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Chemical engineering education

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Title:
Chemical engineering education
Alternate Title:
CEE
Abbreviated Title:
Chem. eng. educ.
Creator:
American Society for Engineering Education -- Chemical Engineering Division
Publisher:
Chemical Engineering Division, American Society for Engineering Education
Publication Date:
Frequency:
Quarterly[1962-]
Annual[ FORMER 1960-1961]
quarterly
regular
Language:
English
Physical Description:
v. : ill. ; 22-28 cm.

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Subjects / Keywords:
Chemical engineering -- Study and teaching -- Periodicals ( lcsh )
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serial ( sobekcm )
periodical ( marcgt )

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Chemical abstracts
Additional Physical Form:
Also issued online.
Dates or Sequential Designation:
1960-June 1964 ; v. 1, no. 1 (Oct. 1965)-
Numbering Peculiarities:
Publication suspended briefly: issue designated v. 1, no. 4 (June 1966) published Nov. 1967.
General Note:
Title from cover.
General Note:
Place of publication varies: Rochester, N.Y., 1965-1967; Gainesville, Fla., 1968-

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University of Florida
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All applicable rights reserved by the source institution and holding location.
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01151209 ( OCLC )
70013732 ( LCCN )
0009-2479 ( ISSN )
AA00000383_00155 ( sobekcm )
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TP165 .C18 ( lcc )
660/.2/071 ( ddc )

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Chemical Engineering Documents

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CEE



VOLUME' 36 NUMBER 3 S L J M M F" R 2 002









Feature Articles ...
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EDITORIAL AND BUSINESS ADDRESS:
Chemical Engineering Education
Department of Chemical Engineering
University of Florida Gainesville, FL 32611
PHONE and FAX: 352-392-0861
e-mail: cee@che.ufl.edu

EDITOR
Tim Anderson
ASSOCIATE EDITOR
Phillip C. Wankat
MANAGING EDITOR
Carole Yocum
EDITORIAL ASSISTANT
Christina Smart
PROBLEM EDITOR
James 0. Wilkes, U. Michigan
LEARNING IN INDUSTRY EDITOR
William J. Koros, Georgia Institute of Technology


-PUBLICATIONS BOARD

CHAIRMAN *
E. Dendy Sloan, Jr.
Colorado School ofMines

MEMBERS
Pablo Debenedetti
Princeton University
Dianne Dorland
Rowan University
Thomas F. Edgar
University of Texas at Austin
Richard M. Felder
North Carolina State University
Bruce A. Finlayson
University of Washington
H. Scott Fogler
University of Michigan
William J. Koros
Georgia Institute of Technology
David F. Ollis
North Carolina State University
Ronald W. Rousseau
Georgia Institute of Technology
Stanley I Sandler
University of Delaware
Richard C. Seagrave
Iowa State University
C. Stewart Slater
Rowan University
James E. Stice
University of Texas at Austin
Donald R. Woods
McMaster University


Chemical Engineering Education


Volume 36


Number 3


Summer 2002


> EDUCATOR
178 L.K. Doraiswamy of Iowa State University,
Thomas D. Wheelock, Peter J. Reilly

> LABORATORY
182 Experimental Projects for the Process Control Laboratory,
Siong Ang, Richard D. Braatz
198 An Introduction to Drug Delivery for Chemical Engineers,
Stephanie Farrell, Robert P. Hesketh
216 Mass Transfer and Cell Growth Kinetics in a Bioreactor, Ken K.
Robinson, Joshua S. Dranoff Christopher Tomas, Seshu Tummala
226 Integrating Kinetics Characterization and Materials Processing in the
Lab Experience,
Dennis J. Michaud, Rajeev L. Gorowara, Roy L. McCullough

> CLASSROOM
188 Using Test Results for Assessment of Teaching and Learning,
H. Henning Winter
212 Rubric Development and Inter-Rater Reliability Issues in Assessing
Learning Outcomes,
James A. Newell, Kevin D. Dahm, Heidi L. Newell
232 Scaling of Differential Equations: "Analysis of the Fourth Kind,"
Paul J. Sides
236 The Use of Software Tools for ChE Education: Students' Evaluations,
Abderrahim Abbas, Nader Al-Bastaki
242 Teaching Process Control with a Numerical Approach Based on
Spreadsheets, Christopher Rives, Daniel J. Lacks

> CURRICULUM
192 Is Process Simulation Used Effectively in ChE Courses?
Kevin D. Dahm, Robert P. Hesketh, Mariano J. Savelski
222 Teaching ChE to Business and Science Students, Ka M. Ng

> RANDOM THOUGHTS
204 FAQs. v. Designing Fair Tests, Richard M. Felder Rebecca Brent

> CLASS AND HOME PROBLEMS
206 Boiling-Liquid Expanding-Vapor Explosion (BLEVE): An Introduc-
tion to Consequence and Vulnerability Analysis, C. Tillez, J.A. Peiia

231 Errata

CHEMICAL ENGINEERING EDUCATION (ISSN 0009-2479) is published quarterly by the Chemical Engineering
Division, American Society for Engineering Education, and is edited at the University of Florida. Correspondence
regarding editorial matter, circulation, and changes of address should be sent to CEE, Chemical Engineering Department,
University of Florida, Gainesville, FL 32611-6005. Copyright 2002 by the Chemical Engineering Division, American
Society for Engineering Education. The statements and opinions expressed in this periodical are those of the writers and not
necessarily those of the ChE Division, ASEE, which body assumes no responsibility for them. Defective copies replaced if
notified within 120 days of publication. Write for information on subscription costs andfor back copy costs and availability.
POSTMASTER: Send address changes to Chemical Engineering Education, Chemical Engineering Department., University
of Florida, Gainesville, FL 32611-6005. Periodicals Postage Paid at Gainesville, Florida and additional post offices.


Summer 2002









educator


L. K. Doraiswamy


of Iowa State University


THOMAS D. WHEELOCK, PETER J. REILLY
Iowa State University Ames, IA 50011


K. Doraiswamy came to Iowa State University (ISU)
in a most unusual manner. One of the authors (PR)
was attending a meeting in New Delhi in 1984 and,
since he had previously helped two scientists at the National
Chemical Laboratory (NCL) in Pune with some chromatog-
raphy for a project of theirs, he asked if he could visit them
there. He took the train to Pune during the dry season, arriv-
ing a bit hot and dusty, but quite exhilarated after experienc-
ing one of the world's great train rides-the climb through
the Western Ghats. He and a former graduate student were
picked up by two NCL scientists on their motor scooters and
were delivered to the laboratory, where they were eventually
ushered into the baronial office of the NCL Director, occu-
pied in fine style by one L.K. Doraiswamy. Although L.K. was
chagrined that the visitors had not been met by an air-condi-
tioned NCL car, things went so well after that, the ISU visitor
ended by participating in ajoint enzyme project with the NCL.
Some years later, L.K. (as he is known to his friends and
colleagues, except at Wisconsin-Madison where he goes by
Dorai) arrived by very small plane in Des Moines to see how
the ISU end of the joint project was progressing. During that
visit L.K. was asked by his host what he planned to do after
his (imminent) NCL retirement. L.K. mentioned how much
he liked small midwestern university towns, and sensing a
very good thing, the host passed this word on to his depart-
ment chair (Dick Seagrave). Soon an appointment was hur-
tling through the university hierarchy in record time.
That first appointment, in 1989, was the Glenn Murphy
Chair, meant for a distinguished visiting professor in the
College of Engineering. It was followed by the Department
of Chemical Engineering's Herbert Stiles Chair in 1992, and
then in 1996 L.K. became Anson Marston Distinguished Pro-
fessor in Engineering. His first office was anything but baro-
nial, being the standard 120 ft2 with hardly any window area,
but eventually a nice office opened up when Sweeney Hall
was expanded. L.K. still occupies it, even after his retire-
ment from ISU in December 2000.
Copyright ChEDivision ofASEE 1999


EARLY STIRRING
L.K. was born in Bangalore in 1927 to L.S. and Kamala
Krishnamurthy, the only boy of four children. His father led
the Hyderabad Branch of the Geological Survey of India. For
part of his childhood, L.K. and his family lived in the small
village of Lingsagur. Later they moved to Hyderabad, the
state capital, where L.K. graduated from Methodist Boys High
School. He studied chemistry at Nizam College in Hyderabad,
part of the University of Madras, and then was faced with
several opportunities for further education. One was to study
organic chemistry, a subject he thoroughly enjoyed. But the
rapidly developing field of chemical engineering also attracted
him, and he ultimately decided to study it at the Algappe
Chettiar College of Technology, also part of the University
of Madras. Such an opportunity was very rare in India at the
time, since only two schools with limited enrollments and
very high entrance standards offered chemical engineering.

ON TO WISCONSIN
As a result of his successful record in pursuing chemical
engineering at Madras, L.K. received a scholarship from the
Hyderabad government to study in the United States. An uncle
with a Wisconsin PhD in chemistry suggested that he apply
there-he did, he was accepted, and he arrived during the
winter cold of December 1948.
L.K. was lucky enough to secure Olaf Hougen as his major
professor, and after he earned his MS in 1950 and his Indian
scholarship had expired, Hougen convinced the Hyderabad gov-
ernment to continue funding L.K. for a PhD (which he received
in 1952). His dissertation was on semichemical pulping, done
under the joint supervision of Hougen and John McGovem of
the USDA Forest Products Laboratory in Madison.
Hougen's perception that he had found a promising chemi-
cal engineer was even truer than he thought-in 1987 L.K.
became the Olaf Hougen Visiting Professor of Chemical En-
gineering at Wisconsin, an honor given to only five other
distinguished educators. Then in 1991, he received an honor-


Chemical Engineering Education































(Top) L.K. evinced a clear
penchantfor things mechanical
at an early age.
(Above) L.K. and his wife
Rajalakshmi (now deceased)
after their 1952 wedding.
(Right) Today's L.K.
(Below) L.K.'s present family;
left to right, Rahul, Sandhya,
Sankar, L.K., Deepak, and
Priya.


L.K. and six of his seven ISU doctoral students. from the
left, Leigh Hagenson Thompson, L.K., Sanjeev Naik, Holger
Glatzer, Jennifer Anderson, Ore Sofekun, and Sridhar
Desikan. Missing is Justinus Satrio.


ary DSc from Wisconsin to go with his 1982 hon-
orary DSc from Salford in England.

BACK HOME TO THE NATIONAL
CHEMICAL LABORATORY
After graduating from Wisconsin, L.K. worked
on emulsion paints for a year at Carlisle Chemical
and Manufacturing in Brooklyn. Although the
company urged him to stay, L.K. believed he could
make a greater contribution in India, and in 1954
he joined the NCL as a senior scientist. He rose
rapidly through the ranks, becoming Assistant Di-
rector and head of the Division of Organic Inter-
mediates and Dyes in 1961, Deputy Director and
head of the Division of Chemical Engineering and
Process Development in 1966, and finally becom-
ing Director in 1978. He was the fifth director and
the first nonchemist to head the NCL, and he led
it until he retired in 1989. After his retirement, he
came to the United States to be nearer to his chil-
dren and grandchildren, and (not incidentally) to
continue his research career without the burden of
administrative duties.
L.K. had a tremendous impact on NCL, both as
a tireless and innovative researcher and as a highly
respected and visionary leader who promoted re-
search excellence. When he retired he received a
scroll that reviewed his accomplishments and
summed up his contributions by stating, "You
epitomize the finest in scientific research, man-
agement, planning, and execution. We will always
remember you, as a compassionate human being
who combined in himself the attributes of great
scholarship and visionary leadership." His contri-
butions to the growth of the Indian chemical in-
dustry were also cited, as was his extensive ser-
vice as an advisor to the Indian government and
as a member of various key committees.
Early in his NCL tenure, L.K. established a
strong base of fundamental and applied research,
especially in chemical reaction engineering. Un-
der his leadership, many commercially important
technologies were developed, including fluidized-
bed processes for making chloromethanes and
methylchlorosilanes, continuous processes for
dimethylaniline and ethylenediamine, a new pro-
cess for vitamin B6, and a complete process for
methyl, ethyl, butyl, and 2-ethylhexyl acrylates.
The dimethylaniline technology was the first va-
por-phase catalytic process for making that prod-
uct, while that for ethylenediamine was apparently
the first continuous organic chemical process de-
veloped in India. His teams also developed zeo-


Summer 2002









lite catalysts and processes for xylene isomerization and for
making alkylating benzene with alcohols. Many of these de-
velopments led to awards from the Indian Chemical
Manufacturer's Association.
L.K. lavished care and attention on the NCL by streamlin-
ing departments, doing what was needed to attract the best
people, and attending to the needs of the whole community.
His son Deepak tells us that on occasion this involved such
matters as "compassionate appointments" for poor or recently
widowed employees, special housing allotments for deserv-
ing cases, and investment of resources for welfare purposes
such as the local school and a shopping center (which has
since become a major attraction in the city and is named
after his late wife).
To highlight his human side, one instance is worth special
mention. One night, a poor family was evicted from the NCL
campus for building and occupying an illegal accommoda-
tion. L.K., moved by their plight (and against the administra-
tive officer's advice), gave them permission to stay overnight
until they could make other arrangements. This eventually
led to a protracted legal battle and illustrates how his softer
side sometimes leads him to take risks.
His professionalism concerning matters such as punctual-
ity, returning phone calls, meeting deadlines, and making al-
lowances for potential mistakes in planning is also a hall-
mark of his character. His approach is simply "to get and
maintain the best," and it has led to a legacy of excellence
that he is especially proud of. He maintains that "excellence
is a state of mind" and he never tires of repeating it.
While at NCL, L.K. wrote a book on catalytic reactors and
reactions (Pergamon, 1991) and was coauthor of two vol-



Students and
faculty at the
Wisconsin summer
laboratory course
in 1977, with L.K. at
the far right
and Roger Altpeter
and Richard
Grieger-Block at
the far left.
Wisconsonians,
and others,
beyond a certain
age will enjoy
identifying the
others pictured
here.


umes on heterogeneous reactions with his close friend M.M.
Sharma at the University of Bombay (Wiley, 1984) and one
on stochastic modeling with his NCL colleague B.D. Kulkarni
(Gordon and Breach, 1987). He also edited or coedited four
books and contributed chapters to six others. L.K. personally
guided the thesis research of 45 students who received PhDs
from various Indian universities and collaborated with the
late Tony Holland at Salford in guiding fifteen others and
with Mike Davidson at Edinburgh in an additional two. He
has been author or coauthor of some 155 international jour-
nal articles. They were mainly on adsorption and catalysis;
gas-solid, gas-liquid, solid-solid, and slurry reactions; fluidi-
zation; and stochastic modeling and analysis of reacting sys-
tems. For five years he also served as editor of the Indian
Chemical Engineer.
L.K. is reputed to have received every major scientific and
technical award in India open to chemical engineers. Among
the most noteworthy are the Om Prakash Bhasin Award for
Science and Technology, given by Indian President Zail Singh
in 1986, the Jawaharlal Nehru Award for lifetime achieve-
ment in engineering and technology (1987), and the Repub-
lic Day honor Padma Bhushan presented by Indian President
R. Venkataraman in 1990. Notable awards from outside of
India but honoring his work there are election to the Third
World Academy of Science in 1997, the Richard H. Wilhelm
Award from AIChE in 1990, and the Personal Achievement
in Chemical Engineering Award in 1988 from Chemical
Engineering magazine.

THE FAMILY MAN
Soon after returning to India, L.K. married his wife
Rajalakshmi. She was always a source of great emotional


Chemical Engineering Education









strength and happiness to him, and her early death after a
prolonged and painful illness was a devastating blow. L.K.
has two children, Sandhya and Deepak, who remember their
dad teaching them by gentle example and with the adage that
discipline is doing what you don't like to do. Sandhya com-
pleted a MPhil at the University of Poona and became a CPA
after she arrived in the United States. She and her husband
Sankar Raghavan have two children, Rahul and Priya, the
apples of their grandfather's eyes. L.K.'s son Deepak received
a PhD in chemical engineering from Delaware after earning
a BTech from the University of Bombay. He completed a
postdoctoral fellowship in the Rutgers Department of Ceram-
ics and Materials Engineering and then joined the DuPont
Experimental Station in Wilmington, Delaware. He is also
an adjunct professor at West Virginia University. L.K.'s chil-
dren and the department at ISU engage in a gentle tug-of-war
over where L.K. will live in retirement. So far, to our delight,
he remains in Ames, with frequent trips east.
Deepak tells us that true to his sense of filial and family
responsibility, L.K. took under his wing his parents, an un-
married sister, and a widowed sister and her children, all while
supporting his own young wife and two small children.
L.K. is a lover of the English language, both written and
spoken. He writes beautifully and his spoken English is free
of slang and interjections. He is a purist about word usage
and delights in good sentence construction. As a child, his
school principal advised him to become an author, if pos-
sible, and he managed to do that, although certainly not in
the manner the former expected.

A SECOND CAREER
Starting a second career at ISU in 1989 did not slow L.K.'s
pace at all. In fact, relinquishing administrative duties at the
NCL gave him a second wind. He has continued to thrive
through his writing, lecturing, teaching, and research. He
taught undergraduate and graduate chemical reaction en-
gineering courses, established a new research program
from scratch, and guided the research of seven ISU doc-
toral students.
L.K.'s research has focused primarily on chemical reac-
tion engineering, especially on rate enhancement strategies
in organic synthesis. His group was worked on phase trans-
fer catalysis and has showed that many of its problems can
be overcome by immobilizing the catalyst on a polymer sup-
port. They have developed and published new mathematical
models and have investigated the effect of ultrasound on solid-
liquid reactions mediated by phase transfer catalysts. In ad-
dition to his own seven doctoral students, L.K. collaborated
with Terry King and Tom Wheelock in supervising two oth-
ers. He worked with the late Mauri Larson on developing
and validating a microphase-assisted reaction model, and he
continues to develop an advanced calciuim-based sorbent for
desulfurizing hot coal gas with Tom Wheelock.


Writing and publishing continue to draw much of L.K.'s
attention. He has published 25 research papers and several
comprehensive reviews, mainly in Chemical Engineering
Science and IEC Research, while at ISU. At the same time,
he was absorbed in writing his 26-chapter Organic Synthesis
Engineering, published by Oxford University Press in 2001.
The book integrates synthetic organic chemistry with chemi-
cal engineering through many illustrative examples, so it will
benefit both chemists and engineers who work together on
manufacturing processes.

L.K. was also honored by a special session at the 1997
AIChE Annual Meeting in Los Angeles and by the publica-
tion of special collections of research papers written by many
of his colleagues and friends. One of these collections ap-
peared as the "L.K. Doraiswamy Festschrift," which honored
his 70th birthday and filled the June 1998 issue of IEC Re-
search. The Indian Academy of Sciences published an ear-
lier collection, titled "Reactions and Reaction Engineering,"
to mark his 60th birthday. In spite of these accolades, L.K.
remarked in the preface to Organic Synthesis Engineering:
"If the truth be told, I am not sure to this day whether I learned
more from my students at NCL and ISU or they from me."

To further honor L.K.'s contributions in both the United
States and India, ISU and NCL established a Doraiswamy
Honor Lectureship, filled by a distinguished chemical engi-
neer who annually delivers lectures at both places. The first
three lecturers have been Jimmy Wei (Princeton), Alex Bell
(UC Berkeley), and Klavs Jensen (MIT). It was the first ex-
posure to India for all three.
Along with L.K.'s ISU Distinguished Professorship came
the Margaret Ellen White Graduate Faculty Award (2000) for
superior mentoring of graduate students. Selection for this
honor reflects the sentiments of a former student, who wrote
"The dedication, persistence, and attention to detail that I
learned from Dr. Doraiswamy has guided me in more ways
than I ever dreamed possible." L.K. not only has a high re-
gard for students but also enjoys assisting and working with
them without completely solving their technical problems.
He is well known for inviting groups of students to his home
for serious as well as humorous discussions of science, phi-
losophy, and politics, subjects in which he has deep interest.

One of his graduate students sums up quite nicely the men-
tor-teacher-friend we know as L.K.: "In addition to being a
fine research mentor, I found Dr. Doraiswamy to be a caring
individual. I was able to talk with him about other things
outside my research--even some personal matters. The well-
being of his students was also Dr. Doraiswamy's concern.
There was a period of time when I had been struggling with
my health. Whenever we met, Dr. Doraiswamy would ask
me about my health. When I mentioned this to a research
group colleague, he said 'That's funny. Dr. Doraiswamy al-
ways asks me whether my old car is running.'" 0


Summer 2002










r laboratory


EXPERIMENTAL PROJECTS

FOR THE

PROCESS CONTROL LABORATORY


SIONG ANG, RICHARD D. BRAATZ
University of Illinois at Urbana-Champaign Urbana, IL 61801


Digital control has been used in the Department of
Chemical Engineering at the University of Illinois
more than twenty-five years, but the process control
laboratory underwent a major renovation and expansion from
1994-2000, in which the total number of control apparatuses
was increased from a dozen to twenty-six (some of the appa-
ratuses are duplicates). The cost for lab renovation was ap-
proximately $100,000, and the lab is maintained by a teach-
ing assistant working fewer than ten hours per week. This
expansion enabled all University of Illinois seniors (approxi-
mately 80 students/4 lab sections) to take the process control
course in one semester, working in groups of two students
during lab. Also, a modem control interface was designed
and implemented in HP-VEE, which is a modern visual pro-
gramming environment for instrument control.[1 The twenty-
six control apparatuses include
1. Temperature control in an air bath
2. Water-flow control under oscillatory load disturbances
3. Single-tankpH control
4. Interacting water-tank level control
5. Temperature control with variable-measurement time
delay
6. Integrating tank-level control
7. Cascade control of temperature in a water tank
8. Dye-concentration control with load disturbances
9. Four-tank water-level control
10. Temperature and level control in a water tank
11. MultitankpH control
The experiments were designed based on three underlying
principles. First, the experiments should emulate real indus-
trial processes and the control problems associated with those
processes. Second, collectively the apparatuses should teach
students a wide variety of techniques for addressing chemi-
cal process control problems. Third, the students should com-
municate with the apparatuses via a modem control inter-
face.M1 Following these principles ensures that the students
receive the appropriate training to productively solve control
problems they may encounter in the industry.


The last three control apparatuses are the most sophisti-
cated. Control apparatus #9 is similar to an apparatus in Pro-
fessor Frank Doyle's control lab at the University of Dela-
wareE2] and in a control lab at the Lund Institute of Technol-
ogy.3' The apparatus is used to teach multiloop and decoupling
control and to illustrate how the controller design becomes
more difficult as the interactions increase. Control apparatus
#10 uses two oversized valves as the final actuation devices
and temperature, water level, and two flow rates as the mea-
sured variables. This two-input four-output process is con-
trolled using multivariable cascade control. Control appara-
tus #11, the multitank pH control apparatus, is a novel lab
apparatus that exhibits significant nonlinearity.[4] In addition
to a multiloop control strategy, students can also apply
feedforward-feedback control loops and observe the dependence
of their performance on the accuracy of disturbance models.

SOFTWARE AND HARDWARE IN THE
PROCESS CONTROL LABORATORY
A laboratory course in process control constitutes an im-
portant component of a chemical engineer's education.[561
It should provide hands-on training in the application of
control to real processes. The design of the process con-
trol laboratory is instrumental to the quality of a chemi-
cal engineering education.
Figure 1 shows the flow of information between the com-
puter hardware and the physical apparatus. Each computer is
connected to a wet-lab experiment and an air-bath experi-


Siong Ang received his BS in chemical engineering from the University of
Illinois in 2000 under a Singapore Armed Forces Overseas Merit Scholar-
ship. He received an MS degree in chemical engineering at Stanford Uni-
versity in 2001 and is now serving in the Singapore Armed Forces.
Richard Braatz received his BS from Oregon State University and his MS
and PhD from the California Institute of Technology. After a postdoctoral
year at DuPont, he joined the faculty of chemical engineering at the Uni-
versity of Illinois. His main research interests are in complex systems theory
and its application.


Copyright ChE Division of ASEE 2002


Chemical Engineering Education









ment. Modem industrial process installations have graphic
operator interfaces for communication between the process
control engineer and the industrial process. Undergraduate
engineers should be exposed to such a graphic user interface
and be provided with experience in controlling real processes
using such interfaces.'5,61 The interfaces are designed to have
the professional look and feel of real industrial operator in-
terfaces, exposing students to a realistic control environment.

The Hewlett Packard Visual Engineering Environment (HP-
VEE) is a visual programming language designed for instru-
mental control.711 This software uses boxes to represent pro-
cesses and controllers, and lines to represent information
flows. The software has advantages over traditional program-
ming languages. The visual interface of HP-VEE allows nov-
ice users to quickly mas-
ter its programming lan-
Wet lab Air bath guage and therefore en-
apparatus apparatus courage more active
Student participation.
Getting the program to
I/O data acquisition boards work in a certain man-
S t V ner merely requires
HP-VEE software changing line connec-
Stions between boxes or
Figure 1. Computer hardware/ modifying control struc-
software architecture. tures. Every change is a

TABLE 1
Course Schedule


Week Lecture
1 Introductory concepts
2 Review: mathematical modeling & Laplace transform

3 Building transfer function models
Dynamics of simple processes
4 Higher-order dynamic behavior
Stability
5 Nonlinear systems, linearization
Parameter estimation


6 Feedback control, introduction to PID
7 Closed-loop time response and stability
8 Direct synthesis
Introduction to frequency domain
9 Frequency domain identification and analysis
10 Cascade control
Feedforward/ratio control
11 Review
12 Introduction to MIMO systems
Interaction Analysis
13 Design of decouplers
Model predictive control
14 On-line optimization
Statistical process control
15 Case study: distillation columns, packed-bed reactors


few mouse clicks away. The program is also equipped with
debugging capabilities with direct reference to the error
source, thus reducing time spent for debugging. More ad-
vanced algorithms such as model predictive control'"1 can be
implemented by linking to compiled programs written in
popular languages such as Fortran or Visual Basic. For iden-
tification, the data are imported to Excel, and the parameters
are fit using a variety of fitting routines. To assist the stu-
dents in programming, an HP-VEE program is stored in
the server for reference. The latest version of HP-VEE is
called Agilent VEE.

DESCRIPTION OF THE UNDERGRADUATE
PROCESS CONTROL COURSE
The control class covers a broad range of control topics
relevant in industrial problems encountered today. The syl-
labus includes first-principles modeling, process identifica-
tion, and both single-loop and multivariable control systems.
Students are exposed to a wide variety of real-life control
restrictions such as time delays, non-minimum phase zeros,
model uncertainties, unmeasured disturbances, measurement
noise, and ill-conditioning.
Students have three hours of lectures and three hours of
laboratory per week. The students spend about four hours
per week outside of class to study for this course. The allo-
cated lab time is sufficient for students to complete the lab.
Students apply techniques in
the laboratory shortly after they
are covered in a lecture. Table
1 shows how the lecture topics
are coordinated with lab ex-


Introduction to control lab
Review of lab equipment
On/off control of air bath

Response of a shielded thermocouple

Response of a shielded thermocouple


PID air bath temperature control
PID air bath temperature control
PID air bath temperature control


Group project: open-loop identification


Group project: open-loop identification


Group project: open-loop identification


Group project: model, design, and implement controllers

Group project: model, design, and implement controllers

Group project: model, design, and implement controllers


periments. The first series of
laboratory sessions are devoted
to an air-bath experiment from
which students gain familiar-
ity with the HP-VEE software,
first-principles modeling, pa-
rameter estimation, filtering,
on-off control, and single-loop
PID control. This training pre-
pares them for the second se-
ries of laboratory sessions,
which are more open-ended
and demanding. The students
are split into several teams,
with one wet-lab project as-
signed to each team. During
the first three weeks of these
experiments, the students write
a visual program in HP-VEE
to control the wet-lab experi-
ment and carry out open-loop
identification experiments. In


Summer 2002


I









the remaining weeks the students construct process models,
design controllers, implement the controllers on the labora-
tory apparatus, analyze the results, and write lab reports. The
analysis is required to include a comparison between theo-
retical predictions and laboratory results with a discussion of
potential causes for disagreement. The suggested work sched-
ule is shown in Table 2.

LABORATORY PROJECTS
To achieve a flavor for the experiments, the air-bath and
some individual wet-lab experiments are described below.
Table 3 provides a summary of the inputs and outputs of the
data acquisition boards to the experimental projects.
Temperature Control in an Air Bath
This apparatus dominates the laboratory curriculum as it is
studied by all students during the first seven weeks of class. An
air bath measures 12 in by 10 in and is available at all computer
terminals. Its temperature is measured by a thermocouple, and
its measurement is sent to the computer running the HP-VEE
program. A fan keeps the air well-mixed. The manipulated vari-
able in the process is the voltage sent to a blackened light bulb
(see Ref. 1 for apparatus schematic). This air-bath experiment
serves partly to familiarize students with the HP-VEE software
as students will be expected to develop a control algorithm for


their assigned wet-lab experiments. The students are asked to
model the air bath and develop simplified models.
Step changes are performed to derive the process param-
eters used for controller tuning. The students apply first-or-


TABLE 2
Proposed Schedule for Wet-Lab Experiments

Week 1 Familiarize with the equipment for the wet-lab experiment.
Construct a block diagram showing all equipment.
Derive transfer function models for all the blocks and clearly
identify which model parameters can be looked up or directly
measured and which must be determined from process reaction
curves.
Propose a control strategy that will satisfy the given control
objectives and further familiarize yourself with the software.
Weeks 2/3 Make changes in the visual program to record all measurements,
send all manipulated variable moves computed by the controller
to the laboratory apparatus, save all variables of interest to the
data file, plot all variables in the correct units.
Implement open-loop step responses.
Week 4 Construct models from process response curve experiments.
Week 5 Implement control algorithms and collect closed-loop response
data.
Week 6 Analyze data and compare theory with both open-loop and
closed-loop experiments.
Write lab report.


TABLE 3
Summary of Information of Experimental Projects


Inputs (I/P) of Acquisition Board


Outputs (O/P) of acquisition board


1 13 Air bath SISO I/P 00-Bath temperature (OC) O/P 00-Bulb voltage (V)
2 1 Oscillatory load SISO I/P 00-Flow rate (V) O/P 00-Valve voltage (V)
3 1 Single-tank pH SISO I/P 00-pH level (no units) O/P 00-Base pump voltage (V)
4 1 Liquid level Single cascade/MIMO cascade I/P 00-Flow rate to upper tank (V) O/P 01-Valve voltage (V)
I/P 01-Upper tank height (inch)
I/P 02-Flow rate to lower tank (V)
IP 03-Lower tank height (inch)
5 3 Temperature time delay SISO IP 00 thru 03-Temperature (C) O/P 00-Pump voltage (V)
6 1 Integrating tank SISO with P controller UP 00-Tank height (inch) O/P 00-Pump voltage (V)
7 1 Temperature cascade Single cascade I/P 00-Tank temperature (oC) O/P 01-Valve voltage (V)
IP 01-Flow rate of hot water (V)
8 1 Dye concentration SISO UP 00-Absorbance (no units) O/P 00-Pump voltage (V)
9 1 Liquid level & temperature MIMO cascade/Multiloop VP 00-Tank temperature (OC) O/P 00-Cold water valve (V)
UP 01-Flow rate of hot water (V) O/P 01-Hot water valve (V)
UP 02-Tank height (inch)
UP 03-Flow rate of cold water (V)
10 2 4-tank 2x2 MIMO/Multiloop/Decouplers UP 00-Tank 1 height (inch) O/P 00-Pump 1 voltage (V)
IP 01-Tank 2 height (inch) O/P 01-Pump 2 voltage (V)
I/P 02-Tank 3 height (inch)
I/P 03-Tank 4 height (inch)
11 1 Multi-pH 3x3 MIMO/Multiloop/Feedforward UP 00-pH of Tank 1 (pH units) O/P 00-Base pump 1 voltage (V)


UP 01-pH of Tank 2 (pH units)
UP 02-pH of Tank 3 (pH units)
UP 03-pH of Tank 3 (pH units)


O/P 01-Base pump 2 voltage (V)
O/P 02-Base pump 3 voltage (V)
O/P 03-Acid pump voltage (V)


Chemical Engineering Education


Qt Experiment


Algorithm










der and second-order filtering to the data with a variety of
filter time constants, to reduce the effect of measurement noise
on their estimates. Students then apply a variety of tuning
rules (e.g., Cohen Coon, direct synthesis, internal model con-
trol[8, 10, 11,12]) to design PID controllers and compare the closed-
loop performance obtained with each tuning rule. The stu-
dents also apply an on/off control, where the bulb either
switches completely off or on based on the sign of the offset.
Students are asked to compare the performances of both types
of control. The air-bath apparatus is the simplest and least
expensive of all the apparatuses in the lab. We recommend
that instructors interested in building a similar lab start with
the air-bath apparatus.
[I Water-Flow Control under Oscillatory Load Disturbances
The objective is to control the flow rate downstream of a
valve while the pressure downstream of the valve is continu-
ously varying. The downstream pressure oscillates by vary-
ing the liquid level in a tank downstream from the valve us-
ing a float system, which is separate from the computer. The
flow rate downstream from the valve is measured as a pres-
sure difference across an orifice. A transducer measures this
pressure difference as a voltage, which is sent to the data-
acquisitions board in the computer (Figure 2).
Students construct process-reaction curves with respect to
valve voltage. When analyzing these curves, the oscillations


Tap VI
Fl

Water
tank
Float
switch
S_ Computer/
controller

Drain Drain
F2 F3
V2 F2 F3"' Flowmeterl
V2 V3

Figure 2. Water-level control under oscillatory load
disturbances.


---------------------------------------------------------------- --------
Computer
......... 1::::::::::::::::::::::::::::: Computer/
Tap Controller
*r- ~ l c| ~~' --- ^ H11 --------------------- ___
Flowmeteri
Fl
Upper
tank ---- -------.


Figure 3. Interacting water tank-level control.


are significant. By first subtracting the oscillatory disturbance,
a process gain, time constant, and time delay can be deter-
mined. Several PI and PID tunings are used for varying mag-
nitudes of the oscillation. A goal of this experiment is to ob-
tain some understanding of the effect of disturbances on the
measured variable and that modeling the disturbances can
result in improved input-output models and improved closed-
loop performance.
L1 Single-Tank pH Control The objective is to control the
pH tank with a continuous flow of acid solution by adjusting
the feed rate of a basic solution. The main tank is fed by two
peristaltic pumps that draw liquid from two reservoirs, one
for acid and one for base. The students do not have access to
the flow rate of the acid stream.
The control strategy is to use single-control loop. The acid
feed rate is set at 1.8 V. Open-loop responses are implemented
by step changing the pump voltage over its full range. The
process dynamics of a single pH tank are highly nonlinear,
so the model parameters vary significantly as a function of
the operating region. For testing closed-loop performances,
several PI and PID tunings are used with different set points
(pH = 6, 7, and 8). Students observe the varying setpoint track-
ing performances obtained by different tunings.
Another interesting aspect of this experiment is that the pH
probe is located far from the input and output feed streams
for the tank and that the mixers are selected to give relatively
poor mixing. Because of this, each step response experiment
gives slightly different results even when carried out in an
identical manner. It is important that students encounter pro-
cesses that are not completely ideal because this is usually
what occurs in practice.
E1 Interacting Water Tanks Level Control The objective is to
control the liquid level in the second of two interacting tanks
by adjusting the flow of liquid to the first tank. Water flows
from the tap to the pneumatic valve and from the valve into
the first tank. From the first tank, the water may flow through
either of two valves so that it is possible to choose whether
the tanks interact. All levels are measured as pressure dif-
ferences, which are converted into voltages by transduc-
ers (Figure 3).
The preferred control strategy for this experi-
ment is cascade control. Aggressive P or PI
tunings are used to control the flow rate in the
inner (slave) loop. When the slave loop has been
tuned, a second set of process response curves
(measuring the level in the second tank with re-
spect to the set point of the inner loop) is con-
structed. The outer (master) loop is tuned using
H2 several PI and PID tunings based on the process
Drain parameters obtained. An alternative strategy is
to use a simple PID controller that controls the
level of the second tank by manipulating the
valve voltage. The performance of both strate-


Summer 2002


V2
t2

V3 Lo\er
F3 tank -










gies can be compared. A goal of this experiment is to recognize
the performance improvement obtainable by cascade control.
E Temperature Control with Variable-Measurement Time
Delay The objective is to control the temperature at one of
several thermocouples downstream from a mixing tank. The
manipulated variable is the hot-water feed rate into the mix-
ing tank. A reservoir provides a constant head for a cold-
water feed, and a peristaltic pump transfers hot water from a
reservoir into the mixing tank. Four thermocouples are lo-
cated downstream from the outlet of the mixing tank.
Students construct process reaction curves with respect to
pump voltage for each of the four thermocouples downstream.
They should observe that the time delay in their step responses
is greater for thermocouples located further downstream. PI
and PID controllers are implemented using each of the ther-
mocouples as the measured variable. Students investigate the
effect of changing the time delay on the closed-loop stability
and performance by using one thermocouple's tuning rules
for the other thermocouples.

E[ Integrating Tank-Level Control The water level in an
integrating tank is the control variable. This tank receives a
constant flow of water from the tap. The water level in the
tank is measured as a pressure difference signal. Water is re-
moved from the tank by a peristaltic pump under the control
of the computer. An interesting feature is that the HP-VEE
software assumes that the gain of the process is positive.
This would be true if the pump was feeding water into the
tank. In the integrating tank, however, the pump drains wa-
ter away from the tank; therefore, the sign of the controller
gain should be negative.
Step changes in the pump voltage are implemented to de-
termine the model parameters, which the students use to tune
P, PI, and PID controllers. The integrating characteristics of
the tank do not require integral action in the controller to
have zero steady-state closed-loop error. Hence, this particu-
lar process can be controlled using a single-loop P controller,
which can be tuned using direct synthesis. The controller is
tuned so that the closed-loop response is as fast as possible,
without too much overshoot. Students can test the disturb-
ing response of their controller parameters by implement-
ing the controller under conditions in which the tapwater
feed rate changes.

El Cascade Control of Temperature in a Water Tank The
objective is to control the temperature in a stirred tank by
adjusting a hot-water flow rate. Cold water is supplied to the
mixing tank from a reservoir that uses an overflow to main-
tain a constant level. Hot water flows through a pneumatic
valve, and a computer records its temperature and flow rate.
The flow rate is measured as a pressure difference across an
orifice by a transducer with output in units of volts.
The preferred method is to implement a single cascade loop.
Open-loop responses for the flow rate of hot water into the


tank are constructed by making a step change in the valve
voltage. After determining the gain, time constant, and time
delay, students can try several P and PI tunings for the inner
(slave) loop to control the flow rate. For tuning the master
loop, the steps are the same except that a new set of process
response curves is constructed by measuring the temperature
of the tank with respect to the set point of the inner loop.
Using the same control parameters from the tuning, a single
PID controller is implemented and compared with a cascade
controller in terms of closed-loop performance.
E Dye Concentration Control with Load Disturbances The
objective is to control the dye concentration in a tank under
load disturbances by changing the voltage to the feed pump.
The 3-liter tank is drained both from the bottom and from an
overflow pipe. A pump takes in water from the bottom of the
tank and sends it through a colorimeter, which measures the
absorbance of the solution using the tap water as a reference,
with the outlet of the colorimeter returned to the tank. A peri-
staltic pump sets the flow rate of dye into the tank (Figure 4).
This process can be controlled using PI or PID control.
The absorbance of the solution is measured and compared to
a concentration setpoint. The voltage to the dye feed pump is
the manipulated variable. Besides determining the setpoint
tracking performance, students perform disturbance
changes by decreasing the water-feed rate by partially
closing the valve at the faucet.
EN 4-Tank Water-Level Control The objective is to control
the water levels in the bottom two tanks (Tanks 1 and 2) with
the levels at least two-thirds of the maximum height. On each
side, water is pumped upward from a cylindrical beaker and
split into two channels at a Y-junction. The relative amount
of water entering the two split tubings can be adjusted manu-
ally. All liquid levels are measured by pressure transducers.
The two pumps adjust the flow of water to the tanks accord-
ing to voltage signals sent by the PID controllers.
A straightforward control strategy is to use two PID loops
to control the process. Both pumps must be calibrated before
reliable data can be obtained. By making step changes to the
pumps, the process reaction curves for the tank levels are


Stream
Computer/
_- Controller .--- Mixing
tank
Drain
-P2 Absorbance
sensor
L-------------------------------------------- _-_-

Figure 4. Dye concentration control
with load disturbances.


Chemical Engineering Education








obtained. The gains, time constants, and time delays of each
process are determined. Each PID loop is tuned separately so
that the closed-loop speed of response is as fast as possible,
without too much overshoot. After tuning the two single loops,
the control loops are implemented simultaneously, and the in-
teractions between the loops are observed. To provide adequate
setpoint tracking, the two loops are detuned as necessary.
Decouplers are capable of reducing loop interactions. Stu-
dents can use the HP-VEE software to implement partial
decouplers and assess any improvements/deterioration in the
closed-loop performance.
E1 Temperature and Level Control in a Water Tank The
objective is to control the liquid level and temperature in a
tank by adjusting the pneumatic valves on hot and cold water
feed-flow rates. Both the feed-flow rates and liquid level in
the tank are indirectly measured as pressure differences by
transducers, which output in units of volts. The presence of
two possible actuators suggests the possibility of implement-
ing multiple loops. Since it is possible to receive four mea-
sured signals, two cascade-control loops can be used. Stu-
dents construct process reaction curves for the flow rates into
the tank with respect to the voltage sent to the valves. The
gain, time constant, and time delay for each of the four trans-
fer functions can then be defined.
The inner (slave) loops should be tuned aggressively with-
out excessive overshoot to control the flow rates. After ob-
taining good tuning parameters, a second set of process re-
sponse curves measuring the level and temperature in the tank
with respect to the set points of the inner loops is constructed.
The process gain, time constant, and time delay for each of
the four transfer functions are collected. At this stage, stu-
dents should be able to assess the level of interaction between
the two loops and decide on the pairing. Another possible
strategy is to implement two simple PID controllers, control
level and temperature, and manipulate the valve voltages.
Students can observe and compare the difference in
closed-loop performance between the cascade controllers
and the PID controllers.
E MultitankpH Control The objective is to control the pH
of an acid stream, which flows through three tanks connected
in series. This is accomplished by adjusting the feed rates of
a basic solution. Three tanks are connected in series. The acid
stream enters a pulse dampener before a pH probe measures
its pH. The acid stream will enter Tank 1, Tank 2, and Tank 3
before it is drained into a safety reservoir. Each tank has its
base flow regulated by one base pump. In addition, a pH probe
is located in each tank to measure the pH of the solution (see
Ref. 4 for apparatus schematic).
Pumps are calibrated, and their threshold voltages are de-
termined. Step changes should be made in the range bounded
by the threshold voltages. The acid flow rate is set through-
out the experiment. There are many ways to design a cascade
control loop with one master and two slave loops. Yet an-


other way is to implement a full multivariable controller with
three inputs and three outputs, and to use partial decoupling
followed by multiloop control. Regardless of strategies, stu-
dents should be able to report any loop interactions. The closed-
loop performance is compared with different set points for the
third tank (pH = 6, 7, and 8). Since this experiment can be con-
trolled by different strategies, it is especially suited for chal-
lenging students to consider and test various control strategies.
[1 Integration ofExperiments with Control Curriculum The
control apparatuses, coupled with the use of a HP-VEE as
the control software, have been designed to equip seniors with
a practical experience in process control. With emphasis on
project-based learning, students are given the opportunity to
apply theoretical concepts on real industrial processes. They
are exposed to the phenomena that limit the achievable closed-
loop performance, including process nonlinearity, time de-
lays, disturbances, measurement noise, valve hysteresis, and
loop interactions. This provides them with experience in han-
dling real physical systems and practice in applying theoreti-
cal concepts to the real process.
Students rated the organization of this course highly but
indicated that too much effort was involved in writing the lab
report. Based on student feedback over the years, several
improvements have been made to the course, including a
shorter lab report requirement.

ACKNOWLEDGMENTS
The Dreyfus Foundation, DuPont, and the University of Illi-
nois IBHE program are acknowledged for support of this project.

REFERENCES
1. Braatz, R.D., and M.R. Johnson,"Process Control Laboratory Educa-
tion Using a Graphical Operator Interface," Comp. Appl. Eng. Ed., p. 6
(1998)
2. Gatzke, E.P., E.S. Meadows, C. Wang, and F.J. Doyle, III, "Model-Based
Control of a Four-Tank System," Comp. & Chem. Eng., 24, p. 1503
(2000)
3. Johansson, K.H., and J.L.R. Nunes, "A Multivariable Laboratory Pro-
cess with an Adjustable Zero," Proc. of the Amer Cont. Conf., IEEE
Press, Piscataway, NJ, p. 2045 (1998)
4. Siong, A., M.R. Johnson, and R.D. Braatz, "Control of a Multivariable
pH Neutralization Process," Proc. of the Educational Topical Conf.,
AIChE Annual Meeting, Los Angeles, CA, Paper 61a. (2000)
5. Skliar, M., J.W. Price, and C.A. Tyler, "Experimental Projects in Teach-
ing Process Control," Chem. Eng. Ed., 34, p. 254 (1998)
6. Rivera, D.E., K.S. Jun, V.E. Sater, and M.K. Shetty, "Teaching Process
Dynamics and Control Using an Industrial-Scale Real-Time Comput-
ing Environment," Comp. Appl. Eng. Ed., 4, p. 191 (1996)
7. Heisel, R., Visual Programming with HP-VEE, 2nd ed., Prentice Hall
PTR, Upper Saddle River, NJ (1997)
8. Ogunnaike, B.A., and W.H. Ray, Process Dynamics, Modeling, and
Control, Oxford University Press, New York, NY (1994)
9.
10. Skogestad, S., and I. Postlethwaite, Multivariable Feedback Control --
Analysis and Design, Wiley, New York, NY (1996)
11. Braatz, R.D., "Internal Model Control," in Control Systems Fundamen-
tals, ed. by W.S. Levine, CRC Press, Boca Raton, FL, p. 215 (2000)
12. Morari, M., and E. Zafiriou, Robust Process Control, Prentice-Hall,
Englewood Cliffs, NJ (1989) 0


Summer 2002









MRS classroom


Using Test Results for

ASSESSMENT OF

TEACHING AND LEARNING



H. HENNING WINTER
University of Massachusetts Amherst, MA 01003


Examination time can be filled with anxiety. Teachers
design a mid-term or final exam to cover the most
important subjects of their courses and expect the stu-
dent to apply the learned material successfully. Most gratify-
ing for teacher and student alike is an exam in which the
student answers all questions and receives a top grade. In-
complete or wrong answers generate dissatisfaction with both
the student and the teacher. Reality is somewhere between
these extremes, depending on the degree of success of the
teaching and student commitment. The exam results often
suggest that the teaching needs to be improved, but the ques-
tions are where it can be improved and how. Direction can
come from an assessment of exams. They contain a wealth of
information, much more than just a grade for the student.0'1
Methods have been developed for assessing entire engi-
neering programs, curricula as well as individual courses, and
educational research projects.12'31 Student portfolios[2,'3 allow
quantitative assessment of the students' work during the year
with feedback to the campus community. This report describes
a teaching tool that works on the assumption that the educa-
tional program as a whole has already been assessed and that
a plan exists for individual courses. Instead of the large-scale
approach, this paper will focus on methods of analyzing a
single exam and generating direct feedback for the teaching
of a course with well-defined objectives.
I have introduced the concept of a "grading matrix" for
analyzing the results of tests in chemical engineering. The
grading matrix has the purpose of detecting academic
strengths and weaknesses of individual students as well as
strengths and weaknesses of teaching. Most important is the
identification of weaknesses so that they can be corrected in
the classroom (or outside) and possibly re-assessed. The in-
creased interest in teaching assessment has motivated me to


describe the grading matrix in this report. Until now, I have
used it by myself in all undergraduate and graduate teaching
for over a decade and have gradually refined it. The matrix
method is somewhat related to the Primary Trait Analysis of
Loyd-Jones,E~' which was recently pointed out to me. But, in
addition to student performance, the grading matrix also as-
sesses teaching success. This paper briefly describes the grad-
ing matrix together with suggestions for its use in teaching
and curriculum development.

THE GRADING MATRIX
The definition and use of the grading matrix can be seen in
Figure 1. The example is deliberately kept simple: a typical
written test is broken down into N individual subtopics (task,
to task16 since N=16 was chosen for this test) shown across
the top of the matrix. Student names appear on the left side.
Separately for each of the subtopics, the student's exam is
evaluated on a scale from 0% to 100%. Grades are finely
varied between 0% and 100% or, in yes/no fashion of a
quiz, with either 1 or 0 in the matrix. This choice depends
on the nature of the test or quiz. A row of grades across
the matrix shows the strengths and weaknesses of that
individual student. The average over the row constitutes


@ Copyright ChE Division of ASEE 2002


Chemical Engineering Education


H. Henning Winter is Distinguished Univer-
sity Professor of Chemical Engineering at the
University of Massachusetts atAmherst. He
has degrees from Stanford University (MS)
and the University of Stuttgart (Dr. Ing). His
research includes experimental theology, poly-
mer gelation, and crystallization.










his or her final grade:

100
grade [%] =-- (task, + task2 + task3 ... + taskN) (1)
N
where N is the number of tasks (=number of columns in
the matrix). The actual grading process is complete at this
point.

When returning the graded test, each student receives two
items: their own exam booklet and the grading matrix (with-
out names) of the entire class. No grades are written in the
booklet except for the final grade on the booklet cover. In-
stead of grades, I write occasional comments into the exam
booklet with the purpose of helping the student to understand
the course material. For identification on the matrix, students
need to find the row with their final grade on the right side.
By knowing the row, students obtain an analysis of their per-
sonal performance in each of the subtopics of the test. This
allows them not only to assess their personal knowledge but
also to compare it with the rest of the class. Students told me
that they especially like this comparison to others. Note that,
different from Figure 1, no student names are listed on the
students' copy of the matrix; privacy is maintained. Students
can reveal their grade to fellow students, but their perfor-
mance remains otherwise unknown. I have not had any prob-


lems arising from this procedure.

The most critical part of the entire assessment process is
the design of the grading matrix itself; e.g. the selection of
test questions (called "task" in Figure 1), which the student
will be asked on the test. These tasks need to be representa-
tive for the course objectives according to an overall plan.[2,3,6]
Consider the example of a Fluid Mechanics course, which
has the objective that students learn to solve certain flow prob-
lems. This can be tested in an exam where one such flow
problem is broken down into: (task,) schematic drawing of
the expected velocity field, choice of coordinate system, and
definition of boundary conditions; taskk) equation for con-
servation of mass; taskk) equation for conservation of linear
momentum; taskk) solution for obtaining the velocity field;
(tasks) statement of all simplifying assumptions and limita-
tions of the solution; taskk) discussion of properties of cal-
culated flow field; and (task,) prediction of pressure and stress.
Most written tests are easily structured in this way.


TEACHING ASSESSMENT
AND CORRECTIONS

Until this point, the exam grading has followed conven-
tional paths, except that the data is filed in a spreadsheet,


I I I[0


S I
A_


1 1 11 1 1 1


1 1 1
1 1 1
1 1 1


1 0.9 0.9
.1 0.9 0.8
1 0.8 0.6
----- --
. ._ -


1I 1 0.9


1 1 1


1 1 1! 1


01 1


~mi% i


1 1 1_ 1 0.3 1 0 1 1 1 1 1
1 1 1 1 1 1 1 1 0 1 0


1 1 1


1 1 0
1| 1 1


1 1


1L 0.2
1 0


1 1


0 0.9
0 0.9


1 0.9


tf10 I

I s I a1
n


11 0 21


1 1 0.9


1
21
1


100 %
96:%


921%
79i%
771%


- . .-. -................ .......... ........ .... -... I ........


1, 1


nt 11 0.8 0.5 1 0.9 1 1 0.2 0 0 1 0 0 1 0 0 53 %
nt 1 0.5 1 1 1 0 0 0 0 1 0 0 0.8 0 0 1 52 %
ent 1 0.8i 1 1 1 1 0 0 0 0.8 0 0.7 0 0 0 52 %
nt 1 1 1 1 1 0.8 0 0 0.8 0 0 0.8 0 0 46 %
int 1 0.3 0.8 1 1 0 1 0 0 0 1 1 0 0.2 0 0 46
ent 1!0. 1 1 1 1 0 0 0 0 0.5 0 00.8 0 0 441%
nt 1 0 0.8 0 0 0 1 1 1 0 1 0 0 0 0 0 41!%
ent 1 0 0.41 1 1 1 1 0 0 0 0.7 0 0 0 0 0 38 %


100 84 78 96 92 86 89 27 47 16 85


i *


221 81 221 9


S_


%:


Figure 1: Example of
the grading matrix of a
test. Grades are filed
in a spreadsheet.
Task,, task,, task,, etc.
stand for test ques-
tions. Number codes
for grades are
1=100%, 0.9=90%,
0.8=80%, ...and 0=0%.
Different weights can
be assigned to each of
the tasks, though here
all weights are set to
the same value of 1.
Teaching is assessed
by taking an average
over entire columns,
top to bottom; the
result shows in the
bottom row. An
asterisk marks topics
which are not under-
stood by the majority
of the class and need
to be addressed. In
real application, the
left column of names
will be removed. All
data in this example
are fictitious.


weight


!nt
int
int
int

int
ent



nt
nt


1 stude
2 stude
3. stude
4. stude
5,. stude
_f6_stude



21. stude
22 stude
23 stude
24. stude
251. stude
261. stude
27 stude
28.. stud
29'. stude
30. studs


teaching


-I -


assessment


Summer 2002


I


, '


.L


=


- .


. 01 2


t --- i I-


' '


I


1 1i


- '''' ''''''''


__


11 0.8


0 0.2


0 0.6 0.8


1 0









ready for further assessment. Some of the most important
information is contained in the columns of the grading ma-
trix of Figure 1. A column with mostly high marks (1 = high-
est mark) top to bottom shows that all students know the sub-
ject, at least at the level of the exam question. If a column,
however, has mostly "0" marks, something went wrong. Rea-
sons can be deep-rooted or only superficial (i.e., the question
was confusing or the students ran out of time). Discussions
between teacher and students often bring clarification, and
plans for further action are easily devised. Technical defi-
ciencies and/or misunderstandings are recognized and can
be addressed, for instance, in a special help session or in the
next homework assignment. Experiments can be added or
computer animation can be used to help visualize abstract
concepts. Teachers have an opportunity to become very cre-
ative as soon as the problem is defined. This definition of the
problem is the main purpose of the grading matrix.
Correction of weaknesses can then be re-assessed in the
next test. This is typically done by including appropriate ques-
tions in the next exam, preferably within the same course
and/or in the next homework assignment. Teaching should
be corrected further if necessary. Often it is too late to intro-
duce corrections in the same semester or quarter. If changes
cannot be made in time, the weakness in one course will be
passed on to the teacher of the following course. This


Figure 2:

This is the same
grading matrix as
in Figure 1, but
specific weights are
assigned to each of
the tasks. This
affects the
calculation of the
grade as defined in
Equation 2.
Everything else,
including the
teaching
assignment,
remains unchanged
by the weighting
system. Weights
have little
effect on the
grade of top
students but can
make a large
difference for a
weaker student.


teacher should be alerted to the problem so that correc-
tions can be made there.
The grading matrix provides a record, which can be used
even if another teacher teaches the course the following year.
Adjustments can be made then and can be re-assessed until
teaching weaknesses are resolved. I can imagine, however, a
problem with the existence of such records, since they have a
potential for misuse in the form of over-coaching of teach-
ers. This would interfere with the learning environment and
impair the matrix method. Access to the grading matrix
should be restricted to the teachers and students who are
directly involved.


FEEDBACK
TO STUDENTS

Advising individual students is enhanced by the diagnostic
property of a grading matrix. The teacher sees individual
weaknesses of students and can suggest corrective measures.
(e.g., specific reading material or exercises). This does not
require further preparation on the teacher's part. Information
is available instantly when a student comes to the office for
consultation. The matrix row of grades, in combination with
other observations (attendance, participation during class,
etc.), provides a quantitative basis for a discussion.


Chemical Engineering Education


0 1- C o T 0 11
i o ^-

0 (0 CC

weight= 0.5 1, 31 1 2 1 55 1 2 0.5 2 1 4 1 1 1 27
1 student 1 11 1 1 1 1 1 0 1 1 1 1 1 1 11 0 2 100 %
21. student 1 1 1 1 1 1 1 0.3 1 0 1 1 1 1 1 1 1 99%
3.student 1i 1 1 1 1 1 1 1 1 1 1 0 _1 0i 0 2 _85%
4:. student 11 0 0.9 .9 1 1 1 1 1 1 1 1 0 1 0.9 0 1 831%
5 student 11 0.9 0.8 1 1 0 10.2 0 00.9 1 1 1 1 0.9 1 84%
6 .student 1 0.8 0.6 1 1_ 1 1 0 1 0 0.9 1 1 1 1 0 __ 85%
-
... ....... ....... ............. .... .... ....... ..... ...... ....... ..__

221 student 1 1 1 1 0 1 0.8 0 0.2 0 0.6 0.8 0 1 0 0 51%
23 student 1 0.8 0.5 1 0.9 1 1 0.2 0 0 1 0 0 1 0 55i%
24. student 1 0.5 1 1 1 1 0 0 0 0 1 0 0 01.81 0 0 1 441%
25. student 1 0.8 1 1 1 1 1 0 0 0 0.8 0 0.7 0 0 0_ 661%
261. student 1 1 0 1 1 1 0.8_ 0 0 00.8 0 0 0.8 0 0___ 44%
27. student 1 0.3 08 1 1 0 1 0 0 0 1 1 0 0.2 0 0 53 %
28. student 1, 0.8 1 1 1 1 1 1 0 0 0 0 0.5 0 0 0.8 0 0__ 37%
29. student 1 0.8 0.8 0 0 0 1 1 1 0 1 0 0 0 0 0 511%
30. student 1 0 0.4 1 1 1 1 0 0 0 0.7 0 01 0 0 0_ 45!%

teaching 00 84 78 96 92 861 89 27 47 16 85 42 22 81 22 9%
assessment *
asesm ent1 ~ *_ -_ -__ _^_ -___ -__ __ _









CURRICULUM DEVELOPMENT
Weaknesses in student learning, as detected in the grading
matrices of a course (two midterms and a final, for example)
should be assessed in the context of the entire curriculum.
There is a possibility that students may not be sufficiently


prepared for a specific class. Prevailing weak-
nesses should, in this case, be addressed by chang-
ing the course content of the responsible preced-
ing course. Relevant results from the grading
matrix can be integrated into the systematic cur-
riculum development."3 Discussions along these
lines are in progress in our department.

ADAPTATION
OF THE MATRIX METHOD
There are many ways of integrating the infor-
mation from the grading matrix into personal
approaches to teaching and student advising. It
goes without saying that assessment of test per-
formance as reported here needs to be integrated
with classroom assessment. This is a dynamic
process, which differs from year to year, since
each group of students interacts differently and
varies in its needs. As the learning process
evolves, teachers adapt in their classroom assess-


questions arise in high school teaching and even in elemen-
tary schools where standardization of tests is considered.17'
The matrix method can also be adapted to examinations of
much wider scope, such as oral presentations or essay-type
exams. Oral exams or essays tend to be less uniform in their


...this
paper
[focuses]
on methods

of
analyzing
a single
exam
and
generating
direct
feedback...


ment and in their creative teaching approaches. The integra-
tion of the grading matrix in day-to-day teaching works well
for me, but a general discussion of this topic would exceed
the scope of this report.
Obviously, the matrix itself can be tailored in many differ-
ent ways, and adaptations are straightforward. A few will be
mentioned here. It is possible, for instance, to emphasize se-
lected parts of an exam by adding weight to some of the tasks.
While I normally give uniform weight to all questions (see
top row of the matrix in Figure 1), more important questions
can be given an increased weight, as shown in Figure 2. The
row of grades across the matrix needs to be rescaled accord-
ingly when calculating the final grade:

N
I weight taski
grade [%]=100 i=1 (2)
Sweighti
i=l

where N is the number of columns. Additional bonus points
can be added wherever appropriate. The overall scale of the
test will not be affected by assigning bonus points to indi-
vidual students.
The concept of a grading matrix is introduced here with a
chemical engineering example and on the most straightfor-
ward type of test. The proposed method for assessment of
teaching is applicable at many levels, however. It is equally
useful for students and teachers outside of engineering. Similar


structure than the written tests discussed above.
This, however, does not make their grading less
amenable to matrix format. New categories
need to be added to the list of tasks, such as
style and expression, logic of argument, depth
of discussion, format of graphs, validity of con-
clusions, and more. The choice of categories
needs to be explained to the students well in
advance of the exam.

SUMMARY
The three main functions of the grading ma-
trix are providing a grade for the student, label-
ing areas of weakness in the student's knowl-
edge, and labeling areas of weakness in the
teaching. For me personally, the grading ma-
trix helped to fairly assess the abilities of stu-
dents since my grading became more uniform,
something I tried with less success with other
grading methods. The grading matrix also
alerted me to problems that students encoun-


tered with course material. It labeled weaknesses in my teach-
ing so that I could devise different teaching methods when
needed. I feel that, during office hours, my advice became
better directed to the needs of individual students. The de-
sign of test content with the matrix structure in mind and the
feedback from tests have positively affected my teaching and
my continued search for ways to motivate students. While still
being a stressful experience for the students, examinations have
turned into an effective instrument for improved teaching.

ACKNOWLEDGMENTS
Support from the von Humboldt Foundation, many lively
discussions with colleagues and students, and helpful sug-
gestions from the reviewers are gratefully acknowledged.

REFERENCES
1. Walvoord, G. and V.J. Anderson, Effective Grading: A Toolfor Learn-
ing and Assessment, Jossey-Bass, San Francisco, CA (1998)
2. Olds, B.M. and R.L. Miller, "An Assessment Matrix for Evaluating
Engineering Programs," J. Eng. Ed., 87, p. 173 (1998)
3. McNeill B. and L. Bellamy, "The Articulation Matrix, a Tool for De-
fining and Assessing a Course." Chem. Eng. Ed., 33, p. 122 (1999)
4. Taylor, R. Basic Principles of Curriculum and Instruction, University
of Chicago Press. Chicago, IL (1949)
5. Loyd-Jones, R. "Primary Trait Analysis" in Cooper C. and L. Odell
(eds.) Evaluating Writing: Describing, Measuring, Judging. Urbana,
IL Council of Teachers of English, Urbana (1977)
6. Olds, B.M. and R.L. Miller, "Using Portfolios to Assess a Chemical
Engineering Program," Chem. Eng. Ed., 33, p. 110 (1999)
7. Saltet, J.K. "How is my Child Doing?" J. WaldofEducation, 10(2), p.
5 (2001) 0


Summer 2002









Joel curriculum


IS PROCESS SIMULATION

USED EFFECTIVELY IN ChE

COURSES?


KEVIN D. DAHM, ROBERT P. HESKETH, MARIANO
Rowan University Glassboro, NJ 08028
Process simulators are becoming basic tools in chemi-
cal engineering programs. Senior-level design projects
typically involve the use of either a commercial simu-
lator or an academic simulator such as ASPENPLUS,
ChemCAD, ChemShare, FLOWTRAN, HYSYS, and ProII
w/PROVISION. Many design textbooks now include exer-
cises specifically prepared for a particular simulator. For ex-
ample, the text by Seider, Seader, and Lewin1l[ has examples
written for use with ASPENPLUS, HYSYS, GAMS,'12 and
DYNAPLUS.[3] Professor Lewin has prepared a new CD-
ROM version of this courseware giving interactive self-paced
tutorials on the use of HYSYS and ASPEN PLUS through-
out the curriculum.[4,5]
This paper will analyze how effective it is to include com-
puting (particularly process simulation) in the chemical en-
gineering curriculum. Among the topics of interest will be
vertical integration of process simulation vs. traditional use
in the senior design courses, the role of computer program-
ming in the age of sophisticated software packages, and the
real pedagogical value of these tools based on industry needs
and future technology trends. A course-by-course analysis
will present examples of specific methods of effective use of
these tools in chemical engineering courses, both from the
literature and from the authors' experience.

DISCUSSION
In the past, most chemical engineering programs viewed
process simulation as a tool to be taught and used solely in
senior design courses. Lately, however, the chemical engi-
neering community has seen a strong movement toward ver-
tical integration of design throughout the curriculum.[6-91 Some
of these initiatives are driven by the new ABET criteria.1 01
This integration could be highly enhanced by early introduc-
tion to process simulation.
Process simulation can also be used in lower-level courses
as a pedagogical aid. The thermodynamics and separations
areas have a lot to gain from simulation packages. One of the
advantages of process simulation software is that it enables


J. SAVELSKI


the instructor to present information in an inductive manner.
For example, in a course on equilibrium staged operations,
one concept a student must learn is the optimum feed loca-
tion. Standard texts such as Wankat11I present these concepts
in a deductive manner. The inductive presentation used at
Rowan University is outlined below in the section on equi-
librium staged separations.
Some courses in chemical engineering, such as process
dynamics and control and process optimization, are computer
intensive and can benefit from dynamic process simulators
and other software packages. Henson and Zhang['2] present
an example problem in which HYSYS.Plant (a commercial
dynamic simulator) is used in the process control course. The
process features the production of ethylene glycol in a CSTR
and purification of the product through distillation. The au-
thors use this simple process to illustrate concepts such as
feedback control and open-loop dynamics. Clough[131 presents
a good overview of the use of dynamic simulation in teach-
ing plantwide control strategies.
A potential pedagogical drawback to simulation packages
such as HYSYS and ASPEN is that it is possible for students
to successfully construct and use models without really un-
derstanding the physical phenomena within each unit opera-
tion. Clough emphasizes the difference between "students
using vs. students creating simulations." Care must be taken
to insure that simulation enhances student understanding,
rather than simply providing a crutch that allows them to solve

Kevin D. Dahm is Assistant Professor of Chemical Engineering at Rowan
University. He received his BS from Worcester Polytechnic Institute in 1992
and his PhD from Massachusetts Institute of Technology in 1998.
Robert P. Hesketh is Professor of Chemical Engineering at Rowan Uni-
versity. He received his BS in 1982 from the University of Illinois and his
PhD from the University of Delaware in 1987. Robert's teaching and re-
search interests are in reaction engineering, freshman engineering, and
separations.
Mariano J. Savelski is Assistant Professor of Chemical Engineering at
Rowan University He received his BS in 1991 from the University of Buenos
Aires, his ME in 1994 from the University of Tulsa, and his PhD in 1999
from the University of Oklahoma. His technical research is in the area of
process design and optimization.
Copyright ChE Division ofASEE 2002


Chemical Engineering Education








problems with only a surface understanding of the processes
they are modeling. This concern about process simulators
motivated development of the phenomenological modeling
package ModelLA. '14 This package allows the user to de-
clare what physical and chemical phenomena are operative
in a process or part of a process. Examples include choosing
a specific model for the finite rate of interphase transport or
the species behavior of multiphase equilibrium situations. One
uses engineering science in a user-selected hierarchical sequence
of modeling decisions. The focus is on physical and chemical
phenomena, and equations are derived by the software.
Despite these concerns, the survey results discussed in the
next section indicate that HYSYS, ASPEN, and Proll remain
the primary simulation packages currently in use.

SURVEY: COMPUTER USE IN CHEMICAL
PROCESS SIMULATION
In 1996, CACHE conducted a study discussing the role of
computers in chemical engineering education and practice.
The study surveyed both faculty members and practicing en-
gineers, but little emphasis was placed on the specific use of
process simulation. To fill this gap and obtain up-to-date re-
sults, a survey on computer use in the chemical engineering
curriculum was distributed to U.S. chemical engineering de-
partment heads in the spring of 2001. It addressed how ex-
tensively simulation software is used in the curriculum, as
well as motivation for its use. The use of mathematical soft-
ware and computer programming was also examined. A total
of 84 responses was received, making the response rate approxi-
mately 48%. Tables 1-7 summarize the results. The wording of
questions and responses in the tables is taken verbatim from the
survey. The survey also provided a space for written comments
and some of these are presented throughout this paper.
In a 1996 publication that discussed the results of the


CACHE survey, Kantor and Edgartl51 observed that comput-
ing was generally accepted as an integral component of teach-
ing design, but that it had not significantly permeated the rest
of the curriculum. The survey results suggest that this per-
ception is outdated. Table 1 shows that only 20% of depart-
ments reported that process simulation software is used ex-
clusively in the design course, and Tables 2 and 3 show that
it is particularly prevalent in the teaching of equilibrium staged
separations, process control, and thermodynamics. It must
be noted, however, that the survey did not ask respondents to
quantify the extent of use; a "yes" response could indicate as
little as a single exercise conducted using a simulator.
Table 1 also indicates that over one-fourth of the respond-
ing departments felt that their faculty have "an overall, uni-
formly applied strategy for teaching simulation to their stu-
dents that starts early in the program and continues in subse-
quent courses." Many other respondents acknowledged the
merit of such a plan but cited interpersonal obstacles, with
comments such as
With each faculty member having their own pet piece of software,
it's tough to come to a consensus.
Not many faculty use ASPEN in their courses because they haven't
learned it, think it will take too much time to learn, and aren't
motivated to do so.
I would like to see the use offlowsheet simulators expanded to
other courses in our curriculum but haven't been able to talk
anybody else into it yet.
At Rowan University, the incorporation of mini-modules
(described further in the next section) into sophomore-and-
junior-level courses has proved to be an effective solution to
this problem. They require only limited knowledge of the
simulation package on the part of the instructor because they
employ models that contain only a single unit operation.
Table 4 (next page) summarizes the responses to a ques-
tion on motivation for using simulation software. Four op-
tions were given, and the respondent
TABLE 2 was asked to check all that apply. The
Responses to: most common choice was "It's a tool
indicate the courses in that graduating chemical engineers
ofessors require the use should be familiar with, and is thus
-state chemical process
lationprograms." taught for its own sake." A total of
83% of the respondents selected this
% Yes option, and in 15% of the responses it
and/or II 94% was the only one chosen.


Summer 2002


TABLE 1
Responses to:
"Which of these best describes your department's use
of process simulation software?"

Response % Yes
E The faculty has an overall, uniformly applied strategy for
teaching simulation to their students that starts early in the
program and continues in subsequent courses. 27%
E There is some coordination between individual faculty
members, but the department as a whole has not
adopted a curriculum-wide strategy. 35%
E Several instructors use it at their discretion, but there
is little or no coordination. 18%
E Only the design instructor requires the use of chemical
process simulation software. 20%
E No professor currently requires simulation in under-
graduate courses. 1%


"Please
which pr
of steady
simi
Course
E Design I


E Process Safety 4%
a Process Dynamics and Control 10%
E Unit Operations 31%
E Equilibrium Staged Separations 57%
[ Chemical Reaction Engineering 19%
E ChE Thermodynamics 36%
E Fluid Mechanics 7%
E Heat Transfer 13%
[ Chemical Principles 29%


TABLE 3
Responses to:
"Please indicate the courses in
which professors require the use
of dynamic chemical process
simulation programs."
Course % Yes
[ Design I and/or II 12%
E[ Process Dynamics and Control 52%









In their 1996 study of computer skills in chemical engineering,
Kantor and Edgar[l4] analyzed survey results from both faculty and
practicing engineers, finding that faculty tended to drastically under-
estimate time spent at the computer by practicing engineers in indus-
try. The main software tools they used, however, did not include simu-
lators; they were spreadsheets (74%), graphics presentation packages
(80%), database systems (70%), and electronic communications (89%).
Indeed, many engineers will not even have access to process simulators.
Our department collaborates with many small companies and has
found that they use self-made Excel macros to solve problems that
are readily solved with commercial simulators, simply because they
cannot afford the software. These observations certainly do not in-
validate the opinion that process simulation software is "a tool that
graduating chemical engineers should be familiar with." They do, how-
ever, suggest that a department would do well to examine how much
time it is spending on activities designed to familiarize the student with
simulation software while serving no other purpose.
Another finding presented in the 1996 study by Kantor and Edgar
was that computer programming (in languages such as FORTRAN,
C, or PASCAL) is not a vital skill for chemical engineers in industry.
Indeed, "many companies explicitly tell their engineers not to write
software because of the difficulty of maintaining such programs writ-
ten by individuals." Courses on computer programming appear to re-
main a staple of undergraduate programs. Table 5 shows that 83% of
the respondents require a computer-programming course (taught by
either computer science or engineering faculty) and 45% require pro-
gramming in "several" subsequent courses. There is a shift away from
teaching traditional computer programming, however. A total of 17%
of the respondents indicated that their curriculum no longer contains
computer programming at all, with a number of them mentioning that
programming had been recently phased out. Many other respondents
indicated that the programming present in their curriculum does
not employ traditional languages such as C or FORTRAN, but
instead uses higher-level programming environments such as
Maple. Example comments are
Our situation is that we teach a course that introduces students to Excel and
Maple. Maple is the programming tool. They are not required to program
thereafter but many of them choose to do so in later courses.
We dropped our programming course last year, because simulation packages
(as well as general equation solvers, spreadsheets, etc.) were becoming so
powerful that it was becoming much less important to know how to program
and more important to know how to configure/use existing packages.
Our undergraduate students no longer take a computer programming course,
per se. Instead, they learn and make extensive use of packaged software (e.g.,
Matlab) in an integrated freshman sequence on engineering analysis.
Subsequent classes draw upon this experience.
This is a trend that may well continue to grow. The CACHE survey
indicates that 5% of respondents said it "is not important" to teach
computer programming to undergrads, and 57% thought it was "be-
coming less important." In addition, the current ABET Chemical En-
gineering criteriat'61 requires that graduates have a knowledge of "ap-
propriate modem experimental and computing techniques" but does
not specifically mention programming as it did in the past.
Two respondents identify one potential drawback to this shift away
from traditional computer programming. They emphasize the impor-


tance of the logic and problem-solving skills that pro-
gramming experience stimulates, even if the ability to
program in itself is unnecessary for chemical engineers.
The specific comments were
We dropped our programming course a number of years ago
as the capabilities of the various software packages
increased to the point where programming input from the
user became insignificant. We're now seeing a drop in the
logical approach to problem solving in our students that we
feel is related to this lack of exposure to programming. As
the software becomes more powerful, however, hit-or-miss or
brute-force techniques work so is there really a need for a
more reasoned approach to problem solving?
Although programming languages (FORTRAN) are in some
disfavor at present and probably will pass from the scene, I
find that students develop an increased ability for the logic
of solutions and of thinking about problems when they learn
a language... Ifind that students can use programs such as
POLYMATH, etc. with a great deal more understanding and
efficiency once they have learned a language.
The chemical engineering community thus may have a
use for teaching tools and techniques that challenge stu-
dents to think logically and develop algorithms without
necessarily taking the time to learn a full programming
language. One option is template-based programming
as developed by Silverstein.0171

TABLE 4
Responses to:
"Which of the following best describes your motivation to
use simulation packages? Please check all that apply."
Response % Yes
1 It helps to illustrate essential chemical engineering concepts. 64%
[ It makes numerical computations less time consuming. 70%
[I The modernity is good for attracting and retaining students. 30%
[I It's a tool that graduating chemical engineers should be
familiar with, and is thus taught for its own sake. 83%


TABLE 5
Responses to:
"Which of the following best describes your department's
use of computer programming languages?"
Response % Yes
1 One required course taught by computer science and no
programming required in subsequent chemical engineering
courses. 13%
1 One required course taught by chemical engineering and no
programming required in subsequent chemical engineering
courses. 11%
[I After students take the required programming course, they
are required to program in one subsequent ChE course. 7%
E After students take the required programming course, they
are required to program in several subsequent ChE courses. 45%
E[ Students are required to program in upper level chemical
engineering courses without having taken a formal program-
ming course. 8%
[I None of the above selected. 16%


Chemical Engineering Education









EXAMPLES OF CHEMICAL PROCESS
SIMULATORS IN CHEMICAL ENGINEERING
In this section of the paper we give some practical ideas on
how to effectively implement chemical process simulators
into courses other than the capstone design course.
Freshman Engineering
At Rowan University, an inductive approach has been used
to introduce freshmen and sophomores to chemical process
simulators. The methodology used was
* Show the students a heat exchanger. This can be either a
laboratory unit or part of a cogeneration plant.[18] The stu-
dents are asked to record their observations of fluid flowrate
and temperatures.
Next, have the students start a process simulator and put
these experimental results into a simple heat-exchange unit
operation of a process simulator to determine the heat duty.
Finally, have the students conduct an energy balance by hand
on the system. In this manner the students have first seen
the equipment and then modeled it using a simulator on hand
calculations. This helps to familiarize them with what a simu-
lator actually does and what sort of problem can be tackled
with simulation.
Chemical Principles or Stoichiometry
In many programs with vertical integration of design
throughout the curriculum, the design project starts in this
typically sophomore-level course. Many project examples can
be found in the literature. Bailie, et al., [191 proposed a design
experience for the sophomore and junior years. In the first
semester of the sophomore year, the students are given a single
chemical design project, and they focus on material balances
and simple economic evaluations such as raw material cost
and the products' selling prices. Throughout the sequence,
the students must apply newly acquired knowledge to im-
prove and optimize the process. The ultimate goal is to pro-
duce a fully sized and optimized design, including the analy-


sis of the capital and operating costs by the end of the junior
year. This approach is comparable to problem-based learning.120
There have been other contributions to this vertical approach.[21-
23] In the above work it is unclear how process simulators are
being used and it is not mentioned if the simulators are used
in the early stages of integration. Process simulators cer-
tainly can be used for such problems, however, since they
provide an efficient way to evaluate many variations on a
single design concept.
Chemical Principles-Energy Balances
In Felder and Rousseaut24] (a standard text for this course),
the chapter on multiphase systems introduces the concepts of
bubble and dew points. An inductive method of teaching these
concepts is to start with an experiment on a binary system, us-
ing a IL distillation unit or an interactive computer module[25]
with a visual examination of the bubble and dewpoint. These
methods result in the students examing their data by using a
binary T-x-y diagram. The next step is to use the process simu-
lator to predict bubble and dewpoints for binary and multicom-
ponent systems. In using HYSYS, the dewpoint temperature is
automatically calculated after specifying the vapor fraction as
1.0 dewpointt), the compositions, and pressure in a single
stream. The calculations for multicomponent systems are usu-
ally reserved for an equilibrium staged operations course.
In new editions of many textbooks for the chemical process
principles course there are chapters on process simulation.t24'261
They give examples with solutions done by calculators, Excel
spreadsheets, and FORTRAN. This gives the students an ex-
cellent reference on how a system of equations is used by chemi-
cal process simulators. In section 10.4 of Felder & Rousseau,
commercial process-simulation packages are discussed, but no
examples are given. The last problem in the chapter suggests,
however, that any of the other fourteen homework problems
could be solved by a chemical process simulator. This could be
another starting point for introducing commercial process simu-
lators in this course.

Equilibrium Staged Operations
In teaching distillation, the standard modeling approach is to
use the McCabe-Thiele graphical method. This is an excellent
tool for introducing students to binary distillation problems.
Before extensive use of the computer became feasible, the next
step was to add the energy balance and use the Ponchon-Savarit
method. Many professors no longer teach this method, using
the simulator instead. This decreasing use of Ponchon-Savarit
has been promoted by Wankat, et al.,[27] and recently published
textbook descriptions of the method have been shortened.[28]
Using simulators throughout the curriculum requires that fac-
ulty have knowledge of the simulator that the students are us-
ing. In the discussion of the survey results, there were concerns
about the faculty time and motivation required to be come pro-
ficient in using a simulator. One possible solution is to imple-
ment mini-modules of the type used at Rowan University. In


Summer 2002


TABLE 6
Responses to:
"Indicate the mathematical
applications software required
of chemical engineering
undergraduates.
Check all that apply."
Response % Yes
l POLYMATH"4 37%
E MATLAB 65%
a Maple 24%
E MathCAD 37%
E EZ-Solve 5%
E Spreadsheets 82%
E Mathematica 13%
E Other 15%


TABLE 7
Responses to:
"Please indicate all
applicable steady-state
Chemical Process Simula-
tion programs currently
being used in your
department's undergraduate
courses. Check all that
apply."
Resonse % Yes
E ProIl/Provision 12%
E HYSYS orHysim 32%
E Aspen Plus 45%
E ChemCAD 32%
E Other 13%










equilibrium staged operations, a student must learn the opti-
mum feed location and the improved separation resulting from
increasing reflux ratio for a given number of stages; in an ap-
proach that has been used at Rowan University
The instructor prepares a complete HYSYS model of a distillation col-
umn and distributes it to the class.
The class receives a brief(less thanfive minutes) tutorial on modeling
columns with HYSYS-just enough to tell them how to change specific
parameters such as the reflux ratio and where to locate the resulting
stream compositions and other output parameters of interest.
The students take a column through a series of configurations, vary-
ing the reflux ratio, number of stages, and feed stage location, and
then answers a series of questions about the results. The students are
thus introduced to concepts in an inductive manner.
Subsequent classroom instruction further examines the "whys" of the
results. This is used as a starting point in deductive derivation of the
McCabe-Thiele model.
Mini-modules analogous to this have been integrated through-
out the course, as well as in thermodynamics and principles of
chemical processes. The primary purpose of the modules is that
the HYSIS model provides a time-efficient and effective way
for students to examine the cause-effect relationships among
column operational parameters. The modules also serve a cur-
ricular purpose in that they begin to introduce process simula-
tion. This is accomplished with a minimal requirement of faculty
time. It is not necessary for professors to learn all aspects of the
simulation package; they merely need to learn how to model one
particular unit operation.
Other forms of mini-modules have been proposed where stu-
dents learn the process simulator in self-paced tutorials."'41 The
proposal is that these modules be given to the students-the
professor does not need to prepare time-consuming tutorials
and may not need to learn how to use the simulator. Another
paper by Chitturt29] discusses preparing tutorials forASPEN Plus
simulators using HTML. Finally, the University of Florida
maintains a web site for ASPEN where tutorials are available.1301
Chemical Engineering Thermodynamics
Judging from the survey results, it seems that process simu-
lators are now widely used in thermodynamics (see Table 2).
This is fertile ground for a pedagogical use of the process simu-
lators, and the first thing a new user of a simulator faces is the
variety of thermodynamics packages that are available. The new
user will quickly learn that an incorrect choice of a thermody-
namic model will yield meaningless results regardless of the
convergence of the simulation case. Unfortunately, there are so
many thermodynamics models in commercial simulators that
it is impossible to educate our students in each one of them.
Elliott and Lira[31] present a decision tree for the proper selec-
tion of the thermodynamic model.
Traditionally, students are taught how to perform equilibrium
and properties calculations by hand or, in the best scenario, with
the aid of custom-made software programs for hand calcula-
tors or computers. The increasing influence of process simula-
tors opens up a completely new spectrum of possibilities. Since
simulation results are only as good as the thermodynamic pack-


age chosen, there is value in teaching the fundamental as-
pects that will permit students to pick the right thermody-
namic package for a system. Simulators also offer the advan-
tages of combining thermodynamic models in the same simu-
lation and picking different models for certain properties
within the overall process model; PRO II with Provision is
very versatile in this respect. For instance, an equation of
state such as Soave-Redlich-Kwong (SRK) is chosen as
the overall simulation package, but it is modified so liq-
uid density is calculated using the American Petroleum
Institute (API) equation.
In many cases, professors have been taught thermodynam-
ics using earlier versions of Sandler'321 and Smith and Van
Ness,t33] which did not emphasize predictions of thermody-
namic properties based on an equation of state. More recent
versions of both texts and new texts such as Elliott and Lira
now contain at least one chapter devoted to predicting ther-
modynamic properties from other equations of state. One of
the fundamental aspects of a modern chemical thermodynam-
ics course is not only to teach students how to use these equa-
tions, but also which equation of state they should select for
a particular problem. An example of the prediction of the
enthalpy of a single component where values of the correlat-
ing parameters of a=f(T) and b are from the Peng-Robinson
equation of state is

(H- Hi) = Z -1- f+1+ )B A [1,Ji
RT Z +(-1~2)B BJ8 -Va

where B bP/RT and A aP/(RT)2
From the above equations it is easily seen how compli-
cated these predictions can become compared to a table or a
graph in a standard handbook.[34,35' Many recent thermody-
namic textbooks have included computer programs that al-
low the reader to use various equations of state to solve home-
work problems. The drawback of these programs is that a
student will only use them for the thermodynamics course.
Instead of using these textbook computer programs, a pro-
fessor can encourage use of the thermodynamic packages
contained in the chemical process simulators. In this manner,
the students can become familiar with the available options
in the various simulators.

Chemical Reaction Engineering
In the current chemical reaction engineering course, most
students are familiar with ODE solvers found in POLYMATH
or MatLab. The philosophy given by Fogler1361 is to have the
students use the mole, momentum, and energy balances ap-
propriate for a given reactor type. In this manner a fairly de-
tailed model of industrial reactors can be developed for de-
sign projects.[371 By using POLYMATH or MatLab, a student
can easily see the equations used to model the reactor. In mod-
ern process simulators there are several reactors that can be
used. For example, in HYSYS 2.2 there are the two ideal


Chemical Engineering Education









reactor models of a CSTR and a PFR. The CSTR model is a
standard algebraic model that has been in simulation pack-
ages for a number of years. The ODE's of the PFR are a re-
cent addition to simulation packages and are solved by di-
viding the volume into small segments and then finding a
sequential solution for each volume element. In these more
recent models, the reactors not only include energy balances,
but pressure drop calculations are also a standard feature for
packed-bed reactors.
With the above set of reactions, chemical reaction engi-
neering courses can easily use the process simulator. Simula-
tion can be integrated throughout the course and used in par-
allel with the textbook, or it can be introduced in the latter
stages of the course, after the students have developed profi-
ciency in modeling these processes by hand. As mentioned
in the discussion section, the primary dilemma is how to in-
sure that the simulator is used to help teach the material rather
than simply giving students a way to complete the assign-
ment without learning the material. Taking care that assign-
ments require synthesis, analysis, and evaluation in addition
to simple reporting of numerical results will help in this re-
gard. Requiring that students do calculations by hand will
ensure that they understand what the simulator is actually do-
ing. The professor can select chemical compounds that are not
in the simulator database to ensure that these are done by hand.
Rate-Based Separations
An example of an integrated approach to teaching rate-based
separations with design is given by Lewin, Seider, and Seader
(1998).381 In this paper the authors state that while design
courses fully use advances in modern computing through the
process simulators, many other courses in the curriculum still
use methods employed over sixty years ago. Many modern


computing methods are visual and are thus very useful in teach-
ing chemical engineering concepts. The authors suggest that
professors who teach junior courses) in separations, equilib-
rium-stage operations, rate-based operations, and/or mass trans-
fer consider including
* Approximate methods (Fenske-Underwood-Gililand and Kremser al-
gebraic method)
Rigorous multicomponent
Enhanced distillation using triangular diagrams
Rate-based methods contained in the ChemLSep program and the
RATEFRAC program ofAspen Plus
Adsorption, ion exchange, chromatography
Membrane separations
which are similar to Chapters 9 through 12 in the new Seader
and Henley text.28]
One major drawback in current process simulators is a lack
of standard unit operations for membrane and other novel sepa-
rators. This can be partially addressed by importing programs
into the process simulators. For example, on the HYSYS web
site, an extension program can be downloaded for a membrane
separator and other operations.1391 As simulators develop, we
believe that more unit operations will become available.

CONCLUSIONS
Chemical process simulation is currently underused in the
chemical engineering curriculum at many schools. According
to survey results, process simulators are used in essentially all
design courses and are also heavily used in equilibrium stage
operations, primarily with respect to multicomponent distilla-
tion. But many respondents acknowledge that the role of simu-
lators could be beneficially expanded in their curriculum. Pro-
cess-simulation designers can make their products more valu-
able to chemical engineering educators by adding new and in-
novative unit operations while they
Continue to improve their thermody-


I namic models.


This paper contains practical sug-
gestions and references for imple-
menting a unified strategy for teach-
ing simulation to their students, start-
ing early in the program and continu-
ing in subsequent courses. We be-
lieve that simulation packages are a
fundamental tool for the future
chemical engineer.

REFERENCES
1. Seider, Warren D., J.D. Seader, and Daniel R.
Lewin, Process Design Principles: Synthesis,
Analysis and Evaluation, John Wiley and Sons,
New York, NY(1999)
2. GAMS, see
ters/fa197_art2.pdf>
3. Aspen Technology, Inc.
SContinued on page 203.


Summer 2002


TABLE 8
Reaction Type Descrition
Conversion Fi = Fo FAXA

Equilibrium Keq = f(T); equilibrium-based on reaction stoichiometry; Keq predicted or specified.
Gibbs minimization of Gibbs free energy of all components
Kinetic rA = -kfC CP + krevCRCs where the reverse rate parameters must be thermody-

namically consistent and rate constants are given by k = AT"exp(-E / RT)
Heterogeneous Catalytic Yang and Hougen form, which includes Langmuir-Hinshelwood, Eley-Rideal and Mars-
van Krevelen, etc.

(k CB CrCR

-rA=
I+ KiCa'


Simple Rate rA = -kf C ,Ke in which Kq is predicted from equilibrium data
e q )










, laboratory


AN INTRODUCTION TO


DRUG DELIVERY


FOR CHEMICAL ENGINEERS



STEPHANIE FARRELL, ROBERT P. HESKETH
Rowan University Glassboro, NJ 08028-1701


Rowan University is pioneering a progressive engineer-
ing program that uses innovative methods of teaching
and learning to prepare students for a rapidly changing
and highly competitive marketplace, as recommended by
ASEE.'1' Key features of the program include
Multidisciplinary education through collaborative laboratory and
course work
Teamwork as the necessary framework for solving complex
problems
Incorporation of state-of-the-art technologies throughout the
curricula
Creation of continuous opportunities for technical communica-
tion.121
The Rowan program emphasizes these essential features in an
eight-semester, multidisciplinary, engineering clinic sequence
that is common to the four engineering programs (civil, chemi-
cal, electrical, and mechanical).
A two-semester Freshman Clinic sequence introduces all
freshmen engineering students to engineering at Rowan Uni-
versity. The first semester of the course focuses on
multidisciplinary engineering experiments using engineering
measurements as a common thread. In the spring semester, stu-
dents are immersed in a semester-long project that focuses on
the reverse engineering of a product or a process. In addition to
introducing engineering concepts, the Freshman Clinic incor-
porates the four key features mentioned above.
This paper describes an experiment that was performed both
in our Freshman Clinic to introduce students to drug delivery,
and in a senior-level elective on pharmaceutical and biomedi-
cal topics to apply concepts of mass transfer and mathematical
modeling. Drug delivery is a burgeoning field that represents
one of the major research and development focus areas of the
pharmaceutical industry today, with new drug delivery system
sales exceeding $10 billion per year.t3] With projected double-
digit growth, the market is expected to reach $30 billion per
year by 2005.[4] Drug delivery is an inherently multidisciplinary
field that combines knowledge from fields of medicine, phar-
maceutical sciences, engineering, and chemistry. Chemical en-


gineers play an important role in this exciting field by apply-
ing their knowledge of physical and chemical properties,
chemical reactions, mass transfer rates, polymer materials, and
system models to the design of drug-delivery systems, yet un-
dergraduate chemical engineering students are rarely exposed
to drug delivery through their coursework.
This experiment introduces freshman engineering students
to chemical engineering principles and their application to
the field of drug delivery. Students are introduced to concen-
tration measurements and simple analysis of rate data.
Through this experiment, students explore concepts and tools
that they will use throughout their careers, such as
Novel application of chemical engineering principles
SConcentration measurement
Calibration
Material balances
SUse of spreadsheetsfor calculations and graphing
Parameter evaluation
SSemi-log plots and trendlines
Comparison of experimental concentration data to predicted concentrations
Testing a transient model at the limits of initial time and infinite time
SDevelopment of a mathematical model (in the senior level class)

BACKGROUND
Periodic administration of a drug by conventional means,
such as taking a tablet every four hours, can result in con-
stantly changing systemic drug concentrations with alternat-
ing periods of ineffectiveness and toxicity. Controlled-release
systems attempt to maintain a therapeutic concentration of a
drug in the body for an extended time by controlling its rate
of delivery. A comparison of systemic drug profiles estab-
Stephanie Farrell is Associate Professor of Chemical Engineering at
Rowan University. She received her BS in 1986 from the University of
Pennsylvania, her MS in 1992 from Stevens Institute of Technology, and
her PhD in 1996 from New Jersey Institute of Technology. Her teaching
and research interests are in controlled drug delivery and biomedical en-
gineering.
Robert Heaketh is Professor of Chemical Engineering at Rowan Univer-
sity. He received his BS in 1982 from the University of Illinois and his PhD
from the University of Delaware in 1987. His research is in the areas of
reaction engineering, novel separations, and green engineering.
Copyright ChE Division of ASEE2002


Chemical Engineering Education










lished by conventional administration and controlled release
is shown in Figure 1.
Historically, drug-delivery systems were developed prima-
rily for traditional routes of administration, such as oral and
intravenous, but recently there has been an explosion in re-
search on delivery by so-called nonconventional routes, such
as transdermal (skin), nasal, ocular (eyes), and pulmonary
(lung) administration. Drug-delivery applications have ex-
panded from traditional drugs to therapeutic peptides, vac-
cines, hormones, and viral vectors for gene therapy. These
systems employ a variety of rate-controlling mechanisms,
including matrix diffusion, membrane diffusion, biodegra-
dation, and osmosis. To design and produce a new drug-de-
livery system, an engineer must fully understand the drug
and its material properties as well as processing variables that
affect its release from the system. This requires a solid grasp
of the fundamentals of mass transfer, reaction kinetics, ther-
modynamics, and transport phenomena. The engineer must
also be skilled in characterization techniques and physical
property testing of the delivery system, and practiced in analy-
sis of the drug-release data.
We present a simple experiment in which students are in-
troduced to the basic concepts of drug delivery by studying
the dissolution of a lozenge into water. This is the type of
experiment that would be performed by a drug company to
determine the rate of drug release from a dissolution-limited
system. As the lozenge dissolves, the drug is released (along
with a coloring agent added by the manufacturer) into the
surrounding water. Students observe the increasing color in-
tensity of the water and are able to measure the increasing
drug concentration periodically using a spectrophotometer.
After calculating the mass of drug released at any time t, they
plot a release profile. They must calculate by material bal-


Summer 2002


ance the mass of drug remaining in the lozenge at any time.
They are also able to compare their data to a model after evalu-
ating a single parameter in the model.
Through this experiment, students are exposed to the excit-
ing field of drug delivery and are introduced to some basic
principles of chemical engineering. They perform a calibra-
tion that enables them to determine the concentration of drug
in their samples. A spreadsheet is used to perform calculations
necessary to determine the release profile, and a plot of the
release profile of drug from their lozenge is created. Finally,
they evaluate what is needed to apply a model to their sys-
tem, and they compare their experimental release profile
to that described by the model.
The experiment begins with a short lecture of drug delivery
in which students are introduced to the two main objectives to
drug delivery: drug targeting (to deliver a drug to the desired
location in the body), and controlled release (to deliver a drug
at a desired rate for a desired length of time). These two objec-
tives are illustrated through familiar examples of drug-deliv-
ery systems, and the important role of chemical engineers in
designing drug-delivery systems is explained to the students.
The release mechanism of three commercial drug-delivery
systems are explored in the lecture: enteric coated aspirin,
Efidac 24-hour-nasal decongestant, and Contac 12-hour
cold capsules. The experiment explores drug release from
an analgesic throat lozenge.

The objective of drug targeting is illustrated by enteric-coated
aspirin, which accomplishes a drug targeting objective by
avoiding dissolution of the aspirin in the stomach where it can
cause irritation. The enteric coating (such as hydroxypropyl
methylcellulose or methacrylic acid copolymer) is specifically
designed to prevent dissolution in the low pH of the stomach,
so that the aspirin tablet passes intact to the intes-
tine. In the more neutral environment of the intes-
tine, the coating dissolves, allowing the aspirin to
release dissolve as well. The absorption of drugs in the
small intestine is usually quite good due to the large
surface area available. The function of the enteric-
coating is illustrated by placing one enteric-coated
aspirin tablet in an environment simulating the
stomach (hydrochloric acid, pH 2), and another en-
teric-coated aspirin tablet in an environment simu-
lating the intestine (sodium hydroxide, pH 8). Stu-
dents see that within about thirty seconds the tablet
in the intestine environment has begun to dissolve,
while the tablet in the stomach environment remains
intact. Within a couple of minutes, the tablet in the
intestine has essentially disintegrated, but the other
tablet remains completely unchanged for the entire
class period (and for several weeks thereafter).
The second objective of drug delivery or con-
ed by trolled release (or the release of a drug at a desired
rate for a desired time) is illustrated through famil-
199


Figure 1. A comparison of systemic drug profiles establish
conventional administration and controlled release.









iar controlled-release products such as Contac 12-hour cold cap-
sules and Efidac 24-hour nasal decongestants. Contac is a mem-
brane-based controlled-release system, and Efidac is an oral
osmotic (OROS) pump device. Both mechanisms of controlled
release are explained to the students, and a brief description of
each is included here. For more details the reader is referred to
a comprehensive text on drug delivery such as Robinson and
Leem51 or Mathiowitz. 61
Contac is a capsule that contains
many tiny beads of different colors.
Each bead contains the drug in a
core region that is surrounded by a
coating material. While the coating
material is biodegradable, the rate
at which it degrades is slow com-
pared with the rate at which the drug
is released through the coating ma-
terial. Hence, the coating controls
the drug's rate of release and is
therefore considered a rate-control-
ling membrane. Some beads have
coatings that allow rapid release of
the drug for immediate relief of cold
symptoms. Some coatings allow
release at an intermediate rate, and
others effect a slow diffusion rate
for extended release, providing re-
lief for up to twelve hours.
The osmotic pump developed by Figure 2. The c
Alza exploits osmosis to achieve a Adapted from Robi
constant drug-release rate for an
extended time. This technology has been applied to implant
systems for delivery of drugs for treatment of diseases such as
Parkinson's and Alzheimer's, cancer, diabetes, and cardiovas-
cular disorders. Efidac 24-hour nasal decongestants are an ex-
ample of an oral system that uses the same technology.
The osmotic pump comprises three concentric layers: an in-
nermost drug reservoir contained within an impermeable mem-
brane, an osmotic solution, and a rigid outer layer of a rate-
controlling semipermeable membrane (see Figure 2). As wa-
ter from the body permeates through the outermost membrane
and into the osmotic "sleeve,", the sleeve expands and com-
presses the innermost drug reservoir, squeezing the drug out
of the reservoir through a delivery portal.17
The experiment that the students perform uses a lozenge for-
mulation, and the short introduction to drug delivery concludes
with an explanation of lozenge formulations and their applica-
tions. The most familiar lozenge formulation is used to deliver
topical anesthetics to relieve sore throat pain. But lozenges are
also an important formulation used to deliver a wide range of
very powerful drugs used to treat very serious ailments, such
as cancer and AIDS. These include pain relief medication, an-
tifungal agents, central nervous system depressants (used to


200


treat anxiety, depression, and insomnia), anti-psychotic
drugs, antiflammatory agents, and anticholinergic agents
used to treat Parkinson disease.

LOZENGE DISSOLUTION
The rate at which a lozenge dissolves is important because
it is directly related to the rate at which the active drug is
delivered to the body or the specified
target site. If the target site is the throat,
as is the case with a topical anaesthetic,
fast dissolution could result in the drug
being "lost" if it were swallowed before
acting to numb the irritated throat. Drug
formulations can be engineered to dis-
solve at the desired rate. In this ex-
/ Reservoir periment, we investigate the dissolu-
S. tion rate of a lozenge.


dsmotic pump.
inson and Lee.5'


When placed in water (or in the
mouth), the lozenge becomes smaller as
it dissolves from the surface into the
water. A mathematical model can be de-
veloped to express the amount of drug
released as a function of time, in terms of
quantities that can be measured experi-
mentally. We begin with a rate expression
for the dissolution rate of the lozenge

d -kaA(Cs -Caq) (1)
where M is the mass of drug remaining
in the lozenge (mg), t is time (s), k is the


mass transfer coefficient (cm/s), a is the
mass fraction of drug in the lozenge, and A is the surface area
of the lozenge (cm2). The lozenge is a sugar-based matrix,
and its rate of dissolution is proportional to the concentration
driving force across a boundary layer in the liquid adjacent
to the solid matrix. The concentration difference is assumed
to be C Caq, where C is the saturation concentration of sugar
in water and Caq is the concentration of sugar in the bulk wa-
ter. Caq is assumed to be negligible since the solubility of su-
crose in water at 250C is 674 g/L8, while the maximum su-
crose concentration from a completely dissolved cough drop
of pure sucrose would be 46 g/L in this experiment. The
shape of the lozenge is approximated as a cylinder, and
the surface area can therefore be expressed in terms of
radius r and height h:
A = 27r2 + 2nrh (2)
To simplify the model solution and analysis, the area of the
sides (2nrh) was neglected. The mass of drug remaining in
the lozenge can similarly be represented in terms of r:
itr2h
M = M0 r (3)
Twr02h
where Mo is the amount of drug present in the lozenge ini-

Chemical Engineering Education


\JMIIULIlt b1CCVC

Semipermeable
membrane









tially (known) and ro is the radius of the lozenge initially.
Combining Eq. (1-3) and integrating from time 0 to time t
results in an intermediate expression for the mass of drug
remaining in the lozenge as a function of time:

M= Mo exp[- A t (4)
L Mo
A plot of fn (M/Mo) vs t should yield a line with a slope of
-AoCsk/M. The amount of drug released from the lozenge,
M,, is related to the amount remaining, M, by the material
balance
M =M+Md (5)
Combining Eqs. (4) and (5), an expression for the amount
of dissolved drug at time t is obtained by

Md -M[ exp AoCsk t (6)

Equation (4) is adequate for describing mass transfer in the
lozenge system since it provides an expression for the amount
of drug remaining in the lozenge, but the expression for Md
provided by Eq. (6) is more meaningful for two reasons: the
amount of released drug is directly related to systemic drug
concentrations in the body, and the concentration of released
drug will be measured in the experiment. In the transport
phenomena course where model development is emphasized,
this expression for area in Eq. (2) was retained. When it is
substituted into Eq. (1), the resulting differential equation
contains two time-dependent spatial variables (r and h) that
are independent of one another. The equation can be solved
by splitting the equation into two differential equations and
solving each separately. This is an interesting exercise for ad-
vanced chemical engineering students, but is not necessary to
achieve good agreement between the model and the data.

0.1
0.09
0.08
0.07
0.06

8 0 y = 0.2733x
0.04
S0.03
0.02
0.01


The experiment involves the
release of a drug from a lozenge
formulation, which is an example of a
matrix-type drug-delivery system.


EXPERIMENTAL SET-UP
The dissolution experiment is simple to implement. Each
group is provided with
One magnetic stir plate
One magnetic stirrer
One graduated cylinder
SOne 100-mi beaker
One cuvette
One dropper or Pasteur pipette
One lozenge (cherryflavor)
The beaker is filled with 80 ml of water and placed on a
magnetic stir plate. Before the lozenge is introduced, the first
sample (t=0) is taken and analyzed spectrophotometrically to
obtain a background reading for the solution. After analysis,
the sample liquid is returned to the beaker. The magnetic stir-
rer and the lozenge are then placed in the beaker, the solution
is agitated gently, and samples are taken at intervals of ap-
proximately 5 minutes.
Similar experimental set-ups have been developed9,101 to in-
vestigate mass transfer between a solid and a surrounding liq-
uid using a dissolving candy. The experiment described here
introduces the application of mass transfer principles to drug
delivery and the measurement of concentration (instead of
solid-mass determination) in dissolution analysis.

CONCENTRATION MEASUREMENT
The release profile of the drug, or amount of drug released
as a function of time, is obtained through indirect
measurement of the concentration of dissolved drug
in solution as a function of time, using red dye as a
marker. The red dye used in the manufacturer's for-
mulation provides a convenient method of analysis.
As the drug dissolves, it is released into the surround-
ing aqueous solution along with the coloring agent
present in the lozenge. Since the drug and dye are
considered to be evenly distributed throughout the
matrix, the dye can be used as a marker for indirect
spectrophotometric determination of drug concentra-
tion present in samples.


Students prepare a simple calibration plot using a
lozenge (containing a known amount of drug) dis-
solved in a known amount of water (see Figure 3).
The calibration plot (or calibration equation) can be
used to determine drug concentrations of samples
taken during the experiment.
The amount of drug that has dissolved from the
lozenge can be calculated once the menthol concen-


Summer 2002


0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
Absorbance at 540 nm
Figure 3. A calibration plot for spectrophotometric determination of
menthol concentration. The coloringin the lozenge serves as a marker
that is released in proportion to the drug, menthol, as the lozenge
dissolves.










tration is determined.


ANALYSIS
Chemical engineers who work on drug formulations are con-
cerned with obtaining the desired dissolution rate. They must
be able to measure the drug dissolution rate and describe the
drug dissolution using a mathematical model. The concentrations
by the model should match the experimental data.
To use Eq. (6) to describe the experimental data, the parameter
AoCsk.o (7)
Mo
must be evaluated.

PARAMETER EVALUATION
Equation (6) can be rearranged to


0 5 10 15 20 25 30 35
time (min)

Figure 4. Parameter evaluation. The parameter 3 is determined
from the slope of the line.
8

7 -

6

5.

S4-
a Md (expt)
3-M
~Md (model)
2

1 -

0 -i -
0 10 20 30 40 50
time (min)

Figure 5. Comparison of the experimental release data to that
described by the model.


nM M tM )=Pt (8)
M0M (8)

this equation, the term in parentheses represents the frac-
n of total drug that remains in the undissolved lozenge. A
)t of the left-hand side of the equation as a function of time
lds a straight line with a slope of 3, which can be deter-
ned using the "trendline" feature of Excel. In Figure 4, the
>pe of -0.0938 (min-') is equal to p3. It is important to em-
asize that the parameter p is evaluated using experimental
ta. Students can make this plot by calculating values of the
action of drug remaining or by generating a semilog plot.
e equivalence of these two methods can be emphasized by
ving the students make both plots.
The amount of drug initially contained in the lozenge, M0,
found on the package label. The Eckerd-brand cough drops
used in our laboratory contain 7.6 mg of menthol.

COMPARISON OF MODEL
TO EXPERIMENTAL DATA
After determining the value of p, Eq. (6) can be
used to describe the experimental release data (see
Figure 5). Students are asked to observe the agree-
ment between the model and the data. Freshman stu-
dents are stepped through the basic steps of the model
development, testing the validity of the model at short
times and at long times. They discover that the model
predicts Md = 0 for t = 0, and Md = M for t o, and
this is in agreement with "common sense." Thus, the
point is emphasized that models can easily be tested
for simple or limiting cases.

CONCLUSIONS
This paper describes a simple experiment that ex-
poses students to basic principles of drug delivery and
chemical engineering. The experiment involves the
release of a drug from a lozenge formulation, which
is an example of a matrix-type drug-delivery system.
Students study the dissolution of a lozenge into
water. As the lozenge dissolves, the drug is released
(along with a coloring agent) into the surrounding wa-
ter. Students observe the increasing dissolved-drug
concentration as reflected by the increasing color in-
tensity of the water, and they are able to measure the
drug concentration spectrophotometrically. They cre-
ate a calibration plot that enables them to determine
the drug concentration from their absorbance measure-
ment. They perform a material balance to determine
the fraction of drug released and perform an experi-
mental parameter evaluation. Using a spreadsheet, they
perform calculations necessary to determine the re-
lease profile, and they generate plots of both the ex-
perimental release profile and that described by the
Chemical Engineering Education










model. Finally, they test the validity of their model for the lim-
iting cases of initial and long times.

Through this experiment and lecture, students are intro-
duced to the role that chemical engineers have in the area of
drug delivery and pharmaceutical production. This experi-
ment has also been used in senior-level courses such as trans-
port phenomena and as an elective in drug delivery. Here,
students develop their own model, compare their experimen-
tal results to those described by the model, and examine the
validity of their simplifying assumptions.

ACKNOWLEDGMENTS
This work was funded through a grant from the National
Science Foundation's Course, Curriculum and Laboratory
Improvement Program, under grant DUE-0126902.

REFERENCES
1. Engineering Education for a Changing World, joint project report by the Engi-
neering Deans Council and Corporate Roundtable of the American Society for
Engineering Education, Washington, DC (1994)
2. Rowan School of Engineering-A Blueprintfor Progress, Rowan College (1995)
3. Langer, R., Foreward to Encyclopedia of Controlled Drug Delivery, Vol. 1, Edith
Mathiowitz, ed., John Wiley and Sons, New York, NY (1999)
4. Van-Amum, P., "Drug Delivery Market Poised for Five Years of Strong Growth,"
Chem. Market Reporter, 258(23), p. 16 (2000)
5. Robinson, J., and V. Lee, eds, Controlled Drug Delivery Fundamentals and Ap-
plications, 2nd ed., Marcel Dekker, New York, NY (1987)
6. Mathiowitz, E., Encyclopedia of Drug Delivery, Vol. 2, John Wiley and Sons,
New York, NY (1999)
7. Theeuwes, E, and S.I. Yum, "Principles of the Design and Operation of Generic
Osmotic Pumps for the Delivery of Semisolid or Liquid Drug Formulations,"
Ann. Biomed. Eng., 4(4), p. 343 (1976)
8. Bubnik, Z., and P. Kadlec, in Sucrose Properties andApplications, M. Mathlouthi
and P. Reiser, eds., Aspen Publishers, Inc., New York, NY (1995)
9. Fraser, D.M., "Introducing Students to Basic ChE Concepts: Four Simple Experi-
ments," Chem. Eng. Ed., 33(3), (1999)
10. Sensel, M.E., and KJ. Myers, "Add Some Flavor to YourAgitation Experiments,"
Chem. Eng. Ed., 26, 156 (1992) 0





Process Simulation
Continued from page 197.

4. Lewin, D.R., W.D. Seider, J.D. Seader, E. Dassau, J. Golbert, G. Zaiats, D.
Schweitzer, and D. Goldberg, Using Process Simulators in Chemical Engineer-
ing: A Multimedia Guide for the Core Curriculum," John Wiley and Sons, Inc.,
New York, NY (2001)
5. Lewin, D.R., W.D. Seider, and J.D. Seader, "Teaching Process Design: An Inte-
grated Approach," AIChE Paper 63d, 2000 AIChE Annual Meeting, Los Ange-
les, CA
6. L.G. Richards and S. Carson-Skalak, "Faculty Reactions to Teaching Engineer-
ing Design to First Year Students," J. of Engg. Ed., 86(3), p. 233 (1997)
7. ASME, Innovations in Engineering Design Education: Resource Guide, Ameri-
can Society of Mechanical Engineers, New York, NY (1993)
8. King, R.H., T.E. Parker, T.P. Grover, JP. Gosink, and N.T. Middleton, "A
Multidisciplinary Engineering Laboratory Course," J. ofEngg. Ed., 88(3), p. 311
(1999)
9. Courter, S.S., S.B. Millar, and L. Lyons, "From the Students's Point of View:
Experiences in a Freshman Engineering Design Course," J. of Engg. Ed., 87(3),
p. 283 (1998)
10. Engineering Criteria 2000: Criteria for Accrediting Programs in Engineering in

Summer 2002


the United States, 3rd ed., Engineering Accreditation Commission, Accreditation
Board for Engineering and Technology, Inc., Baltimore, MD (1999) www.abet.org/eac/eac.htm>
11. Wankat, Phillip C., Equilibrium-Staged Separations, Prentice-Hall, Upper Saddle
River, NJ(1988)
12. Henson, MichaelA., and Yougchun Zhang, "Integration of Commercial Dynamic
Simulators into the Undergraduate Process Control Curriculum." Proc. of the
AIChEAn. Meet., Los Angeles, CA (2000)
13. Clough, David E., "Using Process Simulators with Dynamics/Control Capabili-
ties to Teach Unit and Plantwide Control Strategies." Proc. of the AIChE An.
Meet., Los Angeles, CA (2000)
14. Foss, A.S., K.R. Guerts, PJ. Goodeve, K.D. Dahm, G. Stephanopoulos, J.
Bieszczad, and A. Koulouris, "A Phenomena-Oriented Environment for Teach-
ing Process Modeling: Novel Modeling Software and Its Use in Problem Solv-
ing," Chem. Engg. Ed., 33(4), (1999)
15. Kantor, Jeffrey C., and Thomas E Edgar, "Computing Skills in the Chemical
Engineering Curriculum," Computers in ChE, CACHE Corp. (1996)
16.
17. Silverstein, D. "Template-Based Programmingin Chemical Engineering Courses,"
Proc. of the 2001 ASEE An. Conf and Expo., Albuquerque, NM (2001)
18. Hesketh, R.P., and C.S. Slater, "Using a Cogeneration Facility to Illustrate Engi-
neering Practice to Lower Level Students," Chem. Engg. Ed., 33(4), p. 316(1999)
19. Bailie, R.C., J.A. Shaeiwitz, and W.B. Whiting, "An Integrated Design Sequence"
Chem. Engg. Ed., 28(1), p. 52(1994)
20. Woods, D.R., Problem-Based Learning: How to Gain the Most from PBL, W.L.
Griffin Printing Limited, Hamilton, Ontario, Canada (1994)
21. Gatehouse, Ronald J., George J. Selembo, Jr., and John R. McWhirter, "The Ver-
tical Integration of Design in Chemical Engineering," Session 2213, Proc. of the
1999 ASEE An. Conf and Expo. (1999)
22. Shaeiwitz, J.A. "Chemical Engineering Design Projects," www.cemr.wvu.edu/~wwwche/publications/projects/index.html>
23. Hirt, Douglas, "Integrating Design Throughout the ChE Curriculum: Lessons
Learned," Chem. Engg. Ed., 32(4), p. 290 (1998)
24. Felder, R.M., and R.W. Rousseau, Elementary Principles of Chemical Processes,
3rd Ed. John Wiley & Sons, Inc., New York, NY (1999)
25. Montgomery, S. "The Multimedia Educational Laboratory," www.engin.umich.edu/labs/mel/>
26. Himmelblau, D.M., Basic Principles and Calculations in Chemical Engineering,
6th Ed., Prentice Hall PTR, Upper Saddle River, NJ (1996)
27. Wankat, P.C., R.P. Hesketh, K.H. Schulz, and C.S. Slater, "Separations What to
Teach Undergraduates." Chem. Engg. Ed., 28(1), (1994)
28. Seader, J.D., and E.J. Henley, Separation Process Principles, John Wiley & Sons,
Inc., New York, NY (1998)
29. Chittur, Krishnan K., "Integration of Aspenplus (and Other Computer Tools) into
the Undergraduate Chemical Engineering Curriculum," 1998 ASEE An. Conf.
Session 3613. (1998)
30. Kirmse, Dale, ASPEN PLUS Virtual Library,
31. Elliott, J.R., and C.T. Lira, Introductory Chemical Engineering Thermodynam-
ics, Prentice Hall, Upper Saddle River, NJ (1999)
32. Sandler, Stanley I. Chemical and Engineering Thermodynamics, John Wiley and
Sons, New York, NY (1977)
33. Smith, J.M., and H.C. VanNess, Introduction to Chemical Engineering Thermo-
dynamics, 3rd Ed., McGraw-Hill, New York, NY (1975)
34. Engineering Data Book, 10th Ed., Gas Processors Suppliers Association, Tulsa
OK (1987)
35. Perry's Chemical Engineers' Handbook, R.H. Perry and D.W. Green eds., 7th
Ed. McGraw Hill, New York, NY (1997)
36. Fogler, H. Scott, Elements of Chemical Reaction Engineering, 3rd Ed. Prentice
Hall PTR, Upper Saddle River, NJ (1999)
37. Hesketh, R.P. "Incorporating Reactor Design Projects into the Course," Paper
149e, 1999 An. AIChE Meet., Dallas, TX (1999)
38. Seader, J.D., Warren D. Seider, and Daniel R. Lewin, "Coordinating Equilib-
rium-Based and Rate-Based Separations Courses with the Senior Process Design
Course," Session 3613, Proc. of the 1998 ASEEAn. Conf. and Expo. (1998)
39. HYSYS Programmability/Extensibility (OLE) Examples www.hyprotech.com/ole> (2001)
40. Cutlip, M.B., and M. Shacham, Problem Solving in Chemical Engineering with
Numerical Methods, Prentice Hall PTR, Upper Saddle River, NJ (1999) 0










Random Thoughts...





FAQS.

V.

DESIGNING FAIR TESTSMi


RICHARD M. FIELDER AND REBECCA BRENT
North Carolina State University Raleigh, NC 27695
he subject that sets off the most heated discussions in
our workshops is testing. When we suggest giving tests
that can be finished in the allotted time by most of the
students, contain only material covered in lectures or assign-
ments, involve no unfamiliar or tricky solution methods, and
have average grades in the 70-75 range, a few participants
always leap up to raise objections:
1. What's wrong with tests that only the best students
have time to finish?
Engineers constantly have to face deadlines; besides,
if you really understand course material you should be
able to solve problems quickly.
2. Why do I have to teach everything on the test?
We shouldn't spoon-feed the students-they need to
learn to think for themselves!
3. If I curve grades, what difference does it make if my
averages are in the 50's?
Let's consider these questions, starting with the first one.
One problem with long tests is that students have different
learning and test-taking styles.[2] Some ("intuitors") tend to
work quickly and are not inclined to check their calculations,
even if they have enough time. Fortunately for them, their
style doesn't hurt them too badly on tests: they are usually
fast enough to finish and their careless mistakes only lead to
minor point deductions. Others ("sensors") are characteristi-
cally methodical and tend to go over their calculations ex-
haustively. They may understand the material just as well as
the intuitors do, but their painstaking way of working often
leads to their failing exams they could have passed with fly-
ing colors if they had more time.
Being methodical and careful is not exactly a liability in an
engineer, and sensors are every bit as likely as intuitors to
succeed in engineering careers. (Frankly, we would prefer


them to design the bridges we drive across and the planes we
fly in, even if their insistence on checking their results re-
peatedly slows them down compared to the intuitors.) Stud-
ies have shown, however, that sensors tend to get signifi-
cantly lower grades than intuitors in engineering coursest[2
and that minimizing speed as a factor in test performance
may help level the playing field.31
Tests that are too long thus discriminate against some stu-
dents on the basis of an attribute that has little to do with
conceptual understanding or aptitude for engineering. (True,
engineers have deadlines, but not on a time scale of minutes
for the types of problems on most engineering exams.) More-
over, while overlong tests inevitably frustrate and demoral-
ize students, there is not a scrap of research evidence that
they either predict professional success or help students to
become better or faster problem solvers.


Richard M. Felder is Hoechst Celanese Pro-
fessor Emeritus of Chemical Engineering at
North Carolina State University. He received his
BChE from City College of CUNY and his PhD
from Princeton. He is coauthor of the text El-
ementary Principles of Chemical Processes
(Wiley, 2000) and codirector of the ASEE Na-
tional Effective Teaching Institute



Rebecca Brent is an education consultant spe-
cializing in faculty development for effective uni-
versity teaching, classroom and computer-
based simulations in teacher education, and K-
12 staff development in language arts and class-
room management. She co-directs the SUC-
CEED Coalition faculty development program
and has published articles on a variety of topics
including writing in undergraduate courses, co-
operative learning, public school reform, and
effective university teaching.


Copyright ChEDivision of ASEE 2002


Chemical Engineering Education












How long is too long? Unless problems are trivial, students
need time to stop and think about how to solve them while
the author of the problems does not. A well-known rule-of-
thumb is that if a test involves quantitative problem solving,
the author should be able to work out the test in less than
one-third of the time the students have to do it (and less than
one-fourth or one-fifth if particularly complex or computa-
tion-heavy problems are included). If a test fails to meet this
criterion, it should be shortened by eliminating some ques-
tions, giving some formulas instead of requiring their deriva-
tions, or asking for some solution outlines rather than requir-
ing all the algebra and arithmetic to be worked out in detail.
How about those problems with unfamiliar twists that sup-
posedly show whether the students can think independently?
The logic here is questionable, to say the least. Figuring out a
new way to tackle a quantitative problem on a time-limited
test reflects puzzle-solving ability as much as anything else.
If tricky problems count for more than about 10-15% of a
test, the good puzzle-solvers will get high grades and the poor
ones will get low grades, even if they understand the course
content quite well. This outcome is unfair.
But (a workshop participant protests) shouldn't engineer-
ing students learn to think for themselves? Of course, but
people learn through practice and feedback, period; no one
has ever demonstrated that testing unpracticed skills teaches
anyone anything.Therefore, there should be no surprises on
tests: no content should appear that the students could not
have anticipated, no skill tested that has not been taught and
repeatedly practiced. To equip students to solve problems that
require, say, critical or creative thinking, try working through
one or two such problems in class, then put several more on
homework assignments, and then put one on the test. If for
some reason you want students to be faster problem solvers,
give speed drills in class and on assignments and then give
longer tests. The test grades will be higher-not because
you're lowering standards, but because you're teaching the
students the skills you want them to have (which is, after all,
what teachers are supposed to do).
Finally, what's wrong with a test on which the average grade
is 55, especially if the grades are curved? It is that given the
hurdles students have to jump over to matriculate in engi-
neering and survive the freshman year, an entire engineer-
ing class is unlikely to be incompetent enough to deserve
a failing average grade on a fair test. If most students in a
class can only work out half of a test correctly, it is prob-
ably because the test was poorly designed (too long, too
tricky) or the instructor didn't do a good job of teaching


the necessary skills. Either way, there's a problem.
One way to make tests fair without sacrificing their rigor is
to post a detailed study guide before each one. The guide
should include statements of every type of question that might
show up on the test, especially the types that require high-
level thinking skills.[41 The statements should begin with ob-
servable action words (explain, identify, calculate, derive,
design, formulate, evaluate,...) and not vague terms such as
know, learn, understand, or appreciate. (You wouldn't ask
students to understand something on a test-you would
ask them to do something to demonstrate their understand-
ing.) A typical study guide for a mid-semester test might
be between one and two pages long, single-spaced. Draw-
ing from the study guides when planning lectures and as-
signments and constructing tests makes the course both
coherent and effective.
Peter Elbow observes that faculty members have two con-
flicting functions-gatekeeper and coach.51 As gatekeepers,
we set high standards to assure that our students are qualified
for professional practice by the time they graduate, and as
coaches we do everything we can to help them meet and sur-
pass those standards. Tests are at the heart of both functions.
We fulfill the gatekeeper role by making our tests compre-
hensive and rigorous, and we satisfy our mission as coaches
by ensuring that the tests are fair and doing our best to pre-
pare our students for them. The suggestions given in this pa-
per and its predecessor"' address both sets of goals. Adopt-
ing them may take some effort, but it is hard to imagine an
effort more important for both our students and the profes-
sions they will serve.

REFERENCES
1. This column is based on R.M. Felder, "Designing Tests to Maximize
Learning," J. Prof Issues in Engr Education & Practice, 128(1), 1-3
(2002). Available at
.
2. R.M. Felder, "Reaching the Second Tier: Learning and Teaching Styles
in College Science Education," J. College Science Teaching, 23(5),
286-290 (1993). Available at
.
3. R.M. Felder, G.N. Felder, and E.J. Dietz, "The Effects of Personality
Type on Engineering Student Performance and Attitudes," J. Engr
Education, 91(1), 3-17 (2002). Available at
.
4. R.M. Felder and R. Brent, "Objectively Speaking," Chemical Engi-
neering Education, 31(3), 178-179 (1997). Available at www.ncsu.edu/felder-public/Columns/Objectives.html>. Illustrative
study guides may be found at che205site/guides.html>
5. P. Elbow, Embracing Contraries: Explorations in Learning and Teach-
ing, New York, Oxford University Press, 1986.


Summer 2002


All of the Random Thoughts columns are now available on the World Wide Web at
http://www.ncsu.edu/effective_teaching and at http://che.ufl.edu/~cee/


205









MR% -class and home problems )




The object of this column is to enhance our readers' collections of interesting and novel prob-
lems in chemical engineering. Problems of the type that can be used to motivate the student by
presenting a particular principle in class, or in a new light, or that can be assigned as a novel home
problem, are requested, as well as those that are more traditional in nature and that elucidate
difficult concepts. Manuscripts should not exceed ten double-spaced pages if possible and should
be accompanied by the originals of any figures or photographs. Please submit them to Professor
James O. Wilkes (e-mail: wilkes@umich.edu), Chemical Engineering Department, University of
Michigan, Ann Arbor, MI 48109-2136.





BOILING-LIQUID EXPANDING-VAPOR

EXPLOSION (BLEVE)

An Introduction to Consequence and

Vulnerability Analysis


C. TELLEZ, J.A. PENA
University ofZaragoza Zaragoza, Spain


he chemical engineering curriculum should include
information on safety, health, and loss prevention in
the chemical industries.[1-41 A special sensitivity has
developed in the industry as a result of the real possibility of
accidents of catastrophic proportions, such as
The Flixborough accident (1974) at the Nypro plant in
the United Kingdom when an unconfined vapor cloud
explosion of cyclohexane resulted in 28 deaths and
hundreds of injuries.
The Sevesso (Italy, 1976) accident, where a runaway
reaction caused toxic emissions of dioxin and methyl
isocynate that caused animal deaths, dried vegetation,
and affected 2000 people.
The Bophal (India, 1984) accident, which is the
greatest industrial disaster in the world to date, with
about 2,500 deaths and between 100,000 and 250,000
injuries.
The Mexico (1984) accident at St. J. Ixhuatepec where
a BLEVE (Boiling Liquid Expanding Vapor Explo-
sion) of a storage tank of LPG produced more than
500 deaths and 4,500 injuries.


After the Sevesso accident, developed countries established
compulsory legislation regulating declarations of risk by in-
dustry,[5] developed emergency plans inside plants and in the
surrounding areas, and created coordinating organizations for
emergency events. In the European community, the Sevesso
I (formerly) and the Sevesso II (currently) directives cover

Carlos Tdllez received his PhD in 1998 at the
University of Zaragoza, where he is currently
Assistant Professor teaching chemical engi-
neering fundamentals. His research is focused
on fundamental studies in the preparation of
zeolite membranes and inorganic membranes
for pervaporation and gas separation.



Jose Angel Pefia is Associate Professor of
Chemical Engineering at the University of
Zaragoza. His research interests include de-
velopment of new methods for hydrogen stor-
age and transport, development of a new sys-
tem of indicators to estimate the risk of major
accidents involving chemical reactors, and im-
proved systems for early detection of runaway
reactions.


Copyright ChE Division of ASEE 2002


Chemical Engineering Education










Universities should act as a mirror for society, and during the past few decades the chemical
engineering curriculum has made an effort to develop awareness of safety, health,
and loss prevention, but there is still a need for greater awareness.


such actions, while in the United States, legislation has re-
quired development of both external and internal emergency
plans. OSHA has published laws regarding industrial health
and safety for the last thirty years, while other federal agen-
cies, such as EPA, DOE, DOT, and associations such as
API and AIChE, have developed their own legislation and
codes for good practice.
Universities should act as a mirror for society, and during
the past few decades the chemical engineering curriculum
has made an effort to develop awareness of safety, health,
and loss prevention, but there is still a need for greater aware-
ness. The Center for Chemical Process Safety (CCPS), cre-
ated in 1985, is an industry-driven center affiliated with the
American Institute of Chemical Engineers (AIChE) that ini-
tiated a close relationship with engineering schools in 1992
by creating the Safety and Chemical Engineering Education
program (SACHE). It provides teaching materials and pro-
grams that bring elements of process safety into the curricu-
lum . The AIChE www.aiche.org/education/crsindex.asp> and the Institution of
Chemical Engineers in the United Kingdom www.icheme.org/she/tps/index.html> also provide a variety
of safety courses for the chemical engineering curriculum. In
Spain, a legislative article (R.D. 923/92) of the year 1992,
established a degree of chemical engineering, and while some
subjects on health and safety were included as obligatory, it
is clearly insufficient.
To increase knowledge of safety during the undergraduate
years of chemical engineering, several solutions have been
proposed in the U.S.r6,7] The first proposal is to introduce an
obligatory safety course, but that would increase the length
of the curriculum and would be difficult for departments and
ABET to agree upon. A second possibility, already incorpo-
rated in some programs, is to include safety courses as elec-
tives for undergraduates. The third proposal, perhaps more
useful and easier to incorporate, is to give the students small
"pills" of safety during their studies. One useful pill for show-
ing students how to improve the safety of a process is the so-
called "risk analysis." This technique gives a quantitative
estimation of the risk involved in a given process.
In Spain, some knowledge of risk has been included as
obligatory as a part of some courses on safety and/or health,
and some universities have this program separated as elec-
tive options. For example, the University of Zaragoza has an
elective course titled "Analysis and Risk Reduction in the
Chemical Industry."
The objective of this article is to familiarize the student


with risk analysis. The case selected for this is a boiling-
liquid expanding-vapor explosion (BLEVE) of a tank truck
of liquid propane. A brief introduction to consequence and
vulnerability analysis models is included.

BRIEF DESCRIPTION OF THE CASE
A tank truck of 50 m3 containing 19,000 kg of liquefied
propane under its vapor pressure was discharging inside a
factory. Due to unknown reasons, the tank developed a leak
and propane gas discharged into the atmosphere. About five
minutes later, some propane and oxygen (from the atmo-
sphere) produced a mixture within the LFL (lower flamma-
bility limit) and the UFL (upper flammability limit). An un-
known ignition source produced a weak explosion and started
a fire close to the tank. The heat flux coming from the fire
increased the temperature of the tank wall and the liquid pro-
pane within it. The liquid propane tracked its boiling point
curve (po vs T), substantially increasing the pressure in the
tank. As a consequence, the tank ruptured catastrophically.
This kind of phenomenon is a BLEVE (Boiling-Liquid
Expanding-Vapor Explosion). At the moment of the acci-
dent, the ambient temperature was 360C and the atmo-
spheric pressure and relative humidity were 760 mm Hg
and 41%, respectively.
The students should
Use consequence analysis models to study the
possibility of a BLEVE occurrence and its effects
(fireball radiation, damage due to overpressure) on
the surrounding area.
Use the Probit methodology for vulnerability
analysis to speculate on the percentage of victims
(deaths, injuries, etc.) for a given area.

INTRODUCTION TO
CONSEQUENCE ANALYSIS MODELS

STAGE 1
Is It Possible for a BLEVE to Take Place?
A BLEVE is the worst possible outcome when an LPG tank
is exposed to fire. The possibility of a BLEVE occurring can
be checked by using Reid's "massive nucleation theory."''g
This theory is based on the phenomenon of "spontaneous
nucleation" that consists of a massive, instantaneous forma-
tion of tiny bubbles within the liquid mass, caused by a sud-
den depressurization of the vessel contents. When this phe-
nomenon takes place, the possibility of a BLEVE occurs.


Summer 2002









The zone of spontaneous nucleation can be seen in the
pressure vs. temperature diagram shown in Figure 1. It
represents the liquid-gas equilibrium as mathematically
described by the appropriate Antoine equation for the ma-
terial being used (e.g., propane). (The equilibrium rela-
tionship, as well as the critical temperature and pressure
for such material, can be obtained from the literature.181)
From the critical point (e.g., the critical temperature and
pressure), a tangent line to the po-vs.-T curve must be traced
up to a point where the ordinate represents the atmospheric
pressure. The squared dot in Figure 1 shows the condi-
tions inside the tank before the fire engulfment. As de-
scribed by the Reid theory, every point located to the right
of this imaginary vertical line (dashed and arrowed) that
connects the above described tangent line at atmospheric
pressure, is a suitable scenario for a BLEVE. This means
that when the tank is exposed to a fire, the heat coming
from it will increase the temperature (and correspondingly
the pressure) inside the vessel, and the original conditions
will begin to ascend, following the po-vs.-T curve. This
progressive heating will lead to a point where the above-
mentioned vertical line will be trespassed. Once this con-
dition has been achieved, a sudden rupture of the vessel
would lead to a BLEVE because of the sudden
depresurization.

STAGE 2
Mathematical Models that
Describe the Effects of BLEVEs

The literature describes three types of BLEVE effects:
the shock wave (overpressure effects), the thermal radia-
tion, and the fragment projection. This paper focuses on
the shock wave and thermal effects as the main events in a
BLEVE scenario.

Thermal Effects The thermal effects of a BLEVE are
related to radiation coming from the fireball. They are usu-
ally accounted for through empirical equations related to
the quantity of substance involved in the BLEVE. Table 1
shows expressions that have been proposed by different
authors to calculate the maximum diameter of the fireball,
Dma[m], the duration of the fireball, tBLEVE[s], and the height
at the center of the fireball, HBLEVE[m], as well as the re-
sults obtained with them for the given case.


The flow of radiation per unit of emissive surface area
and time (I) in kW/m2 can be calculated using
CCPS110'

FR(-AHcm)b)M
S(Dmax) tBLEVE


:1)


Elia model[121


0.27 M(-AHcomb )032
I = (2)
1 (Dmax )2 tBLEVE

Pape, et al., model[l31

I = 235 P0.39 (3)
where FR is defined as the ratio between the energy emitted by
radiation and the total energy released by the combustion (the
suggested value as stated in the literaturel"0 ranges from 0.25 to
0.4); -AHcmb is the heat of combustion of the material [kJ/kg];
P, is the initial pressure at which the liquid is stored [MPa]; and
P, is the vapor pressure of the stored liquid [MPa].


250 300 350 400
Temperature (K)

Figure 1. Vapor pressure vs. temperature diagram showing
the zone of spontaneous nucleation for propane, as
described by Reid's Theory!9'


TABLE 1
Fireball Characteristic Parameters as Calculated
by Different Authors
(M) Initial Mass of Flammable Liquid [kg]
(Dx) = maximum diameter of the fireball [m]
(HBLEVE) = height at the center of fireball [m]
(tBLEVE) = duration of fireball [s]


CCPS 1o'


CCPS m"


Dx = 6.48 M"325 = 159.3 m
tBLEVE = 0.825 M026 = 10.7 s
HBLEVE = 0.75 DAx = 119.5 m


D'm = 5.8 M" = 154.8 m
tLEVE = 0.45 MS = 12 s


Chemical Engineering Education


TABLE 2
Flow of Radiation Per Unit of Surface Area and Time (I)
for Different Models

CCPS Model'l" Elia Model"2' Pape, et al., Model'3'
I(kW/m2) 336 301 306


(1)









Typical radiation values of fireballs associated with BLEVEs
are quoted in the range of 200 to 350 kW/m2. Taking a value of
FR = 0.325, the heat of combustion from reference 14, and the
pressure inside the tank (1976 kPa) calculated as the vapor pres-
sure of liquid propane at its superheat temperature (331 K
using a Redlich-Kwong EOS approximation), the results are
shown in Table 2. The value is inside the typical range for
BLEVEs and close to the values reported by CCPSt1ol (350
kW/m2) for the intensity of radiation emitted by propane in
BLEVE experiments.
The radiation received by a surface at a distance X from the
emitting point can be calculated once the geometric view factor
(Fvg) and the fraction of energy transmitted (atmospheric trans-
missivity, T) are known:
R= ITFvg (4)
In this respect, when considering the vulnerability of people to
the effects of a BLEVE, it is appropriate to use a geometric
view factor corresponding to a surface perpendicular to a sphere:

D2
Fvg = 2 (5)
4X

Considering only the partial pressure of water present in the
atmosphere at the moment of the accident, T can be calculated
approximately by[20]
= 2.02(P,X)-.09 (6)


where P is the partial pressure of the water at ambient tem-
perature [Pa].
Another, simpler, model has been proposed by Roberts["
where the intensity of radiation received by a surface at a dis-
tance X is given by an expression depending only on the mass
of fuel:
IR = 828 M0.771X-2 (7)


1000

100


Distance (m)

Figure 2. Radiation received by a vertical surface as a
function of distance.


Overpressure Effects Overpressures are difficult to pre-
dict in the event of a BLEVE. The vaporization and pres-
surization prior to the receptacle's collapse, and the dura-
tion of the rupture-depressurization, is extremely difficult
to quantify. Experiments with explosives have demon-
strated that the overpressure can be estimated using an
equivalent mass of TNT. An approximate way to calculate
the equivalent weight of TNT (Wr) for a BLEVE has
been described by Prugh1'm] as

k-1 (I)
PV ( 1 1
WTNT= 0.024 1- (8)
-k- 1 P

where P is the pressure existing in the receptacle before
the rupture [bar]. V* is given as

V* =V, +V f D1 (9)
( Dv
where V and V, are the volumes of vapor and liquid [m3]
in the vessel before the explosion; D, and Dv are the densi-
ties of liquid and vapor at the pressure and temperature of
the system before the explosion; k is the ratio of Cp and
Cv; and f is the fraction of liquid that flashes after depres-
surization. This can be calculated by the simple energy
balance


Cp(T -Tb )
f =m =1 e AHv
mo


where m0 and mv are the initial mass of liquid and the
amount vaporized in the flash, respectively, To is the ini-
tial temperature, Tb is the normal boiling temperature, C
is the heat capacity, and AHv is the heat of vaporization.
This expression to calculate f usually gives values on the
order of two times smaller than those observed experimen-
tally,1161 concluding that a flash fraction well above 20%
might be considered as a total vaporization.
To calculate the equivalent TNT mass, the following data
can be used:
* Liquid and vapor density are taken from reference 14
* Values for C (2.64 kJ/kg-K) and AHv (430 kJ/kg) are
taken from reference 5.
Boiling temperature of propane at atmospheric
pressure is 231 K
The value of f obtained with these data is 0.38. It has been
mentioned that a more realistic value of the fraction that
flashes is two times the value obtained with Eq. (10); there-
fore, the final estimation of f = 0.76 is close to 1. With f
equal to 1, the equivalent TNT is 423.6 kg.
The TNT model is based on an empirical law established
from trials using explosives.171 This "cubic root law" es-


Summer 2002


-- CCPS Model [10]
Elia Model [12]
.,.-. Pape et al Model [13]
S-- Roberts Model [11










S100 1000


1










tablishes equivalent overpressure effects for explosions oc-
curring at the same normalized distances, expressed as

R
z= 1/3(11)
(WTNT)3 (

where z is the normalized distance [mnkg-m] and R is the
real distance [m]. The experimental relation between over-
pressure and normalized distance for unconfined explo-
sions can be found in several references.5'11 Figure 3
shows the overpressure profile along distance for the
proposed scenario.

INTRODUCTION TO
VULNERABILITY ANALYSIS
The objective is to calculate the vulnerability to persons
or installations expressed as the number of individuals or
installations that could possibly be affected to a certain
level of injury because of an accident. A possible method
for estimating vulnerability consists of relating the dose
received with the effect considered. This can be achieved
from empirical evidence showing that individuals who
have been subjected to a certain dose of the injuring agent
(e.g., a certain radiation intensity level during a given time)
have suffered a particular effect (e.g., death by burn).
Therefore, the methods that relate causes directly with ef-
fects are hardly used, and the approximations to the prob-
lem of estimation of vulnerability generally follow a proba-
bilistic approach. The Probit scale is a way of dealing with
such approximations. The connection between Probit units
(Y) and probability (P) is given by

Y-5 u2
P=- e 2du (12)


The result of this expression is the Probit distribution with
mean 5 and variance 1. The curve relating percentages and
Probit units is shown in Figure 4.
Given the characteristics of the Probit variable, the fol-
lowing relationship can be written

Y = k +k2 fnV (13)

where Y is the number of Probit units, k, and k2 are em-
pirical constants depending on the causative factor and the
level of damage to be analyzed, and V measures the inten-
sity of the damage causative factor. The way in which V is
expressed depends on the type of effect studied. Table 3
shows some values of the empirical constants (k, and k2)
and the expression related with V.
The Probit expressions for prediction of the effects pro-
duced by a given radiation intensity level during a given
time use a causative factor, V, proportional to the product
t'IR4/3 (t is the exposure time and IR is the intensity of radia-
tion level). Regarding vulnerability to explosions, V is the


10 100 1000
Distance (m)

Figure 3. Overpressure along distance for the BLEVE
proposed scenario.


2 3 4 5 6 7 8
Probit Units

Figure 4. Probability and Probit units relationship.


TABLE 3
Probit Correlations for a
Variety of Causes and Effectsi18s211


Cause
Explosion
Explosion
Explosion
Explosion
Thermal effects
Thermal effects


Effect
Lung hemorrhage
Eardrum rupture
Structural damages
Glass breakage
Mortality
Secnnd-deeree burns


Thermal effects First-degree bums


k, k,
-77.1 6.91
-15.6 1.93
-23.8 2.92
-18.1 2.79
-38.5 2.56
-39.8 3.02
-43.1 3.02


V
Overpressure peak'"
Overpressure peak")
Overpressure peak()
Overpressure peak("
IR4/3*t(2)
IR4/3*t(2)
IR 4/3t(2
18 2


(1) Overpressure expressed in [Pa]
(2) IR the intensity of radiation level received [W/m2]
and t the exposure time [s]


Chemical Engineering Education











overpressure at a given point.

Figure 5 shows the percentage of people and installations af-
fected by different effects and causes. The values of overpres-
sure and radiation intensity received by a surface at a distance
X (Elia model) obtained in the previous section (consequence
analysis models) were used; the exposure time was taken as

tLEVE obtained with the Elia model.[121 Table 4 shows the esti-
mated distances at which 1% and 50% of the population or struc-
tures can be affected by a given effect. The limit at which 1% of
the population may die is called "mortality threshold."

CONCLUSIONS

Risk analysis of major accidents is a useful tool for future
chemical engineers; it gives not only a quantitative estimation
of the risk involved in a given process, but also a suitable method
for estimation of possible victims (environment, persons, and


"o

a.
a)
m 0

"o
0)
a-a



"i



oa.
a)


a)
0.


200 300
Distance (m)


Figure 5. Percentage of people and installations affected
by different effects and causes at a given point:
overpressure effects (solid line) and
thermal effects (dotted line).

TABLE 4
Distance at which 1% and 50% of the Population
(People or Objects) are Affected

Cause Effct Distance Distance
ml 50% [mll%
Explosion Lung hemorrhage 18.8 22.3
Explosion Eardrum rupture 34.4 63.0
Explosion Structural damages 51.6 84.7
Explosion Breakage of glass 162 321
Thermal effects Mortality due to thermal radiation 153 212
Thermal effects Second-degree bums'" 222 293
Thermal effects First-degree bums" 329 436

Epidermis and part of the dermis are burned
2 A superficial bum in which the top layer of skin (part of the epidermis) has
been slightly burned


properties). A boiling-liquid expanding-vapor explosion
(BLEVE) of a tank truck of liquid propane has been used
to demonstrate this technique, and the blast and thermal
effects have been calculated with several methods. The vul-
nerability of persons and/or installations affected in both
cases has been calculated using the Probit methodology.


REFERENCES
1. Lane,A.M., "Incorporating Health, Safety, Environmental, and Ethi-
cal Issues into the Curriculum," Chem. Eng. Ed., 23, 70 (1989)
2. Cohen, Y, W. Tsai, and S. Chetty, "A Course on Multimedia Envi-
ronmental Transport, Exposure, and Risk Assessment," Chem. Eng.
Ed., 24, 212 (1990)
3. Gupta, J.P., "AChemical Plant Safety and Hazard Analysis Course,"
Chem. Eng. Ed., 23, 194 (1989)
4. Mannan, M.S., A. Akgerman, R.G. Anthony, R. Darby, P.T. Eubank,
and R.K. Hall, "Integrating Process Safety into the Education and
Research," Chem. Eng. Ed., 33, 198 (1999)
5. Santamaria, J.M., and P.A. Brafa, "Risk Analysis and Reduction
in the Chemical Process Industry," Blackie Academic & Profes-
sional (1998)
6. Golder, A., "Safety Relevance in Undergraduate Education,"
SACHE News, Spring 4 (2000)
7. Rossignol, A.M., and B.H. Hanes, "Introducing Occupational Safety
and Health Material into Engineering Courses," Eng. Ed., 80,430
(1990)
8. Reid, R.C., J.M. Prausnitz, and B.E. Poling, The Properties of Gases
and Liquids, McGraw-Hill, New York, NY (1987)
9. Reid, R.C., "Possible Mechanism for Pressurized-Liquid Tank Ex-
plosions or BLEVEs," Science, 3, 203 (1979)
10. CCPS (Center for Chemical Process Safety), Guidelinesfor Chemi-
cal Process Quantitative Risk Analysis, AIChE, New York, NY
(1989)
11. Roberts, A.E, "Thermal Radiation Hazards from Release of LPG
Fires from Pressurized Storage," Fire Safety J., 4, 197 (1982)
12. Elia, E, Risk Assessment and Risk Managementfor the Chemical
Process Industry, H.R. Greenberg and J.J. Cramer, eds., Van
Nostrand Reinhold, New York, NY (1991)
13. Pape, R.P., et al., "Calculation of the Intensity of Thermal Radia-
tion from Large Fires," Loss. Prev. Bull., 82, 1 (1988)
14. Perry, R.H., and D. Green, eds, Perry's Chemical Engineer's Hand-
book, 6th ed., McGraw-Hill, New York, NY (1984)
15. Prugh, R.W., "Quantify BLEVE Hazards," Chem. Eng. Prog., 87,
66(1991)
16. Kletz, T. "Unconfined Vapor Explosions," Loss Prevention 11,
Chem. Eng. Prog. Tech. Manual, AIChE, New York, NY (1977)
17. Hopkinson, B., British Ordnance Board Minutes 13565 (1915)
18. Crowl, D.A., and J.F. Louvar, Chemical Process Safety: Funda-
mentals with Applications, Prentice Hall, Englewood Cliffs, NJ
(1990)
19. CCPS (Center for Chemical Process Safety): "Guidelines for Evalu-
ating the Characteristics of Vapor Cloud Explosions, Flash Fires,
and BLEVEs," AIChE, New York, NY (1994)
20. Pietersen, C.M., and S.C. Huerta, "Analysis of the LPG Incident in
San Juan Ixhuapetec, Mexico City, 19-11-84," TNO Report B4-
0222, TNO, Directorate General of Labor, 2273 KH Vooburg, Hol-
land (1985)
21. TNO, "Methods for the Determination of Possible Damage to
People and Objects Resulting from Release of Hazardous Materi-
als," CPR 16E, Vooburg, Holland (1992) 0


Summer 2002










[Aw classroom


RUBRIC DEVELOPMENT AND


INTER-RATER RELIABILITY ISSUES

In Assessing Learning Outcomes



JAMES A. NEWELL, KEVIN D. DAHM, AND HEIDI L. NEWELL
Rowan University Glassboro, NJ 08028


With the increased emphasis placed by ABET"11 on
assessing learning outcomes, many faculty
struggle to develop meaningful assessment instru-
ments. In developing these instruments, the faculty members
in the Chemical Engineering Department at Rowan Univer-
sity wanted to ensure that each instrument addressed the three
fundamental program tasks as specified by Diamond:121
E The basic competencies for all students must be stated in
terms that are measurable and demonstrable.
El A comprehensive plan must be developed to ensure that
basic competencies are learned and reinforced throughout
the time the students are enrolled in the institution.
[E Each discipline must specify learning outcomes congruent
with the required competencies.
Like many other institutions,3]' Rowan University's Chemi-
cal Engineering Department chose to use items that address
multiple constituencies including alumni, industry, and the
students themselves. Assessment data from these groups were
obtained through alumni surveys, student peer-reviews, and
employer surveys. These instruments were fairly straightfor-
ward to design and could be mapped directly to the educa-
tion objectives specified in Engineering Criteria 2000 (Crite-
rion 3, A-K) as well as the AIChE requirements and other
department-specific goals. Regrettably, over-reliance on sur-
vey data often neglects those most qualified to assess student
performance-the faculty themselves.
The faculty agreed that student portfolios would provide a
valuable means of including faculty input into the process. The
difficulty arose when the discussion turned to evaluating the
portfolios. Paulson, et al.,[4 define portfolios as a "purposeful
collection of student work that exhibits the students' efforts,
progress, and achievement." As Rogers and Williamst'5 noted,
however, there is no single correct way to design a portfolio
process. Essentially everyone agreed that a portfolio should
contain representative samples of work gathered primarily
from junior- and senior-year courses. The ABET educational
objectives are summative rather than formative in nature, so


the faculty decided to focus on work generated near the end
of the student's undergraduate career. A variety of assign-
ments would be required to ensure that all of the diverse cri-
teria covered in Criterion 3 could be addressed by at least
some part of the portfolio. At the same time, we were acutely
aware that these portfolios would be evaluated every year and
were understandably interested in minimizing the total amount
of work collected. Ultimately, we selected the following items:
El A report from a year-long, industrially sponsored research
project through the Junior/Senior Clinics
EL The Senior Plant Design final report
El A hazardous operations (haz-op) report
El One final examination from a junior-level chemical
engineering class (Reaction Engineering or Heat Transfer)
3 One laboratory report from the senior-level Unit Opera-
tions Laboratory Course
These items were all constructed-response formats[6-8' in which
a student furnished an authentic response to a given assign-
ment or test question. This format was selected over multiple
choice selected response formats because it better represents
realistic behavior.[9] The selected-response format presents
alternative responses from which the student selects the cor-
rect answer; specific selected response formats include true-
false, matching, or multiple choice exams, while constructed
response formats include essay questions or mathematical

James Newell is Associate Professor of Chemical Engineering at Rowan
University. He is currently Secretary/Treasurer of the Chemical Engineer-
ing Division ofASEE. His research interests include high performance poly-
mers, outcomes assessment and integrating communication skills through
the curriculum.
Kevin Dahm is Assistant Professor of Chemical Engineering at Rowan
University. He received his PhD in 1998 from Massachusetss Institute of
Technology. Before joining the faculty of Rowan University, he served as
Adjunct Professor of Chemical Engineering at North Carolina A& T State
University.
Heldl Newell is the Assessment Consultant for the College of Engineering
at Rowan University She holds a PhD in Educational Leadership from the
University of North Dakota, a MS in Industrial/Organizational Psychol-
ogy from Clemson University, and a BA in Sociology from Bloomsburg
University of Pennsylvania.
Copyright ChE Division of ASEE 2002


Chemical Engineering Education


212









problem solving.1101 Although the items contained in the port-
folio provided a wide range of work samples, they could not
be as neatly mapped to the ABET criteria. There was simply
no way to look at a laboratory report and assign a number
evaluating the student's ability to apply math, science, and
engineering. The immediate question that arose from the fac-
ulty was, "Compared to whom?" A numerical ranking com-
paring Rowan University's chemical engineering students to
undergraduates from other schools may be very different than
one comparing students to previous classes. It became clear
that specific descriptions of the performance level in each
area would be required so that all faculty could understand
the difference between a 4 and a 2. As Bantat"1 stated, "The
challenge for assessment specialists, faculty, and administra-
tors is not collecting data but connecting them." The chal-
lenge became one of developing rubrics that would help map
student classroom assignments to the educational objectives
of the program. The four-point assessment rubric also fol-
lowed the format developed by Olds and Millert121 for
evaluating unit operations laboratory reports at the Colo-
rado School of Mines.

COURSE VS PROGRAMMATIC ASSESSMENT
Other chemical engineering departments are also develop-
ing rubrics for other purposes. In their exceptional (and Mar-
tin-Award winning) paper on developing rubrics for scoring
reports in a unit operations lab, Young, et al.,E 31 discuss the
development of a criterion-based grading system to clarify
expectations to students and to reduce inter-rater variability
in grading, based on the ideas developed by Walvoord and
Anderson.E141 This effort represents a significant step forward
in course assessment. The goals of course assessment and
program assessment are quite different, however.
For graded assignments to capture the programmatic ob-
jectives, a daunting set of conditions would have to be met.
Specifically,
[ Every faculty member must set proper course objectives
that arise exclusively from the program's educational
objectives and fully encompass all of these objectives
[I Tests and other graded assignments must completely
capture these objectives
E Performance on exams or assignments must be a direct
reflection of the student's abilities and not be influenced by
test anxiety, poor test-taking skills, etc.
If all of these conditions are met, there should be a direct
correlation between student performance in courses and the
student's overall learning. Moreover, much of the pedagogi-
cal research warns of numerous pitfalls associated with us-
ing evaluative instruments (grades on exams, papers, etc.)
within courses as the primary basis for program assessment.t151
One of the immediate difficulties is that many criteria are
blended into the grade. A student with terrific math skills could
handle the partial differential equations of transport phenom-
ena but might never understand how to apply the model to


practical physical situations. Another student might under-
stand the physical situation perfectly but struggle with the
math. In each case, the student could wind up with a C on an
exam, but for very different reasons. This is not a problem from
the perspective of the evaluation; both students deserve a C.
But, from an assessment standpoint, the grade does not provide
enough data to indicate areas for programmatic improvement.
Moreover, if exams or course grades are used as the pri-
mary assessment tool, the impact of the entire learning experi-
ence on the student is entirely ignoredt161. Community activi-
ties, field trips, service projects, speakers, and campus activi-
ties all help shape the diverse, well-rounded professional with
leadership skills that industry seeks. The influence of these non-
classroom factors cannot be measured by course grades alone.
The goal of our rubrics was to map student work directly
to the individual learning outcomes. This also put us in a po-
sition to more directly compare our assessment of student
work with the assessment of performance provided by stu-
dent peer reviews, employers, and alumni.

RUBRIC DEVELOPMENT
The first step was to take each educational objective and
develop indicators, which are measurable examples of an
outcome through phrases that could be answered with "yes"
or "no." A specific educational objective and indicator is
shown below.
Goal 1, Objective 1: The Chemical Engineering Program
at Rowan University will produce graduates who demon-
strate an ability to apply knowledge of mathematics, sci-
ence, and engineering (ABET-A).
Indicators:
1. Formulates appropriate solution strategies
2. Identifies relevantprinciples, equations, and data
3. Systematically executes the solution strategy
4. Applies engineering judgment to evaluate answers
Once the indicators for each objective were developed, the
next task involved defining the levels of student achievement.
Clearly, the lowest level should be what a novice demon-
strates when confronted with a problem. The highest level
should show metacognition,t161 the students' awareness of their
own learning skills, performance, and habits. To achieve the
highest level, students not only have to approach the prob-
lem correctly, but they must also demonstrate an understand-
ing of their problem-solving strategies and limitations. The
intermediate scores represent steps between a metacognitive
expert and a novice. It is important to note that the numbers
are ordinal rather than cardinal. A score of four does not im-
ply "twice as good" as a score of two.
All of the other assessment instruments used by the Chemi-
cal Engineering Department had a five-point Likert scale,
so a faculty team set out to develop meaningful scoring ru-
brics using a five-point scoring system. Initially, the scores
contained labels (5 = excellent, 4 = very good, 3 = good, 2 =
marginal, 1 = poor), but the qualitative nature of the descrip-


Summer 2002


213









tive phrases should stand alone, without the need for additional
clarifiers. Ultimately, it was decided to eliminate all labels.
It became apparent that a four-point scale allowed for more
meaningful distinctions in developing the scoring rubrics for
the portfolios. Providing four options instead of five elimi-
nates the default "neutral" answer and forces the evaluator to
choose a more definitive ranking. The four-option scale also
made it easier to write descriptive phrases that were meaning-
fully different from the levels above and below. In developing
these phrases, the following heuristic was used: for the four-
point phrases, the writer attempted to describe what a
metacognitive expert would demonstrate; for the three-point
phrases, the target was what a skilled problem solver who lacked
metacognition would display; for the two-point words, the writ-
ers attempted to characterize a student with some skills, but
who failed to display the level of performance required for an
engineering graduate; the one-point value captured the perfor-
mance of a novice problem solver.
To evaluate a given indicator, professors would read the left-
most description. If it did not accurately describe the perfor-
mance of the student, they would continue to the next block to
the right until the work was properly described. A sample ru-
bric is shown in Table 1.

RUBRIC TESTING
AND INTER-RATER RELIABILITY
Once the lengthy process of developing scoring rubrics for
each objective was completed, the rubrics needed testing. C.
Robert Pace""1 succinctly stated the challenge of accurate
assessment, saying "The difficulty in using faculty for the


assessment of student outcomes lies in the fact that different
professors have different criteria for judging students' per-
formance." The intent of the rubrics was to create specific
and uniform assessment criteria so that the role of subjective
opinions would be minimized. The ideal result would be that
all faculty members using the rubrics would assign the same
scores every time to a given piece of student work.

To evaluate if the rubrics were successful in this respect,
six samples of student work (four exams and two engineer-
ing clinic reports) were distributed to the entire faculty (seven
members at that time). All of them assigned a score of 1,2,3,
4, or "not applicable" to every student assignment for every
indicator. This produced 160 distinct score sets (excluding
those that were all "not applicable") that were examined
for inter-rater reliability.

The results, in general, were excellent. Every faculty mem-
ber scored the items within one level of each other in 93% of
the items. In 47% of the score sets (75 of 160), agreement
was perfect-all faculty members assigned exactly the same
score. In another 46%, all assigned scores were within 1.
Rubrics for which this level of agreement was not achieved
were examined more closely for possible modification. After
all of the scoring sheets had been compared, the faculty met
to discuss discrepancies in their evaluations.

The primary example of a rubric that required modifica-
tion is shown in Table 2. "Solutions based on chemical engi-
neering principles are reasonable," in the originally devel-
oped scheme, was an indicator that applied to a number of
different educational objectives. This was the only rubric for


TABLE 1


problems to equations;
sees what must be done


Identifies relevant principles,
equations, and data

Systematically executes the
solution strategy

Applies engineering judgment
to evaluate answers


Consistently uses relevant
items with little or no
extraneous efforts
Consistently implements strategy;
gets correct answers


Has no unrecognized
implausible answers


3
Forms workable
strategies, but may not be
optimal; occasional
reliance on brute force
Ultimately identifies relevant
items but may start with
extraneous information
Implements well;
occasional minor errors
may occur


Has no more than one, if any,
unrecognized implausible
answers; if any, it is minor
and obscure


2
Has difficulty in
planning an approach;
tends to leave some
problems unsolved
Indentifies some principles
but seems to have difficulty
in distinguishing what is needed


Has some difficulty in solving
the problem when data are
assembled; frequent errors


Attempts to evaluate answers
but has difficulty; recognizes
that numbers have meaning
but cannot fully relate


1
Has difficulty getting
beyond the given unless
directly instructed

Cannot identify and assemble
relevant information

Often is unable to solve
problem, even when all data
are given
Makes little, if any, effort
to interpret results; numbers
appear to have little meaning


TABLE 2
4 3 2 1
Solutions based upon Has no unrecognized Has no more than one, if any, Attempts to evaluate answers Makes little, if any, effort to
chemical engineering principles implausible answers unrecognized implausible answers; but has difficulty; recognizes interpret results; numbers
are reasonable if any, it is minor and obscure that numbers have meaning appear to have little meaning
but cannot fully relate.


strategies


4
Formulates appropriate solution Can easily convert word


214


Chemical Engineering Education









which scores were not routinely consistent. One heat-trans-
fer exam received a range of scores that included multiple
occurrences of both 4 and 1.
In the ensuing discussion, we found that the difficulty with
this exam was that nothing recognizable as a final answer
was presented for any question. The student formulated a
solution strategy and progressed through some work but never
finished solving the equations. Interpreting the rubric word-
ing in one way, some faculty chose to assign 4. This interpre-
tation is understandable because no answer was given, and
there was no "unrecognized implausible answer." By the let-
ter of the criteria, the student earned a 4. Some faculty inter-
preted the criteria differently, however, resulting in the as-
signment of 1. This interpretation is also reasonable-since
there were no results, there was no attempt to interpret the
results. The rubric was simply re-written to specify that a
rating of N/A be given if no recognizable "final answer" was
provided, and the discrepancies in scoring were not present
in subsequent evaluations.
In addition to pointing out necessary revisions, this testing
provided a good measure of inter-rater reliability. Having
every faculty member review every item in an annual assess-
ment portfolio would be a laborious task. Consequently, the
results of this test were examined to determine what level of
accuracy could be expected when a group of three faculty
reviewed an item. For example, in the score set 2, 2, 2, 2, 1,
3, 2; the mean score assigned by the faculty was 2, and the
mean of a three-score subset could be 1.67, 2, or 2.33. This
means that any panel of three faculty members would have
assessed this sample of work with a score within 0.5 of that
assigned by the entire faculty. We found (after one rubric was
revised as described above) that 95% (153 of 160) of the score
sets showed this level of consistency. Thus, we concluded that
when using the rubrics, a randomly constituted panel of three
faculty members would be reasonably representative of the de-
partment. Detailed rubrics are available through the web at


CLOSING THE LOOP
Ultimately, the purpose of gathering detailed assessment
data is to improve student learning. Once each year, we re-
view the data in a two-day assessment meetingm3 where we
discuss all aspects of the program, including the data from
each tool. We identify strengths and areas for improvement
and make decisions affecting curriculum and policies. Spe-
cific changes resulting from these meetings have included
a decision to introduce product engineering and econom-
ics earlier in the curriculum and to adjust topical cover-
age in thermodynamics.

THE NEXT LEVEL
The next goal is to use the rubrics to help guide selection
of course objectives across the curriculum. With detailed edu-


national objectives in place and rubrics to assist in their as-
sessment, we hope improved course objectives will be de-
veloped that more directly link classroom activities and evalu-
ations with the program goals. The rubrics described in this
paper should provide the basis for a more in-depth, forma-
tive assessment. Although the ABET criteria are summative,
the educational process itself centers around formative
changes, incrementally enhancing a student's knowledge, skill
set, and problem-solving capabilities.

CONCLUSIONS
A complete set of rubrics was developed and tested that
maps student performance of a variety of junior/senior-level
assignments directly to program educational objectives. These
rubrics were tested for inter-rater reliability and were shown
to yield the same mean (within 0.5) regardless of which set
of three faculty members evaluated the material. These re-
sults, in conjunction with input from alumni, employers, and
the students themselves, serve as a basis for assessment of
the chemical engineering program.

REFERENCES

1. Engineering Accreditation Commission, Engineering Criteria 2000, Ac-
creditation Board for Engineering and Technology, Inc., Baltimore (1998)
2. Diamond, R.M., Designing andAssessing Courses and Curricula: A Prac-
tical Guide," Jossey-Bass Inc., San Francisco (1998)
3. Newell, J.A., H.L. Newell, T.C. Owens, J. Erjavec, R. Hasan, and S.P.K.
Sternberg, "Issues in Developing and Implementing an Assessment Plan in
Chemical Engineering Departments," Chem. Eng. Ed., 34(3), p. 268 (2000)
4. Paulson, L.F., P.R. Paulson, and C. Meyer, "What Makes a Portfolio a
Portfolio?" Educational Leadership, 48(5), p. 60 (1991)
5. Rogers, G.M., and J.M. Williams, "Asynchronous Assessment: Using Elec-
tronic Portfolios to Assess Student Outcomes," Proc. of the 1999 ASEE
Nat. Mtng., Session 2330, Charlotte (1999)
6. Morris, L.L., C.T. Fitz-Gibbon, and E. Lindheim, How to Measure Per-
formance and Use Tests, Sage Publishers, Newberry Park, CA (1987)
7. Roid, G.H., and T.M. Haladyna, A Technologyfor Test-Item Writing, Aca-
demic Press, San Diego (1982)
8. Robertson, G.J., "Classic Measurement Work Revised: An Interview with
Editor Robert L. Linn," The Score, p.1 (1989)
9. Fitzpatrick, R., and E.J. Morrison, "Performance and Product Evaluation,"
in Educational Measurement, R. Thomdike ed., American Council of Edu-
cation, Washington DC (1989)
10. Erwin, T. Dary, Assessing Student Learning and Development, Jossey-
Bass, San Francisco (1991)
11. Banta, T.W., J.P. Lund, K.E. Black, and FW. Oblander, Assessment in Prac-
tice, Jossey-Bass Inc., San Francisco (1996)
12. Olds, B.M., and R.L. Miller, "Using Portfolios to Assess a ChE Program,"
Chem. Eng. Ed., 33(2), 110 (1999)
13. Young, V.L., D. Ridgway, M.E. Prudich, D.J. Goetz, B.J. Stuart, "Crite-
rion-based Grading for Learning and Assessment in the Unit Operations
Laboratory," Proc. of the 2001 ASEE Nat. Mtng., Albuquerque (2001)
14. Walvoord, B.E., and V.J. Anderson, Effective Grading: A Tool for Learn-
ing and Assessment, Jossey-Bass Inc., San Francisco (1998)
15. Terzini, PT., and E.T. Pascarella, How College Affects Students: Findings and
Insights from Twenty Years ofResearch, Jossey-Bass Inc., San Francisco (1991)
16. Angelo, T.A., and K.P. Cross, Classroom Assessment Techniques: A Hand-
bookfor College Teachers, 2nded., Jossey Bass Inc., San Francisco (1993)
17. Pace, C.R., "Perspectives and Problems in Student Outcomes Research,"
in Assessing Educational Outcomes, Peter Ewell ed., Jossey-Bass Inc.,
San Francisco (1985) 0


Summer 2002










,f lIaboratory


MASS TRANSFER

AND CELL GROWTH KINETICS

IN A BIOREACTOR



KEN K. ROBINSON, JOSHUA S. DRANOFF, CHRISTOPHER TOMAS, SESHU TUMMALA
Northwestern University Evanston, IL 60208-3120


Biotechnology is an increasingly important factor in
the chemical process industries. The last decade has
seen rapid growth in the resources committed to the
development of biologically based processes. At the same
time, the market value of new products generated by biologi-
cal means has continued to grow at an accelerating rate. Ac-
cordingly, more and more chemical engineers are being em-
ployed in the development, design, and operation of
bioprocesses for production of pharmaceuticals, foods, and
specialty chemicals, with no indication that the demands and
opportunities in this area will moderate in the future.
In recognition of this trend, we have developed a new "bio-
technology experiment" for Northwestern's senior laboratory
course.m1 This experiment is aimed at giving our students an
opportunity to become familiar with various factors involved
in the implementation of bioprocesses and some of the atten-
dant technologies. We hope this will introduce them to this
broad field while they are still at Northwestern and also en-
hance their attractiveness to potential employers.
The experiment provides a means for studying two basic
chemical engineering operations (mass transfer and cell
growth kinetics) that occur in a three-liter stirred fermenta-
Ken Robinson is a Lecturer at Northwestern University with primary re-
sponsibility for the undergraduate chemical engineeirng laboratory. He
received his BS and MS from the University of Michigan and his DSc from
Washington University. He has worked in industry for both Amoco and
Monsanto.
Joshua Dranoff is Professor of Chemical Engineering at Northwestern
University. He received his BE degree from Yale University and his MSE
and PhD from Princeton University. His research interests are in chemical
reaction engineering and chromatographic separations.
Christopher Tomas is a PhD candidate at Northwestern University work-
ing under the direction of Professor E. Terry Papoutsakis. He received his
BS in Chemical Engineering from the University of Illinois, Urbana-
Champaign, in 1996, and his MS in Biotechnology from Northwestern
University in 1998.
Seshu Tummala is a PhD candidate at Northwestern University working
under the direction of Professor E. Terry Papoutsakis. He received his BS
degree from The Johns Hopkins University in 1996 and his MS degree
from Northwestern University in 1999, both in chemical engineering.
Copyright ChE Division of ASEE 2002


tion reactor. The initial part of the experiment involves the
study of oxygen transfer rates from gas to liquid phases; tran-
sient dissolved oxygen profiles resulting from step changes
in feed gas oxygen concentration are measured with a dis-
solved oxygen probe. The growth kinetics of Escherichia coli
are then studied in the same reactor under standard condi-
tions. Cell growth is monitored by spectrophotometric analy-
sis of samples removed from the reactor at specific times.
The complete experiment is normally run in two successive
laboratory sessions, each about eight hours long, separated
by one week. It is also necessary to perform some short pre-
parative steps the day prior to the second laboratory session.

EXPERIMENT SETUP
Equipment The principal apparatus used is an Applikon
three-liter glass stirred bioreactor. It was obtained as part of a
complete package that included a number of ancillary items,
such as temperature, pH, and oxygen probes and control sys-
tems. Additional major items obtained for this purpose in-
cluded an Innova 4200 shaken-cell incubator and a basic spec-
trophotometer (Spectronic 20+). The approximate cost of this
equipment is indicated in Tablel. Not included in the indi-
cated cost, but of critical importance for this experiment, is a
steam sterilizer large enough to accommodate the fermenta-
tion reactor. We had access to such a unit in our department
(AMSCO Eagle 2300 Autoclave) and assume that similar
equipment is likely to be available in chemical engineering
or related departments at other institutions.
A sketch of the reactor is shown in Figure 1. It is stirred
with dual turbine blade impellers on a single shaft, driven by
an electrical motor with an adjustable speed control. The re-
actor top is a stainless steel disk equipped with multiple ports
for sampling, introduction of inoculum, gas feed and outlet
lines, and insertion of temperature, pH, and dissolved oxy-
gen measuring probes. Additional specifications are indi-
cated in the Appendix.


Chemical Engineering Education









Gas is fed into the reactor and dispersed into the liquid
through an L-shaped sparger tube that has multiple holes along
the horizontal section that is located near the bottom of the
reactor vessel. Outlet gas passes through a small water-cooled
condenser tube that serves to prevent evaporation of water
from the normally warm liquid contents of the reactor.
Temperature in the vessel is sensed by a type-J thermo-
couple inserted through one of the reactor ports and controlled
by a simple electronic control system. An electrically heated
jacket provides required heat input, while cooling water can
be simultaneously circulated through a small cooling coil
immersed in the reactor liquid. Stable control of the reactor
temperature at 370C is easily achieved with this system.
The bioreactor can be fed with three different gases. Air is
supplied by an air pump with an inlet microfilter; pure oxy-
gen and nitrogen are provided from pressurized cylinders.
The nitrogen is used in calibrating and spanning the dissolved
oxygen probe and in the oxygen transfer-rate experiments.
Air and oxygen are used in the cell-growth kinetics studies in
conjunction with the dissolved oxygen (DO) controller. Dur-
ing a typical cell-growth experiment, air is continuously
sparged into the liquid medium in the reactor with the con-
troller set point at 70% of total saturation relative to pure air.
Whenever the measured oxygen concentration falls below
70%, a three-way valve is actuated automatically to switch
the sparging gas from air to pure oxygen. This control scheme
is normally quite effective in returning the DO level back to
the set point within a few minutes, except during the high
oxygen uptake portion of the cell-growth curve (exponential

TABLE 1
Major Equipment Needed for Experiment

1 Applikon 3-liter fermentor, with control system and $15,000
oxygen, temperature, and pH probes
El Innova 4200 Incubator $ 5,000
El Spectronic Instruments 20+ Spectrophotometer $ 1,700
Total Cost $21,700


Gas Outlet
Stirrer motor



Gas Inlet-
Sample
bottle



Shermowell
Gas"L" sparger
Double blade impeller

Figure 1. Fermentation reactor.

Summer 2002


phase described below). At such times, the stirrer speed can
be increased from 250 rpm (normal operating level) to 350
rpm in order to increase the gas-liquid interfacial area enough
to permit increased oxygen transfer to the liquid phase. Op-
eration at these stirrer speeds was found to be convenient
and minimized foam formation during experiments (no anti-
foaming agents were used).
Expendable Supplies To perform the following experi-
ments, a number of reagents and other expendable supplies
are required. They include sodium chloride, Ampicillin,
Tryptone, yeast extract, Agar, ethanol, deionized water, and
bleach, as well as disposable gas-line filters.

DESCRIPTION OF THE EXPERIMENTS
(A) Determination of the Oxygen Transfer Coefficient
The first quantity measured with this system is the com-
bined mass transfer coefficient for oxygen transfer from the
gas to the liquid phase, ka. (Since the interfacial area avail-
able for mass transfer cannot be readily determined in these
experiments, it has been incorporated in the definition of the
coefficient in the usual fashion.) This simple experiment pro-
vides an opportunity for the student to become familiar with
various parts of the apparatus while illustrating an important
chemical engineering principle.
The reactor is assembled and filled with 2 liters of deion-
ized water. With the stirring speed set at 250 rpm, the tem-
perature control system is activated and the system is allowed
to reach a steady temperature of 370C.
The DO probe, having been previously polarized by op-
eration for two hours in deionized water, is connected. The
reactor is sparged with nitrogen at a rate of approximately
0.5 liters/minute until the DO signal has stabilized (normally
about 30-45 minutes), at which point the zero of the DO con-
troller is set to read 0% oxygen. The nitrogen flow is then
replaced by air at the same volumetric rate and flow is main-
tained until the DO probe output remains constant. At this
point the controller span is adjusted to read 100% (i.e., satu-
ration with respect to the oxygen content of air).
The feed gas is then rapidly switched back to nitrogen
(step down in feed gas oxygen concentration), and the DO
concentration is recorded every 30 seconds to 1 minute until
it returns to 0%. The feed is then rapidly switched back to air
(step up in feed gas oxygen concentration), and DO concen-
tration is recorded every minute until it returns to 100%. These
"step-up" and "step-down" data are then analyzed as indi-
cated below to determine kLa.
(B) Determination of Cell Growth Kinetics
This is the more difficult and demanding part of the ex-
periment, especially for students unfamiliar with the proto-
cols used in biochemical research. It involves two separate
operations: the preparation of a stock culture of active cells
and the subsequent measurement of cell growth kinetics.









Throughout this portion of the experiment, emphasis is placed
on the need to maintain sterility and cleanliness of the appa-
ratus and the work area.
> (1) Preparation of stock culture. This part of the proce-
dure is normally carried out during the first laboratory ses-
sion along with the oxygen transfer measurements described
earlier. Steps involved include:
Preparation of Luria-Bertani (LB) culture media (see
also the Discussion section).
Liquid LB medium is a mixture of sodium chloride,
Tryptone, yeast extract, and deionized water (composition
given in the Appendix).
Solid LB medium is a mixture of sodium chloride, Tryptone,
yeast extract, Agar, and deionized water (composition given
in the Appendix).
Each of these media is placed in an Erlenmeyer flask that is
then covered with aluminum foil and autoclaved for 20
minutes in the sterilizer. The liquid medium can be used in
the reactor as prepared.
The solid medium is used to prepare solid culture plates.
After the initial sterilization, the solutions are allowed to
equilibrate at 550C and then antibiotic solution is added
(see the Appendix for composition of antibiotic solution).
The medium is then poured into sterile culture plates that
are stacked and allowed to solidify in a sterile hood at
room temperature (several hours).
Preparation of Cell Cultures. The cells used in these
experiments are from an E.coli strain, ER 2275, furnished
by New England Bio Labs, Beverly, Massachusetts, and
modified (pImPl) as described by Mermelstein, et al.[2]
A stock of E.coli on the solid medium is prepared by
streaking a fresh solid medium plate with a colony of
E.coli and then incubating the plate at 370C overnight. If
individual colonies of E.coli are then easily visible on the
plate, it is placed in the refrigerator for storage. If not,
another plate is streaked and incubated, as above. This
process has proven to be easily reproducible.
Preparation of inoculum. The inoculum is a solution
containing living cells that is used to initiate the growth
process within the bioreactor. It is prepared the day prior
to the fermentation experiment. An individual colony from
a stock plate is combined in a 250-ml. Erlenmeyer flask
with 200 ml of liquid LB medium equilibrated at 370C,
antibiotic solution is added, and the inoculum is allowed
to grow overnight (for approximately 12 hours) with shak-
ing at 200 rpm in the incubator.
(2) Preparation of the Reactor for Growth Kinetics
Studies. The reactor vessel is assembled and filled with deion-
ized water and then autoclaved for approximately 20 min-
utes along with a supply of liquid LB medium prepared as
described above. After the reactor has cooled to room tem-
perature, the water is pumped out and replaced by 1.8 liters


of the LB medium. The reactor is then allowed to come to
thermal equilibrium at 37C and the control systems are acti-
vated. (The DO probe must first be polarized and calibrated,
as described above.)
(3) Growth Kinetics Studies. When the system is ready,
200 ml of the inoculum solution is pumped into the reactor
and the DO level is set to approximately 70%. A small sample
(10-15 ml) of the reactor contents is then removed every 10-
15 minutes and its turbidity measured in the spectrophotom-
eter (at a wavelength of 600 nm). If the cell concentration
gets too high, the sample is first diluted in order to keep it
within the mid-range of the spectrophotometer. The experi-
ment is concluded when the fermentation appears to have
reached the stationary phase (see below). This normally re-
quires 4 to 6 hours.
The final liquid medium still left in the reactor is auto-
claved before disposal, and all equipment is carefully
cleaned with bleach and soap.

DATA ANALYSIS
(A) Determination of Oxygen Transfer Coefficient
Typical data obtained in the "step-down" (nitrogen feed)
and "step-up" (air feed) experiments described above are
shown in Figure 2. These data were obtained with a reactor
volume of 2.0 liters, a gas flow rate of 0.38 liters per minute,
and a mixer rpm of 250. The data clearly exhibit an initial
time lag that is the same for both experiments. This lag is
apparently due to dynamic response of the dissolved oxygen
probe itself. Since it was consistent and relatively small com-
pared to the overall time scale of the experiment, the response
data have been corrected by subtracting a lag of 1.5 minutes
from the measured time in each transient experiment.
For either experiment, the oxygen transfer rate per unit
volume of liquid (OTR) is given by the following equation,
which also defined the volumetric liquid phase mass transfer
coefficient:


OTR=kLa(C*-C)


where


120 1 T_
1001
&E 80


6 40
S 20

0 5 10 15 20 25 30 35
Time, minutes
Figure 2. Typical oxygen transfer data: Dissolved oxygen
concentration vs. time.


Chemical Engineering Education










C* saturated dissolved oxygen concentration at the gas-
liquid interface, mmol/L
C dissolved oxygen concentration in the bulk liquid
phase, mmol/L
kLa liquid phase oxygen mass transfer coefficient, 1/
minute
OTR oxygen transfer rate, mmol/L/minute
The transfer coefficient typically depends on the gas fl
rate, the bioreactor working volume, and the power inpu
the agitator (or stirrer speed). It may also depend on the
rameters of the reactor design, such as impeller and spar
design and configuration, and the physical properties of
culturing medium, such as viscosity and interfacial tensi
A transient oxygen balance for the reactor volume is

dC
-=OTR=kLa(C*-C)
dt
Considering the experiment in which the initially oxyg
free solution is contacted with oxygen containing gas,
(2) must be integrated with initial concentration = 0 and c
centration C* held constant. The well-known result is

S(C -C)
in C--= -kLat
C*

For the reverse experiment in which the solution is initi;
saturated at concentration C* and the gas concentration
0, the solution is


1000
0




o
| 100 100exp(-0.155[t-1.51)



10


0.1 -. .
-5 0 5 10 15 20 25
Time, minutes
Figure 3. Typical Oxygen transfer data: Determination
kLa with nitrogen sparging.

1000 -- -- ---- --- ----- --
1000

S10texp(-0.145[t-1 .5



o 1
E 0
0

0.1
-5 0 5 10 15 20 25 3
Time, minutes


C
enC = -kLat (4)
C*

Logarithmic plots of the corrected step-down and step-up
data according to Eqs. (3) and (4) are shown in Figures 3 and
4, respectively. It can be seen that the data conform quite
well to the expected form, yielding the values for the mass
transfer coefficient of 0.155 min' for the nitrogen sparging
or step-up experiment, and 0.145 min'1 for the air sparging or
step-down experiment, for an average value of 0.15 min'.


low
t to
pa-


ger One other measurement of kLa was made with air sparging
the into the OB medium prior to the beginning of the cell-growth
on. experiments. In this case, the mixer speed was set to 150 rpm
while the other conditions remained as before. It was found
that the data once again showed a time lag of 1.5 minutes and
(2) fit the expected exponential decay similar to Figure 4. The
value of kLa determined, however, was 0.075 min-. Thus, it
;en- is clear that this mass transfer coefficient is a strong func-
Eq. tion of the degree of agitation in the vessel and the prop-
on- erties of the liquid.
It should be noted that Roberts, et al.,[3] previously described
a laboratory experiment to measure oxygen transfer in a 1-
(3) liter stirred fermentor. In that case, the stirring rate was con-
siderably higher (500 to 700 rpm) and the method of deter-
ally mining kLa was different; those authors measured the quasi-
is = steady-state rate of oxygen consumption by yeast in the ab-
sence of oxygen feed (the vessel contents were previously
saturated with air). Although conditions were quite different
in that experiment compared to the present case, the mass
transfer coefficients reported were of the same order of mag-
nitude-approximately 0.6 min- at a stirrer speed of 500 rpm.
Using their exponent of 2.75 for the effect of mixer rpm, the
expected value of ka at 250 rpm would be 0.089 min', which
is unexpectedly close to the value of 0.15 min- found here
under considerably different conditions.

(B) Determination of Cell Growth Kinetics


30


of













0


The immediate objective of the second part of the experi-
ment is to measure the specific growth rate of the E.coli cul-
ture in the batch fermentation reactor system. Typically, such
bacteria growing in a batch culture exhibit four distinct growth
phases following inoculation with an active culture. As shown
in Figure 5, growth usually begins with a very slow lag phase
as cells introduced into the inoculum adjust to their new en-
vironment. This is followed by a rapid, exponential phase as
acclimated cells reproduce via binary fission as quickly as
nutrient and oxygen concentrations within the medium per-
mit. This phase is followed by a stationary phase where the
rate of oxygen supplied to the cells equals their rate of oxy-
gen consumption. Finally, the cell concentration falls during
the death phase due to the depletion of nutrients and the
buildup of toxic byproducts.
The specific growth rate ( ~) of the cells is determined dur-
ing the exponential binary fission phase. This process is au-


Figure 4. Typical oxygen transfer data: Determination of
kLa with air sparging.

Summer 2002










tocatalytic and is usually represented as a first-order reac-
tion, i.e.,
dX
S=PX (5)
dt
Integration of this differential cell balance yields
X(t)=Xoexp[.t(t-to)] (6)
where
X cell concentration, number/volume
t time, minutes
p. cell specific growth rate, 1/minute
o as a subscript refers to initial conditions

In the present experiments, cell concentration in the reac-
tor is monitored at 10- to 15-minute intervals by measure-
ment of the absorbance (at 600 mm) of a small sample of
solution using the spectrophotometer. According to the usual
Beer-Lambert law, the light transmitted through a solution is
related to the incident light and the concentration of absorb-
ing species, as shown in
I
-=exp(-ecl) (7)
Io
where
I/I fractional light intensity relative to incident intensity
c concentration of absorbing species, number per unit volume
1 length of light path through solution
E extinction coefficient of absorbing species, area per number

Strictly speaking, for the present experiments E should be
regarded as an appropriate fitting parameter since changes in
measured light intensity are no doubt due to a combination
of absorption and scattering.
Since absorbance A is defined as -loglf(I/I), it follows from
Eqs. (6) and (7) that

A= EC EXo exp [x(t -to)] (8)
2.303 2.303
Taking natural logs of Eq. (8) yields

fn(A) = p(t to)+ fn X (9)
2.303)

Thus, a plot of in(A) against time should be linear with a
slope equal to the specific cell-growth rate (p) during the
exponential growth phase. A cell doubling time, td, can be
calculated once the growth rate is determined, according to


t n(2)
td -


Figure 6 shows typical data obtained over a 4-hour period
following the experimental procedure described earlier. These
data indicate an expected initial lag of 15 minutes, followed
by an apparent exponential growth phase that levels off some-
time after 200 minutes. When these data are plotted in accord
with Eq. (9), a good fit to the exponential model is obtained,
as shown in Figure 7. The corresponding specific growth rate


Station Phase


Sas Death Phase


U
Exponential Phase




Lag Phase

Time
Figure 5. Typical batch culture growth phases.

of the E.coli in this experiment was 0.013 minor This is equiva-
lent to a doubling time td of 53 minutes. This relatively long
doubling time confirms that the E.coli strain, while adequate
for these experiments, is not particularly robust.
The only difficulty encountered in carrying out the cell-
growth experiments has been maintaining the dissolved oxy-
gen concentration at 70%. Large swings in the oxygen level
(between 50% and 90% of saturation) have been observed
even with increases in gas-flow rate and stirring speed. These
variations, however, apparently do not have any significant
effect on the observed growth rates.


3.5 -
3
S2.5


1.5
*
0.5
0
0 60 120 180 240 300
Time, minutes

Figure 6. E.coli growth data: solution absorbance vs. time.

10 r --

jm= : 0.16.5exp(0.013(t-1 ])






0.1
-15 15 45 75 105 135 165 195 225
Time-Lag, minutes

Figure 7. Determination of specific cell-growth rate.


Chemical Engineering Education









DISCUSSION
The experiments described here have provided a means for
introducing senior students to some aspects of bioprocessing.
During the course of this experiment, students are exposed to
standard procedures for preparing and handling a bacterial
culture, including preparation of growth media, development
of active bacterial colonies, and incubation and sterilization
processes. They also become aware of the mass transfer pro-
cesses involved, the underlying theoretical analysis, and rel-
evant methods of data analysis, as well as the relatively long
time scale of the experiments. The latter is not a serious prob-
lem in our laboratory since we are able to devote two 8-hour
sessions to this experiment. Some compromises, such as more
pre-lab preparations carried out by the instructors, would
undoubtedly be necessary in order to perform similar experi-
ments in a shorter laboratory session.
In designing this experiment, we have attempted to include
as many of the preparative and analytical steps mentioned
above as possible without unduly burdening the students, since
our goal is to provide opportunies for "hands-on" experiences
whenever possible. At the same time, we are not attempting
to develop research-level competencies in our students by
this means. Selection of LB culture media as opposed to
chemically defined media is a case in point. While the former
may yield somewhat less reproducible results from one stu-
dent group to another, the LB media have proven to be robust
and easy to use. Some lack of reproducibility was not con-
sidered to be a significant drawback in the present context.
A related laboratory experiment[14 used the growth of yeast
(Saccharomyces cerevisiae) and involved the simultaneous
use of two fermenters. The rate of oxygen transfer to the liq-
uid phase was studied with and without cell growth, and the
rates of cell growth during the exponential phase were also
measured under aerobic conditions with various concentra-
tions of added ethanol. No performance data were presented,
so a more direct comparison to the present experiment is not
possible. It should be noted, however, that while the overall
goals of these two experiments are similar, the systems of
choice and the methods of data analysis differ somewhat.
Another experiment'5 based on ethanol production using
Saccharomyces cerevisiae yeast used 1 liter fermentors and
measured CO2 generated during fermentation to follow the
course of the process. As in the above-mentioned case, the
overall objective of the experiment is similar to the present
case, although it is much more limited in scope.
We have now run this experiment successfully for two years,
with increasing numbers of students and with very positive
results. While the immediate and ancillary equipment required
to mount such an experiment is not trivial or inexpensive,
such equipment is becoming relatively common and is likely
within reach of most chemical engineering departments in-
terested in providing some direct introduction to biotechnol-
ogy in their curricula. Of even greater importance than equip-


ment in the successful development of such an experiment
are skilled and experienced people who can help in the early
planning and implementation stages. We were particularly
fortunate to be able to call on Professors E.T. Papoutsakis
and W.M. Miller and some of their graduate students for tech-
nical assistance and agement.

ACKNOWLE GEM TS
We wish to thank te-ftlowing Northwestern graduate students
for their assistance and advice during the development and start-up
of this experiment: Kathy Carswell, Dominic Chow, Rick Desai,
Sanjay Patel, Albert Schmelzer, and Vivian DeZengotita. We also
thank the recent undergraduate laboratory group whose data were
used to illustrate the features of this experiment: Michael Gerlach,
Julie Nguyen, Edward Ruble, and Chris Spelbring. Finally, we are
especially thankful to Kraft, Abbott Laboratories, and the Murphy
Society of the McCormick School of Engineering and Applied Sci-
ence for the financial support that made it possible to develop and
bring this new experiment to full realization.

REFERENCES
I. Robinson, K.K., and J.S. Dranoff, Chem. Eng. Ed., 30, 98 (1996)
2. Mermelstein, L.D., N.E. Welker, C.N. Bennett, and E.T. Papoutsakis,
Bio/Technology, 10, 190 (1992)
3. Roberts, R.S., J.R. Kastner, M. Ahmad, and D.W. Tedder, Chem. Eng.
Ed., 26, 142 (1992)
4. Shuler, M.L., N. Mufti, M. Donaldson, and R. Taticek, Chem. Eng.
Ed., 28, 24(1994)
5. Badino, Jr., A.C., and C.O. Hokka, Chem. Eng. Ed., 33, 54 (1999)
Useful references for this general area are:
Biochemical Engineering, by Harvey W. Blanch and Douglas S. Clark,
Dekker(1996)
Biochemical Engineering Fundamentals, 2nd ed., by James E. Bailey
and David F. Ollis, McGraw-Hill (1986)
Bioprocess Engineering: Basic Concepts, by M. L. Shuler and F Kargi,
Prentice-Hall (1992) 0


APPENDIX

1. Composition of Luria-Bertani liquid medium:
Per liter of solution: NaCI 10 grams
Tryptone 10 grams
Yeast extract 5 grams
Deionized water 1 liter
2. Composition of Luria-Bertani solid medium:
Per liter of solution NaCl 10 grams
Tryptone 10 grams
Yeast extract 5 grams
Agar 15 grams
Deionized water 1 liter
3. Composition of antibiotic solution:
Ampicillin 1 gram dissolved in 1 ml of deionized water
Added to LB medium at concentration of 100 micrograms/ml
4. Reactor dimensions
Type: 3 liter, dished bottom
Inside diameter: 130 mm
Impeller: Two 6-bladed Rushton turbines
Turbine diameter: 45 mm
Turbine distance from vessel bottom: 45 mm and 75 mm
Baffles: Three, equally spaced baffles, each 220 mm long


Summer 2002









curriculum


TEACHING ChE TO

BUSINESS AND SCIENCE STUDENTS




KAM. NG
Hong Kong University of Science and Technology Clear Water Bay, Hong Kong


he chemical processing industries (CPI) have under-
gone profound changes, and companies are under con-
siderable pressure to restructure and innovate in a glo-
bal environment where information, technology, capital, and
human resources flow easily. Supply chain management and
e-business is used to improve the overall efficiency of an
enterprise, and there is a tendency to farm out non-core tech-
nologies. For example, recognizing that drug discovery is their
main business, pharmaceutical firms tend to outsource the
production of active pharmaceutical ingredient intermediates.
There is increasing emphasis on product design, which is
closely linked to market demands. '.2] This creates new busi-
ness opportunities and the need for better understanding of
the global issues of chemical processing. In response, there
is considerable effort to broaden chemical engineering edu-
cation to include emphasis on entrepreneurship, lifelong learn-
ing, management, business, international experience, etc.
Obviously, chemical engineering is not the only profession
reacting to the challenges of the new global environment.
Other disciplines also strive to enhance the depth and breadth
of their curriculum in order to expand employment opportu-
nities for students. A case in point is an elective course about
chemical engineering offered to business and science students
at the Hong Kong University of Science and Technology
(HKUST). Here, the semester system is identical to that of


the US, and all classes are conducted in English. There are
two similar but separate courses: one for business and one
for science students. The course for business students covers
more basic chemistry, while the one for science students is
more detailed in business concepts. We will discuss what we
teach and why, how the students respond to the course, and
what we can learn from this experience.

COURSE OBJECTIVES
Hong Kong (a Special Administrative Region of China since
1997) is a vibrant, international city of 6.7 million inhabit-
ants from all over the world. It is located in the heart of the
Asia-Pacific region where chemical processing industries
have been growing at a rate in excess of 10% per year. Hong
Kong has a strong financial sector with an interest in chemi-
cal-related businesses. While the manufacturing sector within
Hong Kong is comparatively small, extensive manufactur-
ing takes place north of Hong Kong in Shenzhen, Guangzhou,
Zhuhai, Huizhou, and other municipalities. Also, since the
GNP per capital of Hong Kong is comparable to that of other
developed countries, there is keen interest in chemical prod-
ucts that can offer a higher return on assets. Of particular
interest are high-value-added chemicals and pharmaceuti-
cals. The allure is clear when one compares the 8% profit
margin in a typical chemical firm to the 20% figure of a
US drug company.[13
The overall goal of the course is to provide business and
science students with an overview of chemical engineering.
Specifically, the student is expected to gain an appreciation
of


The CPI products
How chemicals are manufactured
The cost of building and operating a typical chemical
plant
ChE Division of ASEE 2002


Chemical Engineering Education


Ka M. Ng is Professor and Head of Chemical
Engineering and Director of the Consortium
of Chemical Products and Processes at
HKUST He obtained his BS and PhD degrees
at Minnesota and Houston, respectively. From
1980 to 2000 he was Professor of Chemical
Engineering at the University of Massachu-
setts. His research interests are in process
systems engineering involving reactions, crys-
tallization, and solids processing of high-value-
added products.










The organization andfinance of a typical chemical
company
Product-centered processing
The history of chemical engineering
The global chemical business

COURSE DESIGN
The course, consisting of six sections (see Table 1) starts
by introducing the students to the US and HK economies.[4',5
In the late '70s the breakdown of the HK GNP was similar
to that of the US. Gradually, financing, insurance, and real
estate have become dominant industries in Hong Kong. In
contrast, the US CPI is one of the largest among manufactur-
ing sectors such as electronic and electric equipment, motor
vehicles, and parts, etc. We show how the return on assets


TABLE 1
Outline of Topics

Section
1. Introduction
The economy and the chemical processing industries (CPI)
Diversity and complexity of products from the CPI
Characteristics of the CPI
2. Chemicals and Their Sources
Basic chemistry
Chemicals in our daily lives
The chemical supply chain
The chemical business hierarchy
3. The Production of Chemicals
The chemical plant and its unit operations
Project evaluation
The cost of manufacture
The criteria of economic performance
4. The Financial Performance of Chemical Corporations
Financial metrics
Financial statements
Capital budgeting
5. Product Design
Approaches to product design
Product-centered process synthesis and development
6. The Modern Chemical Processing Industries
Development of CPI in the UK, Germany, US, and Japan
The scale and economics of the CPI today
The CPI in Asia



TABLE 2
Chemicals in Our Daily Lives

Petroleum
Fibers
Soaps and detergents
Plastics
Oils and fats
Natural products
Traditional Chinese medicines


and profit margins of the CPI have fluctuated with time along
with the overall economy. Innovations such as nylon and
polyester have created new markets for chemical products.
In Section 2 of the course, we discuss selected chemical
products.16] Table 2 lists the products we have considered so
far. Petroleum is normally the first product to be discussed.
The students can easily appreciate the various uses of petro-
leum and the concept of distillation. Soaps and detergents is
another business to which the students can readily relate. They
learn about the composition of a typical detergent formula-
tion, surfactants, detergent builders, bleaching agents, and
enzymes, and how detergency works. There is a wealth of
information on the World Wide Web from the Soap and De-
tergent Association"71 as well as from companies such as
Procter and Gamble and Unilever. A typical assignment is to
read a product report in Chemical and Engineering News.ts1
The students gain an appreciation for both the need for dif-
ferentiated products that drive reformulations and the chal-
lenges faced by suppliers of detergent ingredients. We con-
sider the replacement of sodium tripolyphosphate with zeo-
lites from an environmental viewpoint, and we use pictures
and samples of chemical products such as cellulose triacetate
(for cigarette filters), spandex, sugar esters, superabsorbents
(for diapers), etc., to stimulate students' interest in the sub-
ject. Oils and fats is another business of interest to Hong Kong
students. We discuss the nature of those products, the source
of raw materials, and manufacturing processes.[9',10']
Next we show the students that all of these products origi-
nate from three sources in our environment: air and water;
substances from the ground (which include gas, petroleum,
and minerals); and living things (including plants and ani-
mals). We show the primary reaction for conversion of one
compound (or compounds) to another.1 21 For example, urea
is manufactured from ammonia and carbon dioxide; polyes-
ter results from a polycondensation reaction between ethyl-
ene glycol and terephthalic acid, which is in turn obtained
from the oxidation of paraxylene; and cellulose triacetate
comes from cotton linters. We expected the students to gain
an appreciation of the complexity of the chemical supply chain
and also introduced the concept of mass balance. We point
out the kind of companies that add value to different seg-
ments of the suppy chain, such as oil companies, chemical
companies, specialized engineering firms, pharmaceutical
companies, consumer goods companies, etc.
In Section 3 of the course, we turn our attention to the pro-
duction of chemicals using Douglas' hierarchical approach."13
After covering input-output, recycle structure, and separa-
tion systems, we discuss chemical engineering unit opera-
tions. These include reaction, evaporation, drying, distilla-
tion, absorption, extraction, crystallization, adsorption, fil-
tration, etc.1141 We discuss basic principles but omit equations
for equipment design. We use The Visual Encyclopedia of
Chemical Engineering Equipment developed at the Univer-


Summer 2002









sity of Michigan to supplement the lectures. The animated
equipment operations are very helpful to the non-engineer-
ing students. At this point, we briefly discuss safety and en-
vironment issues related to chemical processing in order to
raise the students' awareness of these issues.
We use a chemical plant in Hong Kong to illustrate pro-
cessing concepts. Towngas, produced by catalytic reaction
of naphtha with steam, is often the example of choice (see
Figure 1). The first stage of the desulfurization unit converts
organic sulfur compounds to hydrogen sulfide, and the sec-
ond stage removes hydrogen sulfide with zinc oxide. In the
reaction system, the desulfurized naptha is converted to meth-
ane and hydrogen, and carbon monoxide is converted to car-
bon dioxide and hydrogen. The carbon dioxide and water is
removed in the gas purification and drying system. Project
evaluation follows Douglas' book. The students do not have
much difficulty in grasping the details of direct costs, indi-
rect costs, working capital, etc. We also cover (particularly
for science students) the time value of money and the dis-
counted cash-flow rate of return on investment. Normally,
we assign a project in which the students perform cost evalu-
ation of a chemical plant. The flowsheet and all major equip-
ment sizes and operating conditions are given, assuming that
this input information has been obtained from chemical en-
gineers in a consulting firm.
Next we turn our attention to the financial performance of
chemical corporations. Various measurements, such as return
on net assets, after-tax profit margin, sales growth, and con-
trolled fixed-cost productivity, are introduced. We usually
examine the financial statements of two US corporations;
recently, we have discussed those of DuPont in class while
those of Eastman Chemical are analyzed in a homework as-
signment. One objective is to learn how to read the balance
sheet, the income statement, and the statement of changes in
financial position. More importantly, we emphasize an ap-
preciation of the financial position of a typical chemical com-
pany in terms of profit margin, new investments,
amount of assets on the ground, etc. This reinforces
the notion that CPI is a capital-intensive business.
To emphasize decision-making in chemical busi- Napht
nesses, we venture into capital budgeting,E151 but
this segment can be skipped if the students have
previously learned these concepts in their business
classes. Retrofit projects, as well as proposals to S
construct a grassroots plant, are considered.


Product design is of great interest to Hong Kong.
We discuss a typical product development cycle-
concept development, design and prototype, pro-
cess planning, piloting, and plant startup. We ex-
plain the use of Quality Function Deployment
(QFD); this is further refined for chemical prod-
ucts where market trends lead to product attributes,
which are in turn decided by material properties


and processing conditions (see Figure 2). We identify the
desired performance of the product, both functional and sen-
sorial, and select the requisite ingredients. The process
flowsheet and the operating conditions are then identified.
We study the modem CPI in Section 6.41 It begins with a
review of the manufacture of soda ash, dyes, and sulfuric
acid in the UK and Germany as well as the emergence of the
CPI in America in the 1900s and in Japan in the 1950s. Then
we turn our attention to today's CPI. Its global enormity is
evident when one compares the global chemical shipment of
$1.59 trillion in 1999 to the HK GDP equivalent of approxi-
mately $200 billion.
We then examine the financial performance of the top glo-
bal chemical companies, emphasizing the top twenty-five
chemical-selling countries in 1999 (see Table 3).[3] It is evi-
dent from the statistics that chemical production per capital in
Asia is below the world average, but (unsurprisingly) it is
rapidly gaining ground. Singapore is a net exporter compet-
ing in the international market. Although China is not ex-
pected to be self-sufficient, its rapid development and pur-
chasing decisions can significantly affect the global CPI. We
examine the recent JVs and investment projects in order to
appreciate the dynamics of the market in this region.1161

COURSE EVALUATION

The impact of the course has been assessed by its students.
While the course is intended for undergraduates, it generally
has around 25% graduate students from all science and busi-
ness disciplines. With rankings ranging from very bad to very
good, about 85% of the respondents ranked the overall course
as good or very good. Most of them expressed that they ac-
quired a good knowledge of chemical engineering. Also,
throughout the semester we hold a 10-to-15 minute oral quiz
every week in order to challenge them to think about interre-
lationships among different decisions. Most students felt that


Figure 1. The production of towngas by catalytic reforming
of naphtha using steam.


Chemical Engineering Education










they have been encouraged to express ideas (84% ranked as
good and very good) and have improved their ability to think
(76% ranked as good and very good).

REFLECTIONS ON
CHEMICAL ENGINEERING EDUCATION

With the reshaping of the global economic landscape, the
demarcation between disciplines has become blurred. It is
highly desirable to have an appreciation of contemporary glo-


Figure 2. Step-by-step procedure for product-centered
process synthesis and development.


TABLE 3
Top Twenty-Five Chemical-Selling Countries in 1999
(in US$ billions)t3]


1. U.S.
2. Japan
3. Germany
4. China
5. France
6. United Kingdom
7. South Korea
8. Italy
9. Brazil
10. Belgium
11. India
12. Spain
13. Taiwan


14. Netherlands
15. Switzerland
16. Russia
17. Canada
18. Mexico
19. Australia
20. Argentina
21. Sweden
22. Malaysia
23. Poland
24. Singapore
25. Thailand


bal economic issues while keeping our core competencies in
chemical engineering practice. The strategy and financial
dealings of the various companies in the global CPI covered
in this course can also serve as an interesting topic in a typi-
cal chemical engineering process design course. In fact, some
of these business concepts were covered in the senior design
course at the University of Massachusetts.
In addition to synthesizing, simulating, and costing a chemi-
cal plant, it is interesting to investigate whether or not a pro-
posed retrofit project or a new investment adds to the share-
holder value. Indeed, it is not uncommon to request that the
engineers and researchers in a company justify an R&D pro-
posal in terms of potential return on investment as well as on
its technical merits. Similarly, the lectures on product-cen-
tered process synthesis and development is suitable for chemi-
cal engineering process design. In this case, the student learns
how market demands dictate what to make, how to make it,
and where to make it, thus gaining an appreciation of the
economic consequences of these decisions in a much wider
context than in a traditional process design course.


ACKNOWLEDGMENTS

I would like to thank Bruce Vrana for his teachings on cor-
porate finance during my stay at DuPont Central R&D,
Francis Lui for providing the HK economics data, and Chi
Ming Chan for teaching the section on product design.


REFERENCES

1. Cussler, E.L., and J.D. Moggridge, Chemical Product Design, Cam-
bridge University Press, Cambridge, UK (2001)
2. Wibowo, C., and K.M. Ng, "Product-Oriented Process Synthesis and
Development: Creams and Pastes," AIChE J., 47, 2746 (2001)
3. "Facts and Figures from the Chemical Industry," C&EN, June 26, p.
48 (2000)
4. Arora, A., R. Landau, and N. Rosenberg, Chemicals and Long-Term
Economic Growth, John Wiley and Sons (1998)
5. "Estimates of Gross Domestic Product 1961 to 1997," Government of
Hong Kong, Feb. (1998)
6. Chenier, P.J., Survey of Industrial Chemistry, 2nd ed., John Wiley &
Sons (1992)
7.
8. Ainsworth, S.J., "Soaps and Detergents," C&EN, Jan 24, p. 34 (1994)
9. Hamm, W., and R.J. Hamilton, eds., Edible Oil Processing, CRC Press
(2000)
10. Hoffmann, G., The Chemistry and Technology of Edible Oils and Fats
and Other High Fat Products, Academic Press (1989)
11. O'Brien, R.D., Fats and Oils Formulating and Processing for Appli-
cations, Technomic Publishing Co., Lancaster, PA (1998)
12. Rudd, D.E, S. Fathi-Afshar, A.A. Trevino, and M.A. Stadtherr, Petro-
chemical Technology Assessment, John Wiley and Sons (1981)
13. Douglas, J.M., Conceptual Design of Chemical Processes, McGraw-
Hill, New York, NY (1988)
14. Walas, S.M., Chemical Process Equipment: Selection and Design,
Butterworths, Boston, MA (1988)
15. Ross, S.A., R.W. Westfield, and B.D. Jordan, Fundamentals of Cor-
porate Finance, 5th ed., McGraw Hill, New York, NY (2000)
16. Bank ofAmerica's Guide to Petrochemicals in Asia, EFP International,
Hong Kong (1997) O


Summer 2002










" laboratory


INTEGRATING

KINETICS CHARACTERIZATION

AND MATERIALS PROCESSING IN THE

LAB EXPERIENCE



DENNIS J. MICHAUD, RAJEEV L. GOROWARA, ROY L. MCCULLOUGH
University of Delaware Newark, DE 19716


At the University of Delaware, we have developed an
integrated sequence of two undergraduate laboratory
experiments (spanning the junior and senior years)
in which the students investigate different aspects of batch
process design. The design task assigned to the students is to
identify adequate processing conditions to produce a quality
one-inch-thick composite laminate within a limited time
frame. Thick-sectioned thermoset composites can be diffi-
cult to process correctly due to the exothermic nature of
the polymerizing resin and the low thermal conductivity
of the laminate.
The Resin Transfer Molding (RTM) process incorporates a
number of core chemical engineering concepts within a labo-
ratory exercise while at the same time introducing students
to the manufacture and properties of composite materials. A
numerical cure simulation of the RTM process,"l developed
within the Center for Composite Materials at the University
of Delaware, is used during each lab's design component to
evaluate different processing scenarios. Figure 1 outlines the
important features of the two experiments and illustrates the
manner in which they are integrated.
In the first experiment, the juniors characterize the resin's
polymerization kinetics and heat of reaction using differen-
tial scanning calorimetry (DSC). Using an empirical nonlin-
ear kinetic model for the thermosetting resin,r21 the data is
correlated to establish the model parameters needed by the
process simulation. The simulation is then used for a pre-
liminary design of the processing conditions required to suc-
cessfully produce a one-inch-thick composite laminate within
a two-hour processing window. The sensitivity of their de-
sign to kinetic parameter variability is also investigated.


The senior composite laboratory experience continues the
simulation-based sensitivity analysis of the RTM process by
including variations of the simulation's heat transfer model
parameters. The students implement their initial design, pro-
ducing a ten-inch-square composite laminate with a one-inch
through-thickness. Density, void fraction, and mechanical
tests of the laminate help students evaluate the success (or
failure) of their experiment. By comparing measurements
from thermocouples embedded within the composite and
those predicted by the simulation, the students make modifi-
cations to the simulation's model parameters (heat transfer
and kinetic) to improve the simulation's accuracy.
Armed with an improved process simulation and more
knowledge of the process, the students then generate a new
set of processing conditions and again implement it experi-
mentally, producing a new (and hopefully improved) com-
posite laminate. The students then use a combined evalua-
tion of the simulation's model parameters and their process-

Dennis J. Michaud is currently Lecturer of Chemical Engineering at the
University of Delaware. He received his BS from Northeastern University
and was awarded a PhD in Chemical Engineering at the University of
Delaware in 2000 for his work in the optimization and control of thick-
sectioned RTM composite processing.
Rajeev L. Gorowara received his PhD in Chemical Engineering under
the direction of Professor McCullough at the University of Delaware in
2001, focusing on interphase formation in glass-fiber vinyl-ester compos-
ites. He received his BS and MS from Ohio State University. He is cur-
rently a Consulting Engineer in the DuPont Engineering Particle Science
and Technology Group.
Roy L. McCullough was Professor of Chemical Engineering at the Uni-
versity of Delaware until his death in December of 2001. He received his
undergraduate chemistry training at Baylor University and was awarded a
PhD in Chemistry by the University of New Mexico in 1960. He published
numerous technical papers and organized symposia in the areas of poly-
mer science and composite materials.


Copyright ChE Division of ASEE 2002


Chemical Engineering Education










ing experience to propose a final design in their written report.

THICK-SECTIONED COMPOSITE MANUFACTURING
The specific problem given to students concerns the manufacture of
thick (greater than one-half inch through-thickness) composite materials
via RTM. This nontraditional subject matter allows students to apply
classroom knowledge of kinetics and transport phenomena while also
introducing process control and the limitations of mathematical models.
Processing thick-sectioned composites is challenging due to the exo-
thermic nature of the reacting resin and the heat transfer limitations
of the polymer and glass fiber composite."1"3 Unfavorable process-
ing conditions of the composite part can lead to poor part quality,
including cases where the laminate cracks internally due to residual
stresses within the part.
The primary design problem for thick-sectioned composite is to iden-
tify an acceptable temperature trajectory (or "cure cycle") that balances

Junior Lab: Senior Lab:
Kinetics ofThermoset Polymer Cure Design and Manufacture of
Thick-Section Composites
DSC Experiment
Review Polymerization M
Measure Reaction Rate
Determine Kinetic Parameter
Simulation-Based
Process Cycle Design
Kinetic I Physical
Parameter Parameter
Sensitivity | Sensitivity Eprmn
RTM Experiment
Anticipate Process Deviations
I Manufacture the Composite
I Validate / Revise Design


Figure 1. Schematic of integrated undergraduate
laboratory experiments.


160 a
~" Mea
140 Mea
S- -- Sim
S 120 (U
I t
100 \

Q 80

| 60

40 Stage 1
S Curing Phase
20
0 50 100
Time,


150 200
minutes


the heat necessary to initiate the polymerization
reaction (cure) with the heat transfer limitations of
the composite once the reaction begins, while also
maintaining a processing time that is economically
feasible. The example cure cycle presented in Fig-
ure 2 shows experimentally measured heater and
composite (measured at the center of a one-inch-
thick laminate) temperatures. The cure cycle is
broken up into different stages, each with a spe-
cific heater set-point.

For the experiment shown in Figure 2, the first
set-point was 620C and the second set-point for the
post-cure was 900C. Due to the low thermal con-
ductivity of the composite, almost 60 minutes of
processing is required for the center of the com-
posite to reach the heater set-point, but once the
resin at the center begins to cure, the heat gener-
ated from the reaction quickly raises the composite's
temperature and drives the polymerization reaction
to completion. A lower temperature curing stage
reduces the temperature gradient within the part as
well as residual stresses, but also increases process-
ing time. Since the surface temperature of the com-
posite remains much closer to the heater set-point,
a post-cure is generally required to ensure the sur-
faces of the composite are adequately cured for re-
moval of the part from the mold.


LABORATORY FORMAT
AND EDUCATIONAL OBJECTIVES

At Delaware, the undergraduate chemical engi-
neering laboratory is a two-course sequence, taken
in the spring of the junior year and the fall of the
senior year. Initially, all students attend five
background lectures in laboratory safety, mea-
surement techniques, statistics, report writing,
and oral presentation.

In the junior course, student groups go through
three experimental cycles, with each cycle center-
ing around a design problem using information
gathered during a laboratory experiment. Over a
four-week period, the students must learn about the
problem, perform the experiment, analyze the
data, prepare a preliminary data report, revise
the data analysis, and complete the design prob-
lem in a final report.

In the first week of a cycle, the students prepare
for the lab by reviewing the experiment and labo-
ratory procedures with the teaching assistant (TA).
They prepare an experimental proposal, and dur-


isured Heater Temperature
isured Center Temperature
ulated Center Temperature
ing Initial Model Parameters)


Figure 2. Example cure cycle and corresponding
internal composite temperature.


Summer 2002









ing the graded pre-lab conference they present it to the su-
pervising faculty member, who must be convinced that valu-
able "research facility" time should be spent on the prob-
lem. The students must also show an understanding of
the safety issues involved.
In the second week the students perform the experiment
under the guidance of the TA, and in the third week they con-
clude the data analysis and
preliminary data report.
The students then use their
lab data during the fourth 2.5
week for the design prob- 2.0 Isother
lem and present the final
report for the cycle to the 1.5
faculty member.
At the conclusion of the 1.0
course, the individual 00 0.5
groups orally present one of p
their experiments to their 0.0
colleagues and faculty and
then critique their video- -0.5 Are
taped performance. The -1.0
format of the senior-year 0 5 10
course is very similar in
approach, but has only two
experiment cycles. Longer Figure 3. Example heat
six-week sequence allows calorimetry (
the students to return to the
lab after their first experiment and either extend or correct
their experimental data.
The integrated lab format allows us to address the entire
hierarchy of educational objectives outlined by Bloom and
colleagues in their famous taxonomy.[41 These objectives in-
clude analysis, synthesis, and evaluation, referred to as
"higher-level skills" by Felder, et al."[5 The fundamental ob-
jectives of knowledge, comprehension, and application are
referred to as "lower-level skills."
We agree with Miller, et al.,1[6 that the engineering labora-
tory is an ideal setting to help students become better engi-
neering practitioners and to enhance their higher-level think-
ing skills. Since the time of Professor Robert Pigford, it has
been the tradition at the University of Delaware to focus the
chemical engineering laboratories not only on the determi-
nation of experimental data, but also on a design problem
using that data. In the terms of Bloom's taxonomy, the higher-
level objectives are not only analysis, but also the synthesis
of this new information into an engineering design. We find
the design problem's requirements to be an excellent motiva-
tion for the laboratory experiments, and that the synthesis
step reinforces the need to succeed in the lower-level skills.
We add the integrated lab to this tradition, as it creates a
situation that stresses evaluation, based on the student's own


15
rime

low
DSC)


depth of experience: evaluation of the validity of experimen-
tal data in comparison to the other groups; evaluation of their
process design in the second experiment; and (after revising
their process model based on the second experiment) evalua-
tion of their ability to evaluate. The supervising professor
focuses on the higher-level skills, guiding students in ana-
lyzing their data, using it in the synthesis of a new process
design, and evaluating that
design in the process ex-
periment.
The TA tends to focus on
S R n the lower-level skills:
iase Ramping Phase
5 OC/min knowledge of polymeriza-
tion kinetics and compos-
ites processing; compre-
hension of the experimen-
tal methods; and applica-
tion of that knowledge to
extract model parameters
from the experimental data.
rxn Area H r dual
Ar res'"idl KINETICS OF

20 25 30 35 THERMOSET
, minutes POLYMER CURE
(JUNIOR YEAR)
of a differential scanning The junior-level com-
experiment. posite laboratory experi-
ment requires that the stu-
dents evaluate the resin's kinetic parameters necessary to pre-
dict the resin curing behavior within a thick-sectioned com-
posite and to develop a preliminary design of the processing
conditions for a one-inch-thick composite laminate. The stu-
dents investigate the resin-curing process of pure (neat) resin
samples using differential scanning calorimetry (DSC), which
accurately measures the heat evolved from the reaction and
the reaction temperature.[7] They are challenged to consis-
tently prepare the small (8 to 12 mg) resin samples and to
interpret the DSC's baseline and endpoint data. The DSC is
used to measure the isothermal heat release rate, dQ/dt, which
is related to the polymerization reaction rate, dx/dt, by
aa 1 dQ (
at Hul dt
and the extent of ploymerization (cure), a


Hul todt
where Ht is the total heat of reaction given by
tf.isothermal (dQ t + t (dQ *d
Hult Hrxn + Hresidual= (i J Idt + dt
t 0 t fisothermal


Chemical Engineering Education


228









Ht is determined by summing the heat measured during the
isothermal cure of the resin with the residual heat measured
at the conclusion of an isothermal run. Using Figure 3 of
experimentally measured heat flows as an example, the value
of Hrxn is evaluated from to = 3.2 minutes (when the DSC pan
is added to the cell) to the final isothermal time point, tisothermal
of 20 minutes. The temperature of the DSC cell is then ramped
at 5C/min until no residual heat is observed.
For the students to simulate resin cure in an actual part,
they need to be able to describe the reaction in a non-isother-
mal cure. The kinetics of the free-radical polymerization can
be described using the popular autocatalytic mode12,81] shown
in Eq. (4), which gives the reaction rate, da/dt, as a function
of the fractional extent of cure, a, the maximum extent of
cure, amax, and an overall reaction order of 2
da- k am(max -a)2-m (4)
dt
and

a(t)= amax (5)
1+ [(1 m)max k. t] (m-1)

An Arrhenius expression is used to account for the tempera-
ture dependence of the rate constant, k

k = A exp--La (6)
RT
For the incomplete curing case in which vitrification occurs
before complete reaction, the maximum extent of cure, amax,
for an isothermal curing temperature is less than one, and a
linear relationship may be used to approximate the effect of
temperature, T, on amax.

amax = ao + a, T for amax < 1 (7)
We have used the resin Derakane 411-C50 (Dow Chemi-
cal), a free-radical polymerizing resin that is 50 wt% DGEBA-
based vinyl ester and 50 wt% styrene, since we use it in other
projects."91 Alternative resin systems can easily be imple-
mented, however. We have also used a variety of initiators
and accelerators to alter the kinetic performance of the resin.
From heat rate and time data, the students estimate the
resin's kinetic parameters (H ,, A, Ea, m, ao, and a,) required
by the cure simulation. We recommend that the students first
determine Hut, then amax(T), and then k(T) and m at each
cure temperature, using nonlinear regression. We make avail-
able for their use KaleidaGraph (Synergy Software), which
allows curve fits of nonlinear functions. To help ensure rea-
sonable curve fitting results, we ask the students to use
their derived kinetic model to predict the extent of cure
(a) as a function of time and compare that to the experi-
mental extent of cure data.
The students estimate the error for some of the parameters


The Resin Transfer Molding (RTM)
process incorporates a number of core
chemical engineering concepts within a
laboratory exercise while at the same time
introducing students to the manufacture and
properties of composite materials.


based on the nonlinear regression fitting of the data, and the
error for the others is determined by propagation of experi-
mental measurement errors. The melting of a standard In-
dium sample is used to estimate error in the DSC heat flow
and temperature measurements.
Once the students submit their preliminary data reports,
the data from all of the groups (including previous cycles) is
circulated via memos in order to provide a larger estimate of
variability from the pooled data. This gives the students an
introduction to the statistical treatment of data, including the
use of significance testing (i.e., t-test) to determine if their
data is within the norm. There is generally a lot of variability
between groups, and this exercise gives the students an ap-
preciation of these statistical techniques as well as refining
the data they will need during the design component. The
students are asked to use these estimates as bounds for the
sensitivity analysis on the simulation parameters.

SIMULATION-BASED
PROCESS CYCLE DESIGN
(INTEGRATED DESIGN PROBLEM)
As part of the junior lab, the students are introduced to
simulation-based batch-process cycle design, focusing pri-
marily on the effects of the resin's kinetic parameters. The
RTM process cure simulations are provided via a course
homepage.* Before their prelab meeting, the students use a
fast, but imperfect, neural net version of the simulation to
explore the dynamics of the system and get a "feel" for their
design problem. Once they have experimentally determined
the resin's kinetic parameters, they use the more accurate fi-
nite difference cure simulation" for their design.
We define the problem of cure-cycle design as the proper
selection of the composite's time-temperature cycle (similar
to Figure 2), within the limits of available equipment, to make
a high-quality part while completing the cure process in as
short a period of time as possible to reduce the production
cost. We define a successful cure cycle in terms of several
quality criteria, such as achieving an acceptable degree of
cure while minimizing void content, thermal degradation,
and residual stresses.


*


Summer 2002









The students are informed of the different process param-
eters that must be controlled to meet the product design lim-
its. For example, void formation is affected by the vaporiza-
tion of styrene, and therefore the students must calculate this
temperature limit at process pressures (approximately 20
psig). To avoid thermal degradation, the student's proposed
temperature cycle should minimize the peak temperature
observed in the center of the composite. To minimize residual
stresses, the students should ensure that the composite cures
inside/out once the resin's gel-point is reached. The resin
shrinks 8% during cure, and significant curing on the outside
of the composite before the
center begins to cure results
in large internal stresses Sta
(and possible delamina-
tions) once the resin at the
center begins to polymerize.
In terms of minimizing
processing time, the stu- Thermocouples
dents are given the goal of Polyurethane
curing the composite Tubing
( surface > 0.75) in less than o Con
2 hours. The juniors present
their proposed design in
their final report for the
DSC experiment. In their
Resin
senior year, they again visit Rein Source
the simulation-based design
problem, but with a new
emphasis on the material
properties o the composite Figure 4. Diagram of resin tr
properties of the composite
(resin content, composite
density, thermal conductivity, etc.), heat transfer coeffi-
cients within the mold, and the effect of fibers on the ki-
netic behavior of the resin.


DESIGN AND MANUFACTURE OF THICK-
SECTIONED RTM COMPOSITES
(SENIOR YEAR)

After an introduction to composite processing in the junior
lab, the seniors are given an opportunity to manufacture a
composite laminate. While they previously only investigated
the kinetic behavior of neat resins, they soon discover that
the heterogeneous nature of composite materials, as well as
other manufacturing realities, can complicate a situation.
One of the challenges they find with manufacturing thick-
sectioned composites is that extrapolating kinetic data down
to the lower temperatures necessary for thick-sectioned cure
can result in significant error."l Other complications include
the change in the resin's kinetic behavior in the presence of
fibers and the effect of inhibitors within the resin system that
are not currently modeled by the simulation. Lastly, the stu-


dents are responsible for measuring and/or estimating the
physical properties of the composite and the mold environ-
ment (e.g., volume fraction of the resin, composite density
and thermal conductivity, and effective heat transfer coeffi-
cients). The students are given the pure component proper-
ties for the resin and glass fibers for their calculations. Heat
capacity of the composite is estimated using the "rule of mix-
tures," and its thermal conductivity can be predicted using a
number of techniques.'10,"
The seniors begin their composite laboratory sequence with
a tour of the composite
manufacturing equipment
and an overview of the ex-
Steel Mold perimental procedure and
safety issues. The experi-
mental RTM equipment is
shown in Figure 4. Using
their experience from the
a Acquisition junior lab, students use the
on-line simulation to iden-
Polyurethane tify the cure cycle they will
Tubing
dT implement experimentally.
The simulation is also used
to analyze the effect of pos-
sible model parameter
variations on the cure cycle
Resin (i.e., sensitivity analysis).
Se The lab begins with the
students filling the stainless


ansfer molding (RTM) equipment.


steel mold with a predeter-
mined volume fraction of


glass fiber reinforcement. The particular fiber reinforcement
has varied over the years to include woven sheets, random
mats, and stitched layers of different fabric types, which can
affect the resulting volume fraction of resin and the
composite's thermal conductivity. During the placement of
the fibers, six J-type thermocouples are placed between the
fabric layers to provide internal temperature data during manu-
facturing. The entire mold assembly is placed within a heat
press to seal the mold components and to provide the heat
necessary to cure the composite. The catalyzed resin, con-
tained within a pressurized pot, is injected into the room-
temperature mold until no air bubbles are seen exiting from
the mold. Once the mold has been filled with resin, the flow
of resin is stopped and the cure cycle is begun.
As discussed earlier, the cure cycle is defined by the tem-
perature set-point of the heat press. A representative cure cycle
for a one-inch-thick composite laminate is shown in Figure
2. LabView is used to observe and collect the internal com-
posite temperatures during processing. When the observed
temperatures do not match those generated by the simula-
tion, the students are challenged with modifying the cure cycle
on-line according to insights from their sensitivity analysis.


Chemical Engineering Education


winless










Once the cure cycle is completed and the mold is cooled, the
composite is removed from the mold and cut into test samples.
The students estimate the composite's quality according to
ASTM standards for density (D792), void fraction (D2584/
D2734), and short-beam shear strength (D2344).
Although some material and heat transfer model param-
eters of the composite and the mold can be measured, a few
of them (e.g., thermal conductivity and the simulation's
boundary condition) must be estimated by the students in order
to improve the accuracy of the cure simulation. By compar-
ing the simulated composite temperatures with those mea-
sured at the beginning of the cure cycle when no resin cure
has occurred, the students identify which of the estimated
heat transfer model parameters is most likely responsible for
the mismatch, and they can then estimate new values. Like-
wise, the students compare simulated composite temperatures
to those measured during the curing phase of the resin to iden-
tify possible changes in kinetic parameters due to lower pro-
cessing temperatures and the effect of fibers.
As is shown in Figure 2, the numerical simulation gener-
ally underpredicts the length of time necessary to cure the
composite when the default model parameters are used (neat
resin kinetics and predicted heat transfer parameters). Since
there are a number of parameters within the simulation that
can be altered to improve the fit of the simulated temperature
profile, the students must defend their choices by using knowl-
edge they have gained about the system and by performing a
sensitivity analysis.
Once the students have improved the simulation, they use
it to redesign their cure cycle (while understanding that they
do not have a perfect model of the system) and use it to manu-
facture another composite part. The experimental results from
this second experiment are then used to further improve the
estimate of the simulation's model parameters. Using model
parameters derived from both experiments and their newly
acquired knowledge of composite processing, the students
generate a final cure-cycle design as part of their written re-
port of the lab. This report also includes a sensitivity analysis
of their final design and recommendations as to how the simu-
lation and the experiments might be improved in order to
better generate an "optimal" cure cycle design that can ac-
count for observed batch-to-batch variability.


CONCLUSION

The double sequence of junior and senior laboratory ex-
periments described in this paper has been implemented suc-
cessfully at the University of Delaware for the past five years.
In order to understand the goals of the experiments and com-
plete the design portion, students are required to integrate a
number of important engineering concepts, including kinet-
ics, heat and mass transfer, and some process control. Both
experiments also provide a good basis for implementing a


statistical treatment of the data. Furthermore, the students are
introduced (through the simulation-based design component)
to the reality of process-model mismatch and the effect of
significant process variabilities on their design.
As a whole, each laboratory sequence allows the students
to demonstrate many of the outcomes defined within the
ABET Engineering Criteria 2000. Unlike many other labora-
tory experiences, the ability to take a piece of the final prod-
uct home with them (e.g., a composite paperweight) has been
well received by the students. We believe that the integrated
concept of this lab and its design aspect in each phase pro-
vides an invaluable experience for the students.


ACKNOWLEDGEMENT

The paper is dedicated to the memory of Professor Roy L.
McCullough, coauthor, educator, mentor, and friend, who
passed away unexpectedly in December of 2001.


REFERENCES
1. Michaud, D.J., A.N. Bers, and P.S. Dhurjati, "Curing Behavior of
Thick-Sectioned RTM Composites," J. ofComp. Mats., 32(14), 1273
(1998)
2. Lam, P.W.K., H.P. Plauman, and T. Tran, "An Improved Kinetic Model
for the Autocatalytic Curing of Styrene-Based Thermoset Resins," J.
ofAppl. Polymer Sci., 41, 3043 (1990)
3. Ciriscioli, P.R., Q. Wang, and G.S. Springer, "Autoclave Curing: Com-
parisons of Model and Test Results," J. of Comp. Mats., 26(1), 90
(1992)
4. Bloom, B.S., ed., Taxonomy of Educational Objectives, David McKay
Co., New York, NY (1956)
5. Felder, R.M., D.R. Woods, J.E. Stice, andA. Rugarcia, "The Future of
Engineering Education: II. Teaching Methods that Work," Chem. Eng.
Ed., 34(1), 26 (2000)
6. Miller, R.L., J.F. Ely, R.M. Baldwin, B.M. Olds, "Higher-Order Think-
ing in the Unit Operations Laboratory," Chem. Eng. Ed., 32(2), 146
(1998)
7. Willard, H.H., L.L. Merritt, Jr., J.A. Dean, and FA. Settle, Instrumen-
tal Methods of Analysis, 7th ed., John Wiley & Sons, New York, NY
(1988)
8. Kamal, M.R., and S. Sourour, "Kinetics and Thermal Characteriza-
tion of Thermoset Cure," Polymer Eng. and Sci., 13(1), 59 (1973)
9. Gorowara, R.L., S.H. McKnight, and R.L. McCullough, "Effect of
Glass Fiber Sizing Variation on Interphase Degradation in Glass Fi-
ber-Vinyl Ester Composites upon Hygrothermal Exposure," Compos-
ites Part A, accepted for publication
10. Springer, G.S., and S.W. Tsai, "Thermal Conductivities of Unidirec-
tional Materials," J. of Comp. Mats., 1, 166 (1967)
11. Farmer, J.D., and E.E. Covert, "Thermal Conductivity of an Anisotro-
pic Thermosetting Advanced Composite During Cure," Am. Inst. of
Aeron. and Astron.:Structures, Structural Dynamics, and Materials,
5(56), 2939 (1995) 0

ERRATA
The phrase "to appear in" in citations 4 and 7 of "Devel-
oping Troubleshooting Skills in the Unit Operations Labo-
ratory," by Aziz M. Abu-Khalaf, published in CEE, 36(2),
p. 122, (2002), should be omitted.


Summer 2002










classroom


SCALING OF

DIFFERENTIAL EQUATIONS

"Analysis of the Fourth Kind"


PAUL J. SIDES
Carnegie Mellon University Pittsburgh, PA 15213


What does it mean to solve a differential equation?
The answer might be in closed form, or it can be
an infinite series. A numerical simulation might
also provide the answer. The first kind of answer is preferred
but not always available or even possible. The second answer
is useful if the series converges well, but this is not guaranteed
in all cases. The third kind of answer is the least flexible, and
doubt about the exactness of the simulation can remain.
This paper concerns a fourth kind of analysis, where a so-
lution per se is not found, but the student learns about the
dependence of the solution on relevant parameters and/or ob-
tains an order of magnitude estimate of various meaningful
quantities, such as the approximate thickness of a boundary
layer. This answer is the result of natural scaling of the dif-
ferential equation; it provides insight into an equation even
when the solution to the equation or set of equations is un-
known. This process of deducing relationships among the
physical properties and significant dimensions of the problem
accelerates physical understanding of its nature. The answers
from this type of analysis often guide experiments, reducing
their number to a minimum. Finally, the analysis can demon-
strate that effects are important or unimportant.
The goal is to present an approach for arriving at the fourth
kind of answer. The procedure is called "all-natural scaling"
of the equation. There is at least one contribution in the lit-
erature on a similar topic. Hellums and Churchillml described
a general method for analyzing equations; their method re-
veals cases where similar solutions are found and at least in-
dicates minimum numbers of parameters and variables. Their
approach is formal and aimed more at deducing constraints on
problems than on deducing physically meaningful quantities.
What need does this contribution fill? It is not a scientific
advance, because scaling of equations has been around for a
long time; scaled equations are the standard form in journal
publications. For most undergraduates, the limited need for
this understanding and the modest potential for comprehen-
sion of its significance are not compelling arguments for in-


troducing them to it. Likewise, this contribution is not in-
tended for the experienced analyst who performs these op-
erations subconsciously or has seen them all.
This method is intended primarily for advanced undergradu-
ates or first-year graduate students who find themselves in
classes where the professor conjures dimensionless groups
without arguing their origins. I introduce this technique to
the students in our core graduate math and transport courses;
they seem not to have seen a direct discussion of this process
before. This contribution is intended to fill that gap.

EXAMPLE 1
Viscous Heating and the Brinkman Number
Consider first the classic problem of viscous heating ap-
pearing in Figure 1. A warm viscous liquid flows laminarly
in a pipe and is cooled by contact with the cold wall; the
concern is whether or not viscous heating of the liquid is im-
portant. For simplicity, it is assumed that axial convection of
energy dominates axial conduction, so that the important heat
transfer terms are radial conduction, and viscous dissipation.
The following equation governs convective heat transfer in
laminar pipe flow under these circumstances:

a I1 a( T av 2
pcv,v -- lk[ tr + (1)
z |z r dr dr Dr
where T = temperature, To = incoming temperature, Tw = wall

Paul J. Sides is currently Professor of Chemi-
cal Engineering at Carnegie Mellon Univer-
sity. He received his BSChE from the Univer-
sity of Utah in 1973 and his PhD in Chemical
Engineering from the University of California
at Berkeley in 1981. He joined the faculty of
the Department of Chemical Engineering at
Carnegie Mellon in 1981. He has published
articles in electrochemical engineering, growth
of advanced materials, and data storage tech-
nology.

Copyright ChE Division of ASEE 2002


Chemical Engineering Education










temperature, v, = axial velocity in laminar pipe flow, p = den-
sity of the fluid, = viscosity, cp = heat capacity, k = thermal
conductivity, r = radial position, and z = axial position.
Equation 1 is the convective conduction equation for the
laminar flow of fluid in a pipe plus a term describing the
local dissipation of mechani-
cal energy into thermal en-
ergy.[21 Before going to the
trouble of solving the equa- To Vz
tion, or looking up the an-
swer, we can use a scaling
analysis to estimate the im-
portance of the effect. This Figure 1. Laminar flow of
circular cross section.
example illustrates the pro-
cess of natural scaling and the deduction of the pertinent di-
mensionless group.
First, we pick all sensible length scales for the independent
variables in the governing equation. R is obvious for radius,
but there is no obvious choice for axial distance. We there-
fore temporarily give the axial length scale a name and de-
duce it during the derivation. This lets the equation exhibit
appropriate relations among the physical properties. Finally,
we define a dimensionless dependent variable preferably so
that its value varies from zero to unity, when its range is
known.


z
Zo
i- -
zo


r
R


T-T,
T -Tw
T T,


For laminar pipe flow: v = 2 < v >(1- 2 )
Substitute these definitions into the equation using the chain
rule for derivatives. The first crucial step is to divide by the
coefficient of an important term in the equation. In this case,
we are exploring the importance of the viscous heating term,
so its coefficient must float. Axial convection of energy is
obviously an important term, so one divides through the equa-
tion by the convective energy transport coefficient

2pc To-Tw- (3)
z o 5
The result is

(2 ) -

kzo [a ( 8 ] 16p o 2
2 pcp < v > R2 2 pcpR2(To -T,)

(4)

Dividing the energy equation by Eq. (3) "scales" the axial
convection term to 0(1); it declares axial convection to be
important. The choice of which term to use in scaling the
equation seems arbitrary at first. (Hellums and Churchill,"1
for example, use the coefficient of the diffusive term to scale
their Eqs. 10-12 but do not comment on the choice.) This


choice is not often critical as long as the term chosen is im-
portant in the problem. The first exercise of the Appendix of
this contribution illustrates this point.
The radial conduction term is also important; after all, this
is how the thermal energy escapes the pipe. Thus, the con-
duction term is scaled to 0(1) by
,, equating its coefficient to unity
\T r and solving for the unknown
Length scale.


ZI
aw z
//////7/////7//
a viscous liquid in a pipe of


2 < v > R2pcp (
Zo (5)
k
With the inclusion of this axial
length scale, the overall energy


equation can now be written as

(1-i ) +[1 C ll +l6Br2

where


Br L < v >2
Br (7)
k(To T,)
The analysis yields two results. First, the temperature of the
incoming fluid changes substantially toward the wall tem-
perature over a distance z that is calculable from known quan-
tities of the problem. Second, the resulting parameter in Eq.
7, (Br), is a dimensionless group that governs the importance
of viscous heating;[21 i.e., we can now quickly determine the
significance of viscous heating relative to the ability of the
system to dissipate the irreversible energy released. If the
thermal conductivity is high relative to heating by viscous
dissipation, the latter is unimportant. The effect of viscous
heating is proportional to the viscosity and the square of the
velocity, and inversely proportional to conductivity of the liq-
uid. If 16Br is very small, we can ignore viscous heating- the
usual case; otherwise, we should consult the published work.[2]
Guidelines U The method used in the previous example
consisted of several steps.
1) Write the governing equation including effects of interest.
2) Make position variables dimensionless with distances over
which the dependent variable assumes the full range of its
possible values. Where there is no obvious appropriate dis-
tance, give it a name and try to deduce it as part of the analy-
sis (remember R and zo).
3) Nondimensionalize the dependent variables with theirfull scale
values.
4) Substitute the definitions into the differential equation using the
chain rule for derivatives. Once students do this a couple of
times, they easily write down the substituted form by inspection.
5) Identify a term of known importance and divide the equation
by the coefficient of that term. This forces that term to order
unity importance in the equation and scales the rest of the
equation to that term. The equation becomes dimensionless.
6) Inspect the remaining terms of the equation. Whenever a co-


Summer 2002


Z//Z









efficient contains only one unknown distance or other nor-
malizing quantity and is also a known important term, set the
coefficient to unity and solve for the unknown quantity (i.e., we
knew the conduction in the radial direction was important, so
we found z, with the coefficient of the conduction term.)
7) Collect remaining terms into as few coefficients as possible.
These terms are generally dimensionless ratios that appear
as parameters of the final solution.
These steps should be considered general guidelines. For
the student, it is useful to try scaling the same equations by
the coefficients of various terms to see the effect on the re-
sults. This process develops insight and experience that make
the analysis meaningful. If one plans to solve the complete equa-
tion in closed form, the choice of reference distances does not
matter. If we plan to solve the equation numerically, it can make
a great deal of difference if the equation is properly scaled.

EXAMPLE 2
Natural Convection Near a Vertical Heated Surface
How much can be said about a classic case of natural con-
vection without actually solving the governing equations in
detail? Consider a heated vertical plate immersed in a fluid
of infinite extent as shown in Figure 2. The well-known equa-
tions for the laminar case (GrPr < 109) are the following:
Continuity

+ -o0 (8)
ay az
Motion

Py + Vz z = +y2 2 + +pgI(T Tc) (9)
SaVy aVz a2 Z a2z
Energy
PCp(y aT aT (a2T a2T)
PC, v +v = k T + ( 10)
a y az y )z
where v = y velocity, vz = z velocity, T = temperature, Th =
wall temperature, T. = bulk fluid temperature, c = thermal
heat capacity, k = thermal conductivity, g = gravity, p = co-
efficient of expansion, p = density, g = viscosity, y = hori-
zontal position, and z = vertical position.
For completeness, no assumption has been made about the
relative importance of cunduction or convection in the direc-
tion parallel to the wall. The first step is to identify scaling
parameters for the independent variables, in this case y and
z. The scaling distance for z is obviously H; the scaling dis-
tance for y is unclear since the domain is infinite in that di-
rection. Thus, define a distance yo as the appropriate scale for
y. This distance is essentially a characteristic hydrodynamic
boundary-layer thickness. Then define the dependent vari-
able over its range


z
H


y
yo


T- T
Th -T


Likewise, there are no natural reference velocities for the
vertical and horizontal velocities, so give them names as well
(z '-V / VozO y =y /Voy) and define B = pgP(Tw -Tc).
After inserting them into the momentum equation, we obtain

pVyVyoz K z pv 2
Yo ly ) H z

v+ (a20) v+B (12)(a2
y2 (2 H2 -T 5J+ (12)
The convection of momentum in the direction parallel to the
wall is surely important; scale the equation by dividing
through by that term's coefficient
Hvoy z )O _
YoVoz + z a -
vH (a2o v (a2z ) BH
YVo +--- J -- + _---2 (13)
yvoz 1 2 Hvoz ) V2oz
At this point, there are two terms that contain only one of the
unknown reference variables-the second and third terms on
the right-hand side. Typically, diffusion of momentum is neg-
ligible compared to convection of momentum in the primary
direction of flow, thus it would not be prudent to base the
definition of the reference velocity in the z-direction on the
coefficient of this term. Furthermore, we know that for natu-
ral convection, the source term for momentum must be 0(1)
or the problem does not make sense. Force the coefficient of
this term to unity. We conclude that a reference velocity for
the flow parallel to the vertical wall should be
BH
vo i (14)

Having this definition, we can now define other reference
quantities by forcing the coefficients of other important terms
to unity. The coefficient of the y-directed momentum diffu-
sion terms yields


(g2H 1/4
Y0 pB p


and Vo (2B 1/4
a v 3H)


and the differential equation becomes

0 a z a20z (2 a2 0 z
y a+ an 2 HpB + e (16)





H z T

Th

Figure 2. Geometryfor natural convection near a heated wall.


Chemical Engineering Education










This is as it should be. The typically important boundary-
layer type terms are all of order unity along with the source
term driving them. The axial diffusion of momentum is mul-
tiplied by a coefficient that allows its importance to be as-
sessed. For even very modest temperature differences between
the wall and the bulk fluid, or for large H, this term is small.
The H-3 dependence of this parameter is very strong.
We now insert the definitions obtained into the energy equa-
tion and obtain

O )+ = ao ( I a20 (22p 1/2( 2)
S+ j( Pj (a2ej (17)
The equation contains two parameters-Pr and a coefficient
multiplying the axial diffusion term. Assuming that the axial
diffusion of energy can be neglected, we find that the Prandtl
number is the sole parameter of the system of Eqs.(8,9)
What happened to the Grashof number? Why does it not
appear in this equation? To see how Gr arises, examine the
flux of heat at the vertical wall, using the derived definitions
to make it dimensionless

q h(Tw T,)=

T hy h pB2H V14 -l
k Nu I (18)
y 0 k k pB =0

Still no Grashof number appears. Note that the appropriate
scaling distance for heat flux normal to the wall is the hydro-
dynamic boundary-layer thickness y The Nusselt number,
i.e., the dimensionless flux of heat, remains solely a function
of Pr. The only way that Gr appears in the equation is if we
convert this "all natural" scaling to one based on H as the
length parameter. Then the flux equation becomes

q -h(Tw T) -k- N =
By |y= o u

Nu H O_ e (pB )/4 H (19)
NuY -Yo 0 2H (19)

The coefficient on the far right-hand side is recognizable as
Gr so that the definition of NuH becomes

NUH Gr1/4 (20)

The dimensionless temperature gradient at the wall is a func-
tion solely of the Pr number, as we found scaling of the sys-
tem of coupled equations and is most often written as

I f(Pr) Pr1/4 (21)

where f(Pr) is a slowly varying function of Pr. This definition
leads to the tidy form
NuH = f(Pr)(Gr Pr)1/4 (22)


which is the one commonly encountered.
As in the first example, there are several useful results. First,
we now have estimates of the velocities achieved in the prob-
lem and the boundary layer thickness (Eqs. 14, 15). Second,
we show that if axial diffusion of momentum and energy is
small, the solution to the problem is only a function of Pr.
Third, the origin of the Grashof number in this problem is
clearly demonstrated.

CONCLUSIONS
Scaled equations are the standard for most journal publica-
tions, but apart from this standard, the process of scaling dif-
ferential equations is a way to learn about their nature and
build arguments about what terms can be neglected. The
method requires that the student be able to read the equations
at hand; in the examples, the student needs to recognize dif-
fusive and convective terms. We suggest that this perspec-
tive be imparted concurrently with the method where neces-
sary. We hope the method presented here helps advanced
undergraduates and first-year graduate students become ac-
customed to the practice of scaling equations and, most of
all, to understand the origin of dimensionless numbers, the
shorthand of our profession.

APPENDIX: Suggested Further Examples
1) Repeat example 1, but divide through by the conductive term
rather than the convective term; compare the results to Eq. 7.
2) One might object and say that it is strange to force all the
terms to unity in example 2, that this must create an imbal-
ance in the equation. We can check for suitability by inserting
the definitions into the continuity equation. Problems with the
scaling might appear there. Put the given definitions for the
reference quantities into the continuity equation and deduce
its form. Does a problem appear?
3) Consider the classic problem of flow of a free stream that meets
and flows parallel to a flat plate. Include the axial diffusion of
momentum. Deduce a parameter that allows one to estimate
the minimum plate length for which axial diffusion of mo-
mentum can be neglected. Deduce an estimate of the thick-
ness of the hydrodynamic boundary layer for a plate of length
L. A close approximation to the exact answer is 5-vL v .
How does your answer compare to this?
4) Write the energy equation for the above example, including
the axial conduction term. Use the reference distances devel-
oped in Prob. 1. Deduce a parameter that allows estimation of
the lengths below which axial conduction must be considered.
5) Instead of using the hydrodynamic boundary layer thickness
in the energy equation, as in the previous problem, define a
new reference length in the direction normal to the plate for
the energy equation. Deduce an estimate of the thermal bound-
ary layer thickness. Show that the ratio of the hydrodynamic
layer thickness to the thermal layer thickness is given by Pr"2.

REFERENCES
1. Hellums, J.D. and S. W. Churchill, AICHE J., 10, p. 110, (1964).
2. Brinkman, H.C., Appl. Sci. Research, A2, p. 120, (1951).


Summer 2002


235










e 1 classroom


THE USE OF SOFTWARE TOOLS

FOR ChE EDUCATION

Students' Evaluations



ABDERRAHIM ABBAS AND NADER AL-BASTAKI
University of Bahrain Bahrain 32038


Over the last two decades, we have witnessed a rapid
decline in the computer price/performance ratio and
the development of fast, reliable, and user-friendly
computer packages. These developments have brought com-
puters within the reach of organizations and people who were
once deterred by cost or by complex mathematics and pro-
gramming expertise. The ease of use and enhanced capa-
bilities of general-purpose software such as Mathcad or
Matlab have made it possible for engineers with limited
or no formal training in programming to solve relatively
complex problems.
The available computing tools have led to large changes in
the industrial world. In contrast, the typical engineering edu-
cator has been slow to incorporate computer-based concepts
in the curriculum and training methods. This situation has
been attributed to a number of factors, including the lack of
computer literacy/inclination among certain staff and the way
popular textbooks are written.[1,21
The positive impact of information technology on teach-
ing and learning is no longer questionable.[3-51 Kulik and
Kulik1[4 reported that most studies found that computer-based
instruction-using technology of the eighties-had positive
effects on students. In particular, students learned more and
faster (the average reduction in instructional time in 23 stud-
ies was 32%). The students also developed more positive at-
titudes and liked classes more when they use computers.
The main objective of this paper is to present our experi-
ence with and students' evaluations of three commercial soft-
ware packages that we at the Department of Chemical Engi-
neering at the University of Bahrain have been using as teach-
ing aids. These packages are the process control training soft-
ware Control Station , the pro-
cess flowsheeting package HYSYS ,
and the general-purpose computational package Mathcad
.


CONTROL STATION
Control Station (CS) is a process dynamics and control train-
ing simulator that provides access to several simulated pro-
cesses.6'7, The case studies include gravity-drained tanks, a
pumped tank, a heat exchanger, ajacketed reactor, a furnace,
a multitank process, and a binary distillation column. The
software also allows the user to build tailor-made processes
and single-loop (or 2 x 2) control structures using a transfer
function block-oriented environment. Linear process models
and Proportional-Integral-Derivative (PID) controller settings
can be developed using the design module of the software
package. The available controllers in version 3.0 of CS in-
clude the classical PID and its variants, cascade, feedforward,
Smith predictor, decoupler, and sampled-data and single-loop
Dynamic Matrix Control (DMC).
During the last few semesters, we have used Control Sta-
tion as a teaching aid in a number of bachelor and diploma
courses on process dynamics and control. We use it for both
assignments and hands-on workshops. As shown later, the

Abderrahim Abbas is Associate Professor of
Chemical Engineering at the University of
Bahrain. He received his degrees from the
University of Salford (BSc), University of
Newcastle upon Tyne (MSc), and University
of Bath (PhD), all in chemical engineering. His
teaching and research interests are process
systems engineering and reverse osmosis.




Nader AI-Bastaki is Associate Professor and
Head of the ChE Department at the University
of Bahrain. He received his BEng and MEng
from McGill University and his PhD from UMIST.
His teaching and research interests are sepa-
ration processes and reverse osmosis. _


Copyright ChE Division of ASEE 2002


Chemical Engineering Education










feedback from the students on the use of the program was
very positive. The program made it easier for them to under-
stand process control material and concepts in a shorter time
than traditional lecture-only classes. It also helped the stu-
dents relate theory to practice.


Two workshop examples of how CS can be used to teach
control concepts are shown in Figures 1 and 2. Figure 1 il-
lustrates why the derivative action should not be employed
for processes having noisy measurements; the addition of the
derivative action to a PI controller leads to a deterioration


fie Bun laBks Help
E L a 12?


U


SE
0

'U


U
Q-


51 1 21 2s 3 44 s 19 17 74
Time [min)


I79 31 MiSec I


Reactant Fee
-1.


Cooing
Jacket nlet
Temp CC)
I 50.0
(Dstubance)


PID (P= DA. I=ARW, D= means


Outlet
Flow ([min) 47.3
Temp [C] 76.3

Conrolloer
Output (
| 525





StI Point


Reactor Emil Tmp [C) 92.7
Conversion (Z)] 95

S Fe Storage: OFF


ile Bun Iasks Help




945O ____ ^ ^ ^_^_ -- i "rlur
-II A ?




cc( 1 94
-1 -





0._ L Ste Controllee
s 2. (kgpmin Dutput (I)_
S222 5 s7.4



-jf j f 2-l
10 IW-- T ( cc

S4u ,U T u n tl .51 Iu lisu n 2n Bottoms -2.5
Composidkon [(
Time (min)
S 2111 Min Top: PIDP=RA.I=ARW, D= off / Bot: PID [P=DA, =ARW, D= off) I FileStoage: OFF


Figure 1.

Impact
of noise
on
derivative
action
(Control
Station).


Figure 2.

Effect of
interaction
on
SISO
loops
(Control
Station).


Summer 2002









(not an improvement) of the closed-loop response. Also,
the derivative term leads to unacceptable fast movement
of the control valve.
The use of CS significantly contributes to teaching advanced
control strategies such as feedforward, cascade, and


decoupling control to undergraduate students. Figure 2 illus-
trates the effect of process interaction on the performance of
conventional controllers in multi-input/multi-output pro-
cesses. The distillate composition controller results in good
closed-loop performance when the bottoms composition con-


tf b *J&md Il E D 10* YJw H AN
Pn ma* =Gx Mo ADe A_ Eni'a"
H HNPAP O I|d.:arSdu -J
















II lIc~iir. I
ionp1.-.- R-t


Caseludy2


.2 0.20

L.
0.15
o
.! 0.10
0o
E
E 0.05
<


200 300 400 500
Reactor Pressure, atm


Calm l ,CaICSl T2 SIG 3

lee | I& r |


Figure 3. Simulation of an ammonia reactor (HYSYS).


r Efe Edit Simulation Fgwsheet eFD lools Window H elp x|
Dg I =l g I A A EnvwoionM estae d(atJ
H o H N A @A |P Default Colour schema v-


I Completed. _4

Figure 4. Methanol synthesis loop (HYSYS).


I ~ -1


I a nHaw "cA.1 |


238


Chemical Engineering Education









troller is on manual mode. Closing this latter loop leads to a
deterioration of the performance of the first loop due to the
"fight" or interaction between the two controllers. The stu-
dents are usually asked to check the loops' interaction by cal-
culating the relative gain arrayE81 and to design and test a
decoupler for the distillation column.

HYSYS
HYSYS is a modular commercial process flowsheeting
program that is widely used by universities and industry (par-
ticularly hydrocarbon-related companies). It is capable of do-
ing material and energy balances for static and dynamic con-
ditions and is a very powerful tool for process simulation. It
has built-in routines to solve many specialized unit opera-
tions. One of the important features of HYSYS is the avail-
ability of an "Oil Manager" option dedicated to support re-
finery simulations. A comprehensive library of thermody-
namic property packages is supplied with HYSYS to enable
the user to design and solve many types of problems. At the
Chemical Engineering Department of the University of
Bahrain, HYSYS is used as an effective teaching tool in a
number of courses including process analysis (material and
energy balances), plant design, and the senior projects.


TABLE 1
Students' Evaluation Forms

1. Justification for the use of program in the course
(1 = unjustified; 5 = absolutely justified)
2. Contribution to study of the subject by program use
(1 = irrelevant; 5 = very effective)
3. Ease of achieving the goal (1 = difficult; 5 = easy)
4. Clarity in the means used to convey knowledge
(1 = confusing; 5 = absolutely clear)
5. Relationship between the complexity of the concept given and
the resources supplied (1 = inadequate; 5 = absolutely adequate)
6. Number of resources (information) simultaneously presented on
screen (1 = excessive; 5 = balanced)
7. Computer skills required (1 = excessive; 5 = null)
8. General quality of presentation (1 = poor; 5 = excellent)
9. Effectiveness of the resources used: graphics, tables, and texts
(1 = ineffective; 5 = very effective)
10. Ease of operation (1 = complex; 5 = very easy)
11. Documentation for user (1 = deficient, 5 = excellent)
12. Clarity of the goal (1 = confusing, 5 = perfectly defined)
13. Correspondence between program and knowledge conveyed in
class (1 = absolute disconnection; 5 = highly related)
14. Amount of specific knowledge required about subject for
program use (1 = excessive; 5 = reasonable)
15. Degree of interaction between user and program
(1 = passive schemes; 5 = very interactive)
16. Time needed for program execution (1 = excessive; 5 = suitable)
Comment on the reasons for which you felt attracted to or bored
by the program.


The use of multimedia and software
packages enhances teaching and learning.
... the students learn more and faster,
allowing the teacher to cover
more material...

In the process analysis course, students follow a system-
atic approach in which they effectively analyze the systems
and develop comprehensive degree-of-freedom tables to de-
termine if a problem is correctly specified and also the order
of solving the various units. The basic concepts used in modu-
lar simulation packages are thoroughly discussed. Among the
problems associated with modular solution is the presence of
recycle streams, which necessitate the iterative tear stream
solution. Determining the number of tear streams, their posi-
tions, the convergence techniques, and the order or sequences
of their converging are basic issues that we clarify.
Figures 3 and 4 show flow diagrams of simple HYSYS
case studies that the students were requested to develop. In
Figure 3, the effect of operating parameters such as tempera-
ture, pressure, and composition of inerts on the production
rate are evaluated for an equilibrium-type ammonia reactor
parametricc analysis). The variation of ammonia output com-
position with the operating pressure is shown in Figure 3.
The significance of the recycle loop and the selection of the
suitable convergence acceleration method are emphasized by
the second case study on a methanol synthesis loop (Figure
4). Solving this problem also gives students insight into the
philosophy of the modular flowsheeting programs and the
nature of the sequential solution strategy.

MATHCAD
Mathcad is one of the four most popular computational
packages used in industry and academia; the other three pro-
grams are Matlab, Maple, and Mathematica. Mathcad com-
bines some of the best features of spreadsheets (like MS Ex-
cel) and symbolic math programs. It provides a good graphi-
cal user interface and can be used to efficiently manipulate
large data arrays, to perform symbolic calculations, and to
easily construct graphs. One of the useful features of Mathcad
that is not found in the aforementioned programs is its ability
to perform calculations with units; this is indeed an impor-
tant feature for engineering students. In a recent survey con-
ducted by the discussion group on Computer Applications in
Chemical Engineering ,
Mathcad was the preferred computational package for 16.2%
of participants. The survey included a large number of known
packages, and the only two programs preferred by more
people were MS Excel (35.3%) and Matlab (23.4%).
As a general programming package, Mathcad is being used
in the Chemical Engineering Department in several courses
including process analysis, process modeling and simulation,


Summer 2002










equipment and plant design and the senior projects.

STUDENTS' EVALUATIONS
To measure the usefulness and effectiveness of the consid-
ered software packages, students filled out the evaluation form
shown in Table 1 at the end of the course for which the soft-
ware was used. The sixteen questions were selected from the
list of 24 questions proposed by Iglesias, et al.[9] Eight ques-
tions were dropped based on the recommendations of the
authors and the inability of students to clearly understand
some of them. Iglesias and co-workers classified the ques-
tions in three categories: teaching content and methodology
(questions 1-5), software and design features (questions 6-
10), and user reaction (questions 11-16).
The first class attempts to test the usefulness of the educa-
tional software in terms of subject content and design fea-
tures, as well as the teaching methodology used in the course.
The second category evaluates mainly the user interface (num-
ber of resources presented, quality and effectiveness of graph-
ics, tables, animation, etc.) and
ease of use of the package. The TABLE
third class tests the user's reac- Evaluation Res
tion to the program by consider- Control Station (1I
ing aspects such as documenta-
tion for user, degree of interac- Question Mean Stand
tion between user and program, 1 4.10
and time needed for program ex- 2 3.70
ecution. Note that the three cat- 3 3.20
egories are not totally indepen- 4 3.30
dent and distinct. The question- 5 3.50
naire ends by asking students to 6 3.90
comment on the reasons they 7 3.40
felt attracted to or bored by the 8 3.50
program. 9 3.90


The students' evaluations for
the three considered packages are
shown in Tables 2 to 7. The over-
all results are presented in Figure
5. Control Station and Mathcad
were, respectively, evaluated by
the process control and process
analysis undergraduate classes.
HYSYS was evaluated by stu-


I Category I
Figure 5. Overall marks for the three packages. CTM= Con-
tent and Teaching Methodology, PCC = Program Design
Characteristics, and UR = Users' Reaction.


Chemical Engineering Education


2
ults for
0 students)

lard Deviation
0.99
0.82
1.03
0.95
0.97
0.88
1.07
0.71
0.74


10 3.40 1.17
11 2.90 1.20
12 3.10 0.88
13 3.90 0.99
14 3.00 0.47
15 3.40 0.84
16 4.10 0.99
Comment on the reasons for which you felt
attracted to or bored by the program.


TABLE 4
Evaluation Results for
HYSYS (21 students)

Question Mean Standard Deviation
1 3.59 1.33
2 4.00 1.07
3 3.50 0.91
4 3.41 1.14
5 3.36 1.05
6 3.59 1.18
7 3.59 1.05
8 3.57 1.16
9 4.27 0.83
10 3.05 1.05
11 2.86 1.08
12 4.18 0.80
13 3.82 1.22
14 3.32 1.09
15 3.32 0.99
16 3.09 1.34


TABLE 3
Overall Marks for Control Station

Category Mean Standard Deviation
Content and teaching methodology 3.56 0.97
Program design characteristics 3.62 0.92
Users' reaction 3.40 0.99
Overall 3.52 0.96


TABLE 5
Overall Marks for HYSYS

Category Mean Standard Deviation
Content and teaching methodology 3.57 1.11
Program design characteristics 3.61 1.11
Users' reaction 3.43 1.17
Overall 3.53 1.12


240










dents from process systems engineering courses. As the tables
and Figure 5 show, the students' evaluations of all three soft-
ware packages were highly favorable; the overall marks var-
ied within a relatively narrow range (3.52 to 3.74).
For the case of control station, questions 1 and 13 received
high marks, indicating a strong correlation between the soft-
ware and the knowledge conveyed in the class, and also that
the use of computer workshops in the course is highly justi-
fied. Question 14 received the second lowest mark (3.0). This
was expected since chemical engineering students do gener-
ally feel that their first process control course includes more
material than an average course and that it is rather difficult.
This is due to the well-known fact that process control is much
different from traditional chemical engineering courses and
that it includes a significant number of new theories and terms.
For HYSYS, questions 2, 9, and 12 received the highest
marks, indicating that the students found the software re-
sources to be very effective and that the program has signifi-
cantly contributed to their study of the courses considered.
Note that prior to the availability of process flowsheeting
packages, the students had to manually carry out lengthy de-


Summer 2002


sign calculations. The students gave their lower ratings to
questions 10 (3.05) and 16 (3.09), i.e., they felt that the pro-
gram was not very easy to operate and that the time for simu-
lating case studies was too long. The speed of execution is,
of course, dependent on the size of the problem at hand. With
HYSYS being a commercial flowsheeting package, even
simple problems include a significant number of details.
High marks were given to questions related to Mathcad
design characteristics; the overall mark is 4.03 (see Table 7).
This is not surprising since the package is truly user-friendly
and the fact that prior to using Mathcad, the students were
programming in FORTRAN. For all three programs, the stu-
dents evaluated the programs' documentation as above aver-
age (see question 11). Although we feel that the material
handed out to the students was very good, this issue is cur-
rently being addressed by conducting more tutorials on the
use of the packages, supplying the students with more copies
of shorter versions of the users' guides, and preparing sim-
pler getting-started handouts.

CONCLUDING REMARKS

The computer has become an integral part of engineering
education. As the power of both hardware and software con-
tinues to rapidly increase, we expect the use of information
technology in the classroom/laboratory to grow at a much
faster rate in the near future.
The use of multimedia and software packages enhances
teaching and learning. In particular, the students learn more
and faster, allowing the teacher to cover more material in the
time allocated for the course. Of course, the information tech-
nology tools have a large number of benefits that are not within
the scope of this paper. For example, they are invaluable tools
for web-based education and distance learning and training.

REFERENCES
1. Kantor, J.C., T.F. Edgar, "Computing Skills in the Chemical Engineer-
ing Curriculum," in B. Carnahan (Ed.), Computers in Chemical Engi-
neering Education, CACHE Corporation, p. 9, (1996)
2. Benyahia, E, "Process Simulation Packages in Undergraduate Chemi-
cal Engineering Courses," The 1998 IchemE Research Event, CD-ROM
(ISBN 0 85295 400 X)
3. Edgar, T.F., "Information Technology and ChE Education: Evolution
or Revolution?" Chem. Eng. Ed., 34(4), p. 290, (2000)
4. Kulik, J.A. and C.C. Kulik, Contemporary Education Psychology, 12,
p. 222, (1987)
5. Montgomery, S., H.S. Fogler, "Interactive Computer-Aided Instruc-
tion," In B. Carnahan (Ed.), Computers in Chemical Engineering Edu-
cation, CACHE Coproration, p. 57, (1996)
6. Cooper D., D. Dougherty, "Enhancing Process Control Education with
Control Station Training Simulator," ComptAppl Eng Edu, 7, p. 203,
(1999)
7. Cooper, D.J., N. Sinha, "Picles + Digest = Control StationT for Win-
dows," CACHE News, 44, p. 14, (1997)
8. Bristol, E.H., "On a New Measure of Interactions for Multivariable
Process Control," IEEE TransAuto ControlAC-11, 133, p. 133, (1966)
9. Iglesias, O.A., C.N. Paniagua, R.A. Pessacq, "Evaluation of Univer-
sity Educational Software," ComptAppl Eng Edu, 5, p. 181, (1997) O

241


TABLE 6
Evaluation Results for
Mathcad (6 students)

Question Mean Standard Deviation
1 3.50 1.52
2 3.33 1.51
3 3.33 1.03
4 3.67 1.21
5 3.33 0.82
6 4.50 0.55
7 3.67 0.52
8 4.00 1.10
9 4.00 0.63
10 4.00 1.10
11 3.17 1.17
12 3.50 1.05
13 4.17 1.60
14 4.50 0.84
15 3.67 1.37
16 3.50 1.05


TABLE 7
Overall Marks for Mathcad

Category Mean Standard Deviation
Content and teaching methodology 3.43 1.17
Program design characteristics 4.03 0.81
Users' reaction 3.75 1.20
Overall 3.74 1.10










rem classroom


TEACHING PROCESS CONTROL


WITH A NUMERICAL APPROACH


BASED ON SPREADSHEETS




CHRISTOPHER RIVES AND DANIEL J. LACKS
Tulane University New Orleans, LA 70118


he traditional method for teaching process control
courses uses analytic techniques based on Laplace
transforms to solve the relevant differential equa-
tions.1'-9] The mathematical manipulations involved in these
analytic solutions are so complex and non-intuitive, however,
that students can lose sight of the physical significance of the
results. Numerical solutions offer a remedy to this problem
and can be used in conjunction with traditional analytic solu-
tions to strengthen the instruction of process control. We
emphasize that numerical solutions are not intended to re-
place analytic methods, but should instead be used in addi-
tion to analytic methods.
The use of computers in obtaining numerical solutions can
give an enhanced physical intuition and understanding that


can be difficult to achieve from
analytic solutions alone. As a re-
port in Science claims, "Many
physics students ... can solve the
calculus-based equations at the
heart of many laws of nature, but
they lack an intuitive feel for how
they work.1101 In contrast, numeri-
cal solutions solve the fundamen-
tal equations directly, allowing stu-
dents to focus on the physical prob-
lem rather than on mathematical
manipulations and approxima-
tions.["1 The interactive nature of
computers allows "what-if' experi-
ments in which values of param-
eters are changed, and the results
are displayed immediately in graphi-
cal form. The usefulness of this
approach is summarized by the


A I B C I D E
1 Process Variables Disturbance
2 K= 5 step 1
3 T = 2 for t 4 = 1 for t>ts,., 1
5
6 Time Step Initial Values
7 At= 0.01 y(0) = 0
8 y'(0) = 0
9
10 t f y y' y"
11 0 if(A11 12 A11+B$7 C11+D11*B$7 D11+E11*B$7
13
14
15


Figure 1. Spreadsheet used to determine the response of a 2nd order process to a step
change in the disturbance. The step function is implemented with an IF function of the
form IF (expression, value if true, value if false). Arrows indicate that cells should be
copied and pasted downward for approximately 5,000 to 10,000 rows.
@ Copyright ChE Division of ASEE 2002


Chemical Engineering Education


Christoper Rives received his BS in chemical
engineering from Tulane University in 2002. He
is currently studying for a PhD in chemical en-
gineering at Northwestern University






Daniel J. Lacks is Professor of Chemical En-
gineering at Tulane University. He received his
BS in chemical engineering from Cornell Uni-
versity and his PhD in chemistry from Harvard
University. His research interests involve the ap-
plication of molecular simulations to chemical
engineering problems.









title of a recent article in Chemical and Engineering News:
"Thinking Instead of Cookbooking: When Computers
Take Over the Dirty Work ... Students Can Focus on the
Bigger Picture."1121
The differential equations that arise in process control ap-
plications are readily solved numerically by using simple
spreadsheets that can be constructed by the students in less
than five minutes. Students can experiment with different
control schemes and parameters in order to gain an under-
standing of how each parameter affects the response of the
system. They develop an intuitive feel for how a system will
respond to input changes and how this response can be con-
trolled. Then, they discover how to optimize the control.
This strategy has been used in the process control course at
Tulane. The numerical approach is used first to introduce a
topic, allowing students to obtain a good physical understand-
ing before proceeding. The topic is then addressed more fully
with the traditional analytical approach based on Laplace
transforms. Students follow the analytical approach more eas-
ily at this point since they already have a solid physical un-
derstanding from the numerical approach.

DESCRIPTION OF APPROACH
This section describes how the numerical approach using
spreadsheets can be used to teach most major topics in a pro-
cess control course, including process dynamics, frequency
response analysis, feedback control, and advanced control


3
25
2
as
15
os-
0
-o.5
0 50 100 150
time


0 20 40
time


3
25
2
15
0 -1 -
0
-05
0 20 40 60 80 100
time


60 80 0


20 40 60
time


Figure 2. Response of a 2nd order process to a step change
in the disturbance for (a) = = 3 (b) = = 0.2 (c) = = 0 (d)
i = -0.1 The bold line is the disturbance, and the thin line
is the response.

Summer 2002


techniques such as feedforward and cascade control.
Process Dynamics
As an example, the response of a linear second-order pro-
cess is examined.1"-" A linear second-order process is de-
scribed in general by
t2y" +2 Ty' +y = Kf(t) (1)

where y is the response of the process (output), y' = dy/dt, y"
= d2y/dt2, f is the disturbance (input), K is the gain, r is the
characteristic time, and is the damping factor.
Differential equations can be solved numerically using
Euler's Method. This method is implemented for second-
order differential equation by repeatedly applying the follow-
ing algebraic equations for small time increments, At:
y(t + At) = y(t) + y' (t)At (2)

y' (t + At) = y' (t) + y" (t)At (3)
Note that the initial values of y and y' must be specified, and
the values of y"(t) are obtained by rearranging Eq. (1).

Kf(t)- 2rty' (t)- y(t) (a)
y"(t)= "c2
T2
Below, we present the implementation of this method for a
step change in f(t).
The spreadsheet used to solve this problem is shown in
Figure 1. The results are easily displayed in graphical form
by plotting y and f together as functions of time. All param-
eters are defined at the top of the spreadsheet, and their cell
locations are referenced in the relevant equations. Upon
changing parameter values, the graphical display of the re-
sults is updated immediately, without rewriting any of the
spreadsheet.
The physical significance of the damping factor, i, in a sec-
ond-order linear differential equation can be demonstrated
with this approach by comparing the response to a step change
for different values of i. For > 1, the response is
overdamped, and it reaches a steady state without oscillating
(Figure 2a). For 0 < < 1, the response is underdamped, and
it exhibits decreasing oscillations as it reaches a steady state
(Figure 2b). For = 0, the response is undamped, and it os-
cillates indefinitely (Figure 2c shows a slight increase in
amplitude with time, due to numerical error-see Discussion
section). For < < 0, the response is unstable, and it increases
without bound (Figure 2d). All of these results are generated
and graphically displayed in a matter of seconds once the
spreadsheet is constructed.

Frequency Response Analysis
The frequency-dependent response to an oscillating distur-
bance is important in many fields, including process control.
The traditional method of teaching frequency response analy-
sis is given in process control textbooks.10-9 A second-order
process (Eq. 1) is examined here, and the spreadsheet used to
solve this problem (Figure 3) is just a slight modification of










the spreadsheet used for the step
function input (only the disturbance
is different).
The frequency response of the sys-
tem can be addressed by comparing
the response obtained with different
values of the angular frequency, ow.
When the frequency is small, the sys-
tem has sufficient time to react to the
changing disturbance, and the re-
sponse is nearly in phase with the
disturbance (Figure 4a). When the
frequency is increased, however, the
system does not have sufficient time
to react, and the response increas-
ingly lags behind the disturbance
(Figures 4b and 4c). Additionally,
the amplitude of the response usu-


A I B C D E
1 Process Variables Disturbance
2 K= 1 A= 1
3 = 2 o)= 0.01
4 C= 1.5
5
6 Time Step Initial Values
7 t = 0.5 y(0) = 0
8 y'(0)= 0
9
10 t f y y' y"
11 0 D$2*sin(D$3*A11) D7 D8 (B$2*B11-2*B$4*B$3"D11-C11)/(B$3)^2
12 A11+B$7 C11+D11*B$7 D11+E11*B$7
13

14
15

Figure 3. Spreadsheet used to determine the response of a 2nd order process to an
oscillating disturbance. Arrows indicate that cells should be copied and pasted down-
ward for approximately 5,000 to 10,000 rows.


ally decreases with increasing frequency (Figures 4a, 4b, and
4c). For < < 1 and small frequencies, however, the behavior
of a-linear second-order system is unusual in that the ampli-
tude increases with increasing frequency (Figure 4d). Note
that the immediate graphical results allow students to quickly
and easily experiment with different values of c and !.
Feedback Control
A feedback control mechanism measures the output of the
process, compares it to the desired value (the set point), and
then alters an input to the process in order to bring the output
closer to the desired value."-"'
The output of a proportional-integral-derivative (PID) con-
troller is given by


TIdt
yc=KcKe +-Jedt+KcTDd- (4)

where e = ysp y, Ysp is the set point, and y is the output of
the process. When the system is not under any control, the
values of K, and tD are set equal to zero, while Tl is set
equal to infinity. The integral term can be calculated numeri-
cally as
t
j Fdt = (ti)At (5)
0
and the derivative term can be calculated numerically as

de(t) (t)- e(t At) (6)
dt At
The numerical approach is applied here to the feedback con-
trol of a process consisting of three first-order systems in se-
ries. The dynamics of the other parts of the control loop (e.g.,
measuring device) are not included for simplicity, but can
easily be included if desired (as pointed out in the Discussion
section). A process consisting of three first-order systems in


Figure 4. Response of a 2nd order process to an oscillating
disturbance for (a) = 1.5, co = 0.1; (b) t = 1.5, o = 0.3; (c)
S= 1.5 o = 2; (d) r = 0.5, o = 0.2. The bold line is the
disturbance, and the thin line is the response.


series is described by three coupled first-order differential
equations,


iYi + i = Kif + Kpy

Tiyi + Yi = Kiyi_i


i=l

i =2,3


where i is the system number. These coupled differential equa-
tions are numerically integrated using Euler's method by re-
peatedly applying the algebraic equations


i = 1,2,3


Chemical Engineering Education


Yi(t + At) = y, (t) + yi (t)At

















3:
I

I








I


+



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o N CO C






o C I (C II
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CC CC CC CC CC CC C


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cO
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.4.
Co

Co





Co

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>4C


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a
CU

W
Co
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11 V

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020







00
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g-



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02O







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2 ^


"52
0. ^
~'S -'S







.a -c;





Cr2 5-.
_ .f 2
02^
('.2-


where the Yi(t) are obtained from Equations 7 and 8. The spreadsheet
used to solve this problem is shown in Figure 5.

By experimenting with different values of the control parameters

(K6, r1 and TD), the relationship between each control parameter
and the response can be determined. If proportional-only control is

used (i.e., tD = 0 and 'T = a large number that approximates -), the

response is offset from the set point (Figure 6a). Increasing the value

of Kc will minimize this offset (Figure 6b), but the system can be-

come unstable if Kc is too large (Figure 6c). Adding integral control

(i.e., decreasing Tl from ) will eliminate this offset (Figure 6d).

But if the value of t1 is too small, the system becomes unstable (Fig-

ure 6e). Adding derivative control (i.e., increasing TD from 0) stabi-

lizes the system (Figure 6f). This stabilization allows a larger K, and
a smaller Tl to be used, but a large TD value also slows the response.

The values of the control parameters should be chosen such that a

quick response with small oscillations and no offset is achieved. The

Zeigler-Nichols tuning method is one way to obtain advantageous

values for the three control parameters, in which


Kmax
Kc =c-
1.7

Pu

2


D Pu
8


(lOa)



(l1b)



(lOc)


where K"'x is the maximum value of K. for which the response is

stable with a proportional-only controller, and Pu is the period of os-

cillation of the response at K'ax The value of K"ma is found by trial


8 (a)
6


2
0-
-2
-4


0 20 40 60 80
time


8 (d)
6


2
0
-2
-4
-6
0 20 40 60 80
time


8 (b)
6


2
. 1 ----------------------

-2
-4
-6
0 20 40 60 80
time


8 (e)
6
4
2


-2
-4
-6
0 20 40 60 80
time


S (c)





2
-4
-6
0


20 40 60


time


8
6






-2
-4
-6
0 50 100 150
time


Figure 6. Response of a process consisting of three first-order systems in
series with feedback control to a step change in the disturbance. (a) P-
only, Kc = 1 ; (b) P-only, K, = 4 ; (c) P-only, Kc = 15 ; (d) PI: Kc = 1, 't = 5; (e)
PI: Kc =1, 1 = 1.3, (f) PID: K =1, rl = 1.3, D = 15. The bold line is the dis-
turbance, and the thin line is the response.


Summer 2002


I I- I -I -I -1- 1'2;: !L el : U!? -


245











7 (a) 7 (b)
6 6
5 5
4 4
3 3
2 2

0 0 ? -\

0 20 40 60 0 20 40 60 I
time time

Figure 7. Tuning of PID parameters with Ziegler-Nichols .
method, for a process consisting of three first-order systems in z
series with feedback control. (a) Determination of Kmax and o
Pu; (b) PID with Ziegler-Nichols parameters: Kc = 3.7, T = 5.4, "u
TD =1.4. The bold line is the disturbance, and the thin line is -
the response. P
o p
and error to be 6.3 (Figure 7a), and the value of Pu is observed < m4 -
to be 10.8. The response using the Ziegler-Nichols parameters
is shown in Figure 7b. .
Feedforward Control
A feedforward control mechanism measures the disturbance
and uses this measured value to adjust an input variable with 4
the goal of keeping the process output at the desired value.1'I U"-
The output of a simple feedforward controller is given by m


Ye =Ayp -Bf (11)
where A and B are controller parameters that will depend on the
particular process to be controlled. I
uwi oo 000
The numerical approach is applied here to the feedforward +
control of a process consisting of three first-order systems in U .
series (Eq. 7 and 8). The spreadsheet for this problem is shown
in Figure 8. Perfect control can be obtained by choosing the 0 0
1 VXi
parameters such that the system is at steady state with the pro- -. J : -
cess output at the set point (i.e., y; = y2 = y3 = 0 and Y3 = Ysp).
From equations 7 and 8, it is easily found that the parameter -
values that yield perfect control are A=1/(KpK2K3) and











0 2 0 t o e o080 0 20 ,4 60 8 ,
7 (a) 7 (b)K0.842 an
6 6 -
w
4 4 Q S
3 3 NmI-0), 5
ca o O0N *'
1 1 10
1 / K=0.625; (b) A= 0.842 and B= 0.5. The bold line
is the disturbance; the thin line is the response.
0 20 40 60 80 0 20 40 60 80 11 It II 111 I 11 IIt
time time N z N l +

Figure 9. Response of a process consisting of three first-or- \ "ID I" 1 0j I I l Dl
der systems in series with feedforward control to a step change
in the disturbance. (a) A=l/(KpK2K3)=0.842 and
B = K / Kp = 0.625;(b) A = 0.842 and B = 0.5. The bold line
is the disturbance; the thin line is the response.


Chemical Engineering Education















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P


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7r'
1








47


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'- m
3053
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(. a 3
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B = K1 / K,. As shown in Figure 9a, perfect control is indeed achieved

with these parameters. Perfect control is no longer achieved when
A I1 / (KpK2K3) or B # K1 / Kp (as shown in Figure 9b). Since real
processes are generally not simple with accurately known parameters,
perfect control is only idealistic, not practical.

Cascade Control

Cascade control uses two control loops (primary and secondary).m The
primary control compares the process output to the desired value (set
point), yielding a second set point to be used for a secondary control.
The secondary control compares an intermediate quantity to this second
set point to determine how to alter an input variable.

The example of a process consisting of three first-order systems in
series (Eq. 7 and 8) is used to examine cascade control. The intermediate
quantity used in the secondary control loop is the output of the first-

order process (yi). A proportional-integral controller is used for the

primary controller, and a proportional-only controller is used for the
secondary controller. The spreadsheet used to solve this problem is
shown in Figure 10.

The response of the system with cascade control is shown in Figure 11
- this response is superior to the response with feedback control (Figure
7b). (Note that this example is somewhat artificial in that the secondary
control loop consists of only a first-order process and will be stable for
any value of the secondary controller gain. Therefore, an arbitrarily large
value of the secondary controller gain can be used to make the response
arbitrarily fast. This arbitrarily fast response is not possible in gen-
eral, e.g., if the secondary loop includes dead time or a process higher
than second-order).


DISCUSSION

Implementation of Approach

This numerical approach using spreadsheets was implemented in the
process control course at Tulane as follows: first, a topic is introduced in
a lecture, and the governing equations are derived; next, the class moves
on to our computer lab, where students solve the governing equations
numerically (all students do this individually on separate comput-
ers), and the physical significance of the results is discussed; finally,
the traditional analytic solutions based on Laplace transforms are
taught, in lecture format.

Homework assignments include problems requiring numerical solu-
tions using spreadsheets, problems requiring analytical solutions, and
problems that use the Control Station software package.113 Some prob-
lems require that students compare results
from numerical solutions to results from
7-
76 analytical solutions. For example, one
5
4
3 Figure 11. Response of a process consist-
2 ing of three first-order systems in series

with cascade control to a step change in
0 2, 0 the disturbance (primary controller: K =2
o 20 40 6o and 1 =5, secondary controller: K=10).
time The bold line is the disturbance, and the
thin line is the response.


Summer 2002


I I-IN(Dl~lnlmlhl~la(PI=INl~lllrlPIFlml









homework problem requires that students find the maximum
value of a controller gain for a proportional-only controller
in a certain process by three methods: by trial and error with
numerical solutions, by deriving the transfer function and find-
ing the gain that leads to positive real parts of its poles, and
by the Bode stability criterion using analytical expressions
for phase lags and amplitude ratios. The students compare
the results for the maximum controller gain from these
different methods and find them to be the same (within
numerical error).
The exams test the students' knowledge of applying nu-
merical methods to process control problems, in addition to
the traditional process control material. One of the exams
includes a computer part (given in class in our computer com-
puter lab), where students solve a problem numerically with
a spreadsheet and turn in the printed result. The other exams
have problems in which students must show how to set up a
spreadsheet to numerically solve a given problem, providing
all of the relevant equations.
Students found the numerical approach using spreadsheets
to be extremely useful in understanding the concepts under-
lying process control. In unsolicited comments on the course
evaluations, two-thirds of the students remarked that the nu-
merical approach was the most valuable aspect of the course.
The students also seemed to genuinely enjoy this approach.
When problems were solved with this method in the com-
puter lab, students were often so eager to discover the ef-
fects of changing some parameters that they would proceed
ahead of the discussion. They would also occasionally con-
tinue experimenting with the effects of different parameters
after the class had ended.

Other Issues
The numerical approach is more general than the analytic
approach, in that it can also be applied to nonlinear differen-
tial equations, i.e., a linearization approximation is not nec-
essary as it is for the analytic approach based on Laplace
transforms. To emphasize this point, a homework problem
was given in which students investigate the frequency re-
sponse for a process described by the nonlinear differential
equation y + ya = f (where a is the number of letters in their
last name divided by five), and then use the results to con-
struct Bode and Nyquist diagrams.
A concern with the numerical approach, of course, is that
there is numerical error in the results. Students should be
aware of the numerical error and that the error can be re-
duced by decreasing the time step At or by using a more
sophisticated integration method (e.g., Runge-Kutta or a pre-
dictor-corrector method). A reasonable time step for these
problems is At = T / 100, where T is the smallest characteris-
tic time for the system.
Although excluded here for simplicity, it is straightforward
to include in this approach the dynamics of other elements of


the control loop, such as actuators (e.g., valves) and measur-
ing devices. Including the dynamics of these elements would
amount to including a few more coupled differential equations, which
translates to a few more columns on the spreadsheet.
Dead time is also straightforward to include in this approach.
To introduce dead time to a variable y, a new variable, Y+dead,
is defined such that y+dead(t)= y(t deadd. The values for
Y+dead are obtained in the spreadsheet from the values of y,
by setting the cell for y+dead at the time, t, equal to the value
of the cell for y at the time t dead (i.e., tdead / At rows above
in the spreadsheet).
The present approach is different than, but complementary
to, an approach that uses packaged software (such as Control
Station[131) for teaching process control. In the present ap-
proach, students are in fact solving the governing equations
themselves, with a numerical method rather than an analytical
method. In contrast, the Control Station softwaret131 presents
results without requiring that students solve the equations.

CONCLUSION
In the usual method for teaching process control, students
are taught to solve the relevant differential equations analyti-
cally by using Laplace transforms. This method involves com-
plex mathematical manipulations, which can cause students
to lose sight of the physical significance of the problem. The
main goal of a process control course should be to provide a
general understanding and intuitive feel for how physical pro-
cesses behave and how they can be controlled. Numerical
solutions for process control problems are extremely easy to
obtain using spreadsheets created by students themselves. This
approach allows students to concentrate on what is physi-
cally happening as opposed to the complex mathematics, yet
the students solve the problems themselves (i.e., the solu-
tion is not given to them by packaged software). This ap-
proach has been used in the Process Control course at Tulane,
and student feedback has been extremely positive.

REFERENCES
1. Stephanopolous, G., Chemical Process Control, Prentice Hall, Englewood Cliffs,
NJ (1984).
2. Riggs, J.B., Chemical Process Control, Ferret, Lubbock, TX (1999).
3. Marlin, T.E., Process Control, McGraw-Hill, New York, NY (1995).
4. Marlin, T.E., Process Control, 2nd ed., McGraw-Hill, New York, NY (2000).
5. Smith, C.A., and A.B. Corripio, Principles and Practice ofAutomatic Process
Control, John Wiley & Sons, New York, NY (1985).
6. Seborg, D.E., T.F. Edgar, and D.A. Mellichamp, Process Dynamics and Con-
trol, John Wiley & Sons, New York, NY (1989).
7. Shinskey, EG., Process Control Systems, 4th ed., McGraw-Hill, New York, NY
(1996).
8. Luyben, W.L., Essentials of Process Control, McGraw-Hill, New York, NY
(1997).
9. Coughanowr, D.R., Process Systems Analysis and Control, 2nd ed., Mc-Graw-
Hill, New York, NY (1991).
10. Gibbons, W., Science, 266, 893 (1994).
11. De Vries, P.L., American Journal of Physics, 64, 364 (1996).
12. Wilson, E.K., Chemical and Engineering News, May 26, p. 33 (1997).
13. Cooper, D.J., Control Station for Windows, Version 2.5 (2000) 0


Chemical Engineering Education
















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published by the Chemical Engineering Division of the American Society for Engineering Education (ASEE).
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If the reader needs the table, omit the graph. Substitute a few typical results for lengthy tables when practical. Avoid computer
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NOMENCLATURE Follow nomenclature style of Chemical Abstracts; avoid trivial names. If trade names are used, define at
point of first use. Trade names should carry an initial capital only, with no accompanying footnote. Use consistent units of mea-
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ACKNOWLEDGMENT Include in acknowledgment only such credits as are essential.

LITERATURE CITED References should be numbered and listed on a separate sheet in the order occurring in the text.

COPY REQUIREMENTS Send two legible copies of the typed (double-spaced) manuscript on standard letter-size paper.
Submit original drawings (or clear prints) of graphs and diagrams on separate sheets of paper, and include clear glossy prints of
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Chemical Engineering Education, c/o Chemical Engineering Department
University of Florida, Gainesville, FL 32611-6005















overpressure at a given point.

Figure 5 shows the percentage of people and installations af-
fected by different effects and causes. The values of overpres-
sure and radiation intensity received by a surface at a distance
X (Elia model) obtained in the previous section (consequence
analysis models) were used; the exposure time was taken as
tBLEVE obtained with the Elia model.[12] Table 4 shows the esti-
mated distances at which 1% and 50% of the population or struc-
tures can be affected by a given effect. The limit at which 1% of
the population may die is called "mortality threshold."


CONCLUSIONS

Risk analysis of major accidents is a useful tool for future
chemical engineers; it gives not only a quantitative estimation
of the risk involved in a given process, but also a suitable method
for estimation of possible victims (environment, persons, and


0)
in c
o r
WC





C
-nc

C-

>>


a4)

ca
- *
C


g"
ci)
(0 Q
mg
r


200 300
Distance (m)


Figure 5. Percentage of people and installations affected
by different effects and causes at a given point:
overpressure effects (solid line) and
thermal effects (dotted line).


TABLE 4
Distance at which 1% and 50% of the Population
(People or Objects) are Affected

Cause Effect Distance Distance
ml 50% fmll%
Explosion Lung hemorrhage 18.8 22.3
Explosion Eardrum rupture 34.4 63.0
Explosion Structural damages 51.6 84.7
Explosion Breakage of glass 162 321
Thermal effects Mortality due to thermal radiation 153 212
Thermal effects Second-degree burns() 222 293
Thermal effects First-degree bums(2) 329 436

(1) Epidermis and part of the dermis are burned
(2) A superficial bum in which the top layer of skin (part of the epidermis) has
been slightly burned


properties). A boiling-liquid expanding-vapor explosion
(BLEVE) of a tank truck of liquid propane has been used
to demonstrate this technique, and the blast and thermal
effects have been calculated with several methods. The vul-
nerability of persons and/or installations affected in both
cases has been calculated using the Probit methodology.


REFERENCES
1. Lane,A .M ., H Oill..-l *1, ..%1_Ii 1 II l1 ,, I h I1i. i1..h h .l.I ll,,-
cal Issues into the Curriculum," Chem. Eng. Ed., 23, 70 (1989)
2. Cohen, Y, W. Tsai, and S. ( IIH "A Course on Multimedia Envi-
ronmental Transport, Exposure, and Risk Assessment," Chem. Eng.
Ed., 24, 212 (1990)
3. Gupta, J.P, "A Chemical Plant Safety and Hazard Analysis Course,"
Chem. Eng. Ed., 23, 194 (1989)
4. Mannan, M.S., A. Akgerman, R.G. Anthony, R. Darby, PT. Eubank,
and R.K. Hall, "Integrating Process Safety into the Education and
Research," Chem. Eng. Ed., 33, 198 (1999)
5. Santamaria, J.M., and PA. Brania, "Risk Analysis and Reduction
in the Chemical Process Industry," Blackie Academic & Profes-
sional (1998)
6. Golder, A., "Safety Relevance in Undergraduate Education,"
SACHENews, Spring 4 (2000)
7. Rossignol, A.M., and B.H. Hanes, "Introducing Occupational Safety
and Health Material into Engineering Courses," Eng. Ed., 80, 430
(1990)
8. Reid, R.C., J.M. Prausnitz, and B.E. Poi... Gases
andLiquids, McGraw-Hill, New York, NY (1987)
9. Reid, R.C., "Possible Mechanism for Pressurized-Liquid Tank Ex-
plosions or BLEVEs," Science, 3, 203 (1979)
10. CCPS (Center for Chemical Process Safety), Guidehnes for Chemi-
cal Process Quantitative Risk Analysis, AIChE, New York, NY
(1989)
11. Roberts, A.F., "Thermal Radiation Hazards from Release of LPG
Fires from Pressurized Storage," Fire Safety J., 4, 197 (1982)
12. Elia, F., Risk Assessment and Risk Management for the Chemical
Process Industry, H.R. Greenberg and J.J. Cramer, eds., Van
Nostrand Reinhold, New York, NY (1991)
13. Pape, R.P, et al., "Calculation of the Intensity of Thermal Radia-
tion from Large Fires," Loss. Prev. Bull., 82, 1 (1988)
14. Perry, R.H., and D. Green, eds,Perry's Chemical Engineer sHand-
book, 6th ed., McGraw-Hill, New York, NY (1984)
15. Prugh, R.W., "Quantify BLEVE Hazards," Chem. Eng. Prog., 87,
66(1991)
16. Kletz, T. "Unconfined Vapor Explosions," Loss Prevention 11,
Chem. Eng. Prog. Tech. Manual, AIChE, New York, NY (1977)
17. Hopkinson, B., British Ordnance Board Minutes 13565 (1915)
18. Crowl, D.A., and J.F. Louvar, Chemical Process Safety: Funda-
mentals with Applications, Prentice Hall, Englewood Cliffs, NJ
(1990)
19. CCPS (Center for Chemical Process Safety): "Guidelines for Evalu-
ating the Characteristics of Vapor Cloud Explosions, Flash Fires,
and BLEVEs," AIChE, New York, NY (1994)
20. Pietersen, C.M., and S.C. Huerta, "Analysis of the LPG Incident in
San Juan Ixhuapetec, Mexico City, 19-11-84," TNO Report B4-
0222, TNO, Directorate General of Labor, 2273 KH Vooburg, Hol-
land (1985)
21. TNO, "Methods for the Determination of Possible Damage to
People and Objects Resulting from Release of Hazardous Materi-
als," CPR 16E, Vooburg, Holland (1992) 5


Summer 2002











^ 9 laboratory


MASS TRANSFER

AND CELL GROWTH KINETICS

IN A BIOREACTOR



KEN K. ROBINSON, JOSHUA S. DRANOFF, CHRISTOPHER TOMAS, SESHU TUMMALA
Northwestern University Evanston, IL 60208-3120


Biotechnology is an increasingly important factor in
the chemical process industries. The last decade has
seen rapid growth in the resources committed to the
development of biologically based processes. At the same
time, the market value of new products generated by biologi-
cal means has continued to grow at an accelerating rate. Ac-
cordingly, more and more chemical engineers are being em-
ployed in the development, design, and operation of
bioprocesses for production of pharmaceuticals, foods, and
specialty chemicals, with no indication that the demands and
opportunities in this area will moderate in the future.
In recognition of this trend, we have developed a new "bio-
technology experiment" for Northwestern's senior laboratory
course.["1 This experiment is aimed at giving our students an
opportunity to become familiar with various factors involved
in the implementation of bioprocesses and some of the atten-
dant technologies. We hope this will introduce them to this
broad field while they are still at Northwestern and also en-
hance their attractiveness to potential employers.
The experiment provides a means for studying two basic
chemical engineering operations (mass transfer and cell
growth kinetics) that occur in a three-liter stirred fermenta-
Ken Robinson is a Lecturer at Northwestern University with primary re-
sponsibility for the undergraduate chemical engineeirng laboratory. He
received his BS and MS from the University of Michigan and his DSc from
Washington University He has worked in industry for both Amoco and
Monsanto.
Joshua Dranoff is Professor of Chemical Engineering at Northwestern
University He received his BE degree from Yale University and his MSE
and PhD from Princeton University His research interests are in chemical
reaction engineering and chromatographic separations.
Christopher Tomas is a PhD candidate at Northwestern University work-
ing under the direction of Professor E Terry Papoutsakis. He received his
BS in Chemical Engineering from the University of Illinois, Urbana-
Champaign, in 1996, and his MS in Biotechnology from Northwestern
University in 1998.
Seshu Tummala is a PhD candidate at Northwestern University working
under the direction of ProfessorE. Terry Papoutsakis. He received his BS
degree from The Johns Hopkins University in 1996 and his MS degree
from Northwestern University in 1999, both in chemical engineering.
Copyright ChE Division ofASEE 2002


tion reactor. The initial part of the experiment involves the
study of oxygen transfer rates from gas to liquid phases; tran-
sient dissolved oxygen profiles resulting from step changes
in feed gas oxygen concentration are measured with a dis-
solved oxygen probe. The growth kinetics ofEscherichia coli
are then studied in the same reactor under standard condi-
tions. Cell growth is monitored by spectrophotometric analy-
sis of samples removed from the reactor at specific times.
The complete experiment is normally run in two successive
laboratory sessions, each about eight hours long, separated
by one week. It is also necessary to perform some short pre-
parative steps the day prior to the second laboratory session.

EXPERIMENT SETUP
Equipment The principal apparatus used is an Applikon
three-liter glass stirred bioreactor. It was obtained as part of a
complete package that included a number of ancillary items,
such as temperature, pH, and oxygen probes and control sys-
tems. Additional major items obtained for this purpose in-
cluded an Innova 4200 shaken-cell incubator and a basic spec-
trophotometer (Spectronic 20+). The approximate cost of this
equipment is indicated in Tablel. Not included in the indi-
cated cost, but of critical importance for this experiment, is a
steam sterilizer large enough to accommodate the fermenta-
tion reactor. We had access to such a unit in our department
(AMSCO Eagle 2300 Autoclave) and assume that similar
equipment is likely to be available in chemical engineering
or related departments at other institutions.
A sketch of the reactor is shown in Figure 1. It is stirred
with dual turbine blade impellers on a single shaft, driven by
an electrical motor with an adjustable speed control. The re-
actor top is a stainless steel disk equipped with multiple ports
for sampling, introduction of inoculum, gas feed and outlet
lines, and insertion of temperature, pH, and dissolved oxy-
gen measuring probes. Additional specifications are indi-
cated in the Appendix.


Chemical Engineering Education










educator


L. K. Doraiswamy


of Iowa State University


THOMAS D. WHEELOCK, PETER J. REILLY
Iowa State University Ames, IA 50011


K. Doraiswamy came to Iowa State University (ISU)
in a most unusual manner. One of the authors (PR)
was attending a meeting in New Delhi in 1984 and,
since he had previously helped two scientists at the National
Chemical Laboratory (NCL) in Pune with some chromatog-
raphy for a project of theirs, he asked if he could visit them
there. He took the train to Pune during the dry season, arriv-
ing a bit hot and dusty, but quite exhilarated after experienc-
ing one of the world's great train rides-the climb through
the Western Ghats. He and a former graduate student were
picked up by two NCL scientists on their motor scooters and
were delivered to the laboratory, where they were eventually
ushered into the baronial office of the NCL Director, occu-
pied in fine style by one L.K. Doraiswamy. Although L.K. was
chagrined that the visitors had not been met by an air-condi-
tioned NCL car, things went so well after that, the ISU visitor
ended by participating in a joint enzyme project with the NCL.
Some years later, L.K. (as he is known to his friends and
colleagues, except at Wisconsin-Madison where he goes by
Dorai) arrived by very small plane in Des Moines to see how
the ISU end of the joint project was progressing. During that
visit L.K. was asked by his host what he planned to do after
his (imminent) NCL retirement. L.K. mentioned how much
he liked small midwestern university towns, and sensing a
very good thing, the host passed this word on to his depart-
ment chair (Dick Seagrave). Soon an appointment was hur-
tling through the university hierarchy in record time.
That first appointment, in 1989, was the Glenn Murphy
Chair, meant for a distinguished visiting professor in the
College of Engineering. It was followed by the Department
of Chemical Engineering's Herbert Stiles Chair in 1992, and
then in 1996 L.K. became Anson Marston Distinguished Pro-
fessor in Engineering. His first office was anything but baro-
nial, being the standard 120 ft2 with hardly any window area,
but eventually a nice office opened up when Sweeney Hall
was expanded. L.K. still occupies it, even after his retire-
ment from ISU in December 2000.
@ Copyright ChE Division ofASEE 1999


EARLY STIRRING
L.K. was born in Bangalore in 1927 to L.S. and Kamala
Krishnamurthy, the only boy of four children. His father led
the Hyderabad Branch of the Geological Survey of India. For
part of his childhood, L.K. and his family lived in the small
village of Lingsagur. Later they moved to Hyderabad, the
state capital, where L.K. graduated from Methodist Boys High
School. He studied chemistry at Nizam College in Hyderabad,
part of the University of Madras, and then was faced with
several opportunities for further education. One was to study
organic chemistry, a subject he thoroughly enjoyed. But the
rapidly developing field of chemical engineering also attracted
him, and he ultimately decided to study it at the Algappe
Chettiar College of Technology, also part of the University
of Madras. Such an opportunity was very rare in India at the
time, since only two schools with limited enrollments and
very high entrance standards offered chemical engineering.

ON TO WISCONSIN
As a result of his successful record in pursuing chemical
engineering at Madras, L.K. received a scholarship from the
Hyderabad government to study in the United States. An uncle
with a Wisconsin PhD in chemistry suggested that he apply
there-he did, he was accepted, and he arrived during the
winter cold of December 1948.
L.K. was lucky enough to secure Olaf Hougen as his major
professor, and after he earned his MS in 1950 and his Indian
scholarship had expired, Hougen convinced the Hyderabad gov-
ernment to continue funding L.K. for a PhD (which he received
in 1952). His dissertation was on semichemical pulping, done
under the joint supervision of Hougen and John McGovern of
the USDA Forest Products Laboratory in Madison.
Hougen's perception that he had found a promising chemi-
cal engineer was even truer than he thought-in 1987 L.K.
became the Olaf Hougen Visiting Professor of Chemical En-
gineering at Wisconsin, an honor given to only five other
distinguished educators. Then in 1991, he received an honor-


Chemical Engineering Education










lite catalysts and processes for xylene isomerization and for
making alkylating benzene with alcohols. Many of these de-
velopments led to awards from the Indian Chemical
Manufacturer's Association.
L.K. lavished care and attention on the NCL by streamlin-
ing departments, doing what was needed to attract the best
people, and attending to the needs of the whole community.
His son Deepak tells us that on occasion this involved such
matters as "compassionate appointments" for poor or recently
widowed employees, special housing allotments for deserv-
ing cases, and investment of resources for welfare purposes
such as the local school and a shopping center (which has
since become a major attraction in the city and is named
after his late wife).
To highlight his human side, one instance is worth special
mention. One night, a poor family was evicted from the NCL
campus for building and occupying an illegal accommoda-
tion. L.K., moved by their plight (and against the administra-
tive officer's advice), gave them permission to stay overnight
until they could make other arrangements. This eventually
led to a protracted legal battle and illustrates how his softer
side sometimes leads him to take risks.
His professionalism concerning matters such as punctual-
ity, returning phone calls, meeting deadlines, and making al-
lowances for potential mistakes in planning is also a hall-
mark of his character. His approach is simply "to get and
maintain the best," and it has led to a legacy of excellence
that he is especially proud of. He maintains that "excellence
is a state of mind" and he never tires of repeating it.
While at NCL, L.K. wrote a book on catalytic reactors and
reactions (Pergamon, 1991) and was coauthor of two vol-



Students and
faculty at the
Wisconsin summer
laboratory course
in 1977, with L.K. at
the far right
and Roger Altpeter
and Richard
Grieger-Block at
the far left.
Wisconsonians,
and others,
beyond a certain
age will enjoy
identifying the -
others pictured
here. A


umes on heterogeneous reactions with his close friend M.M.
Sharma at the University of Bombay (Wiley, 1984) and one
on stochastic modeling with his NCL colleague B.D. Kulkarni
(Gordon and Breach, 1987). He also edited or coedited four
books and contributed chapters to six others. L.K. personally
guided the thesis research of 45 students who received PhDs
from various Indian universities and collaborated with the
late Tony Holland at Salford in guiding fifteen others and
with Mike Davidson at Edinburgh in an additional two. He
has been author or coauthor of some 155 international jour-
nal articles. They were mainly on adsorption and catalysis;
gas-solid, gas-liquid, solid-solid, and slurry reactions; fluidi-
zation; and stochastic modeling and analysis of reacting sys-
tems. For five years he also served as editor of the Indian
Chemical Engineer.
L.K. is reputed to have received every major scientific and
technical award in India open to chemical engineers. Among
the most noteworthy are the Om Prakash Bhasin Award for
Science and Technology, givenby Indian President Zail Singh
in 1986, the Jawaharlal Nehru Award for lifetime achieve-
ment in engineering and technology (1987), and the Repub-
lic Day honor Padma Bhushan presented by Indian President
R. Venkataraman in 1990. Notable awards from outside of
India but honoring his work there are election to the Third
World Academy of Science in 1997, the Richard H. Wilhelm
Award from AIChE in 1990, and the Personal Achievement
in Chemical Engineering Award in 1988 from Chemical
Engineering magazine.

THE FAMILY MAN
Soon after returning to India, L.K. married his wife
Rajalakshmi. She was always a source of great emotional


Chemical Engineering Education










etic iunt contains only one unknown distance or other nor-
malizing quantity and is also a known important term, set the
co'ttic i ut to unity and solve for the unknown quantity (i.e., we
knew the conduction in the radial direction was important, so
we found z with the (coeti ieut oiirel conduction term.)
7) Collect remaining terms into as few c oei'tt it~'nr as possible.
These terms are generally dimensionless ratios that appear
as parameters of the final solution.
These steps should be considered general guidelines. For
the student, it is useful to try scaling the same equations by
the coefficients of various terms to see the effect on the re-
sults. This process develops insight and experience that make
the analysis meaningful. If one plans to solve the complete equa-
tion in closed form, the choice of reference distances does not
matter. If we plant solve the equation numerically, it can make
a great deal of difference if the equation is properly scaled.

EXAMPLE 2
Natural Convection Near a Vertical Heated Surface
How much can be said about a classic case of natural con-
vection without actually solving the governing equations in
detail? Consider a heated vertical plate immersed in a fluid
of infinite extent as shown in Figure 2. The well-known equa-
tions for the laminar case (GrPr < 109) are the following:
Continuity
dVy avz
S+ z 0 (8)
ay az
Motion
( avy + vz + v z a2Vz 2Vz
p Y av z z a= _2-+ z2 )+pgO(T-Tc) (9)
v Y z a)y- z- I
Energy

pCP(- VYT T) k a2+ (10)
a' y y az avy2 az2 )
where y = y velocity, v = z velocity, T = temperature, Th
wall temperature, T b = bulk fluid temperature, c = thermal
heat capacity, k = thermal conductivity, g = gravity, 3 = co-
efficient of expansion, p = density, p = viscosity, y = hori-
zontal position, and z = vertical position.
For completeness, no assumption has been made about the
relative importance of cunduction or convection in the direc-
tion parallel to the wall. The first step is to identify scaling
parameters for the independent variables, in this case y and
z. The scaling distance for z is obviously H; the scaling dis-
tance for y is unclear since the domain is infinite in that di-
rection. Thus, define a distance yo as the appropriate scale for
y. This distance is essentially a characteristic hydrodynamic
boundary-layer thickness. Then define the dependent vari-
able over its range


H
H


Yo
r=^
Yn


T T,
Th Tc


Likewise, there are no natural reference velocities for the
vertical and horizontal velocities, so give them names as well
( z Vz / Voz, )y Vy / Voy) and define B = pgp(T, T,).
After inserting them into the momentum equation, we obtain

pvovoz z pv) 0z,



P V i+P +Bo (12)
y2 ay2 H2 2

The convection of momentum in the direction parallel to the
wall is surely important; scale the equation by dividing
through by that term's coefficient

YoVoz Iy )+ z z


vH 2, v (Da2, BH
+-- 1-,2-2 + --- (13)
yoVoz a2 HVoz pVoz

At this point, there are two terms that contain only one of the
unknown reference variables-the second and third terms on
the right-hand side. Typically, diffusion of momentum is neg-
ligible compared to convection of momentum in the primary
direction of flow, thus it would not be prudent to base the
definition of the reference velocity in the z-direction on the
coefficient of this term. Furthermore, we know that for natu-
ral convection, the source term for momentum must be 0(1)
or the problem does not make sense. Force the coefficient of
this term to unity. We conclude that a reference velocity for
the flow parallel to the vertical wall should be
BH
voz-- H (14)

Having this definition, we can now define other reference
quantities by forcing the coefficients of other important terms
to unity. The coefficient of the y-directed momentum diffu-
sion terms yields


_ (L2H 1/4
Y o p B-


and vo, = 3H)


and the differential equation becomes


STI a afl2 H3pB 1 2 J v
0y_ + z D H- pB- K D +0 (16)





H Tc
Y
Th

Figure 2. Geometryfor natural convection near a heated wall.


Chemical Engineering Education











the remaining weeks the students construct process models,
design controllers, implement the controllers on the labora-
tory apparatus, analyze the results, and write lab reports. The
analysis is required to include a comparison between theo-
retical predictions and laboratory results with a discussion of
potential causes for disagreement. The suggested work sched-
ule is shown in Table 2.

LABORATORY PROJECTS
To achieve a flavor for the experiments, the air-bath and
some individual wet-lab experiments are described below.
Table 3 provides a summary of the inputs and outputs of the
data acquisition boards to the experimental projects.
Temperature Control in an Air Bath
This apparatus dominates the laboratory curriculum as it is
studied by all students during the first seven weeks of class. An
air bath measures 12 inby 10 in and is available at all computer
terminals. Its temperature is measured by a thermocouple, and
its measurement is sent to the computer running the HP-VEE
program. Afankeeps the air well-mixed. The manipulatedvari-
able in the process is the voltage sent to a blackened light bulb
(see Ref. 1 for apparatus schematic). This air-bath experiment
serves partly to familiarize students with the HP-VEE software
as students will be expected to develop a control algorithm for


Algorithm


their assigned wet-lab experiments. The students are asked to
model the air bath and develop simplified models.
Step changes are performed to derive the process param-
eters used for controller tuning. The students apply first-or-


TABLE 2
Proposed Schedule for Wet-Lab Experiments

Week 1 Familiarize with the equipment for the wet-lab experiment.
Construct a block diagram showing all equipment.
Derive transfer function models for all the blocks and clearly
identify which model parameters can be looked up or directly
measured and which must be determined from process reaction
curves.
Propose a control strategy that will satisfy the given control
objectives and further familiarize yourself with the software.
Weeks 2/3 Make changes in the visual program to record all measurements,
send all manipulated variable moves computed by the controller
to the laboratory apparatus, save all variables of interest to the
data file, plot all variables in the correct units.
Implement open-loop step responses.
Week 4 Construct models from process response curve experiments.
Week 5 Implement control algorithms and collect closed-loop response
data.
Week 6 Analyze data and compare theory with both open-loop and
closed-loop experiments.
Write lab report.


TABLE 3
Summary of Information of Experimental Projects


1 13 Air bath SISO I/P 00-Bath temperature ('C) O/P 00-Bulb voltage (V)
2 1 Oscillatory load SISO I/P 00-Flow rate (V) O/P 00-Valve voltage (V)
3 1 Single-tank pH SISO I/P 00-pH level (no units) O/P 00-Base pump voltage (V)
4 1 Liquid level Single cascade/MIMO cascade I/P 00-Flow rate to upper tank (V) O/P 01-Valve voltage (V)
I/P 01-Upper tank height (inch)
I/P 02-Flow rate to lower tank (V)
I/P 03-Lower tank height (inch)
5 3 Temperature time delay SISO I/P 00 thru 03-Temperature ('C) O/P 00-Pump voltage (V)
6 1 Integrating tank SISO with P controller I/P 00-Tank height (inch) O/P 00-Pump voltage (V)
7 1 Temperature cascade Single cascade I/P 00-Tank temperature ('C) O/P 01-Valve voltage (V)
I/P 01-Flow rate of hot water (V)
8 1 Dye concentration SISO I/P 00-Absorbance (no units) O/P 00-Pump voltage (V)
9 1 Liquid level & temperature MIMO cascade/Multiloop I/P 00-Tank temperature ('C) O/P 00-Cold water valve (V)
I/P 01-Flow rate of hot water (V) O/P 01-Hot water valve (V)
I/P 02-Tank height (inch)
I/P 03-Flow rate of cold water (V)
10 2 4-tank 2x2 MIMO/Multiloop/Decouplers I/P 00-Tank 1 height (inch) O/P 00-Pump 1 voltage (V)
I/P 01-Tank 2 height (inch) O/P 01-Pump 2 voltage (V)
I/P 02-Tank 3 height (inch)
I/P 03-Tank 4 height (inch)
11 1 Multi-pH 3x3 MIMO/Multiloop/Feedforward I/P 00-pH of Tank 1 (pH units) O/P 00-Base pump 1 voltage (V)


I/P 01-pH of Tank 2 (pH units)
I/P 02-pH of Tank 3 (pH units)
I/P 03-pH of Tank 3 (pH units)


O/P 01-Base pump 2 voltage (V)
O/P 02-Base pump 3 voltage (V)
O/P 03-Acid pump voltage (V)


Chemical Engineering Education


Inputs (/P) ofAcquisition Board


Qty Experiment


Outputs (O/P) ofacquisition board










ready for further assessment. Some of the most important
information is contained in the columns of the grading ma-
trix of Figure 1. A column with mostly high marks (1 = high-
est mark) top to bottom shows that all students know the sub-
ject, at least at the level of the exam question. If a column,
however, has mostly "0" marks, something went wrong. Rea-
sons can be deep-rooted or only superficial (i.e., the question
was confusing or the students ran out of time). Discussions
between teacher and students often bring clarification, and
plans for further action are easily devised. Technical defi-
ciencies and/or misunderstandings are recognized and can
be addressed, for instance, in a special help session or in the
next homework assignment. Experiments can be added or
computer animation can be used to help visualize abstract
concepts. Teachers have an opportunity to become very cre-
ative as soon as the problem is defined. This definition of the
problem is the main purpose of the grading matrix.
Correction of weaknesses can then be re-assessed in the
next test. This is typically done by including appropriate ques-
tions in the next exam, preferably within the same course
and/or in the next homework assignment. Teaching should
be corrected further if necessary. Often it is too late to intro-
duce corrections in the same semester or quarter. If changes
cannot be made in time, the weakness in one course will be
passed on to the teacher of the following course. This

Figure 2:
This is the same ---- .
grading matrix as a
in Figure 1, but
specific weights are weight 0.5 1 3 1 2
assigned to each of 1 student 1 1 1 1 1
the tasks. This 2 student 1 1 1 1 1
affects the 3 student _1 1 1 1, 1
calculation of the 4 student 1 0.9 0.9 1 1
grade as defined in 5,. student 1 0.9 0.8 1 1
Equation 2. 6. student 1 0.8 0.6 1 1
Everything else, .....
including the .. .... .......
teaching 22. student 1 1 1 0
assignment, 23. student 1 0.8 0.5 1 0.9
assignment,
remains unchanged 24 student 1 0.5 1 1 1
remains unchanged ---------
S 25 student 1 0.8 1 1 1
by the weighting
system. Weights -- -
27 student 1 0.3 0.8i 1 1
have little
28 .student 1 0.8 1 1 1
effect on the
29 student 1 0.8 0.8 0 C
grade of top I
grade of top 30 .student 1 0 0.4, 1 1
students but can
make a large teaching 100 841 78 96i 91
difference for a asses sment
weaker student.


teacher should be alerted to the problem so that correc-
tions can be made there.
The grading matrix provides a record, which can be used
even if another teacher teaches the course the following year.
Adjustments can be made then and can be re-assessed until
teaching weaknesses are resolved. I can imagine, however, a
problem with the existence of such records, since they have a
potential for misuse in the form of over-coaching of teach-
ers. This would interfere with the learning environment and
impair the matrix method. Access to the grading matrix
should be restricted to the teachers and students who are
directly involved.


FEEDBACK
TO STUDENTS

Advising individual students is enhanced by the diagnostic
property of a grading matrix. The teacher sees individual
weaknesses of students and can suggest corrective measures.
(e.g., specific reading material or exercises). This does not
require further preparation on the teacher's part. Information
is available instantly when a student comes to the office for
consultation. The matrix row of grades, in combination with
other observations (attendance, participation during class,
etc.), provides a quantitative basis for a discussion.


Chemical Engineering Education











tocatalytic and is usually represented as a first-order reac-
tion, i.e.,
dX
= X (5)
dt
Integration of this differential cell balance yields
X(t)=X exp[g(t-to)] (6)
where
X cell concentration, number/volume
t time, minutes
g cell specific growth rate, 1/minute
o as a subscript refers to initial conditions

In the present experiments, cell concentration in the reac-
tor is monitored at 10- to 15-minute intervals by measure-
ment of the absorbance (at 600 mm) of a small sample of
solution using the spectrophotometer. According to the usual
Beer-Lambert law, the light transmitted through a solution is
related to the incident light and the concentration of absorb-
ing species, as shown in
I
-=exp(-ecl) (7)
10
Io
where
I/Io fractional light intensity relative to incident intensity
c concentration of absorbing species, number per unit volume
1 length of light path through solution
e extinction coefficient of absorbing species, area per number

Strictly speaking, for the present experiments e should be
regarded as an appropriate fitting parameter since changes in
measured light intensity are no doubt due to a combination
of absorption and scattering.
Since absorbance A is defined as -logl0(I/Io), it follows from
Eqs. (6) and (7) that

A= l o exp g(t to (8)
2.303 2.303
Taking natural logs of Eq. (8) yields
n(A) = g(t to) + ln 2.X (9)
2.303 (9)


Thus, a plot of n(A) against time should be linear with a
slope equal to the specific cell-growth rate (p) during the
exponential growth phase. A cell doubling time, td, can be
calculated once the growth rate is determined, according to

td = (2) (10)

Figure 6 shows typical data obtained over a 4-hour period
following the experimental procedure described earlier. These
data indicate an expected initial lag of 15 minutes, followed
by an apparent exponential growth phase that levels off some-
time after 200 minutes. When these data are plotted in accord
with Eq. (9), a good fit to the exponential model is obtained,
as shown in Figure 7. The corresponding specific growth rate


Stationar Phase


na e death Phase

C /Exponential Phase




Lag Phase


Time
Figure 5. Typical batch culture growth phases.

of the E. coli in this experiment was 0.013 min1. This is equiva-
lent to a doubling time td of 53 minutes. This relatively long
doubling time confirms that the E. coli strain, while adequate
for these experiments, is not particularly robust.
The only difficulty encountered in carrying out the cell-
growth experiments has been maintaining the dissolved oxy-
gen concentration at 70%. Large swings in the oxygen level
(between 50% and 90% of saturation) have been observed
even with increases in gas-flow rate and stirring speed. These
variations, however, apparently do not have any significant
effect on the observed growth rates.


3.5
3

2.5
2
0 1.5 I ......-t




0 60 120 180 240 300
Time, minutes

Figure 6. E.coli growth data: solution absorbance vs. time,
S1





0.16 5exp(o 13[t-15])



L "- J !
0.5









-15 15 45 75 120 135 165 195 2300
Time-Lag, minutes

Figure Detecoli growth data: solution absorbance vs time.
S0.1675exp(O 3[t-15)

0 1




-15 15 45 75 105 135 165 195 225
Time-Lag, minutes

Figure 7. Determination of specific cell-growth rate.


Chemical Engineering Education






















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a Q
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a;
+-j

SuO


3 'i



;



a,

'c I


B = K1 / Kp. As shown in Figure 9a, perfect control is indeed achieved
with these parameters. Perfect control is no longer achieved when
A 1 /(KK2K3) or B K / Kp (as shown in Figure 9b). Since real

processes are generally not simple with accurately known parameters,
perfect control is only idealistic, not practical.

Cascade Control

Cascade control uses two control loops (primary and secondary).[1 The
primary control compares the process output to the desired value (set
point), yielding a second set point to be used for a secondary control.
The secondary control compares an intermediate quantity to this second
set point to determine how to alter an input variable.

The example of a process consisting of three first-order systems in
series (Eq. 7 and 8) is used to examine cascade control. The intermediate
quantity used in the secondary control loop is the output of the first-

order process (yi). A proportional-integral controller is used for the

primary controller, and a proportional-only controller is used for the
secondary controller. The spreadsheet used to solve this problem is
shown in Figure 10.

The response of the system with cascade control is shown in Figure 11
- this response is superior to the response with feedback control (Figure
7b). (Note that this example is somewhat artificial in that the secondary
control loop consists of only a first-order process and will be stable for
any value of the secondary controller gain. Therefore, an arbitrarily large
value of the secondary controller gain can be used to make the response
arbitrarily fast. This arbitrarily fast response is not possible in gen-
eral, e.g., if the secondary loop includes dead time or a process higher
than second-order).


DISCUSSION

Implementation of Approach

This numerical approach using spreadsheets was implemented in the
process control course at Tulane as follows: first, a topic is introduced in
a lecture, and the governing equations are derived; next, the class moves
on to our computer lab, where students solve the governing equations
numerically (all students do this individually on separate comput-
ers), and the physical significance of the results is discussed; finally,
the traditional analytic solutions based on Laplace transforms are
taught, in lecture format.

Homework assignments include problems requiring numerical solu-
tions using spreadsheets, problems requiring analytical solutions, and
problems that use the Control Station software package."13 Some prob-
lems require that students compare results
from numerical solutions to results from

6 analytical solutions. For example, one
5
4
3 Figure 11. Response of a process consist-
2 ing of three first-order systems in series
with cascade control to a step change in
.1 2 the disturbance (primary controller: K=2
0 20 40 60 and 1 =5, secondary controller: KA=10).
time The bold line is the disturbance, and the

thin line is the response.


E
0a

g I
"^^


0 0


Summer 2002


I I~INlmlPlmllgl~lml o, IOI~INlm IPl~nl~olclml










sity of Michigan to supplement the lectures. The animated
equipment operations are very helpful to the non-engineer-
ing students. At this point, we briefly discuss safety and en-
vironment issues related to chemical processing in order to
raise the students' awareness of these issues.
We use a chemical plant in Hong Kong to illustrate pro-
cessing concepts. Towngas, produced by catalytic reaction
of naphtha with steam, is often the example of choice (see
Figure 1). The first stage of the desulfurization unit converts
organic sulfur compounds to hydrogen sulfide, and the sec-
ond stage removes hydrogen sulfide with zinc oxide. In the
reaction system, the desulfurized naptha is converted to meth-
ane and hydrogen, and carbon monoxide is converted to car-
bon dioxide and hydrogen. The carbon dioxide and water is
removed in the gas purification and drying system. Project
evaluation follows Douglas' book. The students do not have
much difficulty in grasping the details of direct costs, indi-
rect costs, working capital, etc. We also cover (particularly
for science students) the time value of money and the dis-
counted cash-flow rate of return on investment. Normally,
we assign a project in which the students perform cost evalu-
ation of a chemical plant. The flowsheet and all major equip-
ment sizes and operating conditions are given, assuming that
this input information has been obtained from chemical en-
gineers in a consulting firm.
Next we turn our attention to the financial performance of
chemical corporations. Various measurements, such as return
on net assets, after-tax profit margin, sales growth, and con-
trolled fixed-cost productivity, are introduced. We usually
examine the financial statements of two US corporations;
recently, we have discussed those of DuPont in class while
those of Eastman Chemical are analyzed in a homework as-
signment. One objective is to learn how to read the balance
sheet, the income statement, and the statement of changes in
financial position. More importantly, we emphasize an ap-
preciation of the financial position of a typical chemical com-
pany in terms of profit margin, new investments,
amount of assets on the ground, etc. This reinforces
the notion that CPI is a capital-intensive business.
To emphasize decision-making in chemical busi- Naphtha
nesses, we venture into capital budgeting,15] but
this segment can be skipped if the students have
previously learned these concepts in their business
classes. Retrofit projects, as well as proposals to
construct a grassroots plant, are considered.


Product design is of great interest to Hong Kong.
We discuss a typical product development cycle-
concept development, design and prototype, pro-
cess planning, piloting, and plant startup. We ex-
plain the use of Quality Function Deployment
(QFD); this is further refined for chemical prod-
ucts where market trends lead to product attributes,
which are in turn decided by material properties


and processing conditions (see Figure 2). We identify the
desired performance of the product, both functional and sen-
sorial, and select the requisite ingredients. The process
flowsheet and the operating conditions are then identified.
We study the modern CPI in Section 6.41 It begins with a
review of the manufacture of soda ash, dyes, and sulfuric
acid in the UK and Germany as well as the emergence of the
CPI in America in the 1900s and in Japan in the 1950s. Then
we turn our attention to today's CPI. Its global enormity is
evident when one compares the global chemical shipment of
$1.59 trillion in 1999 to the HK GDP equivalent of approxi-
mately $200 billion.
We then examine the financial performance of the top glo-
bal chemical companies, emphasizing the top twenty-five
chemical-selling countries in 1999 (see Table 3).131 It is evi-
dent from the statistics that chemical production per capital in
Asia is below the world average, but (unsurprisingly) it is
rapidly gaining ground. Singapore is a net exporter compet-
ing in the international market. Although China is not ex-
pected to be self-sufficient, its rapid development and pur-
chasing decisions can significantly affect the global CPI. We
examine the recent JVs and investment projects in order to
appreciate the dynamics of the market in this region.[161


COURSE EVALUATION

The impact of the course has been assessed by its students.
While the course is intended for undergraduates, it generally
has around 25% graduate students from all science and busi-
ness disciplines. With rankings ranging from very bad to very
good, about 85% of the respondents ranked the overall course
as good or very good. Most of them expressed that they ac-
quired a good knowledge of chemical engineering. Also,
throughout the semester we hold a 10-to-15 minute oral quiz
every week in order to challenge them to think about interre-
lationships among different decisions. Most students felt that


Figure 1. The production of towngas by catalytic reforming
of naphtha using steam.


Chemical Engineering Education


Gas Purification and Drying
Recycle
Dioxide i t
after "- D -
I Towngas




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PAGE 2

I N D E X GRADUATE EDUCATION ADVERTISEMENTS A k ro n U ni ve r s it y of.............................. 32 1 A l a b a m a, Un i versi t y of.... ............ .. ..... 322 A l a b a m a, Hunt sv ill e; U ni versi t y of .............. 323 A l be rt a, U ni ve r si t y of... .............. 324 Ar i zo n a, U ni ve r s it y of. ................. .... 325 Ar i zo n a S t a t e U ni ve r s it y .... .... 326 A uburn U ni ve r s it y .. .. ... .. .. . .. ..... . Bri g h a m Y o un g U ni ve r s it y ......... Briti s h Co lum b i a, U ni ve r s it y o f B row n U ni ve r s it y . .. .. Bu c kn e ll Uni ve r s it y .. . Ca l ga r y, U ni ve r s it y of Ca li fo rni a, B e r ke l ey; Un i vers it y of 327 ..... 427 .... 427 .... 44 1 ......... 428 ...... 328 329 Ca li fo rni a, D avis; U ni ve r s i ty o f ...... 330 Ca li fo rni a, I rv in e; U ni ve r s i ty of .... ......... ... 331 Ca li fo rni a, L os An ge l es; U ni vers it y of .. 332 Ca li fo rni a, R ive r s id e, U ni ve r s it y of . .. . .. .... 333 Ca li fo rni a, S ant a B ar b ara; U ni ve r s it y of . .. 334 Ca lif o rni a In s titut e o f T ec hn o l ogy... .. 335 Car n eg i eM e ll o n U ni vers i ty. .. .. ... .. 336 Case W es t e rn R ese r ve U ni ve r s it y ..... 337 C in c inn a ti U ni ve r s it y of Ci t y Co ll ege of New Y o r k C l eve l a nd St a t e U ni ve r s it y. Co l orado, B o uld e r ; U ni ve r s it y of Co l o rad o S c h oo l o f Min es .. Co l o rad o St a t e U ni ve r s it y .. Co lumbi a U ni vers it y Co nn ec ti c ut U n ive r s it y of .... 338 ..... 339 . 340 .. ......... 34 1 ............... 342 ..... 343 ... 428 ....... 344 Co rn e ll U ni ve r s it y D a rtm o uth Co ll ege .............. ... 345 ... 346 D e l awme, U ni ve r si t y of.. .. .. .. .. ..... 347 D rexe l U ni vers it y .......... .. 348 Eco l e P o l y t ec hniqu e M o nt rea l ...... .. . ... . .. 349 E n g in eer in g R esea r c h Ce nt e r ... ..... -1 29 F lorid a, U ni ve r s it y o f ... ................. 350 Flo ri da A&M/F l o rid a S t a t e U ni versity .. .. ... 351 Fl o ri d a In s titut e o f Tec hn o l ogy .......... 352 Geo r gia In s titut e of Tec hn o l ogy. .... 353 H o u s t o n U ni ve r si t y of ............. ........... ...... 354 H owa rd U ni ve r s it y .. . .... ... .. . .. .................. 355 Id aho, U ni vers it y of..................... .... 429 Illin o i s, C hi cago; U ni ve r s it y of. ... 356 Illin o i s, U rb a n a-C h a m pa i g n ; U ni vers it y of .. 357 Illin o i s In s titut e o f T ec hn o l ogy . ... 358 Iowa, U ni ve r s it y o f ..................... ... 359 I owa S t a t e U ni vers it y ... .. Joh n s H opkins U ni ve r si t y . K a n sas, U ni ve r si t y of Ka n sas S t a t e U n ivers it y . Ken tu cky, U ni versity of L a m a r Un i ve r s it y L ava l Un i vers it e .... L e hi g h U ni vers it y ... 360 .. 361 .. 362 363 .... 36-1 ............. 430 L o ui s i a n a, L afaye tt e: U ni versi t y o f .... Lo ui sia n a S t a t e U n ive r s i ty 365 366 ..... 367 .. 368 ...... 430 431 369 .......... 370 L o ui s i a n a T ec h Unive r s i ty Lo ui svi ll e, U ni vers it y of. Ma nh a tt a n Co ll ege Ma r y l and U ni ve r si t y of Ma r y l and, Ba lt i m ore Co un ty: Un i vers it y of 371 M assac hu se tt s, L owe ll ; Un i ve r s it y of .......... 44 1 Massac hu se tt s, Am h e r s t : U n ive r s it y of 372 M assac hu se tt s In s titut e of Tec hn o l ogy .. .. . 373 M cG ill U ni ve r s it y... 43 1 M cMaster U ni vers i ty Mi c h iga n U ni ve r s it y o f Mi c h igan S t a t e University ................ Mi c h igan Tec hn o l ogica l Un i ve r s it y .. . . ....... 37-1 375 376 -132 Minn eso t a, U ni ve r s it y o f ........... . ... 377 Mi ss i ss i pp i S t a t e U ni vers it y ....... . .. 378 Mi sso uri Co lumbi a; U n ivers it y of ......... .... 379 Mi sso uri R o ll a: U ni ve r si t y of ............ ..... . 380 M o n as h U ni ve r si t y .. 432 M o nt ana S t a t e U n ive r s it y .. -133 Nebraska, U ni ve r sity of.. ................ 381 Neva d a, R e n o; Un i ve r s it y of .. 4 33 New J ersey In sti tut e of Tec hn o l ogy 382 New Mex i co, Un i ve r s it y of .................. 383 New M ex i co St a t e U ni ve r s it y .. 384 New So uth Wa l es, U n ive r s i ty of ............ 434 No rth Caro lin a St a t e U ni ve r s it y .... No rth Dako t a, Unive r s i ty of .... Nor th easte rn U ni versity .. .. Nort h western U n iversi t y No tr e Dame. Un i versity of O hi o S t a t e U ni ve r s it y O h io U n ivers it y .. Ok lah o m a, U ni ve r s it y of .. ......... 385 ... -134 .. 386 ..... 387 . 388 ...... 389 390 39 1 Ok lah o m a S t a t e U ni ve r s it y 392 O r ego n Sta t e U ni ve r s i ty . .. ................. ...... 393 P e nn sy l va ni a, U ni ve r s it y of .... ....... 394 Pe n sy l va ni a Sta t e U ni ve r si t y Pittsburgh. University of Poly t echnic University .. Princeton University ... Purdue Universit) Re n sse l aer Poly t ec hni c In sti tut e ........ ... 395 396 ...... 397 . .. 398 .. 399 ... 400 435 Rhode I sland. Universi t y of R ice Un i versi t y .... ........... 40 1 Roc h ester, Universi t y of. . ..... 402 Rose H u l man I nsti t ute of Tec h no l ogy .. .... . 435 Rowan Univers it y.................... .... 403 Rutgers Uni,ersity .................. Saska t chewan. University of Singapore, Nationa l University of .. .... 404 .... 436 405 .. 406 South Carolina, University of ....... So uth F l or i da, Un i ve r s it y of ........................ -1 37 Sout h ern California, U n ivers i ty of ..... 4 36 Sta t e Un i vers it y of New Yo r k 407 Stevens I nstitu t e 4 08 Sydney University of Syracuse, Unive r s it y of Tennessee. University of Texas, Un i versity of ...... 437 438 .. -109 4 1 0 Texas A&M Un i vers i ty... 4 11 Texas A&M Un i versi t y, Ki n gsvi ll e .. .... .. -138 To l edo. U ni ve r s it y of . .... 4 1 2 Tufts U n iversity Tulane Universi t y .. Tulsa, Uni,ersit) of .... Utah. Universi t y of .. Va n derbi l t University Villanova University Virgi ni a, Un i ve r sity of .... Virgi n ia Tec h . .. Wash in gton. U n iversi t y of Wash in gton Sta t e U ni ve r s it y Washington University .. Waterloo, University of .. Wayne State Univer,ity .... West Virginia University . Wisco n s i n, U n iversi t y of Worcester Poly t ec h nic I nstitute Wyo mi ng, Un i vers i ty of ..... Ya l e U n iversity .. ..... 4 1 3 .. 4 1 4 415 ....... .. .. 439 ......... 4 1 6 439 4 1 7 ................ 4 1 8 .... 4 1 9 ......... 420 .. 421 4-10 422 .... -123 . .. . -1 24 .. -125 .... 440 .. 426

PAGE 3

EDITORIAL AND BUSINESS ADDRESS: Chemical Engineering Education Department of Chemical Engineering University of Florida Gainesville, FL 32611 PHONE and FAX: 352-392-0861 e-mail: cee@che.ufl.edu EDITOR Tim Anderson ASSOCIATE EDITOR Phillip C. Wankat MANAGING EDITOR Carole Yocum EDITORIAL ASSISTANT Christina Smart PROBLEM EDITOR James 0. Wilkes, U. Michigan LEARNING IN INDUSTRY EDITOR William J. Koros, Georgia Institute of Te c hnolog y PUBLICATIONS BOARD CHAIRMAN E. Dendy Sloan, Jr. Colorado School of Mines MEMBERS Pablo Debenedetti Princeton University Dianne Dorland Rowan University Thomas F. Edgar University of Texas at Austin Richard M. Felder North Carolina State University Bruce A. Finlayson University of Washington H. Scott Fogler University of Michigan William J. Koros Georgia Institut e of Technology David F. Ollis North Carolina State University Ronald W. Rousseau Georgia Institut e of Technology Stanley I. Sandler University of Delaware Richard C. Seagrave Iowa State University C. Stewart Slater Rowan University James E. Stice University of Texas at Austin Donald R. Woods McMaster University Summer 2002 Chemical Engineering Education Volume 36 Number 3 Summer 2002 EDUCATOR 178 L.K. Doraiswamy oflowa State University, Thomas D. Wheelock, Peter J. R e ill y LABORATORY 182 Experimental Projects for the Process Control Laboratory Siong Ang, Richard D. Braat z 198 An Introduction to Drug Delivery for Chemical Engineers, Stephanie Farrell, Robert P. Hesketh 216 Mass Transfer and Cell Growth Kinetic s in a Bioreactor, Ken K. Robins on, Jo s hua S. Dranoff, Christopher Tomas, Seshu Tummala 226 Integrating Kinetics Characterization and Materials Processing in the Lab Experience, Denni s J Michaud, Rajee v L. Gorowara, Ro y L. McCullough CLASSROOM 188 Using Test Results for Assessment of Teaching and Learning, H. Henning Winter 212 Rubric Development and Inter-Rater Reliability Issues in Assessing Learning Outcomes Jam es A. Newell, K evin D. Dahm H eid i L. Newell 232 Scaling of Differential Equations: "Analysis of the Fourth Kind," Paul J. Sides 236 The Use of Software Tools for ChE Education : Students' Evaluations, Abderrahim Abbas, Nader Al-Bastaki 242 Teaching Process Control with a Numerical Approach Based on Spreadsheets, Christopher Ri ves Dani el J. Lacks CURRICULUM 192 ls Process Simulation Used Effectively in ChE Courses? Kevin D. Dahm Robert P. Hesketh, Mariano J Savelski 222 Teaching ChE to Business and Science Students, Ka M. Ng RANDOM THOUGHTS 204 FAQs v. Designing Fair Tests, Richard M. Felder, Rebecca Brent CLASS AND HOME PROBLEMS 206 Boiling-Liquid Expanding-Vapor Explosion (BLEVE): An Introduc tion to Consequence and Vulnerability Analysis, C. Telle z, I.A. Pena 231 Errata CHEMICAL ENGINEERING EDUCATION ( ISSN 0009-2479) is published quarterly by the Chemical E11gi11eeri11g Di v ision American Society for E11gilleering Education, a1ld is e dited al the University of Florida. Corresponde11ce regarding editorial mailer circulation, and changes of address should be se11I to CEE, Chemical Engilleeri11g Department University of Florida Gai11esville, FL 32611. Copyright 2002 by the Chemical E11gilleering Divisio11, American Society for E11gilleering Education. The statements and opillio11s expressed in this periodical are those of the writers and ,rot 11ecessarily those of the ChE Divi sio n, ASEE, which body assumes 110 respo11sibility for th e m Defective copies replaced if 11otified within 120 days of publication Write for information 011 subscriptio11 costs and/or back copy co sts and availability POSTMASTER: Send address changes to Chemical Engineering Education, Chemical Engineering Department. University of Florida, Gainesville, FL 32611-6005 Periodicals Postage Paid at Gainesville, Florida and additional post offices. 177

PAGE 4

.,;_5_4.__e_d_u_c_a_to_r __________ ) L. K. Doraiswamy of Iowa State University THOMAS D. WHEELOCK, PETER J. REILLY Iowa State University Ames, IA 500/1 L K. Doraiswarny came to Iowa State University (ISU) in a mo s t unusual manner. One of the authors (PR) was attending a meeting in New Delhi in 1984 and, since he had previously helped two scientists at the National Chemical Laboratory (NCL) in Pune with some chromatog raphy for a project of their s he a s ked if he could visit them there. He took the train to Pune during the dry season arriv ing a bit hot and dusty but quite exhilarated after experienc ing one of the world's great train ride s -the climb through the Western Ghats. He and a former graduate student were picked up by two NCL scientist s on their motor scooters and were delivered to the laboratory where they were eventually ushered into the baronial office of the NCL Director occu pied in fine style by one L.K. Doraiswarny Although L.K. was chagrined that the visitors had not been met by an air-condi tioned NCL car, things went so well after that the ISU visitor ended by participating in a joint enzyme project with the NCL. Some years later L.K. (as he i s known to hi s friends and colleagues, except at Wisconsin-Madison where he goe s by Dorai) arrived by very small plane in Des Moines to see how the ISU end of the joint project was progressing During that visit L.K. was asked by his host what he planned to do after his (imminent) NCL retirement. L.K. mentioned how much he liked small midwestern university towns and sensing a very good thing, the host passed this word on to his depart ment chair (Dick Seagrave) Soon an appointment wa s hur tling through the university hierarchy in record time That first appointment, in 1989, was the Glenn Murphy Chair meant for a distinguished visiting professor in the College of Engineering. It was followed by the Department of Chemical Engineering s Herbert Stiles Chair in 1992 and then in 1996 L.K. became Anson Marston Distinguished Pro fessor in Engineering. His first office was anything but baro nial, being the standard 120 ft 2 with hardly any window area but eventually a nice office opened up when Sweeney Hall was expanded L.K. still occupies it, even after his retire ment from ISU in December 2000 Co p yr i g h t C hE Di v i sio n of ASEE 1999 1 7 8 EARLY STIRRINGS L.K. was born in Bangalore in 1927 to L.S and Kamala Krishnamurthy, the only boy of four children His father led the Hyderabad Branch of the Geological Survey oflndia. For part of his childhood, L.K. and his family lived in the small village of Lingsagur. Later they moved to Hyderabad, the state capital, where L.K. graduated from Methodist Boys High School. He studied chemi s try at Nizarn College in Hyderabad part of the Univer s ity of Madras and then was faced with several opportunities for further education. One wa s to study organic chemistry a subject he thoroughly enjoyed. But the rapidly developing field of chemical engineering also attracted him, and he ultimately decided to study it at the Algappe Chettiar College of Technology also part of the University of Madras. Such an opportunity was very rare in India at the time since only two schools with limited enrollments and very high entrance standards offered chemical engineering ON TO WISCONSIN As a result of his successful record in pursuing chemical engineering a t Madras, L.K. received a scholarship from the Hyderabad government to study in the United States. An uncle with a Wisconsin PhD in chemistry suggested that he apply there-he did, he was accepted, and he arrived during the winter cold of December 1948. L.K. was lucky enough to secure Olaf Hougen as his major professor and after he earned his MS in 1950 and his Indian scholarship had expired, Hougen convinced the Hyderabad gov ernment to continue funding L.K. for a PhD (which he received in 1952). Hi s dissertation was on semichemical pulping, done under the joint supervision of Hougen and John McGovern of the USDA Forest Products Laboratory in Madison. Hougen 's perception that he had found a promising chemi cal engineer wa s even truer than he thought-in 1987 L.K. became the Olaf Hougen Visiting Professor of Chemical En gineering at Wiscon s in, an honor given to only five other distinguished educators. Then in 1991 he recei v ed an honorCh e mi ca l E ng i nee rin g Edu ca tion

PAGE 5

(Top) L.K. evinced a clear penchant for things mechanical at an early age. (Above) L.K. and his wife Rajalakshmi (now deceased) after their 1952 wedding. (Right) Today's L.K. (Below) L.K.'s present family; left to right, Rahul, Sandhya, Sankar, L.K., Deepak, and Priya. L.K and six of his seven ISU doctoral students. From the left, Leigh Hagenson Thompson, L.K., Sanjeev Naik Holger Glatzer, Jennifer Anderson, Ore Sofekun, and Sridhar Desikan. Missing is Justinus Satrio. Summer 2002 ary DSc from Wi sco nsin to go with his 1982 hon orary DSc from Salford in England. BACK HOME TO THE NATIONAL CHEMICAL LABORATORY After graduating from Wisconsin, L.K. worked on emulsion paints for a year at Carlisle Chemical and Manufacturing in Brooklyn. Although the company urged him to stay L.K. believed he could make a greater contribution in India, and in 1954 he joined the NCL as a senior scientist. He rose rapidly through the ranks, becoming Assistant Di rector and head of the Division of Organic Inter mediates and Dyes in 1961, Deputy Director and head of the Division of Chemical Engineering and Process Development in 1966 and finally becom ing Director in 1978. He was the fifth director and the first nonchemist to head the NCL, and he led it until he retired in 1989 After his retirement, he came to the United States to be nearer to his chil dren and grandchildren, and (not incidentally) to continue his research career without the burden of administrative duties. L.K. had a tremendous impact on NCL, both as a tireless and innovative researcher and as a highly respected and visionary leader who promoted re search excellence. When he retired he received a scroll that reviewed his accomplishments and summed up his contributions by stating, "You epitomize the finest in scientific research, man agement, planning, and execution. We will always remember you, as a compassionate human being who combined in himself the attributes of great scholarship and visionary leadership." His contri butions to the growth of the Indian chemical in dustry were also cited, as was his extensive ser vice as an advisor to the Indian government and as a member of various key committees. Early in hi s NCL tenure, L.K. established a strong base of fundamental and applied research especially in chemical reaction engineering. Un der his leadership many commercially important technologies were developed, including fluidized bed processes for making chloromethanes and methylchlorosilanes continuous processes for dimethylaniline and ethylenediamine, a new pro cess for vitamin B 6 and a complete process for methyl, ethyl, butyl and 2-ethylhexyl acrylates. The dimethylaniline technology was the first va por-phase catalytic process for making that prod uct while that for ethylenediarnine was apparently the first continuous organic chemical process de veloped in India. His teams also developed zeo179

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lite catalysts and processes for xylene isomerization and for making alkylating benzene with alcohols Many of the s e de velopments led to awards from the Indian Chemical Manufacturer s Association. L.K. lavished care and attention on the NCL by streamlin ing departments, doing what was needed to attract the best people, and attending to the needs of the whole community. His son Deepak tells us that on occasion this involved such matters as compassionate appointments" for poor or recently widowed employees special housing allotments for deserv ing cases, and investment of resources for welfare purposes such as the local school and a shopping center (which has since become a major attraction in the city and is named after his late wife). To highlight his human side one instance is worth special mention. One night, a poor family was evicted from the NCL campus for building and occupying an illegal accommoda tion. L.K. moved by their plight (and against the administra tive officer's advice), gave them permission to stay overnight until they could make other arrangements. This eventually led to a protracted legal battle and illustrates how his softer side sometimes leads him to take risks. His professionalism concerning matters such as punctual ity, returning phone calls, meeting deadlines, and making al lowances for potential mistakes in planning is also a hall mark of his character. His approach is simply "to get and maintain the best, and it has led to a legacy of excellence that he is especially proud of. He maintains that "excellence is a state of mind" and he never tires of repeating it. While at NCL, L.K. wrote a book on catalytic reactors and reactions (Pergamon, 1991) and was coauthor of two vol180 Students and faculty at the Wisconsin summer laboratory course in 1977, with L.K. at the far right and Roger Altpeter and Richard Grieger-Block at the far left. Wisconsonians, and others, beyond a certain age will enjoy identifying the others pictured here. umes on heterogeneous reactions with his close friend M M. Sharma at the University of Bombay (Wiley, 1984) and one on stochastic modeling with his NCL colleague B.D Kulkarni (Gordon and Breach, 1987) He also edited or coedited four books and contributed chapters to six others. L.K. personally guided the thesis research of 45 students who received PhDs from various Indian universities and collaborated with the late Tony Holland at Salford in guiding fifteen others and with Mike Davidson at Edinburgh in an additional two He has been author or coauthor of some 155 international jour nal articles. They were mainly on adsorption and catalysis; gas-solid, gas-liquid solid-solid, and slurry reactions; fluidi zation; and stochastic modeling and analysis of reacting sys tems. For five years he also served as editor of the Indian Chemical Engineer. L.K. is reputed to have received every major scientific and technical award in India open to chemical engineers. Among the most noteworthy are the Om Prakash Bhasin Award for Science and Technology given by Indian President Zail Singh in 1986, the Jawaharlal Nehru Award for lifetime achieve ment in engineering and technology (1987) and the Repub lic Day honor Padma Bhushan presented by Indian President R. Venkataraman in 1990. Notable awards from outside of India but honoring his work there are election to the Third World Academy of Science in 1997, the Richard H. Wilhelm Award from AIChE in 1990 and the Personal Achievement in Chemical Engineering Award in 1988 from Chemical Engineering magazine THE FAMILY MAN Soon after returning to India, L.K. married his wife Rajalakshmi. She was always a source of great emotional Chemi c al En g ine e rin g Edu c ation

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strength and happiness to him, and her early death after a prolonged and painful illness was a devastating blow. L.K. has two children, Sandhya and Deepak who remember their dad teaching them by gentle example and with the adage that discipline is doing what you don't like to do. Sandhya com pleted a MPhil at the University of Poona and became a CPA after she arrived in the United States. She and her husband Sankar Raghavan have two children Rahul and Priya, the apples of their grandfather s eyes. L. K.' s son Deepak received a PhD in chemical engineering from Delaware after earning a BTech from the University of Bombay. He completed a postdoctoral fellowship in the Rutgers Department of Ceram ics and Materials Engineering and then joined the DuPont Experimental Station in Wilmington, Delaware. He is also an adjunct professor at West Virginia University L.K. 's chil dren and the department at ISU engage in a gentle tug-of-war over where L.K. will live in retirement. So far, to our delight, he remains in Ames, with frequent trips east. Deepak tells us that true to his sense of filial and family responsibility, L.K. took under his wing his parents, an un married sister, and a widowed sister and her children all while supporting his own young wife and two small children. L.K. is a lover of the English language both written and spoken. He writes beautifully and his spoken English is free of slang and interjections. He is a purist about word usage and delights in good sentence construction. As a child, his school principal advised him to become an author if pos sible, and he managed to do that, although certainly not in the manner the former expected. A SECOND CAREER Starting a second career at ISU in 1989 did not slow L.K. s pace at all. In fact, relinquishing administrative duties at the NCL gave him a second wind. He has continued to thrive through his writing, lecturing, teaching, and research. He taught undergraduate and graduate chemical reaction en gineering courses, established a new research program from scratch, and guided the research of seven ISU doc toral students. L.K. 's research has focused primarily on chemical reac tion engineering, especially on rate enhancement strategies in organic synthesis. His group was worked on phase trans fer catalysis and has showed that many of its problems can be overcome by immobilizing the catalyst on a polymer sup port. They have developed and published new mathematical models and have investigated the effect of ultrasound on solid liquid reactions mediated by phase transfer catalysts. In ad dition to his own seven doctoral students, L.K. collaborated with Terry King and Tom Wheelock in supervising two oth ers. He worked with the late Mauri Larson on developing and validating a microphase-assisted reaction model, and he continues to develop an advanced calciuim-based sorbent for desulfurizing hot coal gas with Tom Wheelock. Summer2002 Writing and publishing continue to draw much of L.K. 's attention. He has published 25 research papers and several comprehensive reviews, mainly in Chemical Engineering Science and IEC Research while at ISU. At the same time, he was absorbed in writing his 26-chapter Organic Synthesis Engineering, published by Oxford University Press in 2001. The book integrates synthetic organic chemistry with chemi cal engineering through many illustrative examples, so it will benefit both chemists and engineers who work together on manufacturing processes. L.K. was also honored by a special session at the 1997 AIChE Annual Meeting in Los Angeles and by the publica tion of special collections of research papers written by many of his colleagues and friends. One of these collections ap peared as the "L.K. Doraiswamy Festschrift," which honored his 70th birthday and filled the June 1998 issue of IEC Re search. The Indian Academy of Sciences published an ear lier collection, titled "Reactions and Reaction Engineering, to mark his 60th birthday. In spite of these accolades, L.K. remarked in the preface to Organic Synthesis Engineering: "If the truth be told, I am not sure to this day whether I learned more from my students at NCL and ISU or they from me." To further honor L.K. 's contributions in both the United States and India, ISU and NCL established a Doraiswamy Honor Lectureship, filled by a distinguished chemical engi neer who annually delivers lectures at both places. The first three lecturers have been Jimmy Wei (Princeton), Alex Bell (UC Berkeley), and Klavs Jensen (MIT). It was the first ex posure to India for all three Along with L.K. 's ISU Distinguished Professorship came the Margaret Ellen White Graduate Faculty Award (2000) for superior mentoring of graduate students. Selection for this honor reflects the sentiments of a former student, who wrote "The dedication, persistence, and attention to detail that I learned from Dr. Doraiswamy has guided me in more ways than I ever dreamed possible L.K. not only has a high re gard for students but also enjoys assisting and working with them without completely solving their technical problems. He is well known for inviting groups of students to his home for serious as well as humorous discussions of science, phi losophy, and politics, subjects in which he has deep interest. One of his graduate students sums up quite nicely the men tor-teacher-friend we know as L.K. : In addition to being a fine research mentor, I found Dr. Doraiswamy to be a caring individual. I was able to talk with him about other things outside my research-even some personal matters. The well being of his students was also Dr. Doraiswamy's concern. There was a period of time when I had been struggling with my health. Whenever we met, Dr. Doraiswamy would ask me about my health. When I mentioned this to a research group colleague, he said 'That's funny. Dr. Doraiswamy al ways asks me whether my old car is running.'" 0 181

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.t3 ... 5.3 ...... _1a_b_o_ra_to_r_:y:.._ _______ ) EXPERIMENTAL PROJECTS FOR THE PROCESS CONTROL LABORATORY SIONG ANG, RICHARD D. BRAATZ University of Illinois at Urbana-Champaign Urbana, IL 61801 D igital control has been used in the Department of Chemical Engineering at the University of Illinois more than twenty-five years, but the process control laboratory underwent a major renovation and expansion from 1994-2000, in which the total number of control apparatuses was increased from a dozen to twenty-six (some of the appa ratuses are duplicates). The cost for lab renovation was ap proximately $100,000, and the lab is maintained by a teach ing assistant working fewer than ten hours per week. This expansion enabled all University of Illinois seniors ( approxi mately 80 students/4 lab sections) to take the process control course in one semester, working in groups of two students during lab. Also, a modem control interface was designed and implemented in HP-VEE, which is a modern visual pro gramming environment for instrument control. 111 The twenty six control apparatuses include 1. Temperature control in an air bath 2. Water-flow control und e r oscillatory load disturbances 3. Single-tank pH control 4 Interacting water-tank level control 5. Temperature control with variable-measurement time delay 6. Integrating tank-level control 7. Cascade control of temperature in a water tank 8. Dye-concentration control with load disturbances 9. Four-tank water-level control 10. Temperature and level control in a water tank 11. Multitank pH control The experiments were designed based on three underlying principles. First, the experiments should emulate real indus trial processes and the control problems associated with those processes. Second, collectively the apparatuses should teach students a wide variety of techniques for addressing chemi cal process control problems. Third, the students should com municate with the apparatuses via a modern control inter face. cii Following these principles ensures that the students receive the appropriate training to productively solve control problems they may encounter in the industry. The last three control apparatuses are the most sophisti cated. Control apparatus #9 is similar to an apparatus in Pro fessor Frank Doyle's control lab at the University of Dela warec21 and in a control lab at the Lund Institute of Technol ogy. c 3 J The apparatus is used to teach multiloop and decoupling control and to illustrate how the controller design becomes more difficult as the interactions increase. Control apparatus #10 uses two oversized valves as the final actuation devices and temperature, water level, and two flow rates as the mea sured variables. This two-input four-output process is con trolled using multivariable cascade control. Control appara tus #11, the multitank pH control apparatus is a novel lab apparatus that exhibits significant nonlinearity. 141 In addition to a multiloop control strategy, students can also apply feedforward-feedback control loops and observe the dependence of their performance on the accuracy of disturbance models. SOFTWARE AND HARDWARE IN THE PROCESS CONTROL LABORATORY A laboratory course in process control constitutes an im portant component of a chemical engineer's education.15 61 It should provide hands-on training in the application of control to real processes. The design of the process con trol laboratory is instrumental to the quality of a chemi cal engineering education. Figure 1 shows the flow of information between the com puter hardware and the physical apparatus. Each computer is connected to a wet-lab experiment and an air-bath experiSiong Ang received his BS in chemical engineering from the University of Illinois in 2000 under a Singapore Armed Forces Overseas Merit Scholar ship. He received an MS degree in chemical engineering at Stanford Uni versity in 2001 and is now serving in the Singapore Armed Forces. Richard Braatz received his BS from Oregon State University and his MS and PhD from the California Institute of Technology. After a postdoctoral year at DuPont he joined the faculty of chemical engineering at the Uni versity of Illinois His main research interests are in complex systems theory and its application. Copyright ChE Divi s ion of ASEE 200 2 182 Chemical Engineering Education

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ment. Modem industrial process installations have graphic operator interfaces for communication between the process control engineer and the industrial proces s Undergraduate engineers should be exposed to such a graphic user interface and be provided with experience in controlling real processes using such interfaces.r 5 61 The interfaces are designed to have the professional look and feel of real industrial operator in terfaces exposing students to a realistic control environment. The Hewlett Packard Visual Engineering Environment (HP VEE) is a visual programming language designed for instru mental control.[7 1 This software u ses boxe s to represent pro cesses and controllers, and line s to repre se nt information flows. The software has advantages over traditional program ming languages The visual interface of HP-VEE allows novice u sers to quickly mas ter its programming IanWet lab Air bath guage and therefore enapparatus apparatus 1 / 0 data acquisition boards HP-VEE software Figure 1. Computer hardwar e/ software architecture. I Introdu ctory co ncepts courages more active student participation. Getting the program to work in a certain man ner merely requires changing line connec tions between boxes or modifying control struc tures. Every change is a TABLE 1 Course Schedule few mouse clicks away. The program i s also equipped with debugging capabilities with direct reference to the error source, thus reducing time spent for debugging. More ad vanced algorithms such as model predictive controll 81 can be implemented by linking to compiled programs written in popular languages such as Fortran or Visual Basic For iden tification, the data are imported to Excel and the parameters are fit u si ng a variety of fitting routines. To assist the stu dent s in programming an HPVEE program is stored in the server for reference The latest version of HP-VEE is called Agilent VEE. DESCRIPTION OF THE UNDERGRADUATE PROCESS CONTROL COURSE The control class covers a broad range of control topics relevant in industrial problems encountered today. The syl labus includes first-principles modeling process identifica tion, and both s ingle-loop a nd multi variable control systems. Student s are exposed to a wide variety of real-life control restrictions such as time delays, non-minimum phase zeros, model uncertainties, unmeasured disturbances measurement noise, and ill-conditioning. Students have three hours of lectures and three hours of laboratory per week. The students spend about four hours per week outside of class to study for this course. The allo cated lab time i s sufficient for students to complete the lab Students apply techniques in the laboratory shortly after they are covered in a lecture. Table l shows how the lecture topics are coordinated with lab ex2 Review : mathematical modeling & Laplace transform Introduction to control lab periments The first series of laboratory sessions are devoted to an air-bath experiment from which students gain familiar ity with the HP-VEE software, first-principles modeling, pa rameter estimation, filtering, on-off control, and single-loop PID control. This training pre pares them for the second se ries of laboratory sessions, which are more open-ended and demanding. The students are split into several teams, with one wet-lab project as signed to each team. During the first three weeks of these 3 Building transfer function models Dynamics of sim ple processes 4 Higher -order dynamic behavior Stability 5 Nonlinear systems linearization Param eter est im ation 6 Feedback control, introduction to PID 7 Closed-loop time respon se and stab ilit y 8 Direct synt h esis Introducti on to frequency domain 9 Frequency domain identification and analysis l O Cascade control Feedforward/ratio control Review of lab equ ipment On/off control of air bath Response of a shielded thermocouple Response of a shielded thermocouple PID air bath temperature control PID air bath temperature control PID air bath temperature contro l Group project: openl oop identification Group project: open-loop identification 11 Revi ew Group project: open-loop identification -----------------------------1 2 Intr oduct i on to MIMO systems Int eraction Analysis I 3 De sign of decouplers Model predictive control 14 On-line optimization Statistical process control 15 Case st ud y : distillation columns packed-bed reactors Summer 2002 Group project: model design and implement contro ll ers Group project: model design, and implement controllers Group project: model design and implement controllers experiments, the students write a visual program in HPVEE to control the wet-lab experi ment and carry out open-loop identification experiments. In 183

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the remaining weeks the students construct process models, design controllers, implement the controllers on the labora tory apparatus, analyze the results, and write lab reports. The analysis is required to include a comparison between theo retical predictions and laboratory results with a discussion of potential causes for disagreement. The suggested work sched ule is shown in Table 2. LABORATORY PROJECTS To achieve a flavor for the experiments, the air-bath and some individual wet-lab experiments are described below. Table 3 provides a summary of the inputs and outputs of the data acquisition boards to the experimental projects. Temperature Control in an Air Bath This apparatus dominates the laboratory curriculum as it is studied by all students during the first seven weeks of class. An air bath measures 12 in by 10 in and is available at all computer terminals. Its temperature is measured by a thermocouple and its measurement is sent to the computer running the HP-VEE program. A fan keeps the air well-mixed. The manipulated vari able in the process is the voltage sent to a blackened light bulb (see Ref. 1 for apparatus schematic). This air-bath experiment serves partly to familiarize students with the HP-VEE software as students will be expected to develop a control algorithm for their assigned wet-lab experiments. The students are asked to model the air bath and develop simplified models. Step changes are performed to derive the process param eters used for controller tuning. The students apply first-orTABLE2 Proposed Schedule for Wet-Lab Experiments Week 1 Familiarize with the equipment for the wet-lab experiment. Construct a block diagram showing all equipment. Derive transfer function models for all tbe blocks and clearly identify which model parameters can be looked up or directly measured and which must be determined from process reaction curves. Propose a contro l strategy that will satisfy the given control objectives and further familiarize yourself with tbe software Weeks 2/3 Make change s in the visual program to record all measurements, send all manipulated variable moves computed by the controller to the laboratory apparatus, s ave all variab l e s of interest to the data file plot all variables in the correct units Implement open-loop step responses. Week 4 Construct models from process response curve experiments. Week 5 Implement control algorithms and collect closed-loop response data. Week 6 Analyze data and compare theory with both open-loop and closedl oop experiments. Write lab report. TABLE3 Summary of Information of Experimental Projects lj_ Qb! Experiment Algorithm Inu.uts (1/P) o[Acg_uisitio11 Board Outputs (0/P) o[acg_uisition board 13 Air bath SISO I/P 00-Bath temperature ( C) O/P 00-Bulb voltage (V) 2 Oscillatory lo ad SISO I/P 00-Flow rate (V) O/P 00Valve voltage (V) 3 Single-tank pH SISO I/P 00-pH level (no units) O/P 00-Base pump voltage (V) 4 Liquid level Single cascade/MIMO cascade I/P 00-Flow rate to upper tank (V) O/P0l-Valve voltage (V) I/P 01-Upper tank height (inch) I/P 02-Flow rate to l ower tank (V) I/P 03-Lower tank height (inch) 5 3 Temperature time delay SISO I/P 00 thru 03-Temperature ( C) O/P 00-Pump voltage (V) 6 Integrating tank SISO with P controller I/P 00-Tank height (inch) O/P 00-Pump voltage (V) 7 Temperature cascade Single cascade I/P 00Tank temperature ( C) O/P 01-Valve voltage (V) I/PO 1-Flow rate of hot water (V) 8 Dye concentration SISO I/P 00-Absorbance (no units) O/P 00-Pump voltage (V) 9 Liquid level & temperature MIMO cascade/Multiloop I/P 00-Tank temperature ( 0 C) O/P 00-Cold water valve (V) I/P 01-Flow rate of hot water (V) O/P 01-Hot water valve (V) I/P 02-Tank height (inch) I/P 03-Flow rate of cold water (V) 10 2 4-tank 2x2 MIMO/Multiloop/Decouplers I/P 00-Tank l h eight (inch) O/P 00-Pump I voltage (V) I/POI-Tank 2 height (inch) O/P OJ-Pump 2 voltage (V) I/P 02-Tank 3 height (inch) I/P 03Tank 4 height (inch) 11 l Multi-pH 3x3 MIMO/Multiloop/Feedforward I/P 00-pH of Tank l (pH unit s) O/P 00-Base pump 1 vo lt age (V) I/POI-pH of Tank 2 (pH units) O/P OJ-Base pump 2 voltage (V) I/P 02-pH of Tank 3 (pH units) O/P 02-Base pump 3 voltage (V) I/P 03-pH of Tank 3 (pH units) O/P 03-Acid pump vo lt age (V) 184 Chemical En g ineering Education

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der and second-order filtering to the data with a variety of filter time constants to reduce the effect of measurement noise on their estimates Students then apply a variety of tuning rules (e.g ., Cohen Coon, direct synthesis, internal model con troF 8 10 1 1 12 1) to design PID controllers and compare the closed loop performance obtained with each tuning rule. The stu dents also apply an on/off control, where the bulb either switches completely off or on based on the sign of the offset. Students are asked to compare the performances of both types of control. The air -b ath apparatus is the simplest and least expensive of all the apparatuses in the lab. We recommend that instructors interested in building a similar lab start with the air-bath apparatus. [] Water-Flow Control under Oscillatory Load Disturbances The objective is to control the flow rate downstream of a valve while the pressure downstream of the valve is continu ously varying. The downstream pressure oscillates by vary ing the liquid level in a tank downstream from the valve us ing a float system, which is separate from the computer. The flow rate downstream from the valve is measured as a pres sure difference across an orifice. A transducer measures this pressure difference as a voltage, which is sent to the data acquisitions board in the computer (Figure 2). Students construct process-reaction curves with respect to valve vo ltage When analyzing these curves, the oscillations Fl Water tank ---, ' ' Float : switch : '---~-----j F3 Compute r / -, Controller Drain Flowmeterl Figure 2. Water-level control under oscillatory load disturbances. are significant. By first subtracting the oscillatory disturbance, a process gain, time constant, and time delay can be deter mined Several PI and PID tunings are used for varying mag nitudes of the oscillation A goal of this experiment is to ob tain some understanding of the effect of disturbances on the measured variable and that modeling the disturbances can result in improved input-output models and improved closed loop performance [] Single-Tank pH Control The objective is to control the pH tank with a continuous flow of acid solution by adjusting the feed rate of a basic solution. The main tank is fed by two peristaltic pumps that draw liquid from two reservoirs, one for acid and one for base. The students do not have access to the flow rate of the acid stream. The control strategy is to use single -control loop. The acid feed rate is set at 1.8 V. Open-loop responses are implemented by step changing the pump voltage over its full range. The process dynamics of a single pH tank are highly nonlinear, so the model parameters vary significantly as a function of the operating region. For testing closed-loop performances, several PI and PID tunings are used with different set points (pH= 6 7, and 8). Students observe the varying setpoint track ing performances obtained by different tunings. Another interesting aspect of this experiment is that the pH probe is located far from the input and output feed streams for the tank and that the mixers are selected to give relatively poor mixing. Because of this, each step response experiment gives slightly different results even when carried out in an identical manner. It is important that students encounter pro cesses that are not completely ideal because this is usually what occurs in practice. [] Interacting Water Tanks Level Control The objective is to control the liquid level in the second of two interacting tanks by adjusting the flow of liquid to the first tank. Water flows from the tap to the pneumatic valve and from the valve into the first tank. From the first tank the water may flow through either of two valves so that it is possible to choose whether the tanks interact. All levels are measured as pressure dif ferences, which are converted into voltages by transducers (Figure 3). ,-----------------------------------------------------------I The preferred control strategy for this experi ment is cascade control. Aggressive P or PI tunings are used to control the flow rate in the ,---------------r:: ;: ::::::::: ::::::::::::::: ~~:~~l tl:~ -- HI: : ,--------------------' ,,____ _,, l l ' Upper : : tank : : j i C Flowmeter2 1 H 2 Flowmeterl --' Drain Figure 3. Interacting water tank-level control Summer2002 inner (slave) loop. When the slave loop has been tuned, a second set of process response curves (measuring the level in the second tank with re spect to the set point of the inner loop) is con structed. The outer (master) loop is tuned using several PI and PID tunings based on the process parameters obtained. An alternative strategy is to use a simple PID controller that controls the level of the seco nd tank by manipulating the valve voltage. The performance of both strate185

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gies can be compared. A goal of this experiment is to recognize the performance improvement obtainable by cascade control. [] Temperature Control with Variable-Measurement Time Delay The objective is to control the temperature at one of several thermocouples downstream from a mixing tank. The manipulated variable is the hot-water feed rate into the mix ing tank. A reservoir provides a constant head for a cold water feed, and a peristaltic pump transfers hot water from a reservoir into the mixing tank. Four thermocouples are lo cated downstream from the outlet of the mixing tank. Students construct process reaction curves with respect to pump voltage for each of the four thermocouples downstream. They should observe that the time delay in their step responses is greater for thermocouples located further downstream. PI and PID controllers are implemented using each of the ther mocouples as the measured variable. Students investigate the effect of changing the time delay on the closed-loop stability and performance by using one thermocouple's tuning rules for the other thermocouples. [] Integrating Tank-Level Control The water level in an integrating tank is the control variable. This tank receives a constant flow of water from the tap. The water level in the tank is measured as a pressure difference signal. Water is re moved from the tank by a peristaltic pump under the control of the computer. An interesting feature is that the HP-VEE software assumes that the gain of the process is positive. This would be true if the pump was feeding water into the tank. In the integrating tank, however, the pump drains wa ter away from the tank; therefore, the sign of the controller gain should be negative. Step changes in the pump voltage are implemented to de termine the model parameters, which the students use to tune P, PI, and PID controllers. The integrating characteristics of the tank do not require integral action in the controller to have zero steady-state closed-loop error. Hence, this particu lar process can be controlled using a single-loop P controller, which can be tuned using direct synthesis. The controller is tuned so that the closed-loop response is as fast as possible, without too much overshoot. Students can test the disturb ing response of their controller parameters by implement ing the controller under conditions in which the tapwater feed rate changes. [] Cascade Control of Temperature in a Water Tank The objective is to control the temperature in a stirred tank by adjusting a hot-water flow rate. Cold water is supplied to the mixing tank from a reservoir that uses an overflow to main tain a constant level. Hot water flows through a pneumatic valve and a computer records its temperature and flow rate. The flow rate is measured as a pressure difference across an orifice by a transducer with output in units of volts. The preferred method is to implement a single cascade loop. Open-loop responses for the flow rate of hot water into the 186 tank are constructed by making a step change in the valve voltage. After determining the gain time constant, and time delay, students can try several P and PI tunings for the inner (slave) loop to control the flow rate. For tuning the master loop, the steps are the same except that a new set of process response curves is constructed by measuring the temperature of the tank with respect to the set point of the inner loop. Using the same control parameters from the tuning, a single PID controller is implemented and compared with a cascade controller in terms of closed-loop performance. [] Dye Concentration Control with Load Disturbances The objective is to control the dye concentration in a tank under load disturbances by changing the voltage to the feed pump. The 3-liter tank is drained both from the bottom and from an overflow pipe. A pump takes in water from the bottom of the tank and sends it through a colorimeter, which measures the absorbance of the solution using the tap water as a reference, with the outlet of the colorimeter returned to the tank. A peri staltic pump sets the flow rate of dye into the tank (Figure 4 ) This process can be controlled using PI or PID control. The absorbance of the solution is measured and compared to a concentration setpoint. The voltage to the dye feed pump is the manipulated variable. Besides determining the setpoint tracking performance, students perform disturbance changes by decreasing the water-feed rate by partially closing the valve at the faucet. [] 4-Tank Water-Level Control The objective is to control the water levels in the bottom two tanks (Tanks 1 and 2) with the levels at least two-thirds of the maximum height. On each side, water is pumped upward from a cylindrical beaker and split into two channels at a Y-junction. The relative amount of water entering the two split tubings can be adjusted manu ally. All liquid levels are measured by pressure transducers. The two pumps adjust the flow of water to the tanks accord ing to voltage signals sent by the PID controllers. A straightforward control strategy is to use two PID loops to control the process. Both pumps must be calibrated before reliable data can be obtained. By making step changes to the pumps the process reaction curves for the tank levels are Dye reservoir Compute r/ Controller Reference Stream Recycle stream Reference r-----, Stream Absorbnnce sensor ' ----4 Figure 4. Dye concentration control with load disturbances. Ch e mi c al Engin ee ring Edu c ati o n

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obtained. The gains, time constants, and time delays of each process are determined Each PID loop is tuned separately so that the closed-loop speed of response is as fast as po ss ible without too much overshoot. After tuning the two single loop s, the control loop s are implemented simultaneously, and the in teractions between the loops are observed. To provide adequate setpoint tracking, the two loops are detuned as necessary. Decouplers are capable of reducing loop interactions. Stu dents can use the HP-VEE software to implement partial decouplers and assess any improvements/deterioration in the closed-loop performance. [I Temperature and Level Control in a Water Tank The objective is to control the liquid level and temperature in a tank by adjusting the pneumatic valves on hot and cold water feed-flow rates. Both the feed-flow rates and liquid level in the tank are indirectly measured as pres s ure differences by transducers which output in units of volts. The presence of two possible actuators suggests the po ssi bility of implement ing multiple loops Since it is possible to receive four mea sured signals, two cascade-control loops can be used Stu dents construct process reaction curves for the flow rates into the tank with respect to the voltage sent to the valves. The gain, time constant, and time delay for each of the four tran fer functions can then be defined. The inner (slave) loops s hould be tuned aggressively with out excessive overshoot to control the flow rates. After ob taining good tuning parameters a second set of proces s re sponse curves measuring the level and temperature in the tank with respect to the set point s of the inner loop s is constructed. The process gain, time constant and time delay for each of the four transfer functions are collected. At this stage, stu dents should be able to assess the level of interaction between the two loops and decide on the pairing Another possible strategy is to implement two s imple PID controllers, control level and temperature, and manipulate the valve voltages. Students can observe and compare the difference in closed-loop performance between the cascade controllers and the PID controllers. [I MultitankpH Control The objective i s to control the pH of an acid stream, which flow s through three tanks connected in series. This is accomplished by adjusting the feed rates of a basic so luti on. Three tanks are connected in series The acid stream enters a pulse dampener before a pH probe measure s its pH. The acid stream will enter Tank 1 Tank 2, and Tank 3 before it is drained into a safety reservoir Each tank bas its base flow regulated by one base pump. In addition, a pH probe is located in each tank to measure the pH of the solution (see Ref. 4 for apparatus schematic). Pumps are calibrated, and their threshold voltages are de termined Step changes should be made in the range bounded by the threshold voltages. The acid flow rate is set through out the experiment. There are many ways to design a cascade control loop with one master and two slave loops. Yet anSummer2002 other way is to implement a full multi variable controller with three inputs and three outputs, and to use partial decoupling followed by multiloop control. Regardless of strategies, stu dents should be able to report any loop interactions. The closed loop performance is compared with different set points for the third tank (pH= 6 7, and 8). Since this experiment can be con trolled by different strategies, it is especially suited for chal lenging students to consider and test various control strategies. [I Integration ofExperiments with Control Curriculum The control apparatuses coupled with the use of a HP-VEE as the control software, have been designed to equip seniors with a practical experience in process control. With emphasis on project based learning students are given the opportunity to apply theoretical concepts on real industrial processes. They are exposed to the phenomena that limit the achievable closed loop performance, including process nonlinearity, time de lays disturbances measurement noise valve hysteresis, and loop interactions. This provides them with experience in han dling real physical systems and practice in applying theoreti cal concepts to the real process. Students rated the organization of this course highly but indicated that too much effort was involved in writing the lab report. Based on student feedback over the years, several improvement s have been made to the course, including a shorter lab report requirement. ACKNOWLEDGMENTS The Dreyfus Foundation, DuPont and the University of Illi nois IBHE program are acknowledged for support of this project. REFERENCES 1. Braatz, R.D., a nd M.R. Jolmson,"Process Control Laboratory Educa tion Using a Graphical Operator Interfa ce Comp. Appl. Eng. Ed. p. 6 (1998) 2. Gatzke, E P., E.S. Meadows C Wang and F.J. Do y le, ill "Mode l-Ba se d Control of a Four-Tank System ," Comp. & Chem Eng., 24 p. 1503 (2000) 3. Johansson, K H. and J L.R Nunes, "A Multivariable Laboratory Pro cess witb an Adjustable Zero, Proc. of the Amer. Cont. Conf, IEEE Pre ss, Piscataway NJ p. 2045 (1998) 4. Siong, A., M.R Johnson, and R D. Braatz "Co ntrol of a Multivariable pH Neutralization Proc ess," Proc. of th e Educatio nal Topical Conf, AIChE Annual Meetin g Los Angeles, CA, Paper 61a. (2000) 5. Skliar M., J W Price and C.A. Tyler "Ex perimental Projects in Teach ing Process Control Chem Eng. Ed., 34 p. 254 ( 1998) 6. Rivera D.E. K.S. Jun V.E. Sater, and M.K. Shetty, Teaching Process Dynamics and Control Using an Industrial-S cale Real-Time Comput ing Environment ," Comp. Appl. Eng. Ed. 4 p. 191 (1996) 7. Hei se l R. Visual Pr ogramming w ith HP-VEE 2nd ed., Prentice Hall PTR, Upper Saddle River NJ ( 1997) 8. Ogunnaike B .A., and W H Ray Pr ocess Dynamics Mod e ling and Control Oxford University Press New York, NY ( 1994) 9. 10 Skogestad S. and I. Po st lethwaite, Multivariable Feedback Control Analysis and D esign, Wiley, New York NY ( 1996) 11. Braatz R.D. Internal Model Control," in Control Systems Fundamen tals ed. by W.S. Levine CRC Press Boca Raton FL p. 215 (2000) 12 Morari M ., and E Zafiriou Robust Pro cess Control, Prentice-Hall Englewood Cliffs NJ (1989) 0 187

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f.3 ... 5-.._c_l_a_s_s_r_o_o_m __________ ) Using Test Results for ASSESSMENT OF TEACHING AND LEARNING H. HENNING WINTER University of Massachusetts Amherst, MA 01003 E xamination time can be filled with anxiety. Teachers design a mid-term or final exam to cover the most important subjects of their courses and expect the stu dent to apply the learned material successfully. Most gratify ing for teacher and student alike is an exam in which the student answers all questions and receives a top grade. In complete or wrong answers generate dissatisfaction with both the student and the teacher. Reality is somewhere between these extremes, depending on the degree of success of the teaching and student committment. The exam results often suggest that the teaching needs to be improved, but the ques tions are where it can be improved and how. Direction can come from an assessment of exams. They contain a wealth of information, much more than just a grade for the student. cii Methods have been developed for assessing entire engi neering programs, curricula as well as individual courses, and educational research projects.r 2 3 r Student portfoliosC 2 3 1 allow quantitative assessment of the students' work during the year with feedback to the campus community. This report describes a teaching tool that works on the assumption that the educa tional program as a whole has already been assessed and that a plan exists for individual courses. Instead of the large-scale approach, this paper will focus on methods of analyzing a single exam and generating direct feedback for the teaching of a course with well-defined objectives. I have introduced the concept of a "grading matrix" for analyzing the results of tests in chemical engineering. The grading matrix has the purpose of detecting academic strengths and weaknesses of individual students as well as strengths and weaknesses of teaching. Most important is the identification of weaknesses so that they can be corrected in the classroom (or outside) and possibly re-assessed. The in creased interest in teaching assessment has motivated me to describe the grading matrix in this report. Until now, I have used it by myself in all undergraduate and graduate teaching for over a decade and have gradually refined it. The matrix method is somewhat related to the Primary Trait Analysis of Loyd-Jones,l 51 which was recently pointed out to me. But, in addition to student performance, the grading matrix also as sesses teaching success. This paper briefly describes the grad ing matrix together with suggestions for its use in teaching and curriculum development. THE GRADING MATRIX The definition and use of the grading matrix can be seen in Figure I. The example is deliberately kept simple: a typical written test is broken down into N individual subtopics (task 1 to task 16 since N=16 was chosen for this test) shown across the top of the matrix. Student names appear on the left side. Separately for each of the subtopics, the student's exam is evaluated on a scale from 0% to 100%. Grades are finely varied between 0% and 100% or, in yes/no fashion of a quiz with either I or O in the matrix. This choice depends on the nature of the test or quiz. A row of grades across the matrix shows the strengths and weaknesses of that individual student. The average over the row constitutes H. Henning Winter is Distinguished Univer sity Professor of Chemical Engineering at the University of Massachusetts at Amherst He has degrees from Stanford University (MS) and the University of Stuttgart (Dr. Ing) His resarch includes experimental rheology, poly mer gelation, and crystallization. Copyright ChE Division of ASEE 2002 188 Chemical Engineering Education

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his or her final grade: 100 grade(%]= -(ta s k 1 + ta sk 2 + task 3 ... + task N) (I) N where N is the number of tasks (=number of columns in the matrix). The actual grading process is complete at this point. When returning the graded test each student receives two items: their own exam booklet and the grading matrix (with out names) of the entire class No grades are written in the booklet except for the final grade on the booklet cover In stead of grades, I write occasional comments into the exam booklet with the purpose of helping the student to understand the course material. For identification on the matrix, students need to find the row with their final grade on the right side By knowing the row, st udent s obtain an analysis of their per sona l performance in each of the subtopics of the test. This allows them not only to assess their personal knowledge but also to compare it with the rest of the class Students told me that they especially like this comparison to others. Note that different from Figure 1 no student names are listed on the students' copy of the matrix; privacy is maintained. Students can reveal their grade to fellow students, but their perfor mance remains otherwise unknown. I have not had any prob~ I I CJ) ;:: N "' ..,. "' (&) ,-.. 00 .:ii:: -, .:a.:: l .. ~ : ~ I -"' -"' j -"' -"' -"' -"' I U) (I) i "' "' "' "' "' "' $ g l $ : 2 1 g g g g g g I I I I I I i I weight= : 1 1 1 1 1 1 I 1 1 1 1 1 ; 1 studenl 1 : 1 1 : 1 I 1 1 I 1 0 1 1 1 2 '. student : 1 1 1 1 1 1 1 0 3 1 0 1 Ji. student 1 1 1 1 1 1 1 1 1 1 1 ~t udent 1 0.9 0 9 1 1 1 1 1 1 1 1 5 : student 1 0 9 0.8 1 1 ~~ 1 0 2 0 0 ,..Jl ~ 9 i 6 i. student 1 0.8 0 .6 1 1 1 1 0 1 0 0 9 I ....... ....... ....... ....... ....... ... .. .. ...... ....... .. ..... .. .... ... ... i .... .. ... .. .. .. .. ... ....... ... .. .. ...... . .. .. .. .. .. ....... ....... 21 ; student 1 I 1 0 9 1 1 1 1 0 0 0 1 22 student 1 1 1 1 0 1 0 8 0 0 2 0 0.6 13 _;_ s!udent 1 o.8 i 0.5 1 0 9 1 1 0.2 0 0 1 24 1 student 1 0 5 1 1 1 1 0 0 0 0 1 ~ ~ student 1 0.8 1 1 1 1 1 0 0 0 0 8 2~ ~ 1J_d~_0~ 1 1 0 1 1 1 0.8 0 0 0 0 8 ,P. student 1 I 0.3 : 0.8 1 1 0 1 0 0 0 1 i~ student 1 o 8 1 I 1 1 1 0 0 0 0 0 5 -~ 29 : student 1 ; 0.8 0.8 0 0 0 1 1 1 0 1 30 1 student 1 ; o 0.4 1 1 1 1 0 0 0 0 7 -i I I lteachinA 100 1 84 78 96 92 86 89 27 47 16 85 assessment * -"' "' g 1 1 1 1 1 lem s arising from this procedure. The mo s t critical part of the entire assessment process is the de s ign of the grading matrix itself; e g. the se lection of test que s tions ( called task in Figure I) which the student will be asked on the test. These tasks need to be representa tive for the course objectives according to an overall plan. [ 2 3 61 Consider the example of a Fluid Mechanics course, which has the objective that students learn to solve certain flow prob lems. Thi s can be tested in an exam where one such flow problem is broken down into: (task,) schematic drawing of the expected velocity field, choice of coordinate system, and definition of boundary conditions; (tas~) equation for con servation of mass; (tas~) equation for conservation of linear momentum; (task 4 ) solution for obtaining the velocity field; (task 5 ) statement of all simplifyi ng assumptions and limita tions of the solution; (task 6 ) discus sion of properties of cal culated flow field; and (task) prediction of pressure and stress. Most written tests are easily structured in this way TEACHING ASSESSMENT AND CORRECTIONS Until this point, the exam grading has followed conven tional paths except that the data is filed in a spreads heet "' ..,. "' _.J: t--.!:"'__:,:_ -"' -"' -"" "' "' "' g g g 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 9 (&) -"' "' g "' :, C 0 .0 I 1 : 0 2 1 1 0 2 0 1 .l!! _,:; Cl iii 3: "-I 16 I ~ I <1) ,, I ,, 3
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read y for furth e r asse ss ment. Som e of the mo s t important information i s contained in the column s of the g radin g ma trix of Figure 1 A column with mo s tly high marks (1 = high est mark) top to bottom s how s that all s tudent s know the s ub ject at lea s t a t the level of the exam que s tion. If a column however has mostly O marks, something went wrong. Rea son s can be deep-rooted or only s up e rficial (i. e., the que s tion wa s confusin g or the s tudent s ran out of time) Di s cus s ion s between teacher and student s often bring clarification and plan s for further action are easily devised Technical defi ciencie s and/or misunder s tanding s are recognized and can be a ddre ss ed for instance, in a s pe c ial help s e ss ion or in the next homework assignment. Experiments can be added or computer animation can be used to help vi s ualize abstract concept s Teacher s have an opportunit y to become very cre ative a s soon a s the problem i s defined. Thi s definition of the problem is the main purpose of the grading matrix. Correction of weakne ss e s can then be reass e s sed in the next te s t. Thi s i s typic a lly done by including appropriate que s tions in the next exam preferably within th e same cour s e and/or in the next homework a ss i g nment. T e aching s hould b e corrected further if nece ss ary Often it i s too late to intro duce correction s in the same s emester or quarter. If changes cannot be made in time the weakne ss in one course will be pa ss ed on to the teacher of the following course Thi s Figure 2 : This i s th e sam e g radin g matri x a s in Figur e 1 but s p ec ific we i g hts ar e a s si g ned to e a c h of th e task s. This a ffec t s th e c alculation of th e g rad e a s d e fined in Equation 2 Everythin g e ls e, includin g th e t e a c hin g a ssignment remains un c hanged b y th e w e ightin g sy st e m Wei g hts hav e littl e e ff ec t o n th e g rad e of top students but c an 1 90 mak e a lar ge diff ere n ce for a w eak e r stud e nt. i I N (") ..,. ~ X X X "' "' "' g g g i ' I I weight= 0 5 1 3 1 1 1 student 1 I 1 1 1 2'. student 1 1 1 1 I 3 student 1 1 1 1 i--_ 4 ~ udent 1 0 9 0.9 1 ~ student 1 0 9 0.8 1 6 . student 1 0 8 0.6 1 ...... . I . .. .. .. ---I ....... 1 ....... .... .. . .. .. 22 studen1 1 i 1 1 1 23 1 student 1 0 8 0.5 1 ---24 student 1 i 0 5 1 1 25 l student 1 I 0.8 1 1 --26 1 student 1 1 0 1 21 : student 1 0 3 0.8 1 I 1 1 28 ; student 0 8 1 1 I 29 i student 1 0 8 0 8 0 ~o i student 1 0 0.4 1 ,teaching 100 84 78 96 assessment "' X "' g 2 1 1 1 1 1 1 ... .... .... .. 0 0.9 1 1 1 1 1 0 1 92 CD X "' g 1 1 1 1 1 0 1 ...... ....... 1 1 1 1 1 0 1 0 1 86 teacher s hould b e a l e rted to th e probl e m s o that c orrec tion s can b e made th e re. The grading matrix provide s a record which can be used e ven if a nother teach e r teache s th e cour s e the following ye a r. Adju s tment s can be made then and can be re-a ss e sse d until teaching weaknesse s are re s olved. I can im a gine however a problem with the exi s tence of s uch record s, s ince they have a potential for mi s u s e in the form of over-coachin g of teach er s Thi s would interfere with the learnin g environment and impair the matrix method. A c ce s s to the g radin g matrix s hould be r e s trict e d to the t e a c her s and s tudent s who are directl y in v olved FEEDBACK TO STUDENTS Advising individual s tudent s i s enhanced by the diagnostic propert y of a gradin g m a trix The te a cher s ee s indi v idu a l w e akne sses of s tudent s a nd can s u g gest c orr e ctiv e m e a s ur es. (e g., specific readin g materi a l or exerci s e s ) This does not require further prepar a tion on th e teacher 's part. Information i s a v ailable in s tantly when a s tudent come s to the office for consultation. The matrix row of g rades in combination with other ob s ervation s ( attendan c e particip a tion during cla ss, etc .), provide s a quantitative ba s i s for a di s cu ss ion ;": I ,._ a, Ol ;: I X X X X X X X X X X "' "' "' "' "' "' "' "'
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CURRICULUM DEVELOPMENT Weaknesses in student learning as detected in the grading matrices of a course (two midterms and a final, for example) should be assessed in the context of the entire curriculum There is a possibility that students may not be sufficiently prepared for a specific class. Prevailing weakquestions arise in high school teaching and even in elemen tary schools where standardization of tests is considered .l7 1 The matrix method can also be adapted to examinations of much wider scope such as oral presentations or essay-type exams. Oral exams or essays tend to be less uniform in their structure than the written tests discussed above nesses should in this case, be addressed by chang ing the course content of the responsible preced ing course. Relevant results from the grading matrix can be integrated into the systematic cur riculum development Y 1 Discussions along these lines are in progress in our department. ... this This, however does not make their grading less amenable to matrix format. New categories need to be added to the list of tasks, such as style and expression, logic of argument depth of discussion format of graphs validity of con clusion s, and more. The choice of categories needs to be explained to the students well in ADAPTATION OF THE MATRIX METHOD There are many ways of integrating the infor mation from the grading matrix into personal approaches to teaching and student advising. It goes without saying that assessment of test per formance as reported here needs to be integrated with classroom assessment. Thi s is a dynamic process which differs from year to year, since each group of students interacts differently and varies in its needs. As the learning process evolves, teachers adapt in their classroom assesspaper [focuses] on methods of analyzing a single advance of the exam. SUMMARY The three main functions of the grading ma trix are providing a grade for the student, label ing areas of weakness in the student's knowl edge and labeling areas of weakness in the teaching. For me personally the grading ma trix helped to fairly assess the abilities of stu dents since my grading became more uniform s omething I tried with less success with other exam and generating direct feedback ... ment and in their creative teaching approaches. The integra tion of the grading matrix in day-to-day teaching works well for me, but a general discussion of this topic would exceed the scope of this report. Obviously the matrix itself can be tailored in many differ ent ways, and adaptations are straightforward. A few will be mentioned here It is possible for instance to emphasize se lected parts of an exam by adding weight to some of the tasks. While I normally give uniform weight to all questions (see top row of the matrix in Figure 1 ) more important questions can be given an increased weight as shown in Figure 2 The row of grades across the matrix needs to be rescaled accord ingly when calculating the final grade: N I, weighti taski grade( % ] = I 00 ~i = ~l_ N ____ (2) I, weighti i=l where N is the number of columns. Additional bonus points can be added wherever appropriate. The overall scale of the test will not be affected by assigning bonus points to indi vidual students. The concept of a grading matrix is introduced here with a chemical engineering example and on the most straightfor ward type of test. The proposed method for assessment of teaching is applicable at many levels, however. It is equally useful for students and teachers outside of engineering. Similar Summ e r 2002 grading methods The grading matrix also alerted me to problems that students encoun tered with course material. It labeled weaknesses in my teach ing so that I could devise different teaching methods when needed. I feel that during office hours, my advice became better directed to the needs of individual students The de sign of test content with the matrix structure in mind and the feedback from tests have positively affected my teaching and my continued search for ways to motivate students. While still being a stressful experience for the students examinations have turned into an effective instrument for improved teaching ACKNOWLEDGMENTS Support from the van Humboldt Foundation, many lively discussions with colleagues and students, and helpful sug gestions from the reviewers are gratefully acknowledged. REFERENCES I Wal v oord G and V.J Ander s on E ffec ti ve Gradin g : A T oo /for Learn in g and A sses sm e nt Jo ss ey-Ba ss San Franci s co CA ( 1998 ) 2 Olds B M. and R.L. Mill e r An Asse ss ment Matrix for Evaluating Engineerin g Program s, J En g Ed. 87, p. 173 ( 1998) 3. McN e ill B and L. Bellam y The Articulation Matrix a Tool for De fining and A ss essin g a Course Ch e m Eng. Ed. 33 p 122 (1999) 4 Ta y lor R Basi c Prin c ipl e s of Curri c ulum and Instru c ti o n, University of Chicago Pre s s. Chi c ago IL (1949 ) 5. Loyd-Jone s R Primary Trait Analy s is in Cooper C. and L. Odell (eds.) Evaluatin g Writin g : Describing M e asuring, Judgin g Urbana IL Council of Teachers of English, Urbana ( I 977) 6 Old s, B.M. and R.L. Miller "Using Portfolios to Assess a Chemical Engineering Program Chem En g. Ed ., 33 p 110 (1999) 7 Salte! J K. Ho w i s my Child Doing ?" J. Wale/o f Edu c ation, 10(2), p. 5 ( 2001) 0 191

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.ta.-.5 ... 3._c_u_r,-,_i_c_u_l_u_m _________ ) IS PROCESS SIMULATION USED EFFECTIVELY IN ChE COURSES? KEVIN D. DAHM, ROBERT P. HESKETH, MARIANO J. SAVELSKI Rowan University Glassboro NJ 08028 P rocess simulators are becoming basic tools in chemi cal engineering programs. Senior-level design projects typically involve the u s e of either a commercial s imu lator or an academic s imulator s uch a s ASPENPLUS ChemCAD, ChemShare FLOWTRAN HYSYS and Proll w/PROVISION Man y de s i g n textbooks now include exer cises specifically prepared for a particular s imulator. For ex ample the text by Seider Seader a nd Lewin l 1 1 has example s written for use with ASPENPLUS HYSYS GAMS 121 and DYNAPLUS .13 1 Professor Lewin ha s prepared a new CD ROM version of this courseware givin g interactive self-paced tutorials on the use of HYSYS and ASPEN PLUS through out the curriculum. 1 4 5 1 Thi s paper will analyze how effective it is to include com puting (particularly proce ss s imulation) in the chemical en gineering curriculum. Among the topic s of intere s t will be vertical integration of proce ss s imulation v s tradition a l use in the s enior design cour s e s, the role of computer program ming in the age of sophisticated software packages and the real pedagogical value of these tool s based on industry needs and future technology trends A course-by-course analysis will present examples of specific methods of effective u s e of the s e tools in chemical engineering cour s es both from the literature and from the author s' experience. DISCUSSION In the past most chemical engineering programs viewed process simulation as a tool to be taught and used solely in senior design courses Lately however, the chemical engi neering community has s een a s trong movement toward ver tical integration of design throughout the curriculum.l 6 9 1 Some of these initiatives are driven by the new ABET criteriaJJOJ This integration could be highly enhanced by early introduc tion to process s imulation. Process simulation can al s o be u s ed in lower-level course s a s a pedagogical aid. The thermod y namic s and separation s areas have a lot to gain from simulation packages One of the advantages of process s imulation s oftware is that it enables 192 the instructor to present information in an inductive manner. For example, in a course on equilibrium staged operations one concept a student mu s t learn i s the optimum feed loca tion. Standard texts s uch a s Wankat 11 11 present these concept s in a deductive manner The inductive presentation u s ed at Rowan Univer s ity i s outlined below in the section on equi librium s taged s eparation s. Some cour s e s in chemical engineering such a s proces s dynamic s and control and proce ss optimization, are computer intensive and can benefit from dynamic process simulator s and other software packages. Henson and Zhang 1 1 2 1 present an example problem in which HYSYS.Plant (a commercial dynamic simulator) is used in the process control cour s e. The proces s feature s the production of ethylene glycol in a CSTR and purification of the product through distillation. The au thors use this s imple proce ss to illu s trate concept s s uch a s feedback control and open-loop dynamic s. Clough 1 1 31 pre s ents a good overview of the u s e of dynamic s imulation in teach ing plantwide control strategies A potential pedagogical drawback to simulation packages such as HYSYS and ASPEN is that it is possible for students to successfully construct and use models without really un der s tanding the phy s ical phenomena within each unit opera tion Clough empha s izes the difference between "s tudent s using vs. student s creating simulations ." Care must be taken to insure that s imulation enhance s s tudent under s t a nding rather than simply providing a crutch that allow s them to s olve Kevin D. Dahm is Assistant Professor of Chemical Engineering at Rowan University. He received his BS from Worcester Polytechnic Institute in 1992 and his PhD from Massachusetts Institute of Technology in 1998 Robert P. Hesketh is Professor of Chemical Engineering at Rowan Uni versity He received his BS in 1982 from the University of Illinois and his PhD from the University of Delaware in 1987 Robert s teaching and re search interests are in reaction engineering freshman engineering and separations. Mariano J. Save/ski is Assistant Professor of Chemical Engineering at Rowan University. He received his BS in 1991 from the University of Buenos Aires his ME in 1994 from the Uni v ersit y of Tulsa and his PhD in 1999 from the University of Oklahoma His technical research is in the area of process design and optimization Copyr i gh t C h E D i v i s i o n of ASEE 2002 C h e mi c al En g in ee rin g Edu c ati o n

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problems with only a surface understanding of the processe s they are modeling. Thi s concern about proces s simulators motivated development of the phenomenological modeling package Mode1LA. l 14 J This package allows the user to de clare what physical and chemical phenomena are operative in a process or part of a proces s. Example s include choosing a specific model for the finite rate of interphase transport or the species behavior of multiphase equilibrium situations. One uses engineering science in a userse lected hierarchical seq uence of modeling decisions. The focu s is on physical and chemical phenomena, and equations are derived by the software Despite these concerns, the survey results di sc u sse d in the next section indicate that HYSYS, ASPEN, and Proll remain the primary simulation packages currently in use. SURVE Y: C OMPUTER US E IN CH E M ICA L PROCESS SIMULATION In 1996, CACHE conducted a st udy di sc ussing the role of computers in chemical engineering education and practice The study surveyed both faculty member s and practicing en gineers, but little emphasis was placed on the specific use of process simulation. To fill this gap and obtain up-to-date re sults, a survey on computer use in the chemical engineering curriculum was distributed to U.S. chemical engineering de partment heads in the spring of 2001. It addressed how ex tensively simulation software is used in the curriculum, as well as motivation for its use. The use of mathematical soft ware and computer programming was also examined. A total of 84 responses was received, making the response rate approxi mately 48 %. Tables 17 summarize the results. The wording of questions and responses in the table s is taken verbatim from the survey. The survey also provided a space for written comments and some of these are presented throughout this paper In a 1996 publication that discussed the results of the CACHE survey, Kantor and EdgarC 1 51 observed that comput ing was generally accepted as an integral component of teach ing design but that it had not s ignificantly permeated the rest of the curriculum. The survey results suggest that this per ception is outdated. Table 1 shows that only 20 % of depart ments reported that process simulation software is used ex clusively in the design course, and Tables 2 and 3 show that it is particularly prevalent in the teaching of equilibrium staged separations process control, and thermodynamics. It must be noted, however that the survey did not ask respondents to quantify the extent of use ; a "yes" response could indicate as little as a single exercise conducted using a simulator. Table 1 also indicates that over one-fourth of the respond ing departments felt that their faculty have "a n overall uni formly applied strategy for teaching simulation to their stu dent s that starts early in the program and continues in subse quent courses Many other respondents acknowledged the merit of such a plan but cited interpersonal obstacles with comments such as With each faculty member having their own pet piece of software it's tough to co me to a consensus. Not many fa c ulty use ASPEN in their courses because they haven't learned it, think it will take too much time to learn, and aren't motivated to do so. I would like to see the use of flow sheet simu l ators expanded to other courses in our curriculum but ha ven't been able to talk anybody else into it yet. At Rowan University, the incorporation of mini-modules (described further in the next section) into so phomore-and junior level courses has proved to be an effective solution to this problem. They require only limited knowledge of the simulation package on the part of the instructor because they employ models that contain only a single unit operation. Table 4 (next page) summarizes the responses to a ques tion on motivation for using simulation software. Four op TABLE2 Responses to: tions were given, and the respondent was asked to check all that apply. The TABLE 1 Responses to: "Which of these best describes your department's use of process simulation software?" "Please indicate the courses in which professors require the use of steady-state chemical process simulation programs." most common choice was "It's a tool that graduating chemical engineers should be familiar with, and is thus taught for its own sake." A total of 83% of the respondents selected this [I The faculty ha s an overall, uniforml y app lied strategy for t eac hing s imulation to their st udents that starts early in the program and continues in s ub se quent courses. 27% [I There is so me coordination between individual faculty member s, but the departm e nt as a whole has not a dopted a curriculum-wide s trateg y. 35 % [I Several instructor s use it at their discretion but there is little or no coordination. 18 % [I Only the de sig n instructor requires th e u se of chemical process simulation software 20 % [I No professor currently requires simu l ation in undergraduate courses. 1 % Summer2002 [I Design I and/or II [I Process Safety [I Process Dynamic s and Control [I Un it Operations [I Equilibrium Staged Separations [I Chemical R eact ion Engineering [I ChE Thermodynamics [I Fluid Mechanics [I Heat Transfer [I Chemical Principles % Yes option, and in 15 % of the responses it 94 % was the only one chosen. 4% 10 % TABLE3 31 % Responses to: "Please indicate the courses in 57 % which professors require the use 19 % of dynamic chemical process 36 % simulation programs 7 % Course %Yes 13 % [I Design I and/or II 12% 29% [I Process Dynamics and Control 52% 193

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In their 1996 study of computer skills in chemical engineering, Kantor and Edgarr 141 analyzed survey results from both faculty and practicing engineers, finding that faculty tended to drastically under estimate time spent at the computer by practicing engineers in indus try. The main software tools they used, however, did not include simu lators; they were spreadsheets (74% ), graphics presentation packages (80% ), database systems (70% ), and electronic communications (89% ). Indeed, many engineers will not even have access to process simulators Our department collaborates with many small companies and has found that they use self-made Excel macros to solve problems that are readily solved with commercial simulators, simply because they cannot afford the software These observations certainly do not in validate the opinion that process simulation software is "a tool that graduating chemical engineers should be familiar with." They do, how ever, suggest that a department would do well to examine how much time it is spending on activities designed to familiarize the student with simulation software while serving no other purpose. Another finding presented in the 1996 study by Kantor and Edgar was that computer programming (in languages such as FORTRAN, C, or PASCAL) is not a vital skill for chemical engineers in industry. Indeed, "many companies explicitly tell their engineers not to write software because of the difficulty of maintaining such programs writ ten by individuals." Courses on computer programming appear to re main a staple of undergraduate programs. Table 5 shows that 83% of the respondents require a computer-programming course (taught by either computer science or engineering faculty) and 45% require pro gramming in "several" subsequent courses. There is a shift away from teaching traditional computer programming, however. A total of 17% of the respondents indicated that their curriculum no longer contains computer programming at all, with a number of them mentioning that programming had been recently phased out. Many other respondents indicated that the programming present in their curriculum does not employ traditional languages such as C or FORTRAN, but instead uses higher-level programming environments such as Maple. Example comments are Our situation is that w e teach a course that introduces students to Excel and Maple. Maple is the programming tool. They are not required to program thereafter, but many of them choose to do so in later courses We dropped our programming course last year, because simulation packages (as well as general equation solvers, spreadsheets, etc.) were becoming so powerful that it was becoming much less important to know how to program and more important to know how to configure/use existing packages. Our undergraduate students no longer take a computer programming course, per se. Instead they learn and make e x tensive use of packaged software (e g., Matlab) in an integratedfreshman sequence on engineering analysis. Subsequent classes draw upon this experience. This is a trend that may well continue to grow. The CACHE survey indicates that 5% of respondents said it "is not important" to teach computer programming to undergrads, and 57% thought it was "be coming less important." In addition, the current ABET Chemical En gineering criteriaf' 61 requires that graduates have a knowledge of "ap propriate modern experimental and computing techniques" but does not specifically mention programming as it did in the past. Two respondents identify one potential drawback to this shift away from traditional computer programming They emphasize the impor 194 tance of the logic and problem-solving skills that pro gramming experience stimulates, even if the ability to program in itself is unnecessary for chemical engineers The specific comments were We dropped our programming course a number of years ago as the capabilities of the various software packages increased to the point where programming input from the user became insignificant. We' re now seeing a drop in the logical approach to problem solving in our students that we feel is related to this lack of exposure to programming. As the software becomes more powe,ful, however, hit-or-miss or brute-force techniques work so is there really a need for a more reasoned approach to problem solving? Although programming languages ( FORTRAN) are in some disfavor at present and probably will pass from the scene, I find that students develop an increased ability for the logic of solutions and of thinking about problems when they learn a language ... I find that students can use programs such as POLYMATH, etc. with a great deal more understanding and efficiency once they have learned a language. The chemical engineering community thus may have a use for teaching tools and techniques that challenge stu dents to think logically and develop algorithms without necessarily taking the time to learn a full programming language. One option is template based programming as developed by Silverstein.L 17 i TABLE4 Responses to: "Which of the following best describes your motivation to use simulation packages? Please check all that apply." Response % Yes [I It helps to illustrate essential chemical engineering concepts. 64 % [I It makes numerical computations less time consuming. 70 % [I The modernity is good for attracting and retaining students. 30% [I It's a tool that graduating chemical engineers should be familiar with, and is thus taught for its own sake. 83 % TABLES Responses to: "Which of the following best describes your department's use of computer programming languages?" Response % Yes [I One required course taught by computer science and no programming required in subsequent chemical engineering courses. 13 % [I One required course taught by chemical engineering and no programming required in subsequent chemical engineering courses. 11 % [I After students take the required programming course, they are required to program in one subsequent ChE course. 7 % [I After students take the required programming course, they are required to program in several subsequent ChE courses. 45% [I Students are required to program in upper level chemical engineering courses without having taken a formal programming course. 8 % [I None of the above selected. 16 % Chemical Engineering Education

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EXAMPLES OF CHEMICAL PROCESS SIMULATORS IN CHEMICAL ENGINEERING In this section of the paper we give some practical ideas on how to effectively implement chemical process simulator s into course s other than the capstone design course. Freshman Engineering At Rowan University, an inductive approach has been used to introduce freshmen and sophomores to chemical process simulators The methodology used was Show the student s a he a t exch a nger. Thi s can be either a laboratory unit or part of a cogeneration plant. 118 l The stu dent s are asked to record their observation s of fluid flowrate and temperature s. Next have the students start a proces s s imulator and put these experimental result s into a s imple heat-exchange unit operation of a process simulator to determine the heat duty. Finally, have the students conduct an energy balance by hand on the system. In this manner the s tudent s have first seen the equipment and then modeled it u s ing a simulator on hand calculations This helps to familiarize them with what a simu lator actually does and what sort of problem can be tackled with simulation. Chemical Principles or Stoichiometry In many programs with vertical integration of design throughout the curriculum, the design project starts in this typically sophomore-level course. Many project examples can be found in the literature Bailie et al., L 1 91 proposed a design experience for the sophomore and junior years. In the first semester of the sophomore year, the students are given a single chemical design project, and they focus on material balances and simple economic evaluations such as raw material cost and the products selling prices. Throughout the sequence, the students must apply newly acquired knowledge to im prove and optimize the process. The ultimate goal is to pro duce a fully sized and optimized design, including the analyTABLE6 Responses to: "/11dicate the mathematical applicatio11s software required of chemical e11gi11eeri11g u11dergraduates. Check all that apply." Response %Yes [I POLYMATH 40 37 % [I MATLAB 65 % [I Maple 24 % [I MathCAD 37 % [I EZ-Solve 5 % [I Spreadsheets 82 % [I Mathem a tica 13 % [I Other 15 % Summ e r 2002 TABLE7 Responses to: "Please i11dicate all applicable steady-state Chemical Process Simula tio11 programs currently bei11g used i11 your departme11t's u11dergraduate courses. Check all that apply. Response %Yes [I Proll/Provision 1 2% [I HYSYS or Hysim 32 % [I A s pen Plu s 45 % [I ChemCAD 32 % [I Other 13 % sis of the capital and operating costs by the end of the junior year This approach is comparable to problem-based learning. l 201 There have been other contributions to this vertical approach. 121 231 In the above work it is unclear how process simulators are being u s ed and it is not mentioned if the simulators are used in the early stages of integration. Process simulators cer tainly can be used for such problems, however, since they provide an efficient way to evaluate many variations on a single design concept. Chemical Principles-Energy Balances In Felder and Rousseaul 241 (a standard text for this course), the chapter on multiphase systems introduces the concepts of bubble and dew point s An inductive method of teaching these concepts is to start with an experiment on a binary system, us ing a IL distillation unit or an interactive computer modulel 2 5 1 with a visual examination of the bubble and dewpoint. These methods result in the students exarning their data by using a binary T-x-y diagram. The next step is to use the process simu lator to predict bubble and dewpoints for binary and multicom ponent systems In using HYSYS the dewpoint temperature is automatically calculated after specifying the vapor fraction as 1.0 (dewpoint), the compositions, and pressure in a single stream. The calculations for multicomponent systems are usu ally reserved for an equilibrium staged operations course. In new editions of many textbooks for the chemical process principles course there are chapters on process simulation. L 2 42 61 They give examples with solutions done by calculators, Excel spreadsheets and FORTRAN. This gives the students an ex cellent reference on how a system of equations i s used by chemi cal process simulators. In section 10.4 of Felder & Rousseau commercial process-simulation packages are discussed but no examples are given. The last problem in the chapter suggests, however that any of the other fourteen homework problems could be solved by a chemical process simulator. This could be another starting point for introducing commercial process simu lators in this course. Equilibrium Staged Opera t i o ns In teaching distillation the standard modeling approach is to use the McCabe-Thiele graphical method This is an excellent tool for introducing students to binary distillation problems. Before extensive use of the computer became feasible, the next step was to add the energy balance and use the Ponchon-Savarit method. Many professors no longer teach this method, using the simulator instead. This decreasing use of Ponchon-Savarit bas been promoted by Wankat, et al., l 27 1 and recently published textbook descriptions of the method have been shortened l 2 81 Using simulators throughout the curriculum requires that fac ulty have knowledge of the simulator that the students are us ing. In the discussion of the survey results, there were concerns about the faculty time and motivation required to be come pro ficient in using a simulator One possible solution is to imple ment mini-modules of the type used at Rowan University. In 195

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equilibrium staged operations, a student must learn the opti mum feed location and the improved separation resulting from increasing reflux ratio for a given number of stages; in an ap proach that has been used at Rowan University The instructor prepares a comp l ete HYSYS model of a distillation co umn and distributes it to th e class The class receives a brief(less than fiv e minutes) tutorial on modeling columns with HYSYS -just enough to tell them how to change specifi c parameters s u c h as the reflux ratio and where to lo ca t e the r esulting st r eam compositions and other output parameters of interest. The students take a column through a series of configuratio n s, vary ing the reflux ratio, number of stages, and feed stage location and then answers a series of questions about the results. The students are thus introduced to concepts in an indu ctive manner. Subsequent classroom instru ction further exam in es the "w hys of the r esu lts. This is us ed as a starting point in deductive derivation of the McCabe-Thiele model. Mini modules analogous to this have been integrated through out the course, as well as in thermodynamics and principles of chemical processes The primary purpose of the modules is that the HYSIS model provides a time-efficient and effective way for students to examine the cause-effect relationships among column operational parameters. The modules also serve a cur ricular purpose in that they begin to introduce process simula tion. This is accomplished with a minimal requirement of faculty time. It is not necessary for professors to learn all aspects of the simulation package; they merely need to learn how to model one particular unit operation. Other forms of mini-modules have been proposed where stu dents learn the process simulator in self-paced tutorials L 1 41 The proposal is that these modules be given to the students-the professor does not need to prepare time-consuming tutorials and may not need to learn how to use the simulator. Another paper by Chitturl 29 l discusses preparing tutorials for ASPEN Plus simulators using HTML. Finally, the University of Florida maintains a web site for ASPEN where tutorials are available.l3 1 Chemical Engineering Thermodynamics Judging from the survey results, it seems that process simu lators are now widely used in thermodynamics (see Table 2). This is fertile ground for a pedagogical use of the process simu lators, and the first thing a new user of a simulator faces is the variety of thermodynamics packages that are available. The new user will quickly learn that an incorrect choice of a thermody namic model will yield meaningless results regardless of the convergence of the simulation case. Unfortunately, there are so many thermodynamics models in commercial simulators that it is impossible to educate our students in each one of them. Elliott and Lira [ 311 present a decision tree for the proper selec tion of the thermodynamic model. Traditionally, students are taught how to perform equilibrium and properties calculations by hand or, in the best scenario, with the aid of custom-made software programs for hand calcula tors or computers. The increasing influence of process simula tors opens up a completely new spectrum of possibilities. Since simulation results are only as good as the thermodynamic pack196 age chosen, there is value in teaching the fundamental as pects that will permit students to pick the right thermody namic package for a system. Simulators also offer the advan tages of combining thermodynamic models in the same simu lation and picking different models for certain properties within the overall process model; PRO II with Provision is very versatile in this respect. For instance, an equation of state such as Soave-Redlich-Kwong (SRK) is chosen as the overall simulation package, but it is modified so liq uid density is calculated using the American Petroleum Institute (API) equation. In many cases, professors have been taught thermodynam ics using earlier versions of Sandlerl 32 1 and Smith and Van Ness, [33 1 which did not emphasize predictions of thermody namic properties based on an equation of state. More recent versions of both texts and new texts such as Elliott and Lira now contain at least one chapter devoted to predicting ther modynamic properties from other equations of state. One of the fundamental aspects of a modem chemical thermodynam ics course is not only to teach students how to use these equa tions, but also which equation of state they should select for a particular problem. An example of the prediction of the enthalpy of a single component where values of the correlat ing parameters of a=f(T) and bare from the Peng-Robinson equation of state is (H-Hi g ) Kjr;] RT z + (1where B =bP/RT and A =aP/(RT) 2 From the above equations it is easily seen how compli cated these predictions can become compared to a table or a graph in a standard handbook 4 35 1 Many recent thermody namic textbooks have included computer programs that al low the reader to use various equations of state to solve home work problems. The drawback of these programs is that a student will only use them for the thermodynamics course. Instead of using these textbook computer programs, a pro fessor can encourage use of the thermodynamic packages contained in the chemical process simulators. In this manner, the students can become familiar with the available options in the various simulators. Chemical Reaction Engineering In the current chemical reaction engineering course, most students are familiar with ODE solvers found in POLYMATH or MatLab The philosophy given by Fogler[ 3 6 1 is to have the students use the mole, momentum, and energy balances ap propriate for a given reactor type. In this manner a fairly de tailed model of industrial reactors can be developed for de sign projects .r3 71 By using POLYMATH or MatLab, a student can easily see the equations used to model the reactor. In mod em process simulators there are several reactors that can be used. For example in HYSYS 2.2 there are the two ideal Chemical Engineering Education

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reactor models of a CSTR and a PFR The CSTR model is a standard algebraic model that has been in simulation pack ages for a number of years. The ODE's of the PFR are a re cent addition to simulation packages and are solved by di viding the volume into small segments and then finding a sequential solution for each volume element. In these more recent models the reactors not only include energy balances, but pressure drop calculations are also a standard feature for packed-bed reactors. With the above set of reactions chemical reaction engi~ neering courses can easily use the process simulator. Simula tion can be integrated throughout the course and used in par allel with the textbook, or it can be introduced in the latter stages of the course, after the students have developed profi ciency in modeling these processes by hand. As mentioned in the discussion section, the primary dilemma is how to in sure that the simulator is used to help teach the material rather than simply giving students a way to complete the assign ment without learning the material. Taking care that assign ments require synthesis, analysis, and evaluation in addition to simple reporting of numerical results will help in this re gard. Requiring that students do calculations by hand will ensure that they understand what the simulator is actually do ing. The professor can select chemical compounds that are not in the simulator database to ensure that these are done by hand. Rate-Based Separations An example of an integrated approach to teaching rate-based separations with design is given by Lewin Seider, and Seader (1998)P 81 In this paper the authors state that while design courses fully use advances in modern computing through the process simulators, many other courses in the curriculum still use methods employed over sixty years ago. Many modern Reaction TVJJe Conversion Descriptio11 TABLES computing methods are visual and are thus very useful in teach ing chemical engineering concepts. The authors suggest that professors who teach junior course(s) in separations, equilib rium-stage operations, rate-based operations, and/or mass trans fer consider including Approximate methods (Fens k e-U nd erwood-Gi liland and Kre,nser algebra i c method) Ri gorous multicomponent Enhanced distillation using triangular diagrams R a t e-based methods conta in ed in th e ChemLSep program and the RATEFRAC program of Aspen Plus Adsorption, ion exchange, c hromatography Membrane separations which are similar to Chapters 9 through 12 in the new Seader and Henley text. [ 281 One major drawback in current process sim ulator s is a lack of standard unit operations for membrane and other novel se pa rators. This can be partially addressed by importing programs into the process simulators. For example, on the HYSYS web site, an extension program can be downloaded for a membrane separator and other operations P 91 As simulators develop we believe that more unit operations will become available. CONCLUSIONS Chemical process simulation is currently underused in the chemical engineering curriculum at many schools According to survey results, process simulators are used in essentially all design courses and are also heavily used in equilibrium stage operations, primarily with respect to multicomponent distilla tion. But many respondents acknowledge that the role of sim lators could be beneficially expanded in their curriculum. Pro cess-simulation designers can make their products more valu able to chemical engineering educators by adding new and innovative unit operations while they continue to improve their thermody namic models. This paper contains practical suggestions and references for imple Equilibrium K eq = f(T) ; equilibrium-based on reaction s toichiometry; K eq predicted or specified menting a unified strategy for teach ing simulation to their students, start ing early in the program and continu ing in subsequent courses We be lieve that sim ulation packages are a fundamental tool for the future Gibbs Kinetic Heterogen eo u s Catalytic Simple Rate Summer 2002 minimization of Gibbs free energy of a ll components rA = -k fC~C~ + k,evclcI where the rever se rate parameters mu st be thermodynamicall y consistent and rate co n s tant s are given by k = AT"exp(-E I RT) Yang and Hougen form, which includes Langmuir-Hinshelwood E l ey-Rideal and Mars van Krevelen, e tc. ( c'c I klc cb ~j A B K r k (lccxc~ ctcijl hi h K d df lib d A f A B m w c eq 1s pre 1 cte rom eqm num ata chemical engineer. REFERENCES 1. Seider Warren D. J.D Seader and Daniel R. Lewin Pr ocess D es i g n Principles: S y nth es is Analysis and Evaluation, John Wiley and Sons New York NY(l 999) 2 GAMS, see < http://www .c he utexa s.e du/cache/newslet ters/fall97 _art2.pdf> 3. Aspen Technology, Inc -----Continued on page 203. 197

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~S=i laboratory ) -------------=-----AN INTRODU CTION TO DRUG DELIVERY FOR CHEMICAL ENGINEERS STEPHANIE FARRELL, ROBERT P. HESKETH Rowan University Glassboro, NJ 08028-1701 R owan University i s pioneering a progressive engineer ing program that uses innovative methods of teaching and learning to prepare s tudent s for a rapidly changing and highly competitive marketplace as recommended b y ASEE. l 1 1 Key features of the program include Multidisciplinary education through co ll aborat i ve laborator y and co ur s e work Teamwork as the nece ssa ry framework for solv in g complex problem s In corporat ion of state-of-the-art t ec hnol ogies throu g hout the c urri c ul a Creation of continuous opport uniti es for technical comm unication.1 21 The Rowan program emphasize s these essential features in an eight-semester, multidisciplinary engineering clinic sequence that is common to the four engineering programs (civil, chemi cal, electrical, and mechanical). A twose mester Freshman Clinic seq uence introduces all freshmen engineering students to engineering at Rowan Uni versity The first semester of the course focuses on multidisciplinary engineering experiments using engineering measurements as a common thread In the spring semester, stu dents are immersed in a semester-long project that focuses on the reverse engineering of a product or a process. In addition to introducing engineering concepts, the Freshman Clinic incor porate s the four key features mentioned above. Thi s paper describes an experiment that was performed both in our Freshman Clinic to introduce s tudents to drug delivery and in a se nior-level elective on pharmaceutical and biomedi cal topics to apply concepts of mas s transfer and mathematical modeling. Drug delivery is a burgeoning field that represent s one of the major research and development focus areas of the pharmaceutical industry today, with new drug delivery system sales exceeding $10 billion per year .l3 1 With projected double digit growth, the market i s expected to reach $30 billion per year by 2005. l 41 Drug deliver y i s an inherently multidi s ciplinary field that combines knowledge from fields of medicine phar maceutical sciences, engineering, and chemistry. Chemical en/9 8 gineers play an important role in this exciting field by apply ing their knowledge of phy s ical a nd chemical propertie s, chemical reactions mass tran sfe r rates, polymer materials and system models to the design of drug-delivery systems, yet un dergraduate chemical engineering s tudents are rarely exposed to drug delivery through their coursework This experiment introduce s freshman engineering s tudent s to chemical engineering principle s and their application to the field of drug delivery. Student s are introduced to concen tration measurement s and s imple analysis of rate data Through this experiment, s tudents explore concepts and tools that they will use throughout their careers, s uch as Novel application of chem i cal e n ginee rin g p r in c ipl es Co n cen trati on measurement Calibration Mat e ri a l balances Use of sp r e adsh eets fo r ca lcul a ti o n s and g raphin g P a ram e t e r evaluation Semi-log pl o t s and tr e ndlin es Co mparis o n of ex p e rim e lllal co n ce ntrati o n data t o predicted co n ce ntrati o n s T es tin g a transi e nt m ode l at the limit s of initial tim e and in finite time D eve l o pm ent of a m a th e mati ca l m o d e l ( in th e se ni o r l eve l class) BACKGROUND Periodic administration of a drug by conventional mean s, s uch as taking a tablet every four hours, can result in con sta ntly changing systemic drug concentrations with alternat ing periods of ineffectivene ss and toxicity Controlled-relea s e sys tem s attempt to maintain a therapeutic concentration of a dru g in the body for an extended time by controlling it s rate of delivery. A comparison of s ystemic drug profiles estabStephanie Farrell is Associate Professor of Chemical Engineering at Rowan University. She received her BS in 1986 from the University of Pennsylvania her MS in 1992 from Stevens Institute of Technolog y and her PhD in 1996 from New Jersey Institut e of Technology. Her teaching and research interests are in controlled drug delivery and biomedical en gineering. Robert Hesketh is Professor of Chemical Engineering at Ro wan Univer sity He received his BS in 1982 from the University of Illinois and his PhD from the University of Delaware in 1987 His research is in the areas of reaction engineering novel separations and green engineering. Copyrig h t ChE Division of ASEE 2002 C h em i ca l Eng in ee rin g Edu c ation

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lished b y conventional a dministration and controlled release i s shown in Figure 1 Hi s torically, drug-delivery systems were developed prima rily for traditional routes of administration, such as oral and intravenous, but recently there has been an explosion in re search on delivery by so-called nonconventional routes, such as transdermal (skin), nasal ocular (eyes), and pulmonary (lung) administration. Drug-delivery applications have ex panded from traditional drug s to therapeutic peptides vac cines, hormones and viral vectors for gene therapy These sys tems employ a variety of rate-controlling mechani s m s, including matrix diffusion, membrane diffusion biodegra dation, and osmosis. To design and produce a new drug-de livery system, an engineer must fully understand the drug and its material properties as well as processing variables that affect its release from the system This requires a solid grasp of the fundamentals of mass tran sfe r reaction kinetics, ther modynamics and transport phenomena The engineer must also be ski lled in characterization techniques and phy s ical property te s ting of the delivery syste m and practiced in analy s i s of the drug-release data. We present a simple experiment in which students are in troduced to the basic concepts of drug delivery by studying the dis so lution of a lozenge into water This is the type of experiment that would be performed by a drug company to determine the rate of drug release from a dissolution-limited sys tem A s the lozenge di sso lve s, the drug is released (a long with a coloring agent added by the manufacturer) into the su rrounding water. Student s observe the increasing color in ten s ity of the water and are a ble to measure the increasing drug concentration periodicall y u si ng a spectro photometer After calculating the mas s of drug released at any time t the y plot a release profile They mu s t calculate by material bal-Conve nti onal a nce the ma ss of drug remaining in the lozenge at any time They are also able to compare their data to a model after evalu ating a s ingle parameter in the model. Through this experiment, s tudents are exposed to the excit ing field of drug delivery and are introduced to some basic prin ci pl es of chemical engineering. They perform a calibra tion that enables them to determine the concentration of drug in their samp les A spreadsheet i s u sed to perform calculations nece ssary to determine the release profile and a plot of the release profile of drug from their lozenge is created. Finally they evaluate what i s needed to apply a model to their sys tem and the y compare their experimental release profile to that described by the model. The experiment begin s with a short lecture of drug delivery in which students are introduced to the two main objectives to drug delivery : drug targetin g ( to deliver a drug to the desired location in the body ), and controlled release (to deliver a drug at a de sire d rate for a desired length of time). These two objec tive s are illustrated through familiar examples of drug-deliv ery sys tem s, and the important role of chemical engineers in designing drug-deli very syste m s is explained to the students. The release mechanism of three commercial drug-delivery syste m s are explored in th e lecture: enteric coated a s pirin Efidac 24-hour-nasal decongestant and Contac 12-hour cold capsules. The experiment explores drug release from an a nalge s ic throat lozen ge. The objective of drug targeting is illu s trated by enteric-coated aspirin which accomplishes a drug targeting objective by avoiding di sso lution of the as pirin in the stomach where it can ca u se irritation The enteric coating (s uch as hydroxypropyl methylcellulose or methacryli c acid copolymer) is s pecifically designed to prevent di sso lution in the low pH of the stomach, so that the aspirin tablet pa sses intact to the intes tine In the more neutral environment of the intes Controlled Release tine the coating dissolves, allowing the aspirin to dissolve as well. The absorption of drugs in the small intestine i s u s ually quite good due to the large C 0 I i g e 8 2 C ---------, I Figure 1. A com parison of system i c drug profiles estab li s h ed by co nventional administration and co ntroll e d release. Summer 2002 s urface area available. The function of the enteric coating i s illustrated by placing one enteric coated aspirin tablet in an environment s imulating the stomac h ( h y drochloric acid pH 2), and another en teric-coated aspirin tablet in an environment simu lating the intestine (sodium hydroxide pH 8). Stu dents see that within about thirty seconds the tablet in the intestine environment has begun to dissolve while the tablet in the sto mach environment remains intact. Within a couple of minutes the tablet in the intestine has essentially disintegrated, but the other tablet remains completely unchanged for the entire class period (and for several weeks thereafter). The seco nd objective of drug delivery or con trolled release ( or the release of a drug at a desired rate for a de s ired time) i s illustrated through farnil199

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iar controlled-release products such as Contac 12-hour cold cap sules and Efidac 24-hour nasal decongestants. Contac is a mem brane-based controlled-release system, and Efidac is an oral osmotic (OROS ) pump device Both mechanisms of controlled release are explained to the students, and a brief description of each is included here. For more details the reader is referred to a comprehensive text on drug delivery such as Robinson and Lee[ 5 l or Mathiowitz. [ G J Contac is a capsule that contains many tiny beads of different colors. Each bead contains the drug in a core region that is surrounded by a coating material. While the coating material is biodegradable, the rate at which it degrades is slow com pared with the rate at which the drug is released through the coating ma terial. Hence, the coating controls the drug s rate of release and is therefore considered a rate-control ling membrane. Some beads have coatings that allow rapid release of the drug for immediate relief of cold symptoms. Some coatings allow release at an intermediate rate, and others effect a slow diffusion rate for extended release providing re lief for up to twelve hours treat anxiety, depression, and insomnia), anti-psychotic drugs, antiflammatory agents, and anticholinergic agents used to treat Parkinson disease LOZENGE DISSOLUTION The rate at which a lozenge dissolves is important because it is directly related to the rate at which the active drug is delivered to the body or the specified target site. If the target site is the throat Reservoir Osmotic sleeve Semipermeable membrane as is the case with a topical anaesthetic, fast dissolution could result in the drug being "lost" if it were swallowed before acting to numb the irritated throat. Drug formulations can be engineered to dis solve at the desired rate. In this ex periment, we investigate the dissolu tion rate of a lozenge. When placed in water (or in the mouth), the lozenge becomes smaller as it dissolves from the surface into the water. A mathematical model can be de veloped to express the amount of drug released as a function of time, in terms of quantities that can be measured experi mentally. We begin with a rate expression for the dissolution rate of the lozenge (1) The osmotic pump developed by Alza exploits osmosis to achieve a constant drug-release rate for an Figure 2. The osmotic pump Adapted from Robinson and Lee .1 51 d: =-kaA(C s -C a q) where M is the mass of drug remaining in the lozenge (mg), tis time (s), k is the extended time. This technology has been applied to implant systems for delivery of drugs for treatment of diseases such as Parkinson s and Alzheimer's, cancer, diabetes, and cardiovas cular disorders. Efidac 24-hour nasal decongestants are an ex ample of an oral system that uses the same technology. The osmotic pump comprises three concentric layers: an in nermost drug reservoir contained within an impermeable mem brane an osmotic solution and a rigid outer layer of a rate controlling semipermeable membrane (see Figure 2). As wa ter from the body permeates through the outermost membrane and into the osmotic "sleeve,", the sleeve expands and com presses the innermost drug reservoir, squeezing the drug out of the reservoir through a delivery portaJ. f7 1 The experiment that the students perform uses a lozenge for mulation, and the short introduction to drug delivery concludes with an explanation oflozenge formulations and their applica tions. The most familiar lozenge formulation is used to deliver topical anesthetics to relieve sore throat pain. But lozenges are also an important formulation used to deliver a wide range of very powerful drugs used to treat very serious ailments, such as cancer and AIDS. These include pain relief medication an tifungal agents, central nervous system depressants (used to 200 mass transfer coefficient (emfs), a is the mass fraction of drug in the lozenge, and A is the surface area of the lozenge (cm 2 ). The lozenge is a sugar-based matrix, and its rate of dissolution is proportional to the concentration driving force across a boundary layer in the liquid adjacent to the solid matrix. The concentration difference is assumed to be C C where C is the saturation concentration of sugar s aq s in water and C is the concentration of sugar in the bulk waq ter. c .q is assumed to be negligible since the solubility of sucrose in water at 25 C is 674 g/L 8 while the maximum su crose concentration from a completely dissolved cough drop of pure sucrose would be 46 g/L in this experiment. The shape of the lozenge is approximated as a cylinder, and the surface area can therefore be expressed in terms of radius rand height h: A= 21tr 2 + 2rcrh (2) To simplify the model solution and analysis, the area of the sides ( 21trh) was neglected. The mass of drug remaining in the lozenge can similarly be represented in terms of r: 2 M -M rcr h o-1trJh (3) where M 0 is the amount of drug present in the lozenge iniChemi c al En g in ee rin g Edu c ation

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tially (known) and r 0 is the radius of the lozenge initially. Combining Eq. (1-3) and integrating from time Oto time t results in an intermediate expression for the mass of drug remaining in the lozenge as a function of time: M=M 0 exp[ Ao~:ko:t] (4) A plot of R n (M/M 0 ) vs t should yield a line with a slope of -A 0 Csk/M 0 The amount of drug released from the lozenge, Md, is related to the amount remaining, M, by the material balance Mo =M+Mct (5) Combining Eqs. (4) and (5), an expression for the amount of dissolved drug at time tis obtained by (6) Equation ( 4) is adequate for describing mass transfer in the lozenge system since it provides an expression for the amount of drug remaining in the lozenge but the expression for Md provided by Eq. (6) is more meaningful for two reasons: the amount of released drug is directly related to systemic drug concentrations in the body and the concentration of released drug will be measured in the experiment. In the transport phenomena course where model development is emphasized, this expression for area in Eq. (2) was retained. When it is substituted into Eq. (1), the resulting differential equation contains two time-dependent spatial variables (r and h) that are independent of one another. The equation can be solved by s plitting the equation into two differential equations and so lving each separately. This is an interesting exercise for ad vanced chemical engineering students, but is not necessary to achieve good agreement between the model and the data 0.1 0.09 0.08 0 07 C: .,, 0.06 C: ., ..J g i 0.05 8.s 0 0.04 ,S C: ., ::i: 0.03 0 .02 0 01 0 0 0.05 0.1 0 15 0 2 0 25 0.3 0.35 Absorbance at 540 nm The experiment involves the release of a drug from a lozenge formulation, which is an example of a matrix-type drug-delivery system. EXPERIMENTAL SET-UP The dissolution experiment is simple to implement. Each group is provided with On e magnetic stir plate On e magnetic stirrer On e grad uat ed cy linder One 100-ml beak e r On e cuvette On e dropp e r or Past eu r pip ette On e lo ze ng e (cherry flavor) The beaker is filled with 80 ml of water and placed on a magnetic stir plate. Before the lozenge is introduced, the first sample (t =O ) is taken and analyzed spectrophotometrically to obtain a background reading for the solution. After analysis, the sample liquid is returned to the beaker. The magnetic stir rer and the lozenge are then placed in the beaker the solution is agitated gently, and samp le s are taken at intervals of ap proximately 5 minutes. Similar experimental set-ups have been developed[ 9 10 J to in vestigate mass transfer between a solid and a surrounding liq uid using a dissolving candy The experiment described here introduces the application of mass transfer principles to drug delivery and the measurement of concentration (instead of so lid-m ass determination) in dissolution analysis. CONCENTRATION MEASUREMENT The release profile of the drug, or amount of drug released as a function of time is obtained through indirect measurement of the concentration of dissolved drug in solution as a function of time, using red dye as a marker. The red dye used in the manufacturer's for mulation provides a convenient method of analysis. As the drug dissolves it is released into the surround ing aqueous solution along with the coloring agent present in the lozenge. Since the drug and dye are considered to be evenly distributed throughout the matrix, the dye can be used as a marker for indirect spectrophotometric determination of drug concentra tion present in samples. Students prepare a simple calibration plot using a 0 4 Figure 3. A calibration plot for spectrophotometric determination of menthol concentration. The coloring in the lozenge serves as a marker that is released in proportion to the drug, menthol, as the lozenge dissolves. lozenge (containing a known amount of drug) dis solved in a known amount of water (see Figure 3). The calibration plot ( or calibration equation) can be used to determine drug concentrations of samples taken during the experiment. The amount of drug that has dissolved from the lozenge can be calculated once the menthol concenSummer 2002 201

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tration is determined ANALYSIS Chemical engineers who work on drug formulations are con cerned with obtaining the desired dissolution rate. They must be able to measure the drug dissolution rate and describe the drug dissolution using a mathematical model. The concentrations by the model should match the experimental data To use Eq. (6) to describe the experimental data, the parameter ~= must be evaluated. PARAMETER EVALUATION Equation (6) can be rearranged to (7) O~--------------------, -0 5 -1 i i -1 .5 == .5 -2 -2 5 y=-0 0938x R 2 = 0.9952 -3 +----------~-........ -........ --...---0 5 10 15 20 25 30 35 time(min) (8) In this equation, the term in parentheses represents the frac tion of total drug that remains in the undissolved lozenge. A plot of the left-hand side of the equation as a function of time yields a straight line with a slope of ~, which can be deter mined using the "trendline" feature of Excel. In Figure 4 the slope of -0 0938 (min-') is equal to ~ It is important to em phasize that the parameter is evaluated using experimental data. Students can make this plot by calculating values of the fraction of drug remaining or by generating a sernilog plot. The equivalence of these two methods can be emphasized by having the students make both plots The amount of drug initially contained in the lozenge, M 0 is found on the package label. The Eckerd-brand cough drops used in our laboratory contain 7.6 mg of menthol. COMPARISON OF MODEL TO EXPERIMENTAL DATA After determining the value of ~, Eq. (6) can be used to describe the experimental release data (see Figure 5). Students are asked to observe the agree ment between the model and the data. Freshman stu dents are stepped through the basic steps of the model development testing the validity of the model at short times and at long times They discover that the model predict s M d = 0 fort = 0, and M d = M 0 fort 00 and this is in agreement with common sense. Thus the point is emphasized that models can easily be tested for simple or limiting cases. CONCLUSIONS Figure 4. Parameter evaluation The parameter is determined from the slope of the line This paper describe s a simple experiment that ex poses students to basic principles of drug delivery and chemical engineering The experiment involves the release of a drug from a lozenge formulation, which is an example of a matrix-type drug-delivery system ci .. .; 8 7 6 5 4 3 2 Md (exp!) --Md (model) 0 +----r-------.-----r----,-----1 0 10 20 30 40 50 time (min) Figure 5. Comparison of the experimental release data to that described by the mod e l. 202 Students study the dissolution of a lozenge into water. As the lozenge dissolves, the drug is released (along with a coloring agent) into the surrounding wa ter Students observe the increasing dissolved-drug concentration as reflected by the increasing color in tensity of the water, and they are able to measure the drug concentration spectrophotometrically. They cre ate a calibration plot that enables them to determine the drug concentration from their absorbance measure ment. They perform a material balance to determine the fraction of drug released and perform an experi mental parameter evaluation. Using a spreadsheet, they perform calculations necessary to determine the re lease profile, and they generate plots of both the ex perimental release profile and that described by the Ch e mi c al En g ineerin g Edu c ati o n

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model. Finally, they test the validity of their model for the lim iting cases of initial and long times Through t h is experiment and lecture s tudents are intro d u ced to t h e ro l e that chemical engineer s have in the area of drug delivery and p h armaceutical production. This experi ment h as also been used in senior-level courses s u ch as tran s port p h enomena and as an elective i n drug delivery. Here, s tudents develop their own model compare their experimen tal results to those described by the model and examine the validity of their simplifying as s umptions ACKNOWLEDGMENTS This work was funded through a grant from the National Science Foundation 's Course Curriculum and Laboratory Improvement Program under grant DUE 0126902 REFERENCES 1 E n g in ee rin g Education for a C han gi n g \Vorld joint p ro j ect r epo rt by th e Engi n eeri n g D eans Council a nd Corporate R o undt ab l e of th e American Society for Engineering Education Wa s hingt o n DC ( 1 994) 2. R owan School of Engineering-A Blu ep r int fo r P rogress R owan College ( 1 995) 3. Langer, R ., Foreward to Encyclopedia of Comrol/ed Drug D elivery Vol. I, Edith Mathiowitz ed., John Wil ey and S o n s, New York NY (1999) 4. Van-Arnum P Dru g Deli very Market P o i se d for Fi ve Years of Strong Growth ," Chem Market R e porter, 258 (23), p 1 6 (2000) 5. R o bin so n J. and V. L ee, eds, Con tr olled Drug D elivery F u11d ame111als and Ap plications 2 nd e d ., Marce l D e kk e r New Y ork, NY ( 1 987) 6 Mathiow i t z, E Encyclopedia of Dru g D e li very, V o l. 2 John Wile y and Sons, New York, NY (I 999) 7. Theeuwes, F., and S.I Yum Prin c ipl es of th e D esig n and Op era ti o n of Generic O s motic Pump s for the Delivery of Semisolid or Liquid Dru g Fonnulations ," A nn Bi o m ed Eng. 4 ( 4 ), p 343 (1976) 8. Bubnik Z ., and P K a dl ec, in Sucrose Pr opert i es and Applicat i ons, M. Mathlouthi and P R e i ser, e d s., Aspen Publi s her s, In c New York NY ( 1 995) 9 F rase r D .M., Introducin g Studen ts to B as i c C hE Concepts: Four Simple Experi ments," C h e m Eng Ed., 33 (3), (1999) I 0. Sensel, M.E. and K.J Myer s, Add Some Flavorto Yo ur Agita ti o n Experiment s," Chem. Eng. Ed., 26 156 (1992) 0 Process Simulation Continuted from pa ge 197. 4. L ewi n D R ., W.D S e ider J.D. S eader, E D assa u J. Golbert, G. Zaiats D. Schweitzer, an d D G o ldber g, Using Proc ess Simu l ators in Chemical Eng in ee r in g: A Multimedia Guid e for the Core Curri c ulum, John Wil ey an d Sons, In c New Y o rk NY (200 I ) 5. Lewin D.R. W.D Seider and J.D. S eader, Teaching Pr ocess D esign : An Int e gra t e d Approach AIChE P a per 63d 2000 AIChE Annual Meetin g, Los Ange l es, CA 6. L.G Richard s and S. Carson-Ska l ak, F ac ulty Reactions to Tea c hin g En g ine e ing De s ign to First Year Students ," J of Engg. Ed. 86 (3), p 233 ( 199 7) 7. ASME, Inn ova tion s i n E11 g in ee rin g D es i g n Education: Resourc e Guid e, Ameri c an Society of Mechanical Engin ee r s, New York NY ( 1993) 8. Kin g, R.H. T.E Parker T P Grover, J P Go s ink and N T. Midd l eton, "A Multidi sc iplinary Engineering Laboratory Course, J of Engg. Ed. 88( 3), p 311 ( 1999 ) 9. Courter S S ., S.B. Millar a nd L. L yo n s, "Fro m the Students 's Point of View: Experiences in a Fre s hman Engineering D es i g n Course, J. of En gg. Ed. 87 (3), p 283 ( 1 998) 10 E11 g in ee rin g Criteria 2000: CriteriaforA cc rediting Pr ogra m s in Engineering in Summ e r 2002 th e U11ited States, 3rd ed., E n g in eer in g Acc r ed it a ti o n Commission, Accreditation Board for E n gi neerin g and Technology In c Baltimore, MD ( I 999) < http :// www abe t .o r g/eac/eac. htm > 11 Wankat Phillip C. Equilibri umStaged Separations Prenti ce -H a ll Upper Saddl e Ri ver NJ(l988) 1 2. H e n so n Michael A., and You gc hun Zhang, Int egra tion o f Commercial Dynamic Simulator s into th e U nd e r grad u ate Pr ocess Control Curriculum. Pro c. of th e A / Ch An. Meet. L os Angeles CA (20 00) I 3. Clough, D avi d E., "Usi n g P rocess Simulator s with Dynamic s/ Control Capabili ties to Teach Uni t and Plantwide Co ntr ol Strategies Pro c of th e A/Ch An Meet. Lo s Angeles CA (2000) 1 4. Foss A S K R Guerts, P.J. Goodeve K.D. Dahm, G. Stephanopoulos J. Bie szcza d and A. K o ul ouris A Ph enomenaOri en ted Environment for Tea c ing Pr ocess Modeling: Nove l Modeling Software and I ts Use in Probl em Solv ing, Chem. Engg. Ed., 33 ( 4) (1999) 15 Kant or J eff r ey C., and Th o m as F. Edgar "Co mputin g Skill s in the Chemical Engineering Curriculum ," Computers in Ch, CACHE Corp. ( 1996 ) 1 6. 1 7. Silverstein D T e mpl ate -B ased P rogramming in Chemical Engineering Courses, Pr oc. of the 2001 ASE An. Conf. a nd Expo. Albuquerque, NM (200 1 ) 1 8. He sketh, R P ., and C.S. Slater "Us ing a Cogeneration Facility to Illu stra te Engi neering Practice to L owe r Level Students, Chem. Engg. Ed., 33 (4), p. 3 1 6( 1999 ) 1 9. Bailie, R.C ., J.A. Shaeiwitz and W.B. Whitin g, "An Integrated De s ign Sequence Chem. Engg. Ed. 28 ( I ), p 52(1994) 20 Woods D R. P rob l emB ased Leaming: H ow to Gain the Most from PBL, W.L. Griffin Printin g Limited Hamil ton Ontario Canada (1994) 2 1. Gatehou se, R o n a ld J. George J Selembo, Jr. a nd John R Mc Whirler Th e Ver tical Int egratio n of De s i g n in C hemical E n g in eering, Session 22 13 Pr oc. of the 1 999 ASE An Conf. and Expo. ( 1 999) 22. Shaeiwitz, J. A. "C h emica l Eng in ee rin g D esign Pro jects," 23. Hirt D o u g l as Int egra tin g D esig n Throughout the ChE Curriculum: Lessons Learned ," C h em E11 gg. Ed., 32 (4), p. 290( 1 998) 24. Felder R.M. and R.W. R o u ssea u Eleme nt ary Prin cip l es of Chemical Pr ocesses, 3rd Ed. John Wile y & Sons, In c. New York NY ( 1999 ) 25 Montgomery, S 'T h e Multimedia E du ca ti o n a l Laborator y," 26. Himmelbl a u D M Basi c P rinciples and Calcu l a t ions in Chemical Engineering, 6 th Ed Prentice Hall PTR Uppe r Saddle Ri ve r N J ( 19 96) 27. Wankat P C. R P H eske th K.H Schulz and C.S. Slater, Separations What to Teach Undergrad u ates." Chem. Engg. Ed ., 28 (1), (1994) 28. Seader, J.D ., and E.J. H e nl ey, Separation Pro cess Prin c iples John Wile y & Son s, In c., New York NY (I 998) 29 C hittur Krishnan K., Int egrat i on of A s penplu s (and Other Computer Too l s) int o th e Undergraduate Chemical E n g in ee rin g Curriculum ," 1998 ASEE An Conf. Session 3613. ( 199 8) 30. Kinn se, Dale ASPEN PLUS Virtual Library 3 1. Elliott J R ., and C.T. Lira flllr od u ctory C h e mi ca l Engineering Th e nn o d yn am i cs Pr e ntic e Hall Upper S add l e Ri ve r NJ ( 1999 ) 32. Sandler, Stanley l. C h e mi cal and E n gineeri n g Thennod y nami cs, John Wiley and Sons, New York NY ( 1 977) 33 Smith J.M ., and H C. VanNess lm rod u c tion to Chemi c al Engineering Thenno dynami cs, 3r d Ed., McGraw-Hill New York, NY (1975) 34. Engineering D a t a B ook, 10th Ed., Gas Pro cesso r s Suppliers A ssoc iation Tul sa OK ( 1 987) 35. P erry's Che mi ca l Engineer s' H andbook, R H Perry and D.W Green eds 7th Ed. McGraw Hill New Y o rk NY ( I 997) 36. Fogler H Scott Elements of C h e mi ca l R eactio n En g ineering 3rd Ed. Prentic e Hall PTR Upper S a ddle River NJ ( 1999 ) 37 Hesketh R P. 'Incorporatin g R eac tor D es ign Pr o jects into the Cour se," Paper l 49e 1999 An AIChE Meet. Dal l as, TX ( 1999) 38. Seader J.D Warren D S e id e r a nd Daniel R. Lewin, Coordinatin g Equilib rium-Ba se d and Rate-Ba se d Separ a tion s Cour ses with th e Senior Proce ss Design Course, Se ss ion 3613, P roc. of th e 1 998ASEEA n Conf. and Expo. ( 1998) 39. HYSYS Pro g ramm ab ilit y/Exte n s ibilit y ( OLE ) Example s (200 I ) 40. Cutlip M.B., and M. Shacham Pr ob l em So l v in g in Chemical En gi n ee rin g w ith N um e ri cal M e th ods, Prenti ce H a ll PTR Upper S a ddle River NJ (1999 ) 0 20 3

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Random Thoughts ... FAQS. V. DESIGNING FAIR TESTS[ 1 1 RICHARD M FELDER AND REBECCA BRENT North Carolina State University Raleigh, NC 27695 T he subject that sets off the most heated discussions in our workshops is testing. When we suggest giving tests that can be finished in the allotted time by most of the students contain only material covered in lectures or assign ments, involve no unfamiliar or tricky solution methods, and have average grades in the 70-75 range a few participants always leap up to raise objections: I. What's wrong with tests that only the best students have time to finish? Engineers constantly have to face deadlines; besides, if you really understand course material you should be able to solve problems quickly. 2. Why do I have to teach everything on the test? We shouldn't spoon-feed the students -the y need to learn to think for themselves! 3. If I curve grades, what difference does it make if my averages are in the 50's ? Let's consider these questions, starting with the first one. One problem with long tests is that students have different learning and test-taking stylesP 1 Some ("intuitors") tend to work quickly and are not inclined to check their calculations even if they have enough time. Fortunately for them, their style doesn't hurt them too badly on tests: they are usually fast enough to finish and their careless mistakes only lead to minor point deductions. Others ("sensors") are characteristi cally methodical and tend to go over their calculations ex haustively. They may understand the material just as well as the intuitors do, but their painstaking way of working often lead s to their failing exams they could have passed with fly ing colors if they had more time. Being methodical and careful is not exactly a liability in an engineer, and sensors are every bit as likely as intuitors to succeed in engineering careers. (Frankly we would prefer them to design the bridges we drive across and the planes we fly in even if their insistence on checking their results re peatedly slows them down compared to the intuitors.) Stud ies have shown, however, that sensors tend to get signifi cantly lower grades than intuitors in engineering coursesc 21 and that minimizing speed as a factor in test performance may help level the playing field. c 3 J Tests that are too long thus discriminate against some stu dents on the basis of an attribute that has little to do with conceptual understanding or aptitude for engineering. (True, engineers have deadlines, but not on a time scale of minutes for the types of problems on most engineering exams.) More over, while overlong tests inevitably frustrate and demoral ize students, there is not a scrap of research evidence that they either predict professional success or help students to become better or faster problem solvers. Richard M. Felder is Hoechst Celanese Pro fessor Emeritus of Chemical Engineering at North Carolina State University. He received his BChE from City College of CUNY and his PhD from Princeton He is coauthor of the text El ementary Principles of Chemical Processes (Wiley, 2000) and codirector of the ASEE Na tional Effective Teaching Institute Rebecca Brent is an education consultant spe cializing in faculty development for effective uni versity teaching, classroom and computer based simulations in teacher education and K12 staff development in language arts and class room management. She co-directs the SUC CEED Coalition faculty development program and has published articles on a variety of topics including writing in undergraduate courses co operative learning public school reform, and effective university teaching. Copyright ChE Division of ASEE 2002 204 Chemical Engin eer ing Education

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How long is too long? Unless problems are trivial, students need time to stop and think about how to solve them while the author of the problems does not. A well-known rule-of thumb is that if a test involves quantitative problem solving, the author should be able to work out the test in less than one-third of the time the students have to do it (and less than one-fourth or one-fifth if particularly complex or computa tion-heavy problems are included) If a test fails to meet this criterion, it should be shortened by eliminating some ques tions giving some formulas instead of requiring their deriva tions, or asking for some solution outlines rather than requir ing all the algebra and arithmetic to be worked out in detail. How about those problems with unfamiliar twists that sup posedly show whether the students can think independently? The logic here is questionable, to say the least. Figuring out a new way to tackle a quantitative problem on a time-limited test reflects puzzle-solving ability as much as anything else If tricky problems count for more than about 10-15% of a test, the good puzzle-solvers will get high grades and the poor ones will get low grades, even if they understand the course content quite well. This outcome is unfair. But (a workshop participant protests) shouldn't engineer ing students learn to think for themselves ? Of course, but people learn through practice and feedback, period; no one has ever demonstrated that testing unpracticed skills teaches anyone anything.Therefore, there shou ld be no surprises on tests: no content should appear that the students could not have anticipated, no skill tested that has not been taught and repeatedly practiced To equip students to solve problem s that require say, critical or creative thinking try working through one or two such problems in class, then put several more on homework assignments, and then put one on the test. If for some reason you want students to be faster problem solvers, give speed drills in class and on assignments and then give longer tests. The test grades will be higher-not because you're lowering standards, but because you're teaching the students the skills you want them to have (which is after all, what teachers are supposed to do ). Finally, what's wrong with a test on which the average grade is 55 especially if the grades are curved? It is that given the hurdles students have to jump over to matriculate in engi neering and survive the freshman year, an entire engineer ing class is unlikely to be incompetent enough to deserve a fai lin g average grade on a fair test. If most students in a class can only work out half of a test correctly, it is prob ably because the test was poorly designed (too long, too tricky) or the instructor didn t do a good job of teaching the nece ssa ry skills. Either way, there's a problem One way to make tests fair without sacrificing their rigor is to post a detailed study guide before each one. The guide should include statements of every type of question that might show up on the test, especially the types that require high level thinking skills. r 4 J The statements should begin with ob servable action words (explain, identify, calculate, derive, design, formulate, evaluate, . ) and not vague terms such as know, learn, understand, or appreciate. (You wouldn't ask st udent s to understand so mething on a test-you would ask them to do something to demonstrate their understand ing.) A typical study guide for a mid-semester test might be between one and two pages long, single-spaced. Draw ing from the study guides when planning lectures and as sig nments and constructing tests makes the course both coherent and effective. Peter Elbow observes that faculty members have two con flicting functions-gatekeeper and coach.15 1 As gatekeepers we set high standards to assure that our students are qualified for professional practice by the time they graduate, and as coaches we do everything we can to help them meet and sur pass tho se standards Tests are at the heart of both functions. We fulfill the gatekeeper role by making our tests compre hensive and rigorous and we satisfy our mission as coaches by ensuring that the tests are fair and doing our best to pre pare our students for them The suggestions given in this pa per and its predecessor r 11 address both sets of goals. Adopt ing them may take some effort, but it is hard to imagine an effort more important for both our s tudents and the profes s ion s they will serve. REFERENCES 1 This column i s based on R M. Felder, Designing Tests to Maximize Learning ," J Prof Issu es in Eng,: Education & Practice, 128(1) 1-3 (2002). Available at . 2. R.M. Felder, Reachin g th e Second Tier: Leaming and Teaching Styles in College Science Education ," J College Science Tea c hin g, 23(5) 286-290 (1993). Available at . 3. R.M. Felder, G.N. Felder a nd E.J. Dietz, "T he Effects of Personality Type on Engineering Student Performanc e and Attitudes," J Engr. Education 9 I (1 ), 3-17 (2002). Available at . 4. R .M Felder and R. Brent "Objectively Speaking," Chemical Engi neering Education, 31(3) 178 -179 (1997). Avai labl e at . Illu s trative s tud y g uide s ma y be fou nd at S P. Elbnw, Embracing Contraries: Explorations in Learning and Teach in g, New York Oxford University Press, 1986. All of the Random Thoughts columns are now available on the World Wide Web at http://www.ncsu.edu/effective_teaching and at http://che.ufl edu/~cee/ Summer2002 205

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Mb class and home problems ) The object of this column is to enhance our readers collections of interesting and novel prob lems in chemical engineering Problem s of the type that can be used to motivate the student by pre s enting a particular principle in class, or in a new light, or that can be a s signed a s a novel home pro b lem are requested a s well as those that are more traditional in nature and that elucidate difficult concepts. Manuscript s should not exceed ten double spaced page s if pos s ible and s hould be accompanied by the originals of any figures or photographs Please s ubmit them to Professor James 0 Wilkes (e-mail: wilkes@umich.edu) Chemical Engineering Department University of Michigan, Ann Arbor, MI 48109 2136 BOILING-LIQUID EXPANDING-VAPOR EXPLOSION (BLEVE) An Introduction to Consequence and Vulnerability Analysis C TELLEZ, J .A P ENA Univ e rsity of Zarago z a Zaragoza, Spain T he chemical engineering curriculum sho u ld include information on safety health and loss prevention in the c h emical industries. r 1 41 A specia l sensitivity ha s developed in the industry as a result of the real possibility of accidents of catastrophic proportions such as The Flixborough accident ( 1974) at the Nypro plant in the United Kingdom wh e n an un c onfined vapor cloud explosion of cyclohexan e r e sult e d in 28 deaths and hundreds of injuri e s. The Sevesso (Ital y 1976) accident where a runawa y reaction caused toxic emission s of dioxin and meth y l iso cy nate that caus e d animal d e aths dried vegetation, and affected 2000 p e opl e The Bophal ( India 1984) accident which is the greatest industria l disaster in the world to dat e with about 2,500 deaths and between 100,000 and 250 000 injuries. Th e Mexi c o (1984) a cc id e nt at St J I x huatepec wher e a BLEVE (Boiling Liquid Expanding Vapor Explo sion) of a storage tank of LPG produc e d more than 500 deaths and 4 500 injuries After the Sevesso accident, developed countrie s established compulsory l egislation regulating declaration s of ri s k by in dustry ,[5 1 deve l oped emergency plans inside plants and i n the surrounding areas and created coordinating organizations for emerge n cy events In the European community the Sevesso I (formerly) and the Sevesso II (currently) d irectives cover Carlos T e llez received his P h D in 1998 at the U niversity of Zaragoza wh e r e he is currently Assistant Professor teachi ng c h e m ica l engi neering fundamentals H is r esearch is focused o n f un damental studies in t h e preparation of zeolite membra n es a n d i n or g a n ic me m bra n es for pervaporation and gas se p a r ation Jose Angel Pen a is Associate P rofessor of Ch e mi ca l En gi n ee r i n g at t h e Un iversity of Zaragoza. H is r esea r c h in te r ests in c lud e d ve l opmen t of n ew m ethods for hydroge n stor age and t r a n s p ort, development of a new sys te m of i n dicators to estimate t h e risk of major accidents i n vo l vi n g chemical reactors and im p roved syste m s for early detection of r un away reac t io n s Copyr i ght C h E Di v i s ion of ASEE 2002 20 6 C h e mi c al En g in ee rin g Edu c ati o n

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Universities should act as a mirror for society, and during the past few decades the chemical engineering curriculum has made an effort to develop awareness of safety, health, and loss prevention, but there is still a need for greater awareness. such actions, while in the United States legislation has re quired development of both external and internal emergency plans. OSHA has published laws regarding industrial health and safety for the last thirty years, while other federal agen cies, such as EPA, DOE, DOT and associations such as API and AIChE have developed their own legislation and codes for good practice Universities should act as a mirror for society and during the past few decades the chemical engineering curriculum has made an effort to develop awareness of safety, health, and loss prevention but there is still a need for greater aware ness. The Center for Chemical Process Safety (CCPS), cre ated in 1985 is an industry-driven center affiliated with the American Institute of Chemical Engineers (AIChE) that ini tiated a close relationship with engineering schools in 1992 by creating the Safety and Chemical Engineering Education program (SACHE). It provides teaching materials and pro grams that bring elements of process safety into the curricu lum . The AIChE and the Institution of Chemical Engineers in the United Kingdom also provide a variety of safety courses for the chemical engineering curriculum. In Spain a legislative article (R.D 923/92) of the year 1992, established a degree of chemical engineering, and while some subjects on health and safety were included as obligatory it is clearly insufficient. To increase knowledge of safety during the undergraduate years of chemical engineering several solutions have been proposed in the U.SJ 6 7 1 The first proposal is to introduce an obligatory safety course, but that would increase the length of the curriculum and would be difficult for departments and ABET to agree upon. A second possibility already incorpo rated in some programs is to include safety courses as elec tives for undergraduates. The third proposal, perhaps more useful and easier to incorporate is to give the students small pills" of safety during their studies. One useful pill for show ing students how to improve the safety of a process is the so called "risk analysis." This technique gives a quantitative estimation of the risk involved in a given process. In Spain, some knowledge of risk has been included as obligatory as a part of some courses on safety and/or health, and some universities have this program separated as elec tive options. For example, the University of Zaragoza has an elective course titled "Analysis and Risk Reduction in the Chemical Industry." The objective of this article is to familiarize the student Summer 2002 with risk analysis The case selected for this is a boiling liquid expanding-vapor explosion (BLEVE) of a tank truck of liquid propane. A brief introduction to consequence and vulnerability analysis models is included. BRIEF DESCRIPTION OF THE CASE A tank truck of 50 m 3 containing 19,000 kg of liquefied propane under its vapor pressure was discharging inside a factory. Due to unknown reasons, the tank developed a leak and propane gas discharged into the atmosphere. About five minutes later some propane and oxygen (from the atmo sphere) produced a mixture within the LFL (lower flamma bility limit) and the UFL (upper flammability limit). An un known ignition source produced a weak explosion and started a fire close to the tank. The heat flux coming from the fire increased the temperature of the tank wall and the liquid pro pane within it. The liquid propane tracked its boiling point curve (p 0 vs T), substantially increasing the pressure in the tank. As a consequence, the tank ruptured catastrophically. This kind of phenomenon is a BLEVE (Boiling -Liquid Expanding-Vapor Explosion) At the moment of the acci dent, the ambient temperature was 36 C and the atmo spheric pressure and relative humidity were 760 mm Hg and 41 %, respectively The students should Use consequence anal y sis models to study the possibility of a BLEVE occurrence and its effects (fireball radiation, damage due to overpressure) on the surrounding area. Use the Probit methodology for vulnerability anal y sis to speculate on the percentage of victims (deaths, injuries, etc.)for a given area. INTRODUCTION TO CONSEQUENCE ANALYSIS MODELS STAGE 1 Is It Possible for a BLEVE to Take Place? A BLEVE is the worst possible outcome when an LPG tank is exposed to fire. The possibility of a BLEVE occuring can be checked by using Reid's "massive nucleation theory."l 91 This theory is based on the phenomenon of "spontaneous nucleation" that consists of a massive instantaneous forma tion of tiny bubbles within the liquid mass caused by a sud den depressurization of the vessel contents When this phe nomenon takes place, the possibility of a BLEVE occurs. 207

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The zone of spontaneous nucleation can be seen in the pressure vs. temperature diagram shown in Figure 1. It represents the liquid-gas equilibrium as mathematically described by the appropriate Antoine equation for the ma terial being used (e.g., propane) (The equilibrium rela tionship as well as the critical temperature and pressure for such material can be obtained from the literature. 18 1 ) From the critical point (e.g., the critical temperature and pressure), a tangent line to the p 0 -vs.-T curve must be traced up to a point where the ordinate represents the atmospheric pressure. The squared dot in Figure 1 shows the condi tions inside the tank before the fire engulfment. As de scribed by the Reid theory, every point located to the right of this imaginary vertical line (dashed and arrowed) that connects the above described tangent line at atmospheric pressure, is a suitable scenario for a BLEVE. This means that when the tank is exposed to a fire, the heat coming from it will increase the temperture (and correspondingly the pressure) inside the vessel, and the original conditions will begin to ascend following the p 0 vs.-T curve. This progressive heating will lead to a point where the above mentioned vertical line will be trespassed. Once thi s con dition has been achieved a sudden rupture of the vessel would lead to a BLEVE because of the sudden depresurization. STAGE2 Mathematical Models that Describe the Effects of BLEVEs The literature describes three types of BLEVE effects: the s hock wave (overpressure effects) the thermal radia tion, and the fragment projection. This paper focuses on the shock wave and thermal effects as the main events in a BLEVE scenario. Thermal Effects The thermal effects of a BLEVE are related to radiation coming from the fireball They are usu ally accounted for through empirical equations related to the quantity of substa nc e involved in the BLEVE. Table 1 shows expressions that have been proposed by different authors to calculate the maximum diameter of the fireball Dm a Jm] the duration of the fireball, ~L EVE [s] and the height at the center of the fireball, H 8 L Ev im] as well as the re sults obtained with them for the given case. The flow of radiation per unit of ernissive surface area and time (I) in kW/m 2 can be calculated using CCPS [JO] FR (-6H co mb )M I--~-=---~1t(Dma ,)2 tBL EVE Elia model [ 121 208 (1) 0.27 M(-6H co mb )P3 .3 2 l= ----~--n(Dm ax )2tBLEVE (2) Pape et al., model[ 1 3 1 I= 235 P 39 (3) where FR is defined as the ratio between the energy emitted by radiation and the total energy released by the combustion (the s uggested value as s tated in the literature 1 1 01 ranges from 0 25 to 0.4); -~H co m b is the heat of combustion of the material [kJ/kg]; P 0 is the initial pressure at which the liquid is stored [MPa] ; and P v is the vapor pressure of the stored liquid [MPa]. 45 Critical Point 41 (T,, PJ 37 E 33 :. 29 I 25 ( I ::, "' (/) (/) 21 I I Cl.. T=36 5 C I 17 ----~ P=12 5 atm 13 ~ I 9 ~ / Spont neous nucleation ~, 5 r., r 250 300 350 400 Temperature (K) Figure 1. Vapor pressure vs. temperature diagram showing the zone of spontaneous nucleation for propane, as described by Reid's Theory J 91 TABLE 1 Fireball Characteristic Parameters as Calculated by Different Authors (M) Initial Mass of Flammable Liquid [kg] (D m,x) = maximum diameter of the fireball [m] (HBLEVE) = height at the center of fireball [ml (~LE VE ) = duration of fireball [s] CCPS 1 101 D m., = 6.48 M 0325 = 159 .3 m tBL EVE = 0.825 M 0 26 = 10.7 s H BLEVE = 0 75 D MAX = I 19.5 m CCPS 1 191 D \,,, = 5.8 M 1 13 = 154.8 m t \LEVE = 0.45 M 13 = 12 s TABLE2 Flow of Radiation Per Unit of Surface Area and Time (I) for Different Models CCPS Model 1 01 Elia Model 1 12 1 Pape, et al Mode/ 1 131 I(kW/m 2 ) 336 301 306 Chemical Engineering Education

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Typical radiation values of fireballs associated with BLEVEs are quoted in the range of200 to 350 kW/m 2 Taking a value of FR= 0 .32 5, the heat of combustion from reference 14 and the pressure inside the tank (1976 kPa) calculated as the vapor pres sure of liquid propane at its superheat temperature (331 K using a Redlich-Kwong EOS approximation), the results are shown in Table 2 The value is inside the typical range for BLEVEs and close to the values reported by CCPS [ 101 (350 kW/m 2 ) for the intensity of radiation emitted by propane in BLEVE experiments. The radiation received by a s urface at a distance X from the emitting point can be calculated once the geometric view factor (F vg ) and the fraction of energy transmitted (atmospheric tran missivity 't) are known: (4) In this respect, when considering the vulnerability of people to the effects of a BLEVE, it is appropriate to use a geometric view factor corresponding to a surface perpendicular to a sphere: D2 F =vg 4 x2 (5) Considering only the partial pressure of water present in the atmosphere at the moment of the accident, 't can be calculated approximately by[ 201 (6) where P w is the partial pressure of the water at ambient tem perature [Pa]. Another simpler, model has been proposed by Roberts [ 11 1 where the intensity of radiation received by a surface at a di tance Xis given by an expression depending only on the ma ss of fuel: I _rr. 10000 1000 100 10 s -CCPS Model [10] El i a Model [12] Pape et al. Model [13] -Roberts Model [11] 0 1 .......... --~~~~~........_ __ ......., 10 100 1000 Distance (m) (7) Figure 2. Radiation received by a vertical surface as a function of distance Summer 2002 Overpressure Effects Overpre ss ures are difficult to pre dict in the event of a BLEVE The vaporization and pres s urization prior to the receptacle's collapse, and the dura tion of the rupture-depressurization, is extremely difficult to quantify. Experiments with explosives have demon strated that the overpressure can be estimated using an equivalent mass of TNT. An approximate way to calculate the equivalent weight of TNT (WT NT ) for a BLEVE has been de scri bed by Prugh [ 15 1 as PY ( I 1-k [ k-1 l WTNT =0.024 k-1 1 -lP) (8) where P is the pressure existing in the receptacle before the rupture [bar]. V is given as V* = V v + v{f ~: J (9) where V v and V 1 are the volumes of vapor and liquid [m 3 ) in the vessel before the explosion; D 1 and D v are the densi ties of liquid and vapor a t the pre ssure and temperature of the sys tem before the explosion; k i s the ratio of Cp and Cv ; and f is the fraction of liquid that flashes after depres surization. This can be calculated by the simple energy balance Cp(T 0 T b) f=~=l-e llH (10) mo where m 0 and m v are the initial mass of liquid and the amount vaporized in the flash, respectively, T 0 is the ini tial temperature, T b is the normal boiling temperature, C P is the heat capacity, and ~H v is the heat of vaporization. Thi s expression to calculate f u s ually gives values on the order of two times smaller than those observed experimen tally, 1 1 61 concluding that a flash fraction well above 20 % might be considered as a total vaporization. To calculate the equivalent TNT mass, the following data can be used: Liquid and vapor density are taken from reference 14 Values for C P (2.64 kJ/kg K) and ~H v (430 kJ/kg) are taken from reference 5 Boiling temperature of propane at atmospheric pressure is 231 K The value off obtained with these data is 0.38. It has been mentioned that a more realistic value of the fraction that flashes is two times the value obtained with Eq. ( 10); there fore, the final estimation off= 0.76 is close to 1. With f equal to 1, the equivalent TNT is 423.6 kg. The TNT model is based on an empirical law established from trials using explosives. I171 This "cubic root law" es209

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tablishes equivalent overpressure effects for explosions oc curring at the same normalized distances, expressed as R z =---( W )l/3 TNT (11) where z is the normalized distance [mkg1 13 ] and R is the real distance [m]. The experimental relation between over pressure and normalized distance for unconfined explo sions can be found in severa l references.r 5 1 81 Figure 3 s hows the overpressure profile along distance for the proposed scenario. INTRODUCTION TO VULNERABILITY ANALYSIS The objective is to calculate the vulnerability to persons or installations expressed as the number of individuals or installations that could possibly be affected to a certain level of injury because of an accident. A possible method for estimating vulnerability consists of relating the dose received with the effect considered. This can be achieved from empirical evidence showing that individuals who have been s ubjected to a certain dose of the injuring agent (e.g., a certain radiation intensity level during a given time) have suffered a particular effect (e.g death by burn). Therefore, the methods that relate causes directly with ef fects are hardly used, and the approximations to the prob lem of estimation of vulnerability generally follow a proba bilistic approach. The Probit scale is a way of dealing with such approximations. The connection between Probit units (Y) and probability (P) is given by u 2 l Y-5 _ P = r,;:; J e 2 du -v2rt (12) The result of this expression is the Pro bit distribution with mean 5 and variance 1 The curve relating percentages and Probit units is shown in Figure 4. Given the characteristics of the Probit variable, the following relationship can be written Y=k 1 +k 2 RnV (13) where Y is the number of Probit units, k 1 and Js are em pirical constants depending on the causative factor and the level of damage to be analyzed, and V measures the inten sity of the damage causative factor. The way in which Vis expressed depends on the type of effect studied. Table 3 shows some values of the empirical constants (k 1 and k 2 ) and the expression related with V. The Pro bit expressions for prediction of the effects pro duced by a given radiation intensity level during a given time use a causative factor, V, proportional to the product HR 41 3 (tis the exposure time and IR is the intensity of radia tion level). Regarding vulnerability to explosions, Vis the 210 100 '" a. 10 :::, 1/) 1/) C.
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overpressure at a given point. Figure 5 shows the percentage of people and installations af fected by different effects and causes. The values of overpres sure and radiation intensity received by a surface at a distance X (Elia model) obtained in the previous section (conseq uence analysis models) were u sed; the exposure time was taken as ~L E V E obtained with the Elia model.[ 1 2 1 Table 4 shows the esti mated distances at which 1 % and 50 % of the population or struc tures can be affected by a given effect. The limit at which 1 % of the population may die is called mortality threshold." CONCLUSIONS Risk analysis of major accidents is a useful tool for future chemical engineers; it gives not only a quantitative estimation of the risk involved in a given process but also a suitable method for estimation of possible victims (environment persons, and 'O Q) E Q) ::, 0 (I) C "' "' Cu, Jg ffi "' > ui c, 5 n, Om !!!t5 "' Q) -5 ti:: :~ 'O 0 5 a; > 0 Q) "' 0 cQ) a. 80 60 40 20 un Glas b[eakage . Second degree .burns First degree burns r rum rupture Mo rtal morrhage 100 200 300 Distance (m) Figure 5. Percentage of people and installations affected by different effects and causes at a given point : overpressure effects (solid line) and thermal effects ( dotted line). TABLE4 Distance at which 1 % and 50% of the Population (People or Objects) are Affected Cause Effect Distance Distance fm/50% {mil % Explo s ion Lung hemorrh a ge 18.8 22.3 Explosion Eardrum rupture 34.4 63 0 Explosion Structural damages 51.6 84.7 Explosion Breakage of glass 162 321 Thermal effects Mortality due to thermal radiation 153 212 Thermal effects Second-degre e bum s < > 222 293 Thermal e ffects First-degree bum s < 2 > 329 436 < 1 > Epidermis and part of the dermi s are burned < 2 > A s uperficial bum in which the top la y er of skin ( part of the epidermis) ha s been sli g htly burned Summer 2002 properties). A boiling-liquid expanding-vapor explosion (BLEVE) of a tank truck of liquid propane has been used to demonstrate this technique and the blast and thermal effects have been calculated with several methods. The vul nerability of persons a nd/or installations affected in both cases has been calculated u sing the Probit methodology. REFERENCES I. Lane AM. Incorporating Health Safety Environmental and Ethi c a l I s sue s into the Curriculum ," Ch e m En g. Ed. 23 70 (1989) 2. Cohen, Y W. T s ai and S Chetty A Course on Multimedia Envi ronmental Transport Expo s ure and Ri s k As s es s ment ," Chem Eng Ed 24 212 (1990 ) 3 Gupt a J.P. A Chemical Plant Safety and Hazard Analysis Course Ch e m. En g Ed., 23 194 (1989) 4 Mannan M.S. A Akgerman R.G. Anthony, R. Darby P.T. Eubank, a nd R K. Hall Integrating Process Safety into the Education and Research ," Chem. En g Ed 33 198 (1999) 5. S a ntamaria, J M ., and P A Brana Ri s k Analysis and Reduction in the Chemical Proce ss Indu s try, Blackie Academic & Profes s ional (1998) 6. Golder A S a fety Relevance in Undergradua t e Education ," SACHE N ews, Sprin g 4 ( 2000 ) 7 Ro s signol, A M and B.H. Hane s, Introducing Occupational Safety and He a lth Material into Engineering C o ur s e s," Eng. Ed. 80 430 (1990) 8 Reid R.C. J.M. Prau s nitz, and B.E. Poling The Properties of Gases and Liquids McGraw-Hill New York NY (1987) 9 Reid R.C. Possible Mechanism for Pressurized-Liquid Tank Ex plo s ions or BLEVEs ," S c i e n ce, 3,203 (1979) I 0 CCPS (Center for Chemical Process Safety), Guidelines for Chemi c al Pro cess Quantitati ve Risk Anal y sis, AIChE, New York NY (1989) 11 Robert s A.F. Therm a l Radiation H az ards from Rele a se of LPG Fire s from Pre ss urized Stora g e ," Fir e Saf ety J ., 4 197 (1982) 12. Elia F. Risk Ass e ssm e nt and Ri s k Mana ge m e nt for the Ch e mical Pr oc es s Indust ry H R. Greenberg and JJ. Cramer ed s Van Nostrand Reinhold New York, NY (1991) 13. Pape, R.P. e t al. Calculation of the Intensity of Thermal Radia tion from Large Fires, Loss. Prev Bull ., 82, l (1988) 14. Perry R.H. and D. Green, ed s P e r ry 's Ch e mi c al Engineer's Hand bo o k 6th ed ., McGraw-Hill New York NY (1984) 15. Prugh R.W. "Quantify BLEVE Hazard s Chem Eng. Prag , 87, 66 ( 1991) 16. Kletz T. Unconfined Vapor Explo s ion s ," Loss Pre v ention 11 Chem. En g Pra g. T ec h Manual AIChE New York NY (1977) 17. Hopkinson B. British Ordnance Board Minutes 13565 (1915) 18. Crowl, D A. and J F. Louvar Ch e mi c al Process Safety: Funda m e ntals with Applications Prentice Hall Englewood Cliffs, NJ (1990) 19. CCPS (Center for Chemical Process Safety): "G uidelin es for Evalu ating the Characteristics of Vapor Cloud Explosions, Flash Fires and BLEVEs AIChE, New York NY (1994) 20 Pietersen C.M ., and S .C Huerta Analysis of the LPG Incident in S a n Juan Ixhuapetec Mexico City 19-11-84 ," TNO Report B40222 TNO Directorate General of Labor 2273 KH Vooburg, Hol land ( 1985) 21. TNO Methods for the Determination of Possible Damage to People and Object s Re s ulting from Relea s e of Hazardous Materi a l s CPR 16E, Vooburg, Holland (1992) 0 211

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.ta ... 5 ... 3._c_l_a_s_s_~_o_o_m ________ __,,,) RUBRIC DEVELOPMENT AND INTER-RATER RELIABILITY ISSUES In Assessing Learning Outcomes JAMES A. NEWELL, KEVIN D. DAHM, AND HEIDI L. NEWELL Rowan University Glassboro NJ 08028 W ith the increased emphasis placed by ABET['l on assessing learning outcomes, many faculty struggle to develop meaningful assessment instru ments. In developing these instruments, the faculty members in the Chemical Engineering Department at Rowan Univer sity wanted to ensure that each instrument addressed the three fundamental program tasks as specified by Diamond: 1 21 [] The basic competencies for all students must be stated in term s that are measurable and demonstrable [] A comprehensive plan must be developed to ensure that basic competencies are learned and reinforced throughout the time the students are enrolled in the institution. [] Each discipline must s pecify learning outcomes congruent with the required competencie s. Like many other institutions 1 Rowan University 's Chemi cal Engineering Department chose to use items that address multiple constituencies including alumni, industry, and the students themselves. Assessment data from these groups were obtained through alumni surveys, student peer-reviews, and employer surveys. These instruments were fairly straightfor ward to design and could be mapped directly to the educa tion objectives specified in Engineering Criteria 2000 (Crite rion 3, A-K) as well as the AIChE requirements and other department-specific goals. Regrettably over-reliance on sur vey data often neglects those most qualified to assess student performance-the faculty themselves. The faculty agreed that student portfolios would provide a valuable means of including faculty input into the process. The difficulty arose when the discussion turned to evaluating the portfolios. Paulson, et al. 41 define portfolios as a "purposeful collection of student work that exhibits the students' efforts, progress and achievement." As Rogers and Williamsf 51 noted, however, there is no single correct way to design a portfolio process. Essentially everyone agreed that a portfolio should contain representative samples of work gathered primarily from juniorand senior-year courses. The ABET educational objectives are summative rather than formative in nature, so 212 the faculty decided to focus on work generated near the end of the student's undergraduate career A variety of assign ments would be required to ensure that all of the diverse cri teria covered in Criterion 3 could be addressed by at least some part of the portfolio At the same time, we were acutely aware that these portfolios would be evaluated every year and were understandably interested in minimizing the total amount of work collected. Ultimately, we selected the following items: [] A report from a year-long, industrialJy sponsored research project through the Junior/Senior Clinics [] The Senior Plant Design final report [] A hazardou s operations (haz op) report [] One final examination from a junior-level chemical engineering class (Reaction Engineering or Heat Transfer) [] One laboratory report from the seniorl eve l Unit Operations Laboratory Course These items were all constructed-response formats[ 6 8 1 in which a student furnished an authentic response to a given assign ment or test question This format was selected over multiple choice selected response formats because it better represents realistic behavior. 191 The selected-response format presents alternative responses from which the student selects the cor rect answer; specific selected response formats include true false matching, or multiple choice exams, while constructed response formats include essay questions or mathematical James Newell is Associate Professor of Chemical Engineering at Rowan Uni vers ity. He is currently Secretary/rreasurer of the Chemical Engineer ing Division of ASEE. His research interests include high performance poly mers outcomes assessment and integrating communication skills through the curriculum. Kevin Dahm is Assistant Professor of Chemical Engineering at Rowan University. He received his PhD in 1998 from Massachusetss Institute of Technology. Before joining the faculty of Rowan Universit y, he served as Adjunct Professor of Chemical Engineering at North Carolina A& T State University Heidi Newell is the Assessment Consultant for the College of Engineering at Rowan University She holds a PhD in Educational Leadership from the University of North Dakota a MS in Industrial / Organizational Psychol ogy from Clemson University, and a BA in Sociology from Bloomsburg University of Pennsylvania Copyright ChE Division of ASEE 2002 Chemical Engineering Education

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problem solving.l 1 0 J Although the items contained in the port folio provided a wide range of work samples they could not be as neatly mapped to the ABET criteria There was simply no way to look at a laboratory report and assign a number evaluating the student's ability to apply math, science and engineering. The immediate question that arose from the fac ulty was "Compared to whom?" A numerical ranking com paring Rowan University s chemical engineering students to undergraduates from other schools may be very different than one comparing students to previous classes. It became clear that specific descriptions of the performance level in each area would be required so that all faculty could understand the difference between a 4 and a 2. As Banta l 11 1 stated, "The challenge for assessment specialists faculty and administra tors is not collecting data but connecting them." The chal lenge became one of developing rubrics that would help map student classroom assignments to the educational objectives of the program. The four-point assessment rubric also fol lowed the format developed by Olds and Miller l 12 1 for evaluating unit operations laboratory reports at the Colo rado School of Mines. COURSE VS PROGRAMMATIC ASSESSMENT Other chemical engineering departments are also develop ing rubrics for other purposes. In their exceptional (and Mar tin-Award winning) paper on developing rubrics for scoring reports in a unit operations lab, Young et a/.,r 1 3 l discuss the development of a criterion-based grading system to clarify expectations to students and to reduce inter-rater variability in grading, based on the ideas developed by Walvoord and AndersonJ 1 4 J This effort represents a significant step forward in course assessment. The goals of course assessment and program assessment are quite different, however For graded assignments to capture the programmatic ob jectives, a daunting set of conditions would have to be met. Specifically, [I Every faculty member must set proper course objectives that arise exclusively from the program s educational objectives and fully encompass all of these objectives [I Tests and other graded assignments must completely capture these objectives [I Performance on exams or assignments must be a direct reflection of the student's abilities and not be influenced by test anxiety, poor test-taking skills etc If all of these conditions are met there should be a direct correlation between student performance in courses and the student's overall learning. Moreover, much of the pedagogi cal research warns of numerous pitfalls associated with us ing evaluative instruments (grades on exams, papers, etc.) within courses as the primary basis for program assessment. 115 l One of the immediate difficulties is that many criteria are blended into the grade. A student with terrific math skills could handle the partial differential equations of transport phenom ena but might never understand bow to apply the model to Summer 2002 practical physical situations. Another student might under stand the physical situation perfectly but struggle with the math. In each case, the student could wind up with a C on an exam but for very different reasons. This is not a problem from the perspective of the evaluation; both students deserve a C. But, from an assessment standpoint, the grade does not provide enough data to indicate areas for programmatic improvement. Moreover, if exams or course grades are used as the pri mary assessment tool, the impact of the entire learning experi ence on the student is entirely ignored f 1 6 l Community activi ties, field trips service projects, speakers, and campus activi ties all help shape the diverse, well-rounded professional with leadership skills that industry seeks. The influence of these non classroom factors cannot be measured by course grades alone. The goal of our rubrics was to map student work directly to the individual learning outcomes. This also put us in a po sition to more directly compare our assessment of student work with the assessment of performance provided by stu dent peer reviews employers, and alumni RUBRIC DEVELOPMENT The first step was to take each educational objective and develop indicators, which are measurable examples of an outcome through phrases that could be answered with yes" or "no." A specific educational objective and indicator is shown below. Goal 1 Obje c tiv e 1 : The Chemical Engineering Program at Rowan Uni ve r s ity will produ ce graduates who demon strate an ability to appl y knowl e dge of mathematics sci enc e and engine e ring (ABET-A) Indicators: I. Formulates appropriate solution strategies 2. Identifies rele v ant principles, equations and data 3 S y stematicall y exe c utes the solution strateg y 4 Applies e ngin e ering judgment to evaluate answers Once the indicators for each objective were developed, the next task involved defining the levels of student achievement. Clearly, the lowest level should be what a novice demon strates when confronted with a problem. The highest level should show metacognition ,l 16 l the students' awareness of their own learning skills, performance, and habits. To achieve the highest level, students not only have to approach the prob lem correctly, but they must also demonstrate an understand ing of their problem-solving strategies and limitations. The intermediate scores represent steps between a metacognitive expert and a novice. It is important to note that the numbers are ordinal rather than cardinal. A score of four does not im ply "twice as good" as a score of two All of the other assessment instruments used by the Chemi cal Engineering Department had a five-point Likert scale, so a faculty team set out to develop meaningful scoring ru brics using a five-point scoring system. Initially, the scores contained labels (5 = excellent, 4 = very good, 3 = good, 2 = marginal, 1 = poor), but the qualitative nature of the descrip2 / J

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ti ve phrases should stand alone, without the need for additional clarifiers. Ultimately, it was decided to eliminate all labels It became apparent that a four-point scale allowed for more meaningful distinction s in developing the scoring rubrics for the portfolios. Providing four options instead of five elimi nates the default "neutral" answer and forces the evaluator to choose a more definitive ranking. The four-option scale also made it easier to write descriptive phrases that were meaning fully different from the levels above and below. In developing these phrases, the following heuristic was used : for the four point phrases, the writer attempte d to describe what a metacognitive expert would demonstrate; for the three-point phrases, the target was what a skilled problem solver who lacked metacognition would display ; for the two-point words, the writ ers attempted to characterize a student with some skills, but who failed to display the level of performance required for an engineering graduate; the one-point val ue captured the perfor mance of a novice problem solver. To evaluate a given indicator, professors would read the left most description. If it did not accurately describe the perfor mance of the student, they would continue to the next block to the right until the work was properly described. A sample ru bric is s hown in Table 1. RUBRIC TESTING AND INTER-RATER RELIABILITY Once the lengthy process of developing scoring rubrics for each objective was completed, the rubrics needed testing. C. Robert Pace 1171 succinctly stated the challenge of accurate assessment, saying "The difficulty in using faculty for the assessment of student outcomes lies in the fact that different professors have different criteria for judging students' per formance." The intent of the rubrics was to create specific and uniform assessment criteria so that the role of subjective opinions would be minimized. The ideal result would be that all faculty members using the rubrics would assign the same scores every time to a given piece of student work. To evaluate if the rubrics were successful in this respect, six samples of student work (four exams and two engineer ing clinic reports) were distributed to the entire faculty (seven members at that time). All of them assigned a score of 1 ,2,3, 4, or "not applicable" to every student assignment for every indicator. This produced 160 distinct score sets (excluding those that were all "not applicable") that were examined for inter-rater reliability. The results, in general, were excellent. Every faculty mem ber scored the items within one level of each other in 93% of the items. In 47% of the score sets (75 of 160), agreement was perfect-all faculty members assigned exactly the same score. In another 46%, all assigned scores were within 1. Rubrics for which this level of agreement was not achieved were examined more closely for possible modification. After all of the scoring sheets had been compared, the faculty met to discuss discrepancies in their evaluations. The primary example of a rubric that required modifica tion is shown in Table 2. "Sol utions based on chemical engi neering principles are reasonable," in the originally devel oped scheme, was an indicator that applied to a number of different educational objectives. This was the only rubric for TABLE 1 Formulates appropriate solution strategies Identifies relevant principles, equations, and data Can eas il y convert word problems to equations; sees what must be done Consistently u ses relevant items with little or no extraneous efforts Forms workab l e strategies, but may not be optima l; occasional reliance on brute force Ultimately identifies relevant items but may start with extraneous information Systematically executes the solution strategy Consistently implements stra te gy; gets correct answers Implements well ; occasional minor errors may occur Applies engineering judgment to evaluate answers Solutions based upon chemical engineering principles are reasonable 214 Has no unrecognized implau s ible answers Has no unrecognized implausible answers Has no more than one if any unrecognized implausible answers ; if any, it is minor and obscure TABLE2 Ha s no more than one if any, unrecognized implausible answers; if any it is minor and obscure 1 Has difficulty in planning an approach ; tends to leave some problems un solved lndentifies some principles but seems to have difficulty i n distinguishing what is needed Has some difficulty in so l ving the problem when data are assembled; frequent errors Attempts to eva luat e answers but has difficulty; recognizes that numbers have meaning but cannot fully relate Attempts to evaluate answers but has difficulty; recognizes that numbers have meaning but cannot fully relate. l Has difficulty getting beyond the given unless directly instructed Cannot identify and assemble relevant information Often i s unable to so l ve problem even when all data are given Makes little if any, effort to interpr et results; numbers appear to have little meaning l Makes littl e, if any, effort to interpret results; numbers appear to have little meaning Chemical Engineering Education

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which scores were not routinely consistent. One heat-trans fer exam received a range of scores that included multiple occurrences of both 4 and 1. In the ensuing discussion, we found that the difficulty with this exam was that nothing recognizable as a final answer was presented for any question. The student formulated a solution strategy and progressed through some work but never finished solving the equations Interpreting the rubric word ing in one way, some faculty chose to assign 4. This interpre tation is understandable because no answer was given, and there was no unrecognized implausible answer." By the let ter of the criteria, the student earned a 4. Some faculty inter preted the criteria differently, however, resulting in the as sig nment of 1. This interpretation is also reasonable-since there were no results, there was no attempt to interpret the results. The rubric was simply re-written to specify that a rating of N/ A be given if no recognizable "final answer" was provided, and the discrepancies in scoring were not present in subsequent evaluations. In addition to pointing out neces sary revisions, this testing provided a good measure of inter-rater reliability. Having every faculty member review every item in an annual assess ment portfolio would be a laborious task Consequently, the results of this test were examined to determine what level of accuracy could be expected when a group of three faculty reviewed an item. For example, in the score set 2, 2, 2, 2, 1, 3, 2; the mean score assigned by the faculty was 2, and the mean of a three-score subset could be 1.67, 2, or 2.33. This means that any panel of three faculty members would have assessed this sample of work with a score within 0.5 of that assigned by the entire faculty. We found (after one rubric was revised as described above) that 95% (153 of 160) of the score sets showed this level of consistency. Thus we concluded that when using the rubrics a randomly constituted panel of three faculty members would be reasonably representative of the de partment. Detailed rubrics are available through the web at CLOSING THE LOOP Ultimately, the purpose of gathering detailed assessment data is to improve student learning. Once each year, we re view the data in a two-day assessment meeting 131 where we discuss all aspects of the program, including the data from each tool. We identify strengths and areas for improvement and make decisions affecting curriculum and policies. Spe cific changes resulting from these meetings have included a decision to introduce product engineering and econom ics earlier in the curriculum and to adjust topical cover age in thermodynamics THE NEXT LEVEL The next goal is to use the rubrics to help guide selection of course objectives across the curriculum. With detailed eduSummer 2002 cational objectives in place and rubrics to assist in their as sessment, we hope improved course objectives will be de veloped that more directly link classroom activities and evalu ations with the program goals. The rubrics described in this paper should provide the basis for a more in-depth, forma tive assessment. Although the ABET criteria are summative, the educational process itself centers around formative changes, incrementally enhancing a student's knowledge, skill set, and problem-solving capabilities. CONCLUSIONS A complete set of rubrics was developed and tested that maps st udent performance of a variety of junior/senior-level assignments directly to program educational objectives. These rubrics were tested for inter-rater reliability and were shown to yield the same mean (within 0.5) regardless of which set of three faculty members evaluated the material. These re sults, in conjunction with input from alumni, employers, and the students themselves, serve as a basis for assessment of the chemical engineering program. REFERENCES l. Engineering Accreditation Commission, Engineering Criteria 2000, Ac creditation Board for Engineering and Technology, Inc ., Baltimore ( 1998) 2. Diamond, R.M., Designing and Assessing Courses and Curricula: A Prac tical Guide ," Jossey-Bass Inc., San Francisco (1998) 3 Newell J.A., H L. Newell, T.C. Owens, J. Erjavec, R. Hasan, and S.P.K. Sternberg, Issues in Developing and Implementing an Assessment Plan in Chemical Engineering Departments," Chem. Eng. &1., 34(3) p. 268 (2000) 4. Paulson L.F. P.R. Paulson, and C. Meyer What Makes a Portfolio a Portfolio? Educational leadership, 48 (5), p. 60 (1991) 5. Rogers G.M., and J.M. Williams Asynchronous Assessment: Using Elec tronic Portfolios to Assess Student Outcomes ," Proc. of the 1999 ASEE Nat. Mtng. Session 2330, Charlotte (1999) 6. Morris, L.L. C.T. Fitz-Gibbon, and E. Lindheim, How to Measure Per formance and Use Tests Sage Publishers Newberry Park CA ( 1987) 7. Roid, G.H., and T.M. Haladyna, A Technology for Test-Item Writing Aca demic Press, San Diego ( 1982) 8. Robertson, G.J., "Classic Measurement Work Revised: An Interview with Editor Robert L. Linn," The Score p.l (1989) 9. Fitzpatrick, R., and E.J. Morrison, "Performance and Product Evaluation," in Educational Measurement, R. Thorndike ed ., American Council of Edu cation, Washington DC (1989) 10. Erwin T. Dary Assessing Student learning and Development Jossey Bass San Francisco (1991) 11. Banta, T. W. J.P Lund K.E. Black, and F.W. Oblander, Assessment in Prac tice, Jossey-Bass Inc ., San Francisco (1996) 12. Olds, B.M ., and R.L. Miller, "Using Portfolios to Assess a ChE Program," Chem. Eng. Ed. 33(2), 110 (1999) 13. Young, V.L., D. Ridgway, M.E. Prudich D.J. Goetz, B.J. Stuart "Crite rion-based Grading for Learning and Assessment in the Unit Operations Laboratory," Proc. of the 2001 ASEE Nat. Mtng., Albuquerque (2001) 14. Walvoord B .E., and V.J. Anderson, Effective Grading: A Tool for learn ing and Assessment Jossey-Bass Inc., San Francisco ( 1998) 15. Terzini P.T., and E.T. Pascarella, How College Affects Students: Findings and Insi ghts from Twenty Years of Research, Jossey-Bass In c San Francisco (1991) 16. Angelo T.A., and K.P. Cross Classroom Assessment Techniques: A Hand book for College Teachers 2nd ed., Jossey Bass Inc ., San Francisco (1993) 17. Pace C.R., "Perspectives and Problems in Student Outcomes Re searc h ," in Assessing Educational Outcomes Peter Ewell ed., Jossey-Bass Inc ., San Francisco (1985) 0 215

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.$_ij_._1a_b_o_r._a_t_o_r.:.y ________ ) MASS TRANSFER AND CELL GROWTH KINETICS IN A BIOREACTOR KEN K. ROBINSON, JOSHUA S. DRANOFF, CHRISTOPHER TOMAS, SESHU TUMMALA Northwestern University Evanston, IL 60208-3/20 B iotechnology is an increasingly important factor in the chemical process industries. The last decade has seen rapid growth in the resources committed to the development of biologically based processes. At the same time, the market value of new products generated by biologi cal means has continued to grow at an accelerating rate. Ac cordingly, more and more chemical engineers are being em ployed in the development, design, and operation of bioprocesses for production of pharmaceuticals, foods, and specialty chemicals, with no indication that the demands and opportunities in this area will moderate in the future. In recognition of this trend, we have developed a new "bio technology experiment" for Northwestern 's senior laboratory course.r 1 1 This experiment is aimed at giving our students an opportunity to become familiar with various factors involved in the implementation of bioprocesses and some of the atten dant technologies. We hope this will introduce them to this broad field while they are still at Northwestern and also en hance their attractiveness to potential employers. The experiment provides a means for studying two basic chemical engineering operations (mass transfer and cell growth kinetics) that occur in a three-liter stirred fermentaKen Robinson is a Lecturer at Northwestern University with primary re sponsibility for the undergraduate chemical engineeirng laboratory He received his BS and MS from the University of Michigan and his DSc from Washington University. He has worked in industry for both Amoco and Monsanto. Joshua Dranoff is Professor of Chemical Engineering at Northwestern University He received his BE degree from Yale University and his MSE and PhD from Princeton University. His research interests are in chemical reaction engineering and chromatographic separations. Christopher Tomas is a PhD candidate at Northwestern University work ing under the direction of Professor E. Terry Papoutsakis. He received his BS in Chemical Engineering from the University of Illinois Urbana Champaign, in 1996, and his MS in Biotechnology from Northwestern University in 1998. Seshu Tummala is a PhD candidate at Northwestern University working under the direction of Professor E. Terry Papoutsakis. He received his BS degree from The Johns Hopkins University in 1996 and his MS degree from Northwestern University in 1999, both in chemical engineering. Copyright ChE Division of ASEE 2002 216 tion reactor. The initial part of the experiment involves the study of oxygen transfer rates from gas to liquid phases; tran sient dissolved oxygen profiles resulting from step changes in feed gas oxygen concentration are measured with a dis solved oxygen probe. The growth kinetics of Escherichia coli are then studied in the same reactor under standard condi tions. Cell growth is monitored by spectrophotometric analy sis of samples removed from the reactor at specific times. The complete experiment is normally run in two successive laboratory sessions, each about eight hours long, separated by one week. It is also necessary to perform some short pre parative steps the day prior to the second laboratory session. EXPERIMENT SETUP Equipment The principal apparatus used is an Applikon three-liter glass stirred bioreactor. It was obtained as part of a complete package that included a number of ancillary items, such as temperature, pH, and oxygen probes and control sys tems. Additional major items obtained for this purpose in cluded an Innova 4200 shaken-cell incubator and a basic spec trophotometer (Spectronic 20+ ). The approximate cost of this equipment is indicated in Tablel. Not included in the indi cated cost but of critical importance for this experiment, is a steam sterilizer large enough to accommodate the fermenta tion reactor. We had access to such a unit in our department (AMSCO Eagle 2300 Autoclave) and assume that similar equipment is likely to be available in chemical engineering or related departments at other institutions. A sketch of the reactor is shown in Figure 1. It is stirred with dual turbine blade impellers on a single shaft driven by an electrical motor with an adjustable speed control. The re actor top is a stainless steel disk equipped with multiple ports for sampling, introduction of inoculum, gas feed and outlet lines, and insertion of temperature, pH, and dissolved oxy gen measuring probes. Additional specifications are indi cated in the Appendix. Chemical Engineering Education

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Gas is fed into the reactor and dispersed into the liquid through an L-shaped sparger tube that has multiple holes along the horizontal section that is located near the bottom of the reactor vessel. Outlet gas passe s through a small water-cooled condenser tube that serves to prevent evaporation of water from the normally warm liquid contents of the reactor. Temperature in the vessel is sensed by a type-J thermo couple inserted through one of the reactor ports and controlled by a simple electronic control system. An electrically heated jacket provides required heat input while cooling water can be simultaneously circulated through a small cooling coil immersed in the reactor liquid. Stable control of the reactor temperature at 37 C is easily achieved with this system. The bioreactor can be fed with three different gases. Air is supplied by an air pump with an inlet microfilter; pure oxy gen and nitrogen are provided from pressurized cylinders. The nitrogen is used in calibrating and spanning the dissolved oxygen probe and in the oxygen transfer-rate experiments. Air and oxygen are used in the cell-growth kinetic s s tudies in conjunction with the dissolved oxygen (DO) controller. Dur ing a typical cell-growth experiment, air is continuously sparged into the liquid medium in the reactor with the con troller set point at 70 % of total saturation relative to pure air. Whenever the measured oxygen concentration falls below 70 %, a three-way valve is actuated automatically to switch the sparging gas from air to pure oxygen. This control scheme is normally quite effective in returning the DO level back to the set point within a few minutes, except during the high oxygen uptake portion of the cell-growth curve (exponential TABLE 1 Major Equipment Needed for Experiment [I Applikon 3-liter fermentor, with control system and oxygen, temperature, and pH probes [I Innova 4200 Incubator [I Spectronic In str ument s 20+ Spectrophotometer Total Cost Gas Inlet Gas Ou tl et $ 15 ,000 $5,000 $ 1,700 $21,700 Sample bottle Gas"L" sparger Double blade impell er Figure 1. Fermentation reactor. Summer2002 phase described below). At such times, the stirrer speed can be increased from 250 rpm ( normal operating level) to 350 rpm in order to increase the gas-liquid interfacial area enough to permit increased oxygen transfer to the liquid phase. Op eration at these s tirrer speeds was found to be convenient and minimized foam formation during experiments (no anti foaming agents were used) Expendable Suil[Jlies To perform the following experi ments, a number of reagents and other expendable supplies are required. They include sodium chloride, Ampicillin, Tryptone yeast extract, Agar, ethanol, deionized water, and bleach, as well as disposable gas-line filters. DESCRIPTION OF THE EXPERIMENTS (A) Determination of the Oxygen Transfer Coefficient The first quantity measured with this system is the com bined mass transfer coefficient for oxygen transfer from the gas to the liquid phase k L a. (Since the interfacial area avail able for mass transfer cannot be readily determined in these experiments, it has been incorporated in the definition of the coefficient in the usual fashion.) This simple experiment pro vides an opportunity for the student to become familiar with various parts of the apparatus while illustrating an important chemical engineering principle. The reactor is assembled and filled with 2 liters of deion ized water. With the stirring speed set at 250 rpm, the tem perature control system is activated and the system is allowed to reach a steady temperature of 37C. The DO probe having been previously polarized by op eration for two hours in deionized water, is connected. The reactor is sparged with nitrogen at a rate of approximately 0.5 liters/minute until the DO signal has stabilized (normally about 30 45 minutes), at which point the zero of the DO con troller is set to read 0% oxygen. The nitrogen flow is then replaced by air at the same volumetric rate and flow is main tained until the DO probe output remains constant. At this point the controller span is adjusted to read 100 % (i.e., satu ration with respect to the oxygen content of air). The feed gas is then rapidly switched back to nitrogen (step down in feed gas oxygen concentration), and the DO concentration is recorded every 30 seconds to 1 minute until it returns to 0 % The feed is then rapidly switched back to air (step up in feed gas oxygen concentration), and DO concen tration i s recorded every minute until it returns to 100% These "s tep-up and "s tep-down data are then analyzed as indi cated below to determine a. (B) Determination of Cell Growth Kinetics This is the more difficult and demanding part of the ex periment, especially for students unfamiliar with the proto cols used in biochemical research. It involves two separate operations: the preparation of a stock culture of active cells and the subsequent measurement of cell growth kinetics 217

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Throughout thjs portjon of the experllllent, emphasjs is placed on the need to mruntajn sterility and cleanliness of the appa ratus and the work area. (1) Preparation of stock culture. This part of the proce dure is normally carried out during the first laboratory ses sion along with the oxygen transfer measurements described earlier. Steps involved include: Preparation of Luria-Bertani (LB) culture media (see also the Discussion section). Liquid LB medium is a mixture of sodium chloride, Tryptone, yeast extract, and deionized water (composition given in the Appendix) Solid LB medium is a mixture of sodium chloride, Tryptone yeast extract, Agar, and deionized water (composition given in the Appendix) Each of these media is placed in an Erlenmeyer flask that is then covered with aluminum foil and autoclaved for 20 minutes in the sterilizer. The liquid medium can be used in the reactor as prepared The solid medium is used to prepare solid culture plates. After the initial sterilization, the solutions are allowed to equilibrate at 55C and then antibiotic solution is added (see the Appendix for composition of antibiotic solution). The medium is then poured into sterile culture plates that are stacked and allowed to solidify in a sterile hood at room temperature (several hours). Preparation of Cell Cultures. The cells used in these experiments are from an E.coli strain, ER 2275, furmshed by New England Bio Labs, Beverly Massachusetts and modified (plrnPl) as described by Mermelstein et al.l 21 A stock of E.coli on the solid medium is prepared by streaking a fresh solid medium plate with a colony of E.coli and then incubating the plate at 37C overnight. If individual colonies of E.coli are then easily visible on the plate, it is placed in the refrigerator for storage. If not another plate is streaked and incubated, as above. Trns process has proven to be easily reproducible. Preparation of inoculum. The inoculum is a solution containing living cells that is used to initiate the growth process witrnn the bioreactor. It is prepared the day prior to the fermentation experiment. An individual colony from a stock plate is combined in a 250-ml. Erlenmeyer flask with 200 ml of liquid LB medium equilibrated at 37 C, antibiotic solution is added, and the inoculum is allowed to grow overmght (for approXUllately 12 hours) with shak ing at 200 rpm in the incubator. (2) Preparation of the Reactor for Growth Kinetics Studies. The reactor vessel is assembled and filled with deion ized water and then autoclaved for approXUllately 20 mjn utes along with a supply of liquid LB medium prepared as described above. After the reactor has cooled to room tem perature, the water is pumped out and replaced by 1.8 liters 218 of the LB medium The reactor is then allowed to come to thermal equilibrium at 37 C and the control systems are acti vated. (The DO probe must first be polarized and calibrated, as described above.) (3) Growth Kinetics Studies. When the system is ready 200 ml of the inoculum solution is pumped into the reactor and the DO level is set to approxjmately 70 %. A small sample ( 10-15 ml) of the reactor contents is then removed every 1015 minutes and its turbidity measured in the spectrophotom eter (at a wavelength of 600 nm). If the cell concentration gets too rngh, the sample is first diluted in order to keep it witrnn the mjd-range of the spectrophotometer. The experi ment is concluded when the fermentation appears to have reached the stationary phase (see below). Trns normally re quires 4 to 6 hours The final liquid medium still left in the reactor is auto claved before disposal, and all equipment is carefully cleaned with bleach and soap. DATA ANALYSIS (A) Determination of Oxygen Transfer Coefficient Typical data obtained in the "step-down" (nitrogen feed) and "step-up" (air feed) experiments described above are shown in Figure 2. These data were obtained with a reactor volume of 2.0 liters a gas flow rate of 0 .38 liters per minute and a mixer rpm of 250. The data clearly exrnbit an initial time lag that is the sa me for both experllllents. Trns lag is apparently due to dynamic response of the dissolved oxygen probe itself. Since it was consistent and relatively s mall com pared to the overall tlllle scale of the experiment, the response data have been corrected by subtracting a lag of 1.5 minutes from the measured time in each transient experllllent. For either experllllent, the oxygen transfer rate per unit volume of liquid (OTR) is given by the following equation which also defined the volumetric liquid phase mass transfer coefficient: (1) where 120 100 c ., 80 Cl C s;, .S! o! 60 'Cl::, ., > .. 0 1/1 40 ., ., ci 20 0 0 5 10 15 20 25 30 35 Time, minutes Figure 2. Typical oxygen transfer data : Dissolved oxygen conce ntration vs. time. Chemical Engineering Education

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C* saturated dissolved oxygen concentration at the gas liquid interface rnmol/L C dissolved oxygen concentration in the bulk liquid phase, mmol/L kL a liquid phase oxygen mass transfer coefficient, 1/ minute OTR oxygen transfer rate, rnmol/L/minute The transfer coefficient typically depends on the gas flow rate, the bioreactor working volume, and the power input to the agitator (or stirrer speed). It may also depend on the pa rameters of the reactor design such as impeller and sparger design and configuration, and the physical properties of the culturing medium such as viscosity and interfacial tension. A transient oxygen balance for the reactor volume is dC =OTR=kL a(C*-C) dt (2) Considering the experiment in which the initially oxygen free solution is contacted with oxygen containing gas, Eq. (2) must be integrated with initial concentration = 0 and con centration C* held constant. The well known result is R n (C*-C) =-k at C* L (3) For the reverse experiment in which the solution is initially saturated at concentration C* and the gas concentration is = 0, the solution is 1000 ,,.,,, 1 .,, ,, .. i 5]1 c 0 .:: C: 100 ]: .!2 C 1a ., I } ":, 5 1a 10 0~ C 0 0 0 1 -5 0 5 10 15 20 25 30 T i me, minu te s Figure 3 Typical Oxygen transfer data: Determination of kLa with nitrogen sparging. 1000 ~---~--~-~----.------, ';!. 100 E g _g! 1opexp(-0 145[t-1.5)) C:, 10 0 ... C >., ., "' C 0 'I-. 0 1 -5 0 10 15 20 25 30 Time, minutes Figure 4. T ypica l oxygen transfer data: Determination of k L a with air sparging. Summer 2002 C R n-= -kLat C* (4) Logarithmic plots of the corrected ste p-down and step-up data according to Eqs. (3) and (4) are shown in Figures 3 and 4, respectively. It can be see n that the data conform quite well to the expected form, yielding the values for the mass transfer coefficient of 0.155 miff 1 for the nitrogen sparging or step-up experiment, and 0.145 miff 1 for the air sparging or step-down experiment, for an average value of 0.15 min 1 One other measurement of~ a was made with air sparging into the OB medium prior to the beginning of the cell-growth experiments. In this case, the mixer speed was set to 150 rpm while the other conditions remained as before. It was found that the data once again showed a time lag of 1.5 minutes and fit the expected exponential decay similar to Figure 4. The value of kL a determined, however was 0.075 miff 1 Thus it is clear that this mass transfer coefficient is a strong func tion of the degree of agitation in the vessel and the prop erties of the liquid It should be noted that Roberts et al.,r3 1 previously described a laboratory experiment to measure oxygen transfer in a I liter stirred fermentor. In that case, the stirring rate was con siderably higher (500 to 700 rpm) and the method of deter mining kL a was different; those authors measured the quasi stea dy-state rate of oxygen consumption by yeast in the ab sence of oxygen feed (the vessel contents were previously saturated with air). Although conditions were quite different in that experiment compared to the present case, the mass transfer coefficients reported were of the same order of mag nitude-approximately 0 6 min 1 at a stirrer speed of 500 rpm Using their exponent of 2.75 for the effect of mixer rpm, the expected value ofkL a at 250 rpm would be 0.089 miff 1, which is unexpectedly close to the value of 0.15 miff 1 found here under considerably different conditions. (Bl Determination of Cell Growth Kinetics The immediate objective of the second part of the experi ment is to measure the specific growth rate of the E.coli cul ture in the batch fermentation reactor system. Typically such bacteria growing in a batch culture exhibit four distinct growth phases following inoculation with an active culture. As shown in Figure 5, growth u sually begins with a very s low lag phase as cells introduced into the inoculum adjust to their new en vironment. This is followed by a rapid, exponential phase as acclimated cells reproduce via binary fission as quickly as nutrient and oxygen concentrations within the medium per mit. This phase is followed by a stationary phase where the rate of oxygen supplied to the cells equals their rate of oxy gen consumption. Finally, the cell concentration falls during the death phase due to the depletion of nutrients and the buildup of toxic byproduct s. The specific growth rate ()of the cells is determined dur ing the exponential binary fission phase. This process is au219

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tocatalytic and is usually represented as a first-order reac tion i.e dX_ X dt Integration of this differential cell balance yields X( t )= X 0 exp[( t-t 0 )] where X cell concentration number/volum e t time minutes cell specific growth rate, I/minute o as a subscript refers to initial conditions (5) (6) In the present experiments, cell concentration in the reac tor is monitored at 10to 15 minute intervals by measure ment of the absorbance (at 600 mm) of a small sample of solution using the spectrophotometer. According to the usual Beer-Lambert law, the light transmitted through a solution is related to the incident light and the concentration of absorb ing species, as shown in where I -=exp(-ecl) I o 1/l 0 fractional light intensity relative to incident intensity (7) c concentration of absorbing species, number per unit volume length of light path through solution E extinction coefficient of absorbing species area per number Strictly speaking, for the present experiments e should be regarded as an appropriate fitting parameter since changes in measured light intensity are no doubt due to a combination of absorption and scattering. Since absorbance A is defined as -logwCI/1 0 ), it follows from Eqs. (6) and (7) that Ek elX [( )] A = 2 303 = 2.30; exp t to (8) Taking natural logs of Eq. (8) yields C n(A) = (tt 0 ) + C n(;'.:O;) (9) Thus, a plot of Cn (A) against time should be linear with a slope equal to the specific cell-growth rate ( during the exponential growth phase A cell doubling time, t ct, can be calculated once the growth rate is determined, according to C n(2) tct= -(IO) Figure 6 shows typical data obtained over a 4-hour period following the experimental procedure described earlier These data indicate an expected initial lag of 15 minutes followed by an apparent exponential growth phase that levels off some time after 200 minutes. When these data are plotted in accord with Eq (9), a good fit to the exponential model is obtained, as shown in Figure 7. The corresponding specific growth rate 220 Stationar Phase Q 0 "i !:J eath Phase Q 0 u Q 0 u QJ u Time Figure 5. Typical batch culture growth phases. of the E.coli in this experiment was 0 013 min 1 This is equiva lent to a doubling time tct of 53 minutes. This relatively long doubling time confirms that the E.coli strain, while adequate for these experiments, is not particularly robust. The only difficulty encountered in carrying out the cell growth experiments has been maintaining the dissolved oxy gen concentration at 70 %. Large swings in the oxygen level (between 50 % and 90 % of saturation) have been observed even with increases in gas-flow rate and stirring speed. These variations, however apparently do not have any significant effect on the observed growth rates. .. u 3 ; 2 5 -e i 2 C 1.5 .!:! E 1 Jl 0 .5 I I ; 0 j__..L__.L._--4---L_j__4--'--......L..--'---'---+---'---'---I 0 60 120 180 240 300 Time, minutes Figure 6. E.coli growth data: solution absorbance vs. time. 10 i I I .. I I u 0 : 16 r exp( O p 1 3\1-1 ~]) C .. -e ! I I 0 I ., ... .c I I <( I j C I 0 0 en 0 1 15 15 45 75 105 135 165 195 225 Ti me -La g m i nu t es Figure 7 Determination of specific cell growth rate. Chemical Engineering Edu c ation

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DISCUSSION The experiments described here have provided a means for introducing senior students to some aspects ofbioprocessing. During the course of this experiment, students are exposed to standard procedures for preparing and handling a bacterial culture, including preparation of growth media development of active bacterial colonies, and incubation and sterilization processes. They also become aware of the mass transfer pro cesses involved, the underlying theoretical analysis and rel evant methods of data analysis, as well as the relatively long time scale of the experiments. The latter is not a serious prob lem in our laboratory since we are able to devote two 8-hour sessions to this experiment. Some compromises, such as more pre-lab preparations carried out by the instructors, would undoubtedly be necessary in order to perform similar experi ments in a shorter laboratory session. In designing this experiment we have attempted to include as many of the preparative and analytical steps mentioned above as possible without unduly burdening the students, since our goal is to provide opportunies for "hands-on" experiences whenever possible. At the same time, we are not attempting to develop research-level competencies in our students by this means Selection of LB culture media as opposed to chemically defined media is a case in point. While the former may yield somewhat less reproducible results from one stu dent group to another, the LB media have proven to be robust and easy to use. Some lack of reproducibility was not con sidered to be a significant drawback in the present context. A related laboratory experiment[ 41 used the growth of yeast (Saccharomyces cerevisiae) and involved the simultaneous use of two fermenters. The rate of oxygen transfer to the liq uid phase was studied with and without cell growth, and the rates of cell growth during the exponential phase were also measured under aerobic conditions with various concentra tions of added ethanol. No performance data were presented, so a more direct comparison to the present experiment is not possible. It should be noted, however, that while the overall goals of these two experiments are similar, the systems of choice and the methods of data analysis differ somewhat. Another experiment 151 based on ethanol production using Saccharomyces cerevisiae yeast used 1 liter fermentors and measured CO 2 generated during fermentation to follow the course of the process. As in the above-mentioned case the overall objective of the experiment is similar to the present case, although it is much more limited in scope. We have now run this experiment successfully for two years, with increasing numbers of students and with very positive results. While the immediate and ancillary equipment required to mount such an experiment is not trivial or inexpensive, such equipment is becoming relatively common and is likely within reach of most chemical engineering departments in terested in providing some direct introduction to biotechnol ogy in their curricula. Of even greater importance than equipSummer2002 ment in the successful development of such an experiment are skilled and experienced people who can help in the early planning and implementation stages. We were particularly fortunate to be able to call on Professors E.T. Papoutsakis and W M. Miller and some of their graduate students for tech nical assistance an~e agement. ACKNOWLE GEM TS We wish to thank owing Northwestern graduate students for their assistance and a dvice during the development and start-up of this experiment: Kathy Carswell, Dominic Chow, Rick Desai, Sanjay Patel Albert Schmelzer, a nd Vivian DeZengotita. We also thank the recent undergraduate laboratory group whose data were used to illustrate the features of this experiment: Michael Gerlach Julie Nguyen Edward Ruble and Chris Spelbring. Finally, we are especially thankful to Kraft Abbott Laboratories and the Murphy Society of the McCormick School of Engineering and Applied Sci ence for the financial s upport that made it possible to develop and bring thi s new experiment to full realization REFERENCES I. Robin son, K .K., and J S. Dranoff Chem. Eng. Ed. 30 98 (1996) 2 Mermelstein, L.D., N.E Welker C.N. Bennett and E.T. Papoutsakis, Bio/T echnology, 10 190 (1992) 3. Robert s, R.S. J R. Ka s tner M. Ahmad, and D .W. Tedder Chem. Eng. Ed. 26 142 (1992) 4. Shuler M.L. N. Mufti M. Donaldson and R. Taticek Chem. Eng. Ed., 28 24(1994) 5. Badino Jr. A.C. and C O. Hokka Chem. Eng. Ed. 33 54 (1999) Useful references for this general area are: Biochemical Engineering, by Harvey W. Blanch and Dougla s S. Clark, Dekker (I 996) Biochemical Engineering Fundamentals 2nd ed by James E. Bailey and D av id F. Ollis McGraw-Hill (1986) Biopro cess Engineering: Basi c Concepts, by M. L. Shuler and F. Kargi Prentice-Hall (1992) 0 APPENDIX I. Composition ofLuria-Bertani liquid medium: Per liter of solution: NaCl Tr yp tone Yeast extract D e ionized water 2. Composition ofLuria-Bertani so lid medium : Per liter of solution NaCl Tryptone Yeast extract Agar Deionized water 3 Composition of antibiotic solution: IO grams IO grams 5 grams I liter 10 grams IO grams 5 grams 15 grams I liter Ampicillin I gram di sso lved in 1 ml of deionized water Added to LB medium at concentration of 100 micrograms/ml 4. Reactor dimensions Type: 3 liter dished bottom Inside diameter: 130 mm Impeller: Two 6-bladed Rushton turbines Turbine diameter: 45 mm Turbine distance from vessel bottom : 45 mm and 75 mm Baffles: Three, equally spaced baffles, each 220 mm long 221

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.ta ... 5 ... ._c_u_rr_i_c_u_l_u_m __________ ) TEACHING ChE TO BUSINESS AND SCIENCE STUDENTS KAM.NG Hong Kong University of Science and Te c hnolog y Clear Water Bay, Hong Kong T h e chemical proce ss ing industries (CPI) have under gone profound changes and companies are under con siderable pressure to restructure and innovate in a glo bal environment where information, technology capital, and human resources flow easily. Supply chain management and e-business is used to improve the overall efficiency of an enterprise, and there is a tendency to farm out non-core tech nologies. For example, recognizing that drug discovery is their main business, pharmaceutical firms tend to outsource the production of active pharmaceutical ingredient intermediates. There is increasing emphasis on product design which is clo se ly linked to market demands 11 21 Thi s creates new busi ness opportunities and the need for better understanding of the global issues of chemical proce ss ing. In response, there is considerable effort to broaden chemical engineering edu cation to include emphasis on entrepreneurship, lifelong learn ing, management, business, international experience, etc. Obviously, chemical engineering is not the only profession reacting to the challenges of the new global environment. Other disciplines also strive to enhance the depth and breadth of their curriculum in order to expand employment opportu nities for students. A case in point is an elective course about chemical engineering offered to bu s ine ss and science students at the Hong Kong University of Science and Technology (HKUST). Here the semester sys tem is identical to that of Ka M. Ng is Professor and Head of Chemical Engineering and Director of the Consortium of Chemical Products and Processes at HKUST. He obtained his BS and PhD degrees at Minnesota and Houston respectively. From 1980 to 2000 he was Professor of Chemical Engineering at the University of Massachu setts His research interests are in process systems engineering involving reactions crys tallization, and solids processing of high-value added products the US, and all classes are conducted in English. There are two s imilar but separate courses: one for business and one for science students. The course for business students covers more basic chemistry, while the one for science students is more detailed in business concepts. We will discuss what we teach and why, how the students respond to the course, and what we can learn from this experience. COURSE OBJECTIVES Hong Kong (a Special Administrative Region of China since 1997) is a vibrant, international city of 6 7 million inhabit ants from all over the world. It is located in the heart of the Asia Pacific region where chemical processing industrie s have been growing at a rate in excess of 10 % per year. Hong Kong has a strong financial sector with an interest in chemi cal-related businesses. While the manufacturing sector within Hong Kong is comparatively small, extensive manufactur ing takes place north of Hong Kong in Shenzhen, Guangzhou, Zhuhai, Huizhou, and other municipalities Also, since the GNP per capita of Hong Kong is comparable to that of other developed countries, there is keen interest in chemical prod ucts that can offer a higher return on assets. Of particular interest are high-value-added chemicals and pharmaceuti cals. The allure is clear when one compares the 8 % profit margin in a typical chemical firm to the 20% figure of a US drug company.f31 The overall goal of the course is to provide business and science students with an overview of chemical engineering. Specifically, the student is expected to gain an appreciation of The CPI products How chemicals are manufactured The cost of building and operating a typical chemical plant Copyright ChE Division of ASEE 2002 222 Chemical Engineering Education

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The organization and finance of a typical chemical company Product-centered processing The history of chemical engineering The global chemical business CO U R S E D ES I G N The course, consisting of six sections (see Table 1) starts by introducing the students to the US and HK economies J 4 51 In the late '70s the breakdown of the HK GNP was similar to that of the US. Gradually, financing, insurance and real estate have become dominant industries in Hong Kong. In contrast the US CPI is one of the largest among manufactur ing sectors such as electronic and electric equipment, motor vehicles and parts, etc We show how the return on assets Section TABLE 1 Outline of Topics I llltroduction The eco nomy and the chemical processing industries (C PI ) Di ve r s ity and complexity of product s from th e CPI Characteristics of the CPI 2. Chemicals and Their Sources B as ic chemistry Chemicals in our daily live s The chemical supply chain The chemical business hierarchy 3. The Production of Chemicals The chemical plant and its unit operations Proj ec t evaluation Th e cost of manufacture The criteria of economic perf ormance 4. The Financial Performance of Chemical Corporatio11s Financial metric s Financial statements Capita! budgeting 5. Product Design Approaches to product de s ign Product-centered proce ss sy nthe s i s and development 6. The Modern Chemical Processing llldustries D eve lopment of CPI in th e UK Germany, US and Japan The sca le and economics of th e CPI today The CPI in Asia Summer 2002 TABLE2 Che mi ca l s in Our Daily Lives Petroleum Fibers Soap s and deter ge nt s Pl as tics Oil s and fats Natural product s Traditional Chinese medicin es and profit margins of the CPI have fluctuated with time along with the overall economy. Innovations such as nylon and polyester have created new markets for chemical products In Section 2 of the course, we discuss selected chemical products. 161 Table 2 lists the products we have considered so far. Petroleum is normally the first product to be discussed The students can easily appreciate the various uses of petro leum and the concept of di s tillation. Soaps and detergents is another business to which the students can readily relate They learn about the composition of a typical detergent formula tion, s urfactants, detergent builders bleaching agents, and enzymes, and how detergency works. There is a wealth of information on the World Wide Web from the Soap and De tergent Associationf 71 as well as from companies such as Procter and Gamble and Unilever A typical assignment is to read a product report in Chemical and Engineering News.r s i The students gain an appreciation for both the need for dif ferentiated products that drive reformulations and the chal lenges faced by suppliers of detergent ingredients. We con s ider the replacement of sodium tripolyphosphate with zeo lites from an environmental viewpoint, and we use pictures and samples of chemical products such as cellulose triacetate (for cigarette filters), spandex, sugar esters, superabsorbents (for diapers) etc. to stimulate students' interest in the sub ject. Oils and fats is another business of interest to Hong Kong students. We discuss the nature of those products the source of raw materials and manufacturing processes 1 9 i o 11 1 Next we show the students that all of these products origi nate from three sources in our environment: air and water ; substances from the ground (which include gas, petroleum, and minerals); and living thing s (including plants and ani mals) We show the primary reaction for conversion of one compound (or compounds) to another.f 121 For example, urea i s manufactured from ammonia and carbon dioxide; polyes ter results from a polycondensation reaction between ethyl ene glycol and terephthalic acid which is in turn obtained from the oxidation of paraxylene; and cellulose triacetate comes from cotton !inters We expected the students to gain an appreciation of the complexity of the chemical supply chain and also introduced the concept of mass balance. We point out the kind of companie s that add value to different seg ments of the suppy chain, such as oil companies chemical companies, specialized engineering firms, pharmaceutical companies, consumer goods companies, etc. In Section 3 of the course we turn our attention to the pro duction of chemicals using Douglas' hierarchical approach. 11 31 After covering input-output recycle structure, and separa tion systems, we discuss chemical engineering unit opera tions. These include reaction, evaporation, drying, distilla tion, absorption, extraction, crystallization, adsorption, fil tration etc.r 14 1 We discu ss basic principles but omit equations for equipment design We use Th e Visual Encyclopedia of Ch e mi ca l Engineering Equipment developed at the Univer223

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sity of Michigan to supplement the lectures The animated equipment operations are very helpful to the non-engineer ing students. At this point we briefly discus s safety and en vironment issues related to chemical processing in order to raise the students awareness of these issues We use a chemical plant in Hong Kong to illustrate pro cessing concept s. Towngas produced by catalytic reaction of naphtha with steam, is often the example of choice (see Figure 1 ). The first stage of the desulfurization unit converts organic sulfur compounds to hydrogen sulfide and the sec ond stage removes hydrogen sulfide with zinc oxide. In the reaction system the desulfurized naptha is converted to meth ane and hydrogen and carbon monoxide i s converted to car bon dioxide and hydrogen. The carbon dioxide and water is removed in the gas purification and drying system Project evaluation follows Douglas' book. The students do not have much difficulty in grasping the details of direct costs, indi rect costs, working capital, etc We also cover (particularly for science students) the time value of money and the dis counted cash-flow rate of return on investment. Normally, we assign a project in which the students perform cost evalu ation of a chemical plant. The flowsheet and all major equip ment sizes and operating conditions are given assuming that this input information has been obtained from chemical en gineers in a consulting firm Next we turn our attention to the financial performance of chemical corporations. Variou s measurements, such as return on net assets, after-tax profit margin, sales growth, and con trolled fixed-cost productivity, are introduced We usually examine the financial statements of two US corporations; recently we have discussed those of DuPont in class while those of Eastman Chemical are analyzed in a homework as signment. One objective is to learn how to read the balance sheet, the income statement, and the statement of changes in financial position More importantly, we emphasize an ap preciation of the financial position of a typica l chemical com pany in terms of profit margin, new investments, amount of assets on the ground etc. This reinforces and processing conditions (see Figure 2). We identify the desired performance of the product, both functional and sen sorial, and select the requisite ingredient s The process flowsheet and the operating conditions are then identified. We study the modern CPI in Section 6. r 4 J It begins with a review of the manufacture of soda ash, dyes, and sulfuric acid in the UK and Germany as well as the emergence of the CPI in America in the 1900s and in Japan in the 1950s. Then we turn our attention to today s CPI Its global enormity is evident when one compares the global chemical shipment of $1.59 trillion in 1999 to the HK GDP equivalent of approxi mately $200 billion. We then examine the financial performance of the top glo bal chemical companies emphasizing the top twenty five chemical-selling countries in 1999 (see Table 3).r 3 i It is evi dent from the statistic s that chemical production per capita in Asia is below the world average, but (unsurprisingly) it is rapidly gaining ground. Singapore is a net exporter compet ing in the international market. Although China is not ex pected to be self sufficient, its rapid development and pur chasing decisions can significantly affect the global CPI. We examine the recent JVs and investment projects in order to appreciate the dynamics of the market in this region .1 1 6 1 COURSE EVALUATION The impact of the course has been assessed by its students. While the course is intended for undergraduates it generally has around 25 % graduate students from all science and busi ness disciplines With rankings ranging from very bad to very good, about 85 % of the respondents ranked the overall course as good or very good Most of them expressed that they ac quired a good knowledge of chemical engineering Also throughout the semester we hold a 10-to -15 minute oral quiz every week in order to challenge them to think about interre lationships among different decisions. Most students felt that Gas Recy cl e Gaa PuriflcaUon and Drying Towngas the notion that CPI is a capital intensive business To emphasize decision-making in chemical busi nesses, we venture into capital budgeting, 11 5 1 but this segment can be skipped if the students have previously learned these concepts in their business classes. Retrofit projects, as well as proposals to construct a grassroots plant, are considered. Sutfur Remov al Product design is of great interest to Hong Kong. We discuss a typical product development cycle concept development, design and prototype pro cess planning piloting, and plant startup We ex plain the use of Quality Function Deployment (QFD); this is further refined for chemical products where market trends lead to product attributes, which are in turn decided by material properties 224 Figure 1. The production of towngas by catalytic reforming of naphtha using steam Ch e mi c al En g in ee rin g Edu c ati o n

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they have been encouraged to express ideas (84% ranked as good and very good) and have improved their ability to think (76 % ranked as good and very good). REFLECTIONS ON CHEMICAL ENGINEERING EDUCATION With the reshaping of the global economic landscape the demarcation between disciplines has become blurred. It is highly desirable to have an appreciation of contemporary gloMarket trends Product conceptualization Functionality and packaging Financial analysis Financial Return Identification of quality factors Knowledge base & know-how Product fonms Product packaging Capital budgeting Financial metrics Typical quality factors & perfonmance indices Performance vs Quality factors and material & structure performance indices High Throughput Selection of .---------1 Screening techniques ingredients and for material selection micro structure ~---~---~ Ingredients & structural attributes Generation of process alternatives Process alternatives & operating conditions Process and product evaluation Manufacturing process Generic fiowsheet of manufacturing process Equipment units Heuristics Structure vs Operation Figure 2. Step-by-step procedure for product-centered process synthesis and development. I. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. TABLE3 Top 1\venty-Five Chemical-Selling Countries in 1999 (in US$ billions)C 31 U.S. 435 14 Netherlands 28 Japan 205 15. Switzerland 26 Germany 104 16. Ru ss ia 25 China 91 17. Canada 21 France 78 18. Mexico 15 United Kingdom 50 19. Australia 14 South Korea 50 20. Argentina 10 Italy 49 21. Sweden 9 Brazil 36 22. Malaysia 8 Belgium 35 23. Pol a nd 6 India 31 24. Singapore 5 Spain 30 25. Thailand 5 Taiwan 30 Summer 2002 bal economic issues while keeping our core competencies in chemical engineering practice The stra tegy and financial dealing s of the various companies in the global CPI covered in this course can also se rve as an interesting topic in a typi cal chemical engineering proce ss design course. In fact, some of these business concepts were covered in the senior design course at the University of Massachusetts. In addition to synthesizing, simulating, and costing a chemi cal plant, it is interesting to investigate whether or not a pro posed retrofit project or a new investment adds to the share holder value. Indeed it is not uncommon to request that the engineers and researchers in a company justify an R&D pro posal in terms of potential return on investment as well as on its technical merits. Similarly the lectures on product-cen tered process synthesis and development is suitable for chemi cal engineering proces s design. In this case, the student learns how market demands dictate what to make how to make it, and where to make it, thus gaining an appreciation of the economic consequences of these deci s ions in a much wider context than in a traditional process design course. ACKNOWLEDGMENTS I would like to thank Bruce Vrana for his teachings on cor porate finance during my stay at DuPont Central R&D Francis Lui for providing the HK economics data and Chi Ming Chan for teaching the section on product design. REFERENCES 1. Cussler, E.L., and J.D. Moggridge, Chemical Product D esign, Cam bridge U ni versity Press Cambridge, UK (2001) 2. Wibowo C., and K.M. Ng "Prod u ct -Oriented Process Synthesis and D eve lopm e nt: Creams and Pastes ," AJChE J ., 47, 2746 (2001) 3. "Facts and Figures from the Chemical Industry ," C&EN June 26, p. 48 (2000) 4. Arora, A. R Landau, and N. Ro senbe r g, Chemicals and Long-Term Economic Growili, John Wiley and Sons (1998) 5. Estimates of Gross Domestic Product 1961 to 1997 ," Government of Hong Kong Feb. ( 199 8) 6. Chenier P.J. Surv ey of Indu strial Chemistry 2nd ed. John Wiley & Sons (1992) 7. 8. Ainsworth SJ. Soaps and Detergent s," C&EN, Jan 24, p 34 (1994) 9. Hamm, W. and R.J Hamilton eds., Edible Oil Pr ocessing, CRC Pre ss (2000) I 0. Hoffmann, G. The Chemistry and Technology of Edible Oils and Fats and Other Hi gh Fat Products Academic Press (1989) 11. O Brien R.D Fats and Oils Formulating and Processing for Appli cations, Technomic Publishing Co. Lanc as ter PA (1998) 12. Rudd D.F. S Fathi-Afshar A.A. Trevino, and M.A. Stadtherr, Petro chemical T ec hnolo gy Assessment, John Wiley and Sons ( 1981) 13 Douglas, J.M ., Conceptual Design of Chem i cal Pro cesse s, McGraw Hill New York NY ( 1988 ) 14 Walas, S.M. Chemi c al Pro ce ss Equipm e nt: Selection and Design, Butterworth s Boston, MA (1988) 15 Ro ss, S.A., R.W. Westfield and B.D. Jordan, Fundamentals of Cor porate Finance, 5th ed., McGraw Hill New York, NY (2000) 16. Bank of Americas Guide to Petro c h emica ls in Asia EFP International Hong Kon g ( 1997) 0 225

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.,;_5_3._l_a_b_o_r._a_t_o_r.:.y ________ ) INTEGRATING KINETICS CHARACTERIZATION AND MATERIALS PROCESSING IN THE LAB EXPERIENCE DENNIS J. MICHAUD, RAJEEV L. GoROWARA, Roy L. McCULLOUGH University of Delaware Newark DE 19716 A t the University of Delaware, we have developed an integrated sequence of two undergraduate laboratory experiments (spanning the junior and senior years) in which the students investigate different aspects of batch process design. The design task assigned to the students is to identify adequate processing conditions to produce a quality one-inch-thick composite laminate within a limited time frame. Thick -sec tioned thermoset composites can be diffi cult to process correctly due to the exothermic nature of the polymerizing resin and the low thermal conductivity of the laminate. The Resin Transfer Molding (RTM) process incorporates a number of core chemical engineering concepts within a labo ratory exercise while at the same time introducing students to the manufacture and properties of composite materials. A numerical cure simulation of the RTM process, [IJ developed within the Center for Composite Materials at the University of Delaware is used during each lab 's design component to evaluate different processing scenarios Figure 1 outlines the important features of the two experiments and illustrates the manner in which they are integrated. In the first experiment, the juniors characterize the resin 's polymerization kinetics and heat of reaction using differen tial scanning calorimetry (DSC). Using an empirical nonlin ear kinetic model for the thermosetting resin ,r2 1 the data is correlated to establish the model parameters needed by the process simulation. The simulation is then used for a pre liminary design of the processing conditions required to suc cessfully produce a one-inch-thick composite laminate within a two-hour processing window. The sensitivity of their de sign to kinetic parameter variability is also investigated. The senior composite laboratory experience continues the simulation-based sensitivity analysis of the RTM process by including variations of the simulation's heat transfer model parameters The students implement their initial design, pro ducing a ten-inch-square composite laminate with a one-inch through-thickness Density, void fraction, and mechanical tests of the laminate help students evaluate the success (or failure) of their experiment. By comparing measurements from thermocouples embedded within the composite and those predicted by the simulation, the students make modifi cations to the simulation's model parameters (heat transfer and kinetic) to improve the simulation's accuracy. Armed with an improved process simulation and more knowledge of the process the students then generate a new set of proce ss ing conditions and again implement it experi mentally, producing a new (and hopefully improved) com posite laminate The students then use a combined evalua tion of the simulation's model parameters and their processDennis J. Michaud is currently Lecturer of Chemical Engineering at the University of Delaware He received his BS from Northeastern University and was awarded a PhD in Chemical Engineering at the University of Dela ware in 2000 for his work in the optimization and control of thick sectioned RTM composite processing. Rajeev L. Gorowara received his PhD in Chemical Engineering under the direction of Professor McCullough at the University of Delaware in 2001 focusing on interphase formation in glass-fiber vinyl-ester compos ites He received his BS and MS from Ohio State University He is cur rently a Consulting Engineer in the DuPont Engineering Particle Science and Technolog y Group. Roy L. McCullough was Professor of Chemical Engineering at the Uni versity of Delaware until his death in December of 2001. He received his undergraduate chemistry training at Baylor University and was awarded a PhD in Chemistry by the University of New Mexico in 1960. He published numerous technical papers and organized symposia in the areas of poly mer science and composite materials. Copyright ChE Division of ASEE 2002 226 Chemical Engineering Education

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ing experience to propose a final design in their written report. THICK-SECTIONED COMPOSITE MANUFACTURING The specific problem given to students concerns the manufacture of thick (greater than one-half inch through-thickness) composite materials via RTM. This nontraditional subject matter allows students to apply classroom knowledge of kinetics and transport phenomena while also introducing process control and the limitation s of mathematical models. Processing thick-sectioned composites is challenging due to the exo thermic nature of the reacting resin and the heat transfer limitations of the polymer and glass fiber composite.l 1 3 1 Unfavorable process ing conditions of the composite part can lead to poor part quality including cases where the laminate cracks internally due to residual stresses within the part. The primary design problem for thick-sectioned composite is to iden tify an acceptable temperature trajectory (or "c ure cycle ") that balances Junior Lab: Senior Lab: Kinetics ofThermoset Polymer Cure Design and Manufacture of I Thick-Section Composites u 0 '"' = .... cu '"' Q. e Figure 1. Schematic of integrated undergraduate laboratory experiments. 160 I\ Measured Heater Temperature 11 140 I --Measured Center Temperature I Simulated Center Temperature I 120 I (Using Initial Model Parameters) I I I 100 80 ---60 Stage 2 40 Stage 1 Post-Cure Curing Phase Phase 20 0 50 100 150 200 Time, minutes Figure 2. Example cure cycle and corresponding internal composite temperature Summer 2002 the heat necessary to initiate the polymerization reaction (cure) with the heat transfer limitations of the composite once the reaction begins while also maintaining a processing time that is economically feasible. The example cure cycle presented in Fig ure 2 shows experimentally measured heater and composite (measured at the center of a one-inch thick laminate) temperatures. The cure cycle is broken up into different stages, each with a spe cific heater se t-point. For the experiment shown in Figure 2, the first set-point was 62 C and the second set-point for the post-cure was 90 C. Due to the low thermal con ductivity of the composite, almost 60 minutes of processing is required for the center of the com posite to reach the heater set-point, but once the resin at the center begins to cure, the heat gener ated from the reaction quickly raises the composite's temperature and drives the polymerization reaction to completion. A lower temperature curing stage reduces the temperature gradient within the part as well as residual stresses, but also increases process ing time. Since the surface temperature of the com posite remains much closer to the heater set-point, a post cure is generally required to ensure the sur faces of the composite are adequately cured for re moval of the part from the mold. LABORATORY FORMAT AND EDUCATIONAL OBJECTIVES At Delaware, the undergraduate chemical engi neering laboratory is a two-course sequence, taken in the spring of the junior year and the fall of the senior year. Initially, all students attend five background lectures in laboratory safety, mea surement techniques, statistics, report writing and oral presentation In the junior course student groups go through three experimental cycles, with each cycle center ing around a design problem using information gathered during a laboratory experiment. Over a four-week period the students must learn about the problem, perform the experiment, analyze the data prepare a preliminary data report, revise the data analysis, and complete the design prob lem in a final report. In the first week of a cycle the students prepare for the lab by reviewing the experiment and labo ratory procedures with the teaching assistant (TA). They prepare an experimental proposal, and dur227

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ing the graded pre-lab conference they present it to the su pervising faculty member, who must be convinced that valu able "research facility" time should be spent on the prob lem. The students must also show an understanding of the safety issues involved. In the second week the students perform the experiment under the guidance of the TA, and in the third week they con clude the data analysis and preliminary data report. depth of experience: evaluation of the validity of experimen tal data in comparison to the other groups; evaluation of their process design in the second experiment; and (after revising their process model based on the second expe1iment) evalua tion of their ability to evaluate The supervising professor focuses on the higher-level skills guiding student s in ana lyzing their data using it in the synthesis of a new process design and evaluating that design in the proces s ex periment. The students then use their lab data during the fourth week for the design prob lem and present the final report for the cycle to the faculty member. 2.5 ,----------~------~ 2.0 1.5 1.0 Isothermal Phase Ramping Phase 5 C/min At the conclusion of the course the indi victual groups orally present one of their experiments to their colleagues and faculty and then critique their video taped performance. The format of the senior-year course is very similar in approach, but has only two experiment cycles. A longer six-week sequence allows the students to return to the 0.5 0.0 -0.5 \ Area=H rxn Area= Hresidual The TA tends to focus on the lower-level skills: knowledge of polymeriza tion kinetics and compos ites processing; compre hension of the experimen tal methods; and applica tion of that knowledge to extract model parameters from the experimental data KINETICS OF THERMOSET POLYMER CURE (JUNIOR YEAR) -1.0 ~~~~~~~~~~~......._......._....,__.....,__.....,__.-'-"'--'-' 0 5 10 15 20 25 30 35 Time, minutes Figure 3. Example heat flow of a differential scanning calorimetry (DSC) experiment. The junior-level com posite laboratory experi ment requires that the stu dents evaluate the resin s kinetic parameters necessary to pre dict the resin curing behavior within a thick-sectioned com posite and to develop a preliminary design of the processing conditions for a one-inch-thick composite laminate The stu dents investigate the resin-curing process of pure (neat) resin samples using differential scanning calorimetry (DSC), which accurately measures the heat evolved from the reaction and the reaction temperature. 1 7 1 They are challenged to consis tently prepare the small (8 to 12 mg) resin samples and to interpret the DSC's baseline and endpoint data The DSC is used to measure the isothermal heat release rate dQ/dt, which lab after their first experiment and either extend or correct their experimental data The integrated lab format allows us to address the entire hierarchy of educational objectives outlined by Bloom and colleagues in their famous taxonomy. 141 These objectives in clude analysis synthesis and evaluation referred to as "higher-level skills" by Felder, et al. 1 5 1 The fundamental ob jectives of knowledge, comprehension, and application are referred to as "lower-level skills We agree with Miller, et aL,[ 6 1 that the engineering labora tory is an ideal setting to help students become better engi neering practitioners and to enhance their higher-level think ing skills. Since the time of Professor Robert Pigford it has been the tradition at the University of Delaware to focus the chemical engineering laboratories not only on the determi nation of experimental data, but also on a design problem using that data. In the terms of Bloom's taxonomy the higher level objectives are not only analysis, but also the synthesis of this new information into an engineering design. We find the design problem's requirements to be an excellent motiva tion for the laboratory experiments and that the synthesis step reinforces the need to succeed in the lower-level skills. We add the integrated lab to this tradition, as it creates a situation that stresses evaluation, based on the student's own 228 is related to the polymerization reaction rate, da/dt, by aa dQ ---dt Hult dt (1) and the extent of ploymerization (cure), a a(t) = 1 f ( dQ 1 )dt Hult l o\. dt (2) where H u 1c is the total heat of reaction given by l r ; so t he,ma l ( dQ J t ( dQ J Hult = Hr x n + Hre s idual = f l dt ft+ f l dt ft t o l r i so th e nnal (3) Ch e mi c al En g in ee rin g Edu c ati o n

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Hult is determined by summing the heat measured during the isothermal cure of the resin with the residual beat measured at the conclusion of an isothermal run. Using Figure 3 of experimentally measured beat flows as an example, the value ofH, x n is evaluated from t 0 = 3.2 minutes (when the DSC pan is added to the cell) to the final isothermal time point, t r. h 1 , I SO l enna of20 minutes. The temperature of the DSC cell is then ramped at 5C/min until no residual heat is observed. For the students to simulate resin cure in an actual part they need to be able to describe the reaction in a non-isother mal cure. The kinetics of the free-radical polymerization can be described using the popular autocatalytic model 12 8 1 shown in Eq. ( 4 ), which gives the reaction rate, da./dt, as a function of the fractional extent of cure, a, the maximum extent of cure, amax, and an overall reaction order of 2 da. k m( )2-m -= a a -a dt max (4) and a(t) = Cl.max [( ) ] I /(m 1) I+ 1m Cl.max k t (5) An Arrhenius expression is used to account for the tempera ture dependence of the rate constant, k k = A exp(=~) (6) For the incomplete curing case in which vitrification occurs before complete reaction, the maximum extent of cure, a max, for an isothermal curing temperature is less than one, and a linear relationship may be used to approximate the effect of temperature T, on a max. for (7) We have used the resin Derakane 411-C50 (Dow Chemi cal), a free-radical polymerizing resin that is 50 wt% DGEBA based vinyl ester and 50 wt% styrene, since we use it in other projects. 11 91 Alternative resin systems can easily be imple mented, however. We have also u sed a variety of initiators and accelerators to alter the kinetic performance of the resin. From heat rate and time data, the students estimate the resin's kinetic parameters (Hult' A, E., m, a 0 and a 1 ) required by the cure simulation. We recommend that the students first determine Hult' then amax (T), and then k(T) and m at each cure temperature, using nonlinear regression. We make avail able for their use KaleidaGraph (Synergy Software), which allows curve fits of nonlinear functions. To help ensure rea sonable curve fitting results, we ask the students to use their derived kinetic model to predict the extent of cure ( a) as a function of time and compare that to the experi mental extent of cure data. The students estimate the error for some of the parameters Summer2002 The Resin Transfer Molding (RTM) process incorporates a number of core chemical engineering concepts within a laboratory exercise while at the same time introducing students to the manufacture and properties of composite materials. based on the nonlinear regression fitting of the data, and the error for the others is determined by propagation of experi mental measurement errors. The melting of a standard In dium sample is u sed to estimate error in the DSC heat flow and temperature measurements. Once the students submit their preliminary data reports, the data from all of the groups (including previous cycles) is circulated via memos in order to provide a larger estimate of variability from the pooled data. This gives the students an introduction to the s tatistical treatment of data, including the use of significance testing (i.e., t-test) to determine if their data is within the norm. There is generally a lot of variability between groups, and this exercise gives the studen ts an ap preciation of these sta tistical techniques as well as refining the dat a they will need during the design component. The students are asked to use these estimates as bounds for the sensitivity analysis on the simulation parameters. SIMULATION-BASED PROCESS CYCLE DESIGN (INTEGRATED DESIGN PROBLEM) As part of the junior lab, the st udent s are introducted to sim ulation-ba sed batch-process cycle design, focusing pri marily on the effects of the resin's kinetic parameters. The RTM process cure simulations are provided via a course homepage.* Before their prelab meeting, the students u se a fast, but imperfect, neural net version of the simulation to explore the dynamics of the system and get a "feel" for their design problem. Once they have experimentally determined the resin's kinetic parameters, they use the more accurate fi nite difference cure simulation 111 for their design. We define the problem of cure-cycle design as the proper selection of the composite's time-temperature cycle (similar to Figure 2), within the limits of available equipment, to make a high-quality part while completing the cure process in as short a period of time as possible to reduce the production cost. We define a s uccessful cure cycle in terms of several quality criteria, such as achieving an acceptable degree of cure while minimizing void content, thermal degradation, and residual stresses. 229

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The students are informed of the different process param eters that must be controlled to meet the product design lim its. For example, void formation is affected by the vaporiza tion of styrene, and therefore the students must calculate this temperature limit at process pressures (approximately 20 psig). To avoid thermal degradation, the student's proposed temperature cycle should minimize the peak temperature observed in the center of the composite. To minimize residual stresses, the students should ensure that the composite cures inside/out once the resin's gel-point is reached. The resin shrinks 8% during cure, and significant curing on the outside of the composite before the dents are responsible for measuring and/or estimating the physical properties of the composite and the mold environ ment (e.g., volume fraction of the resin, composite density and thermal conductivity, and effective heat tran sfe r coeffi cients) The students are given the pure component proper ties for the resin and glass fibers for their calculations. Heat capacity of the composite is estimated using the "rule of mix tures, and its thermal conductivity can be predicted using a number of techniques. [Io.i 1 l The seniors begin their composite laboratory sequence with a tour of the composite manufacturing equipment center begins to cure results in large internal stresses (and possible delamina tions) once the resin at the center begins to polymerize. Stainless Steel Mold and an overview of the ex perimental procedure and safety issues. The experi mental RTM equipment is shown in Figure 4. Using their experience from the junior lab students use the In terms of minimizing processing time, the stu dents are given the goal of curing the composite ( 0. 75) in less than 2 hours. The juniors present their proposed design in their final report for the Thermocouples to Data Acquisition Polyurethane Tubing on-line simulation to iden Compressed Air Polyurethane Tubing tify the cure cycle they will implement experimentally. The simulation is also used DSC experiment. In their senior year, they again visit the simulation-based design problem, but with a new Resin Resin Source Resin Collection to analyze the effect of pos sible model parameter variations on the cure cycle (i.e., sensitivity analysis). emphasis on the material properties of the composite (resin content, composite Figure 4. Dia gram of resin transfer molding (RTM) equipment. The lab begins with the students filling the stainless steel mold with a predeter mined volume fraction of density, thermal conductivity, etc.), beat transfer coeffi cients within the mold and the effect of fibers on the kinetic behavior of the resin. DESIGN AND MANUFACTURE OF THICK SECTIONED RTM COMPOSITES (SENIOR YEAR) After an introduction to composite processing in the junior lab, the seniors are given an opportunity to manufacture a composite laminate. While they previously only investigated the kinetic behavior of neat resins they soon discover that the heterogeneous nature of composite materials as well as other manufacturing realities, can complicate a situation. One of the challenges they find with manufacturing thick sectioned composites is that extrapolating kinetic data down to the lower temperatures necessary for thick-sectioned cure can result in significant error_[IJ Other complications include the change in the resin's kinetic behavior in the presence of fibers and the effect of inhibitors within the resin system that are not currently modeled by the simulation. La s tly, the stu230 glass fiber reinforcement. The particular fiber reinforcement has varied over the years to include woven sheets, random mats, and stitched layers of different fabric types, which can affect the resulting volume fraction of resin and the composite's thermal conductivity During the placement of the fibers six J-type thermocouples are placed between the fabric layers to provide internal temperature data during manu facturing. The entire mold assembly is placed within a heat press to seal the mold components and to provide the heat necessary to cure the composite. The catalyzed resin, con tained within a pressurized pot is injected into the room temperature mold until no air bubbles are seen exiting from the mold Once the mold has been filled with resin, the flow of resin is stopped and the cure cycle is begun. As discussed earlier the cure cycle is defined by the tem perature set-point of the heat press A representative cure cycle for a one-inch-thick composite laminate is s hown in Figure 2. Lab View is used to observe and collect the internal com posite temperatures during processing. When the observed temperatures do not match those generated by the simula tion, the students are challenged with modifying the cure cycle on-line according to insights from their sensitivity analysis. Chemical Engineering Education

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Once the cure cycle is completed and the mold is cooled the composite i s removed from the mold and cut into test samples. The students estimate the composite's quality according to ASTM standards for density (D792), void fraction (D2584/ D 2734), and s hort-beam shear strengt h (D2344) Although some material and heat transfer model param eters of the composite and the mold can be measured a few of them (e.g., thermal conductivity and the simulation's boundary condition) must be estimated by the students in order to improve the accuracy of the cure simulation. By compar ing the simulated composite temperatures with those mea sured at the beginning of the cure cycle when no resin cure has occurred the students identify which of the estimated heat transfer model parameters is most likely responsible for the mismatch, and they can then estimate new values. Like wise, the students compare simulated composite temperatures to those measured during the curing phase of the resin to iden tify possible changes in kinetic parameter s due to lower pro cessing temperatures and the effect of fibers. As is shown in Figure 2, the numerical sim ulation gener ally underpredicts the length of time necessary to cure the composite when the default model parameters are used (neat resin kinetics and predicted heat transfer parameters). Since there are a number of parameters within the sim ulation that can be altered to improve the fit of the simulated temperature profile, the students must defend their choices by using knowl edge they have gained about the system and by performing a sensitivity analysis. Once the students have improved the simulation, they use it to redesign their cure cycle (w hile und erstand ing that th ey do not have a perfect model of the system) and use it to manu facture another composite part The experimental results from this second experiment are then used to further improve the estimate of the sim ulation' s model parameters. Using model parameters derived from both experiments and their newly acquired knowledge of composite processing, the students generate a final cure-cycle design as part of their written re port of the lab This report also includes a sensitivity analysis of their final design and recommendations as to how the simu lation and the experiments might be improved in order to better generate an "optimal" cure cycle design that can ac count for observed batch-to-batch variability CONCLUSION The double sequence of junior and senior laboratory ex periments described in this paper has been implemented suc cessfully at the University of Delaware for the past five years. In order to understand the goals of the experiments and com plete the design portion, students are required to integrate a number of important engineering concepts, including kinet ics, heat and mass transfer, and some process control. Both experiment s also provide a good basis for implementing a Summer 2002 statistica l treatment of the data. Furthermore, the students are introduced (through the simulation-based design component) to the reality of process-model mismatch and the effect of significant process variabilities on their design. As a whole, each laboratory seque nce allows the students to demonstrate many of the outcomes defined within the ABET Engineering Criteria 2000 Unlike many other labora tory experiences, the ability to take a piece of the final prod uct home with them (e.g., a composite paperweight) has been well received by the students. We believe that the integrated concept of this lab and its design aspect in each phase pro vides an invaluable experience for the students. ACKNOWLEDGEMENT The paper is dedicated to the memory of Professor Roy L. McCullough, coauthor educator, mentor, and friend, who passed away unexpectedly in December of 2001. REFERENCES I. Michaud, D.J. A.N. B e ri s, and P.S. Dhurjati "C uring Behavior of Thick-S ec tioned RTM Composites ," J of Comp. Mats ., 32( 14) 1273 (1998) 2. L a m P W K. H P. Pl a uman a nd T. Tran "A n Improved Kinetic Model for th e Autocatalytic Curing of Styr e n e -Based Thermoset Re s in s," J. of Appl. Pol ymer Sci 41 3043 (1990) 3. Ciriscioli, P.R. Q Wan g, and G.S. Sprin ger, "A utoclave Curing: Com parisons of Model and Te s t Result s," J of Comp. Mats. 26(1 ), 90 (1992 ) 4. Bloom B.S ., ed. Taxonom y of Educational Objectives David McKay Co. New York NY (1956) 5. Felder R.M., D.R Woods J E Stice and A. Rugarcia, "The Future of Engineering Education : II Teaching Methods that Work ," Chem. Eng Ed., 34( I ), 26 (2000) 6. Miller R L. J .F. Ely R .M Baldwin, B M. Old s, "Higher-Order Think in g in the Unit Operation s Laboratory ," Chem. Eng Ed. 32(2) 146 ( 1998 ) 7. Willard H H ., L.L. Merritt, Jr ., J.A. De a n and F.A. Settle, Instrum e tal M e thods of Analysis 7th ed., John Wiley & Sons New York NY ( 1988 ) 8. Kamal M.R., and S Sourour "Kinetics and Thermal Characteriza tion ofThermoset Cure ," P o l y mer Eng. and Sci. 13(1), 59 (1973) 9. Gorowara R.L. S H. McKnight, and R .L. McCullough, "Effec t of Glass Fiber Sizing Variation on Interphase Degradation in Glass Fi ber-Vinyl Ester Composites upon Hygrothermal Exposure," Compos it es Part A, accepted for publication IO Sprin ge r G S ., a nd S W. T sa i, 'Thermal Conductivities of Unidirec tional Materials ," J of Comp. Mats. 1, 166 (1967) 11. Farmer, J .D ., and E.E. Covert Thermal Conductivity of an Anisotro pic Thermosettin g Advanced Composite During Cure," Am. Inst of Aero11. and Astron.:Structures, Stru c tural D y nami cs, and Mat er ials 5(56) 2939 (1995) 0 ERRATA The phrase "to appear in" in citations 4 and 7 of Devel oping Troubleshooting Skills in the Unit Operations Labo ratory," by Aziz M. Abu-Khalaf, published in CEE, 36(2) p. 122, (2002), should be omitted. 231

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.t3_..ijlllii._c_l_a_s_s_,-,_o_o_m _________ ) SCALING OF DIFFERENTIAL EQUATIONS "Analysis of the Fourth Kind" PAUL J. SIDES Carnegie Mellon University Pittsburgh, PA 15213 W hat does it mean to solve a differential equation? The answer might be in closed form, or it can be an infinite series. A numerical simulation might also provide the answer The first kind of answer is preferred but not always available or even possible. The second answer is useful if the series converges well, but this is not guaranteed in all cases. The third kind of answer is the least flexible, and doubt about the exactness of the simulation can remain. This paper concerns a fourth kind of analysis, where a so lution per se is not found, but the student learns about the dependence of the solution on relevant parameters and/or ob tains an order of magnitude estimate of various meaningful quantities, such as the approximate thickness of a boundary layer. This answer is the result of natural scaling of the dif ferential equation; it provides insight into an equation even when the solution to the equation or set of equations is un known. This process of deducing relationships among the physical properties and significant dimensions of the problem accelerates physical understanding of its nature. The answers from this type of analysis often guide experiments, reducing their number to a minimum. Finally, the analysis can demon strate that effects are important or unimportant. The goal is to present an approach for arriving at the fourth kind of answer The procedure is called "all-natural scaling" of the equation. There is at least one contribution in the lit erature on a similar topic. Hellums and ChurchiW 1 l described a general method for analyzing equations; their method re veals cases where similar solutions are found and at least in dicates minimum numbers of parameters and variables Their approach is formal and aimed more at deducing constraints on problems than on deducing physically meaningful quantities. What need does this contribution fill? It is not a scientific advance, because scaling of equations has been around for a long time; scaled equations are the standard form in journal publications. For most undergraduates, the limited need for this understanding and the modest potential for comprehen sion of its significance are not compelling arguments for in232 troducing them to it. Likewise, this contribution is not in tended for the experienced analyst who performs these op erations subconsciously or has seen them all. This method is intended primarily for advanced undergradu ates or first-year graduate students who find themselves in classes where the professor conjures dimensionless groups without arguing their origins. I introduce this technique to the students in our core graduate math and transport courses; they seem not to have seen a direct discussion of this process before. This contribution is intended to fill that gap. EXAMPLE 1 Viscous Heating and the Brinkman Number Consider first the classic problem of viscous heating ap pearing in Figure 1. A warm viscous liquid flows laminarly in a pipe and is cooled by contact with the cold wall; the concern is whether or not viscous heating of the liquid is im portant. For simplicity, it is assumed that axial convection of energy dominates axial conduction so that the important heat transfer terms are radial conduction, and viscous dissipation. The following equation governs convective heat transfer in laminar pipe flow under these circumstances : [ ( )] ( ) 2 a 1 a aT av 2 pc v z = k r + p az r ar ar ar (1) where T = temperature, T O = incoming temperature, T w = wall Paul J. Sides is currently Professor of Chemi cal Engineering at Carnegie Mellon Univer sity. He received his BSChE from the Univer sity of Utah in 1973 and his PhD in Chemical Engineering from the University of California at Berkeley in 1981. He joined the faculty of the Department of Chemical Engineering at Carnegie Mellon in 1981. He has published articles in electrochemical engineering, growth of advanced materials, and data storage tech nology. Copyright C hE Division of ASEE 2002 Chemical Engineering Education

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temperature, v 2 = axial velocity in laminar pipe flow, p = den sity of the fluid, = viscosity, c = heat capacity, k = thermal p conductivity, r = radial position, and z = axial position. Equation 1 is the convective conduction equation for the laminar flow of fluid in a pipe plus a term describing the local dissipation of mechanichoice is not often critical as long as the term chosen is im portant in the problem. The first exercise of the Appendix of this contribution illustrates this point. The radial conduction term is also important; after all this is how the thermal energy escapes the pipe. Thus, the con duction term is scaled to 0(1) by equating its coefficient to unity and solving for the unknown length scale (5) cal energy into thermal en ergy. [ 2 J Before going to the trouble of solving the equa tion, or looking up the an swer, we can use a scaling analysis to estimate the im portance of the effect. This example illustrates the pro Figure 1. Laminar flow of a viscous liquid in a pipe of circular cross section. With the inclusion of this axial cess of natural scaling and the deduction of the pertinent di mensionless group. First, we pick all sensib l e length scales for the independent variables in the governing equation. R is obvious for radius, but there is no obvious choice for axial distance. We there fore temporarily give the axial length scale a name and de duce it during the derivation. This lets the equation exhibit appropriate relations among the physical properties. Finally, we define a dimensionless dependent variable preferably so that its value varies from zero to unity, when its range is known. r s= R For laminar pipe flow: v 2 = 2 < v > ( I I; 2 ) (2) Substitute these definitions into the equation using the chain rule for derivatives. The first crucial step is to divide by the coefficient of an important term in the equation In this case, we are exploring the importance of the viscous heating term, so its coefficient must float. Axial convection of energy is obviously an important term, so one divides through the equa tion by the convective energy transport coefficient ( T -T J 2pcp < v > l o Zo w (3) The result is (4) Dividing the energy equation by Eq. (3) "scales" the axial convection term to 0(1) ; it declares axial convection to be important. The choice of which term to use in scaling the equation seems arbitrary at first. (Hellums and Churchill,r1 1 for example, use the coefficient of the diffusive term to scale their Eqs. 10-12 but do not comment on the choice ) This Summer 2002 length scale, the overall energy equation can now be written as (6) where (7) The analysis yields two results. First, the temperature of the incoming fluid changes substantially toward the wall tem perature over a distance z 0 that is calculable from known quan tities of the problem. Second, the resulting parameter in Eq. 7, (Br), is a dimensionless group that governs the importance of viscous heating;[2 1 i.e. we can now quickly determine the significance of viscous heating relative to the ability of the system to dissipate the irreversible energy released If the thermal conductivity is high relative to heating by viscous dissipation, the latter is unimportant. The effect of viscous heating is proportional to the viscosity and the square of the velocity, and inversely proportional to conductivity of the liq uid. If 16Br is very small, we can ignore viscous heating-the usual case; otherwise, we should consult the published workP 1 Guidelines The method used in the previous example consisted of several steps. 1) Write the governing equation includin g effects of interest 2) Make position variables dimensionless with distances over which the dependent variable assumes the full range of its possible values. Where there is no obvious appropriate dis tance give it a name and try to deduce it as part of the analy sis (remember Rand z). 3) Nondimensionali ze the dependent variables with their full scale values. 4) Substitute the definitions into the differential equation using the chain rule for derivatives. Once students do this a couple of times, the y easily write down the substituted form by inspection 5) Identi fy a term of known importance and divide the equation by the coefficient of that term This forces that term to order unity importance in the equation and scales the rest of the equation to that term. The equation becomes dimensionless. 6) Inspect the remaining terms of the equation Whenever a co233

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efficient contains only one unknown distance or other nor mali z in g quantity and is also a known important term, set the coefficient to unity and solve for the unknown quantity (i.e., we knew the c onduction in the radial direction was important, so we found z,, with the coefficient of the conduction term ) 7) Collect remaining terms into as few coefficients as possible. These terms are generally dimensionl ess ratios that appear as parameters of the final solution These steps should be considered general guidelines For the student, it is useful to try scaling the same eq u ations by the coefficients of various terms to see the effect on the re sults. This process develops insight and experience that make the analysis meaningful. If one plans to solve the complete equa tion in closed form, the choice of reference distances does not matter. If we plan to solve the equation numerically it can make a great deal of difference if the equation is properly scaled. EXAMPLE 2 Natural Convection Near a Vertical Heated Surface How much can be said about a classic case of natural con vection without actually solving the governing equations in detail? Consider a heated vertical plate immersed in a fluid of infinite extent as shown in Figure 2. The well-known equa tions for the laminar case (GrPr < 10 9 ) are the following : Continuity (8) Motion ( av av J ( a 2 v a 2 v 1 ) p V z +v z z =l __ z +-z j+pg~(T-Tc y ay az ay 2 az 2 (9) Energy ( aT aTJ ( a 2 T a 2 T 1 pep vy-+v =kl2 -+ 2 j (10) ay az ay az where v Y = y velocity, v = z velocity, T = temperature, Th= wall temperature T c = bulk fluid temperature, cP = thermal heat capacity, k = thermal conductivity, g = gravity, = co efficient of expansion, p = density, = viscosity, y = hori zontal position, and z = vertical position For completeness, no assumption has been made about the relative importance of cunduction or convection in the direc tion parallel to the wall. The first step is to identify scaling parameters for the independent variables, in this case y and z. The scaling distance for z is obviously H; the scaling dis tance for y is unclear since the domain is infinite in that di rection. Thus define a distance y O as the appropriate scale for y This distance is essentially a characteristic hydrodynamic boundary-layer thickness Then define the dependent vari able over its range 234 z s= H (11) Likewise, there are no natural reference velocities for the vertical and horizontal velocities so give them names as well ( z = V z / Vo z, y = V y I Vo y ) and define B = pg~(Tw -Tc) After inserting them into the momentum equation, we obtain (12) The convection of momentum in the direction parallel to the wall is surely important; sca le the equation by dividing through by that term's coefficient Hv oy ( y a z J + z a z = YoVo z ari ai; vH ( a 2 <1> z J V ( a 2 <1> z \ BH y~voz l a11 2 r Hvo z l a1; 2 r pv~ z e (13) At this point, there are two terms that contain only one of the unknown reference variables-the second and third terms on the right-hand side. Typically, diffusion of momentum is neg ligible compared to convection of momentum in the primary direction of flow, thus it would not be prudent to base the definition of the reference velocity in the z-direction on the coefficient of this term Furthermore, we know that for natu ral convection, the source term for momentum must be 0(1) or the problem does not make sense Force the coefficient of this term to unity. We conclude that a reference velocity for the flow parallel to the vertical wall should be Vo z = (14) Having this definition, we can now define other reference quantities by forcing the coefficients of other important terms to unity The coefficient of the y-directed momentum diffu sion terms yields and (15) and the differential equation becomes I H l Figure 2. Geometry for natural convection near a heated wall. Chemical Engineering Education

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This is as it should be. The typically important boundary layer type terms are all of order unity along with the source term driving them. The axial diffusion of momentum is mul tiplied by a coefficient that allows its importance to be as sessed. For even very modest temperature differences between the wall and the bulk fluid or for large H this term is small. The H3 dependence of this parameter is very strong We now insert the definitions obtained into the energy equa tion and obtain ( aeJ ( aeJ ( 1 a 2 e 1 ( a 2 p V 12 ( a 2 e 1 y chi + <1> as =l Pr chi 2 tl H 3 B ) l as 2 ) (l 7 ) The equation contains two parameters-Pr and a coefficient multiplying the axial diffusion term. Assuming that the axial diffusion of energy can be neglected we find that the Prandtl number is the sole parameter of the system of Eqs.(8 ,9) What happened to the Grashof number? Why does it not appear in this equation? To see how Gr arises, examine the flux of heat at the vertical wall, using the derived definitions to make it dimensionless q = h(T w -Tc)= aTI hyo h ( 2 H 1 1 1 4 ael k ay y= 0 Nu= -k= ktps) = chi 11 =0 (18) Still no Grashof number appears. Note that the appropriate scaling distance for heat flux normal to the wall is the hydro dynamic boundary-layer thickness y 0 The Nusselt number i.e., the dimensionless flux of heat, remains solely a function of Pr. The only way that Gr appears in the equation is if we convert this "all natural" scaling to one based on H as the length parameter. Then the flux equation becomes The coefficient on the far right-hand side is recognizable as Gr so that the definition of NuH becomes N ael G 1 1 4 UH -r chi 11 =0 (20) The dimensionless temperature gradient at the wall is a func tion solely of the Pr number as we found sca ling of the sys tem of coupled equations and is most often written as _ael =f(Pr)Prl/ 4 (21) a11 11=0 where f(Pr) is a slowly varying function of Pr This definition leads to the tidy form Nutt= f(Pr)(GrPr) 1 1 4 (22) Summer 2002 which is the one commonly encountered. As in the first example there are several useful results. First, we now have estimates of the velocities achieved in the prob lem and the boundary layer thickness (Eqs. 14 15). Second we show that if axial diffusion of momentum and energy is small, the solution to the problem is only a function of Pr. Third the origin of the Grashof number in this problem is clearly demonstrated. CONCLUSIONS Scaled equations are the standard for mo s t journal publica tions but apart from this standard, the process of scaling dif ferential equations is a way to learn about their nature and build arguments about what terms can be neglected. The method requires that the student be able to read the equations at hand; in the examples, the student needs to recognize dif fusive and convective terms. We suggest that this perspec tive be imparted concurrently with the method where neces sary. We hope the method presented here helps advanced undergraduates and first year graduate students become ac customed to the practice of scaling equations and, most of all, to understand the origin of dimensionless numbers, the shorthand of our profession APPENDIX: Suggested Further Examples 1) Repeat example 1 but divide through by the conductive term rather than the convective term ; compare the results to Eq 7. 2) One might object and say that it i s strange to force all the terms to unity in example 2 that this mu s t create an imbal ance in the equation. We can check for s uitability by in se rting the definitions into the continuity equation. Problems with the scaling might appear there. Put the given definitions for the reference quantities into the continuity equation and deduce its form. Does a problem appear? 3) Consider the classic problem of flow of a free stream that meets and flow s parallel to a flat plate Include the axial diffusion of momentum Deduce a parameter that allows one to estimate the minimum plate length for which axial diffusion of mo mentum can be neglected. Deduce an estimate of the thick nes s of the hydrodynamic boundary layer for a plate of length L. A close approximation to the exact answer is / v = How does your answer compare to this? 4) Write the energy equation for the above example, including the axial conduction term Use the reference distances devel oped in Prob 1. Deduce a parameter that allows estimation of the lengths below which axial conduction must be considered. 5) Instead of using the hydrodynamic boundary layer thickness in the energy equation as in the previou s problem define a new reference length in the direction normal to the plate for the energy equation. Deduce an estimate of the thermal bound ary layer thickness. Show that the ratio of the hydrodynamic layer thickness to the thermal l ayer thickness is given by Pr 112 REFERENCES I. Hellum s, J D. and S. W. Churchill, A/CHE J. 10 p 110 (196 4 ). 2. Brinkman, H C. Appl. Sci R ese arch, A2, p. 120 (1951). 235

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.t.~ 111 5 111111 .._c_l_a_s_s_r_o_o_m _________ ) THE USE OF SOFTWARE TOOLS FOR ChE EDUCATION Students' Evaluations ABDERRAHIM ABBAS AND NADER AL-BASTAKI University of Bahrain Bahrain 32038 0 ver the last two decades we have witnessed a rapid decline in the computer price/performance ratio and the development of fast, reliable, and user-friendly computer packages. These developments have brought com puters within the reach of organizations and people who were once deterred by cost or by complex mathematics and pro gramming expertise The ease of use and enhanced capa bilities of general-purpose software such as Mathcad or Matlab have made it possible for engineers with limited or no formal training in programming to solve relatively complex problems The available computing tools have Jed to large changes in the industrial world. In contrast, the typical engineering edu cator has been slow to incorporate computer-based concepts in the curriculum and training methods This situation has been attributed to a number of factors, including the lack of computer literacy/inclination among certain staff and the way popular textbooks are written .l 1 2 1 The positive impact of information technology on teach ing and learning is no longer questionable 5 l Kulik and Kulikl 41 reported that most studies found that computer-based instruction-using technology of the eighties-had positive effects on students. In particular s tudents learned more and faster (the average reduction in instructional time in 23 stud ies was 32%). The students also developed more positive at titudes and liked classes more when they use computers. The main objective of this paper is to present our experi ence with and students' evaluations of three commercial soft ware packages that we at the Department of Chemical Engi neering at the University of Bahrain have been using as teach ing aids. These packages are the process control training soft ware Control Station the pro cess flowsheeting package HYSYS and the general-purpose computational package Mathcad . CONTROL STATION Control Station (CS) i s a proce ss dynamics and control train ing simulator that provides access to several simulated pro cessesJ6 1 The case studies include gravity-drained tank s, a pumped tank a heat exchanger, a jacketed reactor, a furnace a multitank process, and a binary distillation column The software also allows the user to build tailor made processes and single-loop (or 2 x 2) control structures using a transfer function block-oriented environment. Linear process models and Proportional-Integral-Derivative (PID) controller settings can be developed u si ng the de sig n module of the software package. The available controllers in version 3 0 of CS in clude the classical PID and its variants, cascade, feedforward, Smith predictor decoupler and sampled-data and single-loop Dynamic Matrix Control (DMC). During the last few semesters, we have used Control Sta tion as a teaching aid in a number of bachelor and diploma courses on process dynamics and contro l. We use it for both assignments and hands-on workshops. As shown later the Abderrahim Abbas is Associate Professor of Chemical Engineering at the University of Bahrain He received his degrees from the Universit y of Salford (BSc) University of Ne wcastle upon Tyn e (MSc), and University of Bath (PhD) all in chemical engineering. His teaching and research interests are process systems engineering and reverse osmosis Nader AI-Bastaki is Associate Professor and Head of the ChE Department at the University of Bahrain He received his BEng and MEng from McGill University and his PhD from UMIST. His teaching and research interests are sepa ration processes and reverse osmosis Copyr i g ht ChE Division of ASEE 2002 236 Chemica l Engineering Edu ca tion

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feedback from the students on the use of the program was very positive The program made it easier for them to under stand process control material and concepts in a shorter time than traditional lecture-only classes. It also helped the stu dents relate theory to practice. Two workshop examples of how CS can be used to teach control concepts are shown in Figures 1 and 2. Figure 1 il lustrates why the derivative action should not be employed for processes having noisy measurements; the addition of the derivative action to a PI controller lead s to a deterioration 'jS mgle Loop Jacketed Reactor f!l~EI Figure 1. fie BL.l'l la3ks .l:ielp QI ii > ~ u a. t! E a.. QI 'CI !!! :, is QI ~ r -:---. . I 1/{ I I ,rr, I I I tJI \1,, i.l' ) ,.,_ ,/" t I .ti 1 I, 1~' r A I \ J ~ ( i i i i J I I I I I 1 I I I I I I I I I I I : l 1 \:! : : : : ; _J ' ' .. :p1 -: : PIO ~--~-~-;-; ' ' ' ' 1 I I I I I I I I I I I : : : : : : : : : : :/, . . . . I . ~L :~~:~:~f \ i11 1 \11 1 1 ~f~ 1)11~ 1 '~t j~ ~l-t j , , , J 1 fjl 1 1! , I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I 1---,---i---i---t---r-r---t---t1"--1' S 1 J, .J:I a M .._. 5-1 S11 1-7 7~ Time (min] 79:31 Min:Sec PID ( P= DA l=ARW, D= meas) Rflctnl F toe! Coaling Jaoktt lnltt Ttff'4)("C) na.o ---> (Ol1t .. b1nct) Jocl.tt ll
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(not an improvement) of the closed-loop response Also the derivative term leads to unacceptable fast movement of the control valve. The use of CS significantly contributes to teaching advanced control strategies such as feedforward cascade, and decoupling control to undergraduate students. Figure 2 illus trates the effect of proce s s interaction on the performance of conventional controllers in multi-input/multi-output pro cesses. The distillate composition controller results in good closed-loop performance when the bottoms composition con.., Casi HYSYI P,oms ( P fO (die [ Mam[( l!lfJEI 238 . ~;Product 1=:& ::> 1 ----~ I i Ammonia MIX-100 i I : Reactor I' I : : .._, I i -1 Reactor Pressure, atm Figure 3. Simulation of an ammonia reactor (HYSYSJ. ....,meth reactor HYSYS Proce ss (PFO Case IMaonl] 1!11':iJE.1 r i fie .Edit .S.imui..tion FIQw&heel fFD lool Y{indow J:jelp Rec~clc SynGas MIXER Cooler 1 Compressor Cl~. Figure 4. Methanol synthesis loop (HYSYSJ. Environment : Cae (Main) Mode: Stood)' State C\2l I Deloult Colour Scheme iJ Proauct Ch e mi c al En g in e ering Edu c ation

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troller is on manual mode. Clo s ing this latter loop lead s to a deterioration of the performance of the fir s t loop due to the "fight" or interaction between the two controllers The s tu dents are u s ually asked to check the loop s' interaction by cal culating the relative gain arra y[ 81 and to de s ign and te s t a decoupler for the distillation column. HYSYS HYSYS i s a modular commercial pro cess flow s heeting program that i s widely used by universitie s and industry ( par ticularly hydrocarbon-related companies). It i s capable of do ing material and energy balance s for s tatic and dynamic con dition s and i s a very powerful tool for proce ss s imulation. It has built-in routines to solve many s pecialized unit opera tions. One of the important features of HYSYS is the avail ability of an Oil Manager" option dedicated to support re finery simulations A comprehensive library of thermody namic property package s is s upplied with HYSYS to enable the user to design and solve many types of problem s. At the Chemical Engineering Department of the University of Bahrain HYSYS is used as an effective teaching tool in a number of courses including proces s analy sis ( material and energy balance s), plant de s ign and the se nior project s. TABLE 1 Students' Evaluation Forms 1. Ju s tifi ca ti o n fo r the u se of p rogram in the co u rse ( I = unjustified; 5 = absolut e l y Ju stified) 2 Contribution t o s tudy of th e s ubj ec t b y pro gram u se ( I = irr e l eva nt ; 5 = very effect i ve) 3. E ase of achieving th e goa l ( I = difficult; 5 = easy) 4. Clarity in th e means u se d t o co n vey knowledge ( I = co nfusing ; 5 = absolutel y clear) 5. R e lation s hip b e tween th e complexity of the co n ce pt g i ve n and the r eso urce s s upplied ( I = in ade quat e; 5 = absolute l y adequate) 6 Number of r eso urces ( inform a ti o n ) s imultan eo u s l y pr ese nted on scree n ( 1 = excess i ve; 5 = balan ce d) 7. Comput er sk ill s required (1 = excess i ve; 5 = null) 8. General qu a lit y of pr ese ntati o n ( I = poor; 5 = exce llent) 9. Effectiveness of the re so ur ces u se d: gra phi cs, tables and texts ( I = in effec tiv e; 5 = very effec tiv e) I 0. E ase of operation (l = comp l ex; 5 = very easy) 11. Document a tion for u se r(] = defi c ient, 5 = exce ll ent) 1 2. Clarity of th e goa l ( I = co nfusing, 5 = perfe c tl y defined) 13. C o rr es p o ndence between pro gra m and kno w led ge co n veye d in clas s ( I = absolute di sco nn ec ti on; 5 = hi g hl y related) 14 Amount of s p ec ific knowledge required about s ubj ec t for progr a m u se ( I = excess i ve; 5 = reasonable ) 15 Degr ee of interaction b e t wee n u ser and program ( I = pa ss i ve sche m es ; 5 = very int e ra ct i ve) 16 Time needed for program execution ( I = excess iv e; 5 = s uitabl e) Comment on the reason s for which you felt attracted to or bored by the pro gra m Summer 2002 The use of multimedia and software packages enhances teaching and learning. .. the students learn more and faster, allowing the teacher to cover more material ... In the proces s analysis course, s tudent s follow a system atic approach in which they effectively analyze the systems and develop comprehen s ive degree-of freedom tables to de termine if a problem i s correctly specified and also the order of solving the various unit s. The basic concepts used in modu lar s imulation package s are thoroughl y di sc ussed. Among the problem s associated with modular solution is the pre s ence of recycle streams, which nece ss itate the iterative tear stream solution. Determining the number of tear streams, their posi tions, the convergence techniques and the order or sequences of their converging are ba s ic issues that we clarify. Figures 3 and 4 s how flow diagram s of simple HYSYS case s tudie s that the students were requested to develop. In Figure 3, the effect of operating parameter s such as tempera ture pres s ure and composition of inert s on the production rate are evaluated for an equilibrium-type ammonia reactor ( parametric analysis). The variation of ammonia output com position with the operating pressure i s s hown in Figure 3 The significance of the recycle loop and the selection of the s uitable convergence acceleration method are emphasized by the sec ond case study on a methanol sy nthesis loop (Figure 4). Sol vi ng this problem also gives s tudents insight into the philosophy of the modular flowsheeting programs and the nature of the sequential so lution strategy. MATHCAD Mathcad is one of the four most popular computational packages u se d in indu s try and academia; the other three pro grams are Matlab, Maple and Mathematica. Mathcad com bine s s ome of the best features of s pread s heets (like MS Ex cel ) and sy mbolic math programs. It provide s a good graphi cal u ser interface and can be used to efficiently manipulate large data arrays to perform s ymbolic calculations, and to easily con s truct graphs. One of the useful features ofMathcad that is not found in the aforementioned programs is its ability to perform calculations with units ; this i s indeed an impor tant feature for engineering s tudent s In a recent survey con ducted by the discussion group on Computer Applications in Chemical Engineering , Mathcad was the preferred computational package for 16.2 % of participant s The s urvey included a large number of known package s, and the only two program s preferred by more people were MS Excel (35 .3 % ) and Matlab (23.4%). As a general programming package, Mathcad is being used in the Chemical Engineering Departm ent in several courses including process analy s i s, process modeling and simulation, 239

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equipment and plant design and the senior projects STUDENTS EVALUATIONS To measure the usefulness and effectiveness of the consid ered software packages, students filled out the evaluation form shown in Table 1 at the end of the course for which the soft ware was used. The sixteen questions were selected from the list of 24 questions proposed by Iglesias, et al. r 9 i Eight ques tions were dropped based on the recommendations of the authors and the inability of students to clearly understand some of them. Iglesias and co workers classified the ques tions in three categories: teaching content and methodology (questions 1-5), software and design features (questions 610), and user reaction (questions 11-16). The first class attempts to test the usefulness of the educa tional software in terms of subject content and design fea tures, as well as the teaching methodology used in the course. The second category evaluates mainly the user interface (num ber of resources presented, quality and effectiveness of graph ics, tables, animation, etc.) and TA B LE2 5 .-,---_-_-_-_ -_-_ -_-_-_-_:;-------------, Control Station (!EE] HYSYS 4 111!1111111 Mathcad C T M PDC UR Overall Ca t eg o ry Fig ur e 5. Overall marks for the three packages. CTM = Con tent and Teaching Methodology, PCC = Program Design Characteristics, and UR = Users' Reaction. ease of use of the package. The third class tests the user's reac tion to the program by consider ing aspects such as documenta tion for user, degree of interac tion between user and program, and time needed for program ex ecution. Note that the three cat egories are not totally indepen dent and distinct. The question naire ends by asking students to comment on the reasons they felt attracted to or bored by the program. Eva lu a ti on Res ult s for Co n trol S ta tion (1 0 st ud e nt s) TA B LE4 The students' evaluations for the three considered packages are shown in Tables 2 to 7 The over all results are presented in Figure 5. Control Station and Mathcad were, respectively, evaluated by the process control and process analysis undergraduate classes. HYSYS was evaluated by stuTA B LE3 Q u estion Mean Sta nd a r d Deviation 1 4.10 0.99 2 3.70 0 82 3 3.20 1.03 4 3.30 0.95 5 3.50 0.97 6 3.90 0.88 7 3.40 1.07 8 3 50 0.71 9 3.90 0 74 10 3.40 1.17 11 2.90 1.20 12 3 10 0 88 13 3 90 0.99 14 3 00 0.47 15 3.40 0.84 16 4 10 0.99 Comment on the rea s ons for which you felt attracted to or bored by the program. O verall Marks for Contr ol Stati o n Category Mean Standard Deviation Category Eva l uati o n R esults for HYSYS (21 stu d en t s) Question Mean Sta n dard Dev i at i on 3.59 1.33 2 4.00 1.07 3 3.50 0.91 4 3.41 1.14 5 3.36 1.05 6 3.59 1.18 7 3.59 1.05 8 3 57 1.16 9 4.27 0.83 10 3 05 1.05 II 2 86 1.08 12 4 18 0 80 13 3.82 1.22 14 3.32 1.09 15 3.32 0 99 16 3.09 1.34 TA B LES Overa ll Marks for HYSYS Mean Standard Deviat i on Content and teaching methodology Program design characteristics Users reaction 3.56 3 62 3.40 3 52 0.97 0.92 0 99 0 96 Content and teaching methodology 3.57 1.11 Program design characteristics 3 61 1.11 Users' reaction 3.43 1.17 Overall Overall 3 53 1.12 240 Chemical Engineering Education

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dents from process systems engineering courses. As the tables and Figure 5 show, the students' evaluations of all three soft ware packages were highly favorable; the overall marks var ied within a relatively narrow range (3.52 to 3.74). For the case of control station, questions 1 and 13 received high marks indicating a strong correlation between the soft ware and the knowledge conveyed in the class, and also that the use of computer workshops in the course is highly justi fied. Question 14 received the second lowest mark (3.0). This was expected since chemical engineering students do gener ally feel that their first process control course includes more material than an average course and that it is rather difficult. This is due to the well known fact that proce ss control is much different from traditional chemical engineering courses and that it includes a significant number of new theories and terms For HYSYS questions 2, 9, and 12 received the highest marks, indicating that the students found the software re sources to be very effective and that the program has signifi cantly contributed to their study of the courses considered Note that prior to the availability of process flowsheeting packages the students had to manually carry out lengthy deTA B LE6 Eva lu a t i o n Results for Ma th ca d (6 students) Question Mean Standard Deviation 3.50 1.52 2 3.33 1.5 1 3 3.33 1.03 4 3.67 1. 21 s 3.33 0.82 6 4.50 0.55 7 3.67 0.52 8 4.00 1.10 9 4 00 0.63 10 4.00 1.10 11 3.17 1.17 12 3.50 I.OS 13 4.17 1.60 14 4.50 0 84 IS 3 .67 1.37 16 3.50 I.OS TA B LE7 Overa ll Marks for Mathca d Category Mean Standard Deviation Content a nd te achi ng methodolo gy Program desi gn characteristics Users reaction Overall Summer 2002 3.43 4.03 3 75 3.74 1.17 0.81 1.20 1.10 sign calculations. The s tudents gave their lower ratings to que s tion s 10 (3.05) and 16 (3.09), i.e., they felt that the pro gram was not very easy to operate and that the time for simu lating case studies was too long. The speed of execution is, of course, dependent on the s ize of the problem at hand. With HYSYS being a commercial flowsheeting package even simple problems include a significant number of details. High mark s were given to questions related to Mathcad design characteristics; the overall mark is 4.03 (see Table 7). Thi s is not surprising since the package is truly user-friendly and the fact that prior to using Mathcad, the students were programming in FORTRAN. For all three programs the stu dents evaluated the programs documentation as above aver age (see question 11) Although we feel that the material handed out to the students was very good, this issue is cur rently being addressed by conducting more tutorials on the u se of the packages s upplying the students with more copies of s horter versions of the user s' guides, and preparing sim pler getting-started handout s. CONC L UDING REMARKS The computer has become an integral part of engineering education. As the power of both hardware and software con tinues to rapidly increase, we expect the use of information technology in the cla ssroo m/laboratory to grow at a much faster rate in the near future. The use of multimedia and sof tware packages enhances teaching and learning In particular, the students learn more and faster allowing the teacher to cover more material in the time allocated for the course. Of course, the information tech nology tools have a large number of benefits that are not within the scope of this paper. For example, they are invaluable tools for web-based education and distance learning and training REFERENCES 1. Kantor J.C. T.F. Edgar "Computi n g Skills in the Chemical Engineer ing Curriculum ," in B. Carnahan (Ed ), Computers in Chemical Engi neering Education, CACHE Corporation, p 9 (1996) 2. Benyahia F. "Process Simulation Packages in Undergraduate Chemi cal Engineering Courses, The 199 8 lchemE R esea r ch Event, CD-ROM (IS BN 0 85295 400 X) 3. Edgar, T.F., "I nform a tion Technology and ChE Education: Evolution or Re vol ution ?" Chem Eng. Ed ., 34 (4), p 290, (2000) 4. Kulik J.A. and C.C. Kulik, Contemporary Education P syc h o l ogy 12 p. 222 (I 987) 5. Montgomery S. H.S. Fogler "I nter active Computer-Aided Instruc tion," In B. Carnahan (Ed.), Computers in Chemical Engineering Edu ca tion CACHE Coproration p. 57, (1996) 6. Cooper D ., D Dou g h erty Enhancing Process Control Education with Control Station Trainin g Simulator, Compt Appl Eng Edu 1 p. 203, (1999) 7. Cooper, DJ N. Sinha "Pic le s +Digest= Control Station TM for Win dows ," CACHE News 44 p. 14 (1997) 8. Bristol E.H ., "On a New Measure of Interactions for Multivariable Process Control, IEEE Trans Auto ControlAC-11, 133 p. 133, ( 1966 ) 9. I g le sia s, O.A. C.N. Paniagua R .A. Pessacq Evaluation of Univer sity Educational Software, ComptAppl Eng Edu 5 p. 181, (1997) 0 241

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.,~ ... 6 ... -..._ c_l_a_ s_s r o o m __ _______ ) TEACHING PROCESS CONTROL WITH A NUMERICAL APPROACH BASED ON SPREADSHEETS CHRISTOPHER RIVES AND DANIEL J. LACKS Tulane University New Orleans, LA 70118 T he traditional method for teaching process control courses uses analytic techniques based on Laplace transforms to solve the relevant differential equa tionsY -91 The mathematical manipulations involved in these analytic solutions are so complex and non-intuitive, however that students can lose sight of the physical significance of the results. Numerical solutions offer a remedy to this problem and can be used in conjunction with traditional analytic solu tions to strengthen the instruction of process control. We emphasize that numerical solutions are not intended to re place analytic methods but should instead be used in addi tion to analytic methods. The use of computers in obtaining numerical solutions can give an enhanced physical intuition and understanding that can be difficult to achieve from analytic solutions alone. As a reA B 1 Process Vari ab les 2 K= 5 3 t = 2 4 (; = 5 Time Step lit= 0 0 1 t f C hrist ope r Rives received his BS in chemical e n gineering from T u l a n e U niversity in 2002. He is currently studying for a P hD in chemical en gineering at N orthwestern University. Dan i el J. L acks is Professor of Chemica l En gineering at T ulane University. He received his B S in chemical engineering from Cornell Uni versity, and his PhD in chemistry from Harvard University. His research interests involve the ap plication of molecular simu l ations to chemical engineering problems. C D i sturbance ls t ep = D fo r t
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title of a recent article in Chemical and Engineering News: "Thinking Instead of Cookbooking : When Computers Take Over the Dirty Work ... Students Can Focus on the Bigger Picture." 11 21 The differential equations that arise in process control ap plications are readily solved numerically by using simple spreadsheets that can be constructed by the students in Jess than five minutes. Students can experiment with different control schemes and parameters in order to gain an under standing of how each parameter affects the response of the system. They develop an intuitive feel for how a system will respond to input changes and how this response can be con trolled. Then they discover bow to optimize the control. This strategy has been used in the process control course at Tulane. The numerical approach is used first to introduce a topic, allowing students to obtain a good physical understand ing before proceeding The topic is then addressed more fully with the traditional analytical approach based on Laplace transforms. Students follow the analytical approach more eas ily at this point since they already have a solid physical un derstanding from the numerical approach. DESCRIPTION OF APPROACH This section describes how the numerical approach using spreadsheets can be u sed to teach most major topics in a pro cess control course, including process dynamics, frequency response analysis, feedback control, and advanced control 4 5 4 3 5 3 2 5 ( a) 1~ / o~ _j -0 5 50 4 5 4 (c) 3 5 2 5 1 5 1 0 5 0 -0 5 20 100 lime 40 time 150 200 60 BO 4 5 4 3 5 ( b ) 2~ _)= 1 5 1 0 5 0 -0 5 0 20 40 60 BO 100 time 10 (d) -2 -4 -6 20 40 time 60 Figure 2. Response of a 2nd order process to a step change in the disturbance for (a) I;,= 3 (b) I;,= 0.2 (c) I;,= 0 (d) I;,= -0.l The bold lin e is the disturbance and the thin line is the response. Summer 2002 techniques such as feedforward and cascade control. Process Dynamics As an example, the response of a linear second-order pro cess is examined. [1 9 J A linear second-order process is de. scribed in general by "t 2 y" +21;,'ty +y = Kf(t) (l) where y is the response of the process ( output), y' = dy/dt, y" = d 2 y /dt 2, f is the disturbance (input), K is the gain, 't is the characteristic time, and I;, is the damping factor. Differential equations can be solved numerically using Euler's Method. This method is implemented for second order differential equation by repeatedly applying the follow ing algebraic equations for small time increments, ~t: y(t+~t)=y(t)+y'(t)~t (2) y' (t + ~t) = y' (t) + y" (t)~t (3) Note that the initial values of y and y' must be specified, and the values of y"(t) are obtained by rearranging Eq. (1). ,, ( ) Kf(t)21;,'ty' (t)y(t) y t = -~-----,,~~~~ 't2 (la) Below we present the implementation of this method for a step change in f(t). The spreadsheet used to solve this problem is shown in Figure 1. The results are easily displayed in graphical form by plotting y and f together as functions of time. All param eters are defined at the top of the spreadsheet, and their cell locations are referenced in the relevant equations. Upon changing parameter values, the graphical display of the re sults is updated immediately without rewriting any of the spreadsheet. The physical significance of the damping factor I;,, in a sec ond-order linear differential equation can be demonstrated with this approach by comparing the response to a step change for different values of I;, For I;,> I, the response is overdamped, and it reaches a steady state without oscillating (Figure 2a). For O
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the spreadsheet used for the step function input (o nly the di st urbance is different) A B Process Variables K= 1 t= .:i. t= 2 1 5 Time Step 0 5 f C D E Disturbance A= (1 ) = 0 01 Initial Values y(0) = 0 y'(0) = 0 y y' y" The frequency response of the sys tem can be addressed by comparing the response obtained with different values of the angular frequency ro When the frequency i s small, the sys tem ha s sufficient time to react to the changing disturbance, and the re sponse is nearly in phase with the disturbance (Fig ure 4a) When the frequency is increased, however, the system does not have s ufficient time to react and the response increas ingly lags behind the di s turbance (Figures 4b and 4c) Additionally, the amplitude of the response usu 0 D$2'sin(D$3'A 11) D7 DB (B$2*811-2*8$4*8$3"D11-C11)/(B$3)'2 14 15 16 A11+8$7 l l C11+D11*8$7 D11+E11'8$7 l l l Figure 3. Spreadsh ee t used to d eter mine th e response of a 2nd order process to an oscillating disturbance. Arrows indicate that ce lls should be co pied and pasted down ward for approximately 5,000 to 10,000 rows ally decreases with increa si ng frequency (Figures 4a 4b and 4c). For I;< 1 and small frequencies however, the behavior of a linear second-order system is unusual in that the ampli tude increase s with increasing frequency (Figure 4d). Note that the immediate graphical results allow s tudents to quickly and easily experiment with different values of ro and I;. Feedback Control A feedback control mechani s m measures the output of the process, compares it to the de s ired value (the set point) and then alters an input to the process in order to bring the output closer to the desired value_ll 9 1 The output of a proportional integral-derivative (PID) con troller is given by (4) where E = Y s p y, Y s p is the set point, and y is the output of the process When the system is not under any control, the values of Kc and -c 0 are set equal to zero, while -c I is set equal to infinity. The integral term can be calculated numeri cally as I J mt= Ie(ti)~t 0 and the derivative term can be calculated numerically as dE(t) E(t)E(t~t) dt ~t (5) (6) The numerical approach is applied here to the feedback con trol of a process consisting of three first-order systems in se ries. The dynamics of the other parts of the control loop (e.g., measuring device) are not included for simplicity, but can easily be included if de si red (as pointed out in the Discussion section) A process consisting of three first-order systems in 244 1 5 (a) 1 5 (c) o: V'M -0 5 1 0 5 -0 5 -1 1 5 -1 5 0 100 200 300 0 20 40 60 time time 1 5 (c) 1 5 (di o: WM ,: WJ' -0 5 -0 5 -1 1 1 5 0 10 time 1 5 20 -1 5 0 50 100 time 80 150 Figure 4. R esponse of a 2nd order process to an oscillating disturbance for (a) I;= 1.5 ro = 0.1 ; (b) I;= 1.5, ro = 0.3; (c) /;=1.5, ro=2 ; (d) /;=0.5 ro=0.2 The bold lin e is the disturbance and the thin lin e is the response series is described by three coupled first-order differential equations, 'tiY 0 i +Yi= KJ + KpY c 'tiY 0 i + Yi = Ki YiI i = 1 i =2 3 (7) (8) where i is the system number. The se coupled differential equa tions are numerically integrated using Euler's method by re peatedly applying the algebraic equations Yi (t + ~t) = Yi (t) + y i (t)~t i = 1 ,2,3 (9) Chemica l En g in ee ring Education

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I 0 r-.., .., l'i .,; II II II II (!) ,=f! >, u. ..... w "' 0 (D 0 II "i t II Cl e: J .e ;; ,g .... u .... ., .!! "' O> .., ID .c N (') .., r-.. 0 0 j ., ., .... 1l e a. II II II II II II ..: "' !'! .,;: ;: 0 0 II II e: : ::! ;; g. o in .. E i= II II ,,t , .. I ;; ID i .., ll;l >, .., __. i:i ;... .. !!!. (') .. lg_ .., 9 >, :-: __. \-' (D .. !!!. N .. lg_ :-: u ..; ::; ;; : ~ ID + .., iii "' .. !!!. ;;; I" ,.., O> 1 -.;f" >, wI -. + :-: w ;;; I" ;: oo v W ,.... -. (!) + .., i:i ;;; I" "if" >, w. u. + .., u :;;fl> w l'i .. w <'i .. w V .., "' ;;; ... 0 ID + -. .., < -IN I"' I "' I "' 1 10 lr--loo lo, 1~1:::1 ~1 1:1:1~1~1~1~ Summer 2002 (a ) where the y'i ( t) are obtained from Equations 7 and 8. The spreadsheet used to solve this problem is shown in Figure 5. By experimenting with different values of the control parameters ( Kc, t 1 and t 0 ), the relationship between each control parameter and the response can be determined. If proportional-only control is u se d (i.e t 0 = 0 and t 1 = a large number that approximates 00 ), the response is offset from the set point (Figure 6a). Increasing the value of Kc will minimize this offset (Figure 6b ), but the system can be come unstable if Kc is too large (Figure 6c). Adding integral control (i.e., decreasing t 1 from 00 ) will eliminate this offset (Figure 6d). But if the value of t I is too small, the system becomes unstable (Fig ure 6e). Adding derivative control (i e., increasing to from 0) stabi lizes the system (Figure 6f). This stabilization allows a larger Kc and a smaller -c 1 to be used, but a large -c 0 value also slows the response. The values of the control parameters s hould be chosen such that a quick response with small oscillations and no offset is achieved. The Zeigler-Nichol s tuning method is one way to obtain advantageous values for the three control parameters, in which Kma x (10a) K =-cC 1.7 p (10b) "C1 =_!!_ 2 p (10c) to = _!!_ 8 where K~ax is the maximum value of Kc for which the response is sta ble with a proportional-only controller, and Pu is the period of os cillation of the response at K;,"ax The value of K~ax is found by trial (b ) (c) ~ Jr i L . ......... . 2 -2 2 -4 -4 -4 -6 -6 -6 20 40 60 80 0 20 40 60 80 0 20 40 60 time time time (d) (e ) IQ ~ k C -2 -2 2 -4 -4 -4 -6 -6 -6 20 40 60 80 0 20 40 60 80 50 100 150 200 time time time Figure 6. Response of a process consisting of three first order systems in series with feedback control to a step change in the disturbance (a) only, Kc =1 ;{b)P only,Kc =4 ;{c)P -onl y,Kc =15 ;(d)PI: Kc =1, t1 =5;(e) PI: Kc = 1, t 1 = 1.3, (f) Pill: K 0 =1 t 1 = 1.3 t 0 = 15. The bold line is the dis turbance and the thin line is the response. 245

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7 (a) ( b ) 6 -1 -1 0 20 40 60 20 40 60 time time Fig u re 7. Tuning of PID parameters with Ziegler-Nichols method for a process consisting of three first-order systems in series with feedback control. (a] Determination of K~a x and Pu; (b] PID with Ziegler-Nichols parameters: Kc = 3 7, 1: 1 = 5.4 'to = 1.4. The bold line is the disturbance, and the thin line is the response and error to be 6.3 (Figure 7a), and the value of Pu is observed to be 10.8. The response using the Ziegler-Nichols parameters is shown in Figure 7b. Feedforward Control A feedforward control mechanism measures the disturbance and u ses this measured value to adjust an input variable with the goal of keeping the process output at the desired value. 111 The output of a simple feedforward controller is given by Yc=AY s p-Bf (11) where A and B are controller parameters that will depend on the particular process to be controlled The numerical approach is applied here to the feedforward control of a process consisting of three first-order systems in series (Eq. 7 and 8). The spreadsheet for this problem is shown in Figure 8. Perfect control can be obtained by choosing the parameters such that the system is at steady state with the pro cess output at the set point (i e., y' 1 = y 0 2 = y' 3 = 0 a n d Y 3 = Y s p ) From equations 7 and 8 it is easily found that the parameter values that yield perfect control are A= l / (KpK 2 K 3 ) and ( a ) -1 20 40 time 60 80 (b) 5 r--------1 20 40 time 60 80 Figure 9 Response of a process consisting of three first-or der systems in series with feedforward control to a step change in the disturbance. ( a) A= l / (KpK 2 K 3 ) = 0 842 and B = K 1 / KP = 0.625; (b) A= 0 842 and B = 0 5. The bold line is the disturbance; the thin line is the response. 246 N ..,. co 0 "' :i:: "' N 0 r-, I.L W U") 0 tO 0 0 0 "' 0 (.) Q) :::, E II II II JI 11 11 II II 11 11 ~~'!Z-:2~ II ,
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"' :;; :; :,<:: 1o --. "' J: "' _, u "' ------+ s< i J: "' a.;;; u "' --,------+ >, J: + "' ;., "' J: "' a:, "'. ---+ :!:+ "' "' a. w ... .;, --. "' J: "' J: N "' 0 w ;: -. 0 ;... "' !!!. M "' "' " " 0 (!) -2 Ir N ------+ .. >, ,;: >, v "' "' !!!. N "' a:, u u.. ,;; , w c'.,---+ .g .E ,;; ;: + "' o ;;; a:, u ,;; ,.._ :. wu.. ---+ + "' u "' w l"i "' "' Q) "' a, N 0 w a:, :a N M ... ,.._ 0 ... N ------+ "' 0 a. 0 "' .:: Q) w "' u'i V > ... "' Q) :c "' E Q) ;:: ;;;-8 a: ;;; " II " " <( ,= ::::.' ,E -2 ,z
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homework problem requires that students find the maximum value of a controller gain for a proportional-only controller in a certain process by three methods: by trial and error with numerical solutions, by deriving the transfer function and find ing the gain that leads to positive real parts of its poles, and by the Bode stability criterion using analytical expressions for phase lags and amplitude ratios. The students compare the results for the maximum controller gain from these different method s and find them to be the same (within numerical error) The exams test the students' knowledge of applying nu merical methods to process control problems in addition to the traditional process control material. One of the exams includes a computer part (given in class in our computer com puter lab) where students solve a problem numerically with a spreadsheet and turn in the printed result. The other exams have problems in which students must show how to set up a spreadsheet to numerically solve a given problem providing all of the relevant equations. Students found the numerical approach using spreadsheets to be extremely useful in understanding the concepts under lying process control. In unsolicited comments on the course evaluations two-thirds of the students remarked that the nu merical approach was the most valuable aspect of the course The students also seemed to genuinely enjoy this approach. When problems were solved with this method in the com puter lab, students were often so eager to discover the ef fects of changing some parameters that they would proceed ahead of the discussion They would also occasionally con tinue experimenting with the effects of different parameters after the class had ended. Other Issues The numerical approach is more general than the analytic approach, in that it can also be applied to nonlinear differen tial equations, i.e., a linearization approximation is not nec essary as it is for the analytic approach based on Laplace transforms. To emphasize this point, a homework problem was given in which students investigate the frequency re sponse for a process described by the nonlinear differential equation y' + ya = f (where a is the number of letters in their last name divided by five), and then use the results to con struct Bode and Nyquist diagrams A concern with the numerical approach, of course, is that there is numerical error in the results. Students should be aware of the numerical error and that the error can be re duced by decreasing the time step ~t or by using a more sophisticated integration method (e.g Runge-Kutta or a pre dictor-corrector method). A reasonable time s tep for these problems is ~t = 1: / 100 where 1: is the smallest characteris tic time for the system. Although excluded here for simplicity, it is straightforward to include in this approach the dynamics of other elements of 248 the control loop such as actuators (e.g., valves) and measur ing devices Including the dynamics of these elements would amount to including a few more coupled differential equations which translates to a few more columns on the spreadsheet. Dead time is also straightforward to include in this approach. To introduce dead time to a variable y, a new variable, Y +dead, is defined such that y +dead ( t) = y( t tdead) The values for y +dead are obtained in the spreadsheet from the values of y, by setting the cell for y +dead at the time, t, equal to the value of the cell for y at the time t td ead (i.e., td ead I ~t rows above in the spreadsheet). The present approach is different than but complementary to, an approach that uses packaged software (such as Control Station1 13 l) for teaching process control. In the present ap proach students are in fact solving the governing equations themselves, with a numerical method rather than an analytical method. In contrast the Control Station software 1131 presents results without requiring that students solve the equations CONCLUSION In the usual method for teaching process control, students are taught to solve the relevant differential equations analyti cally by using Laplace transforms. This method involves com plex mathematical manipulations which can cause students to lose sight of the physical significance of the problem. The main goal of a process control course should be to provide a general understanding and intuitive feel for how physical pro cesses behave and how they can be controlled. Numerical solutions for process control problems are extremely easy to obtain using spreadsheets created by students themselves This approach allows students to concentrate on what is physi cally happening as opposed to the complex mathematics, yet the students solve the problems themselves (i.e., the solu tion is not given to them by packaged software). This ap proach has been used in the Process Control course at Tulane, and student feedback has been extremely positive. REFERENCES I. Stephanopolous, G., C h emica l Process Control, Prentice Hall Englewood Cliffs, NJ (1984). 2. Rigg s, J.B. Chemical Process Control, Ferret, Lubbock TX (1999). 3. Marlin, T.E., Proc ess Control, McGraw-Hill New York, NY (1995). 4. Marlin T.E Process Control, 2nd ed. McGraw-Hill, New York, NY (2000). 5. Smith, C.A ., and A.B. Corripio, Principles and Practice of Automatic Process Control John Wiley & Sons, New York, NY (1985). 6. Seborg D.E. T.F. Edgar and D.A. Mellichamp, Process Dynamics and Con trol John Wiley & Sons New York, NY ( 19 89). 7. Shin skey, F.G., Pr ocess Control Systems, 4th ed., McGraw-Hill New York, NY (1996). 8. Luyben W.L. Esselllials of Process Control McGraw-Hill New York, NY (1997). 9. Coughanowr, D.R. Process Systems Analysis and Control 2nd ed Mc-GrawHill New York NY ( 1991 ) 10 Gibbons, W. Science, 266 893 (l 994). II. De Vries, P.L., American J ournal of Physics, 64, 364 (1996). 12 Wilson E.K., Chem i ca l and Engineering News, May 26, p. 33 ( 1997 ). 13. Cooper, D.J. Control Station/or Windows, Version 2 5 (2000) 0 Chemical Engineering Education

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U n iversi t y of Akro n U ni v er s it y of A l a b a m a U ni vers it y of A lb erta Un i vers i ty of Arizona Arizo n a Stat e U ni ve r s i ty Un i vers i ty of Arka n sas A uburn U ni versity rig h am Yo u ng University Univers i ty of B ritis h C o lum bia Bro w n U n ivers it y B u cknell U n iversity Un i versity of Calgary U n iversi t y of Ca li fo rni a, B e r ke l ey U ni ve r s i ty of C al i fornia, D avis U ni ve r sity of C alifo rn ia, Los Angeles University of Ca l ifornia, R iverside Uni ve r s it y of C a li fo rni a, S a n ta B arabara Califo rn ia I n s titut e of Tec hn ology California S tate University Carneg i eM e ll on Un i versity Case Wes t e rn R eserve U n iversity U n ive r s i ty of Ci n ci nn ati City Co ll ege of New York C l arkson U n ivers it y Cl e m so n U ni vers i ty U n iversity of Colorado Colorado Sc h oo l of Mines Co l ora d o S tate U n iversi t y Co lumbi a U n ivers i ty University of Connec ti cut Cork R egional Technical Co ll ege Corn e ll U ni ve r s it y D artmo uth College Dal h o u s i e Universi t y U n iversi t y o f D ay t on Un i versity of D e l aware Dr exel Un i ve r sity University of Florida Fl or i da In st i t ut e of Tec hn ology F l or id a St a te/Fl o rid a A& M U n ivers i ty Georgia I nstit ut e of Techno l ogy Hamp t o n University Unive r s it y of H o u ston H owar d U ni ve r sity Un i versity of Idaho University of Ill i n ois, Chicago Un i vers i ty o f Illin o i s, Urb a n a Ill i n ois In stit ut e of Tech n o l ogy I owa S t a t e University Joh n s Hopkins University Un i ve r si t y of Kansas K ansas St ate Universi t y U ni versity of Ke n t u cky Lafayette Co ll ege Lake h ead University L a m ar U n iversity L aval Un i versity Le h ig h University Loug h borough University Lo u isiana St a t e University L o ui sia na Tec hnic al U ni ve r sity University of Louisville U n iversity of Maine M anh attan Co ll ege U n iversity of Mary l and, Co ll ege Park Unive r sity of Mary l and, Bal t imore County U n iversity of Massachusetts U n iversi t y of M assac hu se tt s L owe ll Massac hu setts I n stit u te of Technology McGill University M c M as t e r U n ivers it y M c Neese S t ate U n iversity U ni versity of Mic h igan Michigan S tate University Mic h igan Tec h no l ogical U n iversity U ni ve r si t y of M i ss i ss ippi Mississi p pi State U n iversity University of Missouri, Co l um b ia U n iversity of Misso ur i, R o ll a Monas h U n iversity Mo n tana State University U n iversity of Nebra~ka U ni versi t y of Neva d a a t R e n o University of New Ham p s h ire Unive r sity of New Haven New J ersey I ns titu te of T ec hn o l ogy U ni versity of New Mexico New Mex i co S t ate Un i versity North Carolina A & T University No rth Caro li na State University U ni versi t y of North D a k ota Nort h eastern University Northwes t ern University U n iversity of Notre Dame Ohi o State Univers it y O h io University University of Oklahoma Oklahom a St a t e U n ive r s it y Orego n State U n iversity University of Ottawa University of Pennsy l vania Pen n sy l vania S t ate U n ive r si t y P o l y t ec hni c In s t it ut e of New York Prairie View A&M U n ivers i ty Princeton University Pu r du e Unive r s i ty Q u ee n 's U n iversity R ensselaer Polytechnic Institute University of Rhode Islan d R ice University U ni versi t y of R oc h es t e r Rose-Hulman I n stitute of Techno l ogy R owan Un i versity Ru tge r s, T he St ate U n ive r si t y Sa n Jose State U ni versity University of Saska t chewa n University of S h effie l d University of S h e rb rooke University of South Caro lin a South Dakota School of Mines University of Southern Ca l ifornia State Un i versity of New Yo r k, Buffalo Stevens Insti tut e of Techno l ogy University of Sydney Syrac u se University U ni ve r s i ty of T en n essee Tennessee Techno l ogical University University of Texas Texas A & M U n ive r s i ty, Co ll ege S t atio n University of To l edo T ufts University Tu l ane University Unive r sity of T ul sa Tuskegee Unive r s i ty University of Utah Va n de rbil t U n iversi t y Vi ll anova U n ivers i ty University of V i rgi n ia Virginia Polytechnic Instit u te University of Washing t o n Was hin g t o n Sta t e U n iversi t y Washington University University of Wate rl oo Wayne State Universi t y West V i rgi n ia In sti tu te of Tec h no l ogy West Virgin i a Univers i ty Wide n er University U n ivers i ty of Wisco n s in Worcester Polytechn i c I ns t it u te University of Wyoming Ya l e University Youngs t own S t a t e University

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Chemical & Materials Engineering Graduate Program T he Department of Chemical an d M a t eri als Engineerin g a t the University of Ala bama in Hunt svil le offer s yo u the oppor tunit y for a so lid and rewarding gra duate career that will lead to further success a t the forefront of academia and indu s try. We will provide graduate program s that educate and train students in advanced areas of chemical engineering material s sc i e nc e a nd e n g ineerin g and biotechnology Option s for a n MS a nd PhD de gree in Engineering or Materials Science are available. Our faculty are dedicated to international l ead ership in re searc h. Project s are ongoing in Mass Tran s fer Fluid Mech a nic s Combustion Biosparation s, Biomateri a l s, Microgravi ty Mat rials Proce ss ing and Adhesion. Collaborations have been established with n ea rb y NASA/ Mar s h a ll Space Fli g ht Center as well as leadin g edge biotechnolog y and engineering companies. We are also dedicated to innovation in t eac hin g. Our classes incorporate advances in computational method s and multi media presentations Department of Chemical Enginee ri ng The University of Alabama in Huntsville 130 Engineering Building Huntsville, AL 35899 Fall 2002 FACULTY & RESEARCH AREAS Ramon L Cero Ph.D. (UC-Davis) Professor and Chair Capillary hydrodynamics mult i phase flows enhanced heat transfer surfaces (256) 824-7313 rlc@che.uah.edu C h ien P. Chen Ph.D. (Michigan State) Professor Multiphase flows spray combustion, turbulence modeling, numerical methods in fluids and heat transfer (256) 824-6194 cchen @ che uah edu Krishnan K. Chittur Ph.D. (Rice) Professor Protein adsorption to biomaterials FTR / ATR at solid-liquid i nterfaces biosensing. (2 56) 824-6850 kchittur @c he uah edu Do u glas G. Hayes Ph.D. (Michigan) Associate Professor Enzyme reactions in nonaqueous media separations involving biomolecules lipids and surfactants surfactant-based colloidal aggregates. (256) 824-6874 dhayes @c he.uah edu James E. Smith Jr. Ph D. (South Carolina) Professor Kinetics and catalysis powdered materials processing combustion diagnostics and fluids v i sualization using optical methods ( 256) 824-6439 jesmith@che.uah.edu Jeff r ey J. Weimer Ph. D (MIT) Associate Professor Joint Appointment in Chemistry Adhesion biomaterials surface properties thin film growth surface spectroscopies scanning prode microscopies. (256) 824-6954 jjweimer@matsci.uah edu The Univers i ty of Alabama in Huntsville An Affirmati v e Actio n /E qual Opponunity In s titut i on Web page: http://cheme n g.uah e d u Ph: 256 824 6810 FAX: 256 6839 323

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University of Alberta Chemical and Materials Engineering The University of Alb e rta is well known for its commitment to exce llence in teach ing and research. Th e Department of Chemical and Materials Engineering has 37 professors and over 100 graduate students. Degrees are offered at the M.Sc. and Ph.D. levels in Chemical Engineer ing, Materials Engineering, and Process Control. All full-time graduate students in the res earch program s re c ei v e a stipend to cover living expenses and tuition. 324 For further information contact Gradu a t e Pr ogram O ffice r D epa rtm en t of C h e mi ca l an d M ate r ials E n g in ee r i n g U n ive r s i ty of A lb er t a Ed m o nt on Al b e rt a Ca n ada T 6 G 2 G 6 PHO NE (78 0 ) 4 921 8 2 3 FAX (78 0 ) 49 22 88 1 e -m a il : c h e mi ca l. e n g in ee r i n g@ua lb e rt a ca we b : www .ualb e r ta c a /c h e m e n g M. BHUSHAN Ph D ( I.l. T. B o m bay ) Se n so r Lo ca ti o n Fault Di ag n os i s Pr ocess S afety R.E. BURRELL P h D ( U ni ve r s it y of W a t e rl oo ) Na n os tru c /llr e d Bi o m a t e r ia l s Dru g D e li very Bi o film s 1is s u e l 11t eg rati o n w ith Mat e rial s P. CHOI Ph D (U ni ve r s it y of W a t e rl oo ) M olec ular M ode lin g of P o l y m e r s Th e rmod y nami cs of Pol y m e r S o l wion s a nd Bl e nd s K. T. CHUANG Ph D. ( U ni ve r s i ty of A lb e rt a ) Fu e l C e ll C ata l y si s S e parati o n Pro ces s es P o lluti o n C o nt ro l I. G. DALLA LANA Ph.D (U n iv of Minn e s ota ) E MERIT U S Ch e mi c al R e a c tion En g in ee rin g H e t e ro ge n eo u s Cat a l ys i s J. A. W. ELLIOTT, P h.D ( U ni v e r s i t y ofToro n to ) Th er m o d y nami cs St a ti s ti c al Th e rm o d y nam ics b u e rfa cia l Ph e n o m e n a D. G. FISHER Ph D ( U ni ve r s i t y of M i c hi gan ) EME RIT US P rocess D y nami cs a nd C o ntr o l R ea l-7im e Co mput e r A ppli c ati o n s J.F. FORBES Ph.D ( M c M as t e r U ni ve r s it y ) C HAIR R e al 7im e Op t imi z ation S c h e dulin g and P l ann i n g M. R. GRAY Ph D (Ca li fo rni a In s t. of T ec h ) Bi o r e a c tor s C h e mi c al Kin e ti cs Bi 111 m e n P roce s s i n g R. E. HAYES Ph.D. ( U ni ve r s i ty o fB a th ) N um e ri c al A n a l ys i s R e a cto r M o d e lin g Co mp u tati o na l Fluid D y n a mi cs B. HUANG Ph D (U ni ver s i ty of A l berta ) Co m r o ll e r P erfo rman ce A ssess m e nt Multi va riabl e Co 111ro l S tati s ti c s S. M. KRESTA P h D ( M c Ma s t e r Unive r s i t y ) Tur b ul e nt & Transiti o n a l Fl ows Multiph ase Fl ows C FD S. LIU, Ph D. (U ni vers i ty of A lb ert a) Flu i d P arti cle D y n ami cs Transp o rt Ph e n o m e na Kin e ti c s D. T. LYNCH Ph D (U ni ve r s it y of A lb e rt a) D EAN OF EN GlN EE RIN G Catal ys i s Kin e ti c M o d e l in g N um e r i c al M e th o d s P o l y m e ri z ati o n J. H. MASLIYAH Ph.D. ( U ni ve r s i ty of Briti s h Co lumbi a) T ra n s p o rt Ph e n o m e n a Co ll o id s Particl eFluid D y n a mi cs Oil Sand s A. E. MATHER Ph D ( U ni ve r s i ty o f M i c hi ga n ) Ph ase E qu i li b r ia Flui d P roperties a t H ig h Pr ess ur es Th e rm o d y nami cs E. S. MEADOWS P h D ( U n iv e r s i t y of Tex as) P rocess C o 11tr o l Fu e l Ce ll M o d e lin g and Co ntrol Optimi za ti o n W. C. MCCAFFREY Ph D ( M c Gi ll U ni ve r s i ty ) R eac t i o n K in e ti c s H e a vy Oil Up g radi n g P o l y m e r R ecy clin g Biot ec h nol ogy K. NANDAKUMAR, Ph D ( Prin ce t o n U ni vers it y) Tra n s p o r t Ph e n o m e na Di st ill ati o n Co m p u tatio n a l F lu id D y n a mi c s A.E. NELSON Ph D. ( Mi c hi ga n T ec hn o l og i ca l U ni ve r s it y ) H e t e ro ge n eo u s Cata l ys i s U H V Surfa ce S c i e n ce Ch e m i c al Kin e ti cs M. RAO Ph.D. ( Rut g e r s U ni ve r s it y ) Al l111 e ll ige 111 Co m ro l P rocess Co nt ro l S. L. SHAH Ph D (U n i v e r s it y of A lb erta ) Co m p w e r P rocess Co 11tr o l S ys t e m l d e 11tifi ca ti o n Pro cess a nd P e rf o m ra n ce M o ni tor in g J.M. SHAW Ph D (U ni ve r s i ty of B ri t i s h Co lumbi a ) P e trol e u m Th e rm o d y nami cs Multip h as e M ix in g P ro ces s M o d e lin g U. SUNDARARAJ Ph D. (U ni ver s i ty of Minn eso t a) P o l y m e r Pr o ce ss i n g P o l y m e r Bl e n ds /nt e rfa c i al Ph e n o m e n a H. ULUDAG Ph D (U ni ve r s it y of T o ront o) B i o m a t e ri al s Tiss u e E n g i n ee r in g D ru g D e li ve ry S. E. WANKE Ph D ( U n ive r s it y of Ca li fo rni a D av i s) H e t e r oge n eo u s Catal ys i s Kin e ti cs P o l y m e ri za ti o n M. C. WILLIAMS Ph D ( U ni ve r s i ty of Wi s co n s in ) EMERIT US R h eology P o l y m e r Ch a ra c t e ri za ti o n P o l y m e r P rocess in g Z. XU, Ph D ( Vir g ini a P o l y t ec hni c In s titut e a nd St a t e Uni ve r s it y) S u rf ace S c i e n ce & E n g in ee rin g Min e ral P rocess i n g Wa s t e Mana ge m e nt T. YEUNG Ph.D. (U ni ve r s it y of B ri t i s h Co lumbi a) E m u l sion s /n terfacial P h e no m e n a M i c ro m ec han i cs C h e mi c a l E n g in eer in g Ed u c a ti on

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FACULTY/RESEARCH INTERESTS CHEMICAL AND ROBERT G. ARNOLD, Profe ssor (Ca lTech ) Mi c r obio lo g i ca l Ha za rdous Waste Treatm e nt, M e tal s Speciation and Toxicity ENVIRONMENTAL ENGINEERING PAUL BLOWERS, Assistant Pro fessor (Ill in o i s, Urbana-Champaign) Chem i ca l Kin et i cs, Catalysis, Surface Ph enomena JAMES C. BAYGENTS, Associate Professor ( Prin ceton) Fluid M ec hani cs, Transport and Colloidal Phenomena Bi oseparations WENDELL ELA Assistant P rofessor ( Stanford ) Parti c l eParti cle In teract ion s, Environmental Chemistry JAMES FARRELL, Associate Profes so r ( Stanford ) Sorption/desorptio11 of Or g ani cs in Soils at THE JAMES A. FIELD, Associate Professor (Wage ni gen Agricultural Un i v.) Bior e m e diation Mi cro biolo gy, Whit e Rot Fungi, H aza rdous Waste ROBERTO GUZMAN, Associate Profe sso r ( North Carolina State ) Affinity Prot e in Separations, P o l y m e ri c Su,face Science ANTHONY MUSCAT A ss istant Profe sso r ( Stanford ) Kin e ti cs Surface Chemistry Surfa ce Engineering, Semiconductor P rocessing Mi croco11 taminati on KIMBERLY OGDEN, Associate Profes so r ( Colorado) Bi o r eac tors, Bi o r eme diation, Organics R e moval from Soils THOMAS W. PETERSON, Pro fessor and Dean (Ca !T ech) Aeroso l s, Ha z ardous Waste In c in erat i on, Mi croco ntamination ARA PHILIPOSSIAN, Associate Prof essor (T uft s) Chemical/Mechanical Polishin g, Semiconductor Pro cess in g JERKER PORA TH, Re search Profes so r (U pp sala) Separation S cie 11 ce EDUARDO SAEZ Associate Profe sso r (U C Da v i s) Rh eo l ogy, P oly m er Flows, Multiphase R eactors FARHANG SHADMAN, Pro fessor (Berke l ey) R eac tio11 E11gi11 ee ri11g, Kineti cs, Catalysis, R eac ti ve Membranes, Mi c r oco ntaminatio11 JOST 0. L. WENDT, Profe ssor a nd H ead (Jo hn s Hopkin s) Combustio11 Gen e rat e d Air Polluti o n, ln c i11 era ti o n Waste Ma11agement For further information, write to h1tp :llwww.c he arizona.edu or write Clzairma11, Graduate Study Committee Department of Chemical a11d E11viro11me11tal E11gi11eeri11g P.O. BOX 210011 The U11iversity of Arizona T11cso11, AZ 85721 The University of Arizona i s an equa l opponu nit y educatio n a l institution/equal oppon unit y emp l oye r. Women a n d minoritie s are e n couraged to app l y. Fall 2002 The Chemical and Environme ntal Engineering Department at the University of Arizona offers a wide range of research opportunities in all major areas of chemical engineering and e n vi r o nmental engineering and gra duate courses are offered in most of the research areas li sted here The department offers a fully acc redited und ergrad u ate degree as well as MS and PhD graduate degree s. Strong interdisciplinary programs exist in bioproces si ng and bioseparations microcontamination in electronics manu facture, and environme ntal process modification. Financial support is available through fellowships, government and industrial grants and contracts, teaching and research assistantships Tucson has an exce llent climate and man y recreational opportunities. It is a g rowing modern city that r etains much of the old Southwestern atmosphere. 325

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ARIZONA STATE UNIVERSITY Department of Chemical and Mater i als Engineering A Distinguished and Diverse Faculty Chemical Engineering Jonathan Allen, Ph D. MIT. Atmospheric aerosol c hemistry s in g le-particl e measurement techniques, e n vironme ntal fate of organic pollutants Stephen Beaudoin Ph.D. North Carolina State Semiconductor material s processing, environ ment a lly-beni g n se miconductor processing particle and thin-film adhesion chem i cal mechanical polishing, pol y m er di e lectrics James Beckman Ph.D., Arizona Unit operations, app lied mathematics energy-efficient water purification, fractionation, CMP reclamation Veronica Burrows Ph.D Prin ce ton Surface sc i ence environmental se nsor s, se miconductor processing, interfacial chemical and phy s ic a l processes in sensor proce ssi ng Ann Dillner Ph.D. Illinoi s, Urbana-Champaign. Atmospheric particulate matt er (aerosols) c h e mi st r y a nd physics, ultra fine aerosols, light sca tterin g, climate and health effec ts of aeroso ls Chan Beum Park Ph D POSTTE C H South K orea. Bioprocess in ex tr e mis novel cell-free prot e in sy nthe s i s, biolab-on-a-chip t ec hnol gy Gregory Raupp Ph.D. Wisconsin. Gas-solid surface reactions mechanisms a nd kinetics interacti o n s between s urfac e re act ion s and sim ultaneou s tran s port proce sses, semiconductor material s processing thermal and plasmae nhanced chemica l vapor depo s ition (C VD ) A nneta Razato s Ph.D Texa s at Austin. Bacterial adhesion, co ll oid int eractions, AFM biofilm s, ge n e tic e n gi ne e rin g Daniel Rivera Ph.D., Caltech. Control sys tem s engineering, dynamic modeling via system identification robust con trol com puter-aided control system de s ign Michael Sierks Ph D., Iowa State. Protein engineering, biomedical eng in eering, enzyme kinetics antibody engi neerin g Materials Science and Engineering James Adams Ph.D Atomistic s timulation of metallic surfaces, adhesion, wear, and a ut omotive ca tal ys t s he avy m e tal toxicity Terry Alford Ph .D Cornell Electronic materi a l s, ph ys ical metallurgy, e l ectronic thin film s Nikhilesh Chawla, Ph D. Michigan. Lead-fr ee so ld ers composit emater ial s, powder m e tallur gy Sandwip Dey Ph.D., Alfred Electro-ceramics, MOCVD and ALCVD diel ec tri cs : leakage l oss me c hani s m s and mode lin g Ste phen Krause Ph.D Michigan C haracteriz atio n of st ructural changes in pro cessi ng of semico nductor s A multi-disciplinary research environment w ith opportunities in electronic materials processing biotechnology processing, characterization, and simulation of materials ceramics air and water purification atmospheric chemistry process control Subhash Mahajan (C hair ), Ph.D Berkeley. Semicond u ctor defect s high temp era ture semiconductors s tructural materi a l s deformation James Mayer Ph.D. Purdue Thin film proce ss in g ion beam modification of materials Na te Newman Ph.D Stanford Growth, characterization, and modeling of so lids tate material s S. Tom Picraux Ph.D. Caltech. Nanostructured material s, epitaxy, and thin-film e l ectronic m ate rial s Karl Sieradzki Ph D. Syrac u se. Fracture of so lids thin-film deposition and growth, corrosion Mark van Schilfgaarde Ph.D. Stanford Methods and applications of electro ni c s tructure theory dilute magnet i c se mi co ndu c tors GW approximation For details concerning graduate opportunities in Chemical and Materials Engineering at ASU, please call Marlene Bolf at (480) 965-3313, or write to Subhash Mahajan, Chair, Chemical and Materials Engineering, Ariwna State University, Tempe, Ariwna 85287-6006 (smahajan@asu.edu). 326 Chemi c al Enginee ri ng Edu c a tion

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F a /1 2002 Faculty Robert P. Chamber s Uni ve r s it y o f Californi a Berk e l ey Harr y T. Cullinan Carn eg i e M e llon Un i ve rsity C hristine W. Curti s Fl o rida Stat e U ni ve r s i ty Steve R. Duke Uni ve r s i ty o f Illin o i s Said Elnashaie Uni ve r s i ty of E d i nbur g h Jame s A. Guin U ni ve r s i ty of T exas, Au s tin Ram B. Gupta U n ivers i ty of T ex a s Au s tin Gopal A. Krishnagopalan University of M a in e Y. Y. Lee Io w a Stat e U ni ve r s i ty Glennon Maple s Oklah o ma Stat e U ni ve r s i ty David R. Mills Wa s hin g ton St a t e Uni ve rsi ty Ronald D Neuman Th e I n s titut e o f Pap e r Ch e mi s tr y Stephen A Perusich Uni ve r sity o f Ill in o i s Timoth y D Placek Uni ve r si t y o f K e ntu c k y C hristopher B. Roberts Uni ve rsit y o f N o t r e D am e A. R. Tarrer P u r du e Uni ve r s i ty Bruce J. Tatarchuk U ni v er s i ty o f Wi sc on s in f Research Areas Biochemical En~neering Pulp and Ptper Process Systems En~neering Integrated Process Design Environmental Chemicru En~neering Cat~is and Reaction En~neering Materials Fblymers Surface and lnterfacial Science -~=Thermodynamics Supe[ritical Fluids Electrochemical En~neering Transport Phenomena Fue l CellTechnolo~ Microfibrous Materials Nanotechnolow Di ~c tor of Graduate Recruiting DepartmentofChemicalEngineering Auburn University AL 36849 Phon e ( 3 3 4 ) 844 4827 Fax ( 334 ) 844-2063 http://www.eng.aubum. u mail: chemical@eng.ad6urn.edu 3 27

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DEPARTMENT OF CHEMICAL AND PETROLEUM ENGINEERING FACULTY R G Moore Head (Alberta) J. Azaiez (Sta n fo r d) H. Baheri (Saskatchewan) L.A. Behie (Western Onta r io) C. Bellehumeur (McMaster) P.R. Bishnoi (Alberta) P J Farr e ll (Calgary) R. A Heid e mann (Washington U ) J M. Hill (Wisconsin) A. A Jeje (MIT) M. S. Kallo s (Calgary) A Kantza s (Waterloo) B B. Maini (Univ. Washington) A K M ehrotra (Calgary) S A. Meht a (Calgary) B J M ilne (Calgary) M Pooladi-Darvi s h (Alberta) A. Settari (Calgary) S. Sriniva s an (Stanford) W. Y S v rcek (Alberta) M A Trebble (Calgary) H. W. Y arranton (Alberta) B Young (Canterbury, NZ) L. Zanzotto (Slovak Te c h. Univ. C z echoslovakia) The D epartment offers graduate programs l e ading to t h e M .Sc. and Ph D. degrees in Chemi c al Engineering (fu ll -time) and th e M.Eng. degree in Ch e mi ca l Engineering, Petro l eum R e servoi r Engin ee ring or Engine e ring for th e Env i ronme n t (part-time) in th e following areas: Biochemical Engineering & Biotechnolog y Biomedical Engineering Catalysis and Fuel Cells Environmental Engineering Modeling, Simulation & Control Petroleum Recovery & Reservoir Engineering Polymer Processing & Rheology Process Development Reaction Engineering/Kinetics Thermodynamics Transport Phenomena F ellow s hip s and Re searc h Ass i s tant s hip s a r e a v ail a bl e t o a ll q u a lifi e d a ppli ca n ts Fo r A ddi tio n al J 11fo rma tio11 Write Dr. W Y. Svrcek Assoc i ate Head Graduate Studies Department of C h emical and Petro l eum Engineering University of Calgary Calgary Alberta, Canada T2N I N4 E-mail: gradstud @ uca l gary ca The University is lo c ated in the City of Calgary, the Oil capital of Canada, the home of the world famous Calgary Stampede and th e 1988 Winter Olympics Th e City combines the traditions of t h e Old West with the sophistication of a modern urban center. B e autiful Banff National Park is 110 km west of the City and the ski resorts of Ban.ff, lake Louise .a nd Kananaskis areas are readily accessible. In th e above photo the University Campus is shown in the foreground. The Engin ee ring complex is on the l eft of the pictur e, and the Ol y mpi c Oval i s on the right of the pi c ture 328 Chemi c al Engin e ering Edu c ation

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University of California, Berkeley The Chemical Engineering Department at the University of California, Berkeley one of the pre eminent departments in the field, offers graduate pro grams l eading to the Master of Science and Doctor of Philosophy. Stude nt s also have the opportunity to take part in the many cultural offerings of the San Francisco Bay Area a nd the recreational activities of California's northern coast and mountains. FACULTY N it ash P. Balsara Elton J. Cairns Harvey W. Blanch Douglas S. Clark Arup K. Chakraborty Enrique Iglesia David B. Graves Jay D Keasling A l exander Katz Roya Maboudlan C. Judson King John S. Newman Susan J. Muller Clayton J R a dk e John M. Prausnitz David V. Schaffer Jeffrey A Reimer Rachel A Segalman Alexis T Bell Chairman: Arup K. Chakraborty BIOENGINEERING Blanch Clark Keasling Schaffer Chakraborty Muller Prausnitz & Radke KINETICS THERMODYNAMICS TRANSPORT PHEl\OMENA QUANTUM& STATISTICAL MECHANICS SPECTROSCOPY POLYMERS & SOFT MATERIALS Balsara Chakraborty Muller Prausnitz, Radke Reimer & Segalman CATALYSIS & REACTION ENG Bell Chakraborty, Iglesia Katz & Reimer ELECTROCHEMICAL ENGINEERING Cairns Newman & Reimer ENVIRONMENTAL ENGINEERING Bell Graves Iglesia, Keasling & King MICROELECTRONICS PROCESSING & MEMS Graves, Maboudian Reimer & Segalman FOR FURTHER INFORMATION PLEASE VISIT OUR WEBSITE: http://cheme.berkeley.edu/index.shtm I Fal/ 2002 329

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University of California, Davis Department of Chemical Engineering & Materials Science Offe rin g M .S. a n d Ph D deg r ee p r ogra m s in bo th C h e m ical Eng in ee r i n g a n d M ateria l s Scie n ce and E n ginee rin g ------------F a culty------------Da v id E Block Ass i stant P rofesso r Ph D Univers i ty of Min n esota, 1992 I ndust r ial fennenrarion biochemical processes in phannaceurical industr y Roger B Boulton Professor P h D ., Univers it y of Me l bo u rne, 1 976 Fem,entarion and reaction kinetics c rystall i zation Stephanie R. Dungan Assoc i a t e P rofesso r P h. D ., Massac hu setts In s titut e of Tec h no l ogy 1 992 Mice/le transport colloid and interfacial science in food p r ocessing Roland Faller Assistant Professor P h. D Max-P l anck I nsti t u t e for Poly m er Researc h 2000 Molecular modeling of soft condensed matter Bruce C. Gat es, Professor P h D University of Washi n g t on Seatt l e, 1966 Catalysis solid superacid catalysis ,eolite catalysts bimetallic catalysts catalysis by metal clusters Jeffer y C. Gibeling Professor P h. D S t a n fo r d U ni versity 1 979 Defomwtion fracture and fatigue of metals, layered composites and bone Joanna R. Groza P rofesso r P h. D ., P o l y t ec hni c In s titu te, Bu c h ares t 1 972 P lasma activated s i111 ering and processing of nanostrucru r ed mate r ials Brian G. Higgins P rofessor Ph D. U ni vers it y of Minn eso t a 1 980 Fl u id mechanics a n d i nterfacial phe n ome n a sol gel p r ocessing, coating flows David G. Howitt P rofesso r Ph D ., Unive r s i ty of Ca l ifo rni a Berke l ey 1976 Forensic and failure anal y sis electron mic r oscopy ignition and combustion processes in materials Alan P Jackman Professor Ph.D Un i versity of Mi n nesota 1968 Protein production in plant cell cultures bioremediation Ton y a L. Kuhl Assistan t Professo r P h.D. Un i vers i ty of Cal i fornia Santa B arbara 1996 B iomareria/s, membrane i111eractions i111ennolecularand i111ersurface forces in complex fluid systems Enrique J. Lavernia P rofe so r Ph D ., M assac hu se t ts In s titut e of T ec hn o l ogy, 1 986 Sy n t h esis of s tru c tu ral m a t e r ials and composites; w,ws t rucrured materials and compos i tes, t hennal spray p r ocessing Jiirg F. Loffler Assis t a n t Professor Ph D Swiss Federa l In sti tut e of Tec hn ology (ETH) Zlirich 1 997 Nanostructured and amorphous materials; magnetic, structu r al and them,ophysica/ properties, neutron and x-ray scattering Marjorie L. Lon g o Assistan t P ro f essor Ph .D. U n ivers i ty of California, Sa m a B arbara, 1 993 H ydrophobic protein design for active comrol, surfa c ra111 microst m ctu r e and interact i on of proteins and D NA with biological me m branes Karen A. McDonald P rofesso r Ph D ., U ni vers it y of Mary land Co ll ege P ar k 1 985 P lant cell c u lt u re biop r ocess in g algal c ell c u ltures Ami y a K. Mukherjee P rofesso r D Ph il., University of Oxfo r d 1962 Superplasticity of inte n netallic alloys and ceramics, high temperature creep defomw tion Zuhair A. Munir Professo r P h.D. U ni versity of Califo rni a B e r ke l ey, 1 963 Combustion synthesis, multilayer combustion systems, ft111ctionall y graded mate r ials Alexandra Navrotsk y, Prof esso r Ph D U ni ve rsit y of C hi c a go, 1 967 T h ermody n a mi cs a n d solid state chem i stry; high te m pe r atu r e calo r ime t ry Ahmet N Palazoglu Pr o f esso r Ph D R ensse l ae r P o l y t ec hni c I ns titut e 1 984 P rocess con t rol a n d p r ocess des i gn of env ir onme 111 ally ben i gn p r ocesses Ronald J. Phillips Professor Ph D Massac h use t ts I nsti tut e of Tec hn o l ogy 1 989 Transport processes in bioseparations, Newtonian and non Newtonian suspension mechanics Robert L Powell Professor P h D. Jo h ns H opkins University 1978 R heolog y, suspension mechanics, magneti c resonance imaging of suspensions Subhash H. Risbud Professo r a n d C h ai r Ph D ., U n ivers i ty of California B e r ke l ey 1 976 Semiconductor quantum dots, high T superconducting ceramics p o l y m e r composites for opt i cs Dewey D.Y. Ryu, P rofessor Ph D M assac hu se t ts In s titut e of Tec hn o l ogy 1 967 B iomolec u lar process eng in ee r ing and recombinant bioprocess tec h no l ogy Julie M Schoenung Assoc i a t e P rofesso r Ph D ., M assac hu se tt s In s tit u t e o f T ec h no l ogy, 1 987 M aterials sys t ems analysis; poll w ion preventio n and waste m in imization; process economics James F. Shackelford Professo r Ph D ., University of Califo rni a, B e r ke l ey, I 971 Structure of ma t erials, biomaterials, nondestrncrive testing of engineering materials J.M. Smith, Pr ofesso r E m eri tu s Sc .D. M assac hu se tt s I ns titut e of Tec h no l ogy, 1 943 C h emical k i netics a n d reacto r design Pieter Stroeve P rofessor Sc. D M assac hu se t ts In s t i tu te o fT ech n o l ogy, 1 973 Membrane separa t ions, lnngmui r Blodgett films, colloid and s u rface science Stephen Whitaker, Pro f esso r Ph D U ni versity of De l aware 1959 Multiphase transpo r t phe n omena 33 0 The mu l tifaceted grad u ate s tud y experie n ce in the D e p art m e n t of C h e m ical E n gineering a n d M at e ri a l s Sc i ence a ll ows s tu de nt s t o c h oose r esearc h proj ec t s a n d th es i s adv i sers fro m a n y of o ur fac ult y w ith expe rti se i n chemical engineering b i oc h e mi ca l e n g in eering and/ o r materia l s science a n d e n gi n eering. O ur goal is 10 prov i de the fi n ancial a n d acade m ic s up po rt fo r s tud e n ts 10 co m p l e t e a s ub sta n t i ve r esearc h p ro ject w ith i n 2 years for th e M S an d 4 years fo r th e Ph .D. LOCAIIONs 5oc:rammto: 17 m11a Son p,_.,.,, 72 mlla Lalre Tahoe: 90 m11a Davis is a small bike-friendly university town located 17 miles west of Sacramento and 72 miles northeast of San Francisco, within driving distance of a multitude of recreational activities in Yosemite, Lake Tahoe, Monterey and San Francisco Bay Area. For information about our program, look up our web site at http://www.chms ucdavis.edu or contact us via e-mail at chmsgradasst@ucdavis.edu Graduate admi.tsions on-line applications and printabk forms availabk at http://gradstudies ucdavis edu/b4apply.htm Gradua~ Admission Chair Professor Jeffery C. Gibeling Dept. of Chemical Engineering & Materials Science Univtrsity of California, Davis Ont Shields Av e nue Davis CA 956/6-5294 USA Phonl! (530) 752-7952 Fax (530) 752-/03/ C h e mi ca l E n g in ee rin g E du ca ti o n

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UNIVERSITY OF CALIFORNIA Graduat e Studi es i n JRT TJNE Chemi c al Enginee r in g Y .. and Material s S c i enc e and Engineering for Chemical E ngin ee r i ng En g in ee ring and Material s Sci e nc e M ajo rs Offering degrees at the M S and Ph.D. levels R esearch in frontier areas in chemical engineering, biochemical engineering biomedical engineering and materials science and engineering Strong physical and life science and engineering groups on campus. F A CULTY Ying Chih Chang (Stanford Univers i ty) Nancy A. Da Silva (California I nstitute of T echnology) James C. Earthman (Stanford University) Ste v en C. George (University of Washington) Stanle y B. Grant (California Institute of Technology) Juan Hong ( Purdue University) Enrique J La v ernia (Massachusetts I nstitute of Technology) Henr y C Lim (Northwestern Univers i ty) Jia Grace Lu (Harvard University) Martha L. Mccartne y ( Stanford University) Farghalli A. Mohamed (University of California, Berkeley) Frank G. Shi (California I nstitute of Technology) V asan Venugopalan (Massachusetts I nstitute of Technology) Joint Ap p ointments: G. Wesle y Hatfield ( P urdue University) Noo Li Jeon (University of I llinois) Sunny Jiang (University of South F l o r ida) Roger H. Rangel (University of Ca l ifornia, B erkeley) William A Sirignano ( P rinceton University) Ad i unct P rofessors Ru s sell Chou (Carnegie Mellon Un i versity) Andrew Shapiro (University of Ca l iforia, I rvine) Victoria Tellkamp (University of Ca l iforia I rvine) The 1,510-acre UC Irvine campus is in Orange County.five miles from the Pacific Ocean and 40 miles south of Los Angeles. I rvine is one of the nations fastest growing residential, industrial and business areas Nearby beaches, mountain and desert area recreational activities, and local cultural activities make 1 rvine a pleasant city in which to live and study. For further information and application forms, please visit h tt p: // www.e n g. u ci e du/ c b e/ or contact Department of Chemical Engineering and Materials Science School of Engineering Univer s ity of California Irvine CA 92697-2575 Fall 2002 / B i omedic a l Engineering Bioreactor Engineering Bioremediation Ceramic s Combu s tion Compo s ite Materials Control and Optimiz a tion Environmental Engineering Interf acial Engineering Material s Proce ssi n g Mechanical Propertie s Metabolic Engineering Microelectronics Processing and Modeling Microstructure of Material s N anocry s talline Material s Nucleation, Chrystallizat i on and Glass Transition Process Polymers Recombinant Cell Technology Separation Processes Sol-Gel Processing Two-Phase Flow Water Pollution Control 331

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CHEMICAL ENGINEERING A T RESEARCH AREAS Aerosol Science and Technology Biochemical Engineering Combinatorial Catalysi s Complex Systems Engineering Electrochemistry Membranes Molecular and Cellular Bioengineering Pollution Prevention Polymer Engineering Process Design, Optimization Dynamics, and Control Reaction Kinetics and Combustion Semiconductor Manufacturing FACULTY J.P. Chang P. D. Christofldes Y. Cohen J. Davis ( Vi ce C h a n ce ll o r fo r In fo rmati o n Tec hn o l ogy) S. K. Friedlander R. F. Hicks E L. Knuth ( P rof E m e ritu s) J.C. Liao V. Mano u sio u thakis H. G. Monbouquette K. Nobe L.B. Robinson ( P rof E m e ritu s) S.M.Senkan Y.Tang W. D. Van Vorst ( P rof E m e ritu s) V. L. Vilker ( Pr of E m erit u s) A R. Wazzan PROGRAMS -----------3 32 UCLA s Chemical Engineering Department offer s a program of teaching and research linking fundamental en gineering science and industrial practice. Our Department has strong graduate research programs in Bioengineering, Energy and Environment, Semiconductor Manufacturing Engineering of Materials and Process and Control Sys tems Engineering. Fellowships are available for outstanding applicants intere s ted in Ph.D. degree program s. A fellow s hip in cludes a waiver of tuition and fees plus a s tipend. Located five miles from the Pacific Coast, UCLA's attractive 417-acre campus extends from Bel Air to Westwood Village. Students have access to the highly regarded s cience programs and to a vari ety of experiences in theatre music art, and sport s on campu s. CONTACT Admissions Officer Chemical Engineering Department 5531 Boelter Hall UCLA Los Angeles, CA 90095-1592 Telephone at (310) 825-9063 or visit us at www.chemeng.ucla.edu Ch e mi c al En g in ee rin g Edu c ati o n

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University of California, Riverside Department of Chemical and Environmental Engineering The Graduate Program in Chemical and En vironmental Engineering offers training lead ing to the degrees of Master of Science and Doctor of Philosophy. All applicants are r e quired to submit scores from the general apti tude Graduate Record Examination (GRE). For more information and application mate rials, write: Graduate Advisor Department of Chemical and Environmental Engineering University of California Riverside CA 92521 Visit u s at our website: http://www.engr.ucr.edu/chemenv Faculty ng1neer1ng Wilfred Chen ( Cal T ec h ) Environmental Biotechnolog y, Microbial Engineering Biocatal ys is Da v id R. Cocker ( Caltech ) Air Quality Systems Engineering Marc Deshu s se s ( ETH Zuric h) Environmental Biotechnology, Bioremediation, Modeling Robert C. Haddon ( Penn Stat e) Carbon Nanotubes, Advanced Materials Eric M.V. Hoek (Y ale ) Environmental Membrane Processes Collodial and Inte,fa c ial Phenomena Mark R. Mat s umoto (U C Da v i s) Water and Wastewater Treatment, Ha za rdous Waste, Soil R emediation Ashok Mulchandani ( McGill ) Bioengineering, Biomaterials Biosensors, Environmental Biotechnology Joseph M. Norbeck ( Nebraska ) Advanced Vehicle Technolog y, Air Pollutants, Renewable Fuels Mihri Ozkan (U C Sn Diego ) Biomedical Microdevices Bio-MEMS and Bio-Photonics Anders 0. Wistrom (U C Da vis) Particulate and Colloidal S yste ms Jianzhong Wu ( UC Berkele y) Molecular Simulation, Theory of Complex Fluids, Nanomaterials Yushan Yan ( CalTech ) Zeolite Thin Films Fuel Cells, Nanostructured Materials, Catalysis T h e 1,2 0 0-acre R iversi d e campus of the University of California is located 50 miles east of Los Ange les w i t hin easy d rivi n g d is t a n ce to mos t of t h e m ajor cult u ral a n d recreationa l offe rin gs i n S o u t h ern C a li fornia. I n a ddi tion it is virtua ll y equi d ista nt from t h e desert, the mo un tai n s, an d the ocea n Fall 2002 333

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334 UNIVERSITY OF CALIFORNIA SANTA BARBARA ERAY S. AYDIL Ph D ( H ouston) Microelectronic s and Pla s ma Pro cess in g SANJOY BANERJEE Ph D (Waterloo) Environmental F luid D y n am i cs Multipha se Fl ows Turbulenc e, Comp ut a tional Fluid D y namic s BRADLEY F. CHMELKA Ph D. (U.C. B e rk eley) Molecular Material s S cie nce In organ i c -Or ga nic Composites Porou s Solids NMR Pol y me rs PATRICK S. DAUGHERTY Ph D. (Austin) Protein Engineering a nd De s i g n Library Technologi es MICHAEL F. DOHERTY Ph.D (Camb rid ge) D es i gn and Synth es i s, Separations, Proce ss Dynami cs and Co n tro l FRANCIS J DOYLE III Ph D (Caltech) P rocess Co ntr o l Syste m s Biolo gy, Nonlinear Dyn a mi cs GLENN H. FREDRICKSON Ph D (Stanfo rd ) Statistical Mechanic s, Gla sses Polymer s, Compos it es, A ll oys G.M. HOMSY Ph.D ( Ill inois) Fluid Mechanics In s t abil iti es, Porou s Media Int erfacial Flow s, Convective Heat Tran sfe r JACOB ISRAELACHVILI Ph D. (Ca mbridg e) Co ll oidal and Biomol ec ul ar Int eractions Adhesion and Frictio n EDWARD J. KRAMER Ph.D (Carnegie-Mel l on) Fracture a n d Diffu s i on of Polymer s, Pol y m er Surfaces a nd In terfaces L. GARY LEAL Ph.D. (Stanford) Fluid Mechanics, Phy s i cs a nd Rh eo l ogy of Complex Fluids, in c ludin g P o l y m ers, S u spe n s ion s, and Emulsio n s GLENN E. LUCAS Ph D. (M .I T.) Mechanics of Materials, Structural R e liabilit y. DIMITRIOS MAROUDAS Ph D (M. I .T.) Theoretical a nd Computational Materials Science, Electronic and Structural Material s ERIC McFARLAND Ph D (M. I T.) M.D. ( Harvard ) Combinatorial Materi a l Science Environmental Catalysis, Surface Science DUNCAN A. MELLICHAMP Ph D ( Purdu e) Computer Co ntr ol, P rocess Dynamic s, R eal-Time Co mputin g SAMIR MITRAGOTRI Ph D. (M. I. T.) Drug Deli ve r y and Biomaterial s DAVID J. PINE Ph D (Cornell) (C hair ) Pol y mer, Surfactant and Colloida l Phy sics, Multiple Light Scatt e rin g, Photonic Crystals ORVILLE C. SANDALL Ph D (Berkeley) Tra n s port Phenomena Separation Pro cesses DALEE. SEBORG Ph D (Princeton) Pro cess Control Monitoring a nd Identific at ion MATTHEW V. TIRRELL Ph D (Massac hus etts) Pol yme r s Surfaces, Ad h esion Biom ateria l s T. G. THEOFANOUS Ph.D. (Minnesota) Multipha se F l ow, Ri sk Assessment and Mana geme n t JOSEPH A. ZASADZINSKI Ph D (Minnesota) Surfac e a nd lnt e rfa c ial Phenomena Biomaterial s PROGRAMS AND FINANCIAL SUPPORT The D epart m ent offers M.S. and Ph.D. degree programs Finan cial aid, including fe ll owships, teaching assistantships, and re search assistantships, is avail able. THE UNIVERSITY One of the wo rld' s few seashore campuses, UCSB is located on the Pa cific Coast J OO miles n o rth west of Los Angeles. The student enro llm ent is over 18 000 Th e m et r opo litan Santa Barbara area has over 1 50,000 residents and is famous for its mild even climate For additional information a nd applications write to C hair Graduate A dmi ssio n s Co mmitte e Department of Chemical Engi ne e rin g U n iversity of California Santa Barbara CA 93106 Chemical Eng in ee rin g Education

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Chemical Engineering at the CALIFORNIA INSTITUTE OF TECHNOLOGY '~t the Leading Edge" Frances H. Arnold Anand R. Asthagiri John F. Brady Mark E. Davis Richard C. Flagan George R Gavalas (Emeritus) Konstantinos P. Giapis Julia A. Kornfield Aerosol Science Applied Mathematics Atmospheric Chemistry and Physics Biocatalysis and Bioreactor Engineering Biomaterials Biomedical Engineering Bioseparations Catalysis Chemical Vapor Deposition John H. Seinfeld David A. Tirrell Nicholas W Tschoegl (Emeritus) Zhen-Gang Wang Combustion Colloid Physics Fluid Mechanics Materials Processing Microelectronics Processing Microstructured Fluids Polymer Science Protein Engineering Statistical Mechanics For further information write __________________ Fa/12002 Director of Graduate Stud ies Chemical Engineering 210-41 California Institute of Technology Pasadena California 91125-4100 Also v isit us on the World Wide Web for an on-line brochure : http :// www.che.caltech.edu 335

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I ........ ... .... ......... ... . . . .... .............. . . . . : -: : :-: : : : .. :.: : -: -: . ._ ... :-:-:-:-: ......... ...... .. .. .. ....... .. NelJer fettr~ui&ed bLJ tt f acultLJ thttt rules the e,l)orld of chernicttl er1qir1eerir1tJ, carr1eqie Mellol'I trttiflS LJOU tO &ecorne tt suverfiero il'I LJour oc.lJl'I riqfit.

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Case Western Reserve University M.S. and Ph.D. Programs in Chemical Engineering Research Opportunities Advanced Energy Systems Fuel Cells and Batteries Hydrogen Infrastructure Membrane Transport Sensors Microfabrication Biomedical Engineering Transport in Biological Systems Biomedi cal Sensors and Actuators Wound Healing Inflammation and Cancer Metastasis Neural Prosthetic D ev i ces Advanced Materials and Devices Diam ond and Nitride Synthesis Coatings Thin Films and Surfaces In-Situ Diagno stics Fine Particle Science and Processing Polymer Nanocomposites Electrochemical Microfabrication Fall 2002 For more information on Graduate Re search, Admission, and Financial Aid, contact: Graduate Coordinator Department of Chemica l Engineering E-mail: grad@cheme.cwru.edu Web: http: //www.cwru.edu/cse/ec h e Faculty John Angus Harihara Ba skaran Robert Edwards Donald Feke Jeffrey Glass Uziel Landau Chung-Chiun Liu J. Adin Mann Heidi Martin Ph ilip Morrison Peter Pintauro Syed Qutubuddin Robert Savinell Thomas Zawodzinski Case We ste rn Reserve University 10900 Euclid Avenue Cleve l and, Ohio 44106-7217 337

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Opportunities for Graduat e Study in Chemical Engin eer in g at the 338 UNIVERSITY OF CINCINNATI M.S. and Ph.D. Degrees in Chemical Engineering Faculty Carlos Co Joel Fried Rakesh Govind Vadim Guliants Daniel Hershey Chia-chi Ho Sun-Tak Hwang Yuen-Koh Kao Soon-Jai Khang William Krantz Jerry Y. S. Lin Neville Pinto Peter Smirniotis The Unive r sity of Cincinnati is committed to a policy of non-discrimination in awarding financial aid. For Admission Information Director Graduate St udi es Chemical Engineer in g PO Box 2 10171 University of Cinci nn at i Cincinnati, Ohio 45221-0 I 71 Email: mcarden@alpha.che uc.edu or jlin@alpha.che .u c.ed u The faculty and students in the D epartment of Chemical Engineering are engaged in a diverse range of exciting r esearch topics. Assistantships and tuition scholarships are available to highly qualified applicants to the MS and PhD degree programs. Advanced Materials In organ i c membranes nanostructured materials microporous and mesoporous materials, advanced materials processing, thin film technology, fu e l cell and sensor materials, self assembly Biotechnology (Bioseparations) Novel bioseparation techniques affinity separation, biodegradation of toxic wastes, con trolled drug delivery, two-phase flow Catalysis and Chemical Reaction Engineering H eterogeneous catalysis, environmental catalysis z eolite catalysis novel chemical reactors modeling and design of chemical r eactors Environmental Research D esulfu ri zation and denitrication of flue gas, new technologies for coal combustion power plant, wastewater treatment removal of volatile organic vapors Membrane Technology Membrane synthesis and c h aracte ri z ation, membrane gas separation, membrane reactors, sensors and prob es, pervaporation, biomedical, food and enviro nm ental applications of membranes, hi g h-t emperatu r e membrane technology natural gas processing by membranes Polymers Thennodynamics, polymer blends and composites high-temperature polymers hydrogels pol y mer rheolog y computational pol y mer science pol y meri z ation technolog y Separation Technologies Membrane separation, adsorption, ch r omatography separation system synthesis, chemical reaction-based separation pr ocesses Chemical Eng in eering Educat i on

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Chemical Engineering at The City College of New York CUNY (The City University of New York) A 154-year-old urban University the oldest public University in Ameri c a on a 35-a c r e Gothic and modern campus in the greatest c ity in the w orld FACULTY RESEARCH : 0 Andreas Acrivos* 00 ~ Rh eo l ogy of co n ce ntrated s u s p e n s i o n s; Di e l ec tr ph o r es i s in fl ow in g s u s p e n s i o n s; D y n a mi ca l sys t e m s th eo r y a nd c h ao ti c p a rticl e moti o n s Alexander Couzis: P o l y m o rph se l ec ti ve t e m p l a t e d c r ys t a Hi z ati o n ; M o l ec ularl y thin o r ga ni c b arrier l ayers; Surf ac t a nt fac ilit a t ed we ttin g of h y d ro ph o bi c s urfa ces; sof t ma te ri a l s 0 Morton Denn oo~ : P o l y m e r sc i e n ce a nd rhe o l ogy ; n o n-N ew t o ni a n fluid m ec h a ni cs Lane Gilchrist: Bio e n g in ee rin g w ith ce llular m a t e ri a l s; Spectro sco p y g uid e d m o l ec ular en g in ee rin g; Stru c tu ra l s tudi es o f se lf -asse mblin g pr o t e in s ; Bi o pro cess in g Robert Graff: Coa l liqu efac t io n ; P o llution pr eve nt io n ; R e m ed i a ti o n Leslie Isaacs: Pr e par a ti o n a nd c h arac t e ri zat i o n of n ove l o pti ca l m a t e rial s; R ecy clin g o f p av ement m a t e rial s; Appli c ation o f th e rm o-a nal y tic t ec hnique s in m a t e ri a l s r ese ar c h Jae Lee: Th e ory of reactiv e di s tillation ; Pro c e ss de s i g n and control ; Separ a ti o n s ; B i o p rocess in g ~ Char l es Ma l darelli: lnt e rf ac ial fluid m ec hani cs a nd s t a bilit y; Su rf a ce t e n s i o n dri ve n flo ws a nd rni cro fluidi c a ppli ca ti o n s; Surfa c t a nt ad so rpti o n ph ase b e h a vior and nan os tru c turin g a t inter fa c es Irve n R in ard: Proc ess de s i g n m e thodol-o g y ; D y nami c process s imulation ; Micro-rea c tion te c hn o l ogy; P rocess co nt ro l ; Biopro cess in g David Rumschitzki: T ra n s p o rt a nd r eac ti o n as p ec t s of arte ri a l di sease; F a ll 20 02 lnt e rf ac i a l fl ui d mec h a ni cs and s t a bili ty ; Cata l ys t d eac ti va ti o n a nd r eac ti o n e n g in ee ri ng Reuel Shinnar 00 : Ad va n ce d pro cess d es i g n m et h o d s; C h e mi ca l r eac t or co ntr o l ; Spin o d a l d eco mp os i t i o n of binar y so l ve nt mi x tur es; Pro cess eco n o mi cs ; En e r gy a nd env i ronme nt sys t e m s Carol Steiner: P o l y m e r so luti o n s a nd hydrogels; Sof t bi o m a t eria l s Co n tro ll ed re l ease tec hn o l ogy Gabriel Tardos: P ow d e r t ec hn o l ogy; Gra nul a ti o n ; Fluid p art i c l e sys t ems, E l ec t rosta ti c effec t s ; A ir p o lluti o n Sheldon Weinbaum 00 : Fluid m ec h a ni cs, Bi o t ra n s p o rt i n l iv i ng ti ss u e; M o d e l i n g of ce llul ar mec h a ni s m of bo n e grow th ; bi o h ea t t ra n s f e r ; kidn ey fu n c ti o n Herbert Weinstein: Fl u idi zation a n d multiph ase flows : multi p h ase c h e mi ca l r eac t o r a n a l ys i s a nd d es i g n Multiph ase r eac t o r a n a l ys i s a nd d es i g n ASSOC I ATED FACULTY : 0 Jimm y Feng: ( Mec h a n ica l Eng.) L iq u i d crys t a l s 0 Joel Koplik: (P h ys i cs ) F lu id mec h a n ics; M o l ec ul ar m od e lin g; T ra n s p o rt in ran do m m ed i a 0 Hernan Makse: ( Ph ys i cs ) G ra nul a r m ec h a ni cs 0 Mark Shattuck: ( Ph ys i cs) Experime nt a l gra nul ar rheo l ogy; Co m p u ta ti onal granu l ar fl u id dy n amics; Experimenta l spatio-temporal control of pa n e m s 0 Le vic h I nstitut e Narional A ca dem y of Sciences oo Nationa l A c adem y of Enginee r in g 5Americcm A c ade m y of Arts and Scie n ces CONTACT INFORMATION: D e partm e nt o f C h e mi ca l E n g in ee rin g C it y Co U ege of New Y o rk Co n ve nt Ave n ue a t 140 th Stree t New Y ork NY I 0031 www-che engr.ccny.cu n y.edu c he h r @ao l. co m t \.J.l . ... J ..... .............. ~-; .,,~-""J'l:J 1, ___ f, ..... ..... ... .; I . ": l ... r .. .. .,,.. ----~ ..... , -...... t ...... .... .. ,, ... ..... ... ,_., .. . .. ....... .. .... ., ;;, ... -~ .... ., -.'~I I' l l ~' ~. .. .. --\~ :, ..... : . \. t. .. ~:J ..J.! ...... .,, !--.-,.,,!.! -' I I ' I 11 ., ., . It It f :r .. ,: .. .... ... .. I I I ... ... ~: *:-~ ... ..... ,..... ,, .. ,.. ... .; ........ ;.,,,. ... .. ..... --. ..... ,,..: t, ..... . f I ,,., ..... .... ~........ ,.. .... .. :::..... : : .,:.,.;11 ,,.. ........ :. ,,,.,: .. .. '' ..... ........ .. ..... ........ ... ............. -~ .-:. .. .. . . .. . . -... .... ... .... ... .. .. I .......__ __. ", .. ..: .. : .. ... "', .,. J..,; ..... ,. c. . --. ~~ ..._.... .. :h-f i .. I ,, 339

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Cleveland State University Graduate Studies in Chemical and Applied Biomedical Engineering Engineering Degr~e~e~s~-----Fenn College has more than 75 years of experience in provid M.Sc. D.Eng. D.Eng. Chemical Engineering Applied Biomedical Engineering Chemica l Eng ineerin g CSU Faculty A Annapraga da (U niver s it y o f Michi g an ) J.M. Belovich (Univ e r s it y of Michig a n ) G. Chatzimavro udi s (Georgia Institute of Technology) G A. Coulman ( C as e We s tern Reserv e Univ e r s it y) J.E Gatica ( Stat e Uni v er s it y of New York a t Buffalo ) B. Ghorashi ( Ohio State Univer s ity ) E.S. God leski ( Cornell Univer s ity) R. Lustig ( In s titute of Thermo and Fluiddynamic s of the Ruhr-Univer s ity Bochum Germ a ny) D.B. Shah ( Michigan State University ) 0 Talu ( Arizona St a t e Univer s ity ) S N. Tewari (Purdu e Univer s it y) S. Ungara la (Michig a n Technological Univer s ity ) CCF Collaborating Faculty J. Arendt ( Ohio State Univer s ity) B. Davis (Pennsylvania State Univer s ity ) K. Derwin (Univer s ity o f Michigan ) A. Fleischman ( Ca s e We s t e rn Reserve Univer s ity ) M. Grabiner (University of fllinois) S Halliburton ( Vanderbilt University ) G. Lockwood (University of Toronto Canad a) C. McDevitt (Univer s ity of London U.K. ) S Roy ( Ca se West e rn Reserve Univer s ity ) R. Shekhar ( Ohio Stat e Univer s ity ) W. Smith ( Cleveland State University ) A. van den Bogert (Univer s it y of Utrecht The Netherland s) I. Vesely ( University of W es tern Ontario, Canada ) G. Yue (University of Iow a ) For more information. write to: ing outstanding engineering education. Graduate Studies in Chemical and Applied Biomedical Engineering at Cleveland State University 's (CSU s) Fenn College of Engineering offers a wealth of opportunity in a stimulating environment. Research opportunities are available in collaboration with the Bio medical Engineering Department of the renowned Cleveland Clinic Foundation (CCF), Cleveland's Ad vanced Manufacturing Center local and national industry, and Federal agencies, to name a few. Assistantships and Tuition Fee Waivers are available on a competi tive basis for qualified students Cleveland State University has 16 000 s tudents enrolled in its academic pro grams. It is located in the center of the city of Cleveland, with many outstand ing cultural and recreational opportuni ties nearby. RESEARCH AREAS Adsorption Processes Agile Manufacturing Artificial Heart Valves Biomechanic s Bioreactor Design Bioseparation s Blood F l ow Combustion Computational Fluid Dynamics Drug Delivery System s Environmental Pollution Control Materials Synthesis and Processing Medical Imaging MEMS Technology Orthopedic Devices Process Modeling and Control Reaction Engineering Statistical Mechanics Graduate Program Coordinator Department of Chemical Engineering Cleveland State University Cleveland, OH 44115 Surface Phenomena and Mass Transfer Thermodynamics and Fluid Phase Equilibrium Tis s ue Engineering Tribology Ventricular Assist Devices Telephone: 216-687-2569 E-mail : ChE@csvax.egr.csuohio.edu http://www csuohio.edu/chemical_engineering/ Zeolite s : Synthesis Adsorption, and Diffusion Assistantships and Tuition/Fee Waivers are available on a competitive basis for qualified students 340 Ch e mi c al Engine e rin g Edu c ation

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University of Colorado at Boulder The Boulder campus has a controlled enrollment of about 22 000 undergraduate s and 5 000 graduate s tudent s. The beautiful campus has 200 buildings of rough-cut sandstone with red-tile roofs. The excellent educational opportunities and beautiful location attract outstanding s tudent s from e ver y part of the United State s and 85 countrie s. The University of Colorado ha s it s main campu s located in Boulder an attracti v e community of 90 000 people located at the base of the Rocky Mountain s Boulder ha s o v er 300 day s of s un s hine per year with relativel y mild and dry s easons The city i s an active and innovative town that provide s a rich array of recreational and cultural acti v iti es .---Department of Chemical Engineering Faculty and Re search Interests Fal/ 2002 Kristi S. Anseth Pol y m e rs Biomaterial s, T iss u e En g in eeri n g Chr i stopher N. Bowman Pol y m e r s, M e mbran e M ate rial s David E. Clough Pr ocess Control Appli e d S ta t i st i cs Robert H. Davis Fluid M ec hani c s Bi o t ec hn o l ogy, M e mbr a n es John L. Falconer Catal y sis Zeolite Membran es R. Igor Gamow Bioph y sics High Altitud e Ph ys i o l Og) \ Human P e rf o nnan ce, Di v in g Ph y siolo gy Steven M. George Surfa ce Ch e mi s try Th i n Film s, Nan oe n g in ee rin g Doug Gin Pol y m e rs R y an Gill Biote c hnolog y Christine M. Hrenya Fluidi z a t i on Granul a r S ys t e m s, Fluid M ec hani cs Dhinakar S. Kompala B io t ec hn olog y A nimal Ce ll Cultur es M e tab o li c E ng in ee rin g J. Will Medlin H eteroge n eo u s C a ta l y sis So lid St ate S e n so r s Co mp u ta t i o nal Ch e mi st r y Richard D. Noble M e mbran es, S e parati o n s W. Fred Ramirez Pr oc e ss C o ntr o l Bi o t ec hn o l ogy Theodore W. Randolph B io t ec hnol ogy Sup e r cr iti ca l Fluid s Robert L. Sani Tr a n spo r t Ph e n o m e n a Appli e d M a th e mati cs Daniel K. Schwartz Int e ,f ac ial and Coll o id S c i e n ce A lan W. Weimer C e rami cs, En e r gy, R e a c ti o n En g in ee rin g Graduate stud e nt s ma y parti c ipate in th e int e rdis c iplina ry Bi o t ec hnolog y Trainin g Pro g ram and the interdis c iplinary NSF In dustr y /Uni ve r s i ty Coop e rati ve R e s e ar c h C e nt e r f o r Membrane Appli e d S c i e n ce and T e chnolog y and th e C e nt e r fo r Fund a m e ntal s and Appli cat i o n s of Ph o t o pol y m e ri z ati o n s For information and application Graduate Admi ss ion s Committee Department of Chemical Engineering Univer s ity of Colorado Boulder CO 80309-0424 Phon e (303) 492-7471 Fax (303) 492-4341 E-mail chemeng @s pot.colorado edu http : //www.Colorado EDU/che/ 3 4 /

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Faculty R.M. Baldwin (CSM, 1975) A.L. Bunge (Berkeley, 1982) A.M. Dean (Harvard, 1971) J.R. Dorgan (Berkeley, 1991) J.F. Ely (Indiana, 1971} D.W.M. Marr (Stanford, 199:J) C. McCabe (Sheffield, 1998) J :r. McKinnon (MIT, 1989) R.L. Miller (('SM, 1982) E.D. Sloan (Clemson, 1974) J.D. \\Tay (Colorado, 1986) C.A. Wolden ('.VIIT. 1995) D.T. Wu (Berkeley, 1991} Visit http://\\"\Tw.n1incs.edu 342 Colorado School of Mines E volving from its origins as a school of mining founded in 1873 CSM is a unique highly-focused University dedicated to scholarship and research in materials energy and the environment. The Chemical Engineering Department at CSM maintains a high quality, active and well-funded graduate research program According to the NSF annual survey of research expenditures our department has placed in the top 25 nationally each of the last 5 years Research areas within the department include: Materials Science and Engineering Organic and inorganic membranes (Way Baldwin) Polymeric materials (Dorgan McCabe Wu) Colloids and complex fluids (Marr Wu) Electronic materials (Wolden) Fuel cell membranes (Way) Theoretical and Applied Thermodynamics Natural gas hydrates (Sloan) Molecular simulation and modelling (Ely McCabe) Transport Properties and Processes Dermal absorption (Bunge) Microfluidics (Marr) Space and Microgravity Research Membranes on Mars (Way, Baldwin) Water mist flame suppression (McKinnon) Reacting Flows Flame kinetics (McKinnon Dean) Reaction mechanisms (Dean McKinnon) High-T fuel cell kinetics (Dean) tl :; ~-~ ''! ,i ~ l lll l ,IIJ i-4' tJ. !!illn .~ .... .c-;;il'\ -, .. ~ -!"-Finally located at the foot of the Rocky Mountains and only 15 miles from downtown Denver, Golden enjoys over 300 days of sunshine per year. These factors combine to provide year round cultural, recreational and entertainment opportunities virtually unmatched anywhere in the United States. Chemical En g ine e rin g Edu ca tion

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Fa/12002 M.S. and Ph.D programs in chemical engineering RESEARCH IN .. Advanced Proce ss Control Biochemical Engineering Biomedical Engineering Chemical Thermodynamics Chemical Vapor Depo si tion Computation al Fluid Dynamic s Environmental Biotechnology Environmental Engineering Magnetic Re so nance Imaging Membrane Separations Metabolic Engineering Polymeric Material s Porous Media Phenomena Thin Film s Tissue Engineering FINANCIAL AI D AVA I LABLE Teaching and re se arch assistantships paying a monthl y s tip end plus tuition reimbursement. F or app li cations and further information, write Graduate Advisor, Departm ent of Chemical Engineering Colorado State University Fort Collins CO 80523-1370 tate University CSU is located in Fort Collins, a pleasant commu ni ty of 100 ,000 p eople with the s pirit of the West th e v itali ty of a growing metropolitan area, and th e friendliness of a sma ll town. Fort Collins is lo cated about 65 miles north of D enve r and is adjacent to the foothills of th e R ocky Mountains. Th e climate is excellen t with 300 s unn y da ys per ye a,; mild t em peratures, and low humidity. Opportunities for hik ing, biking camping, boating fishing, and skiing abound in the immediate and nearby areas. Th e ca pu s is wit hin easy walking or biking distance of the town '.s shopping areas and its Center fo r the Per forming Arts. FACULTY Brian C. Batt Ph.D. University of Colorado Laurence A. Belfiore, Ph.D. University of Wisconsin David S. Dandy, Ph.D. California In stitute of T ec hnolog y M. Nazmul Karim, Ph.D. University of Manchester James C. Linden, Ph D Iowa State University Vincent G. Murphy, Ph.D University of Massachusetts Kenneth F. Reardon, Ph.D. California Institute of Technolog y Kristina D. Rinker, Ph. D North Carolina Stat e University A. Ted Watson, Ph.D. California In st itute of Technolog y Ranil Wickramasinghe, Ph. University of Minnesota 343

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University of Connecticut University of Connecticut 191 Auditorium Road Unit 3222 Storrs CT 06269-3222 Tel: (860) 486-4020 Fax: (860) 486-2959 www.engr.uconn.edu/cheg cheginfo@engr.uconn.edu 344 Chemical Engineering Department Graduate Study in Chemical Engineering [] B i o c h e mical E n g in ee rin g a nd Bio te chn o l ogy James D. Br ye rs Ph.D. Ric e University (Joint Appointment) Biochemica l Engi n eering, Biofilm Processe s, Biomaterials Rob e rt W Coughlin, Ph D. Cornell University Biotechnology Biochemical and Environmental Engineering Catalys i s Kinetic s, Separations, Surface Science Ranjan Srivastava, Ph.D., University of Mar yland Experimental a nd Computationa l Biology Biomole c ular Network Analysis Stochastic Biologic a l Phenomena Evolutionary Kinetic s Th omas K. Wood, Ph.D ., North Carolina State University Microbiological Engineering, Bior e mediation with Genetically-Engineered B ac teri a, Enzymatic Green Chemistry, Biochemical Engineering, Biocorro s ion [] P o l y m e r Scie n ce Patrick T. Mather, Ph D. University of California Santa Barbara Pol ymers, Mi cros tru ct ur e and Rh eology, Liquid Crystalinity, Inor ga nic -Orga nic Hybrid s Ri cha rd Parna s, Ph.D ., University of California Los Angeles Composites Biomaterial s Montgomer y T. Shaw, Ph.D. Prin ce ton University Pol y mer Rheolo gy and Proce ss ing Polymer-Solution Thermodynamics Rob e rt A. Wei ss, Ph.D ., University of Massachusetts Polymer Structure-Property R e lation s hips Ion-Containing and Liquid Crystal Polymer s, Pol y mer Bl e nd s L ei Zhu, Ph D., University of Akron Polymer Pha se Transitions, Structures of Morphologie s of Block Copo l ymers, Polymeric Nanocomposites Biodegrabable Block Copolymers for Dru g Deli very [] Co mpu ter A id e d Mo d e lin g Luk e E.K. Achenie, Ph D. Carnegie Mellon University Modeling and Optimization Molecular De s ign, Artificial Intelligen ce, Fl exi bility Analysis Thomas F. Anderson, Ph.D., Univesity of California at Berkele y Modeling of Separation Proce sses, Fluid-Phase Equilibria Douglas J Cooper, Ph.D., University of Colorado Process Modeling Monitorin g a nd Contro l Michael B Cutlip Ph.D., University of Colorado Kinetic s and Catalysis Electrochemical Re act ion Engineering Numerical Method s Suzanne Schadel Fenton, Ph.D., University of Illinoi s, Urbana-Champaign Computational Fluid D y n amics, Turbulence Two-Phase Flow [] E n vi ronm e nt a l a nd E n e r gy E n g in eer i ng Can Erkey, Ph.D. Te xas A&M University Supercritical Fluids, Catalysis, Nanotechnology Jam es M. Fenton, Ph D ., University of Illinois Urbana-Champaign Electrochemical a nd Environmental Engineering, Mass Transfer Proce sses, Electronic Materials Energy Systems Fu e l Cells Jos e ph J. H e lbl e, Ph.D. Massa c hus etts Institut e of Technology Air Pollution Aerosol Science, Nanoscale Mat e rial s Sythesis a nd Characterization, Combustion E m erit u s P rofessors C.O. Bennett J P. Bell A.T. DiBenedetto, G .M. Howard H.E. Klei, D.W. Sund s trom Chemical Engineering Education

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CORNELL UN IV FR SIT Y ,l ., .. . I J ,t ... _ :, \ _ ... ~ 1 \ ,, ,' :., ,. : ,. At Cornell University graduate students in chemical engineering have the flexibility to design research programs that take full advantage of Cornell s unique interdisciplinary environment and enable them to pursue individualized plans of study Cornell graduate programs may draw upon the resources of many excellent departments and research centers such as the Biotechnology Center, the Cornell Center for Materia l s Research, the Cornell anofabrication Facility the Cornell Supercomputing Facility, and the Na n obiotec hn o l ogy Center Degrees granted include Master of Engineering Ma s ter of Science and Doctor of P h ilosophy. All Ph.D. student s are fully funded with tuition coverage and attractive stipends. Re se ar c h A r e a s Advanced Materials Processing B ioc h emica l and Biomedical Engineering Fluid Dynamics, Stability, and Rheology Mo l ecu l ar T h ermodynamics and Computer Simulation Polymer Science and Engineering Reaction Engineering: Surface Science Kinetics and Reactor Design Situated in the s c enic Finger Lakes region of New York State the Cornell campus is one of the most beautiful in the country. Students enjoy sailing, skiing, fishing hiking, bic y cling, boating, wine-tasting and man y other activities. For furth e r information w rit e : Che1111, ul oll(/ 1/1011//1/, 11/m /,11g111, 1 Ill'-: ,0 A. Brad Anton Lynden A. Archer Paulette Clancy Claude Cohen .::: "' Lance Collins T Michael Duncan ; J runes R. Engstrom b.o Fernando A. Escobedo s:: .... .... "' Emmanuel P Giannelis Peter Harriott Yo n g Lak Joo Donald L. Koch Kelvin H. Lee Leonard W. Lion Christopher K. Ober William L. Olbricht David Putnam Ferdinand Rodriguez Michael L. Shuler t i Paul H. Steen Larry Walker Ulrich Wiesner t m e mb e ; Nati o nal A c ad e m y of En g ine e ring t m e mb e r, Am e ri c an A c ad e m y of Art s & S c i e n ce Director of Graduate Studie s School of Chemical Engineering Cornell University 120 Olin Hall Ithaca, NY 14853-5201, e-mail: DGS@CHEME.CORNELL.EDU or "visit our World Wide Web server at: http://www.cheme.comel l. edu Fall 2002 345

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Graduate Stud y & R esearch in Chemical Engineering at Dartmouth's Thayer School of Engineering 346 Dartmouth and its affiliated professional sc h oo ls offer PhD degrees in the full range of science disciphnes as well as MD and MBA degrees. The Tha ye r School of Engineering at Dartmouth College offer s an ABET-accredited BE degree as well as MS Masters of Engineering Management, and PhD degrees. The Chemical and Biochemical Engineering Pro gram features courses in foundational topics in chemical engineering as well as courses serving our areas of re sea rch s pecialization: Biotechno lo gy and biocommodity engi n eering Environmental science and engineering Fluid mechanics Materials science and enginee rin g Process design and evaluation These important research areas are representative of those found in chemical engineering departments around the world. A di s tinctive feature of the Thayer School is that the professors students, and visiting scholars active in these areas have backgrounds in a variety of engineering and scie ntific subdisciplines. This intellectual diversity reflects the reality th a t boundarie s between engineering and scientific subdisciplines are at best fuzzy and overlapping. It also provide s opportu nities for s tudents interested in chemical and biochemical engineering to draw from several intellectual tradition s in coursework and resear c h. Fifteen full-time faculty are active in research involving chemical engineering fund a mental s. Faculty & Research Areas Ian Baker (Oxford) Structure / property relationships of materials, electron microscopy John Collier (Dartmouth) Orthopaedic prostheses, implant/host interfaces Alvin Converse (Delaware) Kinetics & reactor de sign, enzymatic hydrolysis of cellulose Benoit Cushman-Roisin (Florida State) Numerical modeling of environmental fluid dynamics Harold Frost (Harvard) Microstructural evolution, deformation and fracture of material s Tillman Gerngross (Technical University of Vienna) Engineering of glycoproteins, fermentation technology Ursula Gibson (Cornell) Thin film deposition optical materials Francis Kennedy (RPI) Tribology surface mechanics Dani el R. Lynch (Princeton) Computational methods oceanography and water resources Lee Lynd (Dartmouth) Biomass processing pathway engineering, reactor & process design Victor Petrenko (USSR Academy of Science) Phy sica l chemistry of ice Horst Richter (Stuttgart) Thermodynamics, multiphase flow energy conversion, process design Erlan d Sc hul so n (British Columbia) Physical metallurgy of metals and alloys Charles E. Wyman (Princeton) Biomass pretreatment & hydrolysis cellulase synthesis & kinetics, process design For further information, please contact: Chemical Engineering Graduate Advisor Thayer School of Engineering Dartmouth College Hanover, NH 03755 http://thayer.dartmouth.edu/thayer/research/chem-biochem Chemical Engineering Edu ca tion

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University of Delaware www.che.udel.edu/ Faculty Mark A. Barteau ( R o bert L. Pi gfor d P ro f essor ; C h a ir ) S urf ace C hemi s tr y Ca t a l y s i s Kin e tic s Spec t rosco p y, Sca nn i n g Pr obe Micro sco p ies, M a t e ri a l s Antony N. Beris Fluid M ec h a ni cs, Vi scoe l as ti ci t y, N o n e quilibrium T h e rm ody n a m ics N u me ri ca l M e th ods, P ara ll el Co mp u t i n g Douglas J. Buttrey O x i des Th e rm ody n a m ics, Cr ys t a l G row th S t r u c tur e Ca tal ys i s S uper co nduct o r s Jingguang G. Chen ( Materi a l s S c i e n ce a nd En g in ee rin g ; Dir ec t o r Ce nt e r fo r Cataly ti c Sc i e n ce and T ec hn o l ogy) Na n oscale Mi croe l ec t ro ni c D ev i ces, C at a l y ti c M a te r i a l s, E n v ir o nm e nt a l Ca t a l ysis Costel D. Denson M a t e rial s, P o l y m e r s, Co m pos it es Tra n sport Sep ara ti ons Prasad S. Dhurjati Bi o t ec hn o l ogy, Bi o reactor s, M od elin g Bi oinfo rm a ti cs F a ul t D iag n os i s Expe rt Sys t e m s F a ll 2002 The Department of Chemical Engineering The U ni versity of Dela w ar e offers M.Ch.E. and Ph.D. degrees in Chemical Engineering. Bo th degr ees involve research and course work in engi n eering and related sciences. The Delaware tradition is one of st rong interdisciplinary r esea rch on both fundamental and appli ed problems. Jeremy S. Edwards Quan ti ta t ive Ana l ys i s of Metabo li s m a nd Ce llul ar Fate Pr oc e sses; B ioi n fo rm a ti cs a n d Ge n o m ics; Bi o t ec hn o l ogy a n d Metabo l i c E n gi n ee rin g Eric M. F urst Mi cro rh eo l ogy of Co m p l ex Flu ids, Ce llul ar M ec h a ni cs a nd M ove m en t St ru ct ur e a nd D ynamics of Co ll oi d al C r ysta l s, l n t erfacia l Ph e n o m ena Eric W. Kaler (Elizabeth I nez Kelley Professor ; D ea n Co ll ege of E n gi n ee rin g) Co ll oi d s S u rfacta nt s P o l y m e r s T h e rm ody n a mi cs B i o m o l ec ul es Jochen A. Lauterbach co mb i n atoria l cata l ysis a n d hig t hr o u g hp u t sc r eeni n g fabricat i on of co n duc tin g p o l yme r n a n o film s, n o n-li nea r ph e n o m e n a in h eterogeneo u s ca t a l ysis s pectral imaging of diffusio n p r ocesses i n polymers Abraham M. Lenhoff P ro t e i n Bi op h ys i c s, Separati o n s, Co ll oi d s T h e rm ody n a mi cs a nd Tra n spo rt Raul F. Lobo Ad so rpti o n Ca t alysis, Z eo lit es, Mi croporo u s M a t eria l s In o r ganic M a t e ri a l s S y n t h esis Babatunde A. Ogunnaike Pr ocess Con t ro l M ode lin g a n d S i mul a t ion Syste m s B io l ogy A pp li ed St atis ti c s Christopher J. Roberts Kin etics a nd Stati s ti cal T he rm odyna mi cs of L i q ui d s A m o rph o u s So lid s ( Gl asses) P ro t ei n s ; Kin e t ics a nd Th e rm odyna mi cs of P ro t ei n D egra d at i o n ; Pr e di ctio n of Ph ys i c a l and Chemica l S t ab ili ty of Pro t ei n s Anne S. Robinson Bi oc h e m ica l E n g in ee rin g Bi o m o l ec ul e Int eractio n s Bi oreac t o r Co n tro l M olec ul a r E n g in ee r i n g Ce llul a r E n g in eer in g T.W. Fraser Russell ( A ll an P Co l b urn P rofess o r of C h e mi ca l E n g in ee rin g ; Vi ce P rovos t fo r R e s earc h ) P h o t ovo l taic s, M ult ip h a s e F l uid M ec h anic s Stanley I. Sandler (Henry B e lin duP o nt C h ai r ; D irector Ce nt e r fo r M o l ec ul ar and E n gi n ee rin g Th e rm ody n a mi cs ) The rmod y nam ics Sta ti stical Mec h anics Comp u tatio n al C h e mi s try E n v i ro nm e nt Separa t io n s Bi osepara ti o n s Annette D. Shine El ec t ro rh eo l ogy, P o l y m e r Pr ocess in g, Rh eo l ogy S u pe r cri ti cal Fluid s Dionisios G. Vlachos S u rface C h e mi s t r y, Co m b u s ti on, P o ll ution A b a t e m e nt R eac t o r D es i g n ; N u cl eat i o n a nd G ro wth of N a n o pha se Mat e ri a l s and M e mbr a n es; Num e ri ca l M et h ods B i fur ca t io n Th eory P atterni n g of M a t e r ials Norman J. Wagner Co ll o i d a nd P o l y m er Sc i e n ce Rh eo l ogy Sta ti s ti cal M ec h a ni cs o f Com p lex Fl u ids, The r modynam i c s B iopo l y m ers Brian G. Willis C h e mi ca l Ph ys i cal M ec h a n is m s of Co pp e r Meta li za ti o n a nd Se mi co ndu c t or I n t e r co nn ec t Mate ri a l s Co mput a ti o n al C h e mi s tr y Mo d e l s ofC VD G ro wth Mec h a n is m s Ma t e ri a l s P rocess in g r esea r c h of Co mpound Se mi c ondu c t o r Materials fo r Sys t e m o n a C hip I n t egrat i on Richard P. Wool P o l yme r s, Compos it e s, Ad h es i o n Int e rf ace s Ma t e r ia l s fr o m Rene wa bl e R eso ur ces Bi odegra d a bl e Pl as ti cs 34 7

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DREXEL UNIVERSITY M.S. and Ph.D. Programs in CHEMICAL ENGINEERING RESEARCH AREAS Biochemical Engineering Biomaterials Biomedical Engineering Colloids and lnterfacial Engineering Molecular Dynamics Simulations Plasma Processing Polymer Science and Engineering Process Control and Dynamics Rheology and Fluid Mechanics Safety Engineering Systems Analysis and Optimization Tissue Engineering Transport Phenomena ABOUT DREXEL: Full financial support available FACULTY Charles Weinberger, Head (University of Michigan) Cameron Abrams ( University of California) Richard Cairncross (University of Minnesota) Donald Coughanowr (University of Illinois) Nily Dan (University of Minnesota) Elihu Grossmann (University of Pennsylvania) Cato Laurencin (Massachusetts Institute of Technology) Young Lee (Purdue University) Anthony Lowman (Purdue University) Stephen Meyer (Clemson University) Rajakkannu Mutharasan (Drexel University) Giuseppe Palmese (University of Delaware) George Rowell (University of Pennsylvania) Masoud Soroush (University of Michigan) Margaret Wheatley (University of Toronto) Steven Wrenn (University of Delaware) Department is experiencing a dramatic growth in research funding. Drexel is located in downtown Philadelphia with easy access to numerous cultural activities and major pharmaceutical, chemical and petroleum companies. FOR MORE INFORMATION WRITE TO: Professor Tony Lowman alowman@drexel.edu Department of Chemical Engineering Drexel University, Philadelphia PA 19104 Or visit us at: http://www.chemeng.drexel.edu

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.., ECOLE POLYTECHNIQUE MONTREAL Mic h ael D Buschmann Associate P rofesso r, P.En g., Ph.D (MJTI Ti ss u e Eng in ee rin g Biome c hani cs Carti l age Ph ysio l ogy Arthritis R esearc h E-mail : mik e@grbb.po l ym tl. ca Pierre J. Carreau Profe sso r. P.En g., PhD (Wisconsin, Madison ) H ead: Center o n Applied Re sea r c h on P o l y m ers (U RL: www.crasp.po l y mtl.c a) Rh eo l ogica l Prop e rtie s of Su spe n s ion s in P o l y mer s a nd P o l y m e r Blend s Mod e lin g of P o l y m e r Pr ocess in g Mixin g of Nonewtonian Fl uid s E-mail : pierr e carrea u @ mail.pol ym tl. ca Jamal C h aouki Prof essor, Ph D (Polytechnique ) H ead: E n v i ronmental and Bi otec hn o l og i ca l Pr ocess E n g in ee rin g Re sea rch Centre (U RL : www biopro .po l ym tl. ca) Chemical R eact i o n Engineering Multiph ase R eac t o r s Particle Tracking Tomography Fluidization of P owders E-mail : c h ao uki @ bi opro. p oly mtl. ca Louise Deschenes A ssis tant Profe sso r P.En g. Ph.D ( I NRS-Eau ) C o-C hair NSERC Indu s trial Chair on Sit e Biorem e di a tion Intri nsic Soil Bi oremedia ti o n Underground Water Tr eat m e nt E n viro nmental Microbi o lo gy Eco t oxico lo gica l Ri sk Asses s m en t Email : d esc h e n es@ bi opro.po l ym tl. ca Char l es Dubois A ss i sta nt Profe sso r P .Eng Ph.D. (U. Laval ) Rh eo l ogy and Impl e m en t a ti o n of R eac ti ve Med ia P o l ymeriza t ion/Compo undin g E-mail: c harl es. dub ois@polymtl.ca Basil D Favis P rofessor, Ph.D. ( McGill ) Pr ocessi n g-Mo rph o l ogy -Propert y R e l a t io n shi p s in P o l y mer Bl ends Int erface C har ac t e ri za ti o n in Multiph ase S ys tem s E-mai l: favi s @c himie po l ymt l. ca Miroslav Grmela Senior R esearc h A ssoc iate Ph.D. (Prague) Th e rmod y n a mi cs of Irr ev ersible Pr ocesses Mol ec ular Rh eo l og i cal Mod e llin g Flow of Vi scoe l astic Fluid s Pol y m e r Proce ss in g E -m ail : g rm e la @c h i mi e.po l y mtl. ca Marie C laud e Heuzey Assista nt Prof essor, P .E n g., Ph.D ( McGill ) Rh eo l ogy P o l y m e r Pr ocessing E-mail : m c h e u zey@c himie .po l ym tl. ca Chr istoph e Guy Pr ofessor P.En g PhD. (Polytechnique ) Dean of R esea rch Natura l Ga s T ec hn o l ogies Odors Tr e atm e nt of Solid Wa s te s a nd Emis s ion s Multiphase R eacto r s E mai l : c hri s t op h e.g u y@ mail.pol y mtl.c a Mario Jolicoeur, A ss i s tant Profes so r P Eng PhD (Polytechnique ) Bior eac tor E n g ineerin g M yco rrhi za l Fungi-Plant Symbi os i s Metaboli c En g in eeri n g Pharmaceutical En g in ee rin g E-mail: mari o.jo li coe ur @ polymtl.ca I lll 1u1thL1 111!,nm,1111111 uint.tLI u, Danilo Klvana P rofessor Ph.D ( Pra g u e) He ad: G as Technolog y R esearch Group (URL : www .po l y mtl. ca/ udr 7 .htrn ) Catalytic Ga sSolid H ydroge n atio n St orage of Methane Catalytic Combustion Pr e pa rat i o n of Catalysts an d Electrocatalysts E-mai l : d a nilo klv a na @ m a il.p o l ymt l. ca Pierre G Lafleur Pro fessor P En g., PhD (McGill ) D epartmen t C hairman P o l y mer Pr ocessing Comp ut er-A id ed Design Engineering and Manufacturing E-mail : pierre.laneur @ mail.polymtl.ca Robert Legro s, P rofessor P .Eng PhD ( Surre y ) S ol id W aste In c inerati o n Fluidized-Bed Combu s tion Fluidized-Bed D ryi n g Spouted B ed H ydrodynamics Expa nd ed B ed Bi oseparatio n E-mail: robe rt l egros@ m ai l.pol ym tl. ca Jean R Paris Profes so r P .E n g Ph D (Nort h wes t e rn ) H ea d : R esearc h G ro up on Pulp a nd P ape r Scien ce a n d Engineering (URL : www .g re s ip.polymtl.ca ) Pr ocess D esign and A n a l ys is Pr ocess Integrati o n Sy s t em C l os ure in M ec hanic a l and Chemical Pulp Mills Pin c h Analysis Pr ocess Simulation E-mail: jparis @g p apet ier .po l ym tl. ca Miche l Perrier P rofessor In g. PhD ( McGill ) D ynamics and Control of Chemical and Bi ochemical R eactors D ynamics a nd Control of Pulp and P aper Proces ses (URL : www. ur cpc.polym tl. ca/-perrier) E-mail: mi c h e l /pe rrier @ ur cpc. pol y mtl. ca Rejean Samson Profe sso r Ph.D. (Lava l ) NSE R C Indu s trial Chair for Sit e bior e mediation (U RL : www bi op r o polymtl.ca/bioremediation ) Environm e nt al Bi o t ec hn o l ogy W aste Treatment Air P o lluti o n E-Mail: s arn so n @ biopro po l y mtl. ca Henry P. Sc hreib e r S e ni or R esearc h Associate, Ph D (Toronto) Compo si te Material s Surface and Int erface P olymer Properties Microwave Pl as ma Surfac e Treatment E-mail : chreibe r @cras p polymtl.ca Paul Stuart A ssoc i ate Pr ofessor P En g Ph.D (McGill ) NSERC In dustrial Chair Pr ocess Int egration i n th e Pulp in Paper Indu s try E nvironment a l Engineering Pulp and Paper Pro cesses Pr ocess Int egra tion L ow Sludge Produ c tion Waste Water Treatm e nt E-mail: p s tu art@po l y mtl.ca Philippe Tanguy Profe sso r P Eng ., PhD (Laval ) NSERC/Paprican Indu s trial C h air o n Paper Coating (U RL : www urpei pol y mtl. ca) Mi x in g o f Rh eo l og i ca ll y Complex Fluid s Coating Pr ocesses Surface Treatment of Paper E-mail: tanguy @ urpe i po l ym tl. ca lk J1,II llllL lll ill ( lh..'lllll,tl ll~llll'l'I Ill~. I u ik I\ ih ILL lllllLjlll' P() H11\ (10~l) "il,1111 l]l ( l !llll \Ilk \It lll(lL',tl {)LIL hl'L ( d!l,td.1 11 ( '\ PI Hllh..' +I .:;11 ~ -W -Hd; l .t\ +1.::.1--.+ t.+04]",lJ I 111.111 LllL'IIIIL,ilLll!..'.llh...'L'llll~((l111.11l1)(lhlllllL.t \ 1"11 ( llll \\Lh,11L ,ll \\ \\ \\ !..'.L ll pt ih 11111 l ,l Fall 2002 349

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UNIVERSITY OF FLORIDA Graduate Studies in Chemical Engineering leading to M.S. and Ph.D. degrees TIM ANDERSON semiconductor processing thermodynam i cs SEYMOUR S. BLOCK Professor Emeritus biotechnology JASON BUTLER complex flu i ds fluid dynam i cs surface phenomena ANUJ CHAUHAN flu i d mechanics interfac i al phenomena bio materials OSCAR D. CRISALLE process control semiconductors pulp and paper polymer process i ng RICHARD B. DICKINSON cellular enginee ri ng biomedical engineering ARTHUR L. FRICKE Professor Emeritus polymers pu l p & paper characterization GARHOFLUND catalysis surface science semiconductors LEWIS JOHNS transport phenomena applied mathematics DALEKIRMSE computer -aided design process control DMITRY KOPELEVICH multi-scale and molecu l ar modeling TONY LADD statistical mechanics, fluid mechanics biomechanics ATULNARANG kinetics of microbial growth env i ronmental bioengineering RANGA NARAYANAN transport phenomena, applied mathematics low gravity processes MARKE ORAZEM electrochemical engineering CHANG-WON PARK fluid mechanics, polymer processing RAJ RAJAGOPAI.AN colloid physics, particle science FAN REN semiconductor device fabrication and characterization DINEIH. SHAH surface sctences, biomedical engineering lmOSSVOIIONOS Wlll8Wller lr8almenl, particle separations process control JAION F. WEAVER heterogeneous catalysis, dynamics of solid

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Florida A&M Univeristy a,:id Florida State University JOINT COLLEGE OF ENGINEERING GRADUATE EDUCATION AND RESEARCH IN CHEMICAL ENGINEERING and Program in Biomedical Engineering Fall 2002 MS/PhD in CHEMICAL ENGINEERING Advanced Polymers and Materials Process Control and Optimization Reaction Engineering Bioengineering Computational Engineering ar;-id Transport Processes MS/PhD in BIOMEDICAL ENGINEERING T i ssue Engineering Cellular Transport Processes Imaging and Spectroscopy Biointerfacial and Biomedical Engineering Computational Biomedical Engineering For more information contact: Department of Chemical Engineering FAMU FSU College of Engin e erin b g (850) 41 0-6 149 Or visit our websit es: http://www e ng .fsu edu/cheme http : //www.eng fsu edu/bme 35 1

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Graduate Studies in Chemical Engineering Join a small, vibrant campus on Florida 's Space Coast to reach your full academic and professional potential. Florida Tech, the only indepen dent scientific and technological university in the Southeast, has grown to become a university of international standing. Faculty P.A. Jennings, Ph.D. J.R. Brenner Ph.D. D.R. Mason, Ph.D. (emeritus) M.E. Pozo de Fernandez, Ph.D. R.G. Barile Ph.D. M.M. Tomadakis Ph.D. J.E. Whitlow Ph.D. Research Partners NASA/Kennedy Space Center Florida Solar Energy Center Florida Institute of Phosphate Research Department of Energy Florida Space Grant For more information, co nta c t Florida Institute of Technology College of Engineering Dept. of Chemical Engineering 150 West University Boulevard Melbourne, Florida 32901 -6975 (321) 674-8068 Graduate Student Assistantships and Tuition Remission Available ... _, -c ;Wllf .1 1111 Research Interests S1>acecraft Technology Alternative Energy Sources Materials Sl'iem:e Environmental Engineering Expert S:vstems www.fit.edu/ AcadRes/engsci/chemical/chemical.html

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A S Abhiraman: polyme r science and engi neer i ng ; Pradeep K Agrawal : hetereoge nous catalysis surface chemistry reaction kinetics ; Sue Ann Bidstrup Allen : microelec tronics polymer processing ; Andreas Bommarius : biocatalysis b i oprocessing ; L. Victor Breedveld: complex fluids high throughput materials characterization microfluids ; Charles A Eckert: molecular thermodynam i cs chemica l kinetics sepa r t i ons ; Larry J Forney : mechanics of aerosols buoyant plumes and jets ; Martha E Gallivan : process control i nterfac i al sci ence microelectronics ; Denn i s W Hess : microelectronics processing thin film sci ence and technology plasma processes ; Clifford Henderson: microelectronics pro cessing patterning imaging materials thin films ; Jeffery S Hsieh: pulp and paper ; Christopher Jones : catalyst deve l opment for po l ymer synthesis organometallic chem istry ; Paul A Kohl : photochemical process ing chemica l vapor deposition ; William J Koros: structure-permeability relationships for polymers ceramics polymer-ceramic hybrid substrates formation of composite and integrally skinned asymmetr i c mem branes ; Jay Lee : process control integrated sensing and system i dentification ; Charles L. Liotta : synthesis and propert i es of poly meric materials computer modeling of chemical processes ; Peter J Ludovice : molecular modeling of synthet i c and b i olog i cal macromolecules ; J. Carson Meredith : colloid and polymer science and technology related to th i n films and nanotechnology ; John D. Muzzy: polymer engineering energy conservation economics ; Sankar Nair : novel functional materials and nanoscale systems ; Robert M Nerem : biomechanics mammalian cell structures ; Mark R. Prausnitz : bioengineering drug delivery tissue permeabilization ; Matthew J. Realff : optimal process design and scheduling ; Ronald W. Rousseau : separation processes crystallization ; Athanassios Sambanis : bio chemical engineering mic r obial and an i mal cell structures ; Robert J Samuels : polymer science and engineering ; F Joseph Schork : reactor engineering, process control poly merization, reactor dynamics ; Arnold F Stancell : membranes, polymers process econom i cs; Daniel W. Tedder : process syn thesis and simulation chemical separation waste management resource recovery ; Amyn S Teja : thermodynam i c and transport properties phase equilibria supercr i tical extraction ; Mark G White : catalysis kinet ics reactor design ; Timothy M Wick : tissue engineering bioreactor design cell cell interactions biofluid dynamics; Ajit P Yoganathan: biofluid dynamics, rheology transport phenomena Georgia Deru@~ ulliJu@ @uTech[ru@D@@W School of Chemical Engineering Graduate Degree Programs Doctor of Philosophy, PhD Master of Science in Chemical Engineering, MS Doctor of Philosophy in Bioengineering, PhD Master of Science in Bioengineering, MS School Home Page www.che.gatech.edu On-line Graduate Application www.grad.gatech.edu/admissions Contact Information Dr. Ronald W. Rousseau, Chair School of Chemical Engineering Georgia Institute of Technology Atlanta, Georgia 30332-0100 ronald.rousseau@che.gatech.edu

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UNIVERSITY of HOUSTON Chemical Engineering Graduate Program Faculty and Their Research ,:~:-N. R. AMUNDSON (CULLEN PROFESSOR) _____ c he mica/ reactions ; Transport ; Mathematical modeling :~:. V. BALAKOTAIAH ____ C hemica/ reaction engineering ; Applied mathematics :~... A. T. CAPITANO Tissue Engineering ; In Vitro Toxicology a:~V. M. DONNELLY Petroleum engineering; Energy D. J. Eco NO MOU (JOHN & REBECCA MOORES PROFESSOR) Electronic materials ; Composites and ceramics F, M. P. HAROLD (Dow PROFESSOR, CHAIRMAN) Chemical reaction systems 1-_. E. J. HENLEY (EMERITUS) Reliability engineering ; Biomedical engineering "'cZ;_ R. KRISH NAMOO RTI Polymeric materials; Biom ateria/s ~: D. Luss (CULLEN PROFESSOR.) . . Houston Dynamic Hub ot Chem i cal Engineering Houston offers the educational, cultural business, sports and entertainment advantages of a large and diverse metropolitan area with significantly lower costs and crime rates than average Houston is also the increasingly dominant hub of the US energy and petrochemical industries as well as the home of NASA s Johnson Space Center and the wor/d ren owned Texas Medical Center The Chemical Engineering Department at the University of Houston offers excellent tac iii lies, competitive fin an cia/ support and an environment conducive lo personal and professional growth For more information www. che e uh. edu grad-che@uh.edu Graduate Office Chemical Engineering University of Houston Houston, TX 77204-4004

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Chemical Engineering at Howard University Where modern instructional and research laboratori es, to gether with computing facilities, support both student and faculty research pursuit s on an eighty-nine acre main cam pus three miles north of the heart of Washington DC. --Faculty and Research Interests------Mo bolaji E. Aluko, Professor and Chair PhD University of Californi a, Santa Barbara R eactor modeling crystalli z ation microelectronic and ceramic materials pro cessing process control reaction engineering analysis Joseph N. Cannon, Profe ssor PhD University of Colorado Transport phenomena in environmental systems computational fluid mechanics heat transfer Ram esh C. Chawla, Profe ssor PhD Wayne State University Mass transfe r and kinetics in environmental systems bioremediation incineration air and water pollution control William E. Co llin s, Associate Professor PhD University of Wiscon s in-Madison P o l ymer deformation, rheology, and surface science biomaterials bioseparations materials science M. Gopala Rao Profe sso r PhD University of Washington, Seattle Adsorption and ion exchange process energ y systems radioactive waste management remediation of contaminated soils and g round wate r John P. Tharakan, AssociateProfessor PhD University of California, San Diego Bi oprocess engineering protein separations biological ha z ardous waste treatment bio-environmental engineering Robert J. Lutz Visiting Profe sso r PhD University of Penn sy lvania Biomedical enginee rin g hemodynamics drug delivery pharmacokinetics Herbert M Katz Professor Emeritus PhD University of Cincinnati Environmental engineering For further information and a ppli cations, write to M.S. Program Director, Graduate Studies Chemical Engineering Department Howard University Washington, DC 20059 Phone 202-806-6624 Fax 202-806-4635 Fall 2002 355

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UIC The University of Illinois at Chicago Department of Chemical Engineering MS and PhD Graduate Program FACULTY ========= Kenneth Brezinsk y, Professor and Head P h.D City University of New York, 1978 E-Mail: Kenbrez@UIC.EDU John H Kiefer Professor Emeritus P h.D Cornell University, 1961 E Mail: Kiefer @ UIC.EDU Andreas A Linninger Associate Professor Ph .D Vienna University of Techno l ogy, 1992 E Mail: Linninge @ u ic edu G Ali Mansoori Professor Ph D ., University of Oklahoma, 1969 E Mail: Mansoori @ UIC.EDU Sohail Murad Professor Ph D., Cornell University, 1979 E-Mail: Murad @ UIC.EDU Ludwig C. Nitsche Associate Professor Ph D. Massachusetts Instit u te of Technology, 1989 E-Mai l : LCN @ UIC.EDU John Regalbuto Associate Professor Ph D. University of Notre Dame, 1986 E-Mail: JRR @ UIC.EDU Salish C. Saxena Professor Emerit u s P h D Calcu tt a University, 1956 E-Mail: Saxena@UIC.EDU Stephen Szepe Associa t e Professor P h .D., Illi n ois I n sti tu te of Techno l ogy, 1966 E-Mail : SSzepe @ UIC.EDU Christos Takoudis Professor Ph.D ., University of Minnesota 1982 E Mail: Takoudis @ UIC.EDU Raffi M. Turian Professor P h .D ., University of Wisco n sin, 1964 E-Mail: Turian @ UIC.EDU Lewis E. Wedgewood Associa t e Professor P h .D., Uni ve rsity of Wisconsin, 1988 E-Mail : Wedge @ uic ed u RESEARCH AREAS T ransport Ph e nom e na: Tran s port propertie s of fluid s, s lurr y tran s port a nd multiphase fluid flow Fluid me c h a ni cs of pol y mer s and other viscoelastic m e dia. T h er mod y namic s: Mo l ecu l a r s imu l ation and s ta t i s ti ca l mec h anics of liq u id m ix t ures S u pe r ficia l fl ui d extraction/retrograde condensation, asp h a l tene c h aracte ri zation Kinetics and Reaction E n g in ee rin g: Gasso lid reaction kinetic s Energy tra n sfer p rocesses la se r d i agnostic s a nd combustion c h emistry. Env i ronmen t al tec h no l ogy su r face che mi stry a nd opti mi zatio n Cat a l yst preparation and c h aracteriza ti o n s u pported m eta l s. Che mi ca l ki n et i cs i n a u to m otive e n g in e emissio n s Biochemical E n g in ee rin g : Bioin s trumentation Bio se para t ion s. Biodegrada bl e po l y mer s Nonaqueo u s enzymo l ogy Optimi za tion of mycobacter i a l fermentations. Materials: Microelectronic material s and proce ss ing heteroepitax y in group rv materials and in s itu s urface s pe c tro sco pie s at interface s. Comb u stion sy nthe s is of ceramics and s ynthe s i s in s upercrit i ca l fluids. Product and Proce ss D evelopment and design co mpu t er-aided m o delin g and s im u lation po ll ution preve nt ion -------For mor e information writ e to Director of Graduate Stu d ies Department of Chemical Engineering University of Illinois at Chicago 810 S C l inton Chicago I L 60607-7000 (3 12 ) 996 3424 Fax (3 12 ) 996-080 8 URL : http : / / www uic.edu/dept s/ chme /

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Chemical and Biomol ecular Engineering at the University of Illinois at Urbana-Champaign Th e combination of distinguished faculty, outstanding facilities and a di vers i ty of research interests results in exceptional oppo r tunities for gradu ate education. The c h emical and biomolecular engineering department offers graduate programs l eading to the M.S. and Ph.D degrees. Richard C. Alkire E l ectrochemical E n g in eer in g Richard D Braatz Advanced Proces s Control Steve Granick Polymer s a nd Biopolymer s Nanorheologyrrribology, and Surface Spectro sco pie s V ina y K. Gupta lat erfac i a l Phenomena: Structure and D y nami cs in Thin Films Jonathan J. L. Higdon Fluid Mechanic s and Computational Algorithm s Paul J. A. Kenis Microreactor s, Microfluidic Tools and Microfabrication Sangtae Kim Bioinform atics, Microfluidic s /Nanofluidics Mark J. Kushner Pla s ma Chemi s try and Plasma Materials Pro cess in g Deborah E. Leckband Bio e n gi n ee rin g and Bioph ys i cs Jennifer A Lewis Colloidal Assembly Complex Fluids and Mesoscale Fabrication Richard I. Masel Kinetics Cataly s i s, Microfuel Cells and Microchemi ca l System s A nthon y J. McHugh Polymer Science and Engineering Dani e l W. Pack Biomolecular Engineering and Bi otechnology Nikolaos V. Sahinidis Optimi zat i on and Proces s Sy s tems Engineering Kenneth S. Schweizer Macromolecular Colloidal a nd Complex Fluid Theory Edmund G. Seebauer Microelectronic s Proce ss ing and Nanotechnology Michael S. Strano Nanofabricated Materials, Molecular Electronic s, and Fullerene Nanotechnology Huimin Zhao Mo l ec ul ar Bi oe n g i neering a nd Biotechnolo gy Charles F. Zukoski Colloid and lnt erfac i al Science Fall 2002 For information and application forms write: D epartment of Chemical and Bi omolec ul ar Engineering University of Illinoi s at Urbana -Champ aign 114 R oger Adams Lab Box C-3 600 S Mathew s Ave. Urbana, Illinoi s 61801 3792 http://www.chemen g. uiu c.e du CREATING YOUR FUTURE 357

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GRADUATE STUDY IN CHEMICAL AND ENVIRONMENTAL ENGINEERING AT Illinois Institute of Technology THE UNIVERSITY Private, coeducational and research university 1700 under graduate students 3000 graduate students Campus recog nized as an architectural landmark Three miles from downtown Chicago and one mile west of Lake Michigan THE DEPARTMENT Among the oldest chemical engineering programs in the nation Merger of chemical and environmental engineering departments in 1995 created state-of-the-art, interdisciplinary research and educa tion program s M S., Professional Master and Ph.D. degrees in chemical and environmental engineering New food process engi neering program New double Master 's degree program in chemi cal engineering and computer science Fellowships and assistant ships available APPLICATIONS Graduate Admissions Coordinator Chemical and Environmental Engineering Department Illinois Institute of Technology 10 W. 33rd Street Chicago IL 60616-3793 Phone : 312-567-3533; Fax: 312-567-8874 http: // www chee.iit.edu / e-mail: chee@iit edu FACULTY AND RESEARCH AREAS Chairman Hamid Arastoopour Associate Chair for Undergraduate Affairs Fouad Teymour Associate Chair for Graduate A ff airs Salish Parulekar Javad Abbasian; separation processes gas cleaning, air pollution Nader Aderangi; unit ope rati o ns c h emical processes Paul R. Anderson; precipitation kinetics eva luati on of oxide adsorbents for water and wastewater treatment Hamid Arastoopour; computational multiphase flow, fluidization, material processing particle technology fluid -particl e flow Barry Bernstein ; computational fluid mechanics, material properties, polymer rheology Donald J Chmielewski; process control, pollution prevention Ali Cinar; chemical and food process control, nonlinear input-output modeling sta tisti cal process m onitoring Dimitri Gidaspow; hydrodynamics of fluidi z ation using kinetic theory, gas-solid transport Henry R Linden;fossil fuel technologies energ y and resource economics energy and environmental policy Dem e trio s J. Moschandreas; ambient and indoor air pollution statistical analysis, enviro nm ental impact assessment Allan S. Myerson; crystallization from sol uti on, nucleation, molecular modeling Kenneth E. Noll; ai r resources e n g in eering, air pollution meteorology, ha z ardous waste treatment Krishna R. Pagilla ; wate r and wastewater enginee rin g, envi ronm e ntal microbiology, soil remediation sludge treatment Satish Parulekar ; biochemical engineering chemical reaction engineering Victor H. P e re z-L un a; biomedical and tissue engineering Jai Praka s h ; sol id state che mi st y, mat erials sy nth esis and c hara cterizat i on for e n ergy co nv ersion and storage applications Jay D. Schieber; kinetic theory, pol y m e r rheology predictions, transport phenomena, non-Newtonian fluid mechanics J. Robert Selman ; applied e l ect ro c h emistry and elect r oc h emical eng in eer ing, battery and fuel cell design Eugene S. Smotkin; FTIR spectroscopy of electrode surfaces, electroc hemi ca l mas s spectroscopy, fuel ce ll s combinatorial catalyst screening Fouad A. Teymour ; polymer reaction engi n ee rin g, math e mati cal model in g, nonlinear d yna mi cs David C. Venerus; polymer rheology and processing, transport phenomena in pol ymeric syste ms Darsh T. Wasan; thin liquid films; int erfacia l rheology ; foams, em ulsion and dispersion, environmental technologies Research Faculty and Lecturers Said AI-Hallaj Michael Caracotsios E lli s Fields William Franek Ted Knowlton Harold Lindahl Robert Lyczkowski Zoltan Nagy Alex Nikolov Ali Oskouie Giselle Sandi Charles Sizer Hw a-Chi Wang

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Graduate program for M.S. and Ph.D. degrees in Chemical and Biochemical Engineering F A CULTY Gary A. Aurand North Carolina State U 1996 Supercritical fluids / High pressure biochemical reactors Stephen K. Hunter U. of U tah 1989 Bioartificial organs / Microencapsulation technologies David Rethwisch U. of Wisconsin 1985 Membrane science / Polymer science / Catalysis Audrey Bu t ler U of Iowa l 989 Chemical precipitation processes Julie LP Jessop Michigan State U. 1999 Polymers / Microlithography / Spectroscopy V.G.J. Rodgers Washington U. 1989 Transport phenomena in bioseparations / Membrane separations Greg Carmichael U. of Kentucky 1979 Global change / Supercomputing / Air pollution modeling Robert Li nhardt Johns Hopkins 1979 Biopolymers and pharmaceutical applications Alec B. Scranton Purdue U 1990 Photopolymerization / Reversible emulsifiers / Polymerization kinetics f & -, '+:;.,' Vicki H G r assian U. of California Berkeley 1987 Surface chemistry / Heterogeneous processes David Murhammer U. of Houston 1989 Insect cell culture / Bioreactor monitoring Ramaswamy Subraman i an Indian Institute of Science 1992 Structural enzymol ogy/Structure function relationship in proteins C. Allan Guymon U. of Colorado 1997 Polymer reaction engineer ing / UV curable coatings/ Polymer liquid crystal composites Tonya L. Peeples Johns Hopkins 1994 Bioremediation/ Extremophile physiology and biocata l ysis John M. Wiencek Case Western Reserve 1989 Protein crystallization/ Surfactant techno l ogy For information and application: THE U NIVE RS ITY O F I O W A Graduate Admissions Chemica l a n d Bioc h emical E ng i n eeri n g 4133 Seama n s Center Iowa City IA 52242 1527 1-800-5 5 3-IOWA (l-800-5 5 3-4692) chemeng@icaen.u io wa e d u www eng i neering. uio wa.edu/ ~chemeng/

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I OWA STATE UNNERSITY OF SCIENCE AND TECHNOLOGY :-:... :B ro wn Robert: C D o r my LK. Fox, Rodny 0 G ie tz. CharlH E Oon u l u Ram o n Hebert. Kurt A Hlll JamH C Joll Kennath R M a ll ap rapda Surya K Na raN mha n B al JI Reilly PeurJ Aollfn 0..-ric lc K Sc hnder Glan n L Sa .. rave Richard C S hanb Brant H Sh a nb J .c qua U ne V Uh t ch.on Daan L V19U A Da n n ie Whaafoc k. ThomH D Youngqula t. Gordon R. Robert C. Brown Michiga n State L. K. Doraiswamy Wisconsin Charles E. Glatz Wisconsin Graduate Adm i ssions Comm i ttee Department of Chemical Engineering I owa State Un i v er s i ty Am e s I o wa 500 11 515-294-7643 Fax : 5 1 5 294 2689 chemengr@iastate.edu www .i as t at e. edu/ -c h _e Ramon Gonzalez Chile Kurt R Hebert Illinois JamesC Hill Wash i ngton Kenneth R Jolls Illinois Surya Mallapragada Purdue Balajl Narasimhan Pur d ue Peter J Reilly Pennsylvania Derrick K. Rollins Ohio State Richard C Seagrave Iowa State Jacqueline V Shanks Cal Tech Brent H Shanks Cal Tech Glenn L. Schrader Wisconsin Dean L. Ulrichson Iowa State R. Dennis Vigil Mich i gan Thomas D Wheelock Iow a Sta t e Gordon R. Youngquist Ill i nois

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Graduate Study and Research in Chemical Engineering at Johns Hopkins The Johns Hopkins University's Department of Chemical Engineering, established in 1936, features a low student-to-faculty ratio that fosters a highly collaborative research experience. The faculty are internationally known for their contributions in the traditional areas of chemical engineering re search, such as thermodynamic s, fluid dynamics, and rheology, and at the forefront of emerging technologies, such as membrane-based separation processes, recombinant DNA technology, tissue engineering, and molecular/cellular biomedical engineering. Insect Cell Culture Recombinant DNA Technology Protein Folding and Aggregation Michael J B ete nbau g h PhD University of Delaware Equations of State Statistical Thermodynamics Solvent Replacement Marc D Donohue PhD University of California B erke l ey Nanostructured Materials Colloid/Protein Adsorption Rheology of Suspensions Je ff re y J. Gray, PhD University of Texas at Austin Biomaterials Synthesis Controlled/Targeted Drug Delivery Tissue Engi neering Ju s tin S. Hane s, PhD Massachusetts Institute of Technology Biomaterials and Na nocomposite Materials Macromolecular Transport Rheolog y of Soft Materials Jame s L. Harden PhD University of California, Santa Barbara Nucleation Crystallization Flame Generation of Ceramic Powders Joseph L. Katz PhD University of Chicago Fluid Mechanics in Medical Applications Vascular and Cellular Biology Thrombosis, Inflammation, Cancer Metastasis Konstantino s Kon s tantopoulo s, PhD Rice University Th e John s H o pkin s Uni vcrs i1 y d oes not di sc riminat e on the basi s of ra ce. co l or. sex. r e ligi on, sex ual o rient atio n nali o n a l or e thni c origi n age. di s abilit y or ve t eran s tutu s in an y s tudent program or acti vi t y adminis t ered by th e Universi t y o r wi th rega rd to ad mi ss i o n or e mpl oyment. Defense Department d i sc rimin a t ion in ROTC program s o n the ba s i s of hom osex u11li1 y co nfli c t s w ith thi s unive rs ity policy. Th e uni vers it y i s co mm itte d t o e ncoura g in g a c h a n ge i n the Defense Depanm ent policy Questions regarding Tille VI. litle IX and Section 504 should be referred to Yconne M Theodore Affirmative Aclion Officer. 205 Garland Hall ( 410-516-8075) Fall 2002 Molecular Bioengineering Protein Engineering Molecular Evo lution Marc Ostermeier, PhD University of Texas at Austin Surfactant/Supercritical Fluid Phase Behavior Computational Molecu lar Thermodynamics Polymer/Protein Thermodynamics Michael E. Paulaitis, PhD U ni versity of Illinois Interfacial Phenomena Surfactant Transport Kinetics Maragoni Effects Kathleen J. Stebe PhD The City University of New York Phase Transitions and Critical Phenomena Polymer Systems Far from Equilibri um Particle-Tracking Microrheology Denis Wirtz, PhD Stanford University For further information contact: Johns Hopkins University Whiting School of Engineering Department of Chemical Engineering 3400 N Charles Street Baltimore MD 21218-2694 410-516-5455 I che@jhu.edu http://www.jhu.edu/~cheme OHNS HOPKINS 36 1

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Graduate Study in Chemical and Petroleum Engineering at the UNIVERSITY OF KANSAS The University of Kansas is the largest and most comp r ehensive university in Kansa s. It has an en rollm e nt of more than 28 000 and almost 2,000 faculty mem bers. KU offers more than JOO bachelors ', nearly 90 ma sters', and more than 50 d octora l pr ogra ms Th e main ca mpu s is in Lawrence, Kan sas, with ot h e r ca puses in Kansas City, Wi c hita, T o p e k a, and Ov e rland Park Kan sas. Graduate Programs [] M S. degree with a thesi s requirement in both chemical and petroleum engineering [] Ph D degree characterized by moderate and fleX_ible course requirement s and a s trong re searc h emphasis [] T y pical completion time s are 16-18 month s for a M S. degree a nd 4 1/2 years for a Ph.D. degree (fro m B.S .) Faculty Kenneth A. Bishop (Ph.D., Oklah o ma ) Kyle V. Camarda (Ph.D. Illin o i s) John C. Da v i s ( Ph.D. Wyoming) Don W. Green (Ph.D Oklahoma) Colin S Howat ( Ph.D ., Kansa s) Carl E Locke, Jr ., (Ph.D., T exas) Trung V. Nguyen ( Ph.D. T exas A&M) Karen J. Nordheden ( Ph.D ., Illinoi s) Russell D. O s terman (Ph.D ., Kan sas) Marylee Z Southard ( Ph D ., Kansa s) Susan M. William s (Ph.D., Oklah o ma) Bala Subramaniam Chair (Ph.D ., Notr e Dame ) Shapour Vossoughi (Ph.D ., Alb e rta Canada) G. Paul Willhite (Ph.D., Northwestern) Research Catalytic Kinetics and Reaction Engineering Catalytic Material s and Membrane Proce ss in g Controlled Drug Deli very Corro s ion Fuel Cells Batt eries Electrochemical Reactor s and Proce sses Electronic Materials Proce ssing Enhanced Oil Recovery Proce sses Fluid Ph ase Equilibria and Proce ss De s ign Molecular Product De s ign Proce ss Control and Optimization Supercomputer Applications Supercritical Fluid Application s Financial Aid Financial aid i s ava ilable in th e form of re sea rch and teaching assis tantship s at $16,000 a year (pl u s tuition ) and fellowships/scholar s hips s uch a s those noted below Madison & Lila Self Graduate Fellowship Mis s ion : identify recruit, and provide development opportunities for exceptional Ph.D s tudent s. Fourye ar award consistin g of an annual $20,600 s tipend plu s full tuition and fees. An additional bonu s of up to $ 10 000 per year is possible. For add itional information and app lic ation : http://www unkans.edu/~ se l fpro /home/index.html Kansas and Missouri High School Graduates Scholarship of $22,000 annually plu s full tuition and fee s Contacts Web s ite for information and application: http://www.cpe.engr.ku.edu/ Graduate Program Chemical and Petroleum Engineering University of Kan sas -Learned Hall 1530 W. 15 th Street Room 4006 Lawrence KS 660457609 phone: 785 -8 642 900 fax : 785-864-4967 email: cpeinfo@ku .edu

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Durl a nd H a ll H o m e o f Ch e mic a l E n g in ee rin g KANSAS STATE UNIVERSITY M.S. and Ph D. Programs Chemica l Engineering with Interdisciplinary Areas of: Systems Engineering Environmental Engineering Complex Fluid Flows Financial Aid Availabl e Up to $24 500 Per Year For More Information Write T o Professor J H Edgar Durland Hall Kansas State University Manhattan KS 66506 or v i s it ou r web s i te a t http : //www engg ksu edu / CHEDEPT / Fall 200 2 Areas of Study and Research Biopolymers Biotechnology Catalytic Hydrocarbon Conversion Chemical Reaction Engineering Crystal Growth of Semiconductors Environmental Pollution Control Hazardous Waste Treatment Membrane Separations Multiphase Flow Polymeric Materials Properties Process Systems Engineering and Artificial Intelligence Separative Reactors 363

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University of Kentucky Department of Chemical & Materials Engineering Catalysi s Environmental Engineering Biopharmaceutical & Biocellular Engineering The Chemical Engineering Faculty Material s Synthe s i s Advanced Separation & Supercritical Fluids Proce s s in g D o nn Hancher Interim Chair Purdu e U ni ve r s i ty K And e r so n Carn eg i eM e ll o n Univ e r s i ty D. Bhatta c haryya Illin o i s I nstitut e of T ec hn o l ogy A G ee rt se ma Uni ve r s i ty of Karl s ruh e Membranes & Polymer s Aeroso l s For more i11for111atio11: E. Grulk e Ohi o S tat e U ni ve r s i ty C. H a mrin ( Pro fesso r E m e ritu s) No rth wes t e rn U ni vers i ty D K a lik a U ni ve r s it y of Ca l ifo rni a B e r ke l e y M K ea n e Nat i o n a l U ni ve r s i ty of Ir e l a n d R. K e rm o d e No rth weste rn U ni ve r s i ty B Knut so n G eo r g i a In s titut e o f T ec hnol ogy S Rankin U niv e r s i ty of Minn eso ta A R a y C l a rk so n U ni ve r s i ty J.T S c hrodt U ni ve r s it y of Lo ui svi ll e T. T sa n g Uni ve r sity of T exas Paducah KY, Program P. Dunb a r U ni ve r s i ty of T e nn essee R. L ee -D esa ut e l s Ohi o S t a t e Un i ve r s i ty D Sil ve r s t e in V a nd erb ilt U ni v e r s i ty J. Smart Univers i ty of T exas W e b : http://www. e ngr.uky .e du/cme Email : cm e -admit @ en g r.uky. e du Addr ess: D e partment o f Chemical & M a t e rial s En g in ee rin g Dir ec t o r o f Graduat e Studi es, Chemical E n g in ee rin g 177 And e r s on Hall Uni ve r s it y of Kentu cky L exi n g ton KY 40506-0046 Ph o n e (8 59 ) 2 57 -8 0 28 F ax (8 5 9) 3231 929

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Polymer engineering P10eess modelling Rheology Polymer processing Fa/12002 F KUIM DES SC llNC ES ET D GE NIE UNIVERSJ'It !AV AL Research Areas Mosto M. Bousmina (Ph D Eco l e des Hauls Polymeres Strasbourg) bousmlna @gch ul ava l. ca (4 18 ) 656 2769 rhe o l ogy and modelling polymer blends and processing polymer physics and engineering Alain Garnie r (Ph.D Ecole Polytechnique de Montreal) alain garnier@gch ulaval ca (418) 656 3106 biochemical engineering anima l cell cu ltur e v iru s and protein production Suzanne Giasson (Ph.D University of Western O nt ario and IFP Paris) sgiasson@gch ulaval.ca (4 1 8) 656-3774 intermolecular and intersurtace forces comp l ex fluid systems, polymers biomaterials nanorheology nano tr lbology Bernard G r andjean (Ph.D Ecole Polytechnique de Montreal) grandjean@gch ulaval ca (418) 656-2859 catalytic membrane reactors neura l network, genetic algorithm pr ocess m ode llin g Serge Kaliagu i ne (D Ing I GC Toulouse) kaliagui@gch ulaval.ca (418) 656 2708 zeo l ites mesostructured materials perovskites catalytic merrt>ranes and fuel cells industrial catalysis Ren e Lacr o ix (Ph.D UnlverslM Laval) l acro i x@gch ulaval c a (4 18 ) 656 3564 finit e e l e m e nt m e th od numer ica l slmu l ation o f cooling processes therm o-electrk:a l s imulati on Fa "i,; al Larachi (Ph.D INPL Nancy) flarachi@gch.ulaval ca (4 18 ) 656-3566 multiphase reactors we t oxkiat i o n flow instrumentation Anh LeDuy ( Ph.D University o f Western Ontario ) leduy@gch ula v al.ca (418) 656 2634 biochemical and microbial processes biokinetics Jean C l aude Methot (P h.D Unlversile Laval) methot@gch ula val.ca (418) 656 2539 Denis Rodrigue (Ph.D Universite de Sherbrooke) denls rodrigue@gch ulaval ca (418) 656-2903 transport phenomena rheology polymeri c foams Christian Roy (Ph.D Universite de Sherbrooke) c r oy@gch.u la va l .ca (4 18 ) 656 7406 vacuum pyrolysis vapor phase m embranes industrial process engineering Additional information and A pplications may be obtained from : Head of Graduate Programs Al ain Garnier 08parteme n t de G8nle chimique Pavilion Adrien-Pouliot Universlte Laval Quebec (QC) Canada GlK 7P4 alain garnier@gch ulaval.ca www.gc h ulaval.ca Phone : (4 18 ) 656-3106 FAX : (418) 656 5993 365

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Synergistic, interdisciplinary research in .. Biochemical Engineering Catalytic Science & Reaction Engineering Environmental Engineering Interfacial Transport Materials Synthesis Characterization & Processing Microelectronics Processing Polymer Science & E n g in eering Process Modeling & Contro l Two-Phase Flow & Heat Transfer ... leading to M.S., M.E., and Ph.D. degrees in chemical engineering and polymer science and engineering Highly attractive financial aid packages "Which provide tuition and stipend, are available. Philip A. Bl y the (University of Manchester) fluid mechanics heat transfer applied mathematics Hugo S. Caram (Univers it y of Minnesota) gas-solid and gas-liquid systems optical technique s reaction engineering Marvin Charles (Po l ytechnic Institute of B rooklyn) bioprocess design cGMP R&D Manoj K. Chaudhury (SUNY-Buffalo) adhesion thin films surface chemistry John C. Chen (University of Michigan) two-phase vapor-liq u id flow fluidization radiative h eat transfer enviro nm e nt al technology Mohamed S. EI-Aasser (McGill University) polymer colloids and films emulsion copolymerization polymer synthesis and characterization James T. Hsu (Northwestern University) bioseparations applied recombinant DNA technology Andrew Klein (North Carolina State University) em ul s i on polymerization co lloid a l and surface effects in polymerization Mayuresh V. Kothare (Ca li fornia Institute of Technology) model predictive control constrained control microc h emical systems William L. Luyben (U ni versity of Delaware) process d es i gn and co ntr o l distillation William E. Schiesser ( Prin ceto n University) numerical algorithms a nd software in c h emical eng in eering Arup K. Sengupta (University of Houston) use of adsorbents ion exchange reactive polymers, membranes in environmental pollution Cesar A. Silebi (Le hi g h University) se p aration of co lloid a l particles e l ectrop h o r es i s m ass transfer Leslie H. Sperling (Duke University) mechanical and morphological properties of polymers interpenetrating polymer networks Fred P. Stein, Emeritus (University of Michigan) thermodynamic properties of mixtures Harvey G. Stenger, Jr. (Massachusetts In stitute of Technology) reactor engineering Israel E. Wachs (Stanford University) materials c h aracter i zation surface c h emistry heterogeneous catalysis environmen t a l catalysis Leonard A. Wenzel, E m er itu s (U ni versi t y of Michigan) thermodynamics cryoge ni cs and mixed-gas adsorp ti o n Living in B ethlehem, PA allows easy access to cu ltural and recreational opportunities in the New York-Philadelphia area. Additional information and applications may be obtained by writing to: Dr James T. Hsu, Chairman Graduate Committee Department of Chemical Engineering Lehigh University 11 Research Drive lacocca Hall Bethlehem, PA 18015 FAX: (610) 758-5057 E-MAIL: inchegs@lehigh.edu WEBSITE: www.lehigh.edu/~inchm/index.html

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UNIVERSITY -----OF----LQ UJSJAN A Lafayette MS in Engineering Chemical Engineering Faculty C.S. Fang PhD University of Hou sto n TX (1968) F F. Farshad, PhD, University of Oklahoma OK ( 1975) J.D. Garber (Head) PhD Georgia In s titute of Technology GA (197 1 ) A G. Hill PhD, Louisiana Technical University, LA ( 1 980) J.N Linsley PhD Rice University TX ( 1970 ) R.D K. Mi sra, PhD University of Cambridge, UK ( 1984 ) A.B. Ponter DSc Birmingham University, UK ( 1986 ) PhD, Manchester ( 1966 ) J.R. Reinhardt PhD Un i versity of Arkansas AR ( 1977 ) Research Centers Corrosion R esea r ch Center Dr. J D. Garber Director Center for Metals Pol yme r s and Composites R esearch Dr. R D K Misra Director Edith Garland Dupre Library For more information: Atomic Force Microscopy of Deformed High Density Polyethey/ene R esearch Areas Corrosion Gas and Oil Well Modeling Pipeline Steels Hydro ge n-Induced Cracking Materials: Structure/Processing/Performa n ce Irradiation of Polymers with UV /Ozone Deformation Behavior of Polymers and Composites Formability and Fracture Toughness of High-Strength Steels Co ld Work Embrittlement of Int e r stitia Free Steels Casting of Precious Metal s and Alloys Fluid Flow and Transport Phenomena Pha se in vers ion Drop Coalescence Liquid Spreading Multiphase Flow Surface Roughness Thermodynamics and Proces s Engi neerin g Pha se Equi l ibria in Multipha se System s Chemical Reactor Design Stability and Dynamics Pro cess Simulation and Design Department of Chemical E n gi ne ering U niv e r s ity of Louisiana at Lafayette PO Box 44130 Lafayette, LA 70504-4130 www.louisiana.engr.edu/chee/ or e-mail: dmisra@louisiana.edu (Graduate Coordinator) Fall 2002 367

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LOUISIANA STATE UNIVERSITY CHEMICAL ENGINEERING GRADUATE SCHOOL T H E CITY ------Baton Rou ge i s the s tat e capitol a nd home of th e m ajo r s t ate institution for hi g her education LSU. Situated in the Acadian region Baton Rouge blend s the Old South and Cajun Cultures. Baton Roug e i s one of th e nation 's bu s ie st port s a nd the city's economy rests heavil y on the chemical oil pla s ti cs, a nd agricul tural indu s tries. Th e great outdoors provide exce llent recre at ion a l activities year-round especially fishing hunting a nd water s port s The proximity of N ew Orleans provide s for s uperb nightlife especially during Mardi Gras. Th e c ity is a l so only two hour s away from the Mi ss i ss ippi Gulf Coast and four hour s from e ither Gulf Shores or Hou s ton. T H E D E P A R TMENT-----M.S and Ph D Program s Approximately 60 Graduate Students Average research fundin g mor e than $2 million per year DEPARTMENTAL FACILITIES Departmental computing-with mor e than 80 PC s Exten s ive laboratory facilities especially in reaction and environmental engineering transport phenomena and separations, polymer textile and materials proce s ing biochemical engineering thermodynamic s T O A P PLY, CONTACT DIRECTOR OF GRADUATE INSTRUCTION Gordon A. and Mar y Cain Dep a rtment of Chemical Engineering Louisiana St ate University Baton Rou ge, LA 70803 T e lephon e : 1 (8 00) 256 2084 FAX: (225) 578-1476 em a il : gradcoor @c h e. l s u .e du FACULTY T.J. CLEIJ ( Ph D ., Utrecht University) P o l y m er i c M ate ri a ls Science and Engineering A.B. CORRIPIO ( Ph.D. Loui sia na State University) Control, Simulation Computer-Aided D esign K.M. DOOLEY ( Ph D ., University of Delaw are) H ete r ogeneous Ca tal ys is, Hi g h Pr essu re Separations G.L. GRIFFIN (Ph.D., Prin ceto n University) Electronic Materials Surface Chemistry, CVD D.P. HARRISON ( Ph D. University of Texa s) Fluid-Solid R eactions, Ha zardous Waste Treatment M.A. HJORTS0 ( Ph.D. University of H o u sto n ) Bio c hemical R eactio n Engin ee ring Applied Math F.C. KNOPF (P h.D. Purdue University) Supercritical Fluid Extraction, U ltrafast Kinetics B.J. McCOY (P h D ., University of Minnesota) Separation Transport R eaction Engineering R.W. PIKE ( Ph D ., Georgia Institute of Technolo gy) Fluid D y namics R eaction Engineering, Optimi z ation E.J. PODLAHA ( Ph.D. Columbia University) Electrical Ph enomena, Allo y and Composite Materials D D. REIBLE ( Ph.D. Californi a In s titute of Technology ) Environmental Transport, Transport Mod e lin g A.M. STERLING ( Ph.D. University of W as hin gto n ) Transport Ph e n omena, Combustion J.J. SPIVEY ( Ph D ., Louisiana State University) Catalysis L.J. THIBODEAUX ( Ph.D ., Louisiana State University) Chemodynamics Ha za rdous W as t e Transport K.E. THOMPSON ( Ph.D. University of Michigan ) Tran s p or t and R eaction in P orous Media K.T. VALSARAJ ( Ph.D. Vanderbilt University) Environmental Transport, Separations D.M. WETZEL ( Ph D ., University of D e lawar e) H azardous Wast e Treatment Dr y in g M.J. WORNAT ( Ph D ., Ma ssac hu se tt s In st itut e of T ec hnol ogy) Co mbustion H eterogeneo u s R eactio ns FINANCIAL AID-------A ss istantship s at $ 17 ,5 00 $29,20 0 with waiver of out-of-state tuition

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MANHATTAN Offering a Practice-Oriented Master's Degree Program Fall 2002 COLLEGE This well-established graduate program emphasizes the app li ca ti o n of basic principles to the solution of mod e rn e n g in eeri n g problems with new features in e n gi n eering m a n agement, environmental management, and bi oc h emica l engineering Financial aid is available including industrial fellowships in a one-year program involving participation of the following companies: ABB Lummus Global Inc. Air Products and Chemicals, Inc. Consolidated Edison Co. Merck & Co., Inc. Pfizer Inc. Chevron Texaco Global Phillips 66 For information and application form, write to Graduate Program Director Chemical Engineering Department Manhattan College Riverdale, NY 10471 in Chemical Engineering ., ., ., Ill Manhattan College is lo cate d in Ri verdale, an attractive area in th e northwest section of New York City. chmldept@manhattan.edu http://www.en gineering.manha ttan .e du/ gra duate/application/ crea te _a ccount.aspx 369

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CHEMICAL ENGINEERING UNIVERSITY OF Faculty and Research Areas Raymond A. Adomaiti s (IIT) Systems modeling and simulation methodologies ; semiconductor manufacturing Mikhail A Anisimo v (Moscow) Critical phenomena and phase transitions in fluids and flu i d mixtures Timoth y A. Barbari ( T exas A u st in ) Mem b ra n e science, polymer science, biomaterials William E. Bentle y (Co l ora d o) B iochemica l/ meta b olic engineering, applications of molecular biolog y Richard V. Calabrese ( M assac hu se tt s) Mu l t ip hase flow, turb ul ence and mixing K y u Yong Choi (W i sco n sin) Polymer reaction engineering Panagiotis Dimitrakopoulos (Ill i nois-Ur b ana) B iofluid mechanics, bioph y sics and microrheology Sher y l H. Ehrman (UC L A) Aerosol and nanoparticle technology John P. Fisher ( Ri ce) Tissue engineering, biomaterials James W. Gentr y ( T exas A u st in ) Aerosol science and enginee r ing Sandra C. Greer ( Chi cago) P hysical chemistry, po l ymer science, biomacromolecules phase equilib r ia Maria I. Klapa (M IT ) Metabolic engineering, bioinformatics, modeling of biological networks Peter Kofinas ( MIT ) P olymer science and engineeri n g Thoma s J. McAvo y ( Prin ceto n ) P rocess control, fault detection Tracey R. Pulliam Holoman ( M ary l a nd ) B iochem i cal engi n eering and bioremediation Jan V. Sengers ( U Ams t erdam) Critical phenomena, the r moph y sical properties of fluids and fluid mixtures Srinivasa R. Raghavan (N C St a t e) P olymers, co ll oids, comp l ex fluids, self-assembl y Nam Sun Wang ( C a lt ec h ) B iochem i ca l enginee r i n g William A. Weigand ( II T) B iochemical engi n eering bioprocess control and opt i mi z ation Evanghelos Zafiriou ( C a lt ec h ) P r ocess cont r o l identification and optimi z ation Location : T h e U n iversity of Marylan d is l oca t ed in close p roxi mi ty to the natio n s ca p ital, W as h i n gton, D C., a nd a numb e r of government l a b ora t or i es, in clu d i n g NIST NI H N RL A RL U SD A, a nd F D A 37 0 For Applications and Further Information, Write Graduate Admissions Director Department of Chemical Engineering Room 2113 Building 090 University of Maryland College Park MD 20742-2111 http:/ /www.ench.umd.edu Ch e mi ca l E ngin eeri n g Edu c ati o n

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UMBC Universi t y of Maryland B a ltimore Count y EMPHASIS Th e D e p art m e nt of Ch e mi ca l an d Bi oc h e m c al En g in ee rin g at UMBC off e r s gra du a t e pro g ram s l ea din g t o M S a nd Ph D d egrees in Ch e mi ca l En g in ee rin g. Our r esearc h i s h ea vil y foc u se d in bi oc h e mi ca l bi o m e di ca l a nd bi o p rocess e n g in ee rin g a nd cove r s a w id e ra n ge o f areas in c ludin g ferme nt at i o n ce ll c ultur e d ow n s tr e am p rocess in g, dru g d e li ve r y, p ro t e in e n g in ee rin g an d b io op ti cs Uniqu e pro g ram s in th e r eg ul a t o r y-e n g in ee r in g int e rf ace o f bi o pro cess in g a r e offe r e d as we ll. FACILITIES Th e D e partm e nt off er s s t a t e ofth e-a rt fa ciliti es fo r fac ult y and gra du ate st ud e n t r esearc h Th ese m o d e m fac ili ties h ave been d eve l o p e d prim a ril y in th e l as t s i x years a n d co mpri se 6 000 s quar e fee t of l a b ora t o r y s p ace in th e T ec hn o lo gy R esearc h Ce nt er plu s 7 000 s quar e fe e t of d e p art m e nt a l l a b o rat o ri es in th e n ew En g in ee r i n g and C o mput e r S c i e n ce buildin g. LOCATION UMBC i s lo ca t e d in th e B a lt i m o r eW as hin g t o n co rridor a nd w ithin easy access to b o th metrop o litan ar eas. A numb e r of gove rnm e nt r es ear c h faciliti es s u c h as NIH F DA US DA NSA a nd a l arge numb e r of bi o t ec hn o l ogy co mpani es are l oca ted n ear b y a n d prov id e exce ll e nt o pportuniti es fo r r esea r c h int erac ti o n s. FOR FURTHER INFORMATION CONTACT: Graduate Program Coordinator Department of Chemical a nd Bi oc hemi ca l Engine e ring Univer s it y of Maryland B a ltimor e C o unt y IO00 Hilltop Cir c l e Baltim o r e, Mar y land 2 1 2 50 Phon e: ( 4IO ) 4 5534 00 FAX : (4 IO ) 4 55 104 9 F a ll 200 2 Graduate Study in BIOCHEMICAL ENGINEERING For Engineering and Science Majors FACULTY D. D. FREY Ph.D. California-Berkeley Separation and transport processes in biotechnology; protein purification; chromatography. T. GOOD, Ph.D. University of Wisconsin-Madison Cellular Engineering; Protein Aggregation: In Vitro Models of Disease M. R. MARTEN, Ph.D. Purdue Bioprocess engineering; Fermentation; Cell biology and protein secretion; Proteomics A. R. MOREIRA, Ph.D. Pennsylvania rDNA fermentation; Regulatory issues; Scale-up; Downstream processing G. F. PAYNE Ph.D.* Michigan Pl ant cell tissue culture; Streptomyces bioprocessing ; Adsorptive separation; Toxic waste treatment G. RAO Ph.D. Drexel Fluorescence-based sensors and instrumentation; Fermentation and cell culture J.M. ROSS Ph.D. R ice Cellular and biomedical engineering; Cell adhesion; Tissue engineering J oint appointment with t h e University of Maryland B iotechno l ogy In sti tut e 37 1

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372 Come to Chemical Engineering at the University of Massachusetts Amherst Amherst is a prett~ New England n1llege town in Western l\lassadrnsetts. Set amid farmland and rolling hills, the area offers pleasant living conditions and extensive recreational facilities, and urhan pleasures are easily accessihle. Faculty M.F. Malone (Massachusetts), Head S.R. Bhatia (Princeton) W C. Conner Jr. (Johns H op kin s) J.M Dougla s, Emeritu s ( D elaware) N S. Forbe s (Berkeley) V. Haen se l Emeritu s (Northwestern) M.A. Hen so n (U C Santa B a rb a ra) R.L. Laurence Emeritus (Northwestern) E. Kokkoli (Illinois-Urbana) D. Marouda s (MIT) P.A Monson (London) S C Robert s (Cornell) J D Sherman (MIT) M T s apat s is ( Calt ec h ) J.J. Watkin s (Massachusetts) P R. We s tmoreland (MIT) H.H Winter (Stuttgart) Current Areas of MS and PhD Research Process de s ign : Method s, di s till a tion proce ss control Material s: Polym e r s and inor ga ni cs, multi sca l e mod e lin g Kinetic s a nd reaction e n gi n ee rin g: Catalyti c, biolo gica l noncatal y ti c Molecul ar ly b ase d mod e lin g : Stati s tic a l mechani cs, quantum chemistry mole c ul ar s imulation s Fluid mechanic s and polymer rheolo gy Bioengineering and biomaterial s Supercritical fluid pro cess in g For application forms and further information on fellowships and assistantships academic and resea r c h programs, and st u dent h ousing, see: http : //www.ec s. uma ss.e du/che o r w rit e: Graduate Program Director Department of Chemical Engineering 159 Goessmann Laboratory 686 N Pleasant St. University of Ma ssa chusett s Amherst MA 01003-9303 Th e University of Ma ssac hu se tt s Am h e r st prohibits discrimination on th e basi s of race co l o r r e li g ion c r ee d sex, sex u a l o ri e nt a ti o n age, m ar ital s tatu s, n atio n a l o ri g in disability o r h a ndi ca p or vete r a n s t a tu s, in any aspec t of the a dmi ssion or tr eat m e nt of s tud e nt s o r in em plo yme nt. C h e mical Engineerin g Ed u cation

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Chemical Engineering at J -~-. ~ i 1 w i 1 ..-_ .. ,_ . ~,:----MIT is located in Cambridge, just across the Charles River.from Boston, a.few minutes by subway from downtown Boston and Harvard Square. The area is world-renowned for its co/leges, hospitals, research .facilities, and high technology industries, and offers an unending variety <~l theaters, concerts, restaurants, museums, bookstores, sporting events, libraries, and recreational facilities. Fall 2002 Research in ... Biochemical Engineering Biomedical Engineering Biotechnology Catalysis and Chemical Kinetics Colloid Science and Separations Energy Engineering Environmental Engineering Materials Microchemical Systems, Microfluidics Nanotechnology Polymers Process Systems Engineering Thermodynamics Statistical Mechanics, and Molecular Simulation Transport Processes R.C. Armstrong, H ead P.I. Barton K.J. Beers D. Blankschtein H. Brenner R.A.Brown R.E. Cohen C.K. Colton C.L. Cooney W.M.Deen P.S. Doyle With the largest research faculty in the country, th e D epartment of Chemical Engineering at MIT offers programs of research and teaching which span the breadth of chemical engineering with unprecedented depth in fundamentals and applications. Th e D epart ment offers graduate programs leading to the master's and doctor s degrees. Graduate students may also earn a professional masters degree through the David H. Koch School of Chemical Engineering Practice a unique internship program that stresses defining and solving industrial problems by applying chemi ca l engineering fundamentals. In collaboration with the Sloan School of Management, the Department also offers a doctoral program in Chemical Engineering Practice, which integrates chemical engineering, re search, and management. A.P. Gast G.C. Rutledge K.K. Gleason H.H. Sawin W.H.Green K.A. Smith L.G. Griffith Ge. Stephanopoulos P.T. Hammond Gr. Stephanopoulos T.A. Hatton J.W. Tester J.B. Howard B.L. Trout K.F. Jensen P.S. Virk R.S. Langer D.I.C. Wang D.A. Lauffenburger K.D. Wittrup G.J.McRae J.Y. Ying For mor e information, c ontact Chemical Engineering Graduate Office 66-366 Ma ssac hu setts Institute of Technology 77 Massachusetts Avenue Cambridge, MA 02139-4307 Phone (617 ) 253-4579; FAX (617) 253-9695; E-Ma il c h emegrad@ mit. e du URL http ://web .mit .edu/cheme/index. html 373

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McMaster University Chemical Engineering Faculty M.H.I. Baird Emeritus PhD (Cambridge) Mass Transfer Solvent Extraction J.L. Brash Emeritus PhD (Glasgow) B i omedical Engineering Bio Material s Polymers J.M. Dickson PhD (Virginia) Membrane Transport Phenomena Reverse Osmosis C. Filipe PhD ( Clem s on) Environmental Biotechnology Environmental Engineering R. Ghosh DPhil (Oxford) Bioseparation Membrane Technology A.E. Hamielec Emeritus PhD (Toronto) Polymer Reaction Engineering A.N. Hrymak PhD (Carnegie Mellon) Computer Aided Design Polymer Proce ss ing J.F. MacGregor PhD (Wisconsin) Computer Process Control Polymer Reaction Engineering T.E. Marlin PhD (Massachusetts) Computer Proces s Control R.H. Pelton PhD ( Bristol ) Water Soluble Polymers Colloid Polymer Sy s tems Y. Samyudia PhD (Queensland) Computer Process Control C.L.E. Swartz PhD (Wiscon s in ) Computer Process Control Optimization H. Sheardown PhD (Toronto) Biomaterials Tissue Engineering L.W. Shemilt Emeritus PhD (Toronto) Radioactive Waste Management P.A. Taylor PhD (Wales) Computer Proces s Control M. Thompson PhD (Waterloo ) Polymer Processing Extrusion and Reactive Extru s ion J. Vlachopoulos DSc (Washington University) Polymer Processing Rheology Numerical Methods P.E. Wood PhD (Caltech) Experimental and Computational Fl uid Mechanic s Heat Transfer S. Zhu PhD (McMaster) Polymer Reaction Engineering Polymer Synthesis Polymerization Process Modeling Adjunct Faculty T. Kourti PhD ( McMaster) Computer Process Control K. Kostanski PhD (Tech U. Szczecin) Polymerization and Polymer Characterization S.L. Quinn PhD (Queens) Statistical Process Control J.D. Wright PhD (Cambridge) Pulp and Paper Computer Process Control Proces s Dynamics and Modeling 374 Graduate Study in Polymer Processing and Reaction Engineering, Computer Process Control, and much more! We offer a PhD program a nd t hr ee Master's optio n s ( The s i s, P roject Internship) R esearc h sc h o l ars hip s a nd teac h ing ass i s tant s hip s are ava il able Hamilton i s a city of 350,000 s ituated in so uth ern Ont a rio We are located about 100 km fro m b ot h Toronto a nd Niagara F a ll s. Excellent Facilities and R esearch Support through funding from Canadian govern m e nt and extensive interactions with industry Centre for Pulp and Paper Research Center for Advanced Polym er Processing and Design McMaster Advanced Control Consortium McMaster Institute for Polymer Production Technology For Further I nformation, Pl ease Contact Graduate Studies D epartme nt of Chemical Eng in eeri n g McMaster University Hamilton Ont ario Canada L 8S 4L 7 Ph o n e 905-525-9140 Ext 24292 Fax 905-521-1350 e-mail: c h eme n g@ m c m as t er.ca http://www .c h e m e n g. mcm as t er ca Che mi cal E n g in eering Ed u cation.

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I Chemical Engineering at The University of Michigan Faculty 1. Ronald Larson Chair Polymers, DNA complex fluids, fluid mechanic s 2. Stacy G. Bike Colloids, polymers, complex fluids 3 Mark A. Burns Microfabricated analytical systems biochemical separations 4. H. Scott Fogler Fused reactions colloids, gellation kinetic s 5 John L. Gland Surface science 6 Sharon Glotzer Soft materials and complex fluids 7 Erdogan Gulari Catalysis electronic materials, combinational chemistry 8 Jennifer J. Linderman Engineering approaches to cell biolog y 9 Susan Montgomery Undergraduate program advisor 10. David J. Mooney Cellular and tissue engineering 11. Chester Ni Bioinformatics, pharmaceutics 12 Phillip E. Savage Reactions in s up ercritical water, "green" chemistry 13 Johannes Schwank He t erogeneou s catalysis, surface science, gas sensors 14. Christina Smolke Biomolecular and metabolic engineering 15. Michael Solomon Light scattering and rheology of complex fluids 16. Levi T Thompson, Jr. Catal y sis electrocatalysis, materials processing 17 Henry Y. Wang Pharmaceutical engineering, bioproces s ing 18. Walter Weber En v ironmental processes and sustainability 19. Ralph T. Yang Separation s, adsorption, catalysis 20 Robert M. Ziff Percolation catalysis statistical thermodynamic s For More Information, Contact : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 5 16 17 1 8 19 20 Gradua t e Program Office, Department of Chemical Engineering / The University of Michigan / Ann Arbor MI 48109 2136 / 7 3 4 764-2383 Web : http: / / www engin umich edu / dept / cheme /

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Graduate Study in Chemical Engineering and Materials Science The Department of Chemical Engineering and Materials Science offers Graduate Pro grams leading to M S and Ph.D. degrees in Chemical Engineering and Materials Sci ence. The faculty conduct fundamental and applied r esearch in a variety of Chemica l En g in ee rin g and Materials Science disciplines. The Michigan Biotechnology In stitute, the Composite Materials and Structures Center, and the Bioprocessing Center provide a fo rum for interdisciplinary work in current hi gh technology areas ASSISTANTSHIPS Half-tim e grad u ate assistantships for incoming Ma ster's ca ndid a t es are ex p ecte d to p ay $ 18 ,852 per year plus a tuition and fee waiver of nin e credits for Fall and Spring Semesters, four credits for Sum mer Semester. University paid h ea lth insura n ce i s also provided. Theses are written o n th e project cov ered by the re searc h ass i sta nt s hip FELLOWSHIPS Available ap pointm e nt s pa y up to $19,500 p er year. FOR ADDITIONAL INFORMATIO N WRITE Chairperson Department of Chemical Engineering and Materials Science 2527 Engineering Building Michigan State University East Lansing, Michigan 48824-1226 e -mail : grad_rec@egr. m s u .ed u www: http : //www.chem s. m s u .e du / MSU is an Affirmativ e A c tio11/Eqt1al Opport1111ity I nstitution 376 M BAUMANN Ph.D 1 988, Case Western Reserve University Biomaterials, Ceramic Bone Substitutes Bone Tissue Engi n eering, Co ll oida l Processing of Ceramic s and Ceramic Composites K.A BERGLUND Ph.D. 1 98 1 lowa Stat e University Applied Spectroscopy Food and Biochemical Engineering, Crystallization from So l ut i on, New Uses of Agr i cu ltu ral Crops T.R. BIELER Ph.D 1989 University of California High Temperature Creep; Superplasticity ; Texture of Metals lntermetallics, and Composites ; Solder and Electronic H eat Sink Material s ; Metal Matrix Compos it e Fabrication ; High Strain Rate Deformation D.M. BRIEDIS Ph.D. 1 981 lowa State Un i versity Biochemical Engineering, Biobased Industrial Products, Biomass Conversion Life Cycle Ana l ysi s E .D. CASE Ph.D., 1 980 lowa State University M i crocrack.ing in Ceramics, Thermal Fatigue Ceramic/Ceramic Joining, Bioceramics Microwave Proce s sing of Ceramic s and Ceramic Composites C. CHAN Ph.D. 1990 University of Pennsylvania Metabolism and Diabetes Alzheimer and Parkinson's disease, Metabolic Engineering Tissue Engineering, Bioinformatics and Multivariate Analysis M A. CRIMP Ph.D 1987, Case Western Reserve University Transmission Electron Microscopy Diffraction and Channe lin g Studies u si n g Sca nnin g Electron Microscopy Deformation and Fracture lntermetallic Alloys, Magnetic Multilayer Structures L.T. DRZ AL Ph.D. 1974, Case Western Reserve University Surface and lnt erfacia l Phenomena Adhesion, Polymer Composite Materials, Surface Characterization Surface Modification of Polymers, Polymer Composite Processing Ad h esive Bonding D.S. G R UMM O N Ph D., 1 986 University of Michigan Superelasticity and Shape-Memory in Titanium-Nickel Thin Films Microactuators, Thermoelastic Martensite Transformations, Ion Beam Surface Modification of Materials, Surface Effects in Fa ti gue Crack Initi at i on, Mechanical Metallurgy M.C HAWLEY Ph D 196 4, Michigan State University Kinetics, Cata l ys i s, Reactions in Plasmas, P o l ymer i zatio n R eactio n s, Compos it e Processing, Biomass Conversion Reaction Eng in ee rin g K. J AYARAMAN Ph.D., 1975 Princeton University Polymer Rheo l ogy, P rocessing of Polymer Blends and Compos it es, Comp ut ationa l Methods A.LEE Ph.D., 1 987, University of Illinois at Urbana-Champaign Inorganic-Orangic Hybrid Polymers Physical and Mechanica l C har acterization Dynamics of Polymeric Glasses C.T LIRA Ph D 1985, University of Illinois at Urbana-Champaign Thermodynamics a nd Phase Eq uilibri a of Complex Systems Adsorption, Supercritical Fluid Studies J P. LUCAS Ph.D 1981, University of Minnesota Microstructure Evolu ti on/Character i zation of Pb-Free Solders Alloys and their Compos it es; Nanoindentation Characterization of Deformation in Small-Volumes and Thin Films; Moisture Effec t s in R esi n Matrix Composites; Metal Matrix Composite M.E MACKAY Ph.D., 1985, University of Illinois at Urbana-Champaign Polymer Rheology and Thermodynamics Nanotec hn o l ogy Dendrimer s H yperbranc h es P o l yme r s Surface Properties D.J. MILLER Ph.D. 1 982, University of Florida Kinetics and Catalysis, Reaction Engineering Catalytic Conversion of Biomass-Based Materi a l s R. NARAYAN Ph.D. 1 975, University of Bomba y P o l ymer Blends and A ll oys Biodegradable Plastics Biofiber Composites Extru s ion Polymer ization and Reactive Compounding, Biodegradation and Composting Studies J. NOGAMI Ph.D. 1986, Stanford University Electronic Materials Scanned Probe Microscopy, Surface Characterization, Growth of Nanos tru c tur ed Materials R .Y. OFOLI Ph.D., 1 994 Carnegie Mellon University Colloid and Interfacial Science: Colloid Stability, Adsorption of Proteins Receptor-Ligand Interactions at the Liquid-Liquid Interface Micellar So lubili zation C.A. PETTY Ph.D ., 1 970, University of Florida Fluid Mec h anics Turbulent Transport Phenomena Solid-F luid and Liquid-Liquid Separations Hydrocyclones K.N. SUBRAMANIAN Ph.D. 1966 Michigan Stat e Univ e rsity Mechanical Properties of Metals and Ceramics Crysta lli zation of Glasse s, Eros i on Composite Materials, Lead-Free Electronic So ld ers R. M. WORDEN Ph.D 1986, University of Tennessee Biochemical Enginee rin g Microbial Transport Proces s e s Synthesi s Gas Fermentat i ons Metabolic E n g in eer in g Microbial Eco l ogy Chemical Engineering Educat i on

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Leadership and Innovation in CHEMICAL ENGINEERING AND MATERIALS SCIENCE at the UNIVERSITY OF MINNESOTA FACULTY Rutherford Aris (E meritus ) Theoreti ca l s tudi es of chemica l reactors Arnold G. Fredrickson (Eme ritus ) Bi oc h em i cal eng in eer in g, microbial populations C. Daniel Frisbie Frank S. Bates Molecular materials and interfaces, molecular Th e rmod y nami cs and dynamics of polymers elect r onics and pol yme r mixtures Robert W. Carr Chemical kin et i cs, r eaction eng in eering C. Barry Carter Electron mi c r oscopy of semiconductors and ce rami cs, solid-state reactions and grow th of thin films James R. Chelikowsky Structural/electronic prop erties of complex systems Robert F. Cook William W. Gerberich Fracture micromechanics, interfacial defects Wei-Shou Hu B iochemical engineering Yianis Kaznessis Computer modeling of biological systems structura l bi oinfor mati cs, molecular r ecogni tion phenomena Satish Kumar Transport processes in comp l ex fluids stabil i ty, dynamics and manipulation of interfaces, transport processes in microscale s y stems M ec hani ca l behavior of mat e ri a l s, microChris Leighton elec tr o ni c device fabrication and packaging Magnetic and e l ectronic prope r ties of thin film magnetic materials and heterostructures Edward L. Cussler Ma ss transfer, novel separation processes John S. Dahler (E meritus) Nonequilibrium stat i stica l m ec hani cs Prodromos Daoutidis Nonlinear pr ocess co ntrol pr ocess analysis and design H. Ted Davis Colloid and interface science, stat i stical mechanics Jeffrey J. Derb y Materials pro cessing, hi g h p erfo rman ce co mputin g Lorraine Falter Francis Timothy P. Lodge P o l yme r structure an d dynamics, p olymer c hara cterization Christopher W. Macosko P olyme r processing, rh eology polymer networks and blends Richard B. McClurg Th ermodynamics and kinetics of phase c han ges Alon V. McCormick R eaction engineering of materials synthesis spec tr oscopy, molecular simu lati on David C. Morse Statistical mechanics, polymeric and complex fluids David J. Norris Ceramic pr ocessing, elec tri cal and mechaniNanomaterials, photonic crystals, molecular ca l propertie s of ce rami cs s pintroni cs Richard A. Oriani (E meritus ) Co rr osion, thermodynamics of so lids co ld fus i on Christopher Palmstr!'fm Epitaxial g r owt h processes and heterostructure formation, properties of thin film Lanny D. Schmidt Surface chem i stry, heterogeneous catal y sis reaction engineering L. E. Scriven Fluid mechanics and rh eo lo gy, transport reaction and stress ph e n ome na mat er ial s processing David A. Shores H igh temperature co rr osio n f u el ce ll s John M. Sivertsen (E meritus ) Magnetic microelectronic, and tribologi cal materials William H. Smyrl E l ectroc h e mical e n g in ee rin g mod e lin g electrochemical systems, microvisualization of reactive s urfa ces Friedrich Srienc Bi ochemical engineering, cell cycle and growth models, bi o pol ymers Robert T. Tranquillo Cell and tissue eng in eering Michael D. Ward Molecular materials crys tal g r owt h elect r ochemistry Renata M. M. Wentzcovitch Electronic and structura l properties of condensed matter systems; first prin c ipl es molecular dy nami cs For additional information. visit our web site at http://www.ccms.umn.edu Fall 2002 377

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Graduate Studies in Chemical Engineering Environmental R emediation, Electrokinetics, Chemical Extraction, Stabilization/Solidification, Waste Tr eatme nt Heav y Metal Soil s 378 W Todd French Assistant Research Professor Applied Mi cro biolog y, Biorem ed iation Industrial Mi cro biolog y, Mi cro bial Enhanced Oil Re covery Clifford E. George Professor I ndustrial Biot ec hnolog y Industrial Applications of Mi c rowa ve Power/H eating and Electrochemistry, Proc ess Control Chemical Plant /Oil R efinery Operations and Saf ety Priscilla J. Hill Assistant Professor Crystallization Process D es i gn, Solids Processing Irvin A. Jefcoat Professor and Henry Chair Pollution Pr eventio n/Waste Minimi za tion Rudy E Rogers Professor Natural Gas Stora ge and Tran sport, Formation Rat es in O cean S ed im e nts CO 2 S eq uesterin g Natural Gas Produ c tion from S ea b e d H ydrates Kirk H Schulz Director and Deavenport Chair Su,face Science, Catalysis Electronic Mat e rials Hossein Toghiani Associate Professor Composite Material s, Catalysis, Fuel Cells, Thermodynamics of Liquid Mixtur es Rebecca K Toghiani Associate Professor Thermodynamics Separations Mark E Zappi Professor Wa s te Tr eatment, Indust r ial Biote c hnolo gy, Chemical Oxidation, Biotreatment H y ph ena ted R emed iation T ec hniqu es Mississippi State University, located in the Golden Triangle region of Northeast Mississippi, is the largest of eight public institutions of higher learning in the state It is one of two land-grant institutions in Mississippi Area r es idents enjoy numerous university sporting and c ultural events, as well as scenic and recreational activities along the Natche z Trace Parkway and Tennessee Tombigbee Waterway. The Dave C. Swaim School of Chemical Engineering is poised for unprecedented growth in the next decade. A new $18 million facility recently was completed specifically for Chemical Engineering. The school offers both the M.S. and Ph.D. degrees in Chemical Engineering and an M.S. in Industrial Hazardous Waste Management. For more information, contact The Dave C. Swaim School of Chemical Engineering Mississippi State University P O. Box 9595 330 Swaim President's Circle Mississippi State, Mississippi 39762 Phone: (662) 325-2480 Fax: (662) 325-2482 Email: gradstudies@che.msstate.edu www.che.msstate.edu . . . . . . . . For a graduate application, contact The Office of Graduate Studies Phone (662) 325-7404 www.msstate.edu/dept/grad/application htm ,\li,,i,,i1111i .\{aft' { 'nil"tT\/{\' i, 1111 t 'l/11({/ /!/1/li!r/lll/ily i11,tit11tio11. Chemical Engineering Education

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University of Missouri-Columbia Rakesh K. Baipai Ph.D. (/IT, Kanpur) Bio c hemical En gi neering Hazardous Waste Paul C.H. Chan Ph.D. (CalTech) Reactor Anal ys is Fluid Mechanics Patricia A. Darcy Ph.D. ( Iowa) Prot e in Crystalli zatio n Biotechnology Eric Doskocil Ph.D (Virginia) Catal ys is Reaction Engineering William A .Tacob y Ph.D. (Colorado) Photo cata l ysis Transport Sunggyu Lee Ph.D. (Case Western) Pro cess Engine e rin g Polymer s Fuels Stephen .T. Lombardo Ph.D (California-Berkeley) Ceramic Composites Transport Kinetics Sudarshan K. Loyalka Ph.D. (Stanford) Aerosol Mechanics Kinetic Theory Ri ch ard H. Luecke Ph.D. (Oklahoma) Proc ess Control Modeling Thomas R. Ma rr e r o Ph.D. (Maryland) Coal Log Tran s port Conductin g Pol y mers David G. R etzloff Ph.D ( Pittsburgh) Rea c tor Anal ys is Materials Truman S. Storvick Ph.D. ( Purdu e) Nuclear Wast e R e processing Thermodynamics Galen .T. Suppes Ph.D (Johns Hopkins ) Biofuel Processin g Renewable Energy Thermodynamics Dabir S. Viswanath Ph.D ( Ro c hester) Appli e d Thermod y namics Chemical Kinetics Hirotsugu K. Yasuda Ph.D. (SUNY, Syracuse) Pol yme r s Sutfa ce Science The University is one of the most comprehensive institutions in the nation and is situated on a beautiful land grant campus halfway between St Louis and Kansas City, at the foothills of the O z ark Mountains and the recreational Lake of the O zarks. Th e Chemical Engineering D epartment offers M.S. and Ph.D. programs in a wide variety of research areas including surface science, nuclear waste, wastewater treatment, biodegradation, indoor air pollu tion, supercritical processes plasma pol y meri z ation, pol y mer pro cessi ng, coal transportation (hydraulic), fuels, chemical kinetics, protein crystallization, photocatalysis, ceramic composites, and polymer composites. For details contact: The Dir ector of Graduate Studies Department of Chemical Engineering University of Missouri Columbia, MO 65211 Tel: (573) 882-3563 Fax : (573) 884-4940 E-mail : preckshotr@missouri .e du Web s ite : www.mi ss ouri.edu/~chewww Fall 2002 Incentive scholarships available in the form of teaching/research assistantships and fellowships 379

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University of Missouri-Rolla Graduate Studies in Chemical Engineering Offering M.S. and Ph.D. De grees Established in 1870 as th e University of Miss o uri S c hool of Min es and M e t allurgy UMR ha s evo l ved into Miss our i 's t ec hnolo g i ca l uni ve sity. UMR is a medium-si ze d ca mpus of about 5 000 s tud ents lo ca t e d along In terstate 44 approximat e l y JOO mil es from St Louis and Spring field. it s pro x imi ty in th e Missouri Ozarks provid es plenty of scen i c and r ecreational opportunities. Th e University of Missouri-Ro/la's mission is t o e du c ate tomorrow 's l e ad e r s in eng ineerin g and science. UM R offers a full range of exper i e n ces that are v ital to the kind of c omprehensive e du c ation that t urn s yo un g men and wo m en into l e ad e rs UMR ha s a distinguished fa c ul ty dedi c at e d w holeh e art e dly to the teaching, r esea r c h and c r e ati ve a ct i v ties necessa r y for schola rl y l ea rning experiences and advan ce ments t o th e frontiers of kno w l e d ge T eac hin g and R esea r c h Apprenticeships available to M.S. and Ph D students. For additional information : Addr ess: W eb : E-mail: Online Application: 38 0 Graduate Studies Coo rdinat or D e partm e nt of C h e mi ca l En g in ee rin g Un i ve r s i ty of Missouri-Roi/a Rolla MO 65409/ 230 http ://www.umr.edu/~c h e m e n gr cheme 11 gr@umr.edu http://w1 v 1 v. umr. edu/~c i sappslgradap pd.ht111/ Neil L.Book Associate Profe ss or Ph.D. Colorado Computer-Aided Process Design Chemical Process Safety Engineering Data M a n agement Daniel Forciniti Assoc iate Profes so r Ph D North Carolina StaJe Bioseparation s, Thermodynamics Statistical Mech a ni cs A.I. Liapis Professor, Ph.D. ETH-Zurich Transport Phenomena, Adsorp ti on/Desorptio n. Fundamenta l s an d Processe s, Bioseparations C h ro m atogra phi c Separations Cap ill ary Electrochromatography Chemical R eaction E n gineer in g Lyophili zat ion Douglas K Ludlow Profe ssor and Chair, Ph.D Arizona StaJe Surface Characterization of Adsorbents and Cata l ysts Applications of Fractal Geometry to Surface Morpholo gy Nicholas C. Morosoff Professor Emeritus, Ph.D. Brooklyn Polytech Pl asma Polymerizati o n Membranes Parthasakha Neogi Profes s or, Ph.D. Carnegie-Mellon lnt erfacial Phenomena Drug Delivery XB Reed,Jr. Profes so r Ph D. Minnesota Fluid Mechani cs, Transport Phenomena and C h emical Reaction E n g in eeri n g, including those of Particl es, Drops and Bubble s, Lar ge-Sca l e Structure of Shear Turbul ence and Impact of Fine-Scale Structure on Chemical R eac tions Stephen L. Rosen Prof ess or Ph D Come/I Polymerization R eactio n s, Applied Rh eo l ogy, Polymeric Material s Y.T.Shah Professor and Pro vost, Ph.D. MIT Che mi ca l Reaction and React o r Engineering Oliver C. Sitton Associate Profe ss or, Ph D Missouri-Rolla Bi oe n gineering Jee-Ching Wang Assistant Professor, Ph.D Penn State Molecular Simulations of Transport in Co nfin ed Systems Molecular Simulations of Surfactant Sy s t ems, Molecular Propertie s of Material s Yangchuan Xing Assis tant Profe ssor, Ph.D. Yale Synthe s i s, Processing, an d Characterization of Nanomaterials Chemical Engineering Education

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University Nebraska of Graduate Studies in Chemical Engineering Jennifer Brand University of California, San D iego Supercritical Fluid Proce ss in g; Natural Product Proce ss in g; Environmental Remediation L. Davis Clements University of Oklahoma Computer-Aided Proce ss De sig n ; Proce ss S y nthe sis; Fuel s a nd Chemical s from Bioma ss James Eakman University of Minnesota Computer-Aided Proce ss Engineering; Solid s Propertie s & Proc ess in g; Reaction Engineering James Hendrix University of Nebraska Remediation of Mine T ailings Waste; Novel Analytical Chemi stry; Non-Ideal Re actors Gustavo Larsen Yale University Hetero ge neou s Catalysis: Spectroscopic Characterization of Catalysts Lee Lauderback Purd ue University Surface Analysis; Hetero ge neou s Catalysis Michael Meagher Iowa State University F e rment a tion and R eco mbin a nt Protein Expression in the P ichia pastoris; Cross-Flow Membrane Filtra tion; Do w n s tream P rocess, Pu rification, a nd Proc ess D evelop m e nt ; Butan ol Reco very b y Per va poration Chair Graduate Studies Hossein Noureddini University of Nebraska Production of Ch e mical s from Agricultural Product s; Mathematical Modeling of Polymerization Kinetic s Delmar Timm Io wa State University Polymer Compo sites; Step-W ise Polymerization Kinetic s; Kinetic Analysis Using GPC Hendrik Viljoen University of P retoria Fall 2002 Plasma -E nhanced CVD ; Detonation & Combu s tion ; Ceramics For further information, write Dr. Mi chae l Meagher Director of Graduat e Studie s D epa rtment of Ch emica l Engineering University of Nebraska Lin co ln NE 68588-0126 Al s o ple ase vis it u s at our web site at http : //www.unl.edu/chemengr/ Graduate admissions on-line applications and printable forms available at http://www.unl.edu/ gradstud/ gradadmission.html 38 1

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382 The department offers graduate programs leading to both the Master of Science and Doctor of Philosophy degrees. Exciting opportunities exist for interdisciplinary research. Faculty conduct research in a number of areas including: Polymer science/ engineering Membrane technology Hazardous waste treatment Particle technology Pharmaceutical engineering Nanotechnology America's Most Wired Public University Yahoo! Internet Life at New Jersey Institute of Technology The Faculty: P. Armenante; University of Virginia B. Baltzis; University of Minnesota R. Barat; Massachusetts Institute of Technology E. Bart; New York University C. Gogos; Princeton University T. Greenstein ; New York University D. Hahn; Agri Univ. of Wageningen (Netherlands) D. Hanesian; Cornell University M. Huang; University of Massachusetts K. Hyun; University of Missouri-Columbia H. Kimmel; City University of New York D. Knox; Rensselaer Polytechnic Institute G. Lewandowski; Columbia University N. Loney; New Jersey Institute of Technology A. Perna; University of Connecticut R. Pfeffer; New York University L. Simon; Colorado State University K. Sirkar; University of Illinois-Urbana S. Sofer; University of Texas R. Tomkins ; University of London (UK) J. Wu; University of Delaware M. Xanthos; University of Toronto (Canada) For further information contact: Dr Reginald P.T. Tomkins Department of Chemical Engineering New Jersey Institute of Technology University Heights Newark NJ 07102-1982 Phone: (973) 596-5656 Fax: (973) 596-8436 E-mail: tomkinsr@adm.njit.edu JI' A Public Research U niversity UNNERSITY HEIGHTS NEWARK, NJ 07102-1982 ~.njit.edu New Jersey Institute of Technology NJIT does not discriminate on the basis of gender sexua l orientation race handicap veteran s status national or ethnic origin or age In the administration of student programs Campus facilities are accessible to the disabled. Chemical Engineering Educa t ion

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Graduate Research at the Frontier __________________ THE UNIVERSITY OF NEW MEXICO Faculty Plamen Atanaso v Harold M. A nder son C. Jeffrey Brinker Joseph L. Cecchi John G. Curro Ab ha ya K. Datye Julia E. Fulghum SangM .Han David Kauffman Ronald E. Loehman Gabriel P. Lopez Richard W. Mead H. Eric Nuttall Jonathan Phillips Timothy L. Ward Ebtisam S. Wilkins Research Areas The future of chemical engineering is a bright one wit h rapidly developing technologies and exciting new opportunities. Pur s u e your graduate degree in a stim ul ating student ce nt ered intellec tual environment anchored by forward-looking research. We offer full tuition and competitive stipends The ChE faculty are leaders in explori n g phenomena o n th e meso-, micro-, and nanoscales. We offer grad u a te research projects in biotechnology and biomaterials; cata l ysis an d interfa c i a l phenomena; enviro nm enta l technologies and waste manage ment ; microengineered materials and self-asse mbl ed nanostructures; plasma processing and semico nduct or fa brica tion ; polymer theory and modeling The department enjoys extensive interactions and collaborations with New Mexico 's federal laboratories : Los Alamos National Laboratory, Sandia National La b oratories a nd the Air Force Research Laboratory, as well as hi g h technology industries b o th locally and nationally. Albuq ue rq u e is a unique combination of the ve r y old a nd the highly co nt emporary the natural world and the manmade environment the fro nti er town a nd the cosmopolitan city, a harmoniou s blend of diverse cultures and peoples. Join us! Be part of this future! Electroana l ytical Chemistry Biomedical Engineering Plasma Processing, Plasma Di ag n ostics Ceramic s, Sol-Gel Processing, Self-Assembled Nanostructures Semiconductor Manufacturing Technology Plasma Etching and Dep os ition Polymer Theory Computational Modeling Catalysis Interfaces, Adva n ced Materials Surface Characterization 3-D Materials Characterization Semiconductor Manufacturing Technology, Pl asma Etching and Deposition Plant D e s ign Enviro nm ental Engineering Glass-Metal and Ceramic -M eta l Bonding and Interfacial Reactions Chemical Sensors Hybrid Materials, Biotechnology, Interfacial Phenomena Unit Operations Resource Extraction Environmental Science Waste Transport Management Colloid Science Materials Science Catalysis, Plasma Physics and Chemistry Aeroso l Materials Synthesis, Inorganic Membranes Bi omedica l Sensors and Waste Treatment For more information, contact: Fall 200 2 J effrey Brinker Graduate Advisor Chemical an d Nuclear Engineering 209 Farris Engineering Center Albuquerque, NM 871311 341 505 277 5431 Phone 505 277 5433 Fax chne@u nm. ed u www ch n e.unm e du 383

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NEW MEXICO STATE UNIVERSITY Faculty and Research Areas ____________ 3 8 4 Paul K. Andersen, Associate Professor, University of California Berkeley Transport Phenomena Electrochemistry, Environmental Engineering Ron K. Bhada, Professor Emeritus, University of Michigan .Joe L. Creed, Assistant Dean New Mexico State University Engineering Design Francisco R. Del Valle, College Profe ssor, Massachusetts In stitute of Technology Food Engineering Charles L .Johnson, Profe ssor and Head Washington University-St. Louis Richard L. Long, Professor and Associate Head Ri ce University Transport Phenomena, Biomedical Engineering, Separations Martha C. Mitchell, Associate Professor, University of Minnesota Advanced Materials, Statistical Mechanics, Molecular Modeling Stuart H. Munson-McGee, Profe ssor, University of Delaware Advanced Materials, Separations .J oho T. Patton, Professor Emeritus, Oklahoma State University David A. Rockstraw, Associate Profe ssor, University of Oklahoma Separations Environmental Engineering Kinetics Rudi V. Roubicek, Profe sso r Emeritus, Technical University of Prague Edward F. Thode, Professor Emeritus, Massachusetts Institute of Technology D. Bruce Wilson, Professor Emeritus Princeton University LOCATION------~ Southern New Mexico 350 days of s un shine a year For Application and Additional Information Internet http : //chemeng.nmsu.edu/ E-mail chemeng@nmsu edu PO Bo x 30001 MSC 3805 Department of Chemical Engineering New Mexico State University Las Cruces, NM 88003 New Mexico St a te University i s an Equal Opportunity Affirmative Action Employer Chemical Engineering Edu c ation

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North Carolina State University 'Deyartment of Cliemica{ 'Engineering ~--.~ Fall 2002 385

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386 GRADUATE STUDY IN CHEMICAL ENGINEERING in the Heart of Boston Faculty: NurcanBac Gilda Barabino Carolyn Lee-Parsons Albert Sacco Jr. Ronald J. Willey Katherine S. Ziemer Northeastern University Chemical Engineering Department is the home of CAMMP (Center for Advanced Microgravity Materials Processing ) a NASA-sponsored Commercial Space Center. It is one of 16 NASA centers at major universities nationwide and the only one exclusively focused on materials. The Department offers full and part-time graduate programs leading to M.S. and Ph.D degrees. MS students may have the opportunity of co-op experience. The faculty of the chemical engineering program are committed to providing state of the art research areas. Research Areas: Biochemical Engineering Biomedical Engineering Catalysis Microgravity Advanced materials Nanocomposite Membranes Semiconductor Materials Selected Research Topics: Pharmaceutical compounds from plant cell cultures Carbon Nanotubes Mixed-Matrix Membrane Separation Sickle Cell Adhesion Surface Acidity of Ti-silicas Tissue Engineering Thin Film Heterostructures Biosensors For more information write: Chairman Dept of Chemical Eng. 342 SN 360 Huntington Ave. Boston, MA 02115 Visit our web site: http://www.coe.neu.edu/COE/grad _scboo C h e mi ca l Engineering Edu c ation

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Chemical Engineering at Luis A.N. A maral Ph D Bo s ton U ni ve r s it y 1 996 C o mpl ex sy stem s, c omputati o nal ph ys i cs, biol og i c al n etw orks Annelise E. Barron Ph D ., Berkel ey 1 99 5 Bi o s e p a rat io n s biop o l y m e r e n ginee rin g Linda J. Broadbelt PhD D e la wa r e, 1 994 R e a c ti o n e n g in ee rin g, kin e ti cs m ode li ng, p oly m e r r eso ur ce r ecove r y Wesley R. Burghardt Ph D St anford, 1 990 P o l y m e r sc ien ce, rh eo l ogy Buckle y Crist, Jr. Ph D ., Duke 1 966 P o l y m e r sc i e n ce, therm o d y nami cs, m ec h a ni cs Joshua S. Dranoff Ph D. Prin ce t o n 1 96 0 Ch e mi c al r eac ti o n e n g in ee rin g, c hr o m a t og raphi c se par a ti o n s Kimberly A. Gray Ph.D. J o hn s H o p k in s 1 988 Cat a l ys i s tr eat m e nt tec hn o l og i es environmen t a l c h e m istry Bartosz A. Grzybowski Ph D H arvard 2000 C o mpl ex c h e mi ca l sys t e m s Vassily Hatzimanikatis Ph.D ., C a lt ec h 1 996 C o mputational bi o te c hnol ogy, f un c ti o nal ge n o m i c s, bi o informati cs Harold H. Kung Ph D N o rth wes t e rn 1 974 Kin e ti cs, h e t e ro ge n e ou s c at a l ysis William M. Miller Ph.D B e r ke l e y, 1987 Ce ll c ultur e fo r bi o t ec hn o l og y a n d medici n e Lyle F. Mockros Ph.D B e r ke l ey, 1962 Bi o m e di ca l e n g in ee rin g, fluid m ec ha n i cs i n bi o lo g i c al syste m s Monica Olvera de la Cruz Ph D ., C a mbrid ge, 1 984 Stati s ti c al m ec hani cs in p o l y m e r sys t e m s Julio M. Ottino Ph D. Minn eso t a, 1 979 Fluid m ec hani cs, g ranular m a t e ri a l s c h aos mi x in g in m a t e rial s pr ocess in g E. Terr y Papout sa kis Ph D Pur d u e 1 980 Bi o t ec h no l ogy o f a nimal a n d mic r ob i al c e ll s m e tab o li c e n g in ee rin g ge n o mi cs Bruce E. Rittmann Ph.D. St anfo rd 1 979 I n s itu bi o r e m e diati o n biofilm s Gregory R ys kin Ph D C a lt ec h 1 983 Fluid m ec hani c s c omputati o n a l m e th o d s, p o l y m e ri c liquid s Lonnie D. Shea Ph.D ., Mi c hi ga n 1 997 Ti ss u e e n g in ee rin g, ge n e th e rap y Randall Q. Snurr Ph.D Berk e le y, 1 99 4 Ad so rpti o n and diffu s i o n in p o r o u s med ia m o l ec ular m o d e lin g Melody A. Swartz Ph D ., M.l.T 1 998 Bi o m e di c al tran s p o rt ph e n o m ena John M. Torkelson Ph.D. Minn e s t o t a 1 983 P o l y m e r sc i e n ce, m e mbran es Fa/l 20 0 2 Northwestern University For information and application to the graduate program, write Director of Graduate Admissions Department of Chemical Engineering McCormick School of Engineering and Applied Science Northwestern U ni ve r sity Evanston Illinois 60208-3120 Ph o n e: (8 4 7) 49 1 -73 9 8 o r (8 00 ) 8 4 8-5 / 3 5 (U. S. onl y) Em a il : ad mi ss i o 11 s c h e m e n g@ 11 o rtl11 v e s t e rn .e d u o r v i s it o ur we b s it e at www.c h e me n g .n o rthwe s tem .e du 387

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Graduate Studies in Chemical Engineering The University of Not r e Dame Faculty Joan F. Brennecke H.-Chia Chang Davide A. Hill Jeffrey C. Kantor David T. Leighton, Jr. Edward J. Maginn Mark J. McCready Paul J. McGinn Albert E. Miller Agnes E. Ostafin Andre F. Palmer Roger A. Schmitz Mark A. Stadtherr William C. Strieder Arvind Varma For more information and application materials, contact us at Dir ector of Grad u ate R ecruiting Department of Chemical Engineering University of Notre Dame Notre Dame, IN 46556 USA On-Line Application www.nd edu/~gradsch/app l ying/appintro.html 388 http : //www.nd edu/~chegdept chegdept.l@nd.edu Phone: l-800-528-9487 Fax: 1-219-631-8366 Research Areas Biomaterials Biological Photonic Devices Blood Rheology Inorganic Membranes Ionic Liquids Catalysis and Reaction Engineering Combinatorial Materials Synthesis Combustion Synthesis Molecular Modeling Multiphase Flows Nanostructured Materials Nonlinear Dynamics Parallel Computing Polymeric Materials Superconducting Materials Tissue Engineering Drug Delivery Electrochemical Processes Environmentally Conscious Design Enzyme Encapsulation Notre Dam e The University Notre Dame is an independent, national university ranked among the top twenty schools in the coun try. It is located adjacent to the city of South Bend, Indiana, approximately 90 miles southeast of Chi cago The scenic 1,250-acre campus is home to over 10,000 students. The Department The Department of Chemical Engineering is devel oping the next generation of research leaders. Our program is characterized by the close interaction between faculty and students and a focus on cut ting-edge interdisciplinary research that is both aca demically interesting and industrially relevant. Programs and Financial Assistance The Department offers MS and PhD degree pro grams Financially attractive fellowships and assis tantships, which include a full-tuition waiver, are available to student s pursuing either degree Ch e mi c al En g in ee rin g Edu c ati o n.

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FACULTY [I Bhavik Bakshi MIT Industrial Ecology Proce ss Engineering Analysi s of Complex Sy s tem s [I Robert S. Brodkey Wi sco nsin Experimental Mea s urement s for Validation of Computational Fluid Mechanic s and Applications to Mixing Proce ss Applications [I Jeffrey J. Chalmers lmmunumagnetic Cell Sep ara tion Effect of Hydrodynamic Forces on Cells Interfacial Phenomena a nd Cells Bioengine e ring Biotechnolog y, Cancer Detection [I L.S. Fan, W est Virginia Fluidi za tion, Particle Technology Parti c ulate s R eac tion Engineering [I Martin Feinberg, Princ eton Mathematic s of Complex Chemical S ys t e m s [I Winston Ho Illinois-Urbana Membrane Separations with Chemical Re act ion and Fuel-Cell Fuel Proce ss ing [I Kurt W. Koelling, Prin ceton Rheology Polymer Proces s ing Microtluidic s [I Isamu Kusaka, CalTech Nucleation [I L. James Lee, Minn eso ta Polymer and Compo s ite Proce ss ing Micro/-Nano-Fabrication BioMEMS [I Urnit S. Ozkan Io wa State Heterogeneou s Catalysis Kinetic s, Catalytic Materials [I James F. Rathman Oklahom a Colloids, Interfaces Surfactant s, Molecular Self-Assembly, Bioinformatics [I David L. Tomasko, lllin ois-Urbana Separations Molecular Thermodynamics a nd Materials Procesing in Supercritical Fluids [I Shang-Tian Yang, Purdu e Biochemical Engineering, Biotechnolo gy, and Tis s ue Engineering [I Jacques L. Zakin, New York Rheology Drag R eduction Surf ac tant Microstructure s, and Heat Transfer Enhancement Excellent facilities and a unique comb ination of research projects at the frontiers of science and technology. Outstanding faculty and student population who are dedicated and professional. Competitive financial support Close working relationships between graduate students and faculty. Attractive campus minutes away from downtown Columbus. For complete information, write, call, or catch us on the web at http://www.che.eng.ohio-state.edu or write Professor Shang-Tian Yang Department of Chemical Engineering The Ohio State University 140 West 19th Avenue Columbus, Ohio 43210-1180 Phone: ( 614) 292-9076 Fax: (614) 292-3769 E-mail address: che-grad@che.eng.ohio-state.edu The Ohio Stat e University is an equal opportunity/affirmative action institution Fall 2002 389

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390 Ohio University Chemical Engineering For More Information Contact: Graduate Programs Th e Departm e nt of Chemical E n gi n eeri n g offe r s programs l eadi n g to both the M.S a nd Ph.D. de grees. The department's activ iti es are e nh a n ce d b y th e Stocker e nd owme nt w hi c h was made possible by the ge n erosity of Dr C. P a ul a nd B e th K Sto cke r a nd which ha s no w grow n to ove r $ 14 milli o n The intere s t o n thi s e nd ow m e nt i s u se d t o h e lp s upport research effo rt s in s u c h ways as pro vi din g co mp e tit ive gra du a t e fe llow s hip s a nd assoc i a t es hip s, matchin g e quipm e nt funds a nd see d m o n ey for n ew project areas Research Areas Multiph ase Flow a nd Associated Corrosion Coal Conversion T ec hnolo gy a nd D es ulfuri zatio n Aerosol S cie n ce and Technology Pro cess Control Separ at ion s Energy a nd Envir o nm e ntal Engineering Thin Fi lm Materials Chemical R ea ction Engineering Bioreact or An a l ysis Down s tr ea m Proc ess in g of Prot e in s Bi o m e di ca l Engineering Financial Aid Financial s upport include s teaching and gran t-related assoc i ates hip s a nd fe llow s hip s ranging fro m $14,000 t o $ 1 8,000 per twelve months In addition s tudent s are gra nted a full tuiti o n sc holar s hip for both th e r eg ul ar and s ummer aca d e mi c term s. Stocker Fellowships are ava ilable to especially well -qu a lifi e d s tud e nt s. The Faculty Gerardine G Botte ( Ph.D. South Carolina 2000) W. J. Ru sse ll Ch e n ( Ph.D. S yrac us e, 1974) Nichola s Dino s, Emeritus ( Ph.D ., L ehig h 196 7) Dou g l as J Goet z ( Ph.D ., Cornell, 1 995) Tingyue Gu ( Ph.D ., Purdu e, 1990 ) Daniel A. Gulino (Ph .D ., Illin ois, 19 83) Srdjan Ne s ic (Ph.D., Saskat c h ewan, 1991) Michael E. Prudich Chair ( Ph.D ., W est Virginia 1979) D arin Rid gway, P .E. (Ph.D., Florida State, 1990) Kendree J. Samp so n ( Ph D. Purdu e, 19 8 1) Valerie L. Young ( Ph.D. Virginia T ech 199 2) Director of Gradu a t e Studie s Department of Chemical Engineering, 172 Stocker Center Ohio Uni ve r s ity Ath e n s OH 45701-2979 E-mail: c hedept @ bobcat.ent.ohiou.edu Visit our web s ite at: http://www. ent ohiou.edu/che Ohio U ni ve r s i ty i s an affirmativ e action instituti o n C h e mical E n gi n ee rin g Education

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The University of Oklahoma Graduate Studies in Chemical Engineering & Materials Science Join us in research on critical technological problems in the following areas: Environmental Energy Polymers Bioengineering Call, Fax, Write or E-mail: Chairman, Graduate Program Committe e School of Ch e mical Engineering and Material s Scien c e The University of Oklahoma 100 E. Boyd Room T-335 N orman OK 7 3019-1004 Phone: (405) 325-5811 Fax: (405) 325-5813 E-Mail: chegrad @o u.edu For more detailed information visit our World Wide Web site at: http: // www.cems.ou.edu Fall 2002 ""''""' "'""~ , an Equal Opportunity Institution Fa c ul ty & R esea r c h Int erests M igu e l J Bagaje w ic z, Professor process plant simulation & data reconc i liation design of heat/mass exchange networks for waste minimization applications mathematica l background, algorithm development & process design applications of optimiza t ion t h eory high temperature fuel-gas clean i ng reactors modeling of fluid-solid diffusion-react i on problems Brian P. Grad y, Associa t e Professor multiphase & block copolymers ion-contain ing polyme r s polymer-matrix composites biodegradable and bioabsorbable po l ymers nanotechnology at inte1faces Roger G. Harrison Jr. Assoc i ate Professor production of proteins & peptides u s i ng recombinant DNA technology separation & purification of biochemica l s protein engi neering for biomedical and environmental application protein engineering Jeffre y H. Harwell Conoco/D u Po nt Professor, Exec u t i ve Assoc i a t e D ea n fo r t h e Co l l ege of Engineer in g te r tiary oil recovery unconventional low energy separat i o n pr cesses mass transfer dynamics of multicomponent mass transfer processes surface phenomena adsorption kinetics subsurface remediation Llo y d L. Lee C.M. S l iepcev i ch Professor thermodynamics molecu l ar liqu i d t h eory statistical mechanics interactions in nanostructures Monte Car l o & mo l ecu l ar dy namics studies conformal solution theory natural gas properties polar fluids, io n ic solutions & molten salts surface adsorption Lan c e L. Lobban Winn C h a i r & Director catalytic reaction rate mechan i s m s & modeling partial oxidation of hydrocarbons photocatalysis Richard G Mallinson P rofessor chemical r eact i on engineering energy pr oject va l uation synthetic and alternative fuels natural gas utilization me t hane conversion Peter S M cFetrid g e R esearc h Ass i sta n t P rofessor, D irecto r of Ce ll & Ti ss u e C ul t u re Fac il ity vascu l ar tissue engineering biomedical design, development and app li cat i on vascular perfusion reactor engineering Matthia s U N ollert Associate Professor biomedical enginee r ing ce ll u l ar metabo lism and transport platelet and leukocyte adhesion fluid mechanics Edgar A O Rear III Win n Professor drug delive y surface che m istry & p h ys i cs kinetics blood trauma associated with medical devices biorheology organic c h e m is try Dimitrio s Papa v a ss iliou Assista nt Professor integrated p r ocess simu l ations tr a n port phenomena in biological systems small scale transport at the i n terface between statistical mechanics and classical mechanics Daniel E. Re s asco S.A. Wi l so n Professor heterogeneous catalysis, react i on engi neering & kinetics design of catalysts for pollutant abatement carbon nanotu b es physical chemistry of surfaces Melissa M. Rieger Assistan t P rofessor e l ectrochemical pheno m ena and e l ectro c h emical engineering carbon nanotube electro-chem i stly materia l systems an d elec trochemical processes in mic r oelectronic processing electrochemica l be h av i or of p oly meric materials John F. Scamehorn Asahi G l ass Chair surface & colloid science tertiary oi l r ecov ery detergency membrane separations adsorption pollution co n tro l polyme r s paper & plastics deink i ng David W. Schmidtke Assista n t Professor design & deve l op m e nt of new ana l yt i ca l d evices & techno l og i es fo r m edical thera p y biosensors ce ll ad h esion h ig h s p eed/ hi gh reso lu t io n video m ic r osco p y of flu i d m ec h an i cs in th e b l ood s tr eam Robert L. Shambaugh P rofessor po l ymerization chemistry po l y m e r pr ocessing technology fiber spinning, texturing & extrusion wastewater eng in ee ri ng ph ysico c h emica l t r eat m e n t ozonat i on gas-liquid reactions Vassilio s I. Sikavitsas Ass i stant P rofessor tissue enginee r ing biosensors bio r eac t o r s proteomics 39 1

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Oklahoma State University "Where People Are Important" Faculty Gary L. Foutch (Ph.D., University of Missouri-Rolla) K.A.M. Gasem (Ph.D. Oklahoma State University) Karen A. High (Ph.D., Pennsylvania State University) Martin S. High (Ph.D., Pennsylvania State University) A.J. Johannes (Ph.D., University of Kentucky) OSU's School of Chemical Engineering offers programs leading to M.S. and Ph.D. degrees. Qualified students receive financial assistance at nationally competitive levels. Randy Lewis (Ph.D Massachusetts Institute of Technology) Sundarajan V. Madihally (Ph.D. Wayne State University) R. Russell Rhinehart (Ph.D., North Carolina State University) James E. Smay (Ph.D. University of Illinois) D. Alan Tree (Ph.D., University of Illinois) Jan Wagner (Ph.D., University of Kansas) James R. Whiteley (Ph.D. Ohio State University) Vi s i t o ur we b p age at Research Areas Adsorption Artificial Intelligence Biochemical Processes Biomaterials Colloids/Ceramics Environmental Engineering Fluid Flow/CFO Gas Processing Hazardous Wastes Ion Exchange Molecular Design Nanomaterials Phase Equilibria Polymers Process Control Process Simulation Solid Freeform Fabricat i on Tissue Engineering For more information contact Dr. Khaled A. M. Gasem http://www.cheng.ok s tate.edu School of Chem i cal Eng i neering Oklahoma State Un i versity Stillwater, OK 74078-5021 gasem@okstate edu 392 C h e mi ca l E n g in ee ri ng Ed u cat i on

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OREGON STATE Chemical Engineering M.S. and Ph.D. Programs Our programs reflect not onl y traditional chemical engineering.fields but also tech nologies important to the Northwest's industries, such as electronic material processing, forest products, food sci ence, and ocean products. Oregon State is located only a short drive from the Pacific Ocean, white water rivers, hiking I skiing and climbing zn the Cascade Mountains. Fall 200 2 FACULTY M.K. Bothwell Bi o int e rfa c ial Ph e n o m e na C.H. Chang S e mi co ndu c tor Ma te rials Int e grat e d Ch e mica l S y st e ms G. N Jovanovic Fin e P a r t i cle Pr ocess in g Tr a n s p o rt Ph e nom e n a S. Kimura R e a c ti on En g in ee rin g, Hi g h T e mp e r a tur e Materi a l s, Bio ce rami cs, El ec tro ce rami c s and Su1fa c e Modifi c ation. M. D. Koretsky El ec tr o ni c Mat e rial s Pr ocess in g K. L. Levien Pr ocess Optimi z ati o n. and C o ntr o l R e a c tion En g in ee ring C. McConica Ga s S o lid Kin e ti cs S e mi co ndu c tor Pro ces sing J. McGuire Bioint e rfa c ial Ph e nom e na Biomat e rial s R.A. Peattie Bi o m ec hani cs Ph y s i olog y, Fl u id and Bi o fluid D y nami c s W. E. Rochefort Rh eo l og i c al Th e rm a l and M o l ec ular Chara c t e ri z ation of P o l y m e r s; P o l y m e r Pro cess in g; Bi o mat e ria.l s ; En g ine e in g Edu c ation G. L. Rorrer Bio c h e mi c al R e a c ti o n En g in ee ring Competitive research and teaching assistantships are available. For further information write: Ch emica l Engineering Department Or ego n St a t e University 103 Gl eeso n Hall Corvallis, Or egon 97331-2702 Vi s it u s o n the web a t www/c h e/o r st/ed u or ema i l us at mai l @ che.or s t.edu 3 9 3

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394 University of Pe n nsylv an ia Department of Chemical and Biomolecular Engineering Eric T. Bo d er Biomolecular engi neering St u art W. C h urchil l Combustion, incineration crystal growth, rate processes Ru sse ll J Com p osto P olymeric materials sc i ence, surface and interface studies Jo h n C. Crocker Microrheology of biopolymers, recA sea r ching, 3D microscop y, device biophysics Scott L. Di a m o nd Endothelial cell mechano b io lo gy, drug and gene delivery, biotransport phenomena D enn i s E. Di sc h er Cell and molecu l ar mechanics biomembrane and biopol y m e r mesostructures and functions W illi a m C Forsman P o l yme r scie n ce and engineering E du a rdo D. G l a nd t Classical and stat i st i cal th ermodyna m ics, random media R aymo nd J. Gorte H eterogeneous cata l ysis, su pp o rt ed m eta l s, anodes for so lid -oxide fuel ce ll s ze olites D avid J G r aves Bi ochemical and biomedical engi n eer in g, biotechnolog y D a ni e l A. Hammer Cellular bioengineering, bi oi nt erfacia l ph e n omena, adhesion A l a n L. Mye r s Adsorption of gases and liquid s, mol ecu lar simulation D a ni e l D P e rlmutt er Chemical r eac tor design, gas-solid r eac tion s, ge l kin et i cs J o hn A Quinn M embrane transport biochemical/ biom e di ca l e n ginee rin g War r en D Sei d er Pro cess analysis, s imulati on, d esign, and con trol We n K S hi e h Bi oenvironmenta l enginee rin g, e nvironm e ntal syste m s mod e lin g T alid R. S inno Tran s port and r eactio n, sta tisti ca l m ec hani c al modelin g Ly l e H U ngar Artifi cia l intelligence in pro cess contro l n e ural networks John M Vo h s Surfa ce science, cata l ys i s, elec tr on i c mat e rial s pro cess in g Ka r e n I. W in ey Pol y m e r morphology pro cess in g, and prop e r ty int e rr e lation shi p s r "I Penn's graduate program in chemical engineering is designed to be flex i b l e wh i le emphasizing the fundamental nature of chemical and physical processes Students ma y focus their studies in any of the research areas of the department The full resources of this Iv y League university, i ncluding the Wharton S chool of B usiness and one of th i s country's foremost medical centers, are availab l e to students in the program. The cultural advantages, historical assets, and recreational facilities of a great city are with i n wa lki ng dis t ance of the University \. For additional information, write : Di rec t or of Graduate Admissions Chemical a n d Biomolec u lar Engi n eering University of Pennsylvania 220 So u th 33rd Street, Rm. 311A Philadelphia, PA 19104-6393 http: / /www seas up enn.e du / cbe / Chemica l E n g in eer in g Ed u c ation

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PENN STATE Pursue your Chemical Engineering Degree in a diverse Big-Ten University located in a vibrant college community. Individuals with a B.S. degree in related areas are encouraged to apply. For more information contact: C h airperson, Graduate Admissions Cornmjttee Department of Chemical Engineering T h e P ennsy l va n ia State University 158 Fe n ske Laboratory U n iversi t y P ark P A 16802-4400 h ttp://fe n ske che.psu.edu/ Fall 2002 Chemical Engineering Antonios Armaou ( Uni v of CA at Los An ge l es)Pro c e ss Control Sy s tem D y n am i cs Aziz Ben-Jebria (Univ of Pari s)Re s piratory Fluid Flow and Uptake, Inhalation Toxicology Ali Borhan (Stanford)-Fluid D y nam ics Tran s port Phenomena Alfred Carlson (Wisconsin)-Biotechnology Bio se paration s Lance Collins ( P enn)-Turbu l ent Flow Combu s tion Wayne A Curt i s ( Purdu e) -Plant Bi otec hnol ogy Ronald P. Danner (Lehigh)-Polymers Ph ase Equilibria, Diffu s ion J Larry Duda ( D e laware)-Pol y m e r s Diffu sio n Thermodynamic s Tribology Fluid Me c hanic s Rheolog y Kristen Fichthorn (Michigan)-Statistical Me c h a nic s, Fluid-Solid Interface s, Molecular Simulation Henry C. Foley (Penn Stat e) -Nan o porou s Materials Heterogeneous Catalysis Adsorption and Perme a tion Seong Han Kim (No rthwest e m)-Nano-tribology a nd nano-materials Costas D Maranas ( Prin ceton)-Co mput atio n al Chemi stry, Bioinformatic s, Suppl y Chain Optimization Janna Maranas ( Prin ce ton)-Mole c ular Simulation Polymers Thermodynamics Network Gla sses Themis Matsoukas (Michigan)-Aerosol Proce sses, Colloidal Particle s, Ceramic P owders A. Nagarajan ( SUNY at Buffalo )-Colloid and Pol y mer Science Joseph M. Perez ( P e nn Stat e) -Trib ology, Lubri ca tion Michael Pishko (Texas)-Bio -mat erials, Biose nsing, and Ti ss ue Engineering Jonathan Phillips (Wisconsin)-Heterogeneous Catalysi s, Surface Science John M Tarbell (Delaware)-Cardiovascu lar Fluid Mechanic s and Mas s Transfer, Artificial Heart James S. Ultman ( D e la ware)Ph ysio lo gica l Tran s port Pro cesses, Respirator y Mas s Tran sfe r M. Albert Vannice (Stanford)H eterogeneous Catalysis Darrell Velegol (Carnegie Mellon)-Colloidal Sy s tems Colloidal Particle Interaction s James S. Vrentas ( Dela w are )-Transport Phenomena Applied Mathematics Diffu s ion in Polym e r s Rh eology Penn State i s an a ffirmative action, equal opportunity uni ve r s it y 395

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Cheinical Engineering at the University of Pittsburgh RESEARCH AREAS Biotechnology FACULTY Artificial Organs Biocatalysis Biomaterials Metabolic Engineering Modeling & Control Catalysis Surface Chemistry Catalyst Deactivation Chemical Promotion Novel Materials Organometallic Chem i stry Energy and Environment Bioremediation Clean Fuels From Coal Contaminated Soil Cleanup Stack Gas Cleanup Materials Engineering Biocompatible Polymers CO 2 as a Solvent lnterfacial Behavior Polymer/Composite Modeling Polymer Processing Multi-Scale Modeling Molecular Modeling Polymer-Fluid Interactions Process Modeling & Control Particulate Systems Transport Mohammad M Ataai Will i am Federspiel John F. Patzer 11 Jerome S Schultz Julie L. d ltr i Vladimir Kovalchuk Geitz Veser Shiao-Hung Ch i ang Robert M En i ck Badie I Morsi Anna C Balazs Robert M. Enick J Thomas Lindt Sachin Velankar Anna C Balazs Joseph J McCarthy Degree Programs: PhD and MS in Chemical Engineering MS i n Petroleum Engineering Information on Fellowships and Applications: Graduate Coordinator Chemical and Petroleum Engineering 1249 Benedum Hall University of Pittsburgh Pittsburgh PA 15261 412-624-9630 che.pitt edu Eric J Beckman Robert S. Parker Alan J Russell William R Wagne r Dan Farcasiu John W. Tierney Irving Wende r James T. Cobb Jr Gerald D Holder Eric J Beckman George E Klinzing Joseph J. McCarthy J Karl Johnson Robert S. Parker Th e University of Pittsbur gh is an affirmative a c ti on e qu a l o pp o rtuni ty instituti o n. 396 Chemical Engineering Edu c ation

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GRADUATE STUDIES IN THE DEPARTMENT OF CHEMICAL ENGINEERING, CHEMISTRY AND MATERIALS SCIENCE AT POLYTECHNIC UNIVERSITY Come to Polytechnic University in New York City., the nation"s second oldest technological university A number of fellowships are available as a result of the completion of the $275-mill i on Campaign for Polytechnic Fulp/ling the American Dream Join our dynamic research oriented faculty and conduct research in our centers for biocatalysis and biotechnology, polymers and systems engineering. For more information contact Professor Christos Georpkls Head Department of Chemical Engineering, Chemistry and Materials Science Polytechnic Unlvenlty Six MetroTech Center Brooklyn NY I 120 I Phone : 7 1 8/260-3236 Or visits us at: www poly.edu and T op : The Josep h & Vi o l et J J acobs Bu il d in g http://cchems.poly.edu Bottom: Th e Donald F. & M il dred Topp Othmer Residence H all .-~w.w,,,~ Fa ll 2002 FACULTY M.Cowman Conformation and I nteractions I n blopolymers B Garetz Interaction s of lasers with molecules, polarization effects C.Georplds Modeling and contro l of chem i ca l processes s ystems engineering M.Green Ch i rality of macromolecules liquid crystals R.Grou Blosynthes l s blocatalys l s and biotechnology K.Levon Conductive polymers blosensors J Mljovlc Relaxation dynam i cs I n complex systems S.Motzkln Effect of m i crowave radiation on blosystems J Pinto Design scheduling and optimization of chemical processes Y.Shnldman Computational modeling of complex fluids LSdel Thermodynamics and transport properties of fluids I Teraoka Separation of polymers confined systems A.Ulman Surface science and enetneering. nanotechnology E.Zletlw Air polutlon control engineering J.Zlatancwa Chromatin structure and dynamics W. Zurawslcy Pluma polymerization polymer thin films 397

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Princeton University Ph.D. and M.Eng. Programs in Chemical Engineering F acu lty Ilhan A Aksay Jay B B enziger Jeffrey D Carbeck Pablo G. D ebe nedetti (C h air) Christodoulos A Floud as Yannis G Kevrekidis Morton D Kostin Athanassios Z. Pana gio t o poul os Robert K. Prud homm e Richard A. Register William B. Ru ssel Lynn M Ru sse ll Dudle y A Saville George W. Scherer Stanislav Y. Shvartsman Sankaran Sundaresan Sa l vatore T orq u ato Sandra M. Troian T. K y l e Vanderlick James Wei David W. Wood Write to: or call: or email: Dir ector of G ra du a t e Studies Chemical Engineering Princ e t on University Prin ce ton NJ 08544-5263 l-800-238-6169 chegrad@princeton .e du [I Applied and Computational Mathematics Computational Chemistry, Biology, and Materials Systems Modeling and Optimi zat ion [I Biotechnology Biomat er ials M etabolic Engineering Prot ein and Enzyme Engineering Math emat i ca l Biol ogy [I Environmental Science a nd Engineering Aerosol Ph ys i cs and Chemistry Atmospheric Chemistry Art and In frastructure Conservation [I Materials: Synthesis/Processing/Structure/Properties Adhesion and lnt erfac ial Ph eno m ena Ceramics Colloidal Di spersions Complex Fluids Nanoscien ce and Nanotechnolog y P o l y m e r s [I Process Engineering and Science Chemical R eacto r D esign, Stability, and D ynam i cs H ete r ogeneous Catalysis Pr ocess Control and Operations Pr ocess S ynthesis and Design [I Thermodynamic s and Statistical Mechanics Glasses Kin etic and Nucleation Theory liquid State Th eory Molecular Simulation Cl Fluid Mechani cs and Transport Phenomena Electrohydrodynamics Granular and Mul tip has e Flow Microfluidics and Bi o lo g i cal Flows Rh eology Please visit our website: http://www.princeton.edu/~chemical 398 Chemical Engineering Edu ca tion

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PURDUE UNIV E RS IT Y CHEM ICAL \_ E~N~G~I N~E~E=""""'R_I_N_G _________ FACULTY Ronald P Andres Osman A. Basaran Gary E. Blau James M. Caruthers David S. Corti W. Nicholas Delgass Roger E. Eckert Gil U Lee John A. Morgan Joseph F. fckny Nicholas \ Peppas Doraiswami'8amkr" Robert ~Hannemann Gintaras V. Reklaitis Michael T. Harris Jennifer L. Sinclair Hugh W Hillhouse R. N / Houze Financial Assstance Fell~ships / Resea~~ssrstantships Teaching Assistantships Kendall Thomson George T. Tsao Venkat Venkatasubram nian Nien-Hua L. Wang Phillip C. Wankat /\ Degrees O\~d Master of Science Doctor of Philosophy RESEAR(H AREAS e;om, J ";"'";" Engineeri h g Catal ~l { and Reaction Engineering Flu"d Mechanics and Transport Phenomena lnterfacial Engineering and Colloid Science Molecular Modeling and Statistical Mechanics Nanofabrication and Nanomaterials Particle Technology Polymer Materials Process Systems Engineering Separation Processes Surface Science O For More Information Graduate Studies Purdue University 1283 Chemical Engineering Bldg. West Lafayette, Indiana 47907-1283 Phone: (765) 494-4057 www.che.purdue.edu "i~ :::, -2 0 <>. <>. 0 "iii :::, .,. 1 u "' "iii :::, .,. a, C: "' .!!! a, :::, "E :::, Q.

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Chemical Engineering at Rensselaer Polytechnic Institute The Chemical Engineering D e parhnent at R ensselaer ha s long been r ecog ni ze d for its excellence in teaching and research. It s g radu ate pr ograms l ea d to r sea r ch-based M S and Ph.D. degrees and to a co urse-based M.E. degree Pro g rams are a l so offered in cooperation with the School of Mana ge m e nt and Te c nolog y which lead to an M.E. in Chemical Engineering and to an MBA o r the M.S. in Mana geme nt Owin g to funding, cons ulting and previous faculty ex p e ri e n ce, th e department maintains close ties with industry. Department web site: http : // www.eng.rp i .ed u/d ep t/ c hem -eng/ Located in Tro y, New York R e n sse l ae r i s a pri vate sc h oo l with a n e nrollm e nt of so me 6000 s tud e nt s. Situated o n the Hud so n Ri ve r ju st north of New York 's capi tal c ity of Albany, it i s a thr ee -h o ur drive from N ew York City Bo s t o n a nd Montreal. The Adirondack Mountain s of New York, th e Green Mountains of Ver m o nt and the B er k s hire s of Massachusetts are readily accessi bl e. Saratoga with it s battl efie ld racetrack, a nd Performing Arts Center (New York City B a ll et, Phil d e lphi a Orchestra, and j azz festival) i s n ea rb y. 400 Application m ater i a l s a nd informati o n from: Graduate Service s R ensse l aer Polytechnic In s titut e Troy NY 1 2180-3590 Telephone: 518-276-6789 e-ma i I : grad-admissio n s@rp i. ed u http :/ /w ww.rp i .ed u/d ept/grad-serv i ces/ Faculty and Research Interests Michael M. Abbott, abbotm2@rpi.edu Thermodynamic s; equat i ons of state; phase equilibria Elmar R. A lt wicker, altwie@rpi.edu Profe sso r Emeritu s Spouted-bed combustion; incineration; tra ce -pollutant kinetics Georges Belfort, belfo g@ rpi .e du Membran e se parati ons; a d so rption; biocatal ysis; MRI interfacial phenomena B. Wayne Bequette bequeb@rpi.edu Associate Departm en t Chair Process modeling control, de s ign and optimization Henry R. Bungay III, bungah@rpi.edu Professor Emeritus Wastewater treatment ; biochemical engineerin g Timothy S. Cale, ca let @ rpi.edu Semiconductor m ater i a l s proce ssi n g; transport and re ac tion analyses Steven M. Cramer, crames@rpi.edu Di s placement membrane and preparative chromatogra phy ; environmental research Jonathan S. Dordick, dordick @ rpi.edu D e partment Chair Biochemical engineering; biocatalysis polymer sc ience bio se parations Arthur Fontijn, fontia@rpi.edu Combustion; hi g h-temp era ture kinetic s; gas-phase reactions Shekbar Garde, gardes@rpi edu Macromolecular se lf-a sse mbly computer simulations s tatistical thermodynamic s of liquid s, hydration phenomen a William N. Gill, gillw@rpi.edu Microelectronic s; re ve r se osmosis; crystal growth; ceramic composites Ravi S. Kane, kaner @ rpi.edu Pol y mer s: bio s urface s; biomaterial s; nanomaterial s Sanat K. Kumar, kumar @ rpi.edu Polymer nano struct ure s, nanocomposite s, dynamics of glasses and gels thermodynamics of complex fl uid s Howard Littman, littmh @ rpi.edu Profes so r Emeritus Fluid/particle sys t e m s; fluidization spouting, pneumatic transport E. Bruce Nauman, nauman@rpi .e du Polymer blend s; nonlin ea r diffusion; devolatilization ; polymer s tructure and propertie s; plastics recycling Joel L.Plawsky,pl awsky@rp i. edu Electronic a nd photonic materials; interfacial phenom ena; transport phenomen a Susan Sharfstein, sharfs@rpi.edu Biochemic a l engineerig, mammalian cell culture, recombinant protein production Hendrick C. Van Ness, vanneh@rpi.edu In st itut e P rofessor Emeritus Peter C. Wayner, Jr., wayner@rpi.edu He at tr ansfer; inter fac i a l phenomena; porou s material s C h e mi ca l Enginee ri ng Education

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RICE Chemical Engineering at Rice University FACULTY William W. Ake r s t (Michigan 1950 ) THE UNIVERSITY C on sta ntin e D. Ar meniad es (Case W es t e rn R eserve, 1 969) Ri ce i s a leadin g r esearch university-small, private and hi g hly selective-dis tin guis hed b y a co llaborative highly int e rdisciplinary c ultur e Walter G. C hapman (Co rn e ll 19 88) Sam H. Davi s, Jr. t ( M I T, 195 7) Jacqueline L. Goveas ( Prin ce t o n 1 996) J. David Hellums t (Michigan, 1 96 1 ) Joe W. Hightower t ( J o hn s H o pkins 196 3) George J. Hira sak i ( Ri ce, /967 ) Riki Kobayashi t (Michigan, 195/) Paul E. Laibinis ( Harvard University, 1991) Nikolaos V Ma ntzari s (Minneso ta 2000) Clarence A. Miller ( Minn eso ta 1 966 ) Matteo Pa s quali (Minnesota, 2000) Mark A. Robert (Swiss F e d Inst T ec h., 19 8 0 ) Michael S. Wong (M IT, 2000) Kyriacos Zygourakis (Minnesota, 1 98 1 ) THE DEPARTMENT Offer s Ph D ., M.S ., and M.Ch .E degree s Currently ha s 50 g radu ate st udents ( predomin a ntl y Ph D .). L oca ted only a few mile s from downtown Hou s ton, it occupies an architecturally di s tinctive, 3 00ac re campus s h aded by nearly 4 000 tree s. State-of-th e -art facilities and laboratories internationally renowned ce nters and in s titutes, and o n e of the cou ntry 's l argest endowments s upport an ideal learnin g and li v in g e n v ironm e nt. Provide s st ipend s and tuition wa ivers to full-time Ph D s tudent s. Special fellowship s w ith high s tipends are availab l e for outsta ndin g ca didate s. Emphasizes interdi sc iplinary st udies in co llaboration with re searc her s from other Ri ce departm e nt s, NASA the Te xas Medical Center, and R&D centers of p e troch e mical companies. FACULTY RESEARCH AREAS Biochemi ca l Engineering Nanot ec hnolo gy Biomedical Engineering NMR Propertie s of Fluid s Comp l ex Fluid s Petroleum Engineering Computational Engineering Pol y mer Sci e nc e Control and Optimization Re actio n Engineering Environment a l Remediation Rheology Equilibrium Thermodynamic Propertie s Statistical Mechanics Fluid Mechanic s .Toint with Bioengineering Interfacial Phenomena Ti ss ue Engi n eering Transport Phenomena Lary V. McIntire ( Princeton 19 7 0) Antonios G. Mikos ( Purdu e, 19 88) KaY iu San (Caltech, 1984 ) Jennifer L. West (Texas, 1996) t Emeritus Faculty Fall 2002 Kinetic s and C a taly s i s For more information and graduate program applications write to: Or visit our website at: Chair Graduate Admissions Committee Chemical Engineering Department MS-362 Rice Unjversity P O Bo x 1892 Houston, TX 77251-1892 http://www rice edu/ceng 401

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Department of Chemical Engineering UniversitY. of Rochester Graduate Study and Research leading to M.S. and Ph.D. degrees Fellowships to $24,000 plus full tuition S H. CHEN, Ph.D. 1981 Minnesota Pol y m e r Science and Engineering Organic Materials for Optics and Phot o ni cs Molecular D y nami cs Simulation E. H. CHIMOWITZ Ph.D 1982 Connecticut Critical Ph eno m e na Statistical Mechanics of Fluids Computer-Aided D es i gn D.R. HARDING Ph.D. 1986 Cambridge (Eng land ) Chemical Vapor D epos ition Mechanical and Transport Prop e rti es Advanced Aerospace Materials S. D. JACOBS Ph.D 1975 Roche s ter Opti cs Ph o t o ni cs and Optoelectronics Magnetorheology Optics Manufacturing J. JORN E, Ph.D. 1 972, California ( Berkel ey) Electrochemical Engineering Microelectronics Pr ocessi n g Th eo r etical Biology R.H. NOTTER Ph.D 1 969, Washington ( Se a ttle ) M.D. 1980 Rochester Biom e di ca l Engineering Lun g Surfactant Molecular Bioph ysics L. J. ROTHBERG Ph D. 1 984, Harvard Or ga ni c Materials a nd D evice Sciences Li g ht -Em ittin g Diod es Thin Film Transit ors Y SHAPIR Ph.D. 1981 Tel Aviv (Israel) Critical Phenomena Transport in Disord e r ed Media Scaling B e havior of Growing Surfaces S. V SOTIRCHOS Ph D. 1 982, Hou s ton Rea c tion Engineering Transport and R eact i on in Porous M ed ia Pro cess in g of Ceramic Materials and Composites J. H. D. WU Ph.D 1987 M.l.T. Bio c hemical Engineering Fermentation Bi ocatalysis B o n e Marrow Tissue Eng in eer in g Genetic and Prot e in Engineering H. YANG Ph.D. 1998, Toronto Nanostructured Materials Ma g neti c Nanoparticles M esoporo us Solids Mi c roand Nanofabrication Material s and Structu r es for Photoni cs and Bioph o t o ni cs M. YATES, Ph D 1999 Texa s ( Austin ) Colloids and Interfa ces Mat er ials Synthesis in Microemulsions Nanoparticle/Polymer Composites Supercritical Fluids Mi croe ncapsulation 402 For further information and application, write Graduate Admissions Department of Chemical Engineering University of Rochester Rochester, New York 14627 Phone: (585) 275-4913 Fax: (585) 2 73-1348 e-mail: gradadm@che.rochester.edu Chemical Engineering Education

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,,. J: --c-~ J~ll tl!!!!!!!!!!!ll .,._ '.1f; ilt 1 __ l_ ~~ : J [l 1 \ ) _~ __ ROWA.N UNIVERSITY Master of Science Chemical Engineering State-of the Art Facilities Collaboration with Industry Individualized Mentoring Multidisciplinary Research Project Management Experi ence Part-time and Full-time Programs Da y and Evening Classes Assistantships Available The Ch emical Engineering Department at R owan University is housed in Henry M. Rowan Hall, a new $28 mi ll io n 95,000 sq. ft. m u ltidisciplinary teachi n g and research space. An emphasis on pro j ect man agement process research and development, and industrially relevant research prepares students for suc cessfu l careers in high-tech fields A recent award of $6 million as seed money for the South Jersey T ec hn o l ogy Center will provide further opportunities for student training in emerging techno l ogies Located in so u thern New Jersey, the nearby orchards and farms are a daily reminder that this is the G ar d e n St ate. C ul t u ra l a n d recreational o p portuni t ies are p l e n tiful in the area Philade l phia a n d th e sce n ic J ersey Sh ore are only a short drive away, and major metropolitan areas are within easy reach. Faculty---------------C. Ste w art Slater Chair Rutgers University Ke v in Dahm Massachusetts Institute of Technology Stephanie Farrell New Jerse y Institute of Technology Zenaida Gephardt University of Delaware Robert P. Hesketh University of Delaware Kathr y n Hollar Cornell Uni v ersity Jame s Newell Clemson University Mariano J. Sa v elski University of Oklahoma -----------------Re s earch A rea s Membrane Separations Reaction Engineering Mammalian & Insect Cell Culture Pharmaceutica l and Food Processing Technology Biochemical Engineering Green E n gineering Con t rolled Release Novel Separation P rocesses Hig hPerformance Polymer Processi n g Process D esign and O ptimization Particle Technology Supercr i tica l Fluids Environmental Engineering For A dditional information --------------------------D r. Maria n o J. Savelski Graduate Stude n t Advisor D epartment of Chemical Engineering R owan University. 201 Mullica Hill R oad. Glassboro NJ 08028 Ph o n e: (856) 256-5310 Fax : (856 ) 256-5242 E-mail: savelski@rowan.edu Web: http : //engineering eng.rowan.ed u Fall 200 2 403

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RUTGERS Graduate Program in Chemical & Biochemical Engineering Research Areas Biotechnolo gy Reaction Engineering Process Systems Engineering Pharmaceutical Engineering Polymer s Faculty Helen M. Bu e ttner Associate Pro fessor, Ph D. Univers it y of Penn srlXM i I\,.)987 Applied n e ur ob i ology, ce ll m o tili ty cell s ub s trat e int eractio n s, crys t a lli z ation of ph a rma ce uti c al s Yee C C hi ew, Profe sso r ; Ph.D. University of P. ~ ph e nom e na Alki~u~~ e ~~::t:,:;~; :~% ::: ~~;,~i~: ~~~,~,~~~aj~fts i ( ? 197~ : ; ~i~ l ;, ~#//;a l f~ in ~l~ i (jl, Of f.i f;;;!)/Jitl l~pffe rm e ntati o n processes applied Pet e~;;:~:;ap n ; 1 ;,~,:~: ~1,1l1l!::~ : Y~~y i )~~ 1 \76\ 1 Tli ;; ;n ~1J/ 'c~!!;'c ff; ; '. :; nsl ~ i oX JtJ~9pl,; i o 1 / ;)} tat e b;li4lffof single and multi co mp on e nt systems Burton z. Davidson, P~ i~ie 11.o. P.ij ., North~estem .u\l \~s it y f .i 96l $.YJ/,:lS sJ; J; 1 qifo i; t'iia.f pti;; ; i za ti o (e n v iro\wl e;/fi~il~[ rin g, h ea lth a nd safety e n g in ee rin g mana ge m~~~ i}? 1 Pano s G. Geo r g opoulo J1. ~~~s~ifi t ;Ptof1;sso r ; pj {Q., G.a1t{effi\i!OO~tiii;i:IJ!gf '.] i~f;,V/il&f ph~} J~y ;ro;;;;;~utafiJi;tilic;/~ifj1;1eering turbul e nt tran s p o rt Benj :: i : 1 ;~::~~ llt ~: ~; ; ~;;;: r ; ~~ '. ~:,ia1~:;1! 1 1 1 1~! 1 1 1 1;~l i; JlJI O ;; ~:tt ~~';;;~;;\\ ; s :'.::: at'ticulate !l we 11 s i o 1 1s; n o nlin ea r d y nami cs of@'f@jjjft P(
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Graduate Studies in Chemical & Environmental Engineering at National University of Singapore NUS National University of Singapore PROCESS & SYSTEMS ENGINEERING CHEMICAL ENGINEERING FUNDAMENTALS Al Applications Process Dynamics & Control Process Design & Development Pro c ess Model i ng & S i mulation Process Operations & Safety Process Optim i zation MATERIALS & DEVICES Advanced Catalyt i c & Crys t alline Materials Polymeric Electronic & Bio Materials Sensors & Electrochemical Dev i ces Surface Science & Engineering Biochem i cal & Biomedical Engineering lnterfacial Phenomena Reaction Engineering Separation & Purification Thermodynamics Transport Processes ENVIRONMENTAL SCIENCE & TECHNOLOGY A i r & Water Pollution Control Atmospheric & Aquatic Chemistry B i o remediation Environmental Assessment & Modeling Hazardous Waste Treatment National University of Singapore is internationally acknowledged as one of the best universities in the Asia Pacific region with a global outlook and focus on quality teaching research and entrepreneurship W i th more than 45 faculty members from diverse ethnic backgrounds and with excellent academic credentials from leading i nstitutions around the world the Depart ment of Chemical and Environmental Engineering offers graduate programs that provide a stimulating and challenging learn ing experience. The Department has comprehensive top-notch research facilities for carrying out cutting edge research. Close ties with the industry and overseas institutions provide infusion of new ideas and maintain a creative and dynamic atmosphere in the Department. GRADUATE PROGRAMS Coursework-based Master of Science (Chemical Engineering) (with specialization option in biopharmaceutical engineering) Master of Science (Environmental Engineering) Master of Science (Safety Health & Environmental Technology) NUS-UIUC Joint Master of Science (Chemical Engineering) Contact Us At: Department of Chemical & Environmental Engineering National University of Singapore 10 Kent Ridge Crescent Singapore 117576 Tel : (65) 6874-8076 Fax : (65) 6779-1936 E-mail : chegohsp@nus edu sg http :// www.chee.nus.edu sg Fa ll 2002 Research-based Master of Engineering Doctor of Philosophy NUS-UIUC Joint PhD Program Financial assistance is available for qualified applicants in the form of research scholarships. 405

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Z Department ~ U I V E RS I T Y OF ~Q\ROLINA. The Department of Chemical Engineering at USC is booming! Re s earch funding is at an all-time high-exceeding $4 million per year. Thi s progre s s ive department with its dynamic young faculty, i s already recognized a s one of the top teaching and re s earch program s in the South east. Chemical Engineering offer s MS, ME and PhD degrees and PhD candidate s are offered tuition reduction and highly competi tive, twelve month s ti pends ranging from $20,100 to $22,500 per y ear. For further i11Jormatio11 : 406 Th e G r a du a t e Dir ector D e p ar t me nt of C h e mi ca l E n g in eer in g, Swear in ge n E n g i n eer in g Ce n te r U ni versity of So uth Caro li na Co lum b i a SC 29208 P h o n e : 1800 763-05 2 7 Fax : 1 803 777 8265 W e b p ag e: www c h e .s c. e d u of Chemical Engineering Th e Uni ve r s it y of S o uth Car o lin a i s l oca t e d in Co lumbi a th e s t a t e ca pit a l. Co lumbi a i s co n ve ni e ntl y l oca t e d in th e ce nt e r o f th e s t a t e a nd co mbin e s t h e b e n efi t s of a bi g ci t y w ith th e c h ar m and h os pit a lity o f a s mall t ow n. T h e area's s unn y a nd mild clim a t e, co mbin e d w ith it s lak es a nd w oo ded park s pro v id e pl e nt y o f o pp o rtuniti es fo r year ro und o u t d oor r ec r ea ti o n In a dditi o n Co lumbi a i s o nl y h o ur s away fro m th e Blu e Rid ge Mo unt ai n s and th e A tl a nti c Coast. Ch ar l o tt e a nd A tlant a -ci ti es th a t se rv e as Co lumbia s int e rn a ti o n a l ga t e w ays -a r e n e arb y Research Pro,:rams Adsorption Technology Batteries and Fuel Cells Colloids and Interfaces Composite Materials Co"osion Engineering Crossflow Filtration Electrochemistry Heterogeneous Catalysis Molecular Simulations Nanotechnology Numerical Methods Pollution Prevention Process Control Rheology Separations Sol-Gel Processing Solvent Extraction Surface Science Supercritical Fluids Thermodynamics Waste Management Waste Processing C h e mi c a l E n g in ee r in g Edu c a t i o n

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University at Buffalo The State University of New York Faculty Integrative Research at the Frontiers of Chemical Engineering Nanosca le Science and Engineering B1 ochem1cal & Biom ed i cal Engine erin g Paschali s Alexandridis (MIT ) amphiphili c p o l y m e r s, se lf ass e mbl y, co mpl e x fluids nanomat e ri a ls int e rfa c ial ph e nom e na Stelios T. Andreadis ( Michi g an ) bi oe n g in ee rin g, ge n e th e rap y tissu e e n g in ee rin g o f ge n e ticall y modifi e d skin Jeffre y R. Errington ( C o rnell ) m o l ecu lar s imulati o n s tatisti c al th e nn o d y nami cs biopr e s e rvati o n Vladimir Hlavacek ( J CT -Pra g ue ) r e a c tion e n g in ee rin g, nanopo w d e rs, e xplosi ve s and d e tonation s, anal y sis of c h e mi cal plants Mattheo s Koffa s ( MIT ) m e taboli c e n g in ee rin g, bi o in fo nnati c s David A. Kofke ( Pennsylvani a) m o l ec ular m o d e lin g and s imulation s o lid phas e equilibria Carl R. F. Lund ( Wiscon s in ) h e t e r oge n e ous c atal y si s, c h e mi c al kin e ti c s r e a c tion e ngin ee rin g T. J. (Lakis) Mountziaris ( Princeton ) e l ec tr o ni c a nd pho to ni c mat e rials nan o particl e s bios e n s ors multiphas e flo w s Sriram Neelamegham (Rice ) biom e di c al e ngin ee rin g, ce ll biom ec hani c s v as c ular e n g in e erin g Johannes M. Nitsche ( MIT ) fluid m ec hani cs, tran s p o l1 ph e n o m e na bi o a c ti ve s urfa ce s bi o lo g i c al p o r es, transd e nnal transport Eli Rucken s tein ( Bu c h arest ) c atal ys is, surf ace ph e n o m e na c olloids and e mulsions bio c ompatibl e surfa ce s and materials Michael E R ya n ( McGill ) p oly m e r a n d ce r a mi c s pr ocess in g, rh eo l ogy, n o n N ew tonian fluid m ec hani c s Mark T. Swihart ( Minne so t a) c h e mi ca l kin e ti c s, m o d e lin g o f r e a c ti ve fl ow s, co mputational c h e mi s ll y, nanoparticl e Jonna/ion E. (Manolis) S. Tzanakaki s ( Minne so ta ) ce ll and ti s su e e n g in ee rin g, bio c h e mi c al e n g in ee rin g Adiunct Faculty V. James Hernandez ( Microbi o l ogy) r eg ulati o n o f ce llul a r r e spons es William M. Mihalko ( Scho o l o f Medi c in e) o rth o pa e di c s Bruce Nicholson ( Biological Science s) g ap jun c tions and co nn ex ins Athos Petrou (Phy s ic s) sp ec t ro s co p y, se mi co ndu c t o r nan o stru c tur es Carel Jan van Os s ( Microbi o l og y ) co ll o id and i n t e rfa ce s cie n ce Yaoqi Zhou ( Bioph ys ic s) pr ote in f o ldin g, s imul a ti o n o f bi o m o l e cul es Emeritus Faculty in Residence Robert J Good ( Michigan ) adh e sion and int e fa ce s c i e n ce, philosoph y of sci e n c e Thomas W. Weber ( Corne ll ) pro ce ss c ontro l Sol W. W e ller ( Chicag o) c atal y sis, c oal liqu e fa c tion histo ry of c h e mi c al engin ee r in g Chemical engineering faculty participate in m a ny interdisciplinary centers and initiatives, including The Center for Advanced Molecular Biolog y a nd Immunol ogy, The Center for Computational Research, The Center for Advanced Photonic and Electronic Material s, The In s titute for Laser s, Ph oto nic s, and Biophotonics The Institute for Bioinformatics, and The Center for Advanced Techn o l ogy for Bi o medi ca l Device s Fo r mor e in.Jonna/i o n and an appli ca tion writ e t o: Dir ec tor o f Graduat e Studi e s D e partment of Chemi c al Eng in eer in g Univ e rsi ty at Buffal o ( SUNY ), Buffal o, N ew York /4260-4200 o r go t o http: // www.cheme.buffalo.edu Fall 2002 All Ph D students are supported as research or teaching a ss i s tants. Additiona l fe ll o wships spo n sored by P raxai r In c., The Nat.i o nal Science Fo un dation JGERT program, and the State Univer s ity of New York are ava il ab l e to except i ona ll y well qualified app li ca nt s. 407

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Faculty------------R. Besser (PhD Stanford Un iversity) R. Blanks (PhD, University of California at Berkeley) G.8. Delancey ( PhD University of Pittsburgh) H. Du (PhD, Penn State University ) T.E. Fischer (ScD Federal Inst. of Technology Zurich) 8. Gallois (PhD, Carnegie Mellon University) D.M. Kalyon ( PhD McGill University) S. Kovenklioglu (PhD Stevens Institute of Technology) A. Lawal (PhD, McGill University) W.Y. Lee (PhD, Georgia Institute of Technology) M. Libera (ScD, Massachusetts Inst. of Technology ) G. Rothberg (PhD Columbia University) K. Sheppard (PhD University of Birmingham) Research in ____________ Micro-Chem ical Systems Polymer Rheology and Processing Processing of Electronic and Photonic Materials Processing of Highly Filled Materials Chem i cal Reaction Engineering Chemical Vapor Deposition Biomaterials and Thin Films Polymer Characterization and Morphology High Temperature Gas Solid and Solid-Solid Interactions Environmental and Thermal Barrier Coatings Tribochemistry and Tr i bology 408 STEVENS INSTITUTE OF TECHNOLOGY Multidisciplinary environment consisting of chemical and polymer engineering chemistry, and biology Site of a major engineering research center; Highly Filled Materials Institute Scenic campus overlooking the Hudson River and metropolitan New York City Close to the world's center of science and culture At the hub of major highways air, rail and bus lines At the center of the country's largest concentra tion of research laboratories and chemical, petroleum pharmaceutical and b i otechnology companies GRADUATE PROGRAMS IN CHEMICAL ENGINEERING Full and part-time Day and evening programs MASTER'S CHEMICAL ENGINEER PH.D. Fo r application, contact: Offi ce of Graduate Studies Ste ve ns I nstitute of Te c hnolo gy H oboken, NJ 07030 20 / -2 / 6-5234 For additional information co ntact: Ch e mi cal Bio c hem ica l and Materials Engineerin g D e partm e nt Stev e ns I nstitute a/ T ec hn o lo gy H oboken, N J 07030 20 /2 16-554 6 ( Financial Aid is Available to qualified students ) Steve n s Institute of Technology does not di sc riminate against any person because of race creed co lor n a tional ori g in sex, age marilal s tat us, handicap li ability for se rvice in the a rmed forces or sta tu s as a di sa bled or Vietnam era veteran. Chemi c al Engineering Education

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Graduate Studies in Chemical Engineering The University of Tennessee, Knoxville Piece together the elements of a great graduate experience ... I The Faculty ,-----------------------......., Graduate s tudents and faculty w orking together to reach The Research common goals that partnership is at the heart of the University ofTennessee-Knox v ille 's Department of Chemical Engineering. It s a partnership that w orks creating exciting and productive research in six major areas: ( I ) bio-process engineering (2) molecular science and engineering (3) separations and transport phenomena (4) computer aided process simulation and design (5) pol y mer and composite proce ss ing and ( 6) process control. These re sea rch program s reach out to other engineering and science departments to the nearb y Oak Ridge National Laboratory and to industry forming larger partnerships and creating an unsurpassed re sea rch environment. Found e d in 1794 as Blount College the first non The University sectarian college wes t of the Appa l achians The I Paul R Bienkowski (Ph.D ., Purdue 1975) Bioproc essing, Thermod y namics Duane D. Bruns (Ph.D ., Houston 1974) Pro cess Control Modeling John R Collier (Ph D. Case Institute 1966) Pol y m er Pro cessing and Properties Robert M. Counce (Ph.D ., Tennessee 1980) Separations and Transport, Environmental Peter T. Cummings (Ph.D. Melbourne 1980) Molecular Thermodynamics D esig n Environmental Brian J. Edwards (Ph.D ., Delaware 1991) Non Newton ian Fluid D y namic s Paul D. Frymier (Ph D ., Virginia 1995) Bio c h emical E ngin eer in g, Bios e n so rs Da v id J. Keffer (Ph D ., Minnesota 1996) Molecular Modeling of A dsorption Diffusion and R eac tion in Z e oli/es Charles F. Moore (Ph D. Louisiana State 1969) P rocess Control John W. Prados (Ph.D. Tennessee 1957) Safety and Ri sk Assessment Tsewei Wang (Ph.D. M.I.T ., 1977 ) Pro cess Control Biopro cess ing University ofTennessee toda y i s the s tate 's largest uni v ersity and Land-Grant in s titution w ith about 20 000 undergraduate s, 5 700 graduate and professional students and a faculty of 1 200 The University of Tennessee is located in Knox v ille near the headwaters of the Tennessee Ri ve r. Within an hour s drive are six Tenne ssee Valley Authority lakes and the Great Smoky Mountains National Park The Knoxville metropolitan area has a population of 600 000 but enjoys a pleasant generally uncrowded atmosphere and consistently ranks among the nation 's top ten metropolitan areas in surveys on quality of life East Tenne ssee ha s a four-season climate ranging from wa rm s umm er temperatures to winter temperatures cold enough for s now skii ng in nearb y mountain resorts Frederick E. Weber (Ph D ., Minnesota 1982) ~-----------------------.--,1 Computer-Aided D esig n Radiation Chemistry The Next Step For additional information contact : Department of Chemical Engineering University of Tennessee-Knox vi ll e 4 I 9 Dou gherty Hall Knoxville TN 37996 2200 Phone : (865) 974-2421 E-mail : chei nfo @ utk.edu World Wide Web: http :// www.che.utk edu Adjunct and Part-Time Faculty from Oak Ridge National Laboratory Hank D Cochran (Ph.D. M.I.T .): Th ermody nami cs, Statistical Mechanics Brian H Da v i s on (Ph D. Caltech): Bio c h e mical Engineering Jack S Watson (Ph D ., Tennessee ): Separations and Transport Nuclear Fusion

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The Univer at Austin Chemical Engineering at the U n iversity of Texas at A u s t in is an exciting, b roa d b ased an d i nter di sc i pli n ary program, wit h facu l ty of d iverse r esearc h interests. We are o n e of t h e l ea d i n g p rograms in che m ical e n gi n eer ing exce ll i n g i n al l aspects of scho l ars h i p researc h a nd education. Both M S C h E an d Ph.D ChE degrees are offered. Fellow sh i ps and research assistantships are pro vided, including tuition and fees. Faculty and their research David T. Allen Ph.D., Caltech, 1983 env i ronmental modeling reaction engineering Angela M. Belcher Ph D., U. ofC. Sama Barbara, 1997 organic/inorganic biomo l ecular & biological-e l ectronic hybrid materials Roger T. Bonn e caze Ph.D., Caltech, 1991 suspension rheology, transport phenomena electrical impedance tomography Thomas F E dgar Ph.D. Princeton U., 1971 process modeling control optimi z ation John G. Ekerdt Ph.D., U. of C. Berkeley electronic materials chemistry, surface science R. Bruce Eldridge Ph.D. U. ofTexas, 1986 separations r e search Benn y Freeman Ph.D., U. of C. Berkeley, 1988 pol y mer structures processing and properties Venkat Ganesan Ph.D. MIT 1999 statistical mechanics simulat i ons of self-assembly in comple x fluids George Georgiou Ph.D., Cornell U 198 7 microbial prote i n biotechnology Peter F. Green Ph D., Cornell U., 1985 materials science polymer melts Adam Heller Ph.D. Hebrew U., 1961 electrochemical biosensing, environmenta l photoelectrochemistty G y eong S. Hwang Ph.D., Caltech, 1999 multisca l e modeling & simulation semiconductors nanotechnology Keith P. Johnston Ph.D., U. of Illinois 1981 po l ymer and surface thennod y namics supercritical fluids Miguel J ose Yacaman Ph D., National University of Mexico 1973 materials science electron microscop y, nanoparticles Brian A. Korgel Ph.D., U. of C. Los Angeles, 199 7 complex fluids nanostructured materials Douglas R. Llo y d Ph D. U of Waterloo 1977 po l ymeric membrane formation liquid separation s Yueh-Lin Loo Ph D. Princeton U. 2001 polymer physics & chemistry micro& nanostructured materials C. Buddie Mullins Ph.D. Caltech, 1990 surface science, molecular beams semiconductor thin-film growth S. Joseph Qin Ph.D., U. of Maryland, 1992 process modeling and control Gary T Rochelle Ph.D. U. of C. Berkeley, 1977 air pollution control reactive mass transfer Peter J. Rossky Ph.D., Harvard U 1978 theoretical chemistry liquids condensed phase quantum d y namics Isaac C. Sanchez P h D., U of Delaware, 1969 statistical thermodynamics of pol y mer liquids and solutions Christine E. Schmidt Ph.D., University of Illinois, 1995 cell and tissue engineering Makul M. Sharma Ph.D ., U. of Southern California, 1985 surface and colloid chemistry T homas M. Truskett Ph.D., Pr i nceton U., 2001 statistica l mechanics molecular modeling J. Michael White Ph D., U. of Illinois 1966 chemical reactions on surfaces C. Grant Willson Ph D U. of C. Berkeley, 19 7 3 pol y mer s y nthesis photochemical processing Address inquires to: Graduat e Advisor D e p a rtm e nt of Chemic a l Engin ee rin g University of T ex as Austin TX 787 1 2106 2 Phon e: 51 2/47 1 6991 F ax: 51 2/47 1 -7824 ut g r a d @ ch e. ut ex as edu wwwch e .ut exas .edu 410 Ch e mi c al Engin e erin g Edu c ation

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TexasA&M University Large Graduate Program Approximately 120 Graduate Students Strong Ph.D. Program (75% PhD students) Diverse Research Areas Top 10 in Research Funding Quality Living I Work Environment Financial Aid to All Qualified Students Up to $24,000/yr plusTuition and Fees and Medical Insurance Benefits RESEARCH AREAS Biochemical Engineering/Bioprocessing Biomedical Engineering Composite Materials and Asphalts Environmental Remediation/Pollution Prevention Advanced Catalysts Interfacial Transport Kinetics, Catalysis and Reaction Engineering Microelectronic Materials Molecular Simulations Nanomaterials Polymers Computer-Aided Process Design and Modeling Separations Supercritical Phenomenal Technology Thermodynamics For More Information Graduate Admissions Office Department of Chemical Engineering Dwight Look College of Engineering Fall 2002 Texas A&I\I Uniwrsit~ College Station. Texas 778-IJ-Jl22 Phone (979) 8-15-.:U<,t \\!ebsik http://www-chen.tamu.edu Faculty R.G. A nthon y, H ea d Ph.D ., U ni versity ofTexas, 1 966 C. D H o ll a nd Prof essor Catalysis r eac tion e n g in ee rin g ion exc h ange A Akgerman Ph D ., U. of Vir gi ni a 19 7 1 Chevron Profes so r R eac ti on e n gi n ee rin g, was t e tr ea tm e nt J.T. Baldwin Ph D Texas A&M University 1968 P rocess design M A. B eva n Ph D Carne g i e Mellon Uni v ersity 1 999 Collo idal S c i e n ce D.B. Bukur A ssoc iat e H ea d Ph.D ., U.ofMin n esota, 1 974 R eac ti o n e n g in ee rin g, math m e thod s J.A. Bullin Ph D U. o fH o u s t o n 1 972, P rofesso r E meritu s Gas sweetening, asphalt c hara c t e ri za tions R. Darby Ph D Ri ce University 1 972, P rofesso r Emeritu s R heolog y, polymers R.R. Davi so n Ph .D. Texas A&M U. 1962 Pr ofessor Emeritus Asphalt c haracte ri za tion L.D. Durbin Ph D Ric e Univers i ty 1 961, Prof esso r Eme ritu s P rocess co nt rol M. E l-H a lwagi Ph D U ni ve r s it y of Califo rnia 19 90 M c F e rrin Profe sso r P rocess int eg ration P.T. E ubank Ph.D ort h weste m U ni vers it y 1 96 1 J oe M Nesbitt Prof esso r Th e rmod y n amics D.M. Ford Ph.D Univer s i ty of P ennsy l vania 1 996 Molecular m ode lin g/ transp ort G Frome nt Ph D. University of Gent B elg ium 1 957 R eaction e ngineerin g C. J Glover, Ph.D R ice U ni ve r s it y, 1 974 Director Cen t e r for Asphalt & Material s Chemistry P olymer so lwi ons asphah c hara c terizati o n K.R. Hall Ph D. Unive r s ity of Oklahoma 1 967 Jack E. a nd Frances B row n C h a ir Th e rm o d y nami cs D.T. Hanson, Ph D. Unive r s it y of Minnesota 1968 Bi oc h e mi c al engineering C.D. Holland Ph D T exasA&M Univ. 195 3 P ro f essor Emeritu s Separat i on processes distillation unst eady-s tat e processes J.C. Holst e, Ph D I owa State U ni vers it y, 1 973 Th e rmod y nami cs M .T. Holtzapple, Ph.D U niv e r s ity of Penn sy l va nia 1 981 Bi o c h e mi c al e n g in ee rin g Y Kuo Ph D. D ow Prof esso r Col umbi a Unive r si t y 19 79 Microe l ec troni c s S. Ma nnan Ph D U ni ve r s i ty ofOk l ahoma, 19 86 Dir ecto r, M ary K ay O Con n o r Pr ocess Saf e t y Ce nt e r E. Sevick-Muraca Ph.D. Carnegie Mellon Un i vers i ty, 1 989 Biom edica l/Bio c h e mi ca l D .F Shantz Ph D Un i v ersity of D e la ware, 2000 Struc/llre prop e rt y r e l a ti ons hip s of porous mat eria l s, sy n thesis of n ew porous solids V Ugaz, Ph D Northwestern Univer s it y, 19 99 Mi crofab ri ca ted Bi oseparation S y stems 4 11

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(.) ro u. 4 1 2 Chemical & Environment Martin A. Abraham, Professor Ph.D., University of Delaware Green Chemistry and Engineering, Supercritical Fluids Maria R. Coleman Associate Professor Ph.D., University of Texas at Austin Membrane Separations, Bioseparations Kenneth J. DeWitt, Distinguished Professor Ph.D. Northwestern Universit y Transport Phenomena, Mathematical Modeling & Numerical Methods John P. Dismukes, Professor Ph.D., University of Illinois Materials Processing Management of Technological Innovation Isabel C. Escobar, Assistant Professor Ph.D. University of Central Florida Membrane Fouling and Membrane Modifications Saleh Jabarin, Professor Ph D University of Massachusetts Physical Properties of Polymers, Polymer Orientation & Crystallization Dong-Shik Kim, Assistant Professor Ph.D. Universit y of Michigan Biomaterials, Metabolic Pathway Control Steven E. LeB l anc, Professor and Chair Ph.D. University of Michigan Chemical Process Control, Chemical Engineering Education G. G l enn Lipscomb, Professor Ph.D. University of California at Berkele y Membrane Separations, Bioseparations, Education Arunan Nadarajah, Professor Ph.D., University of Florida Transport in Biologi:al Systems, Nanotechnology Bruce E Poling, Professor Ph.D., University of Illinois Thermodynamics and Physical Properties Constance A. Schall, Associate Professor Ph D ., Rutgers University Enzyme Kinetics, Crystallization, Paraffin Deposition Sasidhar Varanasi, Professor Ph.D State Universit y of New York at Buffalo Colloidal & Interfacial Phenomena, Hydrogels The Department of Chemical & Environmental Engineering at the University of To l edo offers graduate programs lead in g to MS and Ph D degrees We are located in state of the art facilities in Nitschke Hall and our dynamic faculty offer a variety of research opportunities in contemporary areas of chemical engineering SEND INQUIRIES TO : Academic Coordinator Chemical & Environmental Enginee r ing 280 1 W Bancroft Street Mail Stop 305 University of Toledo Toledo, Ohio 43606-3390 Ph one : ( 41 9) 5 30 8080 F ax : ( 4 1 9) 5 30 8086 UR L : http : //www che ut o l e d o e d u E-m a il : c h ee d e pt @e n g ut o l e d o e du C h e mi c al En g in ee rin g Edu c ati o n

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Chemical and Biolo gical Engineering at Tufts University TUFTS OFFERS M.S., M.E. and Ph.D degrees in Chemical and Biotechnology Engineering University-wide Bioengineering Center involving Medical Dental and Veterinary Schools A friendly personalized small college" environment with all the advantages of a research university Opportunities to design and contribute to exciting university research at the state-of-the-art Science & Technology Center Small classes that ensure individualized attention from our superb faculty An active graduate student council working to en hance student social and academic life Located 5 miles north of Boston with easy access to the numerous educational and social resources of the local and New England area For further information contact: Graduate Studies Chair Tufts University Chemical and Biological Engineering Department 4 Colby Street Medford MA 02155 PHONE 617-627-3900 FAX 617-627-3991 Fall 2002 Email: chemstudent@infonet. tufts. edu Web: www.ase.tufts.edu/chemical Chemical Engineering at Tufts since 1901 Pr e paring for the next I 00 yea rs FACULTY AND RESEARCH AREAS FULL-TIME PROFESSORS Assoc. Prof. Eliana DeBernardez Clark Ph.D (U .N.L. Argentina) ( on lea ve) Bi oc h emica l engineering, protein folding, protein aggregation Prof. Gregory D. Botsari s Ph .D. (M .I.T. ) Crystalli z ation, nucleation, applied surface science Prof. Maria Flytzani-Stephanopoulos Ph D. (Univ. of Minnesota) Environmental catalysis pollution prevention, clean e n e r gy, and transportation technologies Prof. David L. Kaplan Ph.D. ( Syracuse University) Bi oengineered polymers related to self-assem bl y, biomaterials and tissue engineering Asst. Prof. Kyongbum Lee Ph.D (M .I.T. ) Bi otec hnol ogy, metabolic enginee ring bi o informati cs Assoc. Prof. Jerry H. Meldon Ph D ( M.I.T .) Membrane science and technolog y, mass transfer with c hemi ca l re action including mathematical modeling Assoc. Prof. Daniel F. Ryder Ph.D (Worcester Polytechnic Institute) Advanced pro cess co ntrol applications Prof. Nak-Ho Sung Ph.D (M. I.T. ) P olymers and composites interface science, polymer diffusion, sur face modification Prof. Kenneth A. Van Wormer Sc.D. (M.I.T.) Optimization nucleation, reaction kin etics, VLSI fabrication RESEARCH PROFESSORS Asst. Prof. Aurelie Edwards Ph D ( M.I T .) Transport across biological membranes role of microcirculation in the renal medulla Asst. Prof. Regina Valuzzi Ph.D (U ni v. of Massac hu setts, Amherst) Ordering of highl y structured patterned polymers into co mple x nanostru c tured materials Assoc. Prof. Vladimir Volloch Ph.D (Moscow University) Cellular and molecular biology ADJUNCT PROFESSORS Asst. Prof. Dale Gyure Ph.D (University of Colorado) Prof. Walter Juda Ph D (U niver si ty of Lyons) Electrochemistry and chemical reaction enginee ring Asst. Prof. Brian Kelley Ph D. ( M I.T. ) Novel methods for protein purification, large-scale purifications, high-density bacterial fermentation Prof. Gordana Vunjak-Novakovic Ph.D. (University of Belgrade) Transport phenomena, t issu e engi neerin g, bioreactors Asst. Prof. Stefan Winkler Ph.D (Tufts University) Prot ein assembly 413

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ulane ____ niversit Department of Ch emical Engineering Faculty and Research Areas Dan ie l C. R D eKee Rh eology of Natural and Synthetic Polymers Constitutive Equations Transport Phenomena and Applied Mathematics Richard D. Gonzalez S y nth es is and Characteri za tion of Supported Metal Catal ys ts Fundamental Studies in R eactor D esign Insitu Spectroscopi c Methods R eactions in Organi ze d Media Vijay T. J o hn Biomimetic and Nanostructured Materials l nterfacial Phenom ena Pol y mer-Cerami c Composit es Surfactant Scienc e Daniel J. Lacks Molecular Simulation Thermodynamics of Condensed Phases Dynamical Pro cesses in Solids Ph ys i ca l Properties of Polymer Materials Density Fun c tional Theory Victor J Law Modeling Environmental S ys t e ms Nonlinear Optimi z ation and R egression Transport Phenom e na Numerical Methods Yunf e ng Lu Nanostructured and M icroelectronic Mat erials, Sol-Gel Processes and Organic/Inorganic H y brid Materials Membrane S e parations and Catal ys ts Chemical Sensors and Biosensors Brian S. Mitchell Fiber Technolog y Materials Processing Composites Kim C. O 'C onnor Animal Cell Technology Organ/Tissu e R egeneration R ecombinant Protein Expression Kyriako s D. Papadopoulo s Colloid Stability Coagulation Transport of Multi Phas e Systems Through Porou s Media Colloidal I nteractions For Additional Inf ormation, Pl ease Contact 414 Graduate Advisor Department of Chemical Engineering Tulane University New Orleans, LA 70118 Phone (504) 865-5772 E-mail ddekee @ tulane.edu Tulane is located in a quiet residential area of New Orlean s, approximately six miles from the world-famous French Quarter. The chemical engineering department currently enro ll s approximately 40 full-time grad uat e st udent s. Graduate fellowships include a tu ition waiver plus stipend. Chemical Engineering Education

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Engineering the World The University of Tulsa The Univers it y of Tul sa i s Oklahoma 's o lde s t a nd lar ges t independent uni vers it y Approximately 4 200 s tudent s pur s u e more than 70 major field of s tud y and graduate pro gra m s in mor e than 2 5 di sc ipline s. Tulsa, Oklahoma O ff-camp u s activities abound in Tul sa, o ne of th e nation 's mo s t livable cities Our temperate climate, with fo ur distinct se a so n s, is p erfect for year-round o utd oor ac ti v itie s. With a metropolitan popula ti on of 450 000 the city of Tul sa affords opportunities for s tudent s to gain intern s hip and work exper i e n ce in it s dynamic data proce ss in g petr o l e um medical and financial indu st ri es. One c an also e nj oy world-cla ss b a ll e t sy mphon y a nd theatr e p e rformances a nd ex hibit s in th e c ultu ral commu nity. Ann u a l events include Ma yfest, Oktoberf est, the Chili Cookoff and Blu egrass Festival the Tuls a Run and the J azz and Blu es festivals. Chemical Engineering at TU TU e n joys a so lid int ernat i o nal reputati o n for ex perti se in the petroleum indu s tr y, and offers environmental a nd biochemical program s. The department place s particul a r e mph asis o n experimen tal r esearch and i s proud of it s s tron g co nt act wi th indu s tr y Th e department offers a tradition a l Ph .D. pro gra m an d thr ee ma s ter s pro grams: Ma s ter of Science degree ( the s i s pro g ram ) Ma s ter of Engineering degree (a profe ss ional d eg r ee that can be co mpl ete d in 18 month s witho u t a the s i s) Special Ma s ter 's degre e for nonchemi ca l engineering und e rgr a du a te s Fin a n cia l aid is available includin g fellow s hips and r esea r c h assistantships The Faculty