Chemical engineering education

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Title:
Chemical engineering education
Alternate Title:
CEE
Abbreviated Title:
Chem. eng. educ.
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v. : ill. ; 22-28 cm.
Language:
English
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

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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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Citation/Reference:
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.
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Place of publication varies: Rochester, N.Y., 1965-1967; Gainesville, Fla., 1968-

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oclc - 01151209
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This item is only available as the following downloads:

L.K. Doraiswamy of Iowa State University, Thomas D. Wheelock, Peter J, Reilly ( PDF )

Experimental Projects for the Process Control Laboratory, Siong Ang, Richard D. Braatz ( PDF )

Using Test Results for Assessment of Teaching and Learning, H. Henning Winter ( PDF )

Is Process Simulation Used Effectively in ChE Courses? Kevin D. Dahm, Robert P. Hesketh, Mariano J. Savelski ( PDF )

An Introduction to Drug Delievery for Chemical Engineers, Stephanie Farrell, Robert P. Hesketh ( PDF )

FAQs. v. Designing Fair Tests, Richard M. Felder, Rebecca Brent ( PDF )

Boiling-Liquid Expanding-Vapor Explosion (BLEVE): An Introduction to Consequence and Vulnerability Analysis, C. Tellez, J.A. Pena ( PDF )

Rubric Development and Inter-Rater Reliability Issues in Assessing Learning Outcomes, James A. Newell, Kevin D. Dahm, Heidi L. Newell ( PDF )

Mass Transfer and Cell Growth Kinetics in a Bioreactor, Ken K. Robinson, Joshua S. Dranoff, Christopher Tomas, Seshu Tummala ( PDF )

Teaching ChE to Business and Science Students, Ka M. Ng ( PDF )

Integrating Kinetics Characterization and Materials Processing in the Lab Experience, Dennis J. Michaud, Rajeev L. Gorowara, Roy L. McCullough ( PDF )

Scaling of DIfferential Equations: "Analysis of the Fourth Kind," Paul J. Slides ( PDF )

The Use of Software Tools for ChE Education: Students' Evaluations, Abderrahim Abbas, Nader Al-Bastaki ( PDF )

Teaching Process Control with a Numerical Approach Based on Spreadsheets, Christopher Rives, Daniel J. Lacks ( PDF )

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Full Text







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

















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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


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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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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


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Chemical Engineering Education


Volume 36


Number 3


Summer 2002


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


> LABORATORY
182 Experimental Projects for the Process Control Laboratory,
SiongAng, Richard D. Braatz
198 An Introduction to Drug Delivery for Chemical Engineers,
Stephanie Farrell, RobertP. 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 inAssessing
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, NaderAl-Bastaki
242 Teaching Process Control with a Numerical Approach Based on
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U 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
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> CLASS AND HOME PROBLEMS
206 Boiling-Liquid Expanding-Vapor Explosion (BLEVE): An Introduc-
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231 Errata

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University of Florida Gainesville, FL 32611-6005. Copyright 2002 by the Chemical Engineering Division, American
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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: Sendaddress changesto ChemicalEngineeringEducation, ChemicalEngineeringDepartment., University
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Summer 2002


I














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 ofLuria-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 anE.coli strain, ER 2275, furnished
by New England Bio Labs, Beverly, Massachusetts, and
modified (pImP1) 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 37C,
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.
1 (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 370C 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
100
c 80
o 60
-6 40
0 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













curriculum


TEACHING ChE TO

BUSINESS AND SCIENCE STUDENTS





KA M. 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.[1,2] 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.[3]
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 CPIproducts
How chemicals are manufactured
The cost of building and 7 '.. ,,, a typical chemical
plant

ChE Division ofASEE 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
HKUS T 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.
































(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.


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











The *;i,,, ,a:.,,i andfinance of a typical chemical
company
Product-centeredprocessing
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 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


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


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
anotherbusiness 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 AssociationE7 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. [8
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.9'10,11]

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. 121 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.[131
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.[141 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


TABLE 2
Chemicals in Our Daily Lives

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













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 ifa 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 whichthe 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. [4 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. [5 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 predecessor11' 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, i I i 1-3
(2002). Available at
.r-public/Papers/TestingTips.htm>.
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
.r-pubhc/Learning '
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
.r-pubhc/Learning '
4. R.M. Felder and R. Brent, "Objectively Speaking," Chemical Engi-
neering Education, 31(3), 178-179 (1997). Available at ww . ( . . Illustrative
study guides may be found at .. .. .r-publc/
che205site/guides.html>
5. P. Elbow, Embracing Contraries: Explorations inLearning 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/effectiveteaching and at http://che.ufl.edu/-cee/










his or her final grade:

grade [%] = taskl + 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: taskk) schematic drawing of
the expected velocity field, choice of coordinate system, and
definition of boundary conditions; (lask equation for con-
servation of mass; taskk) equation for conservation of linear
momentum; taskk) solution for obtaining the velocity field;
taskk) 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,


Figure 1: Example of
the grading matrix of a
test. Grades are filed
in a spreadsheet.
Task1, task2, task3, 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.


Summer 2002


U U' 3 U'' CO Ci U' U' U -' i
1 ); I t 1 2 I
t < '
weight= 1 1 1 1 1 1 1 l 1 1 1 1 1 1 1 16
student 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 2 100 %
2.student 1 1 1 1 1 1.3 1 0. 0 1 1 1 1 1 1 96%
student 1 1 1 1 1 1 1 0 1 0 2 2 941%
4.student 1 0.9 0.9 1 1 1 1 1 1 1 o0 10 0.9 0 1 92 %
5. student 1 0.9 0 8 1 1 0 1 O 0 0 _0.9 1 1 1 1 0.9 11 79%
6 student 1 0.8 0.6 1 1 1 1 0 1 0 .9| 1 1 1 0 771%


21 student_ 1 1 0.9 1 1 1 0 0 0 1 0 0 0.5 0 53%
22 student 1 1 1 1 01 1 0.8 0 0.2 0 0.6 0.8 0 1 0 0 53%
I-
23 student 1 0.8 0.5 1 0.9 1 1 0.2 0 0 1_ 0 1 0 1 o0 _0 531%
1 0 o
24. student 1 5 1 0 1 1 10 0.8 0 0 1 52%
25. student 1 0.8 1 1 1 1 1 o 0 0 0.8 0 0.7 0 0 0 52 %
26 student 1 1 0~ 1 1 0.8 0 0 0 0.8 0 00.8 0 0 461%
27.student 10. 0.8 1 1 1 0 0 0 1 1 0 02 0 0 461%
28 student 1 0.8 1 1 1 0 0 0 0 0.5 0 0 0.8 0 0 44 %
29. student 10.8 0.8 0 00 1 1 1 0 1 0 0 0 0O 41 %
30.student 1 0 0.4 1 1 1 1 01 0 00.7 0 0 0 0 38%
---------------------- ----------------

teaching 100 84 78 96 92 86 89 27 47 16 85 42 22 81 22 9%
assessment *










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










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
(and possible delamina-
tions) once the resin at the
center begins to polymerize.


Sta



_


In terms of minimizing i'
processing time, the stu- Thermocouples
dents are given the goal of Polyurethane
curing the composite Tubing
( surface > 0.75) in less than Com
2 hours. The juniors present
their proposed design in
their final report for the
DSC experiment. In their
senior year, they again visitI Rin Soce
the simulation-based design
problem, but with a new
emphasis on the material
emphasis on the material 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.11l 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.110,11]


unless Ste


-*


to Data



pressed
Air


The seniors begin their composite laboratory sequence with
a tour of the composite
manufacturing equipment
and an overview of the ex-
eel Mold perimental procedure and
safety issues. The experi-
Smental RTM equipment is
shown in Figure 4. Using
their experience from the
Acquisition junior lab, students use the
on-line simulation to iden-
Polyurethane tify the cure cycle they will
Tubing implement experimentally.
The simulation is also used
to analyze the effect ofpos-
sible model parameter
variations on the cure cycle
Resin (i.e., sensitivity analysis).
SThe 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. Lab\ ic\ 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











^ 9 laboratory


EXPERIMENTAL PROJECTS

FOR THE

PROCESS CONTROL LABORATORY


SIONG ANG, RICHARD D. BRAATZ
University ofIllinois at Urbana-C hiiin,\l 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 modem visual pro-
gramming environment for instrument control.E1' The twenty-
six control apparatuses include
1. Temperature control in an air bath
2. 'T., , 7. -, 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.[1] 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-
w .11cir: 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. 41 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.[5,6]
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-


SiongAng 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-
versityof Illinois. His main research interests are in complex systems theory
and its application.


Copyright ChE Division ofASEE 2002


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.
E 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.
[1 Multitank pH 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 shouldbe 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 canbe con-
trolled by different strategies, it is especially suited for chal-
lenging students to consider and test various control strategies.
[L 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, Modehng, and
Control, Oxford University Press, New York, NY (1994)
9. 1.11. ,l1 [ nl, i ..., :!, 1. i,- n ,I !'.I. ...Ii I /vee/support/>
10. Skogestad, S., and I. Postlethwaite, Multivariable Feedback Control --
Analysis andDesign, Wiley, New York, NY (1996)
11. Braatz, R.D., "Internal Model Control," inC ..- undamen-
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) O


Summer 2002











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.1141 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 ProII 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 EdgarE151 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
motivated to do so.
I would hke to see the use s" ... 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
professors require the use should be familiar with, and is thus
;-state chemical process
nation proams" taught for its own sake." A total of
ulation programs.
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?"

Resonse % Yes
a 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%
I There is some coordination between individual faculty
members, but the department as a whole has not
adopted a curriculum-wide strategy. 35%
a Several instructors use it at their discretion, but there
is little or no coordination. 18%
I Only the design instructor requires the use of chemical
process simulation software. 20%
I No professor currently requires simulation in under-
graduate courses. 1%


"Please
which pr
of stead
sim
Course
a Design


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


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












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 ofa distillation col-
umn and distributes it to the class.
The class receives a brief(less thanfive minutes) tutorial on modehng
columns with HYSYS-just enough to tell them how to change specific
parameters such as the .- 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 ratio, number of stages, and feed stage location, and
then answers a series ofquestions 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. 1,4] 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 Chitturj291 discusses preparing tutorials forASPEN Plus
simulators using HTML. Finally, the University of Florida
maintains a web site forASPEN where tutorials are available.[301
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 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,[331 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 H1 = Z-1-n Z+(i1+4)B A [
RT Z+( /2)B B78

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.[34,351 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[361 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-
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










Typical radiationvalues 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 CCPSEo10 (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
(F ) and the fraction of energy transmitted (atmospheric trans-
missivity, T) are known:
IR = IFg (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, = X (5)
4 X2
Considering only the partial pressure of water present in the
atmosphere at the moment of the accident, T can be calculated
approximately by[20]
S= 2.02(PX)- 09 (6)
where P is the partial pressure of the water at ambient tem-
perature [Pa].
Another, simpler, model has been proposed by Roberts[1]
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 M 771X-2 (7)


100 1000


Distance (m)


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 (WTNT) for a BLEVE has
been described by PrughE151 as


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 D (9)
SD,
where V and V, are the volumes of vapor and liquid [m3]
in the vessel before the explosion; D1 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(To-Tb)
f= m =l-e av (10)

where m, 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,[16] 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 Cp (2.64 kJ/kgK) 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.[17 This "cubic root law" es-


Summer 2002


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


1











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, co.
Whenthe 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 I D E
1 Process Variables Disturbance
2 K= 1 A= 1
3 = 2 o) = 0.01
4 1 = 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

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 Q.
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.11'9]
The output of a proportional-integral-derivative (PID) con-
troller is given by

K t de
Ye = Ke+ c dt+KCD- (4)
1 f dt

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 Kc and 'D are set equal to zero, while zT is set
equal to infinity. The integral term can be calculated numeri-
cally as


Edt = e(t,)At


and the derivative term can be calculated numerically as

de(t) e(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


5s 15



0 0
-05 -0 5s

-1 51
0 100 200 300 0 20 40 60 80
time time

15 (15



0 0



-1 5
115
0 5 10 15 20
S 2 0 50 100 150
time time

Figure 4. Response of a 2nd order process to an oscillating
disturbance for (a) = 1.5, c = 0.1; (b) = 1.5, c = 0.3; (c)
S= 1.5 c = 2; (d) = 0.5, co= 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,


lyl +y1 =Kf+Kpyc

Ty, + y, = Ky,_1


ii=

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


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


i= 1,2,3


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 of Thermoset Polymer Cure Design and Manufacture of
Thick-Section Composites
laDSC Experimentex
Review Polymerization
Measure Reaction Rate
determine Kinetic Parameter
Simulation-Based
Process Cycle Design
Kinetic I Physical
Parameter Parameter
Sensitivity I Sensitivity Eprmn
RTM Experiment
Anticipate Process Deviations
1 Manufacture the Composite
I Validate / Revise Design


Figure 1. Schematic of integrated undergraduate
laboratory experiments.


II
Ii Measured Heater Temperature
S---- Measured Center Temperature
S- ---- Simulated Center Temperature
(Using Initial Model Parameters)
I




2


Stage 2
Post-Cure
Phase


Stage 1
Curing Phase


0 50 100 150 200
Time, minutes

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


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-


120






60


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.E81)
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,
Dmx[m], the duration of the fireball, tl,, [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(-AHcomb)M
1E(Dmax) tBLEVE


Elia model[121


S0.27 M(-AHcomb)PO 32(
I= (2)
1T(Dmax)2 tBLEVE

Pape, et al., model[13]

I = 235 P39 (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 literature["10 ranges from 0.25 to
0.4); -AHomb is the heat of combustion of the material [kJ/kg];
P0 is the initial pressure at which the liquid is stored [MPa]; and
Pv is the vapor pressure of the stored liquid [MPa].


250 300 350
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]
(D)) = maximum diameter of the fireball [m]
(HBLEVE) = height at the center of fireball [m]
(tBL,,) = duration of fireball [s]


CCPS tl


CCPS 19


Dm = 6.48 M0325 159.3 m
tBLEW = 0.825 M026 10.7s
HBLEVE = 0.75 DMAx= 119.5 m


D* =5.8M1/3 154.8m
t*BLEE = 0.45 M13 12 s


Chemical Engineering Education


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

CCPS Model1f' Elia Modelf"2 Pape, et al, Modelf'1
I(kW/m2) 336 301 306



















>,






_I I


GI

f~
I
C.


II II


O 0 0 0 0


II II I


(N g

o a,
E


II II II II II (F II
c~== "-
^ f ^ .


I












O
~ ci'












Lii
m
















Lfl
(a
m
C
o


















0
m



m,










U}
w
m



C)

w
to
G












*0






w






m
r rei
>;:
O _
3^
ff
'1


ur




M c o ^
>'^o


aQ

o


c-, t
2














O
^
'*B i?
8 o





'
^ s





1^







s

ai
"s 3
S ^^

ar; o


where the Y (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

(K, Tz 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 Tz = a large number that approximates -), the

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

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

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

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

But if the value of TI 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 zt 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
K .7
1.7


Pu
TD =
8


(l1a)



(10b)



(10c)


where Kmax is the maximum value of Kc for which the response is

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

cillation of the response at Kmx The value of K.mx is found by trial


8 (a)




0 -

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



8 (d)


4
2


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


8 (b)



2


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


8 (e)




2


-2
-4
6
0 20 40 60 80
time


8 (c)






-2
-4
-6
0


20 40 60


8 (f)
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, K, =1 ; (b) P-only, K, =4 ; (c) P-only, K, =15 ; (d) PI: K, =1, I = 5; (e)
PI: K, = 1, T = 1.3, (f) PID: K 1 =1, u = 1.3,TD = 15. The bold line is the dis-
turbance, and the thin line is the response.


Summer 2002


I-I-I -f I IR 1n I1o I 0










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
















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











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

R
Z=-- (11)


where z is the normalized distance [mkg-1/3] and R is the
real distance [m]. The experimental relationbetween over-
pressure and normalized distance for unconfined explo-
sions can be found in several references.[5,18 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 bur).
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 u
P = 1 e 2 du (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 = k1 + k2 n V (13)

where Y is the number of Probit units, k1 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 (k1 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
tIR4/3 (t is the exposure time and IR is the intensity of radia-
tion level). Regarding vulnerability to explosions, V is the


Distance (m)

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


100


80


^ 60

n
S 40
0
a-
20


0


4 5
Probit Units


7 8


Figure 4. Probability and Probit units relationship.


TABLE 3
Probit Correlations for a
Variety of Causes and Effects[18,211


Cause
Explosion
Explosion
Explosion
Explosion
Thermal effects
Thermal effects
Thermal effects


Effect k,
Lung hemorrhage -77.1
Eardrum rupture -15.6
Structural damages -23.8
Glass breakage -18.1
Mortality -38.5
Second-degree bums -39.8
First-degree bums -43.1


k,
6.91 Overpressure peak()
1.93 Overpressure peak()
2.92 Overpressure peak()
2.79 Overpressure peak()
2.56 IR4/*t(2)
3.02 I3'*t(2)
3.02 I4/t(2)


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


Chemical Engineering Education













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-


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


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 ChemicalEngi-
neering Education, CACHE Corporation, p. 9, (1996)
2. Benyahia, E, "Process Simulation Packages in Undergraduate Chemi-
cal Engineering Courses," The 1998IchemEResearch 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, ContemporaryEducationPsychology, 12,
p. 222, (1987)
5. Montgomery, S., H.S. Fogler, "Interactive Computer-Aided Instruc-
tion," In B. CE I. . i I i Computers in ChemicalEngineeringEdu-
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," CACHENews, 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," ComptApplEngEdu, 5, p. 181, (1997) O

241










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-


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
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-
trolled release (or the release of a drug at a desired
rate for a desired time) is illustrated through famil-


Summer 2002


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











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

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


S(;as Outlet
J ... motor


o: -- ,._|i -.'.
Gas Inlet
Sample
bottle



ermoen l
Gas"L" sparger
Double blade impelleir

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, kWa. (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 sicp-dow" n" 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.











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." 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
S t I I student participation.
Getting the program to
1/O data acquisition boards work in a certain man-
ner merely requires
HP-VEE software changing line connec-
HP-VEE soft e tions 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[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 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











^ 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











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.P1]
Methods have been developed for assessing entire engi-
neering programs, curricula as well as individual courses, and
educational research projects.[2,3] 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,'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 1. The example is deliberately kept simple: a typical
written test is broken down into N individual subtopics task1
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

H. Henning Winter is Distinguished Univer-
sity Professorof 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 experimentalrheology, poly-
mer gelation, and crystallization.


Copyright ChE Division of ASEE 2002


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










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 H is evaluated from to = 3.2 minutes (when the DSC pan
is added to the cell) to the final isothermal time point, tf othrnal,
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 model[2,81 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

d k am ("max a) (4)
dt
and

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

amax = a0 + a, T for (max < 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.[1,91 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 (Hit, A, Ea, m, a0, and a,) required
by the cure simulation. We recommend that the students first
determine Hit, 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 simulation1' 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











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.E18 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., [9] 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-


TABLE 6
Responses to:
"Indicate the mathematical
applications software required
of chemical engineering
undergraduates.
Check all that apply."


Response
I POLYMATH40
I MATLAB
I Maple
I MathCAD
I EZ-Solve
I Spreadsheets
I Mathematica
I Other


% Yes
37%
65%
24%
37%
5%
82%
13%
15%


sis of the capital and operating costs by the end of the junior
year. This approach is comparable to problem-based learning.I201
There have been other contributions to this vertical approach.[21-
231 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 Rousseau"241 (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 1L distillation unit or an interactive computer module"251
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.E24-261
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 7
Responses to:
"Please indicate all
applicable steady-state
Chemical Process Simula-
tion programs nurr'iily
being used in your
department's undergraduate
courses. Check all that
apply."
Response % Yes
I ProII/Provision 12%
I HYSYS or Hysim 32%
I Aspen Plus 45%
I ChemCAD 32%
I Other 13%










gies canbe compared. A goal of this experiment is to recognize
the performance improvement obtainable by cascade control.
EN 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.
EN 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.
[J 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.
El 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. Apump 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.
EC 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


Figure 4. Dye concentration control
with load disturbances.


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


File Run Tasks help
I %L* E El M?


Reactant Feed
I


Cooing
Jacket Inlet
Temp (C)
D 50.0
(Dicturbance:


s 13 214 2 44 s' sV 67 74
Time [min)


S7931 Min:Sec


PID (P= DA. 1=ARW, D= means


Jacket
Outlet

Temp C] 76.3

Controller
Output [Z)
I 52.5





Set Point
@- 93.0


Reactor Exil TTmp(C) 92.7
Conversion (Z .5

S File Storage: OFF


File Run Tasks Help

IEs ?

Sa I a Reflu?
ON -- (kglmin] Distillate
.m Composition 7)



. s Controller Serpoint 1 ]





tkg in utput (

21 o1
Conmtr o onller





sTime (min) 5. 950




S2111 Min Top: PID I P= RA. 1= ARW, D= off)i Bot: PID ( P= DA, I=ARW, D= off j F File Stafage: OFF


Figure 1.

Impact
of noise
on
derivative
action
(Control
Station).


Figure 2.

Effect of
interaction
on
SISO
loops
(Control
Station).


Summer 2002














tration is determined., _M P

ANALYSIS Mn


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
S ACska (7)
M0

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 P is determine
from the slope of the line.


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


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 3, which can be deter-
mined using the "trendline" feature of Excel. In Figure 4, the
slope of -0.0938 (min'1) is equal to 3. It is important to em-
phasize that the parameter 3 is evaluated using experimental
data. Students can make this plot by calculating values of the
fraction of drug remaining or by generating a semilog 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, M0,
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 p3, 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
'd 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
S perform calculations necessary to determine the re-
t lease profile, and they generate plots of both the ex-
perimental release profile and that described by the
Chemical Engineering Education


10 20 3(
time (min)


0 40 50











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.110' 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.[11 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 D E
1 Process Variables Disturbance
2 K= 5 tstep= 1
3 T= 2 for t 4 = 1 for ttstep 1
5
6 Time Step Initial Values
7 t= 0.01 y(0)= 0
8 y'() = 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
16
15 4


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 fora PhD in chemical en-
gineering at Northwestem 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.
















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 addressedby chang-
ing the course content of the responsible preced-
ing course. Relevant results from the grading p
matrix can be integrated into the systematic cur-
riculum development.3] Discussions along these [fo
lines are in progress in our department. on n

ADAPTATION
OF THE MATRIX METHOD ant
There are many ways of integrating the infor- a
mation from the grading matrix into personal e
approaches to teaching and student advising. It
goes without saying that assessment of test per-
formance as reported here needs to be integrated gen
with classroom assessment. This is a dynamic
process, which differs from year to year, since
each group of students interacts differently and feet
varies in its needs. As the learning process
evolves, teachers adapt in their classroom assess-
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
Weight, task,
grade [%]= 100 -1 (2)
Weight,
1=1

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


questions arise in high school teaching and even in elemen-
tary schools where standardization of tests is considered.[7
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
aper
cuses]
methods

of
ilyzing
single
xam
and
rating
direct
lback...


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, i ........ .... Grc O rLearn-
ing andAssessment, 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. 10(2), p.
5 (2001) 5


Summer 2002











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











Fe Rl c/ass 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. PEIA
University of Zaragoza Zaragoza, Spain


he chemical engineering curriculum should include
information on safety, health, and loss prevention in
the chemical industries.11-4] 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 ofdioxin and methyl
isocynate that caused animal deaths, dried .. .. ri.
and affected 2000 people.
The Bophal (India, 19'-, 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 ofLPG 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,N5' 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 Tllez 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 Pena 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.


0 Copyright ChE Division ofASEE 2002


Chemical Engineering Education











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 Churchill['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 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 ,F ( T l v1 vz2
pCpVz = k[-ar r + (1)
az r ar ar ar
where T= temperature, To = incoming temperature, T. = wall

Paul J. Sides is currentlyProfessorof 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 Califomia
at Berkeley in 1981. He joined the faculty of
the Department of Chemical Engineering at
Canegie Mellon in 1981. He has published
articles in electrochemical engineering, growth
of advanced materials, and data storage tech-
nology

Copyright ChE Division ofASEE 2002


Chemical Engineering Education












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










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


yi)+K Id 1a (17)
y at a Pr a2 H3B 2 (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(T, T,)=


kT hyo h (2H /4
k =O Nu k k p
k =ok k pBI


=oo


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 yo. 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(T, T,)


-k-l = NUH
Dy y=0


Nuy H O p ) H (19)
Yo Dn t=o2H 1

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

NUH =-I Gr1/4 (20)
NH = l=0
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


Dae
L= o


Sf(Pr)Pr1/4


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 Grshof number in this problem is
clearly demonstrated.

CONCLUSIONS
Scaled equations are the standard for mostjournal 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 Pr12.

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


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
course, the individual
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


1.0

0.5

0.0-

-0.5 Area= H

-1.0
0 5 10 15 20
Time, minutes


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.


25 30 35


Figure 3. Example heat flow of a differential scanning
calorimetry (DSC) experiment.


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.[4] These objectives in-
clude analysis, synthesis, and evaluation, referred to as
"higher-level skills" by Felder, et al. [1 The fundamental ob-
jectives of knowledge, comprehension, and application are
referred to as lo\ c i-I\ l skills."
We agree with Miller, et al.,[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


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)
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.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, da/dt, by
a 1 dQ
at Hult dt
and the extent of ploymerization (cure), a

a(t) = l f -)dt (2)
Hul t o to
where Ht is the total heat of reaction given by


Hult = Hrxn + Hresidual


tf,sothermal t
I d dQ dt + dt
t d t tm/ dt
to tf,.sothermal


Chemical Engineering Education


nal Phase













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 ABETM11 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: 2]
L The basic competencies for all students must be stated in
terms that are measurable and demonstrable.
L 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 WilliamsI5] 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:
E A report from a year-long, industrially sponsored research
project through the Junior/Senior Clinics
E The Senior Plant Design final report
E A hazardous operations (haz-op) report
E One final examination from a junior-level chemical
engineering class (Reaction Engineering or Heat Transfer)
E 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.[91 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.
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 ofASEE 2002


Chemical Engineering Education











classroom


THE USE OF SOFTWARE TOOLS

FOR ChE EDUCATION

Students' Evaluations



ABDERRAHIM ABBAS AND NADER AL-BASTAKI
University ofBahrain 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.[1,2]
The positive impact of information technology on teach-
ing and learning is no longer questionable.3-5] Kulik and
Kulik[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 Professorof
Chemical Engineering at the University of
Bahrain. He received his degrees from the
University of Salford (BSc), University of
0 SNewcastle 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 UMIS T
His teaching and research interests are sepa-
ration processes and reverse osmosis.


Copyright ChE Division ofASEE 2002


Chemical Engineering Education











(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-


jp Hsys F S lfo ase faini INUMi
I FiD dt nubjm Fbwhit EfD Isr Yndol ht 191|x|
41oB aP =x:: > Q9 A Env"i [=(an" )
H N2 H M PA P AIDdC|o MSrk J

















2 PFD i/


toClifa.d


:i


r 10 -
* IF I X


Case S 2ii
e-I


.0 0.20
iC


.O
IL 0.15

.2 0.10

E
E 0.05
< 200 300 400 500
Reactor Pressure, atm




Dke re IiGI SES/


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

. . .-t- .. ..... .. .. C l
V File Edt Simulation FlIwsheet PFD lools Window Help _lJ
D d a ( =M ,- Aa Envionment: Case (Main
Sefu lodu Stheme
H IH M AP A @ Default Colour, Sheme


CDmpleted.


Figure 4. Methanol synthesis loop (HYSYS).


Chemical Engineering Education











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


Formulates appropriate soluti
strategies


Identifies relevant principles,
equations, and data

Systematically executes the
solution strategy

Applies engineering judgment
to evaluate answers


in Can easily convert word
problems to equations;
sees what must be done

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


Chemical Engineering Education


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.












temperature, v. = axial velocity in laminar pipe flow, p = den-
sity of the fluid, p = viscosity, c = 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.'2 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.


r
R


Z
Zo


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
,11111A equating its coefficient to unity
S- r and solving for the unknown
length scale.


a


T- T
T, -T,


For laminar pipe flow: vz = 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

2pp < v > T (3)
z o )
The result is




kzo0 [ O ( )] 16_zo 2
2pep R2 2 e- 2 2pcR2(T- 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'l
for example, use the coefficient of the diffusive term to scale
their Eqs. 10-12 but do not comment on the choice.) This


viscous liquid in a pipe of


equation can now be written


2 < v > R2pc,
Zo (5)
k
With the inclusion of this axial
length scale, the overall energy


where


B < 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 z0 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 work.21'
Guidelines U The method used in the previous example
consisted ofseveral 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 ei dl, analy-
sis (remember R and z).
3) Nondimensionalize the dependent variables with tl ,,. nll ..,. Ic
values.
4) Substitute ie ideliuitioun into rthe dlitlerltial equation using the
chain rule for derivatives. Once students do this a couple of
times, they easily write down the substitutedform by inspection.
5) ,lerutii' a term of known importance and divide the equation
by the ( oettk ieut 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


I -
w z
//^^yTTT^_______


//ZZ


)1-^ C, +^=[16 Br
K( a a













curriculum


IS PROCESS SIMULATION

USED EFFECTIVELY IN ChE

COURSES?


KEVIN D. DAHM, ROBERT P. HESKETH, MARIANO J. SAVELSKI
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 Lewin 1f has examples
written for use with ASPENPLUS, HYSYS, GAMS,[2] 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. [6-9 Some
of these initiatives are driven by the new ABET criteria.[10]
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


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 Wankat[11] 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 Zhang1121 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[13] 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













tially (known) and r0 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:
[ ACsko t] (4)
M = Mo exp A- t (4)

A plot of tn (M/Mo) vs t should yield a line with a slope of
-AoCk/Mo. The amount of drug released from the lozenge,
Md, 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= Mo[-exp -AoCsk t (6)
L ( M )\
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
S 0.06
E 0.05
ao E y =0.2733x
E 0.04
0.03
0.02
0.01


0 0.05 0.1 0.15 0.2 0.25
Absorbance at 540 nm


0.3 0.35 0.4


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
SOne graduated cylinder
One 100-ml beaker
One cuvette
SOne dropper or Pasteur pipette
*One lozenge .
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 developed'11] 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


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.











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. Beris, and P.S. Dhurjati, "Curing Behavior of
Thick-Sectioned RTM Composites,"J. ofComp. Mats., 32(14), 1273
(1998)
2. Lam, PW.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, PR., 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 ofEducational, ... .. 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 F.A. Settle, Instrumen-
tal Methods ofAnalysis, 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 PartA, accepted for publication
10. Springer, G.S., and S.W. Tsai, "Thermal Conductivities of Unidirec-
tional Materials," J. ofComp. 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) 5

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











l 9 laboratory


AN INTRODUCTION TO


DRUG DELIVERY


FOR CHEMICAL ENGINEERS



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


R owan 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.[11 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.[]
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.3' With projected double-
digit growth, the market is expected to reach $30 billion per
yearby 2005.[41 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
SNovel apphcation ofchemical engineering principles
SConcentration measurement
SCahbration
Material balances
Use of spreadsheets for calculations and graphing
Parameter evaluation
Semi-log plots and trendihnes
SComparison of expermental concentration data to predicted concentrations
Testing a transient model at the lmits ofinitial time and infinite time
SDevelopment ofa 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 Hesketh 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 ASEE 2002


Chemical Engineering Education











n 9 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,E11 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, 2] 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 Northeastem 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 ofASEE 2002


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













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-
trol18,10, ',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.
El 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
S FI

Water
tank
Float
switch
S--Computer/ -
SI Controller "

Drain Drain
-- F3
v--' V3 Flowmete rl
V2 V3

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


.----------------------------------.------------.----.--.----------.. .--
S----------------------.. _Co mpur er/ .
Tap ----------------------- Controller
Ha ------V-----
Flowmeterl
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.
El 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.
E 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












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."'12

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


45 a
4
35
3
25
2
15
05
-05
0 50 100 150
time


200


45
4 c)
35
3
25
15
05
-05
0 20 40 60 80
time


45
(b)
4
35
3
25
2
15
01
05
-05
0 20 40 60 80
time


100


(d)
10




-2

-4
-6
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)
= -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.E1-9] A linear second-order process is de-
scribed in general by
2yy" +2 y' +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, z 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 ofy"(t) are obtained by rearranging Eq. (1).

Kf(t) 2' (t)- y(t)a)
T2
y" (t) = (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, in a sec-
ond-order linear differential equation can be demonstrated
with this approachby comparing the response to a step change
for different values of Q. For > 1, the response is
overdamped, and it reaches a steady state without oscillating
(Figure 2a). For 0 < t < 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.1-'91 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












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 z 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 includingafew 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 fory 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[13]) 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[13] 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., ChemicalProcess 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 andPractice ofAutomatic Process
Control, John Wiley & Sons, New York, NY (1985).
6. Seborg, D.E., T.E Edgar, and D.A. Mellichamp, Process Dynamics and Con-
trol, John Wiley & Sons, New York, NY (1989).
7. Shinskey, EG., P .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., P .2nd ed., Mc-Graw-
Hill, New York, NY (1991).
10. Gibbons, W., Science, 266, 893 (1994).
11. De Vries, PL., American Journal ofPhysics, 64, 364 (1996).
12. Wilson, E.K., News, May 26, p. 33 (1997).
13. Cooper, D.J., Windows, Version 2.5 (2000) O


Chemical Engineering Education






















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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
















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
LeeE51 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
Alza exploits osmosis to achieve a
constant drug-release rate for an


'I,


rx


Figure 2. The osn
Adapted from Robins.


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 islc.\ c,", the sleeve expands and com-
presses the innermost drug reservoir, squeezing the drug out
of the reservoir through a delivery portal.]71
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


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-
tion rate of a lozenge.


Susmoic sleeve
Semipermeable
Membrane


mntir nnmn


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(C -Caq) (1)
dt


on and Lee.f5l 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 Cq is the concentration of sugar in the bulk wa-
ter. Cq 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 = 2mrr2 + 2rrh (2)
To simplify the model solution and analysis, the area of the
sides (2rrh) was neglected. The mass of drug remaining in
the lozenge can similarly be represented in terms of r:
mr h
M = M r rh (3)
Tr h
where M0 is the amount of drug present in the lozenge ini-

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-0 126902.

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and P Reiser, eds., Aspen Publishers, Inc., New York, NY (1995)
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Process Simulation
Continued from page 197.

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the UnitedStates, 3rd ed., Engineering Accreditation Commission, Accreditation
Board for Engineering and Technology, Inc., Baltimore, MD (1999) www.abet.org/eac/eac.htm>
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ties to Teach Unit and Plantwide Control Strategies." Proc. of the AIChE An.
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Bieszczad, and A. Koulouris, "A Phenomena-Oriented Environment for Teach-
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Hall PTR, Upper Saddle River, NJ (1999)
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149e, 1999 An. AIChE Meet., Dallas, TX (1999)
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Course," Session 3613, Proc. of the 1998ASEEAn. Conf and Expo. (1998)
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NumericalMethods, Prentice Hall PTR, Upper Saddle River, NJ (1999) 5











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 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=kLa(C*-C) (2)
dt
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

(C*-C)
(nC = -kLat (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

100 -
100 100exp -0.155[t- 15])

0 10


0
0 1
a.1 i
-5 0 5 10 15 20 25 30
Time, minutes
Figure 3. Typical Oxygen transfer data: Determination of
ka with nitrogen sparging.


100
100 100exp(-0.145[t-1 5]

E r- 100 o.*.. ,

.| 10
S _

1*
01 - -----
-5 0 5 10 15 20 25 30
Time, minutes
Figure 4. Typical oxygen transfer data: Determination of
ka with air sparging.

Summer 2002


C
n = -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 sparking
or step-up experiment, and 0.145 min1 for the air sparging or
step-down experiment, for an average value of 0.15 min'.
One other measurement of kLa was made with air sparking
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 kLa determined, however, was 0.075 min'. 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.,[31 previously described
a laboratory experiment to measure oxygen transfer in a 1-
liter stirred fermentor. In that case, the stirring rate was con-
siderably higher (500 to 700 rpm) and the method of deter-
mining kLa was different; those authors measured the quasi-
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 mi1 at a stirrer speed of 500 rpm.
Using their exponent of 2.75 for the effect of mixer rpm, the
expected value of kLa at 250 rpm would be 0.089 mi1, which
is unexpectedly close to the value of 0.15 min1 found here
under considerably different conditions.

(B) 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 abatch 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 (gp) of the cells is determined dur-
ing the exponential binary fission phase. This process is au-











In their 1996 study of computer skills in chemical engineering,
Kantor and Edgar[14l 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 .c\ c iil" 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 ofpackaged 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 criteria161 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-


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 ofyears 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 r ,,., a need for a
more reasoned approach to problem solving2
Although programming languages (FORTRAN) are in some
disfavor at present and, 11, 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
.. . .. 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.E171

TABLE 4
Responses to:
"Which of the following best dI,'t nrh' yir ilrn ain'n Ii
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%
E[ 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
[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%
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










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










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 writtenby 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 truthbe 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.'" E


Summer 2002















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

0 0

-1 -1
0 20 40 60 0 20 40 60
time time

Figure 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 and
Pu; (b) PID with Ziegler-Nichols parameters: Kc = 3.7, TI = 5.4,
TD = 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 uses this measured value to adjust an input variable with

the goal of keeping the process output at the desired value.[1

The output of a simple feedforward controller is given by



yc = Aysp -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' = 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 = /(KpK2K3) and



7 ()t 7 (b)
6 6
5 5
4 4
3 3
2 2
1 1
0 o


0 20 40 60 80 0 20 40 60 80
time time


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/(KpK2K3)=0.842 and
B = KI / Kp = 0.625; (b) A = 0.842 and B = 0.5. The bold line
is the disturbance; the thin line is the response.


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Chemical Engineering Education


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Random Thoughts...





FAQS.

V.

DESIGNING FAIR TESTS1


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 ,... o ,,- ,ii tests that only the best students
have time tofinish?
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 i,, i' il- on the test?
We shouldn't spoon-feed the students-they need to
learn to think for themselves!
3. If curve grades, what diterenl ce 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 courses[21
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 teachereducation, 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 varietyof topics
including writing in undergraduate courses, co-
operative learning, public school reform, and
effective university teaching.


Copyright ChE Division ofASEE 2002


Chemical Engineering Education












problem solving.[10' 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[11 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[121 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.,'13 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.E"1 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,
E Every faculty member must set proper course objectives
that arise exclusively from the program's educational
objectives and fully encompass all of these objectives
E 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."15]
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


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[161. 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. I,i, nir, i1, .,- i principles, equations, and data
3. Systematically executes the solution strategy
4. Applies engineeringjudgment 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,161 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-

213











equipment and plant design and the senior projects.


5
STUDENTS' EVALUATIONS Control Station
I u HYSYS
To measure the usefulness and effectiveness of the consid- 4 Mathcad
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 3
list of 24 questions proposed by Iglesias, et al. [9] Eight ques- |
tions were dropped based on the recommendations of the 2 -
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).
CTM PDC UR Overall
The first class attempts to test the usefulness of the educa- Category
tional software in terms of subject content and design fea-
tures, as well as the teaching methodology used in the course. Figure 5. Overall marks for the three packages. CTM= Con-
The second category evaluates mainly the user interface (num- tent and Teaching Methodology, PCC = Program Design
ber of resources presented, quality and effectiveness of graph- Characteristics, and UR = Users' Reaction.
ics, tables, animation, etc.) and
ease of use of the package. The
TABLE 2
third class tests the user's reac-
Evaluation Results for
tion to the program by consider- Control Station (10 students) TABLE 4
ing aspects such as documenta- Evaluation Results for
tion for user, degree of interac- Question Mean Standard Deviation HYSYS (21 students)
tion between user and program, 1 4.10 0.99
and time needed for program ex- 2 3.70 0.82 Ouestion Mean Standard Deviation
ecution. Note that the three cat- 3 3.20 1.03 1 3.59 1.33
egories are not totally indepen- 4 3.30 0.95 2 4.00 1.07
dent and distinct. The question- 5 3.50 0.97 3 3.50 0.91
naire ends by asking students to 6 3.90 0.88 4 3.41 1.14
comment on the reasons they 7 3.40 1.07 5 3.36 1.05
felt attracted to or bored by the 8 3.50 0.71 6 3.59 1.18
program. 9 3.90 0.74 7 3.59 1.05
The students' evaluations for 10 3.40 1.17 8 3.57 1.16
the three considered packages are 11 2.90 1.20 9 4.27 83
shown in Tables 2 to 7. The over- 12 3.10 0.88 10 3.05 1.05
all results are presented in Figure 13 3.90 0.99 11 2.86 1.08
5. Control Station and Mathcad 14 3.00 0.47 12 4.18 0.80
were, respectively, evaluated by 15 3.40 0.84 13 3.82 1.22
the process control and process 16 4.10 0.99 14 3.32 1.09
analysis undergraduate classes. Comment on the reasons for which you felt 15 3.32 0.99
HYSYS was evaluated by stu- attracted to or bored by the program. 16 3.09 1.34


Chemical Engineering Education


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





PAGE 1

Summer 2002 177 Chemical Engineering Education Volume 36 Number 3Summer 2002 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. T he 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 and for 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. EDITORIAL AND BUSINESS ADDRESS:Chemical Engineering Education Department of Chemical Engineering University of Florida Gainesville, FL 32611PHONE and FAX : 352-392-0861 e-mail: cee@che.ufl.eduEDITOR Tim Anderson ASSOCIATE EDITOR Phillip C. Wankat MANAGING EDITOR Carole Yocum EDITORIAL ASSISTANT Christina Smart PROBLEM EDITOR James O. Wilkes, U. Michigan LEARNING IN INDUSTRY EDITOR William J. Koros, Georgia Institute of Technology 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 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 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 Introduction to Consequence and Vulnerability Analysis, C. TŽllez, J.A. Pe–a 231 Errata PUBLICATIONS BOARD

PAGE 2

178 Chemical Engineering Education L. K. Doraiswamy of Iowa State UniversityTHOMAS D. WHEELOCK, PETER J. REILLYIowa State University Ames, IA 50011 L 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 chromatography for a project of theirs, he asked if he could visit them there. He took the train to Pune during the dry season, arriving a bit hot and dusty, but quite exhilarated after experiencing one of the world's great train ridesthe 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, occupied in fine style by one L.K. Doraiswamy. Although L.K. was chagrined that the visitors had not been met by an air-conditioned 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 department chair (Dick Seagrave). Soon an appointment was hurtling 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 Professor in Engineering. His first office was anything but baronial, 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 retirement from ISU in December 2000.EARLY STIRRINGSL.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 WISCONSINAs 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 therehe 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 government 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 chemical engineer was even truer than he thoughtin 1987 L.K. became the Olaf Hougen Visiting Professor of Chemical Engineering at Wisconsin, an honor given to only five other distinguished educators. Then in 1991, he received an honor- Copyright ChE Division of ASEE 1999 ChE educator

PAGE 3

Summer 2002 179 (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.ary DSc from Wisconsin to go with his 1982 honorary DSc from Salford in England.BACK HOME TO THE NATIONAL CHEMICAL LABORATORYAfter 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 Director and head of the Division of Organic Intermediates and Dyes in 1961, Deputy Director and head of the Division of Chemical Engineering and Process Development in 1966, and finally becoming 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 children 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 research 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, management, 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 contributions to the growth of the Indian chemical industry were also cited, as was his extensive service 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. Under his leadership, many commercially important technologies were developed, including fluidizedbed processes for making chloromethanes and methylchlorosilanes, continuous processes for dimethylaniline and ethylenediamine, a new process for vitamin B6, and a complete process for methyl, ethyl, butyl, and 2-ethylhexyl acrylates. The dimethylaniline technology was the first vapor-phase catalytic process for making that product, while that for ethylenediamine was apparently the first continuous organic chemical process developed in India. His teams also developed zeo-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.

PAGE 4

180 Chemical Engineering EducationStudents 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.lite catalysts and processes for xylene isomerization and for making alkylating benzene with alcohols. Many of these developments led to awards from the Indian Chemical Manufacturer's Association. L.K. lavished care and attention on the NCL by streamlining 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 deserving 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 accommodation. L.K., moved by their plight (and against the administrative 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 punctuality, returning phone calls, meeting deadlines, and making allowances for potential mistakes in planning is also a hallmark 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 volumes 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 journal articles. They were mainly on adsorption and catalysis; gas-solid, gas-liquid, solid-solid, and slurry reactions; fluidization; and stochastic modeling and analysis of reacting systems. 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 achievement in engineering and technology (1987), and the Republic 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 MANSoon after returning to India, L.K. married his wife Rajalakshmi. She was always a source of great emotional

PAGE 5

Summer 2002 181strength 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 completed 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 Ceramics 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 children 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 unmarried 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 possible, and he managed to do that, although certainly not in the manner the former expected.A SECOND CAREERStarting 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 engineering courses, established a new research program from scratch, and guided the research of seven ISU doctoral students. L.K.'s research has focused primarily on chemical reaction engineering, especially on rate enhancement strategies in organic synthesis. His group was worked on phase transfer catalysis and has showed that many of its problems can be overcome by immobilizing the catalyst on a polymer support. They have developed and published new mathematical models and have investigated the effect of ultrasound on solidliquid reactions mediated by phase transfer catalysts. In addition to his own seven doctoral students, L.K. collaborated with Terry King and Tom Wheelock in supervising two others. 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 chemical 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 publication of special collections of research papers written by many of his colleagues and friends. One of these collections appeared as the "L.K. Doraiswamy Festschrift," which honored his 70th birthday and filled the June 1998 issue of IEC Research. The Indian Academy of Sciences published an earlier 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 engineer 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 exposure 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 regard 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, philosophy, and politics, subjects in which he has deep interest. One of his graduate students sums up quite nicely the mentor-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 researcheven some personal matters. The wellbeing 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 always asks me whether my old car is running.'"

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182 Chemical Engineering Education EXPERIMENTAL PROJECTS FOR THE PROCESS CONTROL LABORATORY SIONG ANG, RICHARD D. BRAATZUniversity 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 apparatuses are duplicates). The cost for lab renovation was approximately $100,000, and the lab is maintained by a teaching assistant working fewer than ten hours per week. This expansion enabled all University of Illinois seniors (approximately 80 students/4 lab sections) to take the process control course in one semester, working in groups of two students during lab. Also, a modern control interface was designed and implemented in HP-VEE, which is a modern visual programming environment for instrument control.[1] The twentysix control apparatuses include1.Temperature control in an air bath 2.Water-flow control under 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 controlThe experiments were designed based on three underlying principles. First, the experiments should emulate real industrial processes and the control problems associated with those processes. Second, collectively the apparatuses should teach students a wide variety of techniques for addressing chemical process control problems. Third, the students should communicate with the apparatuses via a modern control interface.[1] 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 sophisticated. Control apparatus #9 is similar to an apparatus in Professor Frank Doyle's control lab at the University of Delaware[2] and in a control lab at the Lund Institute of Technology.[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 measured variables. This two-input four-output process is controlled using multivariable cascade control. Control apparatus #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 LABORATORYA laboratory course in process control constitutes an important component of a chemical engineer's education.[5,6]It should provide hands-on training in the application of control to real processes. The design of the process control laboratory is instrumental to the quality of a chemical engineering education. Figure 1 shows the flow of information between the computer hardware and the physical apparatus. Each computer is connected to a wet-lab experiment and an air-bath experi Copyright ChE Division of ASEE 2002 ChE laboratorySiong Ang received his BS in chemical engineering from the University of Illinois in 2000 under a Singapore Armed Forces Overseas Merit Scholarship. He received an MS degree in chemical engineering at Stanford University 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 University of Illinois. His main research interests are in complex systems theory and its application.

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Summer 2002 183ment. Modern 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.[5,6] The interfaces are designed to have the professional look and feel of real industrial operator interfaces, exposing students to a realistic control environment. The Hewlett Packard Visual Engineering Environment (HPVEE) is a visual programming language designed for instrumental control.[7] This software uses boxes to represent processes and controllers, and lines to represent information flows. The software has advantages over traditional programming languages. The visual interface of HP-VEE allows novice users to quickly master its programming language and therefore encourages more active student participation. Getting the program to work in a certain manner merely requires changing line connections between boxes or modifying control structures. Every change is a Figure 1. Computer hardware/ software architecture.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 advanced algorithms such as model predictive control[8] can be implemented by linking to compiled programs written in popular languages such as Fortran or Visual Basic. For identification, the data are imported to Excel, and the parameters are fit using a variety of fitting routines. To assist the students 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 COURSEThe control class covers a broad range of control topics relevant in industrial problems encountered today. The syllabus includes first-principles modeling, process identification, 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 allocated 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 experiments. The first series of laboratory sessions are devoted to an air-bath experiment from which students gain familiarity with the HP-VEE software, first-principles modeling, parameter estimation, filtering, on-off control, and single-loop PID control. This training prepares them for the second series of laboratory sessions, which are more open-ended and demanding. The students are split into several teams, with one wet-lab project assigned 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 experiment and carry out open-loop identification experiments. InTABLE 1Course Schedule W eek Lectur e Lab 1Introductory concepts 2Review: mathematical modeling & Laplace transformIntroduction to control lab Review of lab equipment 3Building transfer function modelsOn/off control of air bath Dynamics of simple processes 4Higher-order dynamic behaviorResponse of a shielded thermocouple Stability 5Nonlinear systems, linearizationResponse of a shielded thermocouple Parameter estimation 6Feedback control, introduction to PIDPID air bath temperature control 7Closed-loop time response and stabilityPID air bath temperature control 8Direct synthesisPID air bath temperature control Introduction to frequency domain 9Frequency domain identification and analysisGroup project: open-loop identification 10Cascade controlGroup project: open-loop identification Feedforward/ratio control 11ReviewGroup project: open-loop identification 12Introduction to MIMO systemsGroup project: model, design, and implement controllers Interaction Analysis 13Design of decouplersGroup project: model, design, and implement controllers Model predictive control 14On-line optimizationGroup project: model, design, and implement controllers Statistical process control 15Case study: distillation columns, packed-bed reactors

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184 Chemical Engineering EducationTABLE 2Proposed Schedule for Wet-Lab ExperimentsWeek 1Familiarize 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 2/3Make 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 4Construct models from process response curve experiments. Week 5Implement control algorithms and collect closed-loop response data. Week 6Analyze data and compare theory with both open-loop and closed-loop experiments. Write lab report.TABLE 3Summary of Information of Experimental Projects # Qty Experiment Algorithm Inputs (I/P) of Acquisition Board Outputs (O/P) of acquisition board113Air bathSISOI/P 00-Bath temperature ( C)O/P 00-Bulb voltage (V) 21Oscillatory loadSISOI/P 00-Flow rate (V)O/P 00-Valve voltage (V) 31Single-tank pHSISOI/P 00-pH level (no units)O/P 00-Base pump voltage (V) 41Liquid levelSingle cascade/MIMO cascadeI/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) 53Temperature time delaySISOI/P 00 thru 03-Temperature ( C)O/P 00-Pump voltage (V) 61Integrating tankSISO with P controllerI/P 00-Tank height (inch)O/P 00-Pump voltage (V) 71Temperature cascadeSingle cascadeI/P 00-Tank temperature ( C)O/P 01-Valve voltage (V) I/P 01-Flow rate of hot water (V) 81Dye concentrationSISOI/P 00-Absorbance (no units)O/P 00-Pump voltage (V) 91Liquid level & temperatureMIMO cascade/MultiloopI/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) 1024-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) 111Multi-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)O/P 01-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 voltage (V) the remaining weeks the students construct process models, design controllers, implement the controllers on the laboratory apparatus, analyze the results, and write lab reports. The analysis is required to include a comparison between theoretical predictions and laboratory results with a discussion of potential causes for disagreement. The suggested work schedule is shown in Table 2.LABORATORY PROJECTSTo 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 variable 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 parameters used for controller tuning. The students apply first-or-

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Summer 2002 185 Figure 2. Water-level control under oscillatory load disturbances. Figure 3. Interacting water tank-level control.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 control[8, 10, 11, 12]) to design PID controllers and compare the closedloop performance obtained with each tuning rule. The students 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. W ater-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 continuously varying. The downstream pressure oscillates by varying the liquid level in a tank downstream from the valve using a float system, which is separate from the computer. The flow rate downstream from the valve is measured as a pressure difference across an orifice. A transducer measures this pressure difference as a voltage, which is sent to the dataacquisitions board in the computer (Figure 2). Students construct process-reaction curves with respect to valve voltage. When analyzing these curves, the oscillations are significant. By first subtracting the oscillatory disturbance, a process gain, time constant, and time delay can be determined. Several PI and PID tunings are used for varying magnitudes of the oscillation. A goal of this experiment is to obtain 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 closedloop performance. Single-T ank 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 tracking 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 processes that are not completely ideal because this is usually what occurs in practice. Interacting W ater T anks 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 val ves so that it is possible to choose whether the tanks interact. All levels are measured as pressure differences, which are converted into voltages by transducers (Figure 3). The preferred control strategy for this experiment 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 respect to the set point of the inner loop) is constructed. 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 second tank by manipulating the valve voltage. The performance of both strate-

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186 Chemical Engineering Education Figure 4. Dye concentration control with load disturbances.gies can be compared. A goal of this experiment is to recognize the performance improvement obtainable by cascade control. T emperature Control with V ariable-Measurement T ime 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 mixing tank. A reservoir provides a constant head for a coldwater feed, and a peristaltic pump transfers hot water from a reservoir into the mixing tank. Four thermocouples are located 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 thermocouples 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 T ank-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 removed from the tank by a peristaltic pump under the control of the computer. An inte resting 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 water away from the tank; therefore, the sign of the controller gain should be negative. Step changes in the pump voltage are implemented to determine 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 particular 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 disturbing response of their c ontroller parameters by implementing the controller under conditions in which the tapwater feed rate changes. Cascade Control of T emperature in a W ater T ank 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 maintain 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. 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 peristaltic 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-T ank W ater-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 manually. All liquid levels are measured by pressure transducers. The two pumps adjust the flow of water to the tanks according 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

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Summer 2002 187obtained. 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 interactions between the loops are observed. To provide adequate setpoint tracking, the two loops are detuned as necessary. Decouplers are capable of reducing loop interactions. Students can use the HP-VEE software to implement partial decouplers and assess any improvements/deterioration in the closed-loop performance. T emperature and Level Control in a W ater T ank 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 implementing multiple loops. Since it is possible to receive four measured signals, two cascade-control loops can be used. Students 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 transfer functions can then be defined. The inner (slave) loops should be tuned aggressively without excessive overshoot to control the flow rates. After obtaining good tuning parameters, a second set of process response 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, students 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. Multitank pH 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 determined. Step changes should be made in the range bounded by the threshold voltages. The acid flow rate is set throughout the experiment. There are many ways to design a cascade control loop with one master and two slave loops. Yet another 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, students should be able to report any loop interactions. The closedloop performance is compared with different set points for the third tank (pH = 6, 7, and 8). Since this experiment can be controlled by different strategies, it is especially suited for challenging students to consider and test various control strategies. Integration of Experiments 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 closedloop performance, including process nonlinearity, time delays, disturbances, measurement noise, valve hysteresis, and loop interactions. This provides them with experience in handling real physical systems and practice in applying theoretical 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.ACKNOWLEDGMENTSThe Dreyfus Foundation, DuPont, and the University of Illinois IBHE program are acknowledged for support of this project.REFERENCES 1.Braatz, R.D., and M.R. Johnson,"Process Control Laboratory Education 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 Process 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 Teaching 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 Computing 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 Fundamentals 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)

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188 Chemical Engineering Education Using Test Results for ASSESSMENT OF TEACHING AND LEARNING H. HENNING WINTERUniversity 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 student to apply the learned material successfully. Most gratifying for teacher and student alike is an exam in which the student answers all questions and receives a top grade. Incomplete 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 questions 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.[1]Methods have been developed for assessing entire engineering programs, curricula as well as individual courses, and educational research projects.[2,3] 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 educational 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 increased 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,[5] which was recently pointed out to me. But, in addition to student performance, the grading matrix also assesses teaching success. This paper briefly describes the grading matrix together with suggestions for its use in teaching and curriculum development.THE GRADING MATRIXThe 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 (task1to 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 ChE classroom H. Henning Winter is Distinguished University 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, polymer gelation, and crystallization.

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Summer 2002 189his or her final grade: grade N tasktasktasktaskN%[]=+++()() 100 1 123K 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 (without names) of the entire class. No grades are written in the booklet except for the final grade on the booklet cover. Instead 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 personal 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 performance remains otherwise unknown. I have not had any problems 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 representative 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 problems. This can be tested in an exam where one such flow problem is broken down into: (task1) schematic drawing of the expected velocity field, choice of coordinate system, and definition of boundary conditions; (task2) equation for conservation of mass; (task3) equation for conservation of linear momentum; (task4) solution for obtaining the velocity field; (task5) statement of all simplifying assumptions and limitations of the solution; (task6) discussion of properties of calculated flow field; and (task7) prediction of pressure and stress. Most written tests are easily structured in this way.TEACHING ASSESSMENT AND CORRECTIONSUntil this point, the exam grading has followed conventional paths, except that the data is filed in a spreadsheet, Figure 1: Example of the grading matrix of a test. Grades are filed in a spreadsheet. Task1, task2, task3, etc. stand for test questions. 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 understood 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.

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190 Chemical Engineering EducationFigure 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. ready for further assessment. Some of the most important information is contained in the columns of the grading matrix of Figure 1. A column with mostly high marks (1 = highest mark) top to bottom shows that all students know the subject, at least at the level of the exam question. If a column, however, has mostly "0" marks, something went wrong. Reasons 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 deficiencies 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 creative 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 questions 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 introduce corrections in the same semester or quarter. If changes cannot be made in time, the weakn ess in one course will be passed on to the teacher of the following course. This teacher should be alerted to the problem so that corrections 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 teachers. This would in terfere 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 STUDENTSAdvising 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.

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Summer 2002 191 ...this paper [focuses] on methods of analyzing a single exam and generating direct feedback...CURRICULUM DEVELOPMENTWeaknesses 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 weaknesses should, in this case, be addressed by changing the course content of the responsible preceding course. Relevant results from the grading matrix can be integrated into the systematic curriculum development.[3] Discussions along these lines are in progress in our department.ADAPTATION OF THE MATRIX METHODThere are many ways of integrating the information from the grading matrix into personal approaches to teaching and student advising. It goes without saying that assessment of test performance 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 assessment and in their creative teaching approaches. The integration 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 different ways, and adaptations are straightforward. A few will be mentioned here. It is possible, for instance, to emphasize selected 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 accordingly when calculating the final grade: grade weighttask weightii i N i i N%[]= ()= = 100 2 1 1 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 individual students. The concept of a grading matrix is introduced here with a chemical engineering example and on the most straightforward 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 questions arise in high school teaching and even in elementary schools where standardization of tests is considered.[7]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. 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 conclusions, and more. The choice of categories needs to be explained to the students well in advance of the exam.SUMMARYThe three main functions of the grading matrix are providing a grade for the student, labeling areas of weakness in the student's knowledge, and labeling areas of weakness in the teaching. For me personally, the grading matrix helped to fairly assess the abilities of students 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 encountered with course material. It labeled weaknesses in my teaching 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 design 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.ACKNOWLEDGMENTSSupport from the von Humboldt Foundation, many lively discussions with colleagues and students, and helpful suggestions from the reviewers are gratefully acknowledged.REFERENCES1.Walvoord, G. and V.J. Anderson, Effective Grading: A Tool for Learning 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 Defining 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. Waldof Education 10 (2), p. 5 (2001)

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192 Chemical Engineering Education IS PROCESS SIMULATION USED EFFECTIVELY IN ChE COURSES? KEVIN D. DAHM, ROBERT P. HESKETH, MARIANO J. SAVELSKIRowan University Glassboro, NJ 08028 P rocess simulators are becoming basic tools in chemical engineering programs. Senior-level design projects typically involve the use of either a commercial simulator or an academic simulator such as ASPENPLUS, ChemCAD, ChemShare, FLOWTRAN, HYSYS, and ProII w/PROVISION. Many design textbooks now include exercises specifically prepared for a particular simulator. For example, the text by Seider, Seader, and Lewin[1] has examples written for use with ASPENPLUS, HYSYS, GAMS,[2] and DYNAPLUS.[3] Professor Lewin has prepared a new CDROM version of this courseware giving interactive self-paced tutorials on the use of HYSYS and ASPEN PLUS throughout the curriculum.[4,5]This paper will analyze how effective it is to include computing (particularly process simulation) in the chemical engineering 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 programming 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.DISCUSSIONIn 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 engineering community has seen a strong movement toward vertical integration of design throughout the curriculum.[6-9] Some of these initiatives are driven by the new ABET criteria.[10]This integration could be highly enhanced by early introduction 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 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 location. Standard texts such as Wankat[11] present these concepts in a deductive manner. The inductive presentation used at Rowan University is outlined below in the section on equilibrium 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[12] 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 authors use this simple process to illustrate concepts such as feedback control and open-loop dynamics. Clough[13] presents a good overview of the use of dynamic simulation in teaching 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 understanding the physical phenomena within each unit operation. 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 solveKevin 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 University. 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 research 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 of ASEE 2002 ChE curriculum

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Summer 2002 193CACHE survey, Kantor and Edgar[15] observed that computing was generally accepted as an integral component of teaching design, but that it had not significantly permeated the rest of the curriculum. The survey results suggest that this perception is outdated. Table 1 shows that only 20% of departments reported that process simulation software is used exclusively 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 responding departments felt that their faculty have "an overall, uniformly applied strategy for teaching simulation to their students that starts early in the program and continues in subsequent courses." Many other respondents acknowledged the merit of such a plan but cited interpersonal obstacles, with comments such asWith 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 of flowsheet 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-andjunior-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 question on motivation for using simulation software. Four options were given, and the respondent was asked to check all that apply. The 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 option, and in 15% of the responses it was the only one chosen.TABLE 1Responses to:"Which of these best describes your department's use of process simulation software?" Response % Y es 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% There is some coordination between individual faculty members, but the department as a whole has not adopted a curriculum-wide strategy.35% Several instructors use it at their discretion, but there is little or no coordination.18% Only the design instructor requires the use of chemical process simulation software.20% No professor currently requires simulation in undergraduate courses.1% TABLE 2Responses to:"Please indicate the courses in which professors require the use of steady-state chemical process simulation programs." Course % Y es Design I and/or II94% Process Safety4% Process Dynamics and Control10% Unit Operations31% Equilibrium Staged Separations57% Chemical Reaction Engineering19% ChE Thermodynamics36% Fluid Mechanics7% Heat Transfer13% Chemical Principles29% TABLE 3Responses to:"Please indicate the courses in which professors require the use of dynamic chemical process simulation programs." Course % Y es Design I and/or II12% Process Dynamics and Control52% 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 declare 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 enginee ring 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 ProII remain the primary simulation packages currently in use.SURVEY: COMPUTER USE IN CHEMICAL PROCESS SIMULATIONIn 1996, CACHE conducted a study discussing the role of computers in chemical engineering education and practice. The study surveyed both faculty members and practicing engineers, but little emphasis was placed on the specific use of process simulation. To fill this gap and obtain up-to-date results, a survey on computer use in the chemical engineering curriculum was distributed to U.S. chemical engineering department heads in the spring of 2001. It addressed how extensively simulation software is used in the curriculum, as well as motivation for its use. The use of mathematical software and computer programming was also examined. A total of 84 responses was received, making the response rate approximately 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

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194 Chemical Engineering EducationTABLE 4Responses to:"Which of the following best describes your motivation to use simulation packages? Please check all that apply." Response % Y es It helps to illustrate essential chemical engineering concepts.64% It makes numerical computations less time consuming.70% The modernity is good for attracting and retaining students.30% It's a tool that graduating chemical engineers should be familiar with, and is thus taught for its own sake.83% TABLE 5Responses to:"Which of the following best describes your department's use of computer programming languages?" Response % Y es One required course taught by computer science and no programming required in subsequent chemical engineering courses.13% One required course taught by chemical engineering and no programming required in subsequent chemical engineering courses.11% After students take the required programming course, they are required to program in one subsequent ChE course.7% After students take the required programming course, they are required to program in several subsequent ChE courses.45% Students are required to program in upper level chemical engineering courses without having taken a formal programming course.8% None of the above selected.16% In their 1996 study of computer skills in chemical engineering, Kantor and Edgar[14] analyzed survey results from both faculty and practicing engineers, finding that faculty tended to drastically underestimate time spent at the computer by practicing engineers in industry. The main software tools they used, however, did not include simulators; they were spreadsheets (74%), graphics presentation packages (80%), database sy stems (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 invalidate the opinion that process simulation software is "a tool that graduating chemical engineers should be familiar with." They do, however, suggest that a departm ent 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 written by individuals." Courses on computer programming appear to remain 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 programming 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 areOur 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 "becoming less important." In addition, the current ABET Chemical Engineering criteria[16] requires that graduates have a knowledge of "appropriate 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 importance of the logic and problem-solving skills that programming experience stimulates, even if the ability to program in itself is unnecessary for chemical engineers. The specific comments wereWe 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... 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 students 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.[17]

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Summer 2002 195TABLE 7Responses to:"Please indicate all applicable steady-state Chemical Process Simulation programs currently being used in your department's undergraduate courses. Check all that apply." Response % Y es ProII/Provision12% HYSYS or Hysim32% Aspen Plus45% ChemCAD32% Other13% TABLE 6Responses to:"Indicate the mathematical applications software required of chemical engineering undergraduates. Check all that apply." Response % Y es POLYMATH4037% MATLAB65% Maple24% MathCAD37% EZ-Solve5% Spreadsheets82% Mathematica13% Other15% EXAMPLES OF CHEMICAL PROCESS SIMULATORS IN CHEMICAL ENGINEERINGIn 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 students 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 simulator 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 ., [19] 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 improve and optimize the process. The ultimate goal is to produce a fully sized and optimized design, including the analysis of the capital and operating costs by the end of the junior year. This approach is comparable to problem-based learning.[20]There have been other contributions to this vertical approach.[2123] 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 certainly can be used for such problems, however, since they provide an efficient way to evaluate many variations on a single design concept. Chemical PrinciplesEnergy Balances In Felder and Rousseau[24] (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, using a 1L 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 simulator to predict bubble and dewpoints for binary and multicomponent 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 usually 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.[24-26]They give examples with solutions done by calculators, Excel spreadsheets, and FORTRAN. This gives the students an excellent reference on how a system of equations is used by chemical 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 simulators 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 faculty have knowledge of the simulator that the students are using. In the discussion of the survey results, there were concerns about the faculty time and motivation required to be come proficient in using a simulator. One possible solution is to implement mini-modules of the type used at Rowan University. In

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196 Chemical Engineering Educationequilibrium staged operations, a student must learn the optimum feed location and the improved separation resulting from increasing reflux ratio for a given number of stages; in an approach that has been used at Rowan University The instructor prepares a complete HYSYS model of a distillation column and distributes it to the class. The class receives a brief (less than five minutes) tutorial on modeling columns with HYSYSjust 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, varying 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 throughout 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 curricular purpose in that they begin to introduce process simulation. 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 students learn the process simulator in self-paced tutorials.[1,4] The proposal is that these modules be given to the studentsthe professor does not need to prepare time-consuming tutorials and may not need to learn how to use the simulator. Another paper by Chittur[29] discusses preparing tutorials for ASPEN Plus simulators using HTML. Finally, the University of Florida maintains a web site for ASPEN where tutorials are available.[30]Chemical Engineering Thermodynamics Judging from the survey results, it seems that process simulators are now widely used in thermodynamics (see Table 2). This is fertile ground for a pedagogical use of the process simulators, 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 thermodynamic 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 selection 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 calculators or computers. The increasing influence of process simulators opens up a completely new spectrum of possibilities. Since simulation results are only as good as the thermodynamic package chosen, there is value in teaching the fundamental aspects that will permit students to pick the right thermodynamic package for a system. Simulators also offer the advantages of combining thermodynamic models in the same simulation 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 Soav e-Redlich-Kwong (SRK) is chosen as the overall simulation package, but it is modified so liquid density is calculated using the American Petroleum Institute (API) equation. In many cases, professors have been taught thermodynamics using earlier versions of Sandler[32] and Smith and Van Ness,[33] which did not emphasize predictions of thermodynamic 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 thermodynamic properties from other equations of state. One of the fundamental aspects of a modern chemical thermodynamics course is not only to teach students how to use these equations, 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 correlating parameters of a=f(T) and b are from the Peng-Robinson equation of state is HH RT Zn ZB ZB A B Tig rŠ()=ŠŠ ++()+Š() + 1 12 12 8 1 l where B bP/RT and A aP/(RT)2From the above equations it is easily seen how complicated these predictions can become compared to a table or a graph in a standard handbook.[34,35] Many recent thermodynamic textbooks have included computer programs that allow the reader to use various equations of state to solve homework 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 professor 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[36] is to have the students use the mole, momentum, and energy balances appropriate for a given reactor type. In this manner a fairly detailed model of industrial reactors can be developed for design projects.[37] By using POLYMATH or MatLab, a student can easily see the equations used to model the reactor. In modern process simulators there are several reactors that can be used. For example, in HYSYS 2.2 there are the two ideal

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Summer 2002 197TABLE 8 Reaction T ype Description Conversion FFFXiiA A =Š00 Equilibrium KfTeq=() ; equilibrium-based on reaction stoichiometry; Ke q predicted or specified. Gibbsminimization of Gibbs free energy of all components Kinetic rkCCkCCAfABrevR S =Š+ where the reverse rate parameters must be thermodynamically consistent and rate constants are given by kATERTn=Š()exp/ Heterogeneous CatalyticYang and Hougen form, which includes Langmuir-Hinshelwood, Eley-Rideal and Marsvan Krevelen, etc. Š= Š + r kCC CC K KCA A a B b R r S s ii yi1 Simple Rate rkCC CC KAfAB RS eq=ŠŠ in which Ke q is predicted from equilibrium data reactor models of a CSTR and a PFR. The CSTR model is a standard algebraic model that has been in simulation packages for a number of years. The ODE's of the PFR are a recent addition to simulation packages and are solved by dividing 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 engineering courses can easily use the process simulator. Simulation can be integrated throughout the course and used in parallel with the textbook, or it can be introduced in the latter stages of the course, after the students have developed proficiency in modeling these processes by hand. As mentioned in the discussion section, the primary dilemma is how to insure that the simulator is used to help teach the material rather than simply giving students a way to complete the assignment without learning the material. Taking care that assignments require synthesis, analysis, and evaluation in addition to simple reporting of numerical results will help in this regard. Requiring that students do calculations by hand will ensure that they understand what the simulator is actually doing. 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).[38] 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 teaching chemical engineering concepts. The authors suggest that professors who teach junior course(s) in separations, equilibrium-stage operations, rate-based operations, and/or mass transfer consider including Approximate methods (Fenske-Underwood-Gililand and Kremser algebraic method) Rigorous multicomponent Enhanced distillation using triangular diagrams Rate-based methods contained in the ChemLSep program and the RATEFRAC program of Aspen Plus Adsorption, ion exchange, chromatography Membrane separationswhich 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 separators. 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.[39] As simulators develop, we believe that more unit operations will become available.CONCLUSIONSChemical 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 distillation. But many respondents acknowledge that the role of simulators could be beneficially expanded in their curriculum. Process-simulation designers can make their products more valuable to chemical engineering educators by adding new and innovative unit operations while they continue to improve their thermodynamic models. This paper contains practical suggestions and references for implementing a unified strategy for teaching simulation to their students, starting early in the program and continuing in subsequent courses. We believe that simulation packages are a fundamental tool for the future chemical engineer.REFERENCES1.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 3.Aspen Technology, Inc.Continued on page 203.

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198 Chemical Engineering Education AN INTRODUCTION TO DRUG DELIVERY FOR CHEMICAL ENGINEERS STEPHANIE FARRELL, ROBERT P. HESKETHRowan University Glassboro, NJ 08028-1701 Copyright ChE Division of ASEE 2002 ChE laboratory R owan University is pioneering a progressive engineering 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 includeMultidisciplinary 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 communication.[2]The Rowan program emphasizes these essential features in an eight-semester, multidisciplinary, engineering clinic sequence that is common to the four engineering programs (civil, chemical, electrical, and mechanical). A two-semester Freshman Clinic sequence introduces all freshmen engineering students to engineering at Rowan University. The first semester of the course focuses on multidisciplinary engineering experiments using engineering measurements as a common thread. In the spring semester, students 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 incorporates 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 biomedical 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.[3] With projected doubledigit 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, pharmaceutical sciences, engineering, and chemistry. Chemical engineers play an important role in this exciting field by applying their knowledge of physical and chemical pro perties, chemical reactions, mass transfer rates, polymer materials, and system models to the design of drug-delivery systems, yet undergraduate 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 concentration measurements and simple analysis of rate data. Through this experiment, students explore concepts and tools that they will use throughout their careers, such asNovel application of chemical engineering principles Concentration measurement Calibration Material balances Use of spreadsheets for calculations and graphing Parameter evaluation Semi-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 Development of a mathematical model (in the senior level class)BACKGROUNDPeriodic administration of a drug by conventional means, such as taking a tablet every four hours, can result in constantly changing systemic drug concentrations with alternating 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 engineering. Robert Hesketh is Professor of Chemical Engineering at Rowan University. 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.

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Summer 2002 199lished by conventional administration and controlled release is shown in Figure 1. Historically, drug-delivery systems were developed primarily for traditional routes of administration, such as oral and intravenous, but recently there has been an explosion in research on delivery by so-called nonconventional routes, such as transdermal (skin), nasal, ocular (eyes), and pulmonary (lung) administration. Drug-delivery applications have expanded from traditional drugs to therapeutic peptides, vaccines, hormones, and viral vectors for gene therapy. These systems employ a variety of rate-controlling mechanisms, including matrix diffusion, membrane diffusion, biodegradation, and osmosis. To design and produce a new drug-delivery 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, thermodynamics, and transport phenomena. The engineer must also be skilled in characterization techniques and physical property testing of the delivery system, and practiced in analysis of the drug-release data. We present a simple experiment in which students are introduced 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 intensity 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 balance the mass of drug remaining in the lozenge at any time. They are also able to compare their data to a model after evaluating a single parameter in the model. Through this experiment, students are exposed to the exciting field of drug delivery and are introduced to some basic principles of chemical engineering. They perform a calibration 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 system, 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 objectives are illustrated through familiar examples of drug-delivery 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 intestine. In the more neutral environment of the intestine, the coating dissolves, allowing the aspirin to 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 entericcoating is illustrated by placing one enteric-coated aspirin tablet in an environment simulating the stomach (hydrochloric acid, pH 2), and another enteric-coated aspirin tablet in an environment simulating the intestine (sodium hydroxide, pH 8). Students 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 controlled release (or the release of a drug at a desired rate for a desired time) is illustrated through famil-Figure 1. A comparison of systemic drug profiles established by conventional administration and controlled release.

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200 Chemical Engineering EducationFigure 2. The osmotic pump. Adapted from Robinson and Lee.[5] iar controlled-release products such as Contac 12-hour cold capsules and Efidac 24-hour nasal decongestants. Contac is a membrane-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] or Mathiowitz.[6]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 compared with the rate at which the drug is released through the coating material. Hence, the coating controls the drug's rate of release and is therefore considered a rate-controlling 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 relief for up to twelve hours. The osmotic pump developed by Alza exploits osmosis to achieve a 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 cardiovascular disorders. Efidac 24-hour nasal decongestants are an example of an oral system that uses the same technology. The osmotic pump comprises three concentric layers: an innermost drug reservoir contained within an impermeable membrane, an osmotic solution, and a rigid outer layer of a ratecontrolling semipermeable membrane (see Figure 2). As water from the body permeates through the outermost membrane and into the osmotic "sleeve,", the sleeve expands and compresses the innermost drug reservoir, squeezing the drug out of the reservoir through a delivery portal.[7]The experiment that the students perform uses a lozenge formulation, and the short introduction to drug delivery concludes with an explanation of lozenge formulations and their applications. 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, antifungal agents, central nervous system depressants (used to treat anxiety, depression, and insomnia), anti-psychotic drugs, antiflammatory agents, and anticholinergic agents used to treat Parkinson disease.LOZENGE DISSOLUTIONThe 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 dissolve at the desired rate. In this experiment, we investigate the dissolution 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 developed to express the amount of drug released as a function of time, in terms of quantities that can be measured experimentally. We begin with a rate expression for the dissolution rate of the lozenge dM dt kACCsaq=ŠŠ()() 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), 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 Cs Caq, where Cs is the saturation concentration of sugar in water and Caq is the concentration of sugar in the bulk water. Caq is assumed to be negligible since the solubility of sucrose in water at 25 C is 674 g/L8, while the maximum sucrose 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: Arrh =+()22 22 To simplify the model solution and analysis, the area of the sides ( 2 rh ) was neglected. The mass of drug remaining in the lozenge can similarly be represented in terms of r: MM rh rh =()0 2 0 2 3 where M0 is the amount of drug present in the lozenge ini-

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Summer 2002 201The experiment involves the release of a drug from a lozenge formulation, which is an example of a matrix-type drug-delivery system. 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.tially (known) and r0 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: MM ACk M ts=Š ()0 0 0 4 exp A plot of l n (M/M0) vs t should yield a line with a slope of -A0Csk/M0. The amount of drug released from the lozenge, Md, is related to the amount remaining, M, by the material balance MMMd 0 5 =+() Combining Eqs. (4) and (5), an expression for the amount of dissolved drug at time t is obtained by MM ACk M td s=Š Š ()0 0 01 6 exp 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 Mdprovided 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 advanced chemical engineering students, but is not necessary to achieve good agreement between the model and the data.EXPERIMENTAL SET-UPThe dissolution experiment is simple to implement. Each group is provided withOne magnetic stir plate One magnetic stirrer One graduated cylinder One 100-ml beaker One cuvette One dropper or Pasteur pipette One lozenge (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=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 stirrer and the lozenge are then placed in the beaker, the solution is agitated gently, and samples are taken at intervals of approximately 5 minutes. Similar experimental set-ups have been developed[9,10] to investigate mass transfer between a solid and a surrounding liquid 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 MEASUREMENTThe 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 formulation provides a convenient method of analysis. As the drug dissolves, it is released into the surrounding 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 concentration present in samples. Students prepare a simple calibration plot using a lozenge (containing a known amount of drug) dissolved 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-

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202 Chemical Engineering EducationFigure 4. Parameter evaluation. The parameter is determined from the slope of the line. Figure 5. Comparison of the experimental release data to that described by the model. tration is determined.ANALYSISChemical engineers who work on drug formulations are concerned 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 =Š()ACk Ms 0 0 7 must be evaluated.PARAMETER EVALUATIONEquation (6) can be rearranged to l n MM M td 0 0 8 Š =() In this equation, the term in parentheses represents the fraction 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 determined using the "trendline" feature of Excel. In Figure 4, the slope of -0.0938 (min-1) is equal to It is important to emphasize 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 semilog 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, M0, 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 DATAAfter determining the value of Eq. (6) can be used to describe the experimental release data (see Figure 5). Students are asked to observe the agreement between the model and the data. Freshman students 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 = M0 for t and this is in agreement with "common sense." Thus, the point is emphasized that models can easily be tested for simple or limiting cases.CONCLUSIONSThis paper describes a simple experiment that exposes 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 water. Students observe the increasing dissolved-drug concentration as reflected by the increasing color intensity of the water, and they are able to measure the drug concentration spectrophotometrically. They create a calibration plot that enables them to determine the drug concentration from their absorbance measurement. They perform a material balance to determine the fraction of drug released and perform an experimental parameter evaluation. Using a spreadsheet, they perform calculations necessary to determine the release profile, and they g enerate plots of both the experimental release profile and that described by the

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Summer 2002 203model. Finally, they test the validity of their model for the limiting cases of initial and long times. Through this experiment and lecture, students are introduced to the role that chemical engineers have in the area of drug delivery and pharmaceutical production. This experiment has also been used in senior-level courses such as transport phenomena and as an elective in drug delivery. Here, students develop their own model, compare their experimental results to those described by the model, and examine the validity of their simplifying assumptions.ACKNOWLEDGMENTSThis work was funded through a grant from the National Science Foundation's Course, Curriculum and Laboratory Improvement Program, under grant DUE-0126902.REFERENCES1. Engineering Education for a Changing World joint project report by the Engineering Deans Council and Corporate Roundtable of the American Society for Engineering Education, Washington, DC (1994) 2. Rowan School of EngineeringA Blueprint for 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-Arnum, 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 Applications 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, F., 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 and Applications 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 Experiments," Chem. Eng. Ed ., 33 (3), (1999) 10.Sensel, M.E., and K.J. Myers, "Add Some Flavor to Your Agitation Experiments," Chem. Eng. Ed ., 26 156 (1992) 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 Engineering: 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 Integrated Approach," AIChE Paper 63d, 2000 AIChE Annual Meeting, Los Angeles, CA 6.L.G. Richards and S. Carson-Skalak, "Faculty Reactions to Teaching Engineering Design to First Year Students," J. of Engg. Ed., 86 (3), p. 233 (1997) 7.ASME, Innovations in Engineering Design Education: Resource Guide, American Society of Mechanical Engineers, New York, NY (1993) 8.King, R.H., T.E. Parker, T.P. Grover, J.P. Gosink, and N.T. Middleton, "A Multidisciplinary Engineering Laboratory Course," J. of Engg. 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 the United States, 3rd ed., Engineering Accreditation Commission, Accreditation Board for Engineering and Technology, Inc., Baltimore, MD (1999) 11.Wankat, Phillip C., Equilibrium-Staged Separations, Prentice-Hall, Upper Saddle River, NJ(1988) 12.Henson, Michael A., and Yougchun Zhang, "Integration of Commercial Dynamic Simulators into the Undergraduate Process Control Curriculum." Proc. of the AIChE An. Meet., Los Angeles, CA (2000) 13.Clough, David E., "Using Process Simulators with Dynamics/Control Capabilities to Teach Unit and Plantwide Control Strategies." Proc. of the AIChE An. 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Griffin Printing Limited, Hamilton, Ontario, Canada (1994) 21.Gatehouse, Ronald J., George J. Selembo, Jr., and John R. McWhirter, "The Vertical 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," 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," 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 Thermodynamics, 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 Thermodynamics, 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 Equilibrium-Based and Rate-Based Separations Courses with the Senior Process Design Course," Session 3613, Proc. of the 1998 ASEE An. Conf. and Expo. (1998) 39.HYSYS Programmability/Extensibility (OLE) Examples (2001) 40.Cutlip, M.B., and M. Shacham, Problem Solving in Chemical Engineering with Numerical Methods, Prentice Hall PTR, Upper Saddle River, NJ (1999) Process SimulationContinuted from page 197.

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204 Chemical Engineering Education 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 assignments, involve no unfamiliar or tricky solution methods, and have average grades in the 7075 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 studentsthey 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 characteristically methodical and tend to go over their calculations exhaustively. 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 flying 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 repeatedly slows them down compared to the intuitors.) Studies have shown, however, that sensors tend to get significantly lower grades than intuitors in engineering courses[2]and that minimizing speed as a factor in test performance may help level the playing field.[3]Tests that are too long thus discriminate against some students 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.) Moreover, while overlong tests inevitably frustrate and demoralize 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. FAQS. V. DESIGNING FAIR TESTS [1] Copyright ChE Division of ASEE 2002 Random Thoughts . RICHARD M. FELDER AND REBECCA BRENT North Carolina State University Raleigh, NC 27695Richard M. Felder is Hoechst Celanese Professor 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 Elementary Principles of Chemical Processes (Wiley, 2000) and codirector of the ASEE National Effective Teaching Institute Rebecca Brent is an education consultant specializing in faculty development for effective university teaching, classroom and computerbased simulations in teacher education, and K12 staff development in language arts and classroom management. She co-directs the SUCCEED Coalition faculty development program and has published articles on a variety of topics including writing in undergraduate courses, cooperative learning, public school reform, and effective university teaching.

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Summer 2002 205How 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-ofthumb 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 computation-heavy problems are included). If a test fails to meet this criterion, it should be shortened by eliminating some questions, giving some formulas instead of requiring their derivations, or asking for some solution outlines rather than requiring all the algebra and arithmetic to be worked out in detail. How about those problems with unfamiliar twists that supposedly 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 1015% 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 engineering 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 highernot 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 engineering and survive the freshman year, an entire engineering 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 probably 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 highlevel thinking skills.[4] The statements should begin with observable 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 unde rstand something on a testyou would ask them to do something to demonstrate their understanding.) A typical study guide for a mid-semester test might be between one and two pages long, single-spaced. Drawing from the study guides when planning lectures and assignments and constructing tests makes the course both coherent and effective. Peter Elbow observes that faculty members have two conflicting functionsgatekeeper and coach.[5] 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 surpass those standards. Tests are at the heart of both functions. We fulfill the gatekeeper role by making our tests comprehensive and rigorous, and we satisfy our mission as coaches by ensuring that the tests are fair and doing our best to prepare our students for them. The suggestions given in this paper and its predecessor[1] address both sets of goals. Adopting them may take some effort, but it is hard to imagine an effort more important for both our students and the professions they will serve.REFERENCES1.This column is based on R.M. Felder, "Designing Tests to Maximize Learning," J. Prof. Issues in Engr. Education & Practice, 128 (1), 13 (2002). Available at < http://www.ncsu.edu/felder-public/Papers/TestingTips.htm >. 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 < http://www.ncsu.edu/felder-public/Learning_Styles.html >. 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), 317 (2002). Available at < http://www.ncsu.edu/felder-public/Learning_Styles.html >. 4.R.M. Felder and R. Brent, "Objectively Speaking," Chemical Engineering Education, 31 (3), 178179 (1997). Available at < http:// www.ncsu.edu/felder-public/Columns/Objectives.html >. Illustrative study guides may be found at < http://www.ncsu.edu/felder-public/ che205site/guides.html > 5.P. Elbow, Embracing Contraries: Explorations in Learning and Teaching 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/

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206 Chemical Engineering Education Copyright ChE Division of ASEE 2002 ChE class and home problems BOILING-LIQUID EXPANDING-VAPOR EXPLOSION (BLEVE) An Introduction to Consequence and Vulnerability AnalysisC. TƒLLEZ, J.A. PE„AUniversity of Zaragoza Zaragoza, Spain T he chemical engineering curriculum should include information on safety, health, and loss prevention in the chemical industries.[1-4] 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 Explosion) 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 industry,[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 coverCarlos TŽllez received his PhD in 1998 at the University of Zaragoza, where he is currently Assistant Professor teaching chemical engineering fundamentals. His research is focused on fundamental studies in the preparation of zeolite membranes and inorganic membranes for pervaporation and gas separation. Jose Angel Pe–a is Associate Professor of Chemical Engineering at the University of Zaragoza. His research interests include development of new methods for hydrogen storage and transport, development of a new system of indicators to estimate the risk of major accidents involving chemical reactors, and improved systems for early detection of runaway reactions. The object of this column is to enhance our readers' collections of interesting and novel problems 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.

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Summer 2002 207such actions, while in the United States, legislation has required 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 agencies, 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 awareness. The Center for Chemical Process Safety (CCPS), created in 1985, is an industry-driven center affiliated with the American Institute of Chemical Engineers (AIChE) that initiated a close relationship with engineering schools in 1992 by creating the Safety and Chemical Engineering Education program (SACHE). It provides teaching materials and programs that bring elements of process safety into the curriculum . 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.S.[6,7] 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 incorporated in some programs, is to include safety courses as electives 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 showing students how to improve the safety of a process is the socalled "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 elective 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 boilingliquid 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 CASEA 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 atmosphere) produced a mixture within the LFL (lower flammability limit) and the UFL (upper flammability limit). An unknown 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 propane within it. The liquid propane tracked its boiling point curve (p0 vs T), substantially increasing the pressure in the tank. As a consequence, the tank ruptured catastrophically. This kind of phe nomenon is a BLEVE (Boiling-Liquid Expanding-Vapor Explosion). At the moment of the accident, the ambient temperature was 36 C and the atmospheric 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 ST AGE 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."[9]This theory is based on the phenomenon of "spontaneous nucleation" that consists of a massive, instantaneous formation of tiny bubbles within the liquid mass, caused by a sudden depressurization of the vessel contents. When this phenomenon takes place, the possibility of a BLEVE occurs.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.

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208 Chemical Engineering EducationTABLE 1Fireball Characteristic Parameters as Calculated by Different Authors (M) Initial Mass of Flammable Liquid [kg] (Dmax) = maximum diameter of the fireball [m] (HBLEVE) = height at the center of fireball [m] (tBLEVE) = duration of fireball [s]CCPS [10]CCPS [19]Dmax = 6.48 M0.325 = 159.3 mD* max = 5.8 M1/3 = 154.8 m tBLEVE = 0.825 M0.26 = 10.7 st* BLEVE = 0.45 M1/3 = 12 s HBLEVE = 0.75 DMAX = 119.5 m TABLE 2Flow of Radiation Per Unit of Surface Area and Time (I) for Different ModelsCCPS Model[10]Elia Model[12]Pape, et al., Model[13]I(kW/m2)336301306 Figure 1. Vapor pressure vs. temperature diagram showing the zone of spontaneous nucleation for propane, as described by Reid's Theory.[9] 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 material being used ( e.g., propane). (The equilibrium relationship, as well as the critical temperature and pressure for such material, can be obtained from the literature.[8]) From the critical point ( e.g., the critical temperature and pressure), a tangent line to the p0-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 conditions inside the tank before the fire engulfment. As described 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 p0-vs.-T curve. This progressive heating will lead to a point where the abovementioned vertical line will be trespassed. Once this condition has been achieved, a sudden rupture of the vessel would lead to a BLEVE because of the sudden depresurization. ST AGE 2 Mathematical Models that Describe the Effects of BLEVEs The literature describes three types of BLEVE effects: the shock wave (overpressure effects), the thermal radiation, and the fragment projection. This paper focuses on the shock wave and thermal effects as the main events in a BLEVE scenario. Thermal Ef fects The thermal effects of a BLEVE are related to radiation coming from the fireball. They are usually 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, Dmax[m], the duration of the fireball, tBLEVE[s], and the height at the center of the fireball, HBLEVE[m], as well as the results 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 CCPS[10] I FHM DtRcomb BLEVE= Š()()() max 2 1 Elia model[12] I MHP Dtcomb BLEVE= Š()()()027 2 0 032 2. max Pape, et al., model[13] IPv=()235 3039 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 literature[10] ranges from 0.25 to 0.4); Hcomb is the heat of combustion of the material [kJ/kg]; P0 is the initial pressure at which the liquid is stored [MPa]; and Pv is the vapor pressure of the stored liquid [MPa].

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Summer 2002 209Figure 2. Radiation received by a vertical surface as a function of distance. 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 pressure of liquid p ropane 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[10] (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 transmissivity, ) are known: IIFRvg=() 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: FvgD X=() 2 24 5 Considering only the partial pressure of water present in the atmosphere at the moment of the accident, can be calculated approximately by[20] =()()Š202 0096 . PXw where Pw is the partial pressure of the water at ambient temperature [Pa]. Another, simpler, model has been proposed by Roberts[11]where the intensity of radiation received by a surface at a distance X is given by an expression depending only on the mass of fuel: IMXR=Š()828 077127. Overpressure Ef fects Overpressures are difficult to predict in the event of a BLEVE. The vaporization and pressurization prior to the receptacle's collapse, and the duration of the rupture-depressurization, is extremely difficult to quantify. Experiments with explosives have demonstrated that the overpressure can be estimated using an equivalent mass of TNT. An approximate way to calculate the equivalent weight of TNT (WTNT) for a BLEVE has been described by Prugh[15] as WTNT k kPV kP=Š Š ()Š00241 1 1 8 1.* where P is the pressure existing in the receptacle before the rupture [bar]. V* is given as VVVvl l vf D D* =+ () 9 where Vv and Vl are the volumes of vapor and liquid [m3] in the vessel before the explosion; Dl and Dv are the densities 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 depressurization. This can be calculated by the simple energy balance fem mv CpTT Hb v==ŠŠ Š()()01010 where m0 and mv are the initial mass of liquid and the amount vaporized in the flash, respectively, T0 is the initial temperature, Tb is the normal boiling temperature, Cpis the heat capacity, and Hv is the heat of vaporization. This expression to calculate f usually gives values on the order of two times smaller than those observed experimentally,[16] 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 Cp (2.64 kJ/kg.K) and Hv (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); therefore, 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.[17] This "cubic root law" es-

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210 Chemical Engineering EducationFigure 4. Probability and Probit units relationship.TABLE 3Probit Correlations for a Variety of Causes and Effects[18,21] Cause Ef fect k 1 k 2 VExplosionLung hemorrhage-77.16.91Overpressure peak(1)ExplosionEardrum rupture-15.61.93Overpressure peak(1)ExplosionStructural damages-23.82.92Overpressure peak(1)ExplosionGlass breakage-18.12.79Overpressure peak(1)Thermal effectsMortality-38.52.56IR 4/3*t(2)Thermal effectsSecond-degree burns-39.83.02IR 4/3*t(2)Thermal effectsFirst-degree burns-43.13.02IR 4/3t(2)(1) Overpressure expressed in [Pa] (2) IR the intensity of radiation level received [W/m2] and t the exposure time [s] Figure 3. Overpressure along distance for the BLEVE proposed scenario. tablishes equivalent overpressure effects for explosions occurring at the same normalized distances, expressed as z R WTNT=()()131 1 / where z is the normalized distance [m.kg-1/3] and R is the real distance [m]. The experimental relation between overpressure and normalized distance for unconfined explosions can be found in several references.[5,18] Figure 3 shows the overpressure profile along distance for the proposed scenario.INTRODUCTION TO VULNERABILITY ANALYSISThe 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 effects are hardly used, and the approximations to the problem of estimation of vulnerability generally follow a probabilistic approach. The Probit scale is a way of dealing with such approximations. The connection between Probit units (Y) and probability (P) is given by Peduu Y=Š Š Š()1 2 1 22 2 5 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 following relationship can be written YkknV =+()121 3 l where Y is the number of Probit units, k1 and k2 are empirical constants depending on the causative factor and the level of damage to be analyzed, and V measures the intensity 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 (k1 and k2) and the expression related with V. The Probit expressions for prediction of the effects produced by a given radiation intensity level during a given time use a causative factor, V, proportional to the product t.IR 4/3 (t is the exposure time and IR is the intensity of radiation level). Regarding vulnerability to explosions, V is the

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Summer 2002 211Figure 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 4Distance at which 1% and 50% of the Population (People or Objects) are Affected Cause Ef fect Distance Distance [m] 50% [m] 1% ExplosionLung hemorrhage18.822.3 ExplosionEardrum rupture34.463.0 ExplosionStructural damages51.684.7 ExplosionBreakage of glass162321 Thermal effectsMortality due to thermal radiation153212 Thermal effectsSecond-degree burns(1)222293 Thermal effectsFirst-degree burns(2)329436(1) Epidermis and part of the dermis are burned(2) A superficial burn in which the top layer of skin (part of the epidermis) has been slightly burned overpressure at a given point. Figure 5 shows the percentage of people and installations affected by different effects and causes. The values of overpressure 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 estimated distances at which 1% and 50% of the population or structures can be affected by a given effect. The limit at which 1% of the population may die is called "mortality threshold."CONCLUSIONSRisk 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 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 vulnerability of persons and/or installations affected in both cases has been calculated using the Probit methodology.REFERENCES1.Lane, A.M., "Incorporating Health, Safety, Environmental, and Ethical Issues into the Curriculum," Chem. Eng. Ed., 23 70 (1989) 2.Cohen, Y., W. Tsai, and S. Chetty, "A Course on Multimedia Environmental 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, 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. Bra–a, "Risk Analysis and Reduction in the Chemical Process Industry," Blackie Academic & Professional (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 Explosions or BLEVEs," Science, 3 203 (1979) 10.CCPS (Center for Chemical Process Safety), Guidelines for Chemical 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 Radiation from Large Fires," Loss. Prev. Bull., 82 1 (1988) 14.Perry, R.H., and D. Green, eds, Perry's Chemical Engineer's Handbook, 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: Fundamentals with Applications, Prentice Hall, Englewood Cliffs, NJ (1990) 19.CCPS (Center for Chemical Process Safety): "Guidelines for Evaluating 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 B40222, TNO, Directorate General of Labor, 2273 KH Vooburg, Holland (1985) 21.TNO, "Methods for the Determination of Possible Damage to People and Objects Resulting from Release of Hazardous Materials," CPR 16E, Vooburg, Holland (1992)

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212 Chemical Engineering Education RUBRIC DEVELOPMENT AND INTER-RATER RELIABILITY ISSUES In Assessing Learning OutcomesJAMES A. NEWELL, KEVIN D. DAHM, AND HEIDI L. NEWELLRowan University Glassboro, NJ 08028 W ith the increased emphasis placed by ABET[1] on assessing learning outcomes, many faculty struggle to develop meaningful assessment instruments. In developing these instruments, the faculty members in the Chemical Engineering Department at Rowan University wanted to ensure that each instrument addressed the three fundamental program tasks as specified by Diamond:[2] The basic competencies for all students must be stated in terms 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 specify learning outcomes congruent with the required competencies.Like many other institutions,[3] Rowan University's Chemical 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 straightforward to design and could be mapped directly to the education objectives specified in Engineering Criteria 2000 (Criterion 3, A-K) as well as the AIChE requirements and other department-specific goals. Regrettably, over-reliance on survey data often neglects those most qualified to assess student performancethe faculty themselves. The faculty agr eed 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 Williams[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 juniorand 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 assignments would be required to ensure that all of the diverse criteria 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, industrially sponsored research project through the Junior/Senior Clinics The Senior Plant Design final report A hazardous operations (haz-op) report One final examination from a junior-level chemical engineering class (Reaction Engineering or Heat Transfer) One laboratory report from the senior-level Unit Operations Laboratory CourseThese items were all constructed-response formats[6-8] in which a student furnished an authentic response to a given assignment 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 correct answer; specific selected response formats include truefalse, 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 Engineering Division of ASEE. His research interests include high performance polymers, 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. 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 Psychology from Clemson University, and a BA in Sociology from Bloomsburg University of Pennsylvania. Copyright ChE Division of ASEE 2002 ChE classroom

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Summer 2002 213problem solving.[10] Although the items contained in the portfolio 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 faculty was, "Compared to whom?" A numerical ranking comparing 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[11] stated, "The challenge for assessment specialists, faculty, and administrators is not collecting data but connecting them." The challenge 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 followed the format developed by Olds and Miller[12] for evaluating unit operations laboratory reports at the Colorado School of Mines.COURSE VS PROGRAMMATIC ASSESSMENTOther chemical engineering departments are also developing rubrics for other purposes. In their exceptional (and Martin-Award winning) paper on developing rubrics for scoring reports in a unit operations lab, Young, et al. ,[13] 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.[14] 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 objectives, 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 Tests and other graded assignments must completely capture these objectives 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 pedagogical research warns of numerous pitfalls associated with using evaluative instruments (grades on exams, papers, etc.) within courses as the primary basis for program assessment.[15]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 phenomena but might never understand how to apply the model to practical physical situations. Another student might understand 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 primary as sessment tool, the impact of the entire learning experience on the student is entirely ignored[16]. Community activities, field trips, service projects, speakers, and campus activities all help shape the diverse, well-rounded professional with leadership skills that industry seeks. The influence of these nonclassroom 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 position to more directly compare our assessment of student work with the assessment of performance provided by student peer reviews, employers, and alumni.RUBRIC DEVELOPMENTThe 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 demonstrate an ability to apply knowledge of mathematics, science, and engineering (ABET-A). Indicators: 1. Formulates appropriate solution strategies 2. Identifies relevant principles, equations, and data 3. Systematically executes the solution strategy 4. Applies engineering judgment to evaluate answersOnce 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 demonstrates when confronted with a problem. The highest level should show metacognition,[16] the students' awareness of their own learning skills, performance, and habits. To achieve the highest level, students not only have to approach the problem correctly, but they must also demonstrate an understanding 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 imply "twice as good" as a score of two. All of the other assessment instruments used by the Chemical Engineering Department had a five-point Likert scale, so a faculty team set out to develop meaningful scoring rubrics 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-

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214 Chemical Engineering EducationTABLE 1 4 3 2 1 Formulates appropriate solution Can easily convert wordForms workableHas difficulty inHas difficulty getting strategies problems to equations;strategies, but may not beplanning an approach;beyond the given unless sees what must be doneoptimal; occasionaltends to leave somedirectly instructed reliance on brute forceproblems unsolved Identifies relevant principles, Consistently uses relevantUltimately identifies relevantIndentifies some principlesCannot identify and assemble equations, and data items with little or noitems but may start withbut seems to have difficultyrelevant information extraneous effortsextraneous informationin distinguishing what is needed Systematically executes the Consistently implements strategy;Implements well;Has some difficulty in solvingOften is unable to solve solution strategy gets correct answersoccasional minor errorsthe problem when data areproblem, even when all data may occurassembled; frequent errorsare given Applies engineering judgment Has no unrecognizedHas no more than one, if any,Attempts to evaluate answersMakes little, if any, effort to evaluate answers implausible answersunrecognized implausiblebut has difficulty; recognizesto interpret results; numbers answers; if any, it is minorthat numbers have meaningappear to have little meaning and obscurebut cannot fully relate TABLE 2 4 3 2 1 Solutions based upon Has no unrecognizedHas no more than one, if any,Attempts to evaluate answersMakes little, if any, effort to chemical engineering principles implausible answersunrecognized implausible answers;but has difficulty; recognizesinterpret results; numbers are reasonable if any, it is minor and obscurethat numbers have meaningappear to have little meaning but cannot fully relate. 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 eliminates 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 meaningfully different from the levels above and below. In developing these phrases, the following heuristic was used: for the fourpoint 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 writers 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 performance of a novice problem solver. To evaluate a given indicator, professors would read the leftmost description. If it did not accurately describe the performance of the student, they would continue to the next block to the right until the work was properly described. A sample rubric is shown in Table 1.RUBRIC TESTING AND INTER-RATER RELIABILITYOnce the lengthy process of developing scoring rubrics for each objective was completed, the rubrics needed testing. C. Robert Pace[17] 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' performance." 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 engineering 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 assignm ent 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 member 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 perfectall 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 modification is shown in Table 2. "Solutions based on chemical engineering principles are reasonable," in the originally developed scheme, was an indicator that applied to a number of different educational objectives. This was the only rubric for

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Summer 2002 215which scores were not routinely consistent. One heat-transfer 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 wording in one way, some faculty chose to assign 4. This interpretation is understandable because no answer was given, and there was no "unrecognized implausible answer." By the letter of the criteria, the student earned a 4. Some faculty interpreted the criteria differently, however, resulting in the assignment of 1. This interpretation is also reasonablesince 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 assessment 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 t his 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 department. Detailed rubrics are available through the web at CLOSING THE LOOPUltimately, the purpose of gathering detailed assessment data is to improve student learning. Once each year, we review the data in a two-day assessment meeting[3] 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. Specific changes resu lting from these meetings have included a decision to introduce product engineering and economics earlier in the curriculum and to adjust topical coverage in thermodynamics.THE NEXT LEVELThe next goal is to use the rubrics to help guide selection of course objectives across the curriculum. With detailed educational objectives in place and rubrics to assist in their assessment, we hope improved course objectives will be developed that more directly link classroom activities and evaluations with the program goals. The rubrics described in this paper should provide the basis for a more in-depth, formative 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.CONCLUSIONSA 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 results, in conjunction with input from alumni, employers, and the students themselves, serve as a basis for assessment of the chemical engineering program.REFERENCES1.Engineering Accreditation Commission, Engineering Criteria 2000 Accreditation Board for Engineering and Technology, Inc., Baltimore (1998) 2.Diamond, R.M., Designing and Assessing Courses and Curricula: A Practical 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 Electronic 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 Performance and Use Tests, Sage Publishers, Newberry Park, CA (1987) 7.Roid, G.H., and T.M. Haladyna, A Technology for Test-Item Writing, Academic 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. Thorndike ed., American Council of Education, Washington DC (1989) 10.Erwin, T. Dary, Assessing Student Learning and Development, JosseyBass, San Francisco (1991) 11.Banta, T.W., J.P. Lund, K.E. Black, and F.W. Oblander, Assessment in Practice 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, "Criterion-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 Learning and Assessment, Jossey-Bass Inc., San Francisco (1998) 15.Terzini, P.T., and E.T. Pascarella, How College Affects Students: Findings and Insights from Twenty Years of Research, Jossey-Bass Inc., San Francisco (1991) 16.Angelo, T.A., and K.P. Cross, Classroom Assessment Techniques: A Handbook for College Teachers, 2nd ed., 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)

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216 Chemical Engineering Education MASS TRANSFER AND CELL GROWTH KINETICS IN A BIOREACTOR KEN K. ROBINSON, JOSHUA S. DRANOFF, CHRISTOPHER TOMAS, SESHU TUMMALANorthwestern University Evanston, IL 60208-3120Ken Robinson is a Lecturer at Northwestern University with primary responsibility 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 working under the direction of Professor E. Terry Papoutsakis. He received his BS in Chemical Engineering from the University of Illinois, UrbanaChampaign, 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. 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 biological means has continued to grow at an accelerating rate. Accordingly, more and more chemical engineers are being employed 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 "biotechnology 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 attendant technologies. We hope this will introduce them to this broad field while they are still at Northwestern and also enhance 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 fermentation reactor. The initial part of the experiment involves the study of oxygen transfer rates from gas to liquid phases; transient dissolved oxygen profiles resulting from step changes in feed gas oxygen concentration are measured with a dissolved oxygen probe. The growth kinetics of Escherichia coli are then studied in the same reactor under standard conditions. Cell growth is monitored by spectrophotometric analysis 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 preparative 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 systems. Additional major items obtained for this purpose included an Innova 4200 shaken-cell incubator and a basic spectrophotometer (Spectronic 20+). The approximate cost of this equipment is indicated in Table1. Not included in the indicated cost, but of critical importance for this experiment, is a steam sterilizer large enough to accommodate the fermentation 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 reactor top is a stainless steel disk equipped with multiple ports for sampling, introduction of inoculum, gas feed and outlet lines, and in sertion of temperature, pH, and dissolved oxygen measuring probes. Additional specifications are indicated in the Appendix. Copyright ChE Division of ASEE 2002 ChE laboratory

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Summer 2002 217TABLE 1Major Equipment Needed for Experiment Applikon 3-liter fermentor, with control system and$15,000 oxygen, temperature, and pH probes Innova 4200 Incubator$ 5,000 Spectronic Instruments 20+ Spectrophotometer$ 1,700 Total Cost$21,700 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 thermocouple 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 oxygen 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. During a typical cell-growth experiment, air is continuously sparged into the liquid medium in the reactor with the controller 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 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. Operation at these stirrer speeds was found to be convenient and minimized foam formation during experiments (no antifoaming agents were used). Expendable Supplies To perform the following experiments, 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 T ransfer Coef ficient The first quantity measured with this system is the combined mass transfer coefficient for oxygen transfer from the gas to the liquid phase, kLa. (Since the interfacial area available 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 provides 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 deionized water. With the stirring speed set at 250 rpm, the temperature control system is activated and the system is allowed to reach a steady temperature of 37 C. The DO probe, having been previously polarized by operation 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 controller is set to read 0% oxygen. The nitrogen flow is then replaced by air at the same volumetric rate and flow is maintained until the DO probe output remains constant. At this point the controller span is adjusted to read 100% (i.e., saturation 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 concentration is recorded every minute until it returns to 100%. These "step-up" and "step-down" data are then analyzed as indicated below to determine kLa. (B) Determination of Cell Growth Kinetics This is the more difficult and demanding part of the experiment, especially for students unfamiliar with the protocols 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.Figure 1. Fermentation reactor.

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218 Chemical Engineering Education Figure 2. Typical oxygen transfer data: Dissolved oxygen concentration vs. time.Throughout this portion of the experiment, emphasis is placed on the need to maintain sterility and cleanliness of the apparatus and the work area. (1) Preparation of stock culture. This part of the procedure is normally carried out during the first laboratory session 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 55 C 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 (pImP1) 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 37 C 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 37 C, antibiotic solution is added, and the inoculum is allowed to grow overnight (for approximately 12 hours) with shaking at 200 rpm in the incubator. (2) Preparation of the Reactor for Growth Kinetics Studies. The reactor vessel is assembled and filled with deionized water and then autoclaved for approximately 20 minutes along with a supply of liquid LB medium prepared as described above. After the reactor has cooled to room temperature, 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 37 C and the control systems are activated. (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 1015 minutes and its turbidity measured in the spectrophotometer (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 experiment is concluded when the fermentation appears to have reached the stationary phase (see below). This normally requires 4 to 6 hours. The final liquid medium still left in the reactor is autoclaved before disposal, and all equipment is carefully cleaned with bleach and soap.DATA ANALYSIS (A) Determination of Oxygen T ransfer Coef ficient 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 compared 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: OTRkaCCL=Š()()* 1 where

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Summer 2002 219 Figure 3. Typical Oxygen transfer data: Determination of kLa with nitrogen sparging. Figure 4. Typical oxygen transfer data: Determination of kLa with air sparging. C*saturated dissolved oxygen concentration at the gasliquid interface, mmol/L Cdissolved oxygen concentration in the bulk liquid phase, mmol/L kLaliquid phase oxygen mass transfer coefficient, 1/ minute OTRoxygen transfer rate, mmol/L/minuteThe 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 parameters 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 dt OTRkaCCL==Š()()* 2 Considering the experiment in which the initially oxygenfree solution is contacted with oxygen containing gas, Eq. (2) must be integrated with initial concentration = 0 and concentration C* held constant. The well-known result is l n CC C katL* Š()=Š() 3 For the reverse experiment in which the solution is initially saturated at concentration C* and the gas concentration is = 0, the solution is l n C C katL* =Š() 4 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-1 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-1. One other measurement of kLa 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 kLa determined, however, was 0.075 min-1. Thus, it is clear that this mass transfer coefficient is a strong function of the degree of agitation in the vessel and the properties of the liquid. It should be noted that Roberts, et al.,[3] previously described a laboratory experiment to measure oxygen transfer in a 1liter stirred fermentor. In that case, the stirring rate was considerably higher (500 to 700 rpm) and the method of determining kLa was different; those authors measured the quasisteady-state rate of oxygen consumption by yeast in the absence 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 magnitudeapproximately 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 of kLa at 250 rpm would be 0.089 min-1, which is unexpectedly close to the value of 0.15 min-1 found here under considerably different conditions. (B) Determination of Cell Growth Kinetics The immediate objective of the second part of the experiment is to measure the specific growth rate of the E.coli culture 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 environment. 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 permit. This phase is followed by a stationary phase where the rate of oxygen supplied to the cells equals their rate of oxygen 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 during the exponential binary fission phase. This process is au-

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220 Chemical Engineering Education Figure 5. Typical batch culture growth phases. Figure 7. Determination of specific cell-growth rate. Figure 6. E.coli growth data: solution absorbance vs. time.tocatalytic and is usually represented as a first-order reaction, i.e., dX dt X =() 5 Integration of this differential cell balance yields XtXttoo()=Š()[]()exp 6 whereXcell concentration, number/volume ttime, minutes cell specific growth rate, 1/minute oas a subscript refers to initial conditionsIn the present experiments, cell concentration in the reactor is monitored at 10to 15-minute intervals by measurement 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 absorbing species, as shown in I I clo=Š()()exp 7 whereI/Iofractional light intensity relative to incident intensity cconcentration of absorbing species, number per unit volume llength of light path through solution extinction coefficient of absorbing species, area per numberStrictly speaking, for the present experiments 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 -log10(I/Io), it follows from Eqs. (6) and (7) that A lc lX tto o==Š()[]() 23032303 8 .. exp Taking natural logs of Eq. (8) yields ll nn Att lXo o()=Š()+ () 2303 9 Thus, a plot of l n 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, td, can be calculated once the growth rate is determined, according to td=()()l n 2 1 0 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 sometime 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 of the E.coli in this experiment was 0.013 min-1. This is equivalent 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 cellgrowth experiments has been maintaining the dissolved oxygen 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.

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Summer 2002 221 DISCUSSIONThe 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 processes involved, the underlying theoretical analysis, and relevant methods of data analysis, as well as the relatively long time scale of the experiments. The latter is not a serious problem 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 experiments 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 student group to another, the LB media have proven to be robust and easy to use. Some lack of reproducibility was not considered to be a significant drawback in the present context. A related laboratory experiment[4] used the growth of yeast ( Saccharomyces cerevisiae ) and involved the simultaneous use of two fermenters. The rate of oxygen transfer to the liquid 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 concentrations 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 interested in providing some direct introduction to biotechnology in their curricula. Of even greater importance than equipment 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 technical assistance and encouragement.ACKNOWLEDGEMENTSWe wish to thank the following 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 Science for the financial support that made it possible to develop and bring this new experiment to full realization.REFERENCES1.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) APPENDIX1. Composition of Luria-Bertani liquid medium: Per liter of solution:NaCl10 grams Tryptone10 grams Yeast extract5 grams Deionized water1 liter 2. Composition of Luria-Bertani solid medium: Per liter of solutionNaCl10 grams Tryptone10 grams Yeast extract5 grams Agar15 grams Deionized water1 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

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222 Chemical Engineering Education TEACHING ChE TO BUSINESS AND SCIENCE STUDENTS KA M. NGHong Kong University of Science and Technology Clear Water Bay, Hong Kong T he chemical processing industries (CPI) have undergone profound changes, and companies are under considerable pressure to restructure and innovate in a global 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 technologies. 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.[1,2] This creates new business opportunities and the need for better understanding of the global issues of chemical processing. In response, there is considerable effort to broaden chemical engineering education to include emphasis on entrepreneurship, lifelong learning, 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 opportunities 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 Copyright ChE Division of ASEE 2002the 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 OBJECTIVESHong Kong (a Special Administrative Region of China since 1997) is a vibrant, international city of 6.7 million inhabitants 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 chemical-related businesses. While the manufacturing sector within Hong Kong is comparatively small, extensive manufacturing 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 products that can offer a higher return on assets. Of particular interest are high-v alue-added chemicals and pharmaceuticals. 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.[3]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 curriculum 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 Massachusetts. His research interests are in process systems engineering involving reactions, crystallization, and solids processing of high-valueadded products.

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Summer 2002 223The organization and finance of a typical chemical company Product-centered processing The history of chemical engineering The global chemical businessCOURSE DESIGNThe 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 manufacturing sectors such as electronic and electric equipment, motor vehicles, and parts, etc. We show how the return on assets 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.[6] 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 petroleum 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 formulation, 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 Detergent Association[7] 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.[8]The students gain an appreciation for both the need for differentiated products that drive reformulations and the challenges faced by suppliers of detergent ingredients. We consider the replacement of sodium tripolyphosphate with zeolites 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 subject. 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.[9,10,11]Next we show the students that all of these products originate 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 animals). We show the primary reaction for conversion of one compound (or compounds) to another.[12] For example, urea is manufactured from ammonia and carbon dioxide; polyester results from a polycondensation reaction between ethylene 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 segments 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 production of chemicals using Douglas' hierarchical approach.[13]After covering input-output, recycle structure, and separation systems, we discuss chemical engineering unit operations. These include reaction, evaporation, drying, distillation, absorption, extraction, crystallization, adsorption, filtration, etc.[14] We discuss basic principles but omit equations for equipment design. We use The Visual Encyclopedia of Chemical Engineering Equipment developed at the Univer-TABLE 1Outline of TopicsSection1. 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 AsiaTABLE 2Chemicals in Our Daily Lives Petroleum Fibers Soaps and detergents Plastics Oils and fats Natural products Traditional Chinese medicines

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224 Chemical Engineering EducationFigure 1. The production of towngas by catalytic reforming of naphtha using steam. sity of Michigan to supplement the lectures. The animated equipment operations are very helpful to the non-engineering students. At this point, we briefly discuss safety and environment 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 processing 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 second stage removes hydrogen sulfide with zinc oxide. In the reaction system, the desulfurized naptha is converted to methane and hydrogen, and carbon monoxide is converted to carbon 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, indirect costs, working capital, etc. We also cover (particularly for science students) the time value of money and the discounted cash-flow rate of return on investment. Normally, we assign a project in which the students perform cost evaluation of a chemical plant. The flowsheet and all major equipment sizes and operating conditions are given, assuming that this input information has been obtained from chemical engineers 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 controlled 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 assignment. 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 appreciation of the financial position of a typical chemical company 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 businesses, 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, process planning, piloting, and plant startup. We explain 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 and processing conditions (see Figure 2). We identify the desired performance of the product, both functional and sensorial, and select the requisite ingredients. The process flowsheet and the operating conditions are then identified. We study the modern CPI in Section 6.[4] 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 approximately $200 billion. We then examine the financial performance of the top global chemical companies, emphasizing the top twenty-five chemical-selling countries in 1999 (see Table 3).[3] It is evident from the statistics that chemical production per capita in Asia is below the world average, but (unsurprisingly) it is rapidly gaining ground. Singapore is a net exporter competing in the international market. Although China is not expected to be self-sufficient, its rapid development and purchasing 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.[16]COURSE EVALUATIONThe 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 business 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 acquired 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 interrelationships among different decisions. Most students felt that

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Summer 2002 225 Figure 2. Step-by-step procedure for product-centered process synthesis and development.1.U.S.435 2.Japan205 3.Germany104 4.China91 5.France78 6.United Kingdom50 7.South Korea50 8.Italy49 9.Brazil36 10.Belgium35 11.India31 12.Spain30 13.Taiwan30TABLE 3Top Twenty-Five Chemical-Selling Countries in 1999 (in US$ billions)[3]14.Netherlands28 15.Switzerland26 16.Russia25 17.Canada21 18.Mexico15 19.Australia`14 20.Argentina10 21.Sweden9 22.Malaysia8 23.Poland6 24.Singapore5 25.Thailand5 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 EDUCATIONWith the reshaping of the global economic landscape, the demarcation between disciplines has become blurred. It is highly desirable to have an appreciation of contemporary global 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 typical 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 chemical plant, it is interesting to investigate whether or not a proposed retrofit project or a new investment adds to the shareholder value. Indeed, it is not uncommon to request that the engineers and researchers in a company justify an R&D proposal in terms of potential return on investment as well as on its technical merits. Similarly, the lectures on product-centered process synthesis and development is suitable for chemical 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.ACKNOWLEDGMENTSI would like to thank Bruce Vrana for his teachings on corporate 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.REFERENCES1.Cussler, E.L., and J.D. Moggridge, Chemical Product Design, Cambridge 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 Applications, Technomic Publishing Co., Lancaster, PA (1998) 12.Rudd, D.F., S. Fathi-Afshar, A.A. Trevino, and M.A. Stadtherr, Petrochemical Technology Assessment, John Wiley and Sons (1981) 13.Douglas, J.M., Conceptual Design of Chemical Processes, McGrawHill, 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 Corporate Finance, 5th ed., McGraw Hill, New York, NY (2000) 16. Bank of America's Guide to Petrochemicals in Asia, EFP International, Hong Kong (1997)

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226 Chemical Engineering Education INTEGRATING KINETICS CHARACTERIZATION AND MATERIALS PROCESSING IN THE LAB EXPERIENCE DENNIS J. MICHAUD, RAJEEV L. GOROWARA, ROY L. MCCULLOUGHUniversity 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-sectioned t hermoset composites can be difficult 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 laboratory exercise while at the same time introducing students to the manufacture and properties of composite materials. A numerical cure simulation of the RTM process,[1] 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 differential scanning calorimetry (DSC). Using an empirical nonlinear kinetic model for the thermosetting resin,[2] the data is correlated to establish the model parameters needed by the process simulation. The simulation is then used for a preliminary design of the processing conditions required to successfully produce a one-inch-thick composite laminate within a two-hour processing window. The sensitivity of their design to kinetic parameter variability is also investigated.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 thicksectioned 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 composites. He received his BS and MS from Ohio State University. He is currently a Consulting Engineer in the DuPont Engineering Particle Science and Technology Group. Roy L. McCullough was Professor of Chemical Engineering at the University 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 polymer science and composite materials. 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, producing 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 modifications 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 experimentally, producing a new (and hopefully improved) composite laminate. The students then use a combined evaluation of the simulation's model parameters and their processChE laboratory Copyright ChE Division of ASEE 2002

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Summer 2002 227 ing experience to propose a final design in their written report.THICK-SECTIONED COMPOSITE MANUFACTURINGThe 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 exothermic nature of the reacting resin and the heat transfer limitations of the polymer and glass fiber composite.[1,3] Unfavorable processing 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 identify an acceptable temperature trajectory (or "cure cycle") that balances 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 Figure 2 shows experimentally measured heater and composite (measured at the center of a one-inchthick laminate) temperatures. The cure cycle is broken up into different stages, each with a specific heater set-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 conductivity of the composite, almost 60 minutes of processing is required for the center of the composite to reach the heater set-point, but once the resin at the center begins to cure, the heat generated 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 processing time. Since the surface temperature of the composite remains much closer to the heater set-point, a post-cure is generally required to ensure the surfaces of the composite are adequately cured for removal of the part from the mold.LABORATORY FORMAT AND EDUCATIONAL OBJECTIVESAt Delaware, the undergraduate chemical engineering 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, measurement techniques, statistics, report writing, and oral presentation. In the junior course, student groups go through three experimental cycles, with each cycle centering around a design problem using information gathered during a laboratory experiment. Over a four-week period, the students must learn about the problem, perf orm the experiment, analyze the data, prepare a preliminary data report, revise the data analysis, and complete the design problem in a final report. In the first week of a cycle, the students prepare for the lab by reviewing the experiment and laboratory procedures with the teaching assistant (TA). They prepare an experimental proposal, and dur-Figure 1. Schematic of integrated undergraduate laboratory experiments. Figure 2. Example cure cycle and corresponding internal composite temperature.

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228 Chemical Engineering EducationFigure 3. Example heat flow of a differential scanning calorimetry (DSC) experiment. ing the graded pre-lab conference they present it to the supervising faculty member, who must be convinced that valuable "research facility" time should be spent on the problem. 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 conclude the data analysis and preliminary data report. The students then use their lab data during the fourth week for the design problem and present the final report for the cycle to the faculty member. At the conclusion of the course, the individual groups orally present one of their experiments to their colleagues and faculty and then critique their videotaped 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 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.[4] These objectives include analysis, synthesis, and evaluation, referred to as "higher-level skills" by Felder, et al.[5] The fundamental objectives of knowledge, comprehension, and application are referred to as "lower-level skills." We agree with Miller, et al.,[6] that the engineering laboratory is an ideal setting to help students become better engineering practitioners and to enhance their higher-level thinking 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 determination of experimental data, but also on a design problem using that data. In the terms of Bloom's taxonomy, the higherlevel 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 motivation 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 depth of experience: evaluation of the validity of experimental 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) evaluation of their ability to evaluate The supervising professor focuses on the higher-level skills, guiding students in analyzing their data, using it in the synthesis of a new process design, and evaluating that design in the process experiment. The TA tends to focus on the lower-level skills: knowledge of polymerization kinetics and composites processing; comprehension of the experimental methods; and application of that knowledge to extract model parameters from the experimental data.KINETICS OF THERMOSET POLYMER CURE (JUNIOR YEAR)The junior-level composite laboratory experiment requires that the students evaluate the resin's kinetic parameters necessary to predict the resin curing behavior within a thick-sectioned composite and to develop a preliminary design of the processing conditions for a one-inch-thick composite laminate. The students 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 consistently 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, ddt by =() tH dQ dtult1 1 and the extent of ploymerization (cure), t H dQ dt dtult t t ()= ()1 2 0 where Hult is the total heat of reaction given by HHH dQ dt dt dQ dt dtultrxnresidual t t t tfisothermal fisothermal=+= + () 0 3 ,

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Summer 2002 229The 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. Hult 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 t0 = 3.2 minutes (when the DSC pan is added to the cell) to the final isothermal time point, tf,isothermal, of 20 minutes. The temperature of the DSC cell is then ramped at 5 C/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-isothermal cure. The kinetics of the free-radical polymerization can be described using the popular autocatalytic model[2,8] shown in Eq. (4), which gives the reaction rate, ddt as a function of the fractional extent of cure, the maximum extent of cure, ma x and an overall reaction order of 2 d dtkm m =Š()()Š max 2 4 and t mktm()+Š()[]()=Š()max max /11 5 11 An Arrhenius expression is used to account for the temperature dependence of the rate constant, k kAE RTa=Š ()exp 6 For the incomplete curing case in which vitrification occurs before complete reaction, the maximum extent of cure, ma x for an isothermal curing temperature is less than one, and a linear relationship may be used to approximate the effect of temperature, T, on ma x maxmax=+<()aaTfor011 7 We have used the resin Derakane 411-C50 (Dow Chemical), a free-radical polymerizing resin that is 50 wt% DGEBAbased vinyl ester and 50 wt% styrene, since we use it in other projects.[1,9] Alternative resin systems can easily be implemented, 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 (Hult, A, Ea, m, a0, and a1) required by the cure simulation. We recommend that the students first determine Hult, then ma x (T), and then k(T) and m at each cure temperature, using nonlinear regression. We make available for their use KaleidaGraph (Synergy Software), which allows curve fits of nonlinear functions. To help ensure reasonable curve fitting results, we ask the students to use their derived kinetic model to predict the extent of cure ( ) as a function of time and compare that to the experimental extent of cure data. The students estimate the error for some of the parameters based on the nonlinear regression fitting of the data, and the error for the others is determined by propagation of experimental measurement errors. The melting of a standard Indium 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 appreciation 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 introducted to simulation-based batch-process cycle design, focusing primarily 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 finite difference cure simulation[1] 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.*

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230 Chemical Engineering Education Figure 4. Diagram of resin transfer molding (RTM) equipment.The students are informed of the different process parameters that must be controlled to meet the product design limits. For example, void formation is affected by the vaporization 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 (and possible delaminations) once the resin at the center begins to polymerize. In terms of minimizing processing time, the students are given the goal of curing the composite ( surfac e > 0.75) in less than 2 hours. The juniors present their proposed design in their final report for the DSC experiment. In their senior year, they again visit the simulation-based design problem, but with a new emphasis on the material properties of the compo site (resin content, composite density, thermal conductivity, etc.), heat transfer coefficients within the mold, and the effect of fibers on the kinetic behavior of the resin.DESIGN AND MANUFACTURE OF THICKSECTIONED 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 thicksectioned composites is that extrapolating kinetic data down to the lower temperatures necessary for thick-sectioned cure can result in significant error.[1] 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 students are responsible for measuring and/or estimating the physical properties of the composite and the mold environment ( e.g., volume fraction of the resin, composite density and thermal conductivity, and effective heat transfer coefficients). The students are given the pure component properties for the resin and glass fibers for their calculations. Heat capacity of the composite is estimated using the "rule of mixtures," and its thermal conductivity can be predicted using a number of techniques.[10,11]The seniors begin their composite laboratory sequence with a tour of the composite manufacturing equipment and an overview of the experimental procedure and safety issues. The experimental RTM equipment is shown in Figure 4. Using their experience from the junior lab, students use the on-line simulation to identify the cure cycle they will implement experimentally. The simulation is also used to analyze the effect of possible model parameter variations on the cure cycle ( i.e., sensitivity analysis). The lab begins with the students filling the stainless steel mold with a predetermined 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 manufacturing. 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, contained within a pressurized pot, is injected into the roomtemperature 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 temperature 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 composite temperatures during processing. When the observed temperatures do not match those generated by the simulation, the students are challenged with modifying the cure cycle on-line according to insights from their sensitivity analysis.

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Summer 2002 231Once 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 parameters 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 comparing the simulated composite temperatures with those measured 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. Likewise, the students compare simulated composite temperatures to those measured during the curing phase of the resin to identify possible changes in kinetic parameters due to lower processing temperatures and the effect of fibers. As is shown in Figure 2, the numerical simulation generally 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 knowledge 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 manufacture 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 report of the lab. This report also includes a sensitivity analysis of their final design and recommendations as to how the simulation and the experiments might be improved in order to better generate an "optimal" cure cycle design that can account for observed batch-to-batch variability.CONCLUSIONThe double sequence of junior and senior laboratory experiments described in this paper has been implemented successfully at the University of Delaware for the past five years. In order to understand the goals of the experiments and complete the design portion, students are required to integrate a number of important engineering concepts, including kinetics, 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 laboratory experiences, the ability to take a piece of the final product 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 provides an invaluable experience for the students.ACKNOWLEDGEMENTThe paper is dedicated to the memory of Professor Roy L. McCullough, coauthor, educator, mentor, and friend, who passed away unexpectedly in December of 2001.REFERENCES1.Michaud, D.J., A.N. Beris, and P.S. Dhurjati, "Curing Behavior of Thick-Sectioned RTM Composites," J. of Comp. 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. of Appl. Polymer Sci., 41 3043 (1990) 3.Ciriscioli, P.R., Q. Wang, and G.S. Springer, "Autoclave Curing: Comparisons 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, and A. 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 Thinking in the Unit Operations Laboratory," Chem. Eng. Ed., 32 (2), 146 (1998) 7.Willard, H.H., L.L. Merritt, Jr., J.A. Dean, and F.A. Settle, Instrumental Methods of Analysis, 7th ed., John Wiley & Sons, New York, NY (1988) 8.Kamal, M.R., and S. Sourour, "Kinetics and Thermal Characterization 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 Fiber-Vinyl Ester Composites upon Hygrothermal Exposure," Composites Part A accepted for publication 10.Springer, G.S., and S.W. Tsai, "Thermal Conductivities of Unidirectional Materials," J. of Comp. Mats., 1 166 (1967) 11.Farmer, J.D., and E.E. Covert, "Thermal Conductivity of an Anisotropic Thermosetting Advanced Composite During Cure," Am. Inst. of Aeron. and Astron.:Structures, Structural Dynamics, and Materials, 5 (56), 2939 (1995) ERRATA The phrase "to appear in" in citations 4 and 7 of "Developing Troubleshooting Skills in the Unit Operations Laboratory," by Aziz M. Abu-Khalaf, published in CEE 36 (2), p. 122, (2002), should be omitted.

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232 Chemical Engineering Education SCALING OF DIFFERENTIAL EQUATIONS "Analysis of the Fourth Kind"Paul J. Sides is currently Professor of Chemical Engineering at Carnegie Mellon University. He received his BSChE from the University 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 technology. Copyright ChE Division of ASEE 2002 PAUL J. SIDESCarnegie 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 availa ble 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 solution per se is not found, but the student learns about the dependence of the solution on relevant parameters and/or obtains 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 differential equation; it provides insight into an equation even when the solution to the equation or set of equations is unknown. This process of d educing 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 demonstrate 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 literature on a similar topic. Hellums and Churchill[1] described a general method for analyzing equations; their method reveals cases where similar solutions are found and at least indicates minimum numbers of par ameters 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 comprehension of its significance are not compelling arguments for introducing them to it. Likewise, this contribution is not intended for the experienced analyst who performs these operations subconsciously or has seen them all. This method is intended primarily for advanced undergraduates 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 1Viscous Heating and the Brinkman Number Consider first the classic problem of viscous heating appearing 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 important. 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: cv z k rr r T r v rpz z = + ()1 12 where T = temperature, To = incoming temperature, Tw = wall ChE classroom

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Summer 2002 233temperat ure, vz = axial velocity in laminar pipe flow, = density 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 mechanical energy into thermal energy.[2] Before going to the trouble of solving the equation, or looking up the answer, we can use a scaling analysis to estimate the importance of the effect. This example illustrates the process of natural scaling and the deduction of the pertinent dimensionless 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 therefore temporarily give the axial length scale a name and deduce 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. !" Š Š() r R z z TT TTo w ow 2 For laminar pipe flow: vvz=<>Š()212 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 equation by the convective energy transport coefficient 2 3 cv TT zp ow o<> Š () The result is 1 2 1 16 2 4 2 22 2Š() = <> + <> Š()() "! kz cvR vz cRTTo p o pow 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 Figure 1. Laminar flow of a viscous liquid in a pipe of circular cross section.choice is not often critical as long as the term chosen is important 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 conduction term is scaled to 0(1) by equating its coefficient to unity and solving for the unknown length scale. z vRc k o p <>()2 5 2 With the inclusion of this axial length scale, the overall energy equation can now be written as 1 1 16622Š() = +() " Br where Br v kTTow <> Š()()27 The analysis yields two results. First, the temperature of the incoming fluid changes substantially toward the wall temperature over a distance zo that is calculable from known quantities of the problem. Second, the resulting parameter in Eq. 7, (Br), is a dimensionless group that governs the importance of viscous heating;[2] 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 propor tional to the viscosity and the square of the velocity, and inversely proportional to conductivity of the liquid. If 16Br is very small, we can ignore viscous heating the usual case; otherwise, we should consult the published work.[2]Guidelines 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 distance, give it a name and try to deduce it as part of the analysis (remember R and zo). 3)Nondimensionalize 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, 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-

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234 Chemical Engineering Education Figure 2. Geometry for natural convection near a heated wall. efficient contains only one unknown distance or other normalizing 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 zo 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 results. This process develops insight and experience that make the analysis meaningful. If one plans to solve the complete equation 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 2Natural Convection Near a Vertical Heated Surface How much can be said about a classic case of natural convection 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 equations for the laminar case (GrPr < 109) are the following: Continuity + =() v y v zy z0 8 Motion v v y v v z v y v z gTTy z z zzz c + = + +Š()()2 2 2 2 9 Energy cv T y v T z k T y T zpyz + = + ()2 2 2 21 0 where vy = y velocity, vz = z velocity, T = temperature, Th = wall temperature, Tc = bulk fluid temperature, cp = thermal heat capacity, k = thermal conductivity, g = gravity, = coefficient of expansion, = density, = viscosity, y = horizontal position, and z = vertical position. For completeness, no assumption has been made about the relative importance of cunduction or convection in the direction 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 distance for y is unclear since the domain is infinite in that direction. 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 variable over its range !#" Š Š() z H y y TT TTo c hc1 1 Likewise, there are no natural reference velocities for the vertical and horizontal velocities, so give them names as well ( zzozyyo yvvvv /,/ ) and define BgTTwc=Š() After inserting them into the momentum equation, we obtain # # vv y v H v y v H Boyoz o y z oz z z oz o z oz z + = + +()2 2 2 22 2 212 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 Hv yv H yv Hv BH voy ooz y z z z ooz z oz z oz # $ # $ + = + +()2 2 2 2 2213 At this point, there are two terms that contain only one of the unknown reference variablesthe second and third terms on the right-hand side. Typically, diffusion of momentum is negligible 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 natural convection, the source term for momentum must be O(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 v BHoz() 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 diffusion terms yields y H B andv B Hooy= = () 2 14 2 3 1415// and the differential equation becomes # # "y z z zzzHB + = + +()2 2 2 3 2 216

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Summer 2002 235This is as it should be. The typically important boundarylayer type terms are all of order unity along with the source term driving them. The axial diffusion of momentum is multiplied by a coefficient that allows its importance to be assessed. 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 equation and obtain # " # !yzHB + = + ()1 1 7 2 2 2 3 12 2 2Pr / The equation contains two parametersPr 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 qhTT k T y Nu hy k h k H Bwc y oŠ()= Š %== =Š ()= = 0 2 14 01 8 ## / Still no Grashof number appears. Note that the appropriate scaling distance for heat flux normal to the wall is the hydrodynamic boundary-layer thickness yo. 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 qhTTk T y Nu Nu H y B H Hwc y H y ooŠ()=Š %= =Š ()= = 0 0 2 141 1 9 # # / The coefficient on the far right-hand side is recognizable as Gr so that the definition of NuH becomes NuGrH=Š ()=" ## 0 142 0 / The dimensionless temperature gradient at the wall is a function solely of the Pr number, as we found scaling of the system of coupled equations and is most often written as Š ()=" ## 0 142 1 f(Pr)Pr/ where f(Pr) is a slowly varying function of Pr. This definition leads to the tidy form NufGrH=()()()PrPr/ 142 2 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 problem 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.CONCLUSIONSScaled equations are the standard for most journal publications, but apart from this standard, the process of scaling differential 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 diffusive and convective terms. We suggest that this perspective be imparted concurrently with the method where necessary. We hope the method presented here helps advanced undergraduates and first-year graduate students become accustomed 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 Examples1)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 imbalance 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 momentum can be neglected. Deduce an estimate of the thickness of the hydrodynamic boundary layer for a plate of length L. A close approximation to the exact answer is 5 $ Lv / How does your answer compare to this? 4)Write the energy equation for the above example, including the a xial conduc tion term. Use the reference distances developed 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 boundary layer thickness. Show that the ratio of the hydrodynamic layer thickness to the thermal layer thickness is given by Pr1/2.REFERENCES1.Hellums, J.D. and S. W. Churchill, AICHE J. 10 p. 110, (1964). 2.Brinkman, H.C., Appl. Sci. Research A2 p. 120, (1951).

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236 Chemical Engineering Education THE USE OF SOFTWARE TOOLS FOR ChE EDUCATION Students' Evaluations Copyright ChE Division of ASEE 2002 ChE classroomABDERRAHIM ABBAS AND NADER AL-BASTAKIUniversity of Bahrain Bahrain 32038 O 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 computers within the reach of organizations and people who were once deterred by cost or by complex mathematics and programming expertise. The ease of use and enhanced capabilities 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 educator 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.[1,2]The positive impact of information technology on teaching and learning is no longer questionable.[3-5] Kulik and Kulik[4] reported that most studies found that computer-based instructionusing technology of the eightieshad positive effects on students. In particular, students learned more and faster (the average reduction in instructional time in 23 studies was 32%). The students also developed more positive attitudes and liked classes more when they use computers. The