Chemical engineering education ( Journal Site )

Material Information

Chemical engineering education
Alternate Title:
Abbreviated Title:
Chem. eng. educ.
Physical Description:
v. : ill. ; 22-28 cm.
American Society for Engineering Education -- Chemical Engineering Division
Chemical Engineering Division, American Society for Engineering Education
Place of Publication:
Storrs, Conn
Publication Date:
annual[ former 1960-1961]


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


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

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Source Institution:
University of Florida
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
oclc - 01151209
lccn - 70013732
issn - 0009-2479
lcc - TP165 .C18
ddc - 660/.2/071
System ID:

Full Text

chemical engineering education



SFeature Articles...
SGetting the Most Out of Graduate School (pg. 258), Rajagopalan
K | A ChE Graduate Course in Materials Design (pg. 262), Mitchell
SA Survey Course In Particle Technology (pg. 266), Sinclair
Experiences With an Experimental Project in a Graduate Control Course (pg. 270)
Gatzke, Vadigepalli, Meadows, Doyle

Essay: Universities...Why? (pg. 288)
"i IHaile
Particle Technology on CD (pg. 282)
SHow to Lie With Engineering Graphics (pg. 304)
" Vesilind
* I Computer Simulation of Tracer Input Experiments (pg. 300)
i .8 Conesa, et al.
t Designing a Petroleum Design Course in a Petroleum Town (pg. 322)
SYarranton, Svrcek
Activities to Enhance Understanding of the Mole and Its Use in ChE (pg. 332)
Fraser, Case
. I A Phenomena-Oriented Environment for Teaching Process Modeling (pg. 292)
a Foss, Geurs, Goodeve, Dahm, Stephanoloulos, Bieszczad, Koulouris
5 AActive Learning vs Covering the Syllabus and Dealing With Large Classes (pg. 276)
it a Felder, Brent
I Class and Home Problems: Beware of Bogus Roots With Cubic Equations of State (pg. 278)
S I Pratt
SIntroducing Process Control Concepts to Senior Students Using Numerical Simulation (pg. 310)
.4 o |Aluko, Ekechukwu
S Using a Cogeneration Facility to Illustrate Engineering Practice to Lower-Level Students (pg. 316)
Hesketh, Slater
Low-Cost Experiments in Mass Transfer: Part 5. Desorption of Ammonia from a Liquid Jet (pg. 328)
Baird, Nirdosh

W-1999 Awards: ASEE ChE Division (pg. 287)



Graduate Education

can be found on

pages 336-337

Chemical Engineering Education
Department of Chemical Engineering
University of Florida Gainesville, FL 32611
PHONE and FAX: 352-392-0861
Web Page:

T. J. Anderson

Phillip C. Wankat

Carole Yocum

James O. Wilkes
University ofMichigan
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University of Texas, Austin

E. Dendy Sloan, Jr.
Colorado School of Mines

Gary Poehlein
Georgia Institute of Technology
Klaus Timmerhaus
University of Colorado

Dianne Dorland
University of Minnesota, Duluth
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
David F. Ollis
North Carolina State University
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New Jersey Institute of Technology
Ronald W. Rousseau
Georgia Institute of Technology
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University of Delaware
Richard C. Seagrave
Iowa State University
M. Sami Selim
Colorado School of Mines
James E. Stice
University of Texas at Austin
Donald R. Woods
McMaster University

Chemical Engineering Education

Volume 33 Number 4 Fall 1999

258 Getting the Most Out of Graduate School, Raj Rajagopalan
262 A ChE Graduate Course in Materials Design, Brian S. Mitchell
266 A Survey Course in Particle Technology, Jennifer L. Sinclair
270 Experiences With an Experimental Project in a Graduate Control Course,
Edward P. Gatzke, Rajanikanth Vadigepalli, Edward S. Meadows, Francis J.
Doyle, III

276 FAQS. II: Active Learning vs Covering the Syllabus and Dealing With Large
Classes, Richard M. Felder, Rebecca Brent

278 Beware of Bogus Roots With Cubic Equations of State, Ronald M. Pratt

282 Particle Technology on CD, Martin J. Rhodes
322 Designing a Petroleum Design Course in a Petroleum Town,
H.W. Yarranton, W.Y. Svrcek

288 Universities...Why?, J.M. Haile

292 A Phenomena-Oriented Environment for Teaching Process Modeling: Novel
Modeling Software and Its Use in Problem Solving,
Alan S. Foss, Kevin R. Geurts, Peter J. Goodeve, Kevin D. Dahm, George
Stephanoloulos, Jerry Bieszczad, Alexandros Koulouris
300 Computer Simulation of Tracer Input Experiments,
J.A. Conesa, J. Gonzdlez-Garcia, J. Iniesta, P. Bonete, M. Inglis, E. Expdsito,
V. Garcia-Garcia, V. Montiel
310 Introducing Process Control Concepts to Senior Students Using Numerical
Simulation, Mobolaji E. Aluko, Kenneth N. Ekechukwu
316 Using a Cogeneration Facility to Illustrate Engineering Practice to Lower-Level
Students, Robert P. Hesketh, C. Stewart Slater
332 Activities to Enhance Understanding of the Mole and Its Use in ChE,
Duncan M. Fraser, Jennifer M. Case

304 How to Lie With Engineering Graphics, P. Aarne Vesilind

328 Low-Cost Experiments in Mass Transfer: Part 5. Desorption of Ammonia from
a Liquid Jet, M.H.I. Baird, I. Nirdosh

> 287 1999 Awards: ASEE ChE Division
I 336 Index of Graduate Education Advertisements

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 0 1999 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 and for back copy costs and
availability. POSTMASTER: Send address changes to CEE, Chemical Engineering Department., University of Florida,
Gainesville, FL 32611-6005. Periodicals Postage Paid at Gainesville, Florida.

Fall 1999

Graduate Education








University of Florida
Gainesville, FL 32611-6005

Raj Rajagopalan is Professor of Chemical
Engineering at the University of Florida, where
he has been since 1996. His research activi-
ties focus on colloid physics and complex flu-
ids. In addition, he maintains an active interest
in teaching, at both
undergraduate and
graduate levels. His
educational activi-
ties include the co-
author-ship of a
textbook on colloids
and the develop-
ment of other in-
structional materi-

Copyright ChE Division of ASEE 1999

What you learn in graduate school can be the foundation for a life-long
learning experience and a successful career, but it takes more than a
good undergraduate preparation and a desire to get an advanced de-
gree to get the most out of graduate school. The following are some bits of advice
and recommendations based on my own experience-as a student and as a faculty
member-and on what I have learned from my students.
Although my comments are addressed largely to graduate students in science
and engineering, many of the observations should be of use to all graduate
students regardless of the discipline. Some of the books in the list of annotated
references at the end of this article have detailed guidelines on specific issues you
may face during your studies and beyond. One of the books, by Carl Djerassi, a
renowned chemist, is a fictionalized account of the competition and personal,
professional, and ethical issues faced by graduate students and faculty involved in
day-to-day research. Another, by James Watson of the DNA fame, is a real-life
story that reads almost like fiction. I have included these two books so that you
have more than a list of dry, self-help books. These two books may possibly teach
you more than all the other books put together.
The first and foremost factor is the attitude one brings to one's life. A major part
of how successful we are in what we set out to do in life often depends on our
attitude. Someone who feels like a "winner" is more likely to be a winner; one
who feels like a victim is likely to end up being one. Most people I have talked
with recall their student years as among the best years of their lives, but how much
you get out of school, professionally and otherwise, depends to a large extent on
what expectations, commitment, and discipline you have and how much you
demand from yourself. Here are some points to ponder:
First, know what to expect from graduate school and what commitments and
responsibilities are expected of you.
Do not postpone learning essential professional skills and basic "life skills"
until you get a job or leave graduate school. For example, I have known many
students who have, either consciously or subconsciously, postponed learning
good communication skills, social skills needed for teamwork, and building a
network of peers-only to regret it later.
As a research student, you are a junior research colleague of the faculty. This
is a privilege, but one that comes with certain responsibilities and account-
ability. Be aware of them.

Discipline Matters
> Graduate research is about generating or implementing new concepts. It is not
a nine-to-five job. Most successful students I know put in at least seventy
hours a week. Expect to work hard, and expect to spend long hours.
- Keep good laboratory notebooks. Divide them into sections, e.g., one for
research papers you read, one for your own work, etc. When you read a
research article, write down the full reference in your research notebook, cut
and paste a xerox copy of the abstract into the notebook, summarize the salient
Chemical Engineering Education

Graduate Education

points and major results, cut and paste key figures and
tables (along with your own annotations), summarize
major questions of interest to you, and make a list of
important cross-references. (You may also want to write
down the dates to make the research notebook a "jour-
nal" of your thoughts and progress.) You'll appreciate
the value of this habit when you are ready to write up
your results for publication or when you write your the-
sis. If you are copying something verbatim from a publi-
cation, make a note that it is a verbatim record, to avoid
later using it inadvertently in your own work without
giving proper credit to the original source. (See my com-
ment later on plagiarism.)
> Spend at least one day a week in the library. It is your
responsibility to keep up with the literature. (It is your
research and your thesis.) Do not expect your research
advisor to do it for you. Take ownership of your project.
- Attend seminars and learn from the experts. Observe
what techniques the speakers use to make their presenta-
tions engaging and understandable. Also observe what
mistakes they make and avoid them in your own presen-
tations. Attend the seminars even if the talks are not on
your research topic-you never know what connections
you can make to your own work or how what you learn
from the talks can help you later in your career.
- Stay focused on your research, but learn about areas
other than your own. The market is fluid, and a typical
employer often wants someone who has a broader back-
ground than the one defined by your specific work. Do
not expect to work on the same problem you studied for
your MA, MS, or PhD when you go into the workforce.
Remember that graduate school is about "learning to
learn" on one's own.
- If your stipend comes from a research grant to your
advisor, learn about the expectations and deadlines the
funding agency places on your advisor. Understand what
pressures your advisor faces in keeping the funds flowing.

Form Networks: Learn from Others
- Take the initiative and form a "journal club" or "research
colloquium club" with other graduate students who have
similar interests. Conduct regular discussion meetings to
learn from each other (and to develop presentation skills).
Use the journal club and similar activities to form a
collegial network of your peers. You never know when
you might need the help of one of your schoolmates!
> Get to know the faculty on your thesis committee and
others from whom you have taken classes. In general,
get to know as many faculty members as possible from
within and outside your department. Make it a point to
meet with them periodically to seek their advice and to
learn from their experiences. Not only do you broaden

Fall 1999

your experience by doing this, but you also create a pool
of faculty members who know you well enough to write
meaningful letters when you need one.
> Form a "global network." If you have questions about a
paper you are reading, write to the authors. Most authors
are pleased to respond. Do not, however, be discouraged
if you do not hear from them-the world is not perfect.
- Attend a few national or international professional meet-
ings in your discipline, even if you have to pay for the
expenses. It is an investment in your future and money
well spent. Use your attendance to meet and to get to
know experts from outside your institution.
> Seek balance in what you do. If you are doing theoretical
work, learn the relevant experimental issues. If you are
an experimentalist, try to get a perspective on the theo-
retical issues. Remember that "theory" does not neces-
sarily mean "dealing with equations." Mathematics is a
language and a medium to achieve an end.
- Seek education beyond the classroom or your research
work. Learn to observe and listen. If you have the right
attitude, you can learn from everyone and from every
experience, positive as well as negative.
College life offers you an opportunity unmatched by any
to broaden your horizon. Break the barriers. Attend talks
or seminars in disciplines very different from yours. For
example, if you are in the "hard" sciences, attend some
seminars in cultural anthropology, art criticism, linguis-
tics or the like. Learn how scholars in the "soft" sciences
approach their research.

Deal with Difficulties Head On; Strive to Stay Positive

> Research is a solitary activity. Do not always expect
others to get excited about what you find exciting. You
can minimize the "isolation" if you build a network of
interested individuals as I suggested earlier.
> Research is full of ups and downs (more downs than ups,
normally), and the key to success is to learn to bear with
or overcome the downs. Try hard to stay motivated. If
you are feeling down, take a break, do something you
enjoy, and then return to your work. If you think you
need someone to cheer you up or urge you on, talk to one
of your friends or see your research advisor and ask
for advice or help. Divide your work into manageable
portions, and make sure that you make progress in at
least one of them on a regular basis. Even incremen-
tal progress is better than none at all and will keep
you motivated.
0 Take a course on "time management." Most people
can use one.
If you feel that you are constantly under stress and have

Graduate Education

difficulty coping, deal with it head on. Talk with a sym-
pathetic friend or with your research advisor or a faculty
member. Take a course on stress management. There are
many self-help books available on both time- and stress-
management, and they can be useful. Most universities
provide professional counseling for students with prob-
lems and run seminars on time-management (among
other things, such as public presentation, interpersonal
skills, and conflict resolution),
usually free of charge. Take TA
advantage of these. References and Web S
- It is not unusual to have differ-
ences of opinion with your ad- Books
visor. You may even "dislike" Alley, M., The Craft of Scie
your advisor sometimes. It is Verlag, New York, NY (1
r Booth, W.C., G.G. Colomb,
natural. It is only human. But Research, University of C
you'll find that in the end the (1995)
overall positive experience will Brusaw, C.T., G.J. Alred, al
Technical Writing, 4th ed.
overcome difficulties you face NY (1993)
along the way. It is how you Day, R.A., How to Write an
respond to setbacks that deter- ed., Oryx Press (1994)
mines your success and the Dodd, J.W., ed., The ACS S
Authors and Editors, 2nd
quality of your life. Washington, DC (1997)
SMatthews, J.R., J.M. Bower
Pay Attention to Ethics Successful Scientific Writii
- Research is a human endeavor Cambridge UK (1996)
and is not always an objective Strunk, Jr., W., and E.B. W
ed., Allyn and Bacon, Bos
search for the truth, but do not
let that discourage you or make Technical Writing Internet
you bend the rules. Strive for Online Writing Lab, Purdu
the highest standards. http://owl.english.pur
Grammar Hotline Directory
0 Pay attention to professional
ethics. Make sure you are Strunk & White's Elements
aware of the rules of author- http://www.columbia
WWWebster Dictionary
ship of publications. Acknowl-,
edge in your publications those WWWebster Thesaurus
who have provided assistance http://www.m-w.comr
in your work (be generous, but *Technical Writing: Books a
get their permission). htwww.
- If you use ideas or results of others, do not forget to cite
the relevant (primary) references. If you use someone
else's writing verbatim, follow the copyright require-
ments. Do not forget to give proper credit. When you are
writing your thesis or papers, it is easy to transfer sen-
tences you may have copied into your notebook from
other sources without attribution. In our profession there
is no greater sin than plagiarism, i.e., trying to get, even
unintentionally, credit for someone else's ideas.

Our profession is about generating ideas and communicat-
ing them to others. You fail if you are poor in either; that is,
you can fail even when you are good at what you do, but are

unable to communicate your achievements to others. I have
known many individuals who have advanced rapidly in their
jobs largely due to their communication skills.
- Do not underestimate the importance of writing effec-
tively and elegantly. Reading only technical articles or
articles in your own profession tends to decrease your
verbal skills and vocabulary. Make it a habit to read, on
a regular basis, the works of authors known for their
command of the language. If you
E 1 are in the sciences or engineering,
rn Technical Writing read well-written popular science
articles in magazines (e.g., Discover,
The New Scientist, Scientific Ameri-
Writing, 3rd ed., Springer- can, etc.) to learn how professional

.M. Williams, The Craft of writers avoid jargon and communi-
Press, Chicago, IL cate complicated concepts in an en-
gaging style. Identify also some
E. Oliu, Handbook of well-known authors of non-techni-
lartin's Press, New York,
martins Press, New York, cal material and read their works

lish a Scientific Paper, 4th periodically so that you keep your
verbal skills honed.
ide: A Manual for c Good writing requires clear think-
ing. Practice writing short summa-
R.W. Matthews, ries of long articles or scholarly es-
imbridge University Press, says at a level accessible to a novice.
e E s of 3 Table 1 contains a list of books and
he Elements of Style, 3rd
IA (1979) internet sites on effective writing.
Try to be gender-neutral in your
ersity, West Lafayette, IN writing. It is not a matter of being
lu/ fashionable or being "politically cor-
rect." It is a matter of recognizing,
:ent/gh/index.htm respecting, and encouraging the par-
cis/bartleby/strunk ticipation of both sexes in our pro-

ites o

and J

nd W.l
,St. l

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ed., Ai

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nd Re

Chemical Engineering Education

t.ntm Learn to make oral presentations
rus.htm effectively. This includes knowing
ference Sources how to organize your thoughts logi-
oltys/twritres.html cally and how to prepare effective
viewgraphs and knowing the proper
body language. If you need help, join a public presenta-
tion group such as the Toastmasters Club (see http:// Make periodic presentations to
your research group and ask your friends and advisor to
criticize your presentation (constructively).
0 In your presentations, focus on your work but present
your results in the context of the broader scope of your
research group and those of others elsewhere. Doing so
is much more impressive to, for example, a prospective
employer, for it shows that you understand the broader
context of your work and that you have the initiative to
learn the broader context of whatever you are assigned.
> Learn to be effective in a group setting. Learn the social


I Graduate Education


E[ Careers in Science and Engineering: A Student Planning
Guide to Grad School and Beyond, National Academy Press,
Washington, DC (1996)
(A planning guide for students considering entering graduate school
or in graduate school. Gives real-life examples of career paths of
scientists and engineers. Contains short case studies of different
career paths and lists of "action points." You can read this book in
the electronic reading room at
E[ On Being a Scientist: Responsible Conduct in Research,
National Academy Press, Washington, DC (1995)
(Advice on professional, personal, and ethical issues that a gradu-
ate student or a beginning researcher faces. Presents a number of
hypothetical, open-ended scenarios designed to draw attention to
ethical issues one may face in research. You can read this book in the
electronic reading room at
E Feibelman, P.J., A PhD Is Not Enough: A Guide to Survival
in Science, Addison-Wesley, New York, NY (1994)
(A concise and easy-to-read volume on what it takes to be success-
ful, especially in an academic career.)
E Medawar, P.B., Advice to a Young Scientist, Basic Books,
New York, NY (1981)
(Advice from a Nobelist to graduate students and scientists in the
early stages of their careers.)
E Watson, J.D., Double Helix: A Personal Account of the Dis-
covery of the Structure ofDNA, G.S. Stent, ed., W.W. Norton,
New York, NY (1980)
(An engaging record of the author's perspective of the competition,
the excitement, and the human side of science in action, written
almost like a racy novel. This special Norton edition, edited by
Stent, a well-known molecular biologist himself, has a number of
critical reviews of Watson's book and additional opinions by other
world-renowned biologists and chemists.)
E Sayre, A., Rosalind Franklin and DNA, W.W. Norton, New
York, NY (1978)
(A discussion of the contributions of Rosalind Franklin to the
discovery of the structure of DNA and an analysis of whether her
contributions were acknowledged appropriately in Watson's ver-
sion of the discovery.)
[ Ambrose, S.A., K.L. Dunkle, B.B. Lazarus, I. Nair, and D.A.
Harkus, Journeys of Women in Science and Engineering: No
Universal Constants (Labor and Social Change), Temple
University Press, Philadelphia, PA (1997)
(Challenges faced by women in science and engineering.)
E Djerassi, C., Cantor's Dilemma, Penguin Books, New York,
NY (1989)
(A gripping novel by a world-renowned chemist known for his
discovery of the birth-control pill, about the fierce competition driv-
ing scientific "superstars." A fictionalized version of real ethical
and personal issues faced by scientists every day. This is the first
volume in a tetralogy. You can visit for
more details on this book and its sequels.)
E Covey, S.R., A.R. Merrill, and R. Merrill, First Things First,
Simon & Schuster, New York, NY (1995)
(One of the most popular time-management books.)
E Seligman, M., Learned Optimism, Pocket Books, New York,
NY (1998)
(The author, a psychologist and clinical researcher, discusses pessi-
mism, optimism, and depression and how they affect quality of life.
The book also discusses the skills needed to change one's attitude
from pessimism to optimism.)

Fall 1999

skills needed to be courteous and generous to others
while making your own points effectively. Do not wait
until you get a job to learn such skills. Your supervisors
generally will not have the time to be your mentors or
advisors. They will merely observe your performance
and will pass you over for promotion or a choice job
assignment if you do not have the necessary skills.
> If English is not your native tongue, speak only English
at work. Ask an English-speaking colleague to proof-
read your papers and listen to your presentations. Take
advantage of technical writing courses and courses on
"English as a second language" offered by the university.

- Mingle with as many peers as possible. Get to know
them. Try to make life-long friendships.
> If you are a foreign student, try to find a roommate who is
not from your own country. Be a cultural ambassador of
your country. If you are a native student, seek out stu-
dents from other countries. Let's make the world a better
place for the next generation.

1. Write a short essay on your goals in life and in your
professional career. Be honest with yourself. Make a list
of your strengths and weaknesses. Identify what you
can do to eliminate or minimize your weaknesses.
2. Examine and analyze the case studies on professional
practice outlined in On Being a Scientist: Responsible
Conduct in Research (National Academy Press, Wash-
ington, DC 1995; accessed through the web at
3. Write a short research proposal on a topic of your choice
following standard guidelines issued by the university
and ask a friend or your graduate advisor to criticize it.
(You may wish to look through the US National Science
Foundation guidelines on proposals and review criteria;

This article is based on a Professional Development Semi-
nar Course developed for the graduate students in the De-
partment of Chemical Engineering at the University of
Florida, and on a couple of seminars I ran recently at the
National University of Singapore. I would like to thank
Jorge Jimenez, Claudia Marin, Anand Jaganathan, and Jonah
Klein, graduate students in the University of Florida Depart-
ment of Chemical Engineering, and my colleague Professor
Mark Orazem, for critically reading a draft of this article and
suggesting improvements. Dr. Paul Huibers, of the Depart-
ment of Chemical Engineering at MIT, read through an
earlier draft and suggested some changes, which I have
incorporated into this version almost verbatim. N

Graduate Education



Tulane University New Orleans, LA 70118

Materials science is perhaps the single largest, multi-
disciplinary, technological subject area, drawing
together experts from such diverse backgrounds
as architecture, physics, biomedical engineering, and even
the arts. Chemical engineers have helped make significant
advances not only in well-established "hard" materials sci-
ence fields such as polymer processing and semiconductors,
but they have also been at the forefront of important emerg-
ing materials science technologies such as tissue engineer-
ing (of the so-called "soft materials"), self-assembling sys-
tems, and nanostructured materials. The irony in these ac-
complishments is that many chemical engineering curricula
neither require a materials science course nor directly pre-
pare undergraduates for careers in these fields, much less
prepare them for advanced study in materials-science-ori-
ented research areas.
The challenge for those of us doing materials-related re-
search in chemical engineering departments, then, is to take
students who may have little or no background in materials
science and prepare them to do state-of-the-art materials
research. Although an undergraduate-level survey course in
materials engineering and science is a good place to start,
what is often needed is a second, "advanced-level survey
course," if there is such a thing, to prepare graduate students
to do research in a wide variety of materials-related areas.

Most undergraduate materials science textbooks take the
survey approach; that is, a wide variety of topics are pre-

sented in relatively little depth or detail, in order to give the
student at least a passing familiarity with a number of differ-
ent materials-science concepts. A working knowledge of
general chemistry, physics, and calculus is required, but
little or no organic chemistry, physical chemistry, or differ-
ential equations are employed.
Textbooks such as that of Callisteri' are excellent in this
approach, especially since they are frequently updated and
thus expose students to the latest trends in materials science.
They are usually written by materials scientists, metallur-
gists, or ceramists, but rarely by chemical engineers. As a
result, there are a number of concepts that are important to
chemical engineers working in the materials field that must
be taught either one-on-one with the graduate student, dur-
ing group meetings, or in a narrower, graduate-level course.
There are a number of excellent texts for such courses, on
topics ranging from polymer rheology,'21 to ceramics pro-
cessing,m3' to electrical properties of materials.[41 There is
very little in between.
The undergraduate survey text is not appropriate for a
graduate-level course, yet to focus on a specific topic (while
it may be well-taught and of academic importance) may not
provide exposure to a sufficient number of concepts to be
useful to graduate students conducting widely varied materi-
als research. Finally, and perhaps most importantly, these
narrower subject courses do not address or discuss the way
in which a number of important materials have been devel-
oped: through design of materials for a specific application.
The course outline for a graduate-level chemical engineer-
ing course, "Advanced Materials Design," is shown in Table
1. Four features of this course will be highlighted here: one
lecture on the history of materials development; a list of
selected advanced topics; a discussion of the design process;
and examples of materials design projects.

There are a number of truly fascinating stories related to
the development of certain types and classes of materials.
:hE Division of ASEE 1999

Chemical Engineering Education

Brian S. Mitchell is Associate Professor of
Chemical Engineering at Tulane University.
He received his BS in chemical engineering
from the University of Illinois-Urbana in 1986
and his MS and PhD degrees in chemical
engineering from the University of Wisconsin-
Madison in 1987 and 1991, respectively. His
research interests are in fiber technology and
composites engineering, and in the use of al-
ternative teaching techniques in the classroom.

I Graduate Education

For example, the work of Wallace
Carothers at DuPont on the develop-
ment of neoprene and nylon demon-
strates not only how materials (in this
case polymers) were developed as sub-
stitutes for specific, naturally occurring
materials such as rubber and silk, but
also how their development was spurred
by historical events (World War II, in
this case). This is not very technical
information, of course, but students
should be made aware (or at least re-
minded) that technological advances
occur as a result of a number of differ-
ent driving forces, including chance,
theoretical predictions, and necessity.
There is a particularly good book by
Ivan Amato called Stuff la that provides
a great deal of historical information
and perspective on the development of
materials science as a discipline, and of
specific materials. Although I stop short
of making this a required text for the
Advanced Materials Design course due
to its lack of technical content, it is
nonetheless on the "must read" list for
anyone in the materials area, and it is
worth recommending to all science and
engineering students. It also shows how
some prominent chemical engineers
have played important roles in devel-
oping new materials, and how they em-
ployed design principles to accomplish
their task. An excellent example is the
work of Ilhan Aksay of Princeton Uni-
versity on biomimetic structures.


Although an
undergraduate-level survey
course in materials
engineering and science is a
good place to start, what is
often needed is a second,
"advanced-level survey
course," if there is such a
thing, to prepare graduate
students to do research in a
wide variety of materials-
related areas.

Course Outline for
Advanced Materials Design
I Selected Advanced Topics in Materials Science
A. The History of Materials Science
B. Crystallography
C. Structure of Glass
D. Structural Imperfections
E. Phase Equilibria
F. Phase Transformations
G. Advanced Characterization Techniques
H. Mechanical Properties
II. Materials Design
A. The Design Process
B. Metals
C. Inorganic Materials
Ceramics (low- and high-tech)
Glass-ceramics, ceramers, and cermets
D. Polymers
E. Composites
F. Case Studies, Design Projects

and a semester-long course in crystal-
lography. This topic is of use to those
students who will be conducting re-
search in practically any materials area,
including polymers. In addition to re-
viewing the seven crystal systems, the
reciprocal lattice, Miller-Bravais indi-
ces, symmetry operations, and X-ray
diffraction are discussed. The advanced
content comes from such exercises as
calculation of single-crystal X-ray dif-
fraction patterns from unit cell dimen-
sions, which can be done on a spread-
The lectures on the crystalline state
lead naturally into lectures on the amor-
phous, or glassy, state. Again, discus-
sions of radial distribution functions,
the glass transition, and phase transfor-
mations are equally applicable to inor-
ganic glasses as well as polymers. Struc-
tural defects often receive a great deal
of attention in an undergraduate mate-
rials science course-deservedly so,
since they directly affect many physi-
cal properties. One topic on structural
defects that does not receive a great deal
of attention, except in courses on ceram-
ics, is point defect equilibrium and
Kroger-Vink notation.171 Once Kroger-
Vink notation has been described, the
determination of equilibrium point de-
fect concentrations is particularly relevant
for graduate-level chemical engineers,
since the point defect species are treated
like any other chemical species, and de-
fect reaction equations are like any other
chemical reaction.

A graduate-level chemical engineering course should be
more than a history lesson, of course. One of the primary
purposes of an advanced materials course should be to teach
advanced topics-those that are only introduced at the un-
dergraduate level or that are not covered in sufficient detail.
The difficult question is which topics those should be. At
Tulane University, we have faculty conducting research in
such areas as heterogeneous catalysis, molecular dynamics
simulations of thermophysical properties in polymer single
crystals, nanostructured ceramics, and tissue engineering.
Some of the advanced topics we have selected to address are
shown in Table 1.
Lectures on crystal structure are given at a level some-
where between the undergraduate materials science course

Phase equilibria is also an area that most chemical engi-
neers get a great deal of exposure to at the undergraduate
level. Most of it deals with the liquid and vapor states,
however, and is mostly applied to binary systems. Ternary,
condensed-phase diagrams are described in Advanced Mate-
rials Design. Although this is not mathematically challeng-
ing material, terms and concepts such as conodes, isopleths,
coprecipitational divariant equilibria lines and alkemade lines
take a bit of practice to fully understand. As a final example
of the advanced survey format of this course, a number of
lectures are spent on advanced materials characterization.
Once again, this can be a course in-and-of itself, but there
are a few techniques that are of particular importance to our
department and its researchers. Tulane University has a com-

Fall 1999

Graduate Education

plete thermal analysis laboratory. It contains, among other
things, a differential scanning calorimeter (DSC), differen- TA- 2
tial thermal analyzer (DTA), thermogravimetric analyzer :_
(TGA), dynamic mechanical analyzer (DMA), thermo- .r<,iS.W -rod re
mechanical analyzer (TMA), dielectric analyzer (DEA), and
various combinations of these instruments. A number of
graduate students in the department wish to use the thermal ULmrL f44i
analysis facility, and without turning a graduate-level course
into a technician training session, the students are introduced
to the operation of these analyzers, and the theory behind
selected instruments is discussed.

It makes sense in a course on "materials design" to discuss Inonla-~-" '
not only the "materials" part, but also the "design" part. The
design aspect of engineering education has certainly not-
been lost on other parts of the chemical engineering curricu- I'
lum, such as process design, and has in fact been emphasized
more recently across the curriculum through emphasis of ct-a companies
design-oriented problems at all levels and in all subject
areas. The design approach has not been taken to any appre-
ciable extent in materials science courses.
In the Advanced Materials Design course, general design -
methodologies such as Cross's methodology,t8t concurrent IT....:_.
engineering,191 and market-driven design10l are discussed. -
More materials-oriented design methodologies are then de-
scribed, in which Ashby's dated, yet still appropriate, text on
mechanical design is used.tI"' Not all materials design prob-
lems, especially newer ones, require a significant mechani-
cal property component, so students are encouraged to use
the more general design strategies to carry out materials a
selection and development.
An excellent text on materials design that contains a great _..
number of case studies is by Lewis.1'2 This book offers a
wealth of information and emphasizes cost analysis-some-
thing that is often lost in academic environs. The remainder
of the course is spent reviewing the traditional grouping of ii
materials science topics, with an emphasis on physical prop-
erties that can be exploited from a design standpoint.

As with many chemical engineering courses, the heart of
the materials design course is the design project. The design
project topics for Advanced Materials Design are gleaned
from the recent literature (see Table 2 and references 13-28).
The Materials Research Society Bulletin is a particularly
useful source for these topics, as the articles in this monthly
journal tend to be of intermediate technical difficulty, yet
represent some of the most current, cutting-edge materials
research being conducted. These articles also generally have
excellent bibliographies, thus providing a good starting point -
for the students' literature reviews. The students get to choose --

Chemical Engineering Education

Graduate Education

the topic, with the stipulation that it be outside of their
research area. Some of the topics are very general, such as
nanoceramics; others are more specific, such as discontinu-
ously reinforced metal matrix composites. Some of the top-
ics already have an application associated with them, such as
materials for flat panel displays; others are still in search of
"break-through" applications.
The project topics are selected and assigned early in the
course. For the first homework assignment, the students
must find three additional articles for their topic, preferably
varied in scope; e.g., one review article, one on molecular
mechanisms, and one on processing. They must also write a
brief summary of the potential significance of the topic and
areas where it can be applied. The requirements for the final
report are listed in Table 3.
The goal is to come up with a specific application for their
material-all the way down to drawing a schematic of the
apparatus or article and describing how it works. This may
seem trivial for a topic like "Materials for Flat Panel Dis-
plays," but in this case the student must reverse engineer the
component, determine what the specific materials constitu-
ents are, and more importantly, why they were selected.
In either scenario, the student must come up with a justifi-
cation for the selected materials. In doing so, the student
applies the design criteria, or sees how others applied them,
uses advanced materials science concepts in the analysis of
the application, and thinks practically, yet critically, about
how newly developed materials can be used.

Graduate courses need not emphasize the use of elliptical
integrals, nor be as narrow as the instructor's most recent
grant proposal in order to be considered advanced. The three
highest levels of Bloom's taxonomy of Education Objec-
tives-analysis, synthesis, and evaluation[291-are sufficiently
challenging for graduate students, even in a survey-type
course, when the technological content of the system being
analyzed and evaluated is sufficiently complex. Today's
materials applications certainly meet the technological re-
quirement, and a design project for chemical engineering
graduate students on advanced materials applications chal-
lenges the students to use many of the chemical engineering
principles they have learned as undergraduates.

1. Callister, William D., Materials Science and Engineering:
An Introduction, 4th ed., John Wiley & Sons, New York, NY
2. Bird, R.B., B.C. Armstrong, and O. Hassager, Dynamics of
Polymeric Liquids, John Wiley & Sons, New York, NY (1977)
3. Reed, J.S., Principles of Ceramics Processing, John Wiley &
Sons, New York, NY (1995)

4. Solymar, L., and D. Walsh, Lectures on the Electrical Prop-
erties of Materials, 5th ed., Oxford Science Publications,
Oxford, England (1993)
5. Amato, I., Stuff-The Materials the World is Made Of, Basic
Books, New York, NY (1997)
6. Shapiro, F.R., "The Calculation of Crystal Diffraction Pat-
terns Using a Spreadsheet," J. Mater. Educ., 14, 93 (1992)
7. Kingery, W.D., H.K. Bowen, and D.R. Uhlmann, Introduc-
tion to Ceramics, 3rd ed., John Wiley & Sons, New York, NY
8. Cross, Nigel, Engineering Design Methods, John Wiley &
Sons, New York, NY (1989)
9. Winner, R.I., J.P. Pennell, H.E. Bertrand, and M.M.G.
Slusarczuk, "The Role of Concurrent Engineering in Weap-
ons System Acquisition," IDA Report R-338 (1988)
10. Pahl, G., and W. Beitz, Engineering Design, translated by
K. Wallace, The Design Council, London and Springer, Ber-
lin, Germany (1984)
11. Ashby, Materials Selection in Mechanical Design,
Butterworth-Heinemann Ltd., Oxford, England (1992)
12. Lewis, G., Selection ofEngineering Materials, Prentice Hall,
New York, NY (1990)
13. Stinson, S., "Delving into Dendrimers," Chem. Eng. News,
p. 28, September 22 (1997)
14. Freemantle, M., "Potential Trigger for Dendrimer Switch,"
Chem. Eng. News, p. 30, May 26 (1997)
15. Service, R.F., "Paving the Information Superhighway with
Plastic," Science, 267, 1921 (1995)
16. Miller, J.S., and A.J. Epstein, "Designer Magnets," Chem.
Eng. News, p. 30, October 2 (1995)
17. Epstein, A.J., "Electrically Conducting Polymers: Science
and Technology," MRS Bull., 22(6), 16 (1997)
18. Rothberg, L.J., and A.J. Lovinger, "Status and Prospects for
Organic Electroluminescence," J. Mater. Res., 11(12), 3174
19. Hanna, J., and I. Shimizu, "Materials in Active-Matrix Liq-
uid Crystal Displays," MRS Bull., 21(3), 35 (1996)
20. Agarwal, M., M.R. DeGuire, and A.H. Heuer, "Synthesis of
ZrO2 and Y203-Doped ZrO, Thin Films Using Self-Assembled
Monolayers,"J. Am. Ceram. Soc., 80(12), 2967 (1997)
21. Ritter, S.K., "Boning Up," Chem. Eng. News, p. 27, August
25 (1997)
22. Alivisatos, A.P., "Semiconductor Nanocrystals," MRS Bull.,
20(8), 23 (1995)
23. Mitomo, M., Y-W Kim, and H. Hirotsuru, "Fabrication of
Silicon Carbide Nanoceramics," J. Mater. Res., 11(7), 1601
24. Rittner, M.N., and R. Abraham, "The Nanostructured Ma-
terials Industry," Am. Ceram. Soc. Bull., 76(6), 51 (1997)
25. Kevorkijan, V.M., "An Ideal Reinforcement for Structural
Composites," Am. Ceram. Soc. Bull., 76(12), 61 (1997)
26. Newnham, R.E., "Molecular Mechanisms in Smart Materi-
als," MRS Bull., 22(5), 20 (1997)
27. Greer, A.L., "Metallic Glasses," Science, 267, 1947 (1995)
28. Archambault, P., and C. Janot, "Thermal Conductivity of
Quasicrystals and Associated Processes," MRS Bull., 22(11),
29. Bloom, B.S., ed., Taxonomy of Educational Objectives Hand-
book I: Cognitive Domain, David McKay Co., (1956) O

Fall 1999

Graduate Education



Purdue University West Lafayette, IN 47907-1283

n the spring semester of 1998, an overview course in
particle technology was launched in the School of Chemi-
cal Engineering at Purdue University. The student en-
rollment in the initial offering of this course was relatively
high (25 students) for an elective course; hence, the course is
now being offered yearly in Purdue's spring semester. It will
also be taught starting in the spring semester of 2000 via
videoconferencing as a part of Purdue's continuing educa-
tion program for practicing engineers, some of whom are
working part-time towards their Masters of Engineering de-
gree at Purdue.
The objective of the course is to provide a broad overview
of the field, with emphasis on concepts and practical appli-
cations. Specific topics include particle characterization, sedi-
mentation, gas fluidization, pneumatic conveying, gas-solid
separation, particle storage, mixing, size reduction and en-
largement, and dust hazards and explosions. About one week
of coverage is given to each of the above topics; the empha-
sis is clearly toward breadth rather than depth.
At Purdue, as well as at most other universities in the U.S.,
the current educational treatment of particle technology is
limited to a one-semester course. This constraint dictates
that the time available in a single-semester course is best
spent in an overview fashion. Hence, emphasis is on relating
to the students an appreciation for the many aspects of this
complex field and on developing an awareness of the resources
available to them if they find themselves working in industries
involved with the processing of particulate solids.
The overview course in particle technology is offered as a
500-level course available to junior and senior undergradu-
ates as well as to graduate students. Enrollment in the two
offerings of the course to date has been an even mix of
undergraduate and graduate students from a range of disci-
plines that includes chemical engineering, mechanical engi-
neering, food science, agricultural and biological engineer-
ing, civil engineering, and pharmacy. The textbook used in
the course is Introduction to Particle Technology by Martin

Rhodes (Wiley, 1998).
The course includes guest speakers from several industrial
companies such as Dow and DuPont. At least one field trip
to an industrial company involved in solids handling is in-
cluded each semester so that students can see first-hand the
many unit operations discussed in the lectures. The course
schedule for the spring 1999 offering of the course can be
found in Table 1.
Slurry flow is the only subject covered in the course that is
not treated in the Rhodes text. Reading for this material is
given as handouts and is based on the text Bulk Solids
Handling by Woodcock and Mason (Blackie Academic,
1987). Supplementary material for other lectures is taken
from the following texts:
* Principles of Powder Technology, Rhodes; Wiley, 1990
* Processing of Particulate Solids, Seville, Tuzun, and
Clift; Blackie Academic, 1997
Principles of Gas-Solid Flows, Fan and Zhu; Cambridge
University Press, 1997
Particle Size Measurement, Allen; Chapman & Hall,
Fluidization Engineering, Kunii and Levenspiel;
Butterworth-Heinemann, 1991
Pneumatic Conveying of Solids, Marcus, Leung,
Klinzing, and Rizk; Chapman & Hall, 1990

Jennifer L. Sinclair is Associate Professor
of Chemical Engineering at Purdue Univer-
sity. Her research interests are in the areas
of gas-solids flow, fluidization, and particle
mechanics. She is the recipient of several
teaching awards, the NSF-PYI award, and
currently serves on the Executive Committee
of the Particle Technology Forum.

Copyright ChEDivision ofASEE 1999

Chemical Engineering Education

Graduate Education

The aim of this lecture is to con-
vince students of the critical im-
portance of particle technology and
also to clearly show them that the
concepts they learned in a typical
engineering fluids course may not
necessarily translate to the flow and
storage of particles. This lecture
sets the stage and motivation for
the course. If done well with lots
of visuals, it is highly effective in
imparting to students the importance
of knowledge in this technical area.
This same lecture works well as
a recruiting tool to attract students
to the course. At Purdue, chemical
engineering undergraduates must
take a chemical engineering semi-
nar course every semester. During
part of one class period in the semi-
nar course, I present some key con-
cepts from this introductory par-
ticle technology lecture to all of
the chemical engineering students.
Many of the students end up regis-
tering for the particle technology
course because of the material con-
tained in this presentation. I have
also given the lecture to industrial
visitors to our university; they are
usually "sold" on particle technol-
ogy after hearing it.
The basic components of this lec-
ture are a definition of particle tech-
nology, a presentation of the im-
portance of particle technology in
industry, and a presentation of ex-

Course Schedule

January 12
February 2
March 2

Particle characterization
Particle characterization
Particle size measurement
Packed beds
Exam #1
Pneumatic conveying
Pneumatic conveying
Gas-solid separation
FIELD TRIP: National Starch
Slurry flow
Particle mixing
Exam #2
Guest Lecturer:
Rachel Anderson, Dow

Reading in
Chapter 3
Chapter 3
Chapter 3
Chapter 3
Chapter 1
Chapter 2
Chapter 4
Chapter 5
Chapter 5

Chapter 6
Chapter 6
Chapter 7

Chapter 9

Chapter 8

"Design of Particle Storage Devices"
25 Guest Lecturer
Rachel Anderson, Dow Chapter 8
"Design of Particle Storage Devices"
30 Guest Lecturer:
Mohsen Khalili, DuPont
"Case Studies in Particle Technology"
April 1 Guest Lecturer:
Mohsen Khalili, DuPont
"Case Studies in Particle Technology"
6 Particle size reduction Chapter 10
8 Particle size enlargement Chapter 11
13 Guest Lecturer:
Professor Wassgren, MechE, Purdue
"Simulation of Particle Flow"
15 Dust hazards/explosions Chapter 12
20 Oral presentations Project
22 Exam #3
27 Oral presentations Project
29 Oral presentations Project

amples in which particles behave
in a unique way, often very differ-
ently than fluids. In most of these examples, I give a visual
picture to the audience either by illustrating with a real
particulate material or through the use of a graph, photo-
graph, etc.
An outline of the introductory lecture can be found in
Table 2 (next page).

One of the key components of the overview course in
particle technology, in addition to the traditional lecture,
homework, and exam format, is the course project. In the

Fall 1999

project, students work in teams to
investigate one specific topic in par-
ticle technology in detail. The team
project comprises one-third of the
course grade, and the last lectures
of the course each semester are de-
voted to presentations of the group
projects. The project brings depth
in one particle technology topic to a
course that emphasizes breadth. The
project also provides additional ex-
perience for the students in the team-
work and communication skills that
are essential on the job.
A course project is very attractive
to the students because they can
work in an open-ended fashion on a
particle technology subject of their
choosing. Most undergraduates en-
joy the team aspect of the course
project since they are accustomed
to working in teams in their senior
engineering design courses and, for
some, in their co-op positions. The
graduate students like the course
project because they are given the
opportunity to probe topics dis-
cussed in lecture in more detail.
Many of the graduate students who
enroll in the course want specific
topics along the lines of their gradu-
ate research developed in greater
detail than the treatment given in
the lectures. Since this is not pos-
sible in the lectures, given the time
constraints, the course project of-
fers another format for meeting these
students' expectations for the course.

In the course project, the students
are engaged in both background re-
search and in making a forward step, that is, moving beyond
what is currently known and putting forth something new.
The "something new" can take the form of a research pro-
posal, new theory, new insight, new calculations, new data
(some students have access to appropriate experimental fa-
cilities in their research group), etc. In the course at Purdue,
the weighting on the background research versus the "some-
thing new" portion of the project is approximately 75/25%.
Students work in teams of three to four people. The teams
and their presentation dates are chosen randomly by picking
numbers out of a hat during the first class meeting. At least


Graduate Education

fifteen minutes is allocated during the first lecture period for
the group members to exchange contact information and
class schedules, briefly get to know one another, and to
decide on a group leader. Other opportunities are given
during the semester towards the end of class periods for
group discussions.
The teams are given three weeks to decide on a project
topic. Students are instructed to peruse the titles of articles in
the journals of Powder Technology and Particle Science &
Technology to stimulate ideas for projects. Often when a
graduate student is one of the team members, he or she takes
on the leadership role and guides the course project to a topic
related to his or her graduate research. This pattern of the
graduate student taking charge of the team has not yet cre-
ated any group conflicts; rather, many of the undergraduate

students seem to be relieved when a project direction is
decided on quickly and easily. The only group conflict that
has arisen to date has been when two graduate students were
on the same team and they had areas of graduate research
specialization in particle technology that involved little over-
lap. For that team, coming to an agreement on a project topic
required a lot of compromising among the group members.
Focus areas for project topics have spanned the spectrum
of particle sizes and particle science and technology applica-
tions. Representative topics have included
Use of Electrokinetic Sonic Amplitude in the Characterization
of Colloidal Suspensions
Particle Size Distribution Effects in Pneumatic Conveying
Novel Designs for FCC Reactors
Use of Simulation Techniques to Improve Cyclone Design

Chemical Engineering Education

Outline of Introductory Lecture

Particle Technology
Particle technology refers to the science and technology related to the handling and processing of particles and powders
Also known as "powder technology" (sometimes when the particle size is less than 100 microns)
Powders or particles also referred to as particulate solids, bulk solids, and granular solids
Particle technology includes solid particles as well as liquid droplets, emulsions, and bubbles
Wet/dry particulate systems-with and without liquid
Importance of Particle Technology
62% of DuPont's 3000 products involve particles (ChE Progress, 1994)
50% of Dow Chemical's products
Rand Corporation Study (ChE Progress, 1985)
37 solids processing plants studied
2/3 operated at less than 80% design capacity
1/4 operated at less than 40% design capacity
(95% is average for the CPI as a whole)
Ignorance of particle technology often results in loss of production, poor product quality, health risks, dust explosions, and storage silo collapse.
Typically, 20-25 deaths occur each year due to failures in particle technology operations.
Particle technology impacts fields of chemical engineering, mechanical engineering, agricultural engineering, food engineering and food science,
electrical engineering, civil engineering, pharmaceuticals, metallurgy, and minerals engineering. In each of these fields I give examples of
processes involving particle technology. I also ask the students for input here because the students in the course come from a diverse set of science
and engineering backgrounds.
Particles Do Not Necessarily Behave Like Fluids
Particles expand in a dry bulk assembly when sheared. Walking on sand at the beach is a good example of particle expansion during shear.
The ability of particles to form a heap. Visual-two different types of particulate material and a discussion of the angle of repose and how particle
cohesivity influences flow behavior.
Differences in pressure drop behavior in pipelines in a single-phase gas versus a gas-particle mixture. Visual-Show and discuss a graph of how it
is possible in dense-phase conveying of particulates to have a reduction in pressure drop with an increase in gas velocity at a constant solids
flowrate. Also discuss how the addition of a small fraction of very fine particles to a turbulent fluid may cause a reduction in the pressure drop.
Particle flow behavior in bends can be very problematic. Visual-An illustration of the roping phenomena; an actual pipe bend ruined by erosion.
Particle storage in hoppers can also be problematic. Visual-Mass flow versus funnel flow-stagnation regions do not occur in fluid storage in a
similar vessel; plugging of the outlet of a hopper.
Particle flowrate out of hopper as the head decreases. Visual-Clear hopper and observation of outlet flowrate as a function of head-contrast
with fluid flow out of a tank as head decreases.
Particle bulk density varies with "tapping." Visual-Tapping of a fine particulate material in a vial and observation of volume occupied by
particulate material-contrast with a single-phase fluid in a vial.
Large particle placed at the bottom of a container of a dry granular material will rise to the surface if the container is vibrated in a vertical plane.
Visual-Shaking of a particulate material in a vial containing one larger particle and observation of the large particle movement.
Fill volume of two types of particles can depend on the order of filling of the container. Visual-Filling ajar with two sizes of nuts and observa-
tion of volume occupied-contrast with combining two fluids and the resulting volume.
Stirring a mixture of two types of particles of different sizes may result in segregation rather than improved mixing quality. Visual-Photographs
of segregation patterns before and after a blending operation-contrast with mixing of two fluids.

Graduate Education

Probing the Mechanisms of Particle Charging
Ultra-High Performance Electrostatic Precipitators
Moisture Content and Caking in Foodstuffs
There are four aspects to be graded in the overall course
1. The first is a written report of at least ten pages prepared by the
group that summarizes the background information on the
topic and the novel aspects of the investigation.
2. The second is an oral presentation of the topic, thirty to forty-
five minutes in length. All of the group members are required
to participate in the oral presentation. Typically, one student
outlines the discussion points in the beginning of the presenta-
tion, and then the team members take turns speaking on each
of the presentation bullets. Often the presentations are
supplemented with visual aids or a short demonstration to
introduce the topic and capture the attention of the audience.
The oral presentation is followed by a questioning period.
During the questioning period, each of the project teams in the
audience is required to ask at least two questions of the
presenting team. This requirement is highly successful in
keeping the class engaged in the presentations; the students
also often generate outstanding questions. This peer question-
ing is one aspect of peer review (see task #3 below) that is
incorporated into the course project to help develop the
communications skills of the students. After the questioning
period is over, the instructor gives the presenting team
immediate feedback, both positive and negative, in front of the
entire class. This helps to improve the quality of the subse-
quent presentations since students get a better understanding of
what is successful, what are some of the pitfalls, and what are
the standards expected for the presentations.
3. Every team performs a peer evaluation of each of the other
teams. The peer evaluation is on both the written and oral
presentations of the course project. In the written report, each
team serves as a reviewer of the other team reports, marking
grammatical and typographical errors directly on the manu-
script. They write a short summary of the manuscript that
includes an overall evaluation, specific positive aspects,
constructive criticisms, and suggestions. For the oral presenta-
tions, a structured evaluation form is used that is provided by
the instructor. Therefore, at the end of the peer evaluation
process, each team has a large amount of anonymous feed-
back-a written and oral report from each of the other teams.
Although these peer evaluations do not influence the grade of
the team being evaluated, they are very instructive; the
students tend to listen and readily accept the comments from
their peers. The peer evaluations prepared by each team are
graded for thoroughness and level of insight by the instructor.
The value of peer review has been documented in the litera-
ture," 5] and its benefits are abundantly evident in the particle
technology course. Perhaps this is due to the fact that the
course is an elective course. Presumably, students already have
some interest in the topic when they enroll in the course and
the peer review merely enhances their involvement in it.
Students take the reviewing task seriously. They do an
excellent job in identifying the strengths and weaknesses in the

work of their fellow students. The peer review also aids the
students in recognizing the strengths and weaknesses of their
own oral and written reports.
4. Finally, each team member submits to the instructor a summary
report of the relative contributions of each of their own group
members, including an assessment of their own contribution to
the team effort. This helps the instructor assign appropriate
individual grades to the group project.

Aside from the technical benefits a particle technology
project brings to the course, there is the additional, more
general benefit of improving team skills. Team skills are a
requirement for a successful workforce; about 80% of U.S.
organizations use teams to accomplish tasks.[J6 In technical
fields, teamwork is particularly crucial as engineers and
scientists become more specialized. Students, through the
course project, gain more experience in how to capitalize on
the unique skills of others, and, in turn, they often learn more
about their own capabilities. In addition, they learn better
how to motivate others, how to organize a group effort, and
how to manage in difficult teams since team members are
not reassigned even if a team is having problems working
together. In fact, student feedback indicates that while being
a member of a "problem team" is certainly not a pleasant
experience, those team members are the ones that make the
strongest comments about their huge learning experiences in
team skills.

A survey course in particle technology is a highly effec-
tive way to introduce the basics in this field to a diverse
group of students. A "gee-whiz"-type introductory lecture
helps sell the importance of particle technology to different
audiences. Visuals enhance presentation of the unique fea-
tures of particulate systems. Incorporation of a team project
into the course allows for students to focus on one particular
topic in particle technology and adds depth to the breadth of
material covered in this survey course. Also, the course
project brings many positive factors to the learning experi-
ence of the students.

1. Elbow, P., Writing Without Teachers, Oxford University
Press, New York, NY (1973)
2. Grimm, N., "Improving Students' Responses to Their Peers'
Essays," College Comp. and Comm., 37, 91 (1986)
3. Herrington, A., and D. Cadman, "Peer Review and Revising
in an Anthropology Course," College Comp. and Comm., 42,
4. Holt, M., "The Value of Written Criticism," College Comp.
and Comm., 43 384 (1992)
5. Newell, J., "Using Peer Review in the Undergraduate Labo-
ratory," Chem. Eng. Ed., 32, 194 (1998)
6. Robbins, S., Organizational Behavior, 8th ed., Prentice-Hall,
Upper Saddle River, NJ, 284 (1998) 0

Fall 1999

Graduate Education
' -------------------------------- __________




University of Delaware Newark, DE 19716

A graduate-level class on process control tradition-
ally employs a standard lecture-style course, possi-
bly coupled with an independent course project car-
ried out in a simulation environment. If one steps back to
critique this approach, it is important to first address the
skills required by a practicing process-systems engineer. As
a guide to the requisite abilities required of a process-
systems engineer, one may consult the list of control design
steps provided by Skogestad and Postlethwaite'" shown in
Table 1. Is the typical engineering graduate well prepared to
accomplish these tasks? There have been no comprehensive
studies to answer this question, but Kheir, et al.,121 reported
the results of an informal survey of industrial employers of
control engineers. The highest rated aspects of the current
methods of control education were control-system knowl-
edge, job preparation, and curriculum. The analytical skills
of the students were considered strong. Such responses seem
to indicate some success for items 7 through 9 of Skogestad's
list of control-design steps, areas that correspond to skills
Ed Gatzke received his BSChE from the Georgia Institute of Technology in
1995. After two years of graduate study at Purdue University, he moved to
the University of Delaware for completion of his PhD. He has held intern-
ships with Teledyne Brown Engineering, Mead Paper, and Honeywell. His
interests include process control, optimization, and artificial intelligence.
Raj Vadigepalli received his BTech from the Indian Institute of Technol-
ogy, Madras, in 1996, and began his PhD research in chemical engineer-
ing at Purdue University in the fall of 1996. He moved to the University of
Delaware in 1997 to complete his doctoral degree with Professor Francis
J. Doyle. His research focus includes modeling and analysis of control
mechanisms in biological systems and distributed hierarchical methods for
control of large-scale process systems.
Edward S. Meadows is a postdoctoral fellow at the University of Dela-
ware, working in the areas of modeling and control of polymerization
reactors as part of a broader research program in optimization and control
of chemical processes. He received his PhD degree from the University of
Texas in 1994.
Frank Doyle received his BSE from Princeton in 1985, his CPGS from
Cambridge in 1986, and his PhD from Caltech in 1991, all in chemical
engineering. He was an Assistant Professor at Purdue University before
coming to the University of Delaware as an Associate Professor in the fall
of 1997, and his research interests are in the areas of process and
biosystems analysis and control.

typically emphasized by a theoretical, textbook-and-lec-
ture control course.
Unfortunately, existing approaches to control engineering
education are not necessarily producing engineers who are
as knowledgeable in other areas. The Kheir survey respon-
dents reported that control engineers received lower ratings
in the areas of laboratories, hands-on experience, and inter-
personal skills. The course described in this paper uses both
a standard lecture class and an experimental group project
related to the course material. This provides an opportunity
to address the deficiencies identified by Kheir and colleagues,
while reinforcing the positive aspects of traditional control
engineering education methods.

In the latest (fall, 1998) offering of this course, Advanced
Process Control, there were seven students enrolled for a
grade and five students auditing the class. Of the seven
students taking the class for a grade, five were University of
Delaware graduate students and two were industrial profes-
sionals enrolled for continuing education credit.
As a main reference, the course used the text by Skogestad
and Postlethwaite,"1 and the major topics covered in the
course included
Classical multivariable control
Analysis of performance limitations
Uncertainty characterization
Robust controller synthesis
Control structure selection and plant-wide control
One of the key strengths of the Skogestad and Postlethwaite
text is the treatment of performance limitations, and this
topic was covered in depth in the lecture and reinforced via
the experimental project. The course project was assigned in

Copyright ChE Division of ASEE 1999

Chemical Engineering Education

Graduate Education

the middle of the semester, and the students were given the choice of a
theoretical independent course project (related to their thesis research)
or the opportunity to work on the experimental system as a group
project. Of the five on-site students, four elected to carry out their
project using the experimental four-tank system.

An interacting four-tank process is currently used in both the elective
multidisciplinary undergraduate control laboratory and the advanced
graduate control course. The design is inspired by the benchtop appara-
tus described in Johansson and Nunes.131 A simple schematic is shown in
Figure 1. Two voltage-controlled pumps are used to pump water from a
basin into four overhead tanks. The two upper tanks drain freely into the
two lower tanks, and the two bottom tanks drain freely into the reservoir
basin. The liquid levels in the bottom two tanks are directly measured
with pressure transducers, and the top tanks have high-level alarm
signals generated by electro-optical sensors. As can be seen from the
schematic, the piping system is configured such that each pump affects
the liquid levels of both measured tanks. A portion of the flow from one
pump flows directly into one of the lower-level tanks where the level is
monitored. The rest of the flow from a single pump is diverted into
an overhead tank, which drains into the other monitored tank. By
adjusting the bypass valves on the system, the amount of interaction
between the two pump flowrates (inputs) and the two lower tank
level heights (outputs) can be varied. For this work, it is assumed
that an external unmeasured disturbance flow may also be present
that drains or fills the top tanks.
The original work of Johansson and Nunes131 employed tanks with a
volume of 0.5L. The present work uses 19L (5 gallon) tanks, attempting
to create a visual impression of practical reality for the students. The
scale of the apparatus is indicated in Figure 2. In the lower right-hand

Steps in Control System Design

1. Study the system (plant) to be controlled and obtain initial information about the
control objectives.
2. Model the system and simplify the model, if necessary.
3. Analyze the resulting model; determine its properties.
4. Decide which variables are to be controlled (controlled outputs).
5. Select the control configuration.
6. Decide on the type of controller to be used.
7. Decide on performance specifications, based on the overall control objectives.
8. Design a controller.
9. Analyze the resulting controlled system to see if the specifications are satisfied; and if
they are not satisfied, modify the specifications or the type of controller.
10. Simulate the resulting controlled system, either on a computer or pilot plant.
11. Repeat from step 2, if necessary.
12. Choose hardware and software, and implement the controller.
13. Test and validate the control system, and tune the controller on-line, if necessary.

corner of the photograph, one can see the display of
a computer control system used as an interface to
the experiment. A Bailey Freelance Distributed Con-
trol System (DCS) was employed to introduce the
students to actual operating software employed
in industry. Furthermore, the PC-based architec-
ture made the system cost-effective for a univer-
sity application and facilitates hardware and soft-
ware upgrade paths.
The experimental package consists of three sepa-

Pumpl T Pump 2

Figure 1. Schematic of the four-tank

figure Z. Laoorarory apparatus.

Fall 1999

Graduate Education

rate components, as shown in Figure 3:
1. Experimental Station: tanks, level sensors, level
alarms, valves, and pumps
2. Process Station: hardware that carries out the control
input-output and communicates between the Experi-
mental Station and the Operator Station
3. Operator Station: PC-based system were Process
Station information is monitored and modified
The Process Station communicates with the Operator Sta-
tion over a private TCP/IP network. The Freelance applica-
tion package DigiTool was used to create a process database
that is loaded onto the Process Station. The DigiVis applica-
tion allows operator interaction with the Process Station and
process database. Operator displays were created that al-
lowed the students to operate the four-tank system (see Fig-
ure 4) as well as to track the trends of key operating variables
(see Figure 5).
For the graduate control class, it is necessary to use more
complex control algorithms than can be easily implemented
using the Freelance packages. Matlab/Simulink can be used
to calculate the control moves needed for the experimental
system. A Dynamic Data Exchange (DDE) interface is used
to link Matlab/Simulink with Freelance. The Simulink dis-
play (Figure 6) emulates a standard simulation flowsheet.
By default, the Bailey DCS controls the process using manual
or PID control. Once the student has toggledd" control (to
Matlab from Bailey), however, the Simulink "simulation"
drives the inputs to the Bailey system as the simulation
proceeds. This creates a very flexible environment for imple-
menting complex control algorithms on a moderately com-
plex experimental system.

Both a nonlinear model and a linearized model are given
in Johansson and Nunest3' for the four-tank system. The
models used for this work include the disturbance effects of
flows in or out of tanks 3 and 4. The nonlinear differential
equations governing the heights in this four-tank system are
given in Table 2, and the linearized version is seen in Table
3. The liquid levels in tanks one and two, h, and h2, are
considered measured variables. The speed of the pumps, v,
and v2, are considered as manipulated inputs. The pump
speeds are manipulated as a percentage of the maximum
pump speed. The disturbances dl and d2 model the unmeasured
disturbance effects of flows in or out of tanks three and four.
This model is a simple mass balance, assuming Bernoulli's
law for flow out of the orifice. The gamma values, yi,
correspond to the portion of the flow going into an upper
tank from pump i. In Johansson and Nunes,l31 it is shown that
inverse response in the modeled outputs will occur when

Matlab DigiTool -----. Process Station *" Process Measurments
DigiVis 0----- Database "'-- Process Inputs
SDigiDDE -*"

Figure 3. Schematic of the control system.

Figure 4. Screenshot of Freelance four-tank schematic.

Figure 5. Screenshot of Freelance tank-level trends.

Chemical Engineering Education

Process Station Experimental Station

Operator Station

Graduate Education

Nonlinear Model Equations
dh1 a- a2 --)2h ylkl
-- -- 2g + -gh + Vi
dt A, A A
dh2 a2 2g2 24 + yk
di A2 A, A1
dh3 a3 (1-y2)k2 kdldl

dh4 a4 2 (1-yl)kl kd,d2
dt A4 A4 A4
A- -- -- A A
Linearized Model Equations

0 0

0 -- 0
0 0
0 0 0





(l- 2)k2

A, 92h(0)
a, y g

Y7 + Y2 < 1. A modification introduced by the students in the class was the
presence of a disturbance introduced by a submersible pump in the upper
tanks. These disturbances' effects are modeled as a constant leak into or
out of the upper tanks.

To illustrate the use of the four-tank system in the graduate control
course, the following projects are briefly described. It should be noted
that each of the four elements (modeling, analysis, synthesis, and imple-
mentation) was performed by each student group. A more detailed theo-
retical treatment of the results can be found
in Vadigepalli, et al.141


Although the fundamental model de-
scribed earlier is a reasonably accurate de-
scription of the system dynamics, many of
the parameters are not available a priori,
which required estimation of several model
parameters. The tank areas A, can be mea-
sured directly from the apparatus. Using
tank drainage data, the cross-sectional out-
let areas ai can also be determined. The
steady-state operating points of v, = 60%
and v, = 60% were used for subsequent
results. The system valves were
ELUS set such that the operating point
S exhibits inverse response
.. (y, + 2 < 1). Time constants, Ti,
for the linear system model were
on the order of 40 seconds.

The students designed a suit-
able test input sequence to gen-
erate data for the estimation of
the remaining parameters. In this
case, they elected to identify the
parameters of the original non-
linear model, requiring the solu-
tion of a nonlinear optimization
problem. The problem was for-
mulated to minimize the 2-norm
of the difference between the
nonlinear model and actual mea-
surements, searching over four
parameters. Using dynamic data
from the experiments, the optimi-
zation routine found the optimal
pump gains k, and gamma values
yi as depicted in Table 4. A simi-
lar routine was employed to model
the characteristics of the distur-
bance introduced by the submers-
ible pumps, kd, and kd2.

Fall 1999

Figure 6. Screenshot of the Matlab interface.
Figure 6. Screenshot of the Matlab interface.


1 0

Graduate Education

A critical step in any identifica-
tion procedure is validation of the
model against novel data. The stu-
dents were successful in validating
the model that resulted from the pre-
vious optimization problem. They
were able to capture the known in-
verse response in the system, and
they also were able to compare the
nonlinear model response to a lin-
ear approximation, which was sub-
sequently used for analysis.

As mentioned earlier, one of the

key insights derived from this course is the limitation to
achievable closed-loop performance due to intrinsic system
properties. Once the students had obtained the physical mod-
els of the system, they computed a linearized approximation
at a steady-state operating point and analyzed the controlla-
bility properties of the resulting linear system. The inputs
and outputs of the system were appropriately scaled before
the controllability analysis was carried out.
The first metric considered was the relative gain array
(RGA) as a function of frequency. For the system configura-
tion employed in this study, the students found that the
diagonal RGA elements were very near to 1 at low fre-
quency, suggesting an easily decoupled system. But as
the frequency increased to the bandwidth region, the
students discovered that the diagonal RGA values de-
creased significantly, indicating the importance of multi-
variable interactions in the bandwidth of interest. Such
an insight is particularly valuable at the graduate control
level to highlight the limited interpretation of the steady-
state RGA value.
Additional insight is derived from an analysis of the singu-
lar values of the system. More specifically, their ratio (the
condition number) gives an indication of the sensitivity of
the plant to uncertainty. The condition number at low fre-
quencies was small, between 1 and 3. But it decreases with
frequency, implying that the plant is more sensitive to uncer-
tainty at steady state than at higher frequencies. In addition,
the low frequency minimum singular value is above 1. This
means that adequate control action should be possible;
the input moves will be able to move the outputs a
sufficient amount to track setpoints. The minimum
singular value of the plant is greater than 1 up to the
frequency of o=0.007 rad/sec. This indicates a poten-
tial constraint on the controller bandwidth because of
high frequency input saturation.
Another quantity of interest in control systems in general,

and the four-tank system in particular,
is the location and direction of multi-
variable process zeros. For the operat-
ing conditions in this study, the multi-
variable zeros are found to be at -0.0791
and 0.0285 rad/sec. The input zero di-
rection corresponding to the right-half-
plane (RHP) zero is [-0.715, 0.699]T,
and the output direction is [0.718,
0.696]T. From these directions, one can
see that forcing one pump up while the
other is forced down causes the sys-
tem to display inverse response. The
presence of the RHP-zero could also
be seen in a plot of the RGA, in that the
elements of the RGA change sign from

frequency o)=0 to frequency o = _. The lesson that the stu-
dents will take away from this analysis is that the RHP-zero
also limits the controller bandwidth.

For completeness in the overall project description, the
topic of uncertainty characterization is briefly mentioned.
The technical details can be found in Vadigepalli, et al.141
The emphasis was on bounding the uncertainty between the
approximate linear model that was used for controller syn-
thesis and the actual physical system with parametric uncer-
tainty. A multiplicative input uncertainty structure was de-
termined by the students to adequately represent the actual
non-ideal behavior of the system. After subjecting the linear
model to parametric variations (10% in yi and k,), approxi-
mate bounds were determined from the corresponding fre-
quency plots of the multiplicative uncertainty. This uncer-
tainty characterization is central to the robust controller de-
sign task that is described below.

The students employed robust control theory to initially
design an H, controller following the procedures detailed
in Balas, et al.J5 Using a D-K iteration procedure, a robust
12l-order controller with a structured singular value, g, less
than 1 was obtained. The controller was implemented in the
real system. As one might expect with a physical system, the
simulations did not precisely match reality. The nonidealities
of the pumps, level sensors, and head losses in the piping all
contributed to these discrepancies. Other unmodeled phe-
nomena witnessed by the students include the formation of
vortices in the upper water tanks above the drainage holes
and spontaneous triggering of the level alarms due to con-
densation. Despite the lack of perfect agreement between
theory and practice, the students were able to generate con-

Chemical Engineering Education

Model Parameters

a, a2 2.3 cm k, 5.51 cm-/s
a,, a4 2.3 cm2 k2 6.58 cm3/s
A A,, A3, A4 730 cm2 g 981 cm/s2
v (0) 60% 7l 0.333
v2(0) 60% Y2 0.307
T, 53.8 sec h,(0) 14.1 cm
T, 48.0 sec h,(0) 11.2 cm
T, 38.5 sec h3(0) 7.2 cm
T4 31.1 sec h,(0) 4.7 cm

Graduate Education

trollers with robust performance guarantees.
Representative results demonstrating the disturbance rejection capa-
bility and setpoint tracking performance of one controller design are
shown in Figures 7 and 8, respectively. This controller was designed
for disturbance rejection, which results in excessive input moves for
setpoint moves. A robustly performing setpoint tracking controller was
also implemented. This design requires an additional setpoint filter in
order to satisfy the constraints on the input moves.
The students clearly mastered a moderately complex control problem.

Time (seconds)
Figure 7. Disturbance rejection using robust controller.

Fall 1999

We have described the use of an elegant experi-
ment for reinforcing the theoretical content of a
typical graduate control course. Although the over-
all physics of the process are not very sophisti-
cated, we have shown that the system exhibits rich
behavior that can be used to exercise principles in
modeling, analysis, and advanced control design.
The use of a PC-based DCS coupled with
MATLAB/Simulink was particularly effective in
the implementation of the laboratory control pro-
cess. The PC-based system was more flexible than
traditional DCS systems, and the DDE interface
facilitated a range of complex control designs that
are appropriate for the graduate level.
Our ongoing efforts with this experiment in-
clude the use of the four-tank system in a
multidisciplinary control engineering laboratory.
The course was first offered in the spring of 1999
as a senior-level elective and drew students from
chemical, electrical, and mechanical engineering.
We plan to report our experiences with this imple-
mentation in a future publication.

We would like to acknowledge the support of
our former Dean, Stuart Cooper, who first encour-
aged us to create such a lab and provided the initial
funding. The continuing support of our department
Chair, Eric Kaler, and Dean, Andras Szeri, is greatly
appreciated. None of this work would have been
possible without the expert craftsmanship of George
Whitmyre, who constructed the four-tank system.
We would also like to acknowledge the additional
graduate students who worked on the four-tank sys-
tem-Luis J. Puig and Radhakrishnan Mahadevan.

1. Skogestad, S., and I. Postlethwaite, Multivariable
Feedback Control, John Wiley & Sons, New York,
NY (1996)
2. Kheir, N.A., K.A. Astrom, D. Auslander, IKC. Cheok,
G.F. Franklin, M.M. Masten, and M. Rabins, "Con-
trol Systems Engineering Education," Automatica,
51(8), 147 (1995)
3. Johansson, K.H., and J.L.R. Nunes, "A Multivari-
able Laboratory Process with an Adjustable Zero,"
in Proc. American Control Conf. (1998)
4. Vadigepalli, R., E.P. Gatzke, and F.J. Doyle III,
"Robust H, Control of a Multivariable Experi-
mental 4-Tank System," in preparation (1999)
5. Balas, G.J., J.C. Doyle, K. Glover, A. Packard, and
R. Smith, p -Analysis and Synthesis Toolbox User's
Guide, The Mathworks, Natick, MA (1995) J



0 __

0 500 1000 1500

Time (seconds)

Figure 8. Reference tracking using robust controller.

1 I

, ,

2000 2500 3000


Random Thoughts...


Active Learning vs. Covering the Syllabus

and Dealing with Large Classes

North Carolina State University Raleigh, NC 27695

In an earlier column,111 we listed the top ten questions we
get at teaching workshops and responded to the first one
("Is there any hard evidence that the instructional meth-
ods we recommend actually work?") In this column we
consider two more questions.
Early in our workshops-usually within the first 15 min-
utes-we suggest that instructors include brief active exer-
cises in their lectures. Some participants invariably express
concern that they have to present a lot of material in their
courses, and one of them poses Question #2: How can I take
the time for those exercises and still cover the syllabus?
Another follows up by observing that he or she teaches a
lecture class to 175 students and raises Question #3: Can you
use these methods in large classes?

Can you use active learning and
still cover the syllabus ?

A huge volume of material can be "covered" in a short
period of time. If you put all of your lecture notes in
PowerPoint or on transparencies and flash through them in
class, you can get through several hundred pages of text in a
month. The question is, what is your objective? If it is
simply to present all of the prescribed course material, re-
gardless of how much or little of it the students actually
absorb, then you should not use active learning exercises-
they do indeed slow things down. On the other hand, if the
objective relates to what the students learn as opposed to
what you present, then the goal should not be to cover the
syllabus but to uncover the most important parts of it.
People acquire knowledge and develop skills only through
repeated practice and feedback, not by watching and listen-
ing to someone else showing and telling them what to do.* In
" For theoretical and empirical support of this claim, see any text
on cognitive psychology written in the last twenty years, e.g.,
Pressley and McCormick.2'

lecture classes, most students are neither practicing nor re-
ceiving feedback on anything. They are just sitting there-
sometimes watching and listening to the lecture, sometimes
thinking of other things, sometimes daydreaming or sleep-
ing. Most of them would learn just as much if the classes
were cancelled and they were simply given the lecture notes
and homework assignments and perhaps review sessions
before the tests.
It's a much different story if lectures are punctuated with
brief active exercises that call on students-working indi-
vidually or in small teams-to answer questions, begin prob-
lem solutions, fill in missing steps in derivations, brain-
storm, formulate questions about material just presented,
summarize, or do anything else that they may subsequently
be asked to do in homework and on tests.13? The exercises
energize the students (sometimes literally waking them up),
direct their focus to the most important points in the lecture,
and increase their subsequent concentration when the lecture
continues. They give the students practice in the methods
and skills the course is intended to teach them and immedi-
ate feedback on their efforts, thus meeting the criteria for
learning to occur. Even if some material were dropped from
the course syllabus to make way for the exercises, the in-
creased learning would more than compensate for the loss.
But there is no need to shorten the syllabus. Suppose that
instead of saying every word and writing every statement
and drawing every diagram and deriving every equation in
class, you were to put a lot of the material in class handouts
that include gaps-skipped steps in derivations, axes with
no curves showing-and exercises with spaces left for re-
sponses. "Estimate the solution of this problem." "If you
increase the temperature, how would you expect the product
yield to vary? Why?" "Draw the free-body diagram" "Fill
in the missing steps between Eqs. (4) and (5)." Further,
suppose you announce that you will not go over most of the
Copyright ChE Division of ASEE 1999

Chemical Engineering Education

details in the handouts in class, but any of it-especially the
gaps and questions-could show up on the test. Most of the
students will then actually read the handouts-at least after
the first test, when they discover that you meant it.
This strategy accomplishes several things. By eliminating
the need to say and write and draw everything in class, you
buy yourself many classroom hours that can be devoted to
the things that make learning happen-spending more time
on conceptually difficult material, giving more examples,
asking and answering questions, and implementing active
learning. You can fill in some of the gaps in the handouts in
class; get the students to fill in others in active learning
exercises; and leave some for them to work out for them-
selves before the test. The students learn more (we learn by
doing, not by watching and listening), the classes are more
lively, daily attendance increases, and the syllabus is safe.

Do active learning methods work in large classes?

The larger a class, the more essential it is to use active
learning.4 7] In a traditional lecture class with 15 students, it
is not too difficult to get almost everyone actively involved
in asking and answering questions and participating in dis-
cussions of course material. In a class with 40 students it is
extremely difficult to do so, and in a class of 75 or more it is
virtually impossible. Few students have the self-confidence
to risk looking foolish by asking or answering questions in
front of a large number of classmates, and the traditional pep
talks proclaiming that there are no dumb questions and that
wrong answers are also valuable generally have little effect.
On the other hand, when a class is periodically given some-
thing to do in groups of two or three, the risk of embarrassment
is minimal-the only real difference between a class of 20 and
a class of 200 is that the latter class is noisier during activities.
A key to making active learning work in large classes is to
stop the activity after the prescribed time interval and call on
individual students or teams to state their results." If you
only call for volunteers to provide responses after a group
exercise, many students will not participate in the activity,
knowing that sooner or later another student or the instructor
will supply the answer. If they know that any of them could
be called on, the same fear of embarrassment that keeps
them from volunteering responses in the whole class will
prompt most of them to work with the small group so they
will be ready with something if they are picked.
Instructors who have never used active learning in a large

When we do this, we tend to overload on the back of the class-
room, where many students go to avoid the instructor's attention.
In our classes the students quickly learn that they can run but they
can't hide.

class usually envision two problems. They worry that some
students will refuse to participate under any circumstances
and that the noise level during the activity will make it
difficult to regain control of the class.
In our experience, more than 90% of the students in a class
routinely participate in active learning exercises after the
first few (when they feel awkward and unsure about what
they are supposed to do), and the usual involvement is closer
to 100%. Nevertheless, it disturbs instructors to see even one
student sitting with arms crossed, pointedly refusing to par-
ticipate, and the instructors often take such observations as
evidence that the method is failing.
That's the wrong way to look at it. Suppose a full 10% of
your students sit on their hands during an active learning
exercise. This means that 90% of your students are engaged
in thinking about what you want them to think about and
trying to apply the concepts you have been teaching, so that
the odds are 9 to 1 in your favor. In a typical traditional
lecture, the percentage of the class actively engaged in think-
ing about the lecture content at any given time, let alone
trying to apply it, is generally very low. Even if it is as high as
10%, which is unlikely, the odds are 9 to 1 against you. No
instructional method-lecturing, active learning, multimedia
tutorials, or anything else-is guaranteed to reach every stu-
dent. As an instructor, the best you can do is go with the odds.
It is true that in a large class the noise level can make it
more difficult to bring the students' attention back to you,
which makes it important to establish a signal (e.g., a buzzer,
whistle, or handclap) for them to finish their sentence and
stop the discussion. After the first few exercises, we have
never had to wait for more than 10 seconds for the room to
quiet down, even with 400 people there. Besides, if you are
teaching a class in which the students are so involved in
answering your questions or working out your problems that
you have trouble getting them to stop, there are far worse
problems you could have.

1. Felder, R.M., and R. Brent, "FAQs," Chem. Eng. Ed., 33(1), 32 (1999)
2. Pressley, M., and C.B. McCormick, Cognition, Teaching and Assess-
ment. HarperCollins, New York, NY (1995)
3. Felder, R.M., (i) "Any Questions?" Chem. Eng. Ed., 28(3), 174 (1994);
(ii) "How About a Quick One?" Chem. Eng. Ed., 26(1), 18 (1992)
4. McKeachie, W.J., Teaching Tips: Strategies, Research, and Theory for
College and University Teachers, 10h ed., Houghton Mifflin Co., Bos-
ton, MA (1999)
5. Felder, R.M., "Beating the Numbers Game: Effective Teaching in
Large Classes<' Proc. 1997 Annual ASEE Conf., American Society for
Engineering Education (1997) users/f/felder/public/Papers/Largeclasses.htm>
6. "On Teaching Large Classes," occasional_papers/large_classes.htm>
7. "Teaching Large Class Sections," The Penn State Teacher. II. Learning
to Teach; Teaching to Learn U

Fall 1999

All of the Random Thoughts columns are now available on the World Wide Web at and at

class and home problems

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:, Chemical Engineering Depart-
ment, University of Michigan, Ann Arbor, MI 48109-2136.




National University of Malaysia Bangi, Selangor, Malaysia 43600

he Peng-Robinson equation of state and its close kin,
the Soave-Redlich-Kwong equation, are simple yet
very effective tools for solving phase equilibria prob-
lems involving hydrocarbons and other nonpolar and slightly
polar species. Being cubic equations, when solved for the
compressibility factor, Z, they will either yield three real
roots or a single real root and a complex pair. It would be
most convenient and is sometimes believed that one single
root implies a single phase while three real roots imply
liquid and vapor phases are in equilibrium. Sadly, such is
not the case. Care must always be taken to extract the
correct root. Major blunders can be made, as we will show
in the following problem.


Pure n-butane at 430K and 60 bars is throttled to a final
pressure of 10 bar, as shown in Figure 1. What is the tem-

Ronald M. Pratt is a lecturer in the engineering department at the
National University of Malaysia. He obtained his BS in mathematics and
in chemical engineering at the Colorado School of Mines, his MS in
mathematics at the Fuxin Mining Institute in Liaoning Province, China,
and his PhD in chemical engineering at the Colorado School of Mines.
Research interests involve molecular dynamics and fractal modeling,
and his teaching responsibilities have included undergraduate, gradu-
ate, and statistical thermodynamics courses and molecular simulation.

Copyright ChE Division of ASEE 1999

T1 = 430K T2 = ?
PI = 60 bar P2 = 10 bar

Figure 1. Throttling of n-butane to known final pressure.

perature of the stream as it exits the valve? Use the Peng-
Robinson equation of state to model the PVT behavior of n-


The Peng-Robinson equation is written as
RT a
P= (1)
v-b v(v+b)+b(v-b)
R universal gas constant
T absolute temperature
v molar volume

a ac [l+m(l- Ti/2 T
a. 0.45723553 R'T'2P

Chemical Engineering Education

m 0.37464 + 1.54226 co-0.26992 (02
b 0.077796074 RT/P
T critical temperature = 425.1K for n-butane1'
P critical pressure = 37.96 bar for n-butane111
o pitzer acentric factor = 0.200 for n-butane1'I

It is common to rewrite the Peng-Robinson equation as a
cubic polynomial,

f(Z)= Z3 + cZ2 + Z + = 0 (2)

a B-l

P A-2B-3B2
y B + B' AB
A aP/(RT)2
B = bP/RT
Since an energy balance written across the valve (assum-
ing residence time of the fluid in the valve is so short that
heat loss through the valve casing is negligible) states that
AH = 0, we therefore need to find the exit temperature, T2,
that satisfies this condition. This requires that we be able to
calculate the enthalpy change across the valve, AH. We will
consider AH to be the sum of two parts, an ideal gas contri-
bution and a residual correction for non-ideal behavior,

AH= AHID + AR (3)

The ideal gas contribution is determined from ideal gas
(low pressure) heat capacity data:

Heat capacities for gases in the ideal gas state are func-
tions of temperature only and are usually given by correla-
tions. A common correlation'" is

CP =R(A+BT+CT2+DT2) (5)

Table 1 shows the coefficients for n-butane in the ideal gas
The residual contribution is calculated using standard equa-
tions12'31 derived from the Peng-Robinson equation of state,

AHR = HR H (6)


Ta'-a Z+B(+ )
HR T8 -nZ -) + RT(Z 1)
bVs Z+B(l-V2)

Ideal Gas State Heat
Capacity Coefficients
for n-butane

A 1.935
B 36.915 x 10-3
C -11.402x 106
D 0

Enthalpy Changes

T2,K) AH(J/mole)



350 2394.94
325 -683.42
330.531 0.00

Sda -ma
dl [l+m(l T-/T,)]TT

HR and H2 are evaluated from Eq. (7) using the compress-
ibility factors corresponding to the initial and final states,
A temperature for the stream leaving the valve may be
guessed and AH calculated as indicated above. Proceeding
by trial and error (the secant method]41 could be used to
impose self-consistency and avoid a trial-and-error solu-
tion), we obtain Table 2. We see that a value of T2 = 330.531K
gives a AH equal to zero (within two decimal places accu-
The "solution" is that the exit stream is n-butane vapor at
330.531K. All done, right? Not quite. Everything seems well
until one looks at a phase diagram for n-butane or notices
that the temperature is well below its boiling point at 10 bar
pressure (T'"'=352.475K), and therefore at these exit condi-
tions (10 bar, 330.531K) n-butane is a subcooled liquid. This
is shown in Figure 2. The actual state of n-butane at 10 bar

0 200 400 600
kJ Ikg

800 1000 1200

Figure 2. Pressure-enthalpy diagram for n-butane.

Fall 1999

and 330.531K is shown by the m in Figure 2, obviously far
from the correct solution (Point 2 in Figure 2), which indi-
cates that the exit stream is a mixture of vapor and liquid.
The calculation has been deceiving us with bogus quasi-
vapor roots. Vapor roots can only be used for temperatures
above the boiling point. Below this temperature, liquid roots
must be used. Therefore, to proceed with the solution to this
problem, we should first determine the boiling point, Tsat, for
n-butane at 10 bar. Since at the boiling point, liquid and
vapor phases must be in equilibrium, we must find the tem-
perature at which the chemical potentials of both phases are
equal. For a pure component, this is equivalent to saying that
the Gibbs free energies must be equal for the two phases.
Since the ideal gas contribution to the Gibbs free energy is
the same for each phase, we only need concern ourselves
with the residual contribution. At the boiling point we re-
quire that

G = (8)
The residual Gibbs free energies are calculated from the
Peng-Robinson equation of state using the standard derived

-0.5 0 0.5 1 1.5
v, m^3/kmnl

GR b = n -(- RT(Z B)+RT(Z 1) (9)
b;8 Z + B(l-V2) I

The vapor GR in Eq. (8) is computed from Eq. (9) using the
largest compressibility factor root. Similarly, the liquid GR
should be calculated using the smallest root. Different tem-
perature values may be selected on a trial-and-error basis
until the equivalence of Eq. (8) is satisfied to within some
tolerance. (Again the secant method can be used to facilitate
coding of this algorithm.) Proceeding in this manner, we
find that at 352.475K, GR = GL = -540.28 J/mole, and there-
fore Tsa = 352.475K.
For this problem, one will find that AH is positive when
using the vapor roots at T'"", and AH is large and negative
when using the liquid root at Tsat. Therefore, the exit stream
is a mixture of liquid and vapor in just the right combination
to make AH = 0. Equation (6) must be modified to include
both a vapor and liquid contribution to the enthalpy of the
exit stream,

AHR = xHRv + (1 x)HR HR (10)

2 25 =23438
2 2.5 3

Figure 3. Magnified pressure-volume diagram in the region
P=10 bar for n-butane

Chemical Engineering Education

Computed Compressibility Factors

T ZL Zmiddle Zv vL(m3/kinol) Vmiddl(m3/kmol) v (m3/kmole)
280 0.03982 none none 0.09270 none none
293 0.03907 0.44221 0.48898 0.09617 1.07724 1.19116
352.475 0.03880 0.13666 0.79982 0.11370 0.40048 2.34383
396 0.05482 0.05694 0.86624 0.18048 0.18746 2.85197
600 none none 0.96897 none none 4.83360


where x is the quality or percent vapor in the two-phase exit
stream. When the calculation is made using Eq. (7), the exit
stream is found to have a quality of 0.8434. Therefore, the
correct solution to the problem is that the exit stream is at its
saturation temperature of 352.475K and consists of 84.34%
vapor and 15.66% liquid (as shown in Figure 2).

To illustrate more clearly what has happened here, we will
look at solutions of the Peng-Robinson equation for Z (and
v) at 10 bar at various temperatures. Obviously, at 10 bar, n-
butane exists as a superheated vapor above Ta"" and as a
subcooled liquid below T"s'. At various temperatures at P=10
bar, we obtain Table 3 from Eq. (2).

Which roots are valid?
Certainly we can rule out
all of the intermediate or
middle values. (These in-
termediate roots that lie
within the saturation en-
velope are of use in sta-
bility analysis,151 some-
thing we are not con-
cerned with here.) Also,
any liquid roots above the
boiling point must be
ruled out, and any vapor
roots below the boiling
point must be eliminated.
Only the values in bold
print hold any physical
significance for us. (The
Peng-Robinson equation
is not recommended for
calculating subcooled liq-
uid values, but values are

0.02 293K



-0.06 -

-0.08 .
0 0.1 0.2 0.3 0.4 0.5

treated in Figure 4, which shows the cubic polynomial in Z
(Eq. 2) as a function of Z. This area is marked "Danger!" in
Figure 4. The polynomial crosses the zero horizontal axis
three times within the danger region 293K fore, in our example above, at 330.531K, the Peng-Robinson
equation cheerfully provided an erroneous vapor root that
gave us a nonsense solution to the problem.
A similar situation exists at temperatures above the boiling
point. Between the boiling point and temperatures as high as
396K (a 43K spread), bogus liquid roots are calculated. This
dangerous region is also marked on Figure 4. At tempera-
tures over 396K, only a single (vapor) root is calculated, and
there is no danger of bogus roots.

The key point is that before we can decide which roots are
valid and which are bo-
gus, we must already
know the boiling point.
0K An equivalent procedure
would be to select the
value of Z that corre-
sponds to the lowest re-
nger sidual Gibbs free energy
or fugacity161 since the
liquid and vapor free en-
ergy curves cross at the
boiling point. Without
K=BoilingPo taking these steps, it is
Danger! rK easy to end up in big
K 600K trouble.
0.6 0.7 0.8 0.9 1 CONCLUSION

In conclusion, it is not

Figure 4. The Peng-Robinson polynomial
f(Z) = Z3 + oZ2 + PZ + y
as a function of Z for n-butane at 10 bar for various temperatures.

physically significant
even if they are of low accuracy.)
Figure 3 is a magnified PV diagram in the region of 10 bar
and shows the bogus liquid root at 396K (0.18048 m3/kmol)
and the bogus vapor root at 293K (1.19116 m3/kmol). Their
respective valid superheated vapor and subcooled liquid vol-
ume roots are also shown. The saturated molar volumes are
also indicated in Figure 3. Note that the bogus roots lie
within the phase envelope.
The danger is that we obtain what appear to be liquid and
vapor roots in regions quite far from the boiling point. A
value of 293K is the minimum temperature (rounded to a
whole number) that will still yield a bogus vapor root. At
temperatures lower than this, the Peng-Robinson equation
will provide just one real (liquid) root, and there is no dan-
ger. In this region between the boiling point and 293K (about
a 60K spread), bogus vapor roots will appear. This is illus-

possible to assume sim-
ply that large roots of Eq.
(2) are vapor values and
that small roots are for

the liquid. There is a large margin on both sides of the
boiling point where bogus liquid or vapor roots are calcu-
lated. Selection of the correct root requires additional infor-
mation, and considerable care must be taken.

1. Smith, J.M., H.C. Van Ness, and M.M. Abbott, Introduction
to Chemical Engineering Thermodynamics, 5th ed., McGraw-
Hill, New York, NY (1996)
2. Sandler, S.I., Chemical and Engineering Thermodynamics,
John Wiley & Sons, New York, NY (1999)
3. Kyle, B.G., Chemical and Process Thermodynamics, Prentice
Hall, Englewood Cliffs, New Jersey (1992)
4. Carnahan, B., H.A. Luther, and J.O. Wilkes, Applied Nu-
merical Methods, John Wiley & Sons, New York, NY (1969)
5. Walas, S.M., Phase Equilibria in Chemical Engineering,
Butterworth-Heinemann, Boston, MA (1985)
6. Savage, P.E., "Spreadsheets for Thermodynamics Instruc-
tion," Chem. Eng. Ed., 29, 262 (1995) U

Fall 1999

, W curriculum



Monash University Clayton, Victoria, Australia 3168

he importance of particle technology to the practic-
ing chemical engineer has been highlighted by ar-
ticles such as those by Ennis, et al.,I'" which drew
attention to "the legacy of neglect in the U.S.," and Nelson,
et al.,121 which invited us to "teach 'em particle technology."
These articles and the excellent series of articles on particle
science and technology in the spring 1998 issue of CEE have
done much to raise awareness in industry and academia of
the importance of particle technology to the process indus-
tries, of the initiatives that have been set in place to address
the problem, and the need to teach it in chemical engineering
Several of the CEE articles drew attention to the shortage
of educational resources for particle technology. Davis and
Fan'31 highlighted the lack of suitable teaching materials and
recommended that booklets of homework problems, soft-
ware, CD-ROMs, laboratory demonstrations, experiments,
and textbooks were required. Nelson and Davies141 announced
the establishment of a web site for gathering multimedia
educational modules prepared by experienced people in the
particles field for dissemination to educators in chemical
engineering. Chase and Jacob151 reported on their successful
introduction of a solids processing course, team-taught by
academia and industry to senior undergraduates at the Uni-
versity of Akron.
These authors highlighted the need to use a range of pri-
mary references and reported on students' strongly voiced
opinion that better textbooks are needed on the subject.
Donnelly and Rajagopalan161 reported on the development of
a series of instructional modules on particle technology top-
ics at the Engineering Research Center for Particle Science
and Technology. The objective of the module series pro-
gram is to permit particle technology topics to be squeezed,
in small doses, into the already overcrowded chemical engi-
neering curriculum.
This article will focus on educational resources for teach-
ing particle technology and, in particular, on laboratory and

classroom demonstrations, which highlight important phe-
nomena and the ways in which the behavior of particulate
solids is surprising and often counter-intuitive when intu-
ition is based on our experience with fluids.
Personal experience gained from teaching particle tech-
nology over a period of fifteen years, at two institutions,
taught me that in order to get students' attention it was often
necessary to do some demonstrations to show how powders
behave differently from liquids and gases. A dust explosion,
for instance, is impressive and never fails to grab attention.
A good-sized fluidized bed, preferably with a floating plas-
tic duck, is another good one for audiences of all ages and
backgrounds. A large steel ball that rises to the top of a
beaker of sand upon shaking, is a favorite, and since it is the
only one that is readily portable, I tended to rely on it more
and more as I grew older and lazier.
I realized at an early stage that demonstrations of the many
interesting phenomena in particle technology are time con-
suming to set up in the lecture theater or the laboratory.
Therefore, I made short videos of the more interesting phe-
nomena and those that are the most difficult to explain
through the spoken and written word, diagrams, or photo-
graphs. I also borrowed video clips from others who are
more talented at setting up the demonstrations (Professor
Derek Geldart of Bradford University on fluidization and
NEU Engineering on pneumatic transport).

Martin Rhodes is Reader in the Department
of Chemical Engineering at Monash Univer-
sity in Australia. With a keen interest in par-
ticle technology education, he has directed
continuing education courses in the area and
is author of the undergraduate textbook In-
troduction to Particle Technology. His re-
search interests include fluidization, gas-par-
ticle flows, interparticle forces, and particle

Copyright ChE Division of ASEE 1999

Chemical Engineering Education


The CD-ROM medium al-
lowed me to combine video clips
and explanatory text in a form
that is easy to use by both the
educator in the classroom and
the student at home. The result-
ing CD-ROM of "Laboratory
Demonstrations in Particle Tech-
nology" was compiled with the
assistance of one of my research
students, Alfi Zakhari, over a
period of several months.

The topics covered in the labo- Figure 1. Core flow of
ratory demonstrations featured 250-micron sand wi
on the CD-ROM include flow to ac
patterns in hoppers, fluidization
phenomena, hindered settling, gas cy-
clones, dust explosions, pneumatic trans-
port, size and density segregation, and
stresses developed in particulate solids.
The user can easily navigate the topics
on the CD by using mouse-activated but-
tons. An explanation of what is featured
under each topic is given below.

Mass Flow and Core Flow
in Storage Hoppers

Mass Flow In perfect mass flow,
all the powder in a storage hopper is in
motion whenever any of it is drawn from
the outlet. Mass-flow hoppers are smooth
and steep. A video sequence shows sand
of mean particle size 250 p.m in mass
flow. The use of alternate layers of col-
ored powder in this sequence clearly Figure 2. A Gel
shows the key features of the flow pat- fluidized
terns: the powder surface remains level
until it reaches the sloping section; the flowing channel
coincides with the walls of the silo; all the powder is in
motion; particle velocity profiles are flat in the parallel-
walled section of the hopper.
Core Flow This occurs when the powder flows toward
the outlet of a silo in a channel formed within the powder
itself. The second video sequence shows the same sand (250
pgm) discharging from a hopper where the sloping walls are
less steep. Core flow results (see Figure 1). Alternate layers
of colored powder highlight the important features of core
flow: the surface of the powder forms an inverted cone;

ith s

in a

during discharge, powder flows
along this surface; the only sol-
ids in motion are those flowing
along these surfaces and down
the central channel or core; the
regions of powder lower down
in the hopper are stagnant until
the hopper is almost empty.
Whether we get mass flow
or core flow depends not only
on the slope of the hopper wall,
but also on the properties of
the powder and the interaction
of the powder with the hopper
wall's material of construction.
This fact is demonstrated in a
third video sequence in which
Is in a hopper. Solids are a different sand (mean size of
ome particles colored 100 gpm) gives core flow in the
tracers. steep-angled hopper.

Geldart Classification of Powders *
Geldartt18 classified powders into four
groups (A, B, C, D) according to their
fluidization properties at ambient con-
ditions. The Geldart Classification of
Powders is now used widely in many
fields of particle technology.
El Group A: Powders that, when flu-
idized by air at ambient conditions,
give a region of non-bubbling fluidi-
zation, beginning at the minimum flu-
idization velocity Um,, followed by bub-
bling fluidization as fluidizing veloc-
ity increases, are classified as Group
A. A video clip shows cracking cata-
lyst, a typical group-A powder, exhib-
iting a region of non-bubbling fluidi-
Group-A powder zation as gas velocity is increased from
"2-D" bed. zero through Umf and on to the mini-
mum bubbling velocity Umb. Expan-
sion of the non-bubbling bed as the gas velocity is increased
is clearly seen.
A second video sequence shows that when the air supply is
interrupted, the bed level initially drops abruptly as the
bubbles escape and then more slowly as the gas escapes
from the expanded emulsion phase-another characteristic
of Group-A powders.
A third video shows a Group-A powder in a backlit two-
dimensional fluidized bed, showing splitting and coales-
cence and maximum stable bubble size (see Figure 2).
L1 Group B: The first video shows beach sand, a typical

Fall 1999

Group-B powder, fluidized by air. The video demonstrates that with
Group-B powders, bubbles appear as soon as the powder fluidizes,
ie., at Um. A second video clip shows Group-B powder in a two-
dimensional fluidized bed, and demonstrates that, in Group-B pow-
ders, bubbles continue to grow as they rise and as gas velocity is
increased, never achieving a maximum size (see Figure 3).
El Group C: These are very fine powders that, because of the domi-
nance of interparticle forces, are incapable of fluidization in the strict
sense. Bubbles do not occur. The gas flow forms channels through the
powder. Cracks form, and sometimes discrete plugs of solids are
lifted by the gas flow. The video shows the formation of cracks,
channels, and rising plugs as an attempt is made to fluidize a Group-C
powder (see Figure 4).
LE Group D: These are powders made up of large particles, which like
Group-B powders give only bubbling fluidization when fluidized by
air at ambient conditions, but are distinguished by their ability to
produce deep spouted beds. The video clip shows a deep spouted bed
of rice grains, a typical Group-D material (see Figure 5).

Slugging When the size of the bubbles is greater than about one-
third of the diameter of the fluid bed vessel, the bubble rise velocity is
controlled by the vessel itself. The bubbles become slugs of gas, and
the bed becomes a slugging fluidized bed. A slugging fluidized bed is
shown in the video sequence.

Pneumatic Transport of
The pneumatic transport
of particulate solids is
broadly classified into two
flow regimes: dilute (or
lean) phase flow and
dense-phase flow.
El Dilute-phase transport:
Under these dilute-phase
flow conditions the solid
particles behave as indi-
viduals, fully suspended in
the gas, and fluid-particle
forces dominate. This is
simulated in the first video
El Dense-phase transport:
At the opposite end of the
scale is dense-phase flow,
characterized by low gas
velocities (1-5 m/s) and *
high solids concentrations
(greater than 30% by vol-
ume). In dense-phase
transport, particles are not .
ra r, s a n Figure 4. Attempting to fluidize a
fully suspended and there Geldart Group-C powder
is much interaction be- (wheat flour).

Figure 3. A Geldart Group-B powder fluidized in
a "2-D" bed.

figure 5. A spoutea jiulazea dea oJ rice,
demonstrating typical Geldart
Group-D behavior.

Chemical Engineering Education

tween the particles. Examples of horizontal and vertical
dense-phase transport are shown in video sequences.
Size and Density Segregation
[ Pouring Powder into a Heap: The video sequence dem-
onstrates the segregation that occurs when a mixture of two
different sizes or particles is poured into a heap. Fine par-
ticles are colored differently from larger particles. The
mixture is poured into a narrow Plexiglas box. The char-
acteristic "Christmas Tree" segregation pattern, with the
fines occupying the center of the heap, is clearly ob-
served (see Figure 6). The dominant segregation mecha-
nism in this case is percolation.
[J Segregation in a Rotating Drum: The video sequence
shows a mixture of different-sized particles rotated in a
Plexiglas drum at around 60 rpm. Bands of finer particles
(dark colored) develop over a period of a minute or two (see
Figure 7). The dominant segregation mechanism is again
probably percolation, although other mechanisms are
likely to be involved.
[E Segregation by Vibration: A steel ball bearing, about 25
mm in diameter, is placed at the bottom of a beaker and the
beaker is filled with sand (about 250 pm, but size is not too
critical). The beaker and contents are shaken vertically by


Figure 6. Segregation pattern formed by pouring a free-
flowing mixture of two sizes of particles into a heap
(the fine particles are the darker ones).

Figure 7. Segregation pattern formed by rotating a free-
flowing mixture of two sizes of particles in a horizontal
drum (fine particles are darker).

Fall 1999

hand. After a few shakes, the steel ball has risen to the top of
the sand. Smaller and less-dense balls may be used to dem-
onstrate that the speed of segregation in this case increases
with size and density of the ball. Several mechanisms have
been proposed to explain this effect, e.g, (a) the steel ball is
driven by convection currents set up in the sand; (b) sand
flows into voids created beneath the ball, causing it to rise;
(c) the large momentum of the ball compared to that of the
sand particles drives the ball upwards during the upward
motion of the beaker. A further video sequence shows a two-
dimensional version of the steel ball experiment described
here. A steel disc is shown rising through a bed of glass
beads in a narrow Plexiglas box that is vibrated vertically.
Colored tracer particles show the motion of the glass ballotini
as the steel disc rises. A strobe effect on the video camera
allows the motion of the box to be eliminated.

Fire and Explosion Hazards of Fine Powders
Finely divided combustible solids, or dusts, dispersed in
air can give rise to explosions in much the same way as
flammable gases. Dust explosions have been known to give
rise to serious property damage and loss of life. Most people
are probably aware that dust explosions have occurred in
grain silos and flourmills, and in the processing of coal. But
explosions of dispersions of fine particles of metals (e.g.,
aluminum), plastics, agricultural products, sugar, and phar-
maceutical products can be particularly potent. Processing
steps where fine powders are in dilute suspensions, or are
heated, have a strong association with dust explosions; ex-
amples include dilute pneumatic conveying and spray dry-
ing, which involves heat and a dilute suspension.
In the video sequence, the explosive potential of a few
grams of brown coal is demonstrated in the vertical tube
apparatus. Corn flour, wheat flour, and ground sugar may
also be used. This apparatus is used to classify the powder as
explosible or non-explosible and to determine the minimum
dust concentration for explosion, minimum energy for
ignition, and in a modified form for minimum oxygen for
combustion. Such information is required for the design
of safe plant and equipment for handling and processing
combustible fine powders.
Gas Cyclone Separators
The reverse-flow gas cyclone is a commonly used device
for separating fine solids from suspension in a gas. Inlet gas
is brought tangentially into the cylindrical section and a
strong vortex is thus created inside the cyclone body. Par-
ticles in the gas are subjected to centrifugal forces that move
them radially outwards, against the inward flow of gas and
towards the inside surface of the cyclone on which the solids
separate. The direction of flow of the vortex reverses near
the bottom of the cylindrical section and the gas leaves the
cyclone via the outlet in the top (the solids outlet is sealed to
gas flow). The solids at the wall of the cyclone are pushed

downwards by the outer vortex and out of the solids exit.
The video sequence shows a Plexiglas cyclone separating
cracking catalyst from suspension in air. The sequence shows
the flow of the suspension into the cyclone and its transport
in the vortex to the solids exit.

Batch (Hindered) Settling of a Suspension
The simple batch-settling test can supply all the informa-
tion for the design of a thickener for separation of particles
from a liquid. In this test, a suspension of particles of known
concentration is prepared in a measuring cylinder. The cyl-
inder is shaken to thoroughly mix the suspension and then is
placed upright to allow the suspension to settle. The posi-
tions of the interfaces that form are monitored in time. The
video sequence shows the type of settling in which three
zones of constant concentration are formed-a top zone of
clear liquid, a middle zone of concentration equal to the
initial suspension concentration, and the lower zone of final
sediment concentration.

Stress Developed in a Powder
The variation in stress with depth of powder, in the case
where no force acts on the free surface of the powder, is
shown in the video sequence. A force is applied to the lower
surface of the bed of particles via a piston and a spring. The
degree of compression of the spring gives an indication of
the force required to move the bed of particles. Stress is
developed within the bed of particles. The magnitude of the
wall friction opposing motion of the bed is proportional to
the stress developed within the bed and increases with bed
depth. The video sequence shows that, beyond a certain
depth, the wall friction is so great that the bed of particles
cannot be moved. Most of the applied force is transmitted to
the tube walls. In the case of powders stored in a bin, the
walls of the bin support most of the weight of the powder.

The CD-ROM is intended for use by students on their own
time, although it can equally well be used by the educator in
a classroom equipped with a video projector linked to a
computer with a CD drive. It is intended to be complemen-
tary to the textbook Introduction to Particle Technology.'71
The primary objective of this textbook is to introduce the
subject of particle technology to students studying chemical
engineering. The approach taken is to take a number of key
topics in particle technology, giving the fundamental science
involved, and linking this, wherever possible, to industrial
practice. The coverage of each topic is intended to be exem-
plary rather than exhaustive.
The topics give coverage of broad areas within particle
technology: characterization (size analysis), processing (flu-
idized beds, granulation), particle formation granulationn,
size reduction), fluid-particle separation (filtration, settling,
gas cyclones), safety (dust explosions), transport (pneumatic

transport and standpipes). The topics included demonstrate
how the behavior of powders is often quite different from the
behavior of liquids and gases. The book includes 37 worked
examples and 76 homework exercises with answers. A
solution manual giving fully worked solutions to all the
homework exercises is now available from the Depart-
ment of Chemical Engineering at Monash University (http:/

Hardware: 486 CPU; 66 MHz; 4 MB RAM; VGA moni-
Operating Systems: CD is self-launching on Microsoft
Windows 95 or NT.

The purpose of putting the demonstrations on CD-ROM
was to be able to make them readily accessible and available
to educators and students. The CD-ROM is available from
the Department of Chemical Engineering at Monash Univer-
sity. Multiple copies are available for distribution to stu-
dents. Those interested should contact the author at
e-mail: martin.rhodes @
fax: Int+61-3-9905-5686

The author is grateful to Alfi Zakhari for putting together
the CD-ROM under the author's guidance, and to research-
ers John Sanderson, Igor Sidorenko, and Mao Qi-Ming for
setting up demonstrations and making videos. The author
would also like to thank Professor Derek Geldart of the
University of Bradford, England, for permission to use his
video clips on fluidization, and to NEU Engineering, Woking,
Surrey, England, for permission to use their video clips on
pneumatic transport.

1. Ennis, B.J., J. Green, and R. Davies, "Key Challenges in
Particle Technology: The Legacy of Neglect in the U.S.,"
Chem. Eng. Prog., 32, April (1994)
2. Nelson, R.D., R. Davies, and K. Jacob, "Teach 'em Particle
Technology," Chem. Eng. Ed., 29, 12 (1995)
3. Davies, R.H., and L-S. Fan, "Teaching Fluid-Particle Pro-
cesses," Chem. Eng. Ed., 31(2), 94 (1998)
4. Nelson, R.D., and R. Davies, "Industrial Perspective on
Teaching Particle Technology," Chem. Eng. Ed., 31(2), 98
5. Chase, G.G., and K.V. Jacob, "Undergraduate Teaching in
Solids Processing and Particle Technology: An Academic/
Industrial Approach," Chem. Eng. Ed., 31(2), 118 (1998)
6. Donnelly, A., and R.J. Rajagopalan, "Particle Science and
Technology Education Initiatives at the University of
Florida," Chem. Eng. Ed., 31(2), 122 (1998)
7. Rhodes, M.J., Introduction to Particle Technology, John Wiley
and Sons, Chichester, England (1998)
8. Geldart, D., "Types of Fluidization," Powder Tech., 7, 285
(1973) U

Chemical Engineering Education

Ray W. Fahien Award

Ray W. Fahien Award

Presented to
Robert P. Hesketh
Rowan University

This award was established in 1996 to honor the memory of Ray Fahien, editor of Chemical Engineering
Educationfrom 1967 until his death in 1995. He was effectively the founding father of the journal,
establishing it as a premier publication vehicle in the field of chemical engineering education. He
selflessly gave his time and talents to advance pedagogical scholarship, particularly in the careers of
young educators, through his dedication to the journal and to the profession.

Given annually to an educator who has demonstrated a scholarly approach to teaching and learning and
who has shown evidence of vision and contribution to chemical engineering education. Educators who
have been faculty members for not more than ten years as of July 1st in the year of the award will be
eligible. Nominees are evaluated based on 1) outstanding teaching effectiveness and 2) educational
scholarship. The award recipient should have made significant contributions to chemical engineering
education that go beyond his or her own institution.

Prior Winners: Kirk H. Schulz (1997); Douglas E. Hirt (1998)

William H. Corcoran Union Carbide Lectureship Joseph J. Martin Award
Award Award presented to
presented to presented to Robert P. Hesketh
John M. Prausnitz Liang-Shi Fan and C. Stewart Slater
U. of California-Berkeley Ohio State University Rowan University

ASEE Fellow Member Honorees
R. Neal House Purdue University U John W. Prados, University of Tennessee

Donald E. Marlowe Award James E. Stice, University of Texas
Chester F. Carlson Award C. Stewart Slater, Rowan University
General Electric Senior Research Award Arthur W. Westerberg, Carnegie-Mellon University

ASEE Section Awards
Dow Outstanding New Faculty Award
Rocky Mountain Section Kristi S. Anseth, University of Colorado
St. Lawrence Section Paschalis Alexandridis, State University of New York-Buffalo
Southeast Section Kimberly E. Forsten, Virginia Polytechnic Institute and State University
Outstanding Teaching Award
Middle Atlantic Section Andrew L. Zydney
Midwest Section Frank Manning, University of Tulsa
North Midwest Section Kirk H. Schulz, Michigan Technological University
Pacific Northwest Section Willie (Skip) Rochefort, Oregon State University
Outstanding Campus Representative Award
Middle Atlantic Section Deran Hanesian, New Jersey Institute of Technology
Effective Teaching Award-Engineering
Francis Manning, Tulsa University
Thomas C. Evans Instructional Unit Award
Douglas E. Hirt, Clemson University

Fall 1999 28

] essay


Clemson University Clemson, SC 29634

As the twentieth century draws to a close, the situa-
tion in many institutions of higher education might
be characterized as one of general frustration .
frustration not only among faculty and students but also
among administrators and the society that supports those
institutions. Students seek to reduce frustration by refusing
to take responsibility for their learning, by ignoring the
advantages offered by the university, or, in short, by relegat-
ing education to the periphery of their lives. Faculties seek to
reduce frustration by shifting their focus from "teaching" to
"research" and by narrowing the definition of education;
now, its meaning is largely confined to the transmission of
knowledge sufficient for students to enter a profession. Ad-
ministrators and state legislatures seek to reduce their frus-
tration by imposing regulations and accountability on uni-
versities in the form of management models transferred from
business. In previous generations, faculty were largely un-
hindered because, in the public's view, universities made
only marginal contributions to society. But today, even the
routine activities of faculty are presumed to be too important
to remain unfettered.
The thesis of this essay is that the problems and frustra-
tions now besetting institutions of higher education stem
largely from a misunderstanding of what such institutions
are supposed to be and misdirection relative to what they are
able to accomplish. Such misunderstanding and misdirec-
tion promote short-term demoralization of students, fac-
ulty, and administrators and lead to long-term degen-
eracy of the entire enterprise."1 So, what are universities
supposed to be about?

In Western cultures, formal schooling first appeared in the
ancient civilizations that thrived along the Tigris, Euphrates,
and Nile Rivers of mid-east Asia. Those schools trained a
class of scribes, some of whom would later become religious
leaders and advisors to the ruling class. The instruction

centered on reading, writing, the arithmetic needed for ac-
counting, and simple reasoning skills that applied to rules for
conducting religious ceremonies and civil actions. In other
words, from the beginning, a formal procedure was deemed
necessary to help students learn to recognize and interpret
abstract symbols and to develop the thought patterns re-
quired to manipulate those symbols. But ancient societies
needed only a few symbol manipulators: the large bulk of
humanity had no use for training in abstractions, for their
needs were fixed on the concrete problem of sustaining life.
Such needs could be met by informal instruction provided
by family and by apprenticeship to craftsmen for learning
specialized skills.
By the Age of Pericles in ancient Greece (ca. 430 BC),
education had spread from the clerics to the youth of a
leisured class. Naturally, this change brought with it a shift
in focus; leisured youth has little patience with the intrica-
cies of either clerical accounting or religious dogma. In-
stead, those ancient youths, like ours today, sought to
understand their relations with the physical world and
their relations with others. The first forms the basis for
scientific inquiry; the second pertains to the proper struc-
ture and function of society.
The ancient Greeks had a genius for reducing matters to
their essentials, and they used education as a vehicle for
seeking such simplifications. Thus, they found that abstract
reasoning simplifies when abstractions are placed in context.
Context simplifies because it shifts our point of view from
an inward one, in which the abstraction is a central issue, to
an outward one, in which an abstraction is seen as part of a
larger whole. In her book, The Greek Way, Edith Hamilton
explained this by the following metaphor.[21

J.M. Haile, Professor of Chemical Engineering at Clemson University, is
the author of Molecular Dynamics Simulation, published by John Wiley &
Sons in 1992 and is the 1998 recipient of the Corcoran Award from the
Chemical Engineering Division of ASEE.

Copyright ChE Division of ASEE 1999

Chemical Engineering Education

In medieval and Renaissance Europe, many communities
undertook wondrous feats of architecture and engineering
that culminated in great cathedrals-complex, vast, and in-
tricate structures of wood, stone, iron, masonry, and glass
woven into arcs, columns, domes, naves, towers, ribs, vaults,
and flying buttresses. By the 14th century,
the elaboration had extended to decoration of
the interiors, including reliefs on walls, stained The the
glass, richly detailed mouldings, surface pat-
terning, networked vaultings, and highly or- essay i
namented interior columns. What remains in- probl
congruous about these impressive structures frustra
is that they were not placed within the con-
text either of their physical surroundings or bes
of the socioeconomic conditions of their so- institf
cities. They did not blend into the environ- higher
ment, but rather dwarfed it-arising from the
earth to intimidate their modest neighbors stem
and the surrounding landscape. A cathe- fir
dral can be beautiful, yet terrifying-inter- misundt
nally consistent, yet elusive-but it always of Wi
draws attention to itself, to its own logic
and grandeur, and away from the world in institU
which it sits. suppol
In contrast, with the temples of the ancient and mih
Greeks we have beauty of a very different relative
kind-a beauty based on simple and restrained
proportion. (The ancient Greeks did not even they ai
have the arch; rather, they had to rely on the accoJ
post and lintel.) Those proportions extend be-
yond the geometry of the building to include
the context into which the temple was placed. The Parthenon
was beautiful not merely because of the precise proportions
and optical illusions that were built into its structure, but also
because it was designed to occupy the Acropolis-the high-
est point in ancient Athens. The Greek temple had no need of
elaborate ostentation, for its beauty was reinforced by its
environment. These same qualities-simplicity, proportion,
and context-characterize sound engineering practice; thus,
Petroski has observed thatl3

Good engineering blends into the environment,
becomes a part of society and culture so naturally
that a special effort is required to notice it.
But the development of minds that can recognize simplic-
ity, proportion, and context, as well as manipulate the ab-
stractions that pertain to them, is no small task; a process of
formal education is required, as the ancient Greeks realized.
How can this be done? Perhaps etymology can offer a hint.
Although the fundamental ideas about education are Greek,
our word education comes from Latin: the sources are the
Latin verbs educare, meaning to raise or bring up, and
educere, meaning to lead or draw out. Thus, an appealing

s th


e to
e a

Fall 1999

metaphor is to liken education to drawing water from a well
(students might prefer to liken it to extracting a tooth).
Such an interpretation serves as the basis for the Socratic
method of teaching, exemplified by any of Plato's dialogs,
but most explicitly in the Meno.[4] Thus, edu-
cation is more than transmitting information
to students; rather, to educate means to draw
of this students out so their minds surround knowl-
at the edge, embrace it, and make it a part of them-
selves. For the instructor, this means that sim-
and ply telling ideas to students is not enough;s51
is now for students, it means that merely adding to
their store of knowledge is not nearly enough.
ng To paraphrase Alfred North Whitehead, the
nS Of merely well-informed person is the most use-
cation less bore on earth.161
gely From ancient times and languages, let us
a now shift to the mid-nineteenth century. In
anding 1852, the Church established the Catholic
University in Dublin so that Catholic youth
such might have access to the same advantages of
iS are education as their Protestant neighbors. But
to be although the University was new, the educa-
tional challenges it faced were neither new
ecUton nor parochial. Indeed, certain of those chal-
what lenges are with us still. On the occasion of
ble to assuming the position as the first Rector of
the University, John Henry (Cardinal)
lish. Newman observed, with dismay, that171

All things now are to be learned at once,
not first one thing, then another,
not one well, but many badly.
Learning is to be without exertion,
without attention, without toil;
without grounding, without advance, without finishing.
There is to be nothing individual in it;
and this, forsooth, is the wonder of the age.
"Nothing individual in it"-fearsome words that are indica-
tive of our age.
By the mid-twentieth century, the scenes and the players
had changed again, but the challenges remained. In address-
ing the deplorable state of Spanish society between the World
Wars, Jos6 Ortega y Gasset asserted that a principal purpose
of the university is to teach the vital ideas of the society.1'
Vital ideas include those of science and engineering, which
interpret the physical world; those of politics and law, which
formulate how society is regulated; those of economics and
business, which try to explain the trade for goods and ser-
vices; those of the humanities, which try to help us under-
stand ourselves and our relations to one another; and those of
the arts, which foster self-expression. Note that this purpose
is concerned solely with ideas, for the mind can grapple only

with ideas, and it is only the mind that can be the recipi-
ent of the teaching. (For a readable introduction to the
connections among objective facts, concepts, and soci-
ety, see Bronowski.81)
The sum total of the vital ideas of a society comprises that
society's culture, so even more important than training pro-
fessionals, universities are to transmit the culture of a society
to succeeding generations. This has become an exceedingly
difficult task, for many reasons. One is the sophisticated
abstract thought that is required to describe and understand
modem societies. E. O. Wilson has pointed out that cultures
evolve in tandem with advances in scientific understanding
and with increased facility at manipulating abstract symbols
that represent those understandings.19' This means that, rela-
tive to earlier generations, we have more to do to bring
students to an appreciation of the culture in which they live.
More here refers not only to the number of abstractions, but
also to the complexities inherent in the network of those
many abstractions by which we represent and manipulate
our environment. Thus, in 1999 a committee of the National
Research Council asserted that U.S. students have a poor
understanding of basic scientific principles and their relation
to everyday life;101 further, that

Institutions of higher education should provide diverse
opportunities for all undergraduates to study science,
mathematics, engineering, and technology.

Note this plea applies to all undergraduates.

We now want to extend the general comments from the
previous section to the education of engineers. To prevent
the discussion from becoming too abstract, we present it
within the context of a hierarchical cognitive model for
learning. In recent years, several such models have been
proposed; at least three are similar and closely related, al-
though they were proposed independently and are based on
different kinds of evidence. Thus, the cognitive hierarchy
proposed by Egan1"1 is based on studies in educational psy-
chology; the hierarchy proposed by Donald1121 is based on
studies of cultural evolution, and that by Haile'13'15] is based
on studies of brain function. These hierarchical models are
all integrative; that is, the progression to a higher level
requires the individual to master skills and to reorganize
knowledge gained at lower levels.

Egan's version of the cognitive hierarchy contains five
levels:1'" somatic, mythic, romantic, philosophic, and ironic.
Each level corresponds to a specific mode for getting thoughts
out of the mind and into forms by which they can be dis-
sected, analyzed, and reassembled. Thus, to oversimplify

considerably, the somatic level includes tactile learning,[161
mythic corresponds to oral learning, romantic involves graph-
ics and written learning, philosophic refers to learning by
formal reasoning, and the ironic level encompasses excep-
tions, limitations, and learning by modeling.
In this hierarchical model, it is the philosophic level that
contains the basic skills required of engineers. At the philo-
sophic level, knowledge and skills mastered at lower levels
may promote development of higher-order thinking skills:
inductive and deductive logic, inferential reasoning, math-
ematical reasoning, analysis and synthesis, critical thinking,
creation of theoretical constructs, and generalizations. These
operations relate, simplify, and extend knowledge gained at
lower levels. To maintain control over the material, we seek
simplifications via patterns, theories, and schema that orga-
nize knowledge into useful structures; that is, in the words of
Mach,'171 we seek economy of thought. The reorganization of
knowledge into abstract and economical structures is the
characteristic activity of learning at the philosophic level.
Note that philosophic understandings may develop, but
they do not necessarily do so. Of the numerous human
cultures that have appeared throughout history, only one-
the ancient Greek-developed to the level of philosophic
understanding. Over the years there has been endless specu-
lation as to why this is so: What was unique to Greek
society? Donald offers the persuasive answer that the break-
through came when the Greeks combined writing with for-
mal logic.1121 Making logic visible through writing clarifies
analysis and communication, and it stimulates further men-
tal growth to levels that, apparently, cannot be reached in
any other way.
The consequences of these observations are profound:
individuals cannot complete the transition to the philosophic
level by themselves. To do so, people must live in a commu-
nity of philosophic and ironic thinkers and learn from them.'"
This is the sine qua non of the university.

The traditional view has been that engineers are problem
solvers; hence, engineering was traditionally taught by hav-
ing students solve many, many problems. In recent years,
this view has broadened to encompass a variety of reasoning
skills that are captured under the general rubric of "critical
thinking." But, based on their experience, many engineering
educators have come to believe that today's students are
generally weak problem solvers and poor critical thinkers.
To cite just one example, Wankatl8"' has noted that

My personal observation is that the average engineering
student of 20 years ago was a better problem solver but not as
skilled at calculating as the average engineering student now.

This observation has provoked educators to subject students

Chemical Engineering Education

to more problem solving and more thinking exercises. Thus,
we find recitation sections, student workshops, and spe-
cialized courses devoted explicitly to problem solving,
and we find problem-based learning, discovery-based
learning, and web-based learning intended to develop
and exercise critical thinking.
It is my contention that "more of the same" will not prove
to be the most effective way to overcome these educational
difficulties. Instead, before we can expect students to func-
tion properly at the philosophic level, we must address their
deficiencies at the somatic, mythic, and romantic levels. For
example, the use of equations in derivations, proofs, and
problem solving are logical exercises and, hence, are philo-
sophic activities; however, equations themselves are collec-
tions of abstract written symbols and, hence, are romantic
devices. Further, individual terms and symbols in an equa-
tion are usually interpreted at cognitive levels other than the
philosophic; thus, the interpretation might be in relation to
equipment (somatic), or in terms of a narrative that describes
a process or procedure (mythic), or it might invoke sche-
matic diagrams and plots (romantic). If we ignore these
lower levels of understanding or if we tacitly assume that
students can invoke these levels on their own initiative, then
their success in such philosophic exercises as performing
derivations and solving problems will be fragmentary.
Similarly, manipulations of data-the inferences and de-
ductions that attach meaning to data-are logical exercises
and, hence, invoke philosophic understanding. However, the
steps used to acquire and organize data combine lower levels
in the cognitive hierarchy. Collecting the data involves so-
matic activities using instruments attached to an apparatus or
processing equipment; narrative descriptions of the process
and the experimental protocol are mythic activities; organiz-
ing the data into tabular and graphic forms is a romantic
activity. Many sophomores and juniors fail to find meaning
in data because they cannot organize the data into tables or
plots that reveal patterns or trends.
With the activity we call problem solving, success re-
quires a much more complex array of cognitive skills than is
usually required to manipulate equations and analyze data.
Problem solving is obviously philosophic: we use abstract
symbols to represent quantities and processes, and we ma-
nipulate those symbols according to logical rules to extract
unknowns from knowns. But to find an algorithm for solving
a problem, we appeal to more cognitive levels than just the
philosophic. Thus, WankatI181 notes

The expert problem solver writes things down, draws sketches,
constructs a variety of different representations of the problem
... expects the problem to eventually make sense and is
looking for this sense ...

The expert expects that finding a sensible interpretation of a
problem will also indicate a direction toward a solution; in

pursuing that search, the expert appeals to many levels in the
cognitive hierarchy. For example, sketches, schematics, and
plots are romantic devices; connecting the problem to hard-
ware and equipment appeals to somatic understandings; nar-
rative descriptions of processes and responses to changes in
variables are mythic activities; models introduced to achieve
appropriate simplifications are ironic devices. In contrast to
the expert, many sophomores flounder at solving problems
because they fail to sketch the situation, or to articulate a
story about the situation, or to connect the situation to hard-
ware, or to recognize what equations might apply-their
low-level cognitive skills are insufficient for the task.
These examples suggest that success at the philosophic
level requires facility with understandings at other levels. If
we accept that the fundamental purpose of the university is
to develop and exercise philosophic understandings, then
our responsibility to today's students seem clear: we must
pay more attention to developing lower-level cognitive
skills rather than simply intensifying our emphasis at the
philosophic level.

The purposes of a university are to develop in students the
ability to interpret and manipulate abstract symbols that
pertain to the vital ideas of modem society. The manipula-
tion of such symbols involves a suite of high-level, critical
thinking skills; however, critical thinking apparently devel-
ops only when the student lives among, and learns from,
those who not only have mastered critical thinking but who
also can shift effortlessly among cognitive levels; these are
attributes of ironic thinkers. Thus, a university faculty is a
community of ironic thinkers intent on elevating students to
high cognitive levels. To become adept at such high-level
skills, students must develop a foundation of low-level cog-
nitive skills; but compared to previous generations, today's
students need more help from university instructors in devel-
oping that necessary foundation.
From Plato to Bacon to Jefferson to Ortega y Gasset to the
present, informed thinkers in Western cultures have argued
that the life of the mind is not merely worth living, but that it
is indispensable for society to flourish: every society needs
philosophic and ironic thinkers who understand how the
world works, how society functions, and how abstract rea-
soning can be deployed to improve society. Today, universi-
ties are the only institutions that can possibly develop the
necessary understandings: church, government, and industry
have all abandoned attempts to develop human potential in
favor of feel-good policies, special interests, and the bottom
line. Universities are under attack to do the same. But if we
succumb to these pressures, if we teach skills that expand
pocketbooks but not minds, if we are satisfied to help stu-
dents feel good rather than challenge them intellectually,
Continued on page 299.

Fall 1999

[e, -classroom




Novel Modeling Software and Its Use in Problem Solving

University of California Berkeley, CA 94720
Massachusetts Institute of Technology Cambridge, MA 02139

New avenues to teaching process modeling are sorely
needed in our discipline. The methods we have
been practicing seem to have been ineffective; they
have not been active and concerted. We have instead de-
pended too much on students individually inventing some
sort of approach as they struggle to find their way through
the modeling maze in homework assignment after home-
work assignment. Missing has been an articulation of a
natural and intuitive hierarchy and its use in declaring the
character of a process representation, a hierarchy comprising
such matters as conservation principles, thermodynamic con-
straints, phase conditions, phase equilibria, reaction phe-
nomena, and transport phenomena. Missing also is an aware-
ness of many in academia that process models are presently
at the heart of industrial control systems and optimization
methods for process operations, and that our graduates ought
to be prepared to contribute to that technology.
In an attempt to address this shortcoming, we propose a
new avenue for teaching modeling and have brought into
being new software embodying a model-building hierarchy
that can guide students in using their engineering science
background in crafting models for problem solving. Our
approach is unconventional, and the opportunity extraordi-
nary for breaking through this instructional omission and
breaking through hurdles that students see in their path.
Our motivation further derives from having observed that
many students are at a loss in identifying the physics and
phenomena operative in a process and how to use their
background in engineering science to make an integrated
representation of the process. Moreover, many sense their

weakness or lack of confidence in writing the equations to
represent the physics. A disciplined focus on stating the
physics and a release from incessant equation writing is
the major contribution that this software brings to stu-
dent learning. Through a selection of examples, we illus-
trate how the teaching of modeling can be enhanced with
features of the software.
"ModelLA" is the name we have given to this program. Be
prepared for something different.
The program offers students a phenomena-oriented envi-
ronment expressed in the fundamental concepts and lan-
guage of chemical engineering, such as mass and energy

Alan Foss has conducted beginning- and senior-level courses in
chemical engineering at Berkeley for three decades, experience that
has motivated this new approach to teaching modeling and this new
modeling software.
Kevin Geurts earned his PhD degree at the University of Washing-
ton in the mathematical modeling of polymer flows and is now a
process systems analyst and modeler at the Exxon Corporation in
Houston, Texas.
Peter Goodeve earned degrees in psychology and human factors
engineering and is a freelance software developer and programmer.
George Stephanopoulos is the A.D. Little Professor of Chemical
Engineering at MIT. He has been teaching and researching various
aspects of process systems engineering for 25 years at the Univer-
sity of Minnesota, National Technical University at Athens, and MIT.
Jerry Bieszczad is a PhD candidate in chemical engineering at MIT
working on the logic of mathematical modeling. He is the principal
designer of ModelLA.
Alex Koulouris earned his PhD at MIT, where he is a Research
Associate. He has been working on the development of ModelLA,
multi-scale systems for estimation and control, and the development
of modeling languages for process systems engineering.
Copyright ChE Division of ASEE 1999

Chemical Engineering Education

balancing, phase equilibria, reaction stoichiometry and rate,
modes of heat and species transport. Through a freely ac-
cessed hierarchy of declarations of such elements of engi-
neering science, the user is assisted in recognizing, for ex-
ample, that energy balances must be declared, that chemical
equilibrium constrains species behavior, and that a zero en-
tropy increase must be imposed to attain minimum compres-
sion work. All levels of the hierarchy are accessible at any
time by user request. Unlike the rigid lists of earlier model-
ing software, elements of the hierarchy are not "pre-wired."
It is just the physics that has to be declared-no equations;
the software writes the equations. That is what is unconven-
tional. And, by simply requesting a solution, the equations
are solved numerically in a few seconds without user inter-
vention in the numerical method. The results are displayed
graphically for rapid assessment of the characteristics of the
model. The feedback about misinformed declarations of the
physics is instantaneous-certainly a major improvement
over the one-week turn-around time of homework sets.
Such modeling capability can complement instruction in
modeling throughout the curriculum. Modeling, in our opin-
ion, should be a part of all instruction in engineering, and it
should be done in the context of resolving an engineering
problem. By teaching modeling in a problem-solving con-
text, one can better demonstrate how the nature of models is
influenced by the context, how models serve problem solv-
ing, and how the structure of efficient problem solving is
shaped by the use of models. The program can be especially
helpful in the process-design course as a complement to the
use of process simulators such as Aspen and Chemcad,
providing students, through model building, a means for
understanding the nature of the relations hidden in the simu-
lator modules. ModelLA is different, having modeling rather
than simulation objectives; its modeling capability is what

Figure 1. Double-effect evaporator modeling project illustrating the
spectrum of phenomena for incorporation in a model.

Knowing where to start is not easy for beginning and
inexperienced students. Take, for example, the double-effect
evaporator process in Figure 1. There is a lot going on here.
In the first place, the instructor will have introduced this
project as one of trading off energy and equipment costs in
synthesizing an economical process for the desalination of
sea water. To build a model for decision making about a
process design, students will need to recognize the presence
of the phenomena identified in the ellipsoidal bubbles. We
emphasize that the evaporators have to be modeled. Unlike
the process units displayed on the screens of process-simula-
tion programs, the pictorial icons here are empty shells; there
is no model associated with them. The model has to be built.
Through interactive Q-and-A with the instructor and concur-
rent use of ModelLA to declare the physics in the bubbles,
students can build a model suitable for investigating, for ex-
ample, the influence of the temperature drop across the train,
the degree of concentration of the brine, and other decisions.
A perennial difficulty in assigning such a project is the
lack of physical feel of many students for the qualitative
cause-effect relations of the process variables to one an-
other. With the availability of the software, those students
can explore an instructor-built model and acquire insight
into the effect of changes in operating conditions before
attempting to craft his or her own model. Figure 2 shows one
such set of explorations: the effect of condenser coolant flow
rate on pressures and vapor flow rates in the various units.


!J97.6 1
Evaporator 1 r
31 -

Evaporator 2 1
9.9 1
kPa -

8.5 Condenser 0
kPa' n

S 5.7
1000 mol/min 2000 mol/min

Vapor Flow Rates
,11.* 44

Evaporator 1

.0 -
Evaporator 2

1000 mol/min 2000 mol/min

Figure 2. Students can gain familiarity with
the evaporation process by interrogating
an instructor-built model for the effect of
coolant rate on pressures and vapor rates.

Fall 1999

All this is calculated in a few seconds with a single request
for such an analysis. The results reveal, perhaps for the first
time for some students, that pressure in the condenser de-
creases with increasing coolant flow, that the pressure de-
crease propagates upstream to the steam generator, that there
is a pressure gradient from one unit to the next, and that
vapor rates in the units differ. Then the question for the
students is, "Why does all this happen?" With such knowl-
edge, students should be able to make intelligent declara-
tions of the physics far better than had they not explored
the behavior.

Exploration of the instructor-built model alerts students
that there are two component parts of the evaporator, that
they are linked by heat exchange, and that they are differ-
ent-one a boiling liquid mixture and the other a condensing
pure vapor. Such recognition suggests that declarations about
the internal structure and the character of the evaporator
parts need to be made in crafting a model. This is accom-
plished in ModelLA by disaggregation of a process unit into
its internal parts. Figure 3 shows how a modeler would use
the disaggregation window (Level 2) to state his or her view
of the evaporator internals through placement of a mixture
unit, a tubes unit, and linkages of convective flows to those
of the parent (Level 1) located on the edges of the disaggre-
gation window. A heat flux between the tubes and mixture is
declared as an essential linkage for effecting evaporation.
One can see that all of this is done in a visual, graphical
manner, a feature quickly grasped by the user.

At this level, declarations can
also be made about the phases
present in each subunit, the species
in them, equations of state, the equi-
librium relations between the
phases, and the "mechanisms" for
heat transport and vapor flow. Such
declarations are made through a hi-
erarchy of dialogs. As these decla-
rations of species and phase equi-
librium are made, a linkage is es-
tablished to a physical-properties
data base for the estimation of
quantities such as vapor pressures,
activity coefficients, densities, and
enthalpy of phase change that are
eventually needed in the numerical
solution of the model equations.
We have operated with a relational
database containing data for over
2000 chemical compounds. The
user may also supply any special

data particular to the process being modeled.
It is at this level where the engineering science concepts
and facts are introduced into the model. To accomplish this
in an organized manner, students need disciplined guidance
in an environment familiar to chemical engineers. They get
it through the hierarchy of declarations of structure and
phenomena shown in the right side of Figure 3. That set of
hierarchical elements applies to just the declarations needed
for this part of the model. The full set available in ModelLA
is much more extensive. The Modeling Assistant shown at
the bottom in Figure 3 guides the user in branching to the
major elements of the hierarchy.
Learning about the physics of the process takes place very
rapidly here because the guidance given by the program at
each juncture is centered on the logical consistency of the
declarations made and because the feedback about inconsis-
tency is essentially instantaneous. One of our student evalu-
ators remarked that ModelLA guards the user from accumu-
lating mistakes. A good amount of self-learning takes place
about the modeling process without instructor prompting.

The top-down synthesis of a process system can be shown
to students as an orderly and rational way to evolve the
structure of a process'] and provides a good example of the
use of the hierarchical structure of the software. Unlike the
bottom-up approach usually employed with process-simula-
tion programs, the top-down approach affords the user a
view of the direction of subsequent development and the
type of model needed to support that development. Some

Add New -e..Unl J Add N. Fj l I Spo Spew and RllI| Ed Roeo Un i. Edl Fkux. Ed CornolLoops. Modd Smul.o
IBlackbo Unit Unl wth Vapo. Lqud Equdibnum 9 Spati ly llnDI ed U
SUrntwthVap.oPhae I UwithLud LqudEquibnum UrnfromTempateL ay
SUn MlWhLLq.dPha.s *IStagedUnim Help

Figure 3. The subunits of the evaporator are identified in a disaggregation
window, fluxes stated, and material properties declared.

Chemical Engineering Education

describe it as a target-directed approach. In a top-down approach, the
progression of one's thoughts about creating process structure is intuitively
organized as a hierarchy, proceeding from overall (top level) objectives and
successively broken down into levels of finer detail. That hierarchy matches
exactly ModelLA's hierarchy of declarations of process structure and con-
tent. As an example of how a top-down synthesis can evolve with the use of
ModelLA, we show three levels of the synthesis of a process for the
hydrodealkylation of toluene to benzene, the HDA process.'
The synthesis starts at Level 1 in Figure 4 with simple declarations of the
products to be produced from candidate raw materials, the chemical species
present in the process, the expected chemical reactions, the expected by-
products and waste products, and energy fluxes between the process and
environment. ModelLA's hierarchical elements accessed for such declara-
tions are shown to the right of the Level 1 depiction in Figure 4. Next, with
an appreciation that a reaction section and a separation section would be
needed, a modeler would place such elements in Level 2, a disaggregation
of Level 1, as shown in Figure 4. Such a step is a top-down step in the
synthesis, the educational benefit being an opportunity for the student to

Hydrodealkylation HDA
Level 1
Toluene Purge Benzene
HDA Plant

Hydrogen Byproducts
eat and Wor k

Level 2 purge
Recycle P
Toluene I Benzene
Mix Reaction Separation
Section Section
Hydrogen S Byproducts
Heat and Work

Figure 4. Process synthesis pursued through successive sublevels
corresponding to the multilevel hierarchy of ModelLA.

express his or her perception of the big pic-
ture. Such declarations are made by access-
ing the hierarchical elements shown to the
right of Level 2 in Figure 4.
The big picture now has to be fleshed out
with some definite proposals for accomplish-
ing the reactions and the separations. A flesh-
ing out of the reaction section is illustrated by
the disaggregation of Level 2 in Figure 5. In
Level 3 of this example, the modeler must
function in a bottom-up mode, placing and
connecting process units whose functional-
ity the modeler must eventually describe,
stating substructure, species, phases, reac-
tions, and fluxes. This is the level where
process inventions are made.
The educational value here is the direction of
attention on a well-defined segment of the en-
tire process so that decisions about process
structure and the modeling of the set of units in
that segment can be made and tested without
interference from other segments. For example,
the type of reactor model, ways to prevent
catalyst coking, and conditions to enhance re-
action selectivity can be scouted. There is plenty
of opportunity here for the instructor to point
out the place and need for a model in problem
solving and synthesis. The opportunity is also
there to point out the structure of problem
solving. The top-down strategy comes to
meet the bottom-up inventions of the sub
levels as the process is further and further
disaggregated into elementary operations and
basic phenomena.
One can also see here an opportunity for
collaboration among team members in a net-
worked environment, say, in the design course,
where one member would develop the reactor
section and another the separation section.
As the user's invention of model structure
proceeds, the software assembles information
about the hierarchy of model units and their
connections and displays it as the hierarchical
tree of model units shown in Figure 5 for the
purpose of keeping the user up-to-date on the
model so far constructed. Such a display is
especially useful when moving back and forth
between several levels of the model.
Completion of the models of the units placed
in Level 3 of Figure 5 is accomplished through
the hierarchical modeling elements shown at
the right. A particularly essential element is

Fall 1999

Hierarchical Elements Accessed
*Global species declared
*Global reactions defined
*Placing flows
*Shaft work
Hierarchical Elements Accessed
*Internal Structure
*Placing Subunits
*Placing Flows
*Shaft Work

Figure 5. Successive disaggregations bring one to the point at which
process subunits can be fully defined and modeled.


the species consistency check. It scans the model units of
each level to determine whether the distribution of spe-
cies declared by the user makes physical sense. If it does
not, guidance is then given to assist the modeler in recti-
fying the omissions.

A very considerable amount of insight and knowledge
about the qualitative cause-effect relationships in a process
system is needed to identify quantities that
fully specify the operating conditions of the
process. It is in this aspect of modeling that "MJ d
the students' engineering ability is thoroughly
taxed, and in the taxing, further developed.
This is also where the instructor can help de- V,-, O"
velop that insight through questions and an- :-.-~
swers in interactive sessions with the students. -:_ p ri
Complex processes often bring to the fore per- -
plexing conflicts in satisfying the degrees of
freedom, particularly when energy and mass -
fluxes interact. This is certainly the case in a
process as complex as the HDA process. O ffe s.
Even models of modest size can have a dozen --.
or more degrees of freedom. Without some _.-
assistance, satisfying these with statements of --ani
operating conditions and physical parameters
would be a daunting and likely an unavailing .I-mp .
task. It is neither of these with the ModelLA J d
program. Candidate quantities are identified COeCej
for user consideration, unit by unit, flux by -?.I-
flux, phase by phase, variable by variable, h
species by species. A running count of the
degrees of freedom yet to be satisfied is dis- :-
played as the user makes selections. This DOF
analytical engine also identifies conflicts in the user's selec-
tions and offers a list of alternative quantities that can be
swapped with the current selection.
In the case of dynamic models, the DOF analytical engine
simultaneously also makes an analysis of the index of the set
of differential algebraic equations and informs the modeler
when the index exceeds 1. Such an analysis is necessary
because selection of a certain combination of quantities to
satisfy the degrees of freedom can sometimes result in a
large index. Current numerical integration algorithms cannot
integrate a differential algebraic equation set with an index
greater than 1. Information regarding the source of the high
index is reported so that the modeler may reconsider the set
of design variables.

When it is confirmed that the index is 1 or less and all
degrees of freedom have been satisfied, the user may launch
a calculation of the model equations. Calculations are made





Chemical Engineering Education

without user intervention by a state-of-the-art numerical en-
gine, gPROMS,[2,3 and for the double-effect evaporator, are
completed in 4 or 5 seconds.
Students and instructors like to see trends of process vari-
ables over ranges of operating and design conditions like
those shown in Figure 2 because insight about the process is
more easily grasped. Trends can be calculated and displayed
for all variables very quickly. The numerical data of such
calculations can be used in deciding on operating conditions
_or examining the trade-offs among design de-
cisions. The engineering problem can be ad-
I" is the dressed in this way.
,: By all means, have the students write the
Md4OF equations-but only after they have gained an
ling. understanding of the physics and an apprecia-
nIt tion of the qualitative cause-effect relation-
ships among the variables.
Ldets a One might think that an assignment in writ-
ing the model equations after having crafted a
model with ModelLA and after having re-
ed solved an engineering problem would be un-
L8fnt necessary and anticlimactic. It is neither. First,
f i 5i e it is necessary because there has to be a clo-
ental sure to the project that is satisfying to the
S..lnd students. We found that students in our trial
group wanted to write equations and to con-
8o f firm that their model and calculations matched
.30 those of ModelLA. Second, one will find that
S,.... even after having articulated the physics, not
everyone is sure-footed in identifying a model
envelope and subunit envelopes. That is,
some run off in wrong directions. Further, one will find a
significant fraction confused about representing such
things as the rate of accumulation of internal energy in a
mass of material in terms of the process variables and the
flux of energy in and out of the process. Instruction is
assuredly needed to straighten out those basic matters.
That instruction is one of the activities that sustains an
interest in writing equations and saves it from being anticli-
mactic. We recommend that the instructor work through the
equation writing with the students in an interactive work-
shop environment because many will still need help with
matters such as those just mentioned and because there is not
much incentive to have students struggle alone through the
equation maze at this stage. Focused and concentrated in-
struction in formulating model equations reinforces an
instructor's continuing admonition for physical thinking at
all stages of modeling and that the equations are just a
symbolic statement of the physics. That will be a revela-
tion to students who have the impression that equation

writing is all mathematics.
An alternative approach, which we think ineffective, is to
have the software display the equations to the student piece
by piece as he or she makes declarations of the process
attributes, that is, the materials, phases, fluxes, and reac-
tions. Such displays are unguided and physically uninterpreted
and are not an effective pedagogical method.
In the workshops there is the further opportunity for the
instructor to point out the character of the set of equations,
how a choice of variables can transform a nonlinear model
into a linear model, how natural divisions in the process
structure result in separate blocks of equations, and how one
can order the terms of the equations to form an easily
computable structure, as for example, the structure of a
linear set. Students left alone in a sea of equations sel-
dom are aware of the equation structure. They can ben-
efit from the instructor's insight here.
A full set of notes for the instructor T_Tub
about writing the equations for every
model is given in a set of course mod-
ules described in a later section. The
equations are laid out following the
logic and cause-effect relations articu-
lated in an earlier section of the module
and thus should appeal to the physical
understanding developed there. Two 635
methods of solving the equations are
developed in the course modules for s 3
every model. A paper-and-pencil ,
method follows directly, step-by-step, 825
the articulation of the physics, the iden- 620
tification of the unknowns, and the na-
ture of the relations needed to complete 615
the model. Solution by use of general- Radial
purpose numerical solvers such as
Matlab, Mathcad, and Polymath is also Figure 6. Two-di
presented. In the case of linear dynamic profile in a phth
models of a single state variable, a calculated bj
closed-form analytic solution is derived.
Numerical calculations of the derived
equations are presented for all models
and compared with the ModelLA cal- C
culations. Thus, the instructor has ma- 4-- _out
trial to close the loop for the students.

Modeling of spatially distributed and
dynamic processes is a frequently en-
countered challenge for students
throughout the curriculum. ModelLA Figure 7. Cascade
has the capability for both. Further, stu- is constructed after
dents can investigate control of pro- can be measu


r ide.
red a

cesses by placing control systems around dynamic process
Spatially Distributed Processes Tubular reactors, ab-
sorption columns, cooling towers, adsorption beds, and tu-
bular heat exchangers all need at least a one-dimensional
spatial representation of species behavior and energy flows.
ModelLA has 3-D spatial modeling capability, offering rect-
angular, cylindrical, and spherical coordinates. As an ex-
ample of that capability, the temperature distribution in a 2-
D model of a phthalic anhydride reactor produced by
ModelLA is shown in Figure 6.
Dynamic Processes Instruction in modeling the dy-
namic behavior of processes can be introduced profitably in
the first course in material and energy balancing. We simply
remark that early experience in dynamic system modeling
gives students an early understanding of interactions in pro-
cess systems and potentially an ap-
ctorrzmatl(r,z) preciation for the evolution of the
steady-state condition. Declaration
of the attributes of a dynamic pro-
cess model proceeds in the same
way as that of a steady-state model
with additional attention needed to
specify the initial conditions. Spa-
tially distributed dynamic pro-
cesses can be modeled.
Multiloop Control Systems Con-
trol loops can be placed on the pro-
A cess flow diagram as shown in Fig-
ure 7, for example, by the cascade
system on a CSTR. The user can
S Fow select a PID controller algorithm or
craft a custom controller action in-
volving, for example, logic elements
and actions triggered by a time se-
sional temperature quence. The salient educational merit
anhydride reactor of ModelLA's use in configuring
'odelLA model. control systems is the challenge to
identify which variables should be
B_ measured, which should be manipu-
lated, and how to link them. Other
1 types of process models in other
c ..n types of software by necessity re-
cw zX veal the measurement transducers
r2 and control valves, thus surrender-
STC2 ing the educational benefit of this
intellectual challenge.

ntifying variables that To assist instructors in using the
nd manipulated. software for instruction in model-

Fall 1999


ing, we have developed several course modules that can be
used in concert with two of the most popular texts on mate-
rial and energy balancing, namely those written by Felder
and Rousseau[41 and Himmelblau.151 Several modules on
more advanced topics have also been developed. The
modules treat the modeling of process systems presented
in those texts, some being simple "warm-up" exercises of
the single-answer variety, and others involving analyses
of trade-off in operating costs with product value in a
search for optimum operating conditions.
One of the important components of each module is an
articulation of the physics and the qualitative cause-effect
relationships of the process, thus giving the instructor back-
ground about the project. Because students can benefit from
such an analysis as well, students are asked in a preliminary
homework assignment to identify the process variables, what
affects them, how they might be determined, and to name the
relations that fully define a model. Students can benefit from
a Q-and-A session with the instructor following such an
assignment and before embarking on building a model.
The modules also lay out a completed ModelLA model,
showing all subunits, fluxes between them, phases, reac-
tions, and transport relations. The full set of design variables
and initial conditions is given and the full set of ModelLA
declarations is provided on a disk file so that a numerical
simulation is ready for execution. For each ModelLA model,
tips and reminders are given to the instructor concerning
certain declarations that may not be obvious or that might be
overlooked. These can be passed on to the students as they
develop the model. Some models may be crafted in more
than one way. In those instances, we have included a discus-
sion of the philosophy of the approaches and have given the
rationale for the approach selected.
Results of the numerical simulation of all models are
given in graphical or tabular form and are discussed in
relation to the physics and cause-effect characteristics treated
in the preliminary homework assignment and also in relation
to questions asked in other assignments. Projects involving
design or operating trade-offs show the behavior of an ob-
jective function as a function of split fractions and fraction
conversions, for example.
As a means of getting students "up to speed" in use of the
program, we have prepared an on-screen tutorial that guides
the learner through the several types of declarations in the
hierarchy interactively with a "live" ModelLA program run-
ning concurrently. Our experience is that students pick up
the general structure and features of the program in about a
2-hour session with the tutorial and the finer details with
subsequent use in modeling projects.

This phenomena-oriented and hierarchically structured soft-
ware propels students quickly into model building and prob-

lem solving. The model can be built expeditiously because
the focus is on the phenomena and because students need not
struggle with equation writing. The hierarchical structure of
the software embodies the same hierarchy used by engineers
in declaration of model characteristics and thus promotes a
natural and intuitive flow of model development.
The release from equation writing permits the students to
push ahead with model development and problem solving
and helps them build a "can-do" confidence in completing
an engineering project. Writing equations is deferred to the
end of the project. Students at that point are much better
informed about process character and more receptive to in-
struction about formulating model equations. Further, the
instructor has the opportunity to describe the structure of the
model, a matter rarely treated, but one of value when consis-
tently brought into view across the curriculum. Thus, we
favor the inversion of the usual order of equation writing and
problem resolution.
The ability to build a model quickly and efficiently with
ModelLA is a major contribution to student learning. Stu-
dents are steadily engaged with the physics and are given
instant feedback about inconsistencies or just plain impos-
sible constructions. Waiting for instructor approval is thus
unnecessary. Efficiency is very important also in satisfying
the degrees of freedom through identification of design and
operating variables. There is a good amount of qualitative
cause-effect analysis needed on the part of the student in
this, and ModelLA helps one to move through selection of
design variables rapidly in an orderly sequence.
This new avenue to teaching modeling can speed student
grasp of using engineering science concepts in any course
environment. Inasmuch as our current method of instruction
rests heavily on quantitative models of fundamental phe-
nomena and on models of process systems, there is consider-
able incentive to improve the efficiency and effectiveness of
that instruction. There is a marked commonality of modeling
needs across the curriculum that can benefit from this hierar-
chical modeling environment.

ModelLA and several course modules will be available in
the near future to faculty members interested in helping
us evaluate its effectiveness in teaching modeling. Re-
quests should be made on departmental letterhead to Pro-
fessor George Stephanopoulos at MIT. The program will
be available to all interested persons following the evalu-
ation period.

Miklos Gerzson assisted in scouting some early ideas for
familiarizing students with process behavior. Michael
Lasinski assisted with coding of some of the graphical dis-
plays. Berkeley junior-year students Adam Cate, Valerie

Chemical Engineering Education

Grill, James Comb, and Ryan Overstreet assisted with evalu-
ation of the software and with the development of process
models. Professor Terje Hetzberg of the Norwegian Institute
of Technology, Trondheim, in a sabbatical year at Berkeley
assisted the Berkeley team with modeling techniques and
evaluation of the software.

We benefited significantly from the help of Professor Costas
Pantelides and his associates at Imperial College, London,
whose state-of-the-art numerical solver (a component of
gPROMS) added importantly to this project. Software for
the analysis of the index of differential-algebraic equations,
generously provided by Professor Wolfgang Marquardt of
the Lehrstuhl Fuer Prozesstechik, RWTH, Aachen, Germany,
was a key element in assessing the numerical solvability of
the equations. Professor Rafiqul Gani of the Chemical En-
gineering Department, Lynby, Denmark, helped us with es-
timating phase conditions in our early scouting work.

We are appreciative of the support of the Directorate
for Education and Human Resources of the National Sci-
ence Foundation for the development of this software
and course modules.

1. Douglas, James M., Conceptual Design of Chemical Pro-
cesses, McGraw-Hill, New York, NY (1988)
2. Barton, P.I., and C.C. Pantelides, "Modeling of Combined
Discrete/Continuous Processes," AIChE J., 40, 966 (1994)
3. Oh, M., and C.C. Pantelides, "A Modeling and Simulation
Language for Combined Lumped and Distributed Param-
eter Systems," Comput. Chem. Eng., 20, 611 (1996)
4. Felder, Richard M., and Ronald W. Rousseau, Elementary
Principles of Chemical Engineering, 2nd ed., John Wiley &
Sons, New York, NY (1986)
5. Himmelblau, David M., Basic Principles and Calculations
in Chemical Engineering," 6th ed., Prentice Hall (1996) U

Universities Why?
Continued from page 291.

then not only do we hinder student growth, but we also
undermine the university and, ultimately, corrupt society.
If this seems farfetched, consider the catastrophic conse-
quences of the Soviet experiment in which a society at-
tempted to provide economic security while suppressing
intellectual growth and development. Consider further the
grave difficulties now being faced by countries of the
former Soviet Union-difficulties engendered because
too many of their people fail to understand how modern

societies function.

In our society, the difficulties of educating are exacerbated
by an astonishing degree of self-satisfaction. It is possible to
operate cars, computers, and microwave ovens without know-
ing anything about how they work; possible to vote and pay
taxes without understanding the rudiments of government;
possible to work at a job without comprehending the larger
workings of the economy; possible to be courteous and well-
meaning while ignoring the deeper implications of human
psychology. In other words, it is possible for many to live
only at the surface of the culture and to be unconcerned
about the underpinnings by which the society functions.
The operative question is this: For a society to survive and
its culture to continue to evolve, what is the smallest fraction
of the population that must comprehend how the society
functions? In modern societies, it is the unique responsibility
of universities to keep that fraction above the minimum.

1. Ortega y Gasset, Jose, Mission of the University, Princeton
University Press, Princeton, NJ (1944)
2. Hamilton, E., The Greek Way, Norton, New York, NY (1930)
3. Petroski, H., The Pencil, A.A. Knopf, New York, NY (1990)
4. Hamilton, E., and H. Cairns, eds, The Collected Dialogues of
Plato, Princeton University Press, Princeton, NJ (1961)
5. Wilson, F.R., The Hand, Pantheon, New York, NY (1998)
6. Whitehead, Alfred North, "The Aims of Education," presi-
dential address to the Mathematical Association of England,
1916; reprinted in Alfred North Whitehead, An Anthology,
edited by F.S.C. Northrop and M.W. Gross, Macmillan, New
York, NY (1953)
7. Newman, John Henry, The Scope and Nature of University
Education, 2nd ed., Longman, Green, Longman, and Rob-
erts, London (1859)
8. Bronowski, J., Science and Human Values, Harper & Row,
New York, NY, Ch. 2 (1956)
9. Wilson, E.O., Consilience, A.A. Knopf, New York, NY (1998)
10. Committee on Undergraduate Science Education, Trans-
forming Undergraduate Education in Science, Mathemat-
ics, Engineering, and Technology, National Research Coun-
cil, National Academy Press, Washington, DC (1999)
11. Egan, K., The Educated Mind, University of Chicago Press,
Chicago, IL (1997)
12. Donald, M., Origins of the Modern Mind, Harvard Univer-
sity Press, Cambridge, MA (1991)
13. Haile, J.M., "Toward Technical Understanding. I. Brain
Structure and Function," Chem. Eng. Ed., 31, 152 (1997)
14. Haile, J.M., "Toward Technical Understanding. II. Elemen-
tary Levels," Chem. Eng. Ed., 31, 214 (1997)
15. Haile, J.M., "Toward Technical Understanding. III. Advanced
Levels," Chem. Eng. Ed., 32, 30 (1998)
16. Petroski, H., "Work and Play," Am. Sci., 87, 208 (1999)
17. Mach, E., The Science of Mechanics, 6th ed., Open Court
Publishing, LaSalle, IL (1960)
18. Wankat, P.C., "Reflective Analysis of Student Learning in a
Sophomore Engineering Course," J. Eng. Ed., 88, 195 (1999)

Fall 1999


, Oclassroom



Universidad de Alicante Alicante, Spain

incorporating a personal computer in the classroom has
brought a new quality to the study of chemical engineer-
ing that was unthinkable just a few years ago. Due to its
mathematical calculation powers, the computer allows us to
study complicated experimental systems that are unapproach-
able from an analytical point of view, and its rapid results
are especially beneficial in chemical engineering. Experi-
mental results of several systems can be simulated by using
the theoretical equations that govern the process. This meth-
odology permits us to quickly analyze the influence of differ-
ent variables that affect the system behavior without the long
and hard experimental testing that can distract a student.
This paper proposes a practical class for undergraduates in
their first year of chemical engineering where the character-
ization of a reactor is achieved through recording the resi-
dence time distribution (experimental response obtained from
a typical test stimulus-response for the hydrodynamic char-
acterization). The distributions are simulated with a theoreti-
cal model allowing the quantification of the parameters char-
acterizing the reactor behavior as a function of the operating
conditions (flow rate).
Specifically, this paper provides the particulars for a prac-
tical session using a versatile computer program that simu-
lates the tracer input experiments. It is necessary, however,
that the students have prior knowledge of reactor design
theory. This background material should have included the
ideal reactor models and the solutions to problems where
several curves were analyzed using the momentum method
or through simple iteration.'11
The practical lesson described here has been designed for
two students working together for a period of approximately
four hours. The students should have some knowledge of
how to program in the BASIC language, but it is not abso-
lutely necessary. The experimental work is short and repeti-
tive, so the students can center their attention more com-

pletely on the simulation. This obliges the students to
make decisions concerning the design and plan of the
lesson in the following ways:
They must elect the number offlow rates to study.
1 They must perform small modifications in the BASIC
program in order to extend its application (with
instructor supervision).
Finally, a student must hand in a report detailing the ex-
perimental procedure, its results, the conclusions gained,
and the important and useful modifications that were made
to the BASIC program. Once the reports have been pre-
sented and the program modifications detailed, the instructor
can propose extending the program to the study of even
Juan A. Conesa is Professor of Chemical Engineering at the University
of Alicante, where he received his Chemistry Science degree in 1992 and
his PhD in Chemical Engineering in 1996. He conducts research on
combustion and pyrolysis of different materials and also focuses on
characterization of chemical reactors and activated carbon production.
Jose Gonzalez-Garcia received his degree in Chemistry in 1990 and his
PhD in 1998 from the University of Alicante, where he is currently a
member of the academic staff. His research interests are in electrochemi-
cal engineering and applied electrochemistry.
Jesus Iniesta is a doctoral student at the University of Alicante, where he
obtained his Master's degree in Electrochemistry. His interest is in elec-
trochemical treatment of hazardous organic compounds.
Pedro Bonete received his degree in Chemistry in 1990 and his PhD in
1995 from the University of Alicante, where he is currently a member of
the academic staff. His academic research involves organic and
electroorganic synthesis.
Marina Ingles received his bachelor degree in 1996 and his Master's in
1998 from Alicante University. His academic research involves organic
Eduardo Expdsito received his Master's degree in 1994 from C6rdoba
University and his PhD from the University of Alicante. His academic
research involves wastewater treatment and organic electrosynthesis.
Vincente Garcia-Garcia received his PhD in chemistry in 1991. His
research interest is applied electrochemistry. He has worked with labora-
tory and pilot-plant experiments.
Vincente Montiel Leguey is Associate Professor in the Department of
Physical Chemistry at the University of Alicante. His research interests
are in electroorganic synthesis and wastewater treatment by electro-
chemical methods.
Copyright ChEDivision of ASEE 1999

Chemical Engineering Education




more complicated systems.
The main objective is to demonstrate how the personal
computer can help in the quantitative characterization, de-
sign, and scale of a reactor-a matter of some difficulty
when other techniques are used. From the technical point of
view, it is interesting to identify the flow pattern, to analyze
the reactor behavior when the flow rate is varied, and to
classify the reactor when the objective is to optimize the
chemical process inside the reactor.

There are two standard models for the ideal behavior of a
continuous chemical reactor: the piston plug flow and the
backmixed reactor (widely studied in specialized litera-
ture[2"31). Nevertheless, real reactors show deviations from
this ideal behavior for a wide number of situations. A vari-
ety of theoretical approaches have developed in the litera-
ture for this non-ideality.
The dispersion model for a tubular reactor is presented by
Levenspiell2] in considering a hydrodynamical behavior with
plug flow and axial dispersion:
aC a2C C
a= ax v (1)
at 8z az
C concentration of property measured
t time
z length in the flow direction
v linear flow rate
Dax axial dispersion coefficient

Equation (1) is usually expressed in dimensionless form as
aC _a 1 (2C
--+ (2)
ae aZ Pe aZ2
using the Peclet number, Pe=vL/Dax, where L is the total
length of the reactor in the flow direction. The dimensionless
time, 0, is defined as

e= t (3)

Different Models for Non-Ide

12 l (1 )2
Open/Open C(0)= exp e
2/CloseCPe Nu 4y c Peq

Closed/Closed Numeric integration. Boundary condition equations:

High Dispersion Degree

tr, the mean residence time, is

Z is the dimensionless length (=z/L), and C is the normalized
concentration of the measured property (C is the conductiv-
ity in this paper).
The solution of Eq. (2) depends on the boundary condi-
tions for the input and the output.[41 Some of these solutions
are analytical. For the resolution of Eq. (2) it is interesting to
consider whether or not the dispersion degree is high. On the
other hand, the boundary conditions must be kept in mind,
i.e., if the system can be considered open or closed. Table 1
shows a scheme of the different cases that can be presented.
This dispersion model defines with great exactness several
practical situations, especially when the shape of the experi-
mental curves is Gaussian with a high symmetry (if the
dispersion degree is low) or with a tail (if the dispersion
degree is high).151
The model presented in this paper considers a more global
non-ideal situation: coexistence of a dead volume inside the
reactor and two flow paths, each modeled with an axially
dispersed plug-flow model. Thus, Eq. (2) is the differential
equation defining the dispersion model for each flow. In this
paper, we considered the most common situation: open-open
flow (inlet and outlet do not change the flow pattern) and a
high axial dispersion degree. Equation (2) has, in this case,
an analytical solution. The solution for the residence time
distribution is, for each flow,

1 Pe Pei
C(0)=I _Pi exp -ei(1-,) (5)
2 It i 46 0i

Other models are available in the literature as the simple
tanks in series model151 with just one parameter, or more
complex multi-parameter differential models.'61 The former
is not attractive from the didactic point of view, since it is
less intuitive. The latter allows a better explanation of the
mass transport phenomena, but it must be used only when
there is evidence of
such phenomena, or
alFlow when the simpler
*al Flow
models are not able to
Low Dispersion Degree reproduce the experi-
mental data.

1 ( (1-6)2
C(e) = P exp 4/Pe
2Vi/Pe I 4/ Pe

1 dC
Z =0 then 8(0)=C dC
Pe dZ

In the proposed simu-
lation model (see Fig-
ure 1), there are two
flows and a dead vol-

Fall 1999

ume, with five fitting parameters to optimize. The five pa-
rameters are: two mean residence times, two Peclet num-
bers, and the ratio between the dead volume and the total
volume (Vd/V). From these parameters the ratio between the
volume used by flow 1 (flow rate = Q,) and the total volume
(V,/V) can be calculated by using

V=Vi +V2 +Vd (6)
Q = Q1 +Q2 (7)
ti = (8)

The optimization method used is the Flexible Simplex
method171 and the objective function (O.F.) is the sum of the
square of the differences between experimental and calcu-
lated values of the residence-time distribution E(t)

O.F.= [Ecac(t)- Eexp(t)]2 (9)

The computer program estimates the best parameters to fit
the experimental data, provides them, calculates the E(t)
curve, and uses the value of the objective function.

Figure 2 shows a schematic diagram of the experimental
system used to measure and analyze the stimulus-response
curve. The procedure is based on the instantaneous modifi-
cation of a property of the fluid in the reactor inlet and
recording the variation of this property with time at the
outlet of the reactor. In this case, the electric conductivity of
the fluid is modified by the injection of a small volume of
saturated solution of the tracer. This response will be the
data file for beginning the parameter-estimate calculation.
Of course, the data acquisition could be computerized and
the data analyzed on the same computer.
Any kind of reactor could undergo this analysis. The sole
condition for the reactor is to know the total volume. In the
practical example that follows, a built-in-house filter-press
electrochemical reactor (UA200.08) is used. The compartment
for the fluid is a drum of dimensions 18x12x0.8 cm. A more
detailed description of this reactor can be found elsewhere.'S'
The design of the hydraulic part of the system is a typical
configuration: a deposit for the fluid (water), pumps, and
flow-rate measurement units controlled by valves permitting
flow-rate adjustment in each experiment. More details can
be found in the literature.[91

Previous to the Experiment Before experimental record-
ing of the curves, it is important to calibrate the measure-
ment apparatus. The conductivity probe and the flow-mea-
surement units must be calibrated for the fluid and tempera-
ture of the experiment. An erroneous measurement of the

flow rate would lead to an overflow situation. In this particu-
lar case the students must calibrate the probe, so the flow
rate calibration is given to them. It is important to keep the
solution temperature constant during the experiment because
conductivity depends on the temperature. This is attained
with a thermostat heat exchanger in the reservoir.
Curve Response Measurement Once the flow rate, the
temperature, and the other conditions are stabilized, the pulse
of tracer can be rapidly injected, collecting the conductivity-
time data shown in Figure 2. The injection of the tracer must
be done as close as possible to the reactor inlet. In this case,
the tracer was 2 mL of a saturated KCl solution (4.3 M). A
conductivity probe (Ingold) measures the conductivity of
the outflow stream reactor. This probe is connected to a
conductivity meter with an analog output of 0 to 10 V (error

Figure 1. Sketch of the model for flow characterization.



Figure 2. Diagram of the experimental set up:
1. reservoir; 2. thermometer; 3. heat exchanger; 4. pump;
5. valve; 6. flowmeter; 7. injection of tracer; 8. reactor; 9.
conductivity probe; 10. conductimeter; 11. computer.

Chemical Engineering Education

Axially dispersed
plug flow
Volume =V,

Axially dispersed
plug flow
Volumen=Vi ,


less than 1%). The conductivity meter, through a data acqui-
sition card (12 bits, monopolar channel, input free tension 0-
10V), permits saving the data in an ASCII file in the com-
puter (PC-compatible system, at least 80386 processor, 2
MB RAM memory). Recording the response must be
done until the value of the conductivity signal reaches
the initial value. This simple procedure is repeated for
every flow rate studied.
Analysis of the Response The computer program pro-
posed in this paper needs the data collected in an ASCII file
with names RV**.GEX, where the flow rate must be in-
cluded. If the experimental system is similar to the one in
this paper (with the data acquisition card), the conductivity-
time curve will be recorded automatically by the computer.
The experiments can also be done by simply recording the
analog X-t curve and digitizing it.
The computer program is written in BASIC; a copy of it
can be requested by e-mailing JA.CONESA@UA.ES. The
program needs the reactor dimensions, the name of the data
file, the flow rate, and the initial value of the parameters to
be fitted (in part II of the program).

r 1

0 5 10 15 20 25

Figure 3. Superposition of experimental and calculated
curves. Volumetric flow 105 L/h.

Estimating Model Parameters for Different Volumetric
Flow Rates

Q/L h' / s Pe, T2 /S Pe2 Vi/V V/V O.F.
66 3.84 15.46 12.72 2.76 0.33 0.42 1.5 103
105 3.38 20.11 8.74 3.58 0.43 0.21 5.0 10-3
155 2.41 22.38 6.65 3.39 0.43 0.10 3.3 10-

Fall 1999

The solution is not strongly dependent on the initial values
of the parameters. Thus the initial values for the mean resi-
dence time could be selected as the ratio between the total
volume and the flow rate (the same value for both ways).
On the other hand, typical values of the Peclet number
are between 1 and 50, and the initial value of Vd/V could
be selected as 0.5.
When these initial parameters, experimental conditions,
and response curve values are entered, the program can be
run. Using the initial values of the parameters, the suc-
cessive iterations will diminish the value of the objective
function. After each iteration, the program shows the
new value of the parameters, the current value of the
objective function, and the minimum value O.F. achieved
until this iteration.
The simulation ends when the value of the O.F. minimum
reached is repeated and the variations of the parameter val-
ues do not change. The program generates an output data file
(with ASCII format and name RV**.CAU) with five col-
umns of data: time, experimental residence time distribu-
tion, calculated residence time distribution, calculated resi-
dence time distribution for flow through path 1, and calcu-
lated residence time distribution for flow through path 2. In
addition, the optimized parameters are also saved at the end
of the output file (Pe and average residence time for each
path, and V,/V), and with the value of the dead volume (V,/
V) and minimum O.F. reached.

The experiments were carried out with three different flow
rates (66, 105, and 155 1/h). Figure 3 shows the experimental
and calculated E(t) curves, E,(t), and E2(t), respectively, for
the flow rate 105 1/h. The optimized parameters for the three
experiments are shown in Table 2. In the Table, "Q" is the
flow rate, Ti is the average residence time of the path "i",
and V,/V and O.F. were defined previously.
Analyzing the results, we can conclude:
El An increase in the flow rate produces a decrease of the
residence time in both flow paths, favoring path 1 (of
minor residence time and a higher Pe, i.e., greater
plug flow behavior) versus path 2, with low Pe. This
conclusion is obtained from the value of the parameter
V,/V. The flow circulating through path 1 can be
calculated using the relationship between the average
residence time and the flow rate (Eq. 8).
[ The flow rate also affects the percentage of dead
volume, decreasing as the flow rate increases. This is
a very important conclusion.

The practical lesson proposed in this paper allows the
Continued on page 309.


105 Uh
- RTD calc
- El
. E2

S ethics



Duke University Durham, NC 27708-0287

he essence of engineering is truthfulness. If we are
truthful in all of our conduct, then we are most likely
acting ethically. While this may seem like an over-
simplification, the idea of truthfulness certainly addresses
the central issue of engineering communication. The nega-
tive side of truthfulness is a bit more ethically complicated.
What does it mean to not tell the truth? Are all non-truths
lies? Are all lies unethical?
In engineering communication, as in all communication,
there are two basic types of non-truths: lies and deceptions.
In both cases the intent is to have the recipient of the infor-
mation draw a false conclusion from the available informa-
tion. While both methods of eliciting false conclusions may
be morally wrong, there is a well-defined operational differ-
ence between a lie and a deception.
A "lie" is a categorical statement known by the teller to be
untrue. An engineer who tells a client that the client's report
has been mailed, for example, while knowing full well that it
is still being prepared, is telling a lie. Most lies are verbal,
but lies may also be told by body language, such as nodding
the head, or by graphics when, say, some data points are
intentionally omitted to "make the graph look better."
Lying requires only that incorrect information is inten-
tionally transmitted. If I say "I am ten feet tall," that is a
lie, regardless of who I tell it to.
In contrast to a lie, a deception is an action that begins with
a statement (verbal or graphical) that may be true, but the
intention is for the listener or reader to draw a false conclu-
sion. If an anxious client asks an engineer for the status of a
report, the engineer can say that "it is essentially finished."
The client might interpet this as meaning that it will be
collated in time to be picked by by FedEx that afternoon, but
the fact might be that not all of it has yet been written. If, by
using such a phrase as "essentially finished," the engineer

* Department of Civil and Environmental Engineering

knows that he or she is creating a false impression (without
overtly lying), he or she is guilty of intentional deception,
and deception with an intent to mislead or obfuscate is not
honorable behavior. If the client is savvy enough to ask what
the engineer means by "essentially finished," the engineer
has a chance to tell the truth or to lie.
Deception in the hands of a professional such as an engi-
neer can have serious repercussions. For example, suppose
an engineer writes a technical article about an explosion at a
chemical plant and intentionally uses incomplete informa-
tion that has the result of deceiving the journal readership.
The engineer is not actually lying, but rather is using partial
data without reporting that other data have been omitted. To
publish such misleading information is unethical behavior.
Other engineers can draw unwarranted conclusions from
such an incomplete report and the result could be additional
industrial accidents.
Just as words can be used to mislead a reader, unscrupu-
lous or incompetent illustrators can distort engineering illus-
trations with both lies and deceit. Thus, we must judge
engineering illustrations not only on the value of their infor-
mation and on their appearance, but also on their integrity.
In most cases, graphics have a certain sacred value to
engineers, and illustrations are seldom blatant lies. Few en-
gineers and scientists will intentionally misplace a data point

Aarne Vesilind received his PhD in environ-
mental engineering from the University of North
Carolina in 1968. Following a post-doctoral year
with the Norwegian Institute for Water Research
and another as a research engineer with Bird
Machine Company, he joined the faculty at
Duke University. He is presently involved in
research on waste management, including pro-
cesses for dewatering wastewater residues,
Management of municipal solid waste, treat-
ment of industrial wastes, and environmental
ethics in engineering.
Copyright ChE Division of ASEE 1999

Chemical Engineering Education

on a graph or add points without having the data. For this
reason, most engineering readers place a great deal of weight
and credibility on data points.
Sometimes, however, some researchers or practitioners
fail to include all the relevant data points or will move data
points to "improve" the graph. Occasionally, data points are
so far off the line that the researcher is tempted to either
discard them as "something went wrong here" or note them
as "rogue points." There even exists a statistical test that can
be used for removing (with full statistical justification) the

data point from the graph. This is
the one "rogue point" may in
fact have been an indicator
of something very important
and totally unexpected.
Fortunately, instances where
engineers and scientists have
used graphs for transmitting in-
correct information (lies) seem
to be rare, and in cases where
there have been fabrications of
data, the scientific community
has properly condemned the
actions. A much more insidi-
ous (and more common) mis-
communication of graphical
information is the use of illus-
trations for purposes of decep-
tion. Graphs don't have to ac-
tually lie to express incorrect
information since it is the per-
ceived information that matters.
Most misleading illustrations
are deceptions rather than lies.
Such deception can be achieved
by several unethical techniques,
including optical illusion, in-
appropriate cause and effect,
unwarranted visual embellish-
ment, and misuse of data.

Optical illusions in engineer-
ing drawing create misper-
ceptions that can cause mis-
takes. Engineering drawings
must not only be accurate, but
they must also be perceived ac-
curately by others. Our eyes
can play tricks on us, and it is
the responsibility of the com-
municator to minimize the po-
tential for misperception.

a risky business since

/ \

< >

Psychologists have identified at least five different visual
1. Illusions of extent-the size or length is misjudged
2. Illusions of direction-the orientation of a line or
figure is misjudged
3. Illusions of shape
4. Illusions of brightness
5. Illusions of motion

Figure 1 shows some

examples of the first illusion-
illusion of extent. In Figure
1A, the length of the straight
line between the arrows is
equal, but the optical illu-
sion is that the lower one is
longer. This same trick can
be applied to space, shown
in Figure IB. Figure IC
shows that the outer circle
of a concentric pair is un-
derestimated while the in-
ner circle is overestimated.
Figure ID shows that for
two equal figures between
converging lines, the one
nearest the "vanishing
point" is perceived as larger.
Illusions of extent can be
used to draw graphics that
misrepresent the facts. Fig-
ure 2 is an example of such
a misrepresentation. Draw-
ing the bar graph inside di-
verging lines suggests a de-
crease that in fact is not true.
Figure 3 shows several
examples of the illusion of
direction in which the ori-
entation of a line or figure is
misjudged. Figure 3A shows
that although the two line
segments intersecting the
parallel line are actually on
the same straight line, they
do not appear that way to
the eye. Figure 3B shows
how the sequence of white
and black squares can make
the lines between them ap-
pear to bend. Figure 3C
shows two parallel lines that
certainly do not appear to
be parallel.

Fall 1999



Figure 1. Illusions of extent.

1 2 3 4 5
Figure 2. Using illusion of extent to deceive the reader

Figure 3. Illusions of direction


The illusion of direction can be used in a graphic as
shown in Figure 4. Even though the two lines are parallel,
the illusion is that the lines are diverging and the casual
observer is left with the impression that the number of
people smoking cigarettes has grown at a faster rate than
the number of people not smoking.
Illusions of shape are illustrated in Figure 5. The straight
lines on top of the pointed arch in Figure 5A appear to
bend to make a sharp point. Figure 5B shows three arcs of
equal sized circles. The more of the arc that is shown, the
more the arc appears to curve. All arcs have, however, the
same radius. Figure 5C is an amazing figure showing how
the circle can appear to be distorted.
The illusion of shape can be shown by the embellished
bar graph in Figure 6. Not only is the last bar fancied up,
but the chevrons also give an optical illusion of fatness on
top, suggesting growth that isn't there in reality.
Illusions of brightness can be illustrated by the block
diagram in Figure 7A. If you look at the figure for a while,
you will see darker spots in the white spaces at the corners
of the black squares.
Illusions of motion are often used to trick the eye. Air-
plane pilots have long been aware of the apparent motion
of spots of light during a dark night, called autokinesis. In
one interesting experiment, the subjects were placed in a
very dark room and asked to focus on a tiny spot of light
on the far wall. They were told that the light would be
moved to spell words and the test (they thought) was for
them to try to read the words. All subjects read what they
wanted to read, while in fact the light never moved!
Figure 7B shows an example of autokinesis. If you stare
at the figure for a while, you will see swirling motion.
Motion is optically created while the figure is obviously
not moving.

Another way graphics can deceive is by implying causa-
tion where none is warranted. Figure 8 shows the correla-
tion between the rate of typhoid fever deaths and the
fraction of the population with public water supplies. Such
graphs have been repeatedly published by environmental
engineers who want to convince the world that they have
performed magnificently and should receive due credit for
their efforts. There is no doubt that the construction of
clean public water supplies helped reduce the typhoid
death rate, but this graph does not prove it. An excellent
correlation also results from plotting the reduction in the
typhoid death rate and the decrease in the manufacture
of buggy whips. The conclusion, if causation is mis-
taken for correlation, is that either buggy whips cause
typhoid or the reduction in typhoid deaths resulted in
the decrease in buggy whips.

number of people
smoking and not
smoking cigarettes

96 97 98 99
Figure 4. Using illusion of direction to deceive the reader.


Figure 5. Illusions of shape.


Price ($) "


1 2 3 4
Figure 6. Using illusion of shape to deceive the reader.


Figure 7, Illusion of brightness and illusion of motion.

Chemical Engineering Education

not smoking

50 75

40 80
Typhoid \ typhoid Population
Fer 30 Served
es 30 85by Public
Deaths Water
(number Wate
per \Supplies
100,00020 90(%)
10 95
water supplies _-
0 100
1890 1900 1910 1920 1930 1940
Figure 8. Correlation between population with public
water supplies and the typhoid fever death rate in the
United States. (After Whipple and Horwood, from Fair,
G.M., J.C. Geyer, and D.A. Okun, Water and Wastewater
Engineering, John Wiley & Sons, New York, NY 1966).

4.5 Ib/cap/day

1960 1990

Figure 9. Deceptive use of two-dimensional pictures to
represent one-dimensional data.


Number of
Nobel 20
Awarded to
Americansl 5 -



0 1901- 1911- 1921- 1931- 1941- 1951- 1961- 1971-
1910 1920 1930 1940 1950 1960 1970 1974
Figure 10. Misleading line graph due to uneven scale.
(National Science Foundation, Science Indicators, 1974,
Washington, DC, 1976)

A third common source of misperceived (and thus un-
ethical) graphics is the use of visual embellishments. One
oft-used visual embellishment technique is the two-dimen-
sional bar graph. A bar in a bar graph is one-dimensional,
showing the quantity as its height. But the bar can also be
shown as a two-dimensional picture. Figure 9 shows how
trash cans represent the bars, but these are actually two-
dimensional pictures. From this figure we see that even
though 4.5 is only 50% larger than 3.0, the appearance is
much greater since the reader sees the area of the trash
cans, not just the height.


Some graphs are clearly intended to mislead. For ex-
ample, Figure 10 shows a notorious graph that appeared in
a government document arguing that the United States was
losing its edge in science and technology since our share of
the Nobel prizes seemed to have dropped precipitously.
The truth is that the last data point (1971-1974) is the total
number of Nobel prizes for only afive-year interval, whereas
all the remaining points represent Nobel prizes received
during ten-year intervals.
Some graphs deceive because the data are plotted cumu-
latively, and the reader is not sufficiently warned to inter-
pret the graph in such a fashion. Figure 11 shows the use of
various forms of power for electricity production. A
quick glance suggests that nuclear power is the most
important energy source and continues to provide the
greatest share of electricity. The data in this graph are,
however, plotted cumulatively so that the energy pro-
duction from various sources is the difference between
the adjacent lines.

Figure 11. An example of a deceptive graph where
the data are summed.

Fall 1999

3 Ib/cap/day

x 1016)

1980 2000

1900 1920 1940 1960

Broken scales can also convey misleading information. Broken scales
occur either when an axis does not start at zero or when the scale is
temporarily discontinued. Note how Figure 12A creates a mistaken im-
pression by not starting at zero and thus conveys a very different impres-
sion from the more honest Figure 12B. Good practice requires that all
broken scales be clearly indicated.
Sometimes it is possible to conceal data points in such a way as to make
the rest of the data appear much more impressive, and thereby mislead the
reader. Like Figure 13A, Figure 13B shows accurate points, but with the
abscissa moved so that the points are concealed.
Sometimes the authors of a graph read more into the data than a reason-
able person might. Figure 14A shows data used as the foundation for a

Solid 4.6 5
Waste 4 -- O,-- '
(b/cap/day) 4- 3
4.2 1

4.0 0 I I
1960 1970 1980 1990 2000 1960 1970 1980 1990 2000
Year Year

Figure 12. Data as presented by the authors (A) and a more honest
presentation without the broken scale (B).


Y 0 00
4 o
00 o
2 2 -2

101 id2 id4 10' idl 10 0 d2 ld4 1'd id
x x


Figure 13. Data obtained in a research experiment (A) and the plot
presented by the authors (B).

8 8

0 C- 0
O oO O
o o
4 004 0

2 2

o I 01 o I 1 00
0 6 12 18 20 24 0 6 12 18 20 24


Figure 14. Data obtained in a research experiment (A) and the line as
drawn by the authors (B).

complex theoretical model. Figure 14B shows
the same data with the line as drawn by the
authors. There is no reason to draw such a line-
except that the model developed by the authors
predicted that this would occur. The most that
can be said in such cases is that the data cer-
tainly did not disprove the model. Claiming that
the data offer proof of the model is, however,
Statistics can be used to suggest causation
where none exists. The famous British prime
minister Benjamin Disraeli is supposed to have
said that there are three kinds of lies-lies, damn
lies, and statistics. Disraeli notwithstanding, as
long as statistics are calculated accurately, the
result cannot represent lies. On the other hand,
statistics opens up a tremendous opportunity
to deceive. There is no doubt that the inter-
pretation of statistics can be manipulated to
serve the desired end.
One of the most used (and abused) statistical
techniques is the least-squares fit of data. The
theory is that the best fit is obtained when the
sum of the squares of the vertical distances be-
tween the experimental values of Y and the
line representing the relationship between X
and Y is minimized. If the sum of the square
of these distances approaches zero, then the
fit is perfect.
The statistic used to measure this goodness of
fit is known as R2. If the calculated R2 = 1.0,
then the data fit perfectly. That is, we get a
perfect straight line and all of the data points

15 15

10 10
5. *
5 5
2 = 0.67 = 0.67
0 0
0 5 10 15 0 5 10 15


5 R R= 0.67
0 0 5 10 15



5 R= 0.67
0 5 10 15

Figure 15. Four plots with data showing
identical R2 values. (After Anscombe, F.J.,
"Graphs in Statistical Analysis," Am.
Statistician, 27, 17, 1973).

Chemical Engineering Education

fall exactly on the line. If R2 = 0, then there is no fit whatso-
ever; there is no correlation between X and Y.
Shady ethics enter when R2 is used to determine the good-
ness of fit without also including the plotted data, or without
using common sense. Figure 15 shows how statistical corre-
lation can be subverted.121 The four data sets can be plotted to
yield exactly the same R2 = 0.67. Each data set has the same
mean and is described by the same least-squares equation.
And yet the data, when plotted and viewed on the plots,
represent four totally different populations. Only by plotting
the data would the reader understand the actual relationship
between the two variables.

Depicting the truth by avoiding both lies and decep-
tions in engineering graphics is invariably the safest course
of action for engineers. Or, as Mark Twain suggested,
"Always tell the truth. That way you don't have to re-
member what you said."

1. The psychology of optical illusion has been studied for over
200 years. Many of the examples used can be found in two
excellent books: J.O. Robinson, The Psychology of Visual
Illusion, Dover Publications, Mineola, NY (1992), and M.
Luckiesh, Visual Illusions, Dover Publications, Mineola, NY
2. Anscombe, F.J., "Graphs in Statistical Analysis," Am. Stat-
istician, 27, 17 (1973); as described in Betthouex, P.M., and
L.C. Brown, Statistics for Environmental Engineers, Lewis
Publishers, Boca Raton, FL (1994) N

Tracer Input Experiments
Continued from page 303.
student to understand and complete the theoretical lessons
concerning chemical reactor design. The versatility and ra-
pidity that comes with using a personal computer has excel-
lent pedagogical aspects. It must be remembered, however,
that it is necessary to refer constantly to the suitability of
using more simple models (lower number of parameters),
making some of the variables in the program constant. In
this way, we avoid the problem of using models with more
parameters than degrees of freedom of the system.
The time necessary for recording the responses and simu-
lation of one curve of residence time distribution, once the
preliminary steps are finished, is estimated to be between 10
and 15 minutes for a reactor of dimensions similar to the one
described in this paper. This allows the student to perform a
series of eight different flow rates in two hours, using the
first hour to prepare and calibrate the system as well as to
prepare the tracer solution. The last hour can be used to
discuss different aspects with the teacher, proposal of pro-
gram modifications, and other applications.

This class was designed for students to familiarize them
with the concepts of reactor design and characterization. The
reasonably good agreement between experimental and cal-
culated values of the RTD makes them feel confident about
applying engineering concepts.
The students find the experimental procedure relatively
uncomplicated and possible to complete within the labora-
tory period. Using personal computers to study an electro-
chemical reactor rather than simply studying the theoretical
concepts provides better comprehension of the reactor flow
pattern and the model development.
It is important that the theoretical concepts be explained in
class before the students attempt the laboratory exercises.
Operational problems also become clear while the students
are performing the experiments. For example, the impor-
tance of rapid injection of the tracer was discovered by
several students who found that the response was "abnor-
mal" in the sense that many peaks were found when non-
instantaneous modification of the conductivity was
achieved in the input.
Another important concept involved in this practice is the
optimization method and its structure in the BASIC pro-
gram. Students appreciate when someone explains how
to run an optimization method such as the Simplex used
in this lab session.
Students find the session interesting and enjoyable, and
they relate well to the engineering principles involved. The
lesson allows them to perform and validate what they have
learned in class.

1. Davis, R.A., J.H. Doyle, and O.C. Sandall, "Liquid-Phase
Axial Dispersion in a Packed Gas Absorption Column," Chem.
Eng. Ed., 27, 20 (1993)
2. Levenspiel, O., The Chemical Reactor Omnibook, OSU Book
Store, Corvallis, OR (1979)
3. Nauman, E.B., Chemical Reactor Design, John Wiley &
Sons, New York, NY (1987)
4. Westerterp, K.R., W.P.M. Swaaij, and A.A.C.M. Beenackers,
Chemical Reactor Design and Operations, John Wiley &
Sons, Amsterdam (1984)
5. Levenspiel, O., Chemical Reaction Engineering, John Wiley
& Sons, New York, NY, p. 253 (1972)
6. King, C.J., Separation Processes, McGraw-Hill, New York,
NY, 570 (1980)
7. Himmelblau, D.M., Process Analysis Statistical Methods,
John Wiley & Sons, New York, NY (1968)
8. Gonzdlez-Garcia, J., V. Montiel, A. Aldaz, J.A. Conesa, J.R.
Perez, and G. Codina, "Hydrodynamic Behaviour of a Filter-
Press Electrochemical Reactor with Carbon Felt as a Three-
Dimensional Electrode," Ind. Eng. Chem. Res., 37, 4501
9. Ingl6s, M., P. Bonete, E. Exp6sito, V. Garcia-Garcia, J.
Gonzalez-Garcia, J. Iniesta, and V. Montiel, "Electrochemi-
cal Regeneration of a Spent Oxidizing Solution: An Ex-
ample of a Clean Chemical Process," J.Chem. Ed., (in press)

Fall 1999


e Stclassroom




Using Numerical Simulation

Howard University Washington, DC 20059

Among all the courses in the chemical engineering
curriculum, students generally find process control
the most challenging. Perhaps one reason is that it is
one of the final courses of their undergraduate curriculum.
Our observation, confirmed by another source,111 was that
students oftentimes characterize the course as an abstract
study with extensive mathematical derivations that bear little
or no relevance to the practice of chemical engineering. This
has prompted a re-examination of process control instruc-
tion at Howard University, with interest focused on how
knowledgeable the graduating students are with respect to
being able to apply their process-control knowledge when
they leave for industry.
Although this re-examination is still proceeding, we want
to share some of our experiences with a learning module that
was introduced for the purpose of helping students rethink
their views about process control. Understanding how the
subject of process control was viewed, we felt there was a
need to stimulate interest in the course by adapting the
course materials in a manner that makes learning exciting.
We have done this through an assignment involving model-
ing and simulation using the Mathcad software package.
The typical tasks covered in the assignment range from
routine material, component, and energy balances to numeri-
cal simulation of uncontrolled and controlled systems. The
assignment thus formulated substantially covers most of the
major topics in the undergraduate process control curricu-
lum. The only set of new material that is needed to comple-
ment the students' knowledge in order for them to be able to
do the assignment is the control law, under which a brief
explanation of the effects of proportional, integral, and de-
rivative control modes are explained. As for modeling and
simulation, the knowledge the students have gained from
prior courses in chemical engineering calculations,1[2 kinet-
ics,131 heat transfer,[41 and advanced calculus are more than

enough to get them through the assignment. The instructors'
responsibility lies solely in guiding the students so that they
will be able to synthesize ideas based on what they have
already learned in these prior courses. With proper guidance,
a successful modeling and simulation of the system was
found to be beneficial to the students in introducing various
aspects of process control, such as the concept of a closed
feedback control loop.151

The simulation assignment was taken from established
sources[6'71 with slight modification. Given was a CSTR
equipped with a cooling jacket in which a first-order exo-
thermic reaction of decomposition of hydrogen peroxide
into water and oxygen occurred in the presence of excess
sodium hydroxide, which acted as a catalyst.

H202 + (NaOH) -- H20+ 02 + (NaOH)
Mobolaji E. Aluko is Professor and Chair of
the Department of Chemical Engineering at
Howard University. He received his BSc from
the University of Ife, Nigeria, his MSc, DIC,
from the Imperial College, London, and his
PhD from the University of California, Santa
Barbara. His research interests are in the math-
ematical modeling of chemical reactors, mate-
rials processing, and coal-related technolo-

Kenneth N. Ekechukwu is a lecturer and se-
nior research fellow at Howard University. He
earned his MSc and PhD from Warsaw Uni-
versity of Technology, Poland. He joined
Howard's Department of Chemical Engineer-
ing in 1992, where he has been pursuing re-
search activities in coal-based technologies,
materials science engineering, and develop-
ment of new products via environmentally be-
nign processes.
Copyright ChEDivision of ASEE 1999

Chemical Engineering Education

The reacting mixture flowed in through a linear valve
while the product was discharged through a square root
valve. We wanted to investigate the uncontrolled and
the controlled dynamic behavior of the concentration
of HO22 and the temperature of the reactor from time
t = 0 to some time after first filling (starting with an
empty reactor), following which control was initiated.
The need to formulate the relevant mass, component,
and energy balances in a manner that was fairly general
and capable of accepting different parameter values
such as inlet concentration, temperature, reactor vol-
ume, and others was emphasized. The parameters of
the system are presented in Table 1. The mean tem-
perature difference between the reacting mixture and
the coolant was expressed as a function of difference in
temperature of the reacting mixture and the inlet tem-
perature of the coolant as

T TCin
T T = F

where Fc = 2Q

The students were asked to show all steps of the
formulation, including the system diagram (Figure 1),
and to state all assumptions clearly. We pointed out
that the units of the data provided were all mixed up,
which meant that conversion to a uniform system of
unit was required before simulation. The problem as-
signment was to culminate in showing the reactor's
liquid height, concentration, and temperature profiles
as a function of time with a short comment on the
stability of the system.

The class was organized into teams averaging four to five stu-
dents for the assignment and met three times a week (usually
before noon on Monday, Wednesday, and Friday) for one-hour
lectures, which was in addition to a 3-hour laboratory period per
week, scheduled on Monday afternoon.
A substantial portion of this open-ended assignment was treated
within the framework of the laboratory. The importance of team-
work was stressed, with an emphasis on including the workload
distribution in the final report as a requirement. The team members
were also asked to exclude the names of any inactive participant in
the report (the student in question would receive a failing grade).
One of the items in the list of topics to be discussed each week was
how well the team members interacted and whether there was a
need to either exclude or split any team into small number of
students. Usually, the students refrained from creating conditions
that would precipitate splitting their team since it would amount to
a heavier workload per student left on the team. Perhaps as a result
of these prior arrangements, no team was split.

The model of the CSTR system was expressed in dimensionless
state-space form, in terms of liquid height (equivalent to liquid
volume for the reactor vessels that were not uniform in cross-
sectional area), concentration, and temperature. Equations (1) and
(2) (see Table 2, next page), respectively, designated the dimen-
sionless state space of the system under uncontrolled and con-
trolled conditions, with applicable initial conditions and Jacobian
defined by Eqs. (3) and (4), respectively. These systems character-
ize the time variations of the state space, which belong to a class of
nonlinear autonomous equations in which there was no time vari-
able involved in the definition.
Large nonlinear systems occur in many important applications
such as the simulation and control of chemically reacting systems


Final Control Element



F=kv h; T, CA,C, p

Coolant Out

Figure 1. A closed feedback control loop of the modeled CSTR showing the
essential components and parameters.

Qc, Tcin

Values of Parameters Used in Simula-

V 16.2 liters
k 5.04 x 10't 1/sec
E 18.620 cal/mol
Cp 0.865 cal/g C
C, 62.5 BTU/(hr ft2oF)/(lb/min)"3
T 220C
T 10C
To 14C
CAO 11.5 mol/liter
F 0.5 liter/min
p 1.1081 g/cm3
AH -22.6 kcal/mol
Qc 900-3600 lb/min
Kv 0.9 cm25/min
Kp 3 liter/min K
D 25.2 cm
A. 3.18 ft2
*Symbol names are stated in the nomenclature

Fall 1999

F, Ti,CAi,Cp,pi ,Pi

whose solutions sometimes precipitate what is known as
"still problems." The still problems usually arise from mix-
ing of terms of fast and slow dynamics with the consequence
that the use of Runge-Kutta's fixed-step technique will yield
unsatisfactory results. Students were therefore reminded that
stiff problems are more competently resolved by employing
varying-step methods such as the modified-adaptive step,
Brulisch-Stoer or Rosenbrock techniques, all of which are

built into the Mathcad software package (see Table 3).
Most students were already familiar with Mathcad and
the decision to use it for the problem assignment enabled a
focused attention on the problem solution rather than be-
ing worried with writing a correct high-level program-
ming language. Additionally, it enabled a different under-
standing of other process control simulation modules, such
as the use of Process Identification and Control Loop

Chemical Engineering Education


Explorer system, PICLES,'8" and MATLAB/SIMULINK,'[9"1
which were introduced later for different process-control
problem assignments. Advanced control system"21 is a simi-
lar software package that is also available.

The tasks of the problem assignment en-
compassed simple case studies aimed at
determining the dynamics of the
Uncontrolled system duringfill-up
Varying Step Te
Uncontrolled system at constant
liquid height Rosenbrock Tec
liquid height
Fixed StepTechr
P-only controlled system at constant
liquid height Burlisch-Stoer T
P-only controlled system right from
Effects of multiple-fold increase in cooling rate

Typical simulation conditions associated with each case
study are explained in Table 4, with representative graphi-
cal results shown in Figures 2 through 4. Prior to simula-
tion, it was instructional for the students to make the
equations dimensionless and to prepare a table of vari-
ables detailing the input values assigned to each param-
eter under each case study, as shown in Table 5.

Our observations can be summarized in two categories:
the pedagogical value of the approach and the usefulness of
simulation as a powerful tool in learning various concepts in
process control. From the standpoint of pedagogy, it was

Various Built-In Solution Tools of Mathcad
Inputs Outputs
chnique Z=Rkadapt(X, 6 6f, points, D( 6 ,X)) Z=F( 6,X,X2,X,)
hnique Z=stiffr(X, 0 O, points, D( 6,X),J) Z=F( 6,X,X,,X,)
lique Z=rkfixed(X, 0 O, points, D( ,X)) Z=F( ,X,,X,,X3)
technique Z=stiffb(X, 6,, points, D( 6,X),J) Z=F( ,X,,X,,X3)

Various Simulation Case Studies Investigated

Case Studies Conditions
I Uncontrolled dynamics during fill-up (1) X,=0; X2=l; X3=l
II Uncontrolled dynamics at constant height (2) F ,=Fo; X,=l; X,=l; X,=l
III P-only controlled system at constant height (3) F,=K,(X3-X ) and (2)
IV P-only controlled system at startup (4) Fo=Kc(X3-Xe) and (1)
V Effects of multiple-fold increase in cooling rate (5) n=l,2..N; Qc=nQcn and (2)

Fall 1999

observed that the problem assignment formulated in this
5 manner gave the students a lot of confidence by focusing
their attention on the subject of the problem, which was to
model and simulate the system. There was an opportunity to
4 investigate as many case studies as possible, the result of
which advanced general understanding of the concepts that
are vital to learning of the course materials.
Toward the end of the assignment, the preconceived no-
3 --- tion of the course being a means of learning mathematics
was suddenly changed to the use of mathematics as a tool in
learning process control. Consequently, there was a general
feeling of "I can do it on my own" among many students.
S2 This was the kind of confidence we wanted them to develop,
I / CFeed Height and our feeling was overwhelming when we saw it work.
Temperature Secondly, breaking up the assignment into various case
..studies enabled the students to answer simple "what-if" ques-
tions associated with them. For example, Case Study I dealt
with investigating the dynamics of the uncontrolled system
during fill-up, with the outlet valve closed (see Figure 2). It
0o 1. was learned that so long as the valve remained closed, the
0 1 2 3 4 5
Dimensionless Time liquid level in the reactor would continue to rise, the effect of
which would possibly lead to overflow. The evidence of the
Figure 2. Case Study I: The dynamic response of the system attaining stability in concentration and temperature,
uncontrolled system during startup, with no effluent i.e., X2 and X3 eventually settling at certain bounded levels
output. The tank fills at dimensionless time=l.. .
Sas time approached infinity even as the liquid level increased,

1.4 1.6

1. 12
0 0

0.84 1 096 ----

I oo P-Only Controlled Temp., Kp=3 Liter/minK
(cona troled)To temperature n cocIE Concentration at coolant rate, Qc=900 Ib/min
K\-x- Concentration Without System Control 0 Concentration at coolant rate, 2*Qc
Concentration at coolant rate, 4*Qc
_t' Concentration With System Control o o Temperature at coolantrate, Qc
a r l o o o Temperature at coolant rate, Qc
S0.56 1 064 00 Temperature at coolant rate, 2*Qc
Temperature at coolant rate, 4*Qc

0.28 032

0 0
0 1 2 3 4 5 0 1 2 3 4 5
Dimensionless Time Dimensionless Time

Figure 3. Comparison of Case Studies I (uncontrolled) and III Figure 4. Case Study V: The effects of multiple-fold increase in
(controlled) for temperature and concentration dynamics. Appli- flowrate of the coolant is marked by decreased frequency in oscil-
cation of proportional control effectively removed the oscillations latory behavior of temperature and concentration, which eventu-
in both variables, ally led to the disappearance of oscillation completely.

314 Chemical Engineering Education

was seen. When a proportional control was applied for con-
trol of temperature using the inlet flowrate of the feed as the
manipulated variable (see Figure 3), the oscillatory behavior
in both concentration (X2) and temperature (X3) was re-
moved. The controlled system experienced gradual decay
rather than oscillatory changes that marked the behavior of
the uncontrolled system.
In Case Study V where the flowrate of the coolant was
increased in multiple-fold of up to four, the system experi-
enced pronounced oscillation in temperature and concentra-
tion despite the fact that control was applied. But the charac-
ter of the oscillatory behavior was marked by a decrease in
frequency, which eventually disappeared with increase in
coolant flowrate (Figure 4). This was an indication that
flowrate of the coolant could alternatively be used as a
manipulated variable to control the system temperature. The
students learned at this point that there was more than one
way of achieving control of the system, having discovered
the two candidates for manipulated variables, i.e., the feed
inlet stream and the flowrate of the coolant.

The assignment also gave the students the opportunity to
revisit past courses such as kinetics, heat transfer, and calcu-
lus, and gave them the chance to apply the knowledge they
have previously learned to this assignment. Since this as-
signment was given in the first two weeks of the course, many
students recognized the need to review some of the earlier
course materials they had taken prior to process control.
Overall, this problem assignment received positive com-
mendations from over two-thirds of the class, with many of
them stating that it helped them integrate ideas and to use
them to study typical problems that occur in many chemical
industries. From our point of view, the project was worth-
while, considering the foundation work it laid for better
understanding of various topics taught in other courses in the
past. Most important in our estimation, however, was its
value in fostering understanding of the subject of process
control as a course.

Grateful acknowledgments are hereby made to all students who
took the course in chemical engineering process control. Our spe-
cial thanks go to Masiane Kabelo, Olive Sikem, and Bashir Elabor
for providing in-depth responses about the course.

A cross sectional area of the tank, cm2
A surface area for heat transfer, cm2
a coefficient (see Eq. 5)
b coefficient (see Eq. 5)
CA concentration of component A in tank (H,0), mole/liter
CA, inlet concentration of component A (H,O,), mol/liter
Cp specific heat capacity, J/g K
C, parameter associated with heat removal rate, BTU/(hr ft2oF)(lb/
c coefficient (see Eq. 5)

D diameter of the reactor, cm
D( 0,X) vector valued function of the state-space variables
d coefficient (see Eq. 5)
E energy of activation, J/mol
F. system parameter associated with cooling, dimensionless
Fo dimensionless flowrate
F, inlet flow rate, liter/min
h height of the liquid in the tank, cm
J Jacobian matrix
ko pre-exponential factor, I/sec
Kc controller gain, liter/min*K
K proportional gain
K valve constant, cm25/sec
P, coefficient (see Eq. 5)
P, coefficient (see Eq. 5)
R universal gas constant, J/mol K
Q coolant volumetric flowrate, liter/min
T temperature of the reacting mixture in the tank, C
Tc temperature of the coolant in the jacket, C
T inlet temperature of the reacting mixture in the tank, C
Te setpoint temperature of the reacting mixture in the tank, C
U overall heat transfer coefficient, J/cm2 K
V volume of the reacting mixture, liters
X, dimensionless height
X, dimensionless concentration
X3 dimensionless temperature
X," dimensionless inlet concentration
X," dimensionless inlet temperature
Xc, dimensionless inlet temperature of the coolant
X_, dimensionless temperature setpoint
y Arrhenius number, dimensionless
AH enthalpy of reaction, J/mol
p density of the reacting mixture in the tank, g/cm3
0 residence time, min

1. Lant, P., and B. Newell, "Problem-Centered Teaching of Process
Control and Dynamics," Chem. Eng. Ed., 30(3), 228 (1996)
2. Himmelblau, D.M., Basic Principles and Calculations in Chemical
Engineering, 6th ed., Prentice Hall (1996)
3. Fogler, H.S., Elements of Chemical Reaction Engineering, 3rd ed.,
Prentice Hall, Chapter 9 (1999)
4. Russell, T.W.F., and M.M. Denn, Introduction to Chemical Engi-
neering Analysis, John Wiley & Sons, New York, NY, Chapters 12,
5. Stephanopoulous, G., Chemical Process Control: An Introduction
to Theory and Practice, Prentice Hall, Chapter 14 (1984)
6. Ramirez, W.F., Process Simulation, Heath Company, p. 81 (1976)
7. Ramirez, W.F., and B.A. Turner, "The Dynamic Modeling, Stabil-
ity, and Control of a Continuously Stirred Tank Chemical Reactor,"
AIChE J., 15(6), 853 (1969)
8. Cooper, D.J., "PICLES: A Simulator for 'Virtual World' Education
and Training in Process Dynamics and Control," Comp. Applica-
tions in Eng. Ed., 4(3), 207 (1996)
9. Doyle III, F.J., E.P. Gatzke, and R.S. Parker, "Practical Case Stud-
ies for Undergraduate Process Dynamics and Control Using the
Process Control Modules (PCM)," Comp. Applications in Eng. Ed.,
(in press, 1999)
10. Bequette, B.W., K.D. Schott, V. Prasad, V. Natarajan, and R.R.
Rao, "Case Study in an Undergraduate Process Control Course,"
Chem. Eng. Ed., 32(3), 214 (1998)
11. Bequette, B.W., "Case Study Projects in an Undergraduate Process
Control Course," Proceedings of Control-97, Sydney, p. 212 (1997)
12. Koppel, L.B., and G.R. Sullivan, "Use of IBM's Advanced Control
System in Undergraduate Process Control Education," Chem. Eng.
Ed., 20, 70 (1986) U

Fall 1999

a classroom


To Illustrate Engineering Practice to Lower-Level Students

Rowan University Glassboro, NJ 08028-1701

Almost every university has a power plant facility
that is an excellent resource for students of engi-
neering processes and equipment. These physical
plants contain many unit operations, such as heat exchang-
ers, combustors, turbines, and boiler water-treatment sys-
tems that may include membrane devices! The systems are
equipped with pumps, compressors, fans, pipes, atomizers,
tanks, finned boiler tubes, inner-wall transfer surfaces, valves,
etc. In addition, a modern facility includes a data-acquisition
system to obtain data to control the plant consisting of ori-
fice plates, pressure transducers, thermocouples, level gauges,
and vibration meters. Concentration measurements are made
using NDIR gas analyzers for CO, CO2 and total hydrocar-
bons. And oxygen is measured using paramagnetic analyz-
ers and NOx using chemiluminescence. Concentration mea-
surements are also made of impurities in the boiler water.
These plants are a rich source of engineering examples that
are readily accessible to engineering students.
At Rowan University, we use our cogeneration facility in
our freshman and sophomore chemical engineering courses.
In the freshman year we introduce our students to measure-
ment devices, process flow diagrams, and process simula-
tion. This is accomplished in the freshman engineering course
in a three-week module on process measurements.

The Rowan engineering faculty are taking a leadership
role by using innovative methods of teaching and learning,
as recommended by ASEE,"11 to better prepare students for
entry into a rapidly changing and highly competitive market-
place. Key program features include
Inter- and multidisciplinary education created through
collaborative laboratory and course work
Stressing teamwork as the necessary framework for
solving complex problems

- Incorporation of state-of-the-art technologies through-
out the curricula
Creation of continuous opportunities for technical com-
To best meet these objectives, the four engineering pro-
grams of chemical, civil, electrical, and mechanical have a
common engineering clinic throughout their program of study.
In addition to the engineering clinic, they share a common
first year of courses. Our first three classes of entering fresh-
men are between 101 and 115 students with an average SAT
score of 1252 and who graduated in the top 14% of their
high school class.
The primary goal of Rowan University's freshman engi-
neering course is to immerse students in multidisciplinary
projects that teach engineering principles using the theme of
engineering measurements in both laboratory and real-world
settings. Many freshman programs focus on either a design

Robert Hesketh is Associate 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. After his PhD he conducted
research at the University of Cambridge, En-
gland. His teaching and research interests are
in reaction engineering, freshman engineer-
ing, and mass transfer.

C. Stewart Slater is Professor and Chair of
Chemical Engineering at Rowan University. He
received his BS, MS, and PhD from Rutgers
University. His teaching and research interests
are in separation and purification technology,
laboratory development, and investigating novel
processes for interdisciplinary fields such as
biotechnology and environmental engineering.
S He has written over 70 papers and several book

Copyright ChE Division of ASEE 1999

Chemical Engineering Education

project or a series of discipline-specific experiments that may not be cohesively inte-
grated. Some institutions have used traditional discipline-specific laboratory experi-
ments at the freshman level,121 while others engage students in discipline-specific
freshman engineering design projects.J31 One of the NSF coalitions, ECSEL, has major
efforts in freshman design that have been widely reported.4'51 At Rowan, freshman
engineers are introduced to industrial problems through a series of four modules and
interactive lectures on problem solving, safety, and ethics. In this paper, we will
discuss a portion of the process engineering module that uses the vehicle of a
cogeneration plant.
Freshmen can be overwhelmed when introduced to real engineering processes, and it
is important to have well-defined objectives. They need to understand that they are only
being introduced to the problems and are not expected to know all of the engineering
principles of the processes. Our overall objectives for the fall-semester freshman engi-
neering clinic are
[ Engineering Measurements Students will understand and apply the concepts of
accuracy, precision, resolution, and linearity; calibrate devices; have a knowledge
of the basics of data acquisition; analyze a problem and select appropriate
measurement devices for actual engineering processes.
E[ Engineering Communication Students will produce plots using Excel to
illustrate engineering principles; use PowerPoint for presentations; use word
processing for reports of actual engineering problems. Students will develop the
ability to work in multidisciplinary teams, have effective meetings, and use a
problem-solving strategy on real engineering problems.
El Engineering Fundamentals Students will convert units, examine equations for
dimensional homogeneity; use engineering equations, apply basic concepts (e.g.,
hydrostatic pressure, Hooke's law, Ohm's law) applied to actual engineering
Four measurement modules are employed in this freshman engineering clinic: manu-
facturing, structural, process, and electrical engineering. Spatial measurements and
measurement fundamentals are introduced to freshman engineering students as they
fabricate a MAG-type flashlight from an aluminum rod. Several structural measure-
ments are shown to the students using a bridge module. Students first survey a bridge
site, conduct strain measurements on a model bridge, and simulate the bridge.
The university cogeneration plant is used to show the use of temperature, pressure,
flow, and concentration measurements. The students tour the cogeneration plant and
record data of temperature, pressure, and flowrate of the water in the cogeneration unit.
They then return to the computer laboratory and simulate two heat exchangers, using
their readings, and perform hand calculations for homework. This is followed by
two weeks of experiments using temperature, pressure, and flowrate devices seen
in the cogeneration plant.
The final module has the students construct a temperature alarm circuit and investi-
gate the use of C++ programming in measurements. Thus, the clinic focuses on mea-
surements in the field and also in traditional laboratory settings. Field trips tend to excite
students by breaking down the monotony of being indoors and helping them prepare for
realistic engineering measurements.

The process measurements module presents a "day in the life of an engineer" to
freshman engineering students. A problem is posed to students requiring them to visit
the university cogeneration facility. At this site both traditional (gauges and thermom-
eters) and data acquisition measurement systems are employed to monitor the steam and
electricity generation process. This laboratory and homework session is followed by

plants contain
many unit
such as heat
and boiler
systems that
may include
devices! The
systems are
equipped with
compressors, fans,
pipes, atomizers,
tanks, finned
boiler tubes,
transfer surfaces,
valves, etc.
In addition, a
modern facility
includes a
system to obtain
data to control
the plant
consisting of
orifice plates,
level gauges, and
vibration meters.

Fall 1999


two more laboratory sessions in which students make pro-
cess measurements using similar equipment to that seen in
the cogeneration plant. The module lasts three weeks, with
each week having a 1-hour and a 3-hour session.
Preceding the cogeneration site visit, students are given
lecture and problem sessions on teamwork, safety, system of
units and unit conversions, dimensional homogeneity, and
significant figures. On the week of the site visit, the students
are given a brief introduction to the cogeneration process
and are shown photographs of equipment that they are re-
quired to identify on the site visit.
At the completion of this module on the cogeneration
facility, freshman students should be able to
Convert units of simple dimensions.
Convert units of a variable, such as flowrate.
Calculate the temperature of saturated steam when given a
gauge pressure and an appropriate equation.
Examine an equation for dimensional homogeneity.
Obtain measurements of temperature, pressure, and mass
flowrate and perform an energy balance on the heat
exchangers in the cogeneration system.
Create a simple heat exchanger network using the chemical
process simulator HYSYS.
Identify from a photograph the following: orifice plate,
pressure transducer, thermometer, and pressure gauge.
Describe the process of cogeneration to a high school

Condensate Return to Plant
(Steam condensed to water)

Figure 1. Overall schematic of Rowan University Steam &
Electricity Generation

Figure 2. Rowan cogeneration plant fabricated by Energy Recovery International.

Chemical Engineering Education

Fuel & Air

Figure 3. Cogeneration process waterflow diagram.

Plant-Trip Readings

Reading Value Units
Feed-water flowrate to cogeneration facility 25.1 1000 lbm/h
Steam flowrate from cogeneration facility 21.6 1000 Ibm/fl
Feed-water temperature 216 F
Feed-water pressure 250 psig
Boiler-inlet water temperature 330 "F
Steam pressure from cogeneration system 150 psig

Rowan University uses steam for both heating and cooling
of its buildings. An additional benefit to the process is the
generation of electricity. We explain to the students that the
process of electricity and steam generation is called cogen-
eration. It is obvious to most students how a building is
heated with steam using radiators, but it is not obvious how
to cool a building with steam! Professors are probably aware
that steam is used with absorption refrigeration[6-81 and
we are very pleased to see that one of the best treatises
on this subject is in Perry's handbook![91
The overall flow diagram for the use of steam at j4
Rowan University is shown in Figure 1. In the Steam
Plant, steam is produced by three conventional boilers
and a cogeneration unit. Steam flows through under-
ground pipelines to each of the university buildings,
through radiator units or the refrigeration absorption
units (air cooling), and then is returned as condensate
to the steam plant. In our new engineering building,
these units are located on the fourth floor, and we are
attempting to modify this laboratory to use this floor
for future engineering classes.
An advantage of using this steam plant is that both
traditional (gauges and thermometers) and data-acqui-
sition measurement systems are employed. Most of the
traditional gauges are for measuring temperature, pres-
sure, and liquid height. At the other end of the spec-

trum is the advanced data-acquisition sys-
tem, which records 65 channels of informa-
.. tion including vibrations, power, voltage,
amperage, temperature, pressure, gas, and
SGP T g, "' flowrates. Students are able to see the advan-
I tages of using both mechanical gauges and
steam..Fow.te pressure transducers. Also given throughout
.R.F this module is the cost of various types of
am Pressure measurement equipment.

Students are given the process flow dia-
gram shown in Figure 2 to illustrate the com-
plexity of a relatively small portion of the
steam plant pertaining to the cogeneration. Figure 3 is also
given to the students and shows only the steam production
side of the cogeneration unit. Using this relatively simple
figure, students relate their knowledge of boiling water to
produce steam to that of steam production in a cogeneration
unit. The students obtain readings (shown in Table 1) from
every device marked in Figure 3. Obtaining readings in the
plant turns the plant trip into an active learning experience.
They need to obtain information from this trip that they will
use immediately in the simulation and homework. In addi-
tion, a quiz is given showing photographs of some of the
common measurement equipment. A map of the cogenera-
tion facility is also given to the students to show placement
of the equipment and measurement devices in the building.
Using these figures, students are guided by professors and
upper-division chemical engineering students through the
combustion process and the production of steam and elec-
These readings are used as input to a chemical process
simulation package, HYSYS, and as input to a set of hand

Figure 4. HYSYS-generated process flow diagram with
summary tables.

Fall 1999

calculations for a homework assignment. In both
the HYSYS simulation and homework assign-
ment, the students determine the heat duties of
two heat exchangers.
Upon completion of this short site visit, stu-
dents have seen actual process equipment and
obtained readings from a real process. The stu-
dents now have motivation to perform engineer-
ing unit conversions and calculations described in
the next two sections.

At the end of the site visit, students return im-
mediately to the computer room and are led
through a simulation program of the two heat
exchangers. The students follow a self-paced tu-
torial on using HYSYS to simulate their process.
They start the computer program and select the
ASME steam tables as the thermodynamic prop-
erty package. Next they simulate the two heat
exchangers as heaters. After installing each heater,
they enter the readings obtained in the plant.
From this simulation, they obtain the tempera-
ture of saturated steam and the values of the
heat duties of both heaters. A process flow
diagram, generated by HYSYS, of these two
heaters is shown in Figure 4.
After completing this laboratory, students have
experienced two of the activities in the "day in the
life of an engineer." They have visited an actual
plant and then have returned to the computer to
simulate a portion of this process.

For homework, students must calculate by hand
the heat duties on both heat exchangers and the
temperature of the saturated steam. In this assign-
ment, they must show all unit conversions and
calculations, and all equations must be dimen-
sionally homogeneous. To aid the students in these
calculations, the answers to most of their calcula-
tions are obtained from the HYSYS simulation
printouts. These printouts contain the plant read-
ings in both the English and SI system of units.
So the student will obtain agreement between
the heat duty on the economizer, but will not
get an exact agreement with the simulated boiler
heat duty.
It is very important to give all the necessary
equations to the freshmen with a clear explana-
tion of the variables. We have also found that the
description or name of each measurement must
be identical to both the HYSYS variable names

and the equation variable names. Finally, the variable names must be
explicitly given in each equation. For example, instead of T in heat
capacity, heat capacity expressions must be given for all of the tempera-
tures (Tw, T,,,,,, TB,).
The heat capacities of the water vapor and liquid were obtained from
empirical correlations based on the ASME steam tables, which are valid
in the range 373 < T < 470 K. In each of these equations, the units for
each constant are explicitly shown. This allows the freshman to deter-
mine if each equation is dimensionally homogeneous.

CF= 4788.26 3.4297 g TFw + 4.885 x 10-3 3 (TW) (1)
P kgK kgK kgK

Hpw = CF(TFw -273.16K) (2)
The temperature of saturated steam is calculated as

= -2.075 x 10-4 1en M steam + 2.683 x 10-3 (3)
Tsteam K 1.01325 x 0Pa) K
An enthalpy balance on each heat exchanger is calculated from

HpWriFW + Qeconomizer = HsBIrFW (4)
Using the results of these energy balances, an estimate of the energy
recovered using the economizer was conducted.
This exercise shows the students the equations that the computer
simulation has used to perform engineering calculations. This removes
the magic of the computer and shows students the equations that the
computer is using in the simulation. After completing this session, the
students have the advantage of seeing a process familiar to them (boiling
water), performing an advanced computer simulation and then conduct-
ing hand calculations of the process.

In the next two laboratory sessions, four experiments are conducted in
which the students use equipment similar to that observed in the cogen-
eration plant. The experiments performed are
Flowrate measurement: rotameter operation and calibration
Temperature measurement: immersion heaters
Pressure measurement: tank efflux and implosion of a 2-L soda
The rotameter operation and tank efflux experiments are classic chemi-
cal engineering experiments that have been adapted from Perna.21 The
tank efflux experiment is modified by adding 3 pressure-measurement
devices; a sight gauge, a pressure transducer, and a low-pressure dia-
phragm gauge. The implosion experiment employs a vacuum pressure
gauge, water aspirator, and a 2-L soda bottle to graphically show stu-
dents the effect of vacuum. The immersion water heater experiment is
unique in that domestic electric kettles are employed and the students
determine that dT/dt = Qin/mCp,' for most of the heating process.

The use of the cogeneration facility continues into the mass and

Chemical Engineering Education

energy balance course typically taken by students in their
second year. Once again, students and professors have an
excellent example of real-world mass and energy balances.
They learn the approximate compositions of natural gas
(fuel oil has a much higher complexity!). Students use their
knowledge of oxidation stoichiometry to determine percent
excess air and predict outlet concentrations. The students
check their answers by comparing them to gas analyzer
measurements of oxygen, carbon dioxide, and carbon mon-
oxide. Water produced is usually not measured, but if de-
sired, a gas stream sample can be obtained and either the
water condensed or a hygrometer can be used to determine
water concentrations. Additionally, mass balances can also
be performed on water-treatment systems, including con-
ventional systems and novel membrane systems.
Since these balances are based on actual measurements,
students find they are not able to obtain an exact balance on
mass and energy. They learn that measurement devices have
a limited accuracy and may need to be recalibrated. This was
the case the first time we tried to complete a mass balance on
the cogeneration fuel stream. Our industrial sponsors are
very excited to hear that students conduct balances on actual
systems, because many of the problems in a real plant are
related to a faulty or out-of-calibration measurement device.
The energy balances on the system were started in the
freshman year to see that heat flows from the hot gas to the
water. Steam tables are introduced and students use their
knowledge of heat capacity and enthalpy gained from chem-
istry and this course to perform energy balances. In this
sophomore course, they now understand the equations that
were employed in the freshman engineering module. In the
sophomore course students calculate the source of energy
production from the combustion reactions. They use stan-
dard heats of combustion, fluid flowrates, and chemical com-
positions to determine outlet gas temperatures. Using mea-
sured values, students determine the magnitude of the en-
ergy losses in a cogeneration system. They can now under-
stand in more detail why an economizer was added to re-
cover additional energy from the exiting gases. In addition,
they can conduct simulations to examine the effect of in-
creasing the surface area of the two heat exchangers on the
energy recovered. This brings engineering economics into
their coursework!

The cogeneration plant is also an excellent vehicle to
introduce safety, health, and environmental concerns. All
freshmen are required to wear safety glasses, hard hats,
closed-toe shoes, ear plugs (near turbines), and clothing that
covers all limbs. Safety features are shown to students dur-
ing the introductory lecture and tour, such as guards for
rotating equipment and pressure-relief valves. The required
EPA monitoring system is shown in which both a continu-

Fall 1999

ous paper printout and electronic data are produced. The
stack emissions sampling point is readily identified from the
catwalk around the stack. Starting freshmen to think about
safety and environmental issues in a plant is an excellent
reinforcement to their use of laboratory safety.

The university cogeneration or physical plant is a rich and
diverse resource that should be used in many chemical engi-
neering classes. The plant is on campus and is easily acces-
sible. In addition to the time savings in not having to travel
to an off-campus site, students can be used as tour guides to
minimize group size. Using small groups to tour a facility
allows students to hear the tour guide as well as to ask
questions about the process. By using real-world examples
of engineering, the student's level of understanding and
motivation to learn new material increases dramatically. The
use of a cogeneration facility in chemical engineering courses
is designed to immerse students into multidisciplinary, real-
world, laboratory projects that teach engineering principles.
Students are excited and challenged by working in real-
world settings and are motivated to learn the underlying
engineering principles.

Special thanks are given to the physical plant staff headed
by Glenn Brewer for help in preparing the cogeneration
module, Mark Showers, chemical engineering students, and
faculty for giving tours of the facility. Funding for this work
was made possible through a grant from the DuPont Educa-
tional Aid Foundation.

1. ASEE, "Engineering Education for a Changing World," Joint
project report by the Engineering Deans Council and Corpo-
rate Roundtable of the American Society for Engineering
Education, Washington, DC (1994)
2. Perna, A., and D. Hanesian, "A Discipline Oriented Fresh-
man Engineering Measurement Laboratory," 1996 ASEE
Annual Conference 2326a, Washington, DC., June (1996)
3. McConica, C., "Freshman Design Course for Chemical Engi-
neers," Chem. Eng. Ed., 30(1), 76 (1996)
4. Dally, J.W., and G.M. Zhang, "A Freshman Engineering
Design Course," J. Eng. Ed., 82(2), 83 (1993)
5. Regan, T.M., and P.A. Minderman, Jr., "Introduction to
Engineering Design: A Major Engineering Education Pro-
cess Improvement," Proc. 4th World Conf. on Eng. Ed., 3,
243, St. Paul, MN (1995)
6. McQuiston, F.C., and J.D. Parker, Heating, Ventilating,
and Air Conditioning: Analysis and Design, 4th ed., Wiley,
New York, NY, 659 (1994)
7. Moran, M., and H.N. Shapiro, Fundamentals of Engineer-
ing Thermodynamics, 3rd ed., Wiley, New York, NY, 659
8. Smith, J.M., H.C. Van Ness, M.M. Abbott, Introduction to
Chemical Engineering Thermodynamics, 5th ed., McGraw-
Hill, New York, NY, 305 (1996)
9. Perry, R.H., and D. Green, Perry's Chemical Engineers' Hand-
book, 7th ed., 11 (1996) E


M.4 1 curriculum




University of Calgary Calgary, Alberta, Canada T2N 1N4

Until recently, the Department of Chemical and Pe-
troleum Engineering at the University of Calgary
offered undergraduate degrees only in Chemical
Engineering and Chemical Engineering with a Petroleum
Minor. As a result of a joint university and industry initia-
tive, however, a new degree in Oil and Gas Engineering was
added to the program in 1998.
The curriculum of the new Oil and Gas Engineering De-
gree was largely based on the advice of an industry advisory
committee consisting of representatives from several major
companies in Calgary's oil and gas sector. The committee
identified the fourth-year Petroleum Design course as a
key component of the new curriculum. This provided an
opportunity for creating a course that draws on the high
concentration of oil and gas companies and petroleum
professionals in Calgary.
Now it was up to us to design the design course. First, we
considered why design is taught at universities.

Perhaps the best way to answer the question is to examine
the difference between undergraduates without design train-
ing and practicing engineers. "Academic" undergraduates
will have taken a number of courses, each dealing with a
specific topic area such as heat transfer, thermodynamics, or
reservoir engineering. They should be familiar with funda-
mental principles of engineering science and are well versed
in solving narrowly defined problems based on those prin-
ciples. For example, they can find the pressure drop of a
specified single-phase fluid in a given pipeline at given
conditions. They will also have received some training in
writing reports and making presentations.
Academic undergraduates are probably not fully aware of
the interrelation of many topics covered in the undergradu-
ate program. They have been trained to tackle problems

individually and, at least in technical courses, they are rarely
called upon to present their results in any format other than
written assignments, short reports, or examinations. They
have little or no experience with managing partial or contra-
dictory data, conducting economic evaluations, and solving
"design" problems. Here, a "design" problem is a problem
that requires "the devising of an artifact, system, or process
to best meet a stated objective.""' For instance, "design a
process to produce styrene for the Alberta market that meets
the corporate economic hurdles." Note that design problems
are open-ended; that is, the number of options and amount of
detail that can be considered is limitless.
Practicing engineers deal primarily with design problems.
They are usually selecting processes or choosing between
competing technologies. Most of the methods and theories
they learned in college are embedded in simulation software.
Their hard-earned university knowledge is used primarily to
check the simulation results for implausible results. But they
must be aware of the interaction of all facets of their under-
graduate knowledge. For example, is there heat loss from
the pipeline and does the flowing fluid enter the two-
Harvey W. Yarranton is Assistant Professor of
Chemical and Petroleum Engineering at the Uni-
versity of Calgary. He received his BSc (1985)
and his PhD (1997) degrees in Chemical Engi-
neering from the University of Alberta. His re-
search interests are in the thermodynamics and
transport of hydrocarbons and the treatment of
water in oil emulsions.

William Y. Svrcek is Professor of Chemical
and Petroleum Engineering at the University of
Calgary. He received his BSc (1962) and his
PhD (1967) degrees in Chemical Engineering
from the University of Alberta. His teaching
and research interests center on process con-
trol and design.

Copyright ChE Division of ASEE 1999

Chemical Engineering Education

phase region? Is erosion or corrosion possible at the
operating conditions?
The university experience has been designed to give prac-
ticing engineers the tools to analyze problems and to adapt to
new circumstances, for they will face new circumstances.
Practicing engineers will often work on projects and tech-
nologies that were barely mentioned in college.
They will usually be part of a team and will be
expected to have good interpersonal and com- The
munication skills. They will often evaluate eco-
nomics and will frequently present the results had
orally and in written form. suffic
Until design was introduced into the engineer- to re
ing curricula, there was a glaring difference be- work
tween the training undergraduate engineers re-
ceived and the work they did as practicing engi- of St
neers. In fact, universities are still criticized for tWO ft
training potential graduate students rather than term
potential industrial engineers.12] Universities have
responded in several ways. Some have intro-
duced design case studies that are introduced in OVerW
the first year and worked on in more detail COmt
throughout the program.31 Many, including
Calgary, have added a co-op or internship pro-
gram where students work in industry jobs for inex]
terms of four to twelve months. Now, all accred- engine
ited North American universities are required to impOf
offer design courses. W
The final-year design course provides the best
opportunity to teach design principles and bridge studeJ
the gap between the university and industry. By with
this time, the students have learned many of the wit
scientific principles they need to solve engineer- o
ing problems. Many have completed an intern-
ship work term and are at least familiar with the and (
industrial environment. The design course al-
lows them to integrate the material from other
courses and to work on a "open-ended" design problem. For
this reason, it is sometimes referred to as the "capstone"
course of an undergraduate program.141

The comparison of an "academic" undergraduate with a
practicing engineer highlighted the elements we wished to
include in the design course. They are
An open-ended design problem
Real data
Experience relevant to industry
Application and integration of all undergraduate
Economic evaluations

Use of commercial software
Written reports
Oral presentations
We then selected projects and structured the course to in-
clude all the above elements.


to be of
ient scope
quire the
of a team
dents for
s and yet
ilex for a
oup of
eers. Most
fantly, we
nted the
nts to deal
real data
& all its

The elements listed above apply equally well
to any design course. The next step was to
develop projects from the petroleum area that
met the requirements of the course. What is
involved in petroleum engineering? It encom-
passes a broad range of activities just as chemi-
cal engineering does. Petroleum engineers may
be called upon to estimate the size of a reser-
voir and to predict production from a well or
the reservoir. They may design waterfloods,
miscible floods, steam floods, or even
firefloods (underground combustion) to dis-
place oil from the reservoir. They may drill,
complete, or stimulate wells. They may de-
sign pipelines or separators, or work in a gas
plant. Economic evaluations, land sale evalu-
ations, and negotiation with joint-interest own-
ers are also part of the job. Petroleum engi-
neering even extends to offshore produc-
tion and oil sands processing. What projects
should we draw on from the broad range of
To apply all the undergraduate material and
gain a perspective of the industry, we desired
projects that included downhole (reservoir) as-
pects as well as facilities (oil batteries, gas
plants, etc.) and the wellbore (drilling and
completions). The projects had to be of suffi-
cient scope to require the work of a team of
students for two four-month terms and yet not

overwhelmingly complex for a group of inexperienced engi-
neers. Most importantly, we wanted the students to deal with
real data with all its contradictions and omissions.
We decided to concentrate on projects involving relatively
straightforward reservoirs, such as a sandstone reservoir or a
homogeneous carbonate reservoir. But the projects them-
selves are broad and open-ended. We ask the students to
examine an existing reservoir and evaluate its reserves and
existing production scenario. The students then have a
choice-they can recommend strategies to increase the value
of the reservoir in its present state, or they can re-engineer
the development of the reservoir. In other words, they could
take all the knowledge of today and develop the reservoir as
if it had just been discovered. The advantage of the second
option is that depleted reservoirs with little remaining poten-
tial can still be used as projects.

Fall 1999

In either case, the students are asked to design appropriate
facilities, construct a drilling and completion program, and
generate production forecasts as well as capital and operat-
ing cost estimates. They then compare the economics of
several strategies and recommend the optimum development

strategy for their reservoir. As in real life, the design
problem statements are deceptively simple
(see Table 1). Note that the progression from
design statement to problem definition to
evaluation of alternatives resembles typical T
chemical engineering design processes,151 al-
though the details differ. Design Pr
With the help of several Calgary companies,'61 E Design
we assembled data sets that included the same for the I
information that practicing engineers deal with retrogra
to assess reservoirs; that is, well logs, conven-
E Design
tional and special core data, pressure data (in- Countes
cluding build-up test data), and PVT data. The reservoir
students were able to access any other required E Optimiz
data, such as well locations, completion data, the Blac
and production rates, from a commercial data- sandstorm
base available at the department or from the E Evaluate
the Spar
AEUB (Alberta Energy and Utilities Board), a sandstorm
provincial regulatory agency.
It is these data sets that make our design
course unique. In petroleum engineering, a ma- T
jor issue is describing the reservoir, its size, Required
thickness, porosity, and permeability distribu- Oil and
tions, etc. This description and the associated
reservoir maps are constructed from well logs Year
and other available data. These data represent a Numeri
tiny fraction of the reservoir and are often con- Engin
tradictory or incomplete. Hence, judgment and Heat an
interpretation are critical. A considerable part Chemic
of the first term of the design course is spent Partial
characterizing the reservoir. This evaluation Equat
draws on many of the petroleum engineering Separat
courses in our program, listed in Table 2. Drilling

The Department of Chemical and Petroleum
Engineering at the University of Calgary has a
four-year undergraduate program (not includ-
ing time spent on internship work terms). The
program includes a three-part series of single-
semester design courses starting in the second
half of the third year and concluding at the end
of the fourth year.
Both chemical and oil and gas engineering
undergraduates are enrolled in the same third-
year design course. Equipment sizing, cost esti-
mation, and profitability analysis are introduced
in this course, and the textbook used is Plant


a gas-c
de con
a water
e the w
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a stea
ky Ah
ie reser

3rd at
jas E

cal Me
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ion Pro
and C

Design and Economics for Chemical Engineers. 71 The stu-
dents are assigned group projects worth 25% of the course
grade. A chemical engineering project involves optimizing a
given process and a HYSYSI81 simulation of the process is
provided. An oil and gas engineering project involves as-
sessing various strategies for developing a given reservoir.
In this case, an EXODUS'91 simulation of the
reservoir is provided. The projects give the
students an opportunity to practice engi-
LE 1 neering design principles and engineering
pie economics on a problem of limited scope.
iStatements The projects also serve to introduce the

cycling scheme simulation software used in the fourth-year
ycling scheme
iNisku D design courses.
densate In the fourth year, the chemical engineer-

flood for the ing and the oil and gas engineering students
flood for the
sandstone enter different design classes: Process Design
I and Petroleum Design II, respectively. The
'aterflood on students taking a petroleum minor can choose
Stratified between the two design courses. While the
rvoir. design courses are run separately, we mod-
m flood for eled the structure of the petroleum design
rvoir. course on the long-standing and very suc-
cessful chemical engineering process de-
sign course.[101
E 2 In both cases, the Design I course is in-
nd 4th Year tended to be a first-pass design where the
engineering students can evaluate "their" process or reser-
es voir and perform preliminary design, costing,
and economics. The level of detail is similar
thods in to a budget cost estimate; that is, shortcut
methods are employed and costs are accurate
STransfer to approximately 25%. In the second fourth-
ineering year course, Design II, the students work on
ential the same project but to a greater level of
detail, similar to an AFE (authorization for
cesses I expenditure) cost estimate. In this case, de-
ompletions tailed design methods are employed and
3r Year manufacturer's quotes are obtained on major
equipment, and costs are expected to be accu-
eservoir rate to 10%.


s Media Petroleum Design I The students are
creating given one week to form a group (3-4 students

and per group) and choose their top three projects
valuation from a list provided in the first class. The key
Well Testing here is that the students are free to make up
ign I their own groups. We do not force students
ign II
[ineering into groups, thus avoiding personality con-
flicts during the term. The projects are allo-
cated as much as possible on a first-come-

Chemical Engineering Education

nII anda as Ec
Economics (
Oil and Gas R
Petroleum Pro
4" Year
Flow in Porou
Oil and Gas T
Well Logging
Formation E
Introduction to
Petroleum Des
Petroleum Des
Petroleum Eng

first-served basis. As a result, the better-organized students
tend to get the projects of their choice. One disadvantage of
this approach is that the poorer students tend to get concen-
trated into groups that will struggle with the course. On the
other hand, these same students cannot coast through the
course hidden in a group of otherwise good performers.
Once the projects are chosen, the students review their
data sets, analyze what they can, and search for missing
information from a petroleum database, AccuMap,"" in the
literature, at the AEUB, or through industry contacts. They
use well logs and core data to construct cross-sections, struc-
ture maps, and appropriate pay maps of their reservoir. They
determine volumetric reserves, and using pressure and pro-
duction history, they perform a material balance to obtain a
second reserves estimate. They usually reach this point at the
midterm of the course. After the midterm, they use analyti-
cal techniques (such as decline analysis, solution gas mod-
els, and Buckley-Leverett-Welge waterflood predictions) to
assess various development scenarios."12 141 They also size
and cost surface facilities (such as oil batteries, gathering
systems, water plants, and gas plants), estimate drilling and
completion costs, and evaluate project economics using the
techniques learned in the Design I course.
The students are required to meet once a week with their
project supervisor, a faculty member. Typically, each fac-
ulty member involved with the course manages two to four
projects. The students, individually or as a group, are free to
visit the supervisor more frequently. These visits tend to
increase exponentially as the midterm approaches. The mid-
term consists of a six-page report and a five-minute oral
presentation given to four supervisors or faculty members.
One representative of each group presents a GANTT chart
of their work schedule, discusses findings to date, and indi-

cates what each member of the group
supervisors can then ask any group
member a question on any part of the
project. The group members are ex-
pected to be familiar with all aspects
of their project, although leeway is
given for detailed questions.
The midterm has a number of posi-
tive aspects. It informs the supervi-
sors of the progress of each group. It
is a milestone that forces the group to
have achieved some results; other-
wise, the students (being human)
might leave it to the end. Finally, the
midterm allows the course coordina-
tor to identify any personality prob-
lems within the groups. The students
also make individual in-class presen-
tations on their part of the project.
The presentations allow the course

is working on. The

coordinator to identify "coasters," students who did not con-
tribute to the project, and they give each student practice in
making individual presentations.
The final phase of the Petroleum Design I is a preliminary
design report. This brief, typed report summarizes the reser-
voir evaluation, development strategy selection, forecasting,
facilities design and costing, and preliminary economic indi-
cators. The students are also expected to produce cross-
sections, reservoir maps, and facility schematics.
Petroleum Design I By the end of Petroleum Design I,
the students are expected to have a good understanding of
their reservoir description and history and to have come up
with some promising development strategies. In Petroleum
Design II, they correct errors from Design I, simulate the
reservoir, usually on EXODUS, and simulate their facilities
on HYSYS. With the reservoir simulator, they are expected
to obtain a history match of the pool production to date and
to generate forecasts for several development strategies. They
are asked to create a PFD of the facilities, a P&ID drawing
of one piece of equipment such as a heater-treater, and to
obtain quotes for major pieces of equipment. They are also
asked to prepare a simple drilling and completion program
and estimate capital and operating costs for the project. They
then evaluate the project economics and perform some risk
and sensitivity analysis. The Design II course is intended to
duplicate as closely as possible the steps an engineer em-
ployed by an oil and gas producing company goes through to
evaluate capital projects.
The Design II course includes weekly meetings with the
supervisor, a midterm, individual presentation, and a final
report. A final oral group presentation is also required after
the winter term exams are finished. It consists of a half-hour
formal presentation followed by a fifteen-minute question
period. We describe it as a "formal" presentation because

not only are supervisors and stu-
dents present, but also other fac-
ulty members and practicing engi-
neers from industry. In fact, as
many as 300 letters go out to major
companies, inviting them to send
interested engineers to these final-
project presentations; typically, 20
to 30 practicing engineers attend.
The atmosphere is one of thesis
defense. The students all partici-
pate in the presentation and then
collectively face questions, first
from other students and guests
(industrial participants and other
faculty), and finally from the
project supervisors. Students are
graded for the final presentation
as well as the midterms and

Fall 1999

Grading Scheme for Petroleum Design II

% of Final Grade
A. Design Project Report
Project Organization 5%
Process Flow Diagram (PFD) and P&ID 5%
Reservoir Maps 5%
Technical Content 25%
Calculations, diligence, and accuracy; design
methods and approach;figures and graphs;
clarity of approximations and design factors
Economics 5%
Summary, conclusions, recommendations 5%
Total Report 50%

B. Weekly Meetings 5%
C. Midterm Examination 15%
D. Classroom Presentation 10%
E. Final Oral Presentation and Project Defense 20%
Total 100%

project reports. An example of the grading
system is shown in Table 3.


Use of Software A significant issue in
undergraduate education is the use of commer-
cial software, especially process and reservoir
simulation packages. Most practicing engineers
use commercial software rather than writing
their own programs or solving lengthy calcula-
tions by hand, yet it is vital to understand
enough of the underlying theory to recognize
when the simulation results are misleading and
to identify appropriate optimization strategies
to test on the simulator. As educators, we want
to avoid promoting the blind acceptance of the
results obtained from commercial software.

We avoid this potential trap by emphasizing
hand calculations in the Design I course. Ap-
plying hand calculations to a reservoir prob-
lem, for example, forces the students to think

Commercial Software Used in Petroleum
Design II course


well information data base
reservoir simulation
reservoir simulation
process simulation
well test analysis
wellbore hydrodynamics
pipeline hydrodynamics
petroleum economics

Lecture Topics

Design II

Project Management
Core Analysis
Log Interpretation
Mapping and Volumetrics
PVT and Material Balance
Primary Production Forecasting
Waterflood Design
Reservoir Simulation
Block Diagrams and PFDs
Gas Treating
Process Design Calculations
Petroleum Economics

Artificial Lift
PFDs and P&IDs
Risk and Economics

about the principles of fluid flow, thermodynamics, and material balances.
With hand calculations, it is often easier to recognize a result that violates
common sense, such as an unrealistically high injection rate for a given
permeability-pay. The hand calculations also provide a check on the simula-
tion results of the Design II course. For example, if a 30% recovery factor is
predicted by hand and a 50% recovery is predicted in a simulation, can the
simulation output be believed?

With appropriate hand calculations and common-sense checks, the stu-
dents can use the commercial software listed in Table 4. Training is provided
for AccuMap, EXODUS, and PEEP. The students usually have been ex-
posed to FAST, WELLFLO, and HYSYS in other courses. The software
training is given outside normal class time.

Since simply learning to use commercial software can be time consuming
(especially reservoir simulators), it is critical to have teaching assistants who
are well versed in the use of the software. Our teaching assistants are
graduate students currently enrolled in petroleum engineering. One challenge
facing the program is securing a stream of graduate students with suitable
backgrounds to act as design-course teaching assistants. We are attempting to
attract part-time Masters of Engineering students who work in local industry.

Use of Lectures The students entering the petroleum design course have
quite varied backgrounds. Some are in the oil and gas program, while others
are taking the petroleum minor. Some have internship or other industry
experience, while others have none. As a result, there are different gaps in
the knowledge and experience of each student. In Design I, we use the
lectures to fill these gaps, with a strong emphasis on applied engineering.
For instance, we spend several lectures on waterflood design leading to the
programming of a waterflood forecast on a spreadsheet. The program is
based on the Buckley-Leverett-Welge method.[121 Voidage replacement,
injectivity calculations, sweep efficiency estimation, and waterflood pat-
tern selection are also covered. There are also several lectures devoted
to process design. Since the necessary material is already covered in the
process design course, these lectures are held in common. A list of
lecture subjects is given in Table 5.

In Design II, the lectures are even more applied, such as artificial lift
design. Most lectures in the second term are given by industry or service

MiOierm Reporr ue NOV. Z
Midterm Oral Exam Nov. 5

Final Report Due
Dec. 8

Figure 1. GANTT chart for a Petroleum Design I project.

Chemical Engineering Education

Design I

SID PTASKNAE SSber 2October November December
ID TASKNAME 3 3 1 4 2 t3 1 4 1 2
1 Log Interpretation
Core Analysis
2 Mapping

Assign Pool Boundaries
4 Test Interpretation uAP
5 Production Plots J
6 Pool History C ,
7 Base Case i
8 Strategies Cavu.
s Reserves
10 Production Forecast
11 Facilities g
12 Economics )P ,


company representatives. Hence, the students can learn from
experts in a given field and develop contacts for work on the
project or in the future.
Project Management Another issue in a design course is
how much time to devote to project management. Is it appro-
priate for the students to prepare detailed schedules, critical
path analysis, etc., for their project? In our opinion, the stu-
dents have too little experience to prepare a meaningful sched-
ule at the beginning of the project. Instead, we ask them to
prepare a simple GANTT chart outlining the major tasks and
assigning duties and target dates to each group member. An
example chart is given in Figure 1. We have found that this
simple chart is sufficient to identify potential bottlenecks and
ensure a fair allocation of tasks. It also demonstrates that
unless certain tasks, such as log interpretation, are completed
early in the project, it will be nearly impossible to complete
the project on time.

The major advantages and disadvantages of the approach to
design taken at the University of Calgary are summarized in
Table 6. In general, the feedback from the students has been
very positive. Examples of anonymous student comments are
An excellent course that provides an overview of industry
tasks required for oilfield development.... Course generally
covered at a high pace.
Challenging but very interesting and makes students look
for other resources of information
Course provides opportunity to learn a lot about general
engineering practices (petroleum). Incorporates all aspects
of reservoir engineering to production engineering.
Good course to get experience of what working as a
reservoir engineer is like.
Very usefulfor "hands on" experience that will be used in
industry. Maybe a little too much work.

Advantages and Disadvantages of the Petroleum Design Coua

Use of industry data
realistic design problems
students faced with real data
Team teaching
topics taught by industry experts
students interact with practicing engineers
Use offaculty supervisor
allows close supervision of each group
grading by consensus
Use of analytical methods
encourages understanding of underlying
encourages "common-sense" checks
Use of software
broad application
training for work in industry

* time consuming to prepare data sets
* dependent on industry cooperation

* difficult to maintain continuity in lectures
* lectures not always delivered at optimum time

* increases teaching load of faculty

*limited application to complex situations
typically faced by design engineers

*encourages button-pushing solutions
* learning software is time intensive
* an experienced TA is essential

These comments are representative of the students' responses
to the request to "please provide general comments about the
course." Negative responses have not been withheld. In all,
13 students out of 20 responded to the request, and half of
the responses referred only to other issues, such as teaching.
The course was rated as 6.1 out of 7, compared with a
faculty average of 5.5 out of 7. The comments and ratings
indicate that students believe they have gained broad and
relevant experience.

We thank Richard Baker, Steve Gordon, Linda van Gastel,
and Dave Douceur for providing data sets for design projects.
We are indebted to Michael Aikman, the first teaching assis-
tant for the design course.


1. Biegler, L.T., I.E. Grossman, and A.W. Westerberg, Systematic Meth-
ods of Chemical Process Design, Prentice-Hall, New Jersey (1997)
2. Horwitz, B.A., and L.G. Nault, "Rethinking Academia; Relate to the
Real World," Chem. Eng. Progress, p. 84, October (1996)
3. Hirt, D.E., "Integrating Design Throughout the ChE Curriculum,"
Chem. Eng. Ed., 32(4), 290 (1998)
4. Rockstraw, D.A., J. Eakman, N. Nabours, and S. Bellner, "An Inte-
grated Course and Design Project in Chemical Process Design,"
Chem. Eng. Ed., 31(2), 94(1998)
5. Brennan, D.J., "Chemical Engineering Design and Undergraduate
Education," Chemica 1995, Proceedings of the 23rd Australian Chemi-
cal Engineering Conference, Adelaide, September 24-27, 2, p. 187
6. Personal communication with Petro-Canada Oil and Gas, PanCanadian
Petroleum Ltd., Altana Exploration Co., and Epic Consulting Ser-
vices Ltd.
7. Peters, M.S., and K.D. Timmerhaus, Plant Design and Economics
for Chemical Engineers, 4th ed., McGraw-Hill, New York, NY (1990)
8. HYSYS"', Reference Manual, AEA Engineering Technology Soft-
ware, Calgary, Alberta, Canada T2E 2R2 (1999)
9. EXODUS"', Reference Manual, T.T. & Associates
Inc., CanTek Group, Calgary, Alberta, Canada T2P
3N3 (1999)
10. Svrcek, W.Y., M.F. Mohtadi, P.R. Bishnoi, and L.A.
Behie, "Undergraduate Process Design: An Open-
rses Ended Approach," ASEE Conference, Atlanta, Geor-
gia, June 16-20(1985)
11. AccuMap"', Reference Manual, EnerData Corp.,
Calgary, Alberta, Canada T2N 1X7 (1999)
12. Dake, L.P. Fundamentals of Reservoir Engineering,
Elsevier, Amsterdam (1978)

13. Bradley, H.B., ed., Petroleum Engineering Handbook,
Society of Petroleum Engineers, Richardson, TX (1992)
14. Craig, F.F., Jr., "The Reservoir Aspects of Water-
flooding," SPE Monograph 9, Henry Doherty Series,
New York, NY (1971)
15. IMEX"', STARS"', GEM"', Reference Manuals, Com-
puter Modeling Group, Calgary, Alberta, Canada T2L
2A6 (1999)
16. FAST'', PIPER"', Reference Manuals, Fekete Asso-
ciates Inc., Calgary Alberta, Canada, T2G 0M2 (1999)
17. WELLFLO'", PIPEFLO"', Reference Manuals,
Neotechnology Consultants Ltd., Calgary, Alberta,
Canada T2E 8A4 (1999)
18. PEEP"', Reference Manual, Merak Projects Ltd.,
Calgary, Alberta, Canada T2P 3R7 (1999) U

Fall 1999



Part 5. Desorption of Ammonia from a Liquid Jet

McMaster University Hamilton, Ontario, Canada

he rate of gas-liquid mass transfer, absorption or
desorption, is controlled by diffusional (film) resis-
tances in the gas and liquid phases. The overall resis-
tance is the sum of the two film resistances. If one film
resistance is much larger than the other, the mass transfer
rate is controlled mainly by the larger resistance. As dis-
cussed in the standard texts, such as Geankoplis,111 the con-
trolling resistance depends on the solubility of the transfer-
ring gas in the liquid phase. For some typical solute gases
transferring between air and water at ambient conditions,
controlling resistances are

Controlling resistance Solute gases
Liquid diffusion Oxygen, carbon dioxide (sparingly soluble)
Liquid and gas diffusion Sulfur dioxide (moderately soluble)
Gas diffusion Ammonia, hydrochloric acid gas (highly

Previous papers in this series1231 have described some simple
undergraduate-level experiments with liquid-phase control,
using oxygen or CO2 as the transferring gas.
It is also desirable to study cases in which mass transfer is
controlled by the gas phase, but as the above table indicates,
the highly water-soluble gases are hazardous substances.
Some years ago, students at McMaster University operated a
packed-absorption tower in which an ammonia/air mixture
was scrubbed with water. Much care was needed in the
control of the ammonia stream to make up the gas mixture,
and there was sometimes trouble with leaking valves on the
ammonia gas cylinder. Eventually the experiment was dis-
continued for safety reasons.
This paper describes a replacement experiment that was
recently developed in which ammonia is desorbed from an
aqueous solution into a stream of air. This is inherently safer
since the ammonia levels in the gas phase are limited by the
ammonia concentration in the aqueous feed solution. The
experiment does not require pure ammonia gas. The objec-

* Address: Lakehead University, Thunder Bay, Ontario, Canada

tive of the experiment is to measure mass transfer coeffi-
cients for the desorption of ammonia from a liquid jet, at
different liquid flow rates and for different jet lengths.
Recommended time for the experiment is two laboratory
periods of three hours.

The schematic flow diagram is shown in Figure 1. The
main items of equipment are mounted on a vertical wooden
panel about four to six feet from floor level. The feed solu-
tion is a 1.5 mol/L standardized solution of ammonia in
water that can be made up by the laboratory technician prior
to the experiment. This concentration was chosen as a com-
promise to balance the needs for rapid rates of mass transfer
(favored by higher concentration) and safety (favored by
lower concentration). At ambient temperatures, the equilib-
rium mol fraction ammonia in the gas phase above this solu-
tion is between 2% and 3%. Accurate values are available from
the literature141 for the temperature of the experiment.
The ammonia solution is fed from an overhead reservoir
through a stainless steel needle valve and rotameter to a
desorption cell. Typically, the feed liquid flow is in the range

Malcolm Baird received his PhD in chemical
engineering from Cambridge University in 1960.
After some industrial experience and a post-
doctoral fellowship at the University of
Edinburgh, he joined the McMaster University
faculty in 1967. His research interests are liq-
uid-liquid extraction, oscillatory fluid flows, and
hydrodynamic modeling of metallurgical pro-
cn.sses. "

SInder Nlrdosh received his BSc and MSc in
chemical engineering from Panjab University
(India) and his PhD from Birmingham Univer-
sity (United Kingdom). He joined Lakehead Uni-
versity in 1981, and his research interests are
in the fields of mineral processing and electro-
chemical engineering.
Copyright ChE Division of ASEE 1999

Chemical Engineering Education

of 50 to 300 mL/min. The desorption cell consists of a
vertical 15-cm section of 5-cm internal diameter glass tub-
ing, closed at each end by rubber stoppers. The feed solution
enters the top of the cell through a glass nozzle of internal
diameter 1.4 mm, which can be made easily by a glass-
blower from standard glass tubing. The free jet of solution
falls from this nozzle to a collector tube (see Figure 1)
located at a measured distance
(L) below the nozzle. The col-
lector tube should be slightly NV(air)
larger in diameter than the jet; Air +
an internal diameter of about
2.5 mm is recommended. At
steady state, the liquid level By-pass\ 2-oy
in the collector tube should
come right up to the open end,
and there should not be any
gas entrainment. This control
can be quite easily achieved /,
by means of a manually ad-
Water Trap Acid
justable overflow leg, as ter fap bubbler
shown in Figure 1. Some lat-
eral adjustment of the posi-
tions of the jet and the collec-
tor can be made by hand, since Figure 1. Schen
the tubes are held by a rubber
stopper that has some flexibility. The variation of jet length
can be achieved by extending or retracting the jet nozzle
between experiments.
It is inevitable that some drops of solution will occasion-
ally spill over the collector tube, and for this reason a few
mL of kerosene are placed at the base of the cell to provide a
mass-transfer barrier. There is also provision to occasionally
drain off any large amount of spilled ammonia from the base
of the cell, as shown. The main liquid flow leaving the cell
still contains most of the ammonia feed, and it is collected in
a receiver for reuse. It is important to avoid the use of copper
or brass in any of the valves or lines handling ammonia
solution since these metals are slowly attacked by aqueous
ammonia; this is apparent from a blue coloration of the
solution with cuprous ammonium salts.
A continuous flow of atmospheric air is drawn through the
cell, entering from the bottom. The exit air, containing des-
orbed ammonia gas, leaves the cell from the top. During the
preliminary adjustment of the system it flows through a
bypass line to a trap, and then to a rotameter, needle valve,
and water aspirator (ejector). The air flow is adjusted to a
fixed value (typically between 1 and 5 L/min) by means of
the needle valve and rotameter. The system should be run for
three to four minutes at the adjusted liquid and gas flows, to
reach a steady state before gas analysis is begun.
The air leaving the cell contains a small amount of ammo-
nia, typically no more than 1 mol%. Analysis is carried out


by a semi-continuous chemical method, by scrubbing with
dilute hydrochloric acid. Before the experiment, a standard
fritted-glass bubbling tube is filled with a known volume
(typically 100 mL) of 0.01 mol/L dilute hydrochloric acid,
with a few drops of methyl red indicator, and is connected to
the system, as shown in Figure 1. The gas analysis is begun
by turning a 2-way cock to divert the exit gas through the
bubbling tube. As the cock is
turned, a stopwatch must be
started. Because of the large
1.5 mol/L gas-liquid interfacial area pro-
NH3 soln duced at the bubbler, the ab-
sorption of the ammonia from
Solution flw the gas phase is extremely ef-
ficient and it reacts stoichio-
Adjstobe metrically with the acid. After

S Vent sufficient time has elapsed, all
I NV the acid will be consumed and
Kerosene (liq)
Serene the pH of the liquid in the bub-
bler will rise sharply, accom-
panied by a change in the me-
to solution thyl red color from pink (acid)
ir in to yellow (alkaline). The color
change occurs over a period
flow diagram. of 1-2 s, which is much smaller
than the neutralization time of
100 s. At the above endpoint, the stopwatch is stopped and
read, and the gas flow is then diverted to the bypass.
The rate of mass transfer is calculated from the moles of
acid neutralized in the bubbler, and the neutralization time
m'= VACA /tN (1)
The molar concentration of ammonia in the exit gas from the
desorption cell can be obtained from the mass transfer rate
and the gas flow rate

c2 = m' /QG (2)
At the end of each experiment, the liquid and gas flows are
shut off and the bubbler is removed for rinsing with distilled

A typical plan for two 3-hour laboratory periods calls for
measurement of the mass transfer coefficient at three differ-
ent jet lengths (for example, 3, 5, and 7 cm) and three or four
different flow rates within the operating range. In addition,
at least some of the experiments should be replicated with
different concentrations of acid in the bubbler. Changes in cA
would affect tN but would not be expected to affect the value
of kg obtained at the same liquid flowrate and jet length.
Because of time constraints, the jet diameter and the air
flowrate are the same in all experiments.
An average time of 15 to 20 minutes is spent on each

Fall 1999

experiment, with students working in groups of two. Al-
though the mass-transfer measurement itself takes only one
to three minutes, additional time is needed for adjustment of
the operating conditions and for achieving steady state. Also,
from time to time the ammonia-feed reservoir has to be
refilled. Goggles and gloves should be worn by the students
when they are handling the ammonia and acid solutions,
e.g., when they are recharging the bubbler. The ammonia
levels in the ambient air are low, but minor discomfort may
be experienced if there is not adequate ventilation in the

The mass-transfer rate, m', is the product of the mass-
transfer coefficient, the interfacial area, and the concentra-
tion driving force
m'= kgA(c -c2) (3)
The terms m' and c2 have already been obtained through
Eqs. (1) and (2). The interfacial area A can be calculated
from the jet length, L, and the orifice diameter, d. For precise
work, allowance should be made for the acceleration of the
jet due to gravity. But for short jets and high velocities
(keeping in mind that this is only an undergraduate-level
experiment), the acceleration effect can be ignored. There-
fore, we use
A = tdL (4)
The equilibrium concentration of ammonia in the gas phase,
c*, can be obtained from standard sources.141 For 1.5 mol/L
ammonia in the solution, the values of c* at 200C and 25C
are, respectively, 0.837 and 1.076 mol/m3; these values are
less than 0.1% of the liquid phase concentration, illustrating
the very high solubility of ammonia. The back-concentration
term is taken as c2 (calculated from Eq. 2), based on the
assumption that the gas space within the cell is "well mixed."
This was found to give consistent results, and it is reasonable
to expect that the combined effects of gas and liquid flow
would result in a well-mixed gas phase in the cell.
Rearranging Eq. (3), the mass transfer coefficient can be
obtained from known data as

kg = m'/[A(c*-c2)] (5)

The conditions of the experiment do not correspond ex-
actly to any of the commonly available predictive equations
because little is known about the hydrodynamic conditions
in the gas space in the cell. It is thought that the gas is
circulating due to the action of the liquid jet and the
throughflowing stream of gas, and the flow is likely to be
weakly turbulent. There are two simple equations that can at

least provide an approximate comparison with the data.

This well-known equation is based on the simple concept
of unsteady diffusion into the gas phase during the "contact
time," t, for which the liquid is exposed to the gas. In using
this equation, it is assumed that the gas film adjacent to the
liquid jet is moving down with it at the same velocity,
U= Q /(7d2/4) (6)
The contact time between liquid and gas is L/U, or

T -=d2L/(4QL) (7)
According to the penetration model,

kg = 2(Dg / )0. (8)

where Dg is the molecular diffusivity of ammonia in air,
which can be estimated from the method of Fuller, et al., as
cited in Perry's Handbook.'61 Values of Dg at 200C and 25C
are, respectively, 2.36 x 10-5 and 2.43 x 10-5 m2/s. Equation
(8) can also be expressed in dimensionless form as

ShL =1.128(ReL Sc)o05 (9)
where the Sherwood and Reynolds numbers are based on jet
length. Equations (8) and (9) can only be regarded as ap-
proximate for the present case, since they assume that the
gas is moving down at the same velocity as the liquid jet; no
allowance is made for a developing boundary layer or the
effects of turbulence in the bulk gas flow.

As an alternative to the penetration model, it could be
assumed that the air is dragged down in a developing lami-
nar boundary layer adjacent to the interface, with ammonia
diffusing through the boundary layer. For this case

ShL = 0.664 Re.5 Sc1/3 (10)
In this work, Re, is always well below the critical value of
500,000 at which there is a transition to turbulent flow in the
boundary layer. But the use of Eq. (10) is also open to
criticism since it was developed for a flat plate, whereas the
interface in this case is cylindrical. Moreover, the bulk gas
may be turbulent, which could affect the boundary layer.

Figure 2 shows 12 directly measured mass-transfer coeffi-
cients from a typical student project,"8 plotted against jet
velocity on a linear scale. It can be seen that kg always
increases with the velocity and in general it decreases with
increasing jet length.
The results can be expressed in dimensionless form by

Chemical Engineering Education


conversion to Sherwood number,
which can then be compared with
the model Eqs. (9) and (10). The
Schmidt number is essentially con-
stant (=0.65) except for minor ef-
fects of temperature between differ-
ent experiments.

Therefore, a plot of Sherwood
number versus Reynolds number al-
lows the data to be compared with
the two model equations, as shown
in Figure 3. Both models predict that
ShL oc ReoL5, so they appear on the
log-log scale as two parallel straight
lines, which are shown dashed. The
data points fall between the two pre-
dictions; lower than the penetration
theory but higher than the boundary
layer prediction.
When students are confronted with
a case like this in which the results
do not agree very well with the "theo-
retical predictions," they are apt to
find fault with the experiment and
the accuracy of their data. But when
an analysis of measurement errors is
done, the measurement accuracy for
kg is found to be better than +10%.
Then a question for the students is:
why are there deviations of 30% or
more between the results and the

1.0 1.5 2.0 2.5
Jet velocity, U, m/s

Figure 2. Effect of jet velocity and jet
length on mass transfer coefficients.

80 -

40- 0 o

i f I
C 0

f 20-


1000 2000 5000
Reynolds number, ReL

The students are encouraged to dis- Figure 3. Dimens
showing comparison
cuss ways in which their actual ex- Symbols are the s
periment departs from the sets of
idealizing assumptions built into the
two simple theoretical models. The curvature of the jet sur-
face and the complex and probably turbulent gas-flow pat-
terns in the cell have already been mentioned. If the theoreti-
cal treatment could be "fine tuned" to account for these
effects, better agreement could be expected, but that would
take us into the realm of graduate research.

This low-cost experiment can be set up with normal labo-
ratory glassware and fittings. It provides students with a
reasonably accurate method of measuring gas-film controlled
mass-transfer coefficients that can then be compared with
simple (though approximate) theoretical predictions.


The apparatus was built with financial support from the
Department of Chemical Engineering at McMaster Univer-
sity. The authors are grateful to Ms. J. Derkach and Mr. Paul


ionless plot of data
with Eqs. (9) and (10).
ame as in Figure 2.

1. Geankoplis, C.J., Transport Processes and Unit Operations,
3rd ed., Prentice Hall, Englewood Cliffs, NJ, p. 600 (1993)
2. Nirdosh, I., and M.H.I. Baird, "Low-Cost Experiments in
Mass Transfer. Part 1.," Chem. Eng. Ed., 30, 50 (1996)
3. Nirdosh, I., L.J. Garred, and M.H.I Baird, "Low-Cost Experi-
ments in Mass Transfer, Part 3. Mass Transfer in a Bubble
Column," Chem. Eng. Ed., 32, 138 (1998)
4. Perry, R.H. and D. Green, eds., Perry's Chemical Engineers'
Handbook, 6th edn., McGraw-Hill, New York, NY, pp. 3-101
5. Geankoplis, C.J., Transport Processes and Unit Operations,
3rd ed., Prentice Hall, Englewood Cliffs, NJ, p. 478 (1993)
6. Perry, R.H., and D. Green, eds., Perry's Chemical Engineers'
Handbook, 6th edn., McGraw-Hill, New York, NY, pp. 3-285
7. Geankoplis, C.J., Transport Processes and Unit Operations,
3rd ed., Prentice Hall, Englewood Cliffs, NJ, p. 477 (1993)
8. Rajkovic, A., "Ammonia Desorption from a Jet," McMaster
University Chemical Engineering Report, March 19, (1998)

Fall 1999

Gatt, who assembled the apparatus and
mounted it on a panel. In addition, the
authors are grateful to the Natural Sci-
ences and Engineering Research Coun-
cil of Canada for providing financial
resources for the preparation of this pa-

A interfacial area, m2
cA initial concentration of acid in
bubbler, mol/m3
c, concentration of ammonia in exit
gas, mol/m3
c* equilibrium concentration of
ammonia in gas, mol/m3
D molecular diffusion coefficient of
ammonia in air, m2/s
d jet diameter, m
k mass transfer coefficient, m/s
L jet length, m
m' mass transfer rate, mol/s
QG air flow rate, m3/s
QL water flow rate, m'/s
ReL Reynolds number, pUL / .t
Sc Schmidt number, t /(pDg)
ShL Sherwood number, k L/D
t, time to neutralize acid in bubbler, s
U jet velocity, m/s
VA volume of acid in bubbler, m3
Greek Symbols
gl viscosity of air, Pa.s
p density of air, kg/m3
t contact time, s


Activities to Enhance



University of Cape Town Rondebosch, 7701 South Africa

Moles are a fundamental unit of measure in chemi-
cal engineering. Our students learn about moles
(in the form of gmol) in chemistry, both at school
and during their first year at university. In chemical engi-
neering, we introduce them to the new units of kmol and
Ibmol, and the problems that arise highlight a general lack of
understanding of the mole concept.
We have been aware for some time that our students have
difficulty with moles, and this led us to tackle student under-
standing of moles as a research project in which we first
quantified the nature and extent of the misunderstanding and
then set out to design and implement a set of activities to
promote conceptual change in this area. The intervention
also dealt with a number of other concepts related to the
mole. Following implementation of the intervention, stu-
dents were again tested to measure the extent of improve-
ment in their understanding.

Duncan Fraser holds degrees of BSc and
PhD, both from the University of Cape Town,
where he has been lecturing since 1979. He
has taught a wide range of courses, from first
year to fourth year, including mass and en-
ergy balances, thermodynamics, transport
phenomena, solid-fluid operations, optimiza-
tion, process control, and design. His pri-
mary research interests are in engineering
education and process synthesis.

Jennifer Case obtained her BSc (Hons) de-
gree in Chemistry from the University of
Stellenbosch, after which she taught for a few
years in a high school She became interested
in the field of educational research after doing
an MEd degree in Science Education at the
University of Leeds, and she currently serves
as the Educational Development Officer in the
Department of Chemical Engineering at the
University of Cape Town.

All of this took place in the context of a new course for
freshman chemical engineering students in which they are
introduced to the basic concepts in chemical engineering as
well as helped in developing certain key skills for the subse-
quent study of chemical engineering.'l One of the skills
developed is unit conversion, and the different mole units
are introduced here. A central element of the course is intro-
ducing students to unfamiliar concepts through the use of
familiar objects.
The full details of this research project are reported else-
where.121 The main objective of this paper is to present an
overview of the test (used to determine understanding) and
the intervention activities, together with evidence for their
effectiveness in dealing with the misconceptions, with the
hope that other chemical engineering educators would be
encouraged to try them out or to use them in a modified

Group interviews were used to explore possible miscon-
ceptions that students might hold about moles. Analysis of
these transcripts identified three common misconceptions:
1. The amounts kmol, lbmol, and gmol are seen as masses.
2. The amounts kmol, Ibmol, and gmol are all the same,
because they are all moles.
3. The volume of a gas is not seen as proportional to its amount.
In order to be able to measure the extent to which these
misconceptions were held in the class, a conceptual test was
developed, based on the above research findings. For ex-
ample, Question 1.2 tested misconception #1:

1.2 60 lbmol of N2 weighs 60 lb. True / False?

Copyright ChE Division of ASEE 1999

Chemical Engineering Education

The main objective of this paper is to present an overview of the test (used to determine understanding)
and the intervention activities, together with evidence for their effectiveness in dealing with the
misconceptions, with the hope that other chemical engineering educators would be
encouraged to try them out or to use them in a modified form.

Question 2.1 (in multiple choice format) is typical of a
number of questions that probed misconception #2:

2.1 Consider the following
1 kmol of CO, 1 Ibmol of CO, 1 gmol of CO2

Which one of the following statements is true?
a) P, Q, and R all have the same number of molecules.
b) P and R have the same number of molecules.
c) P and Q have the same number of molecules.
d) They all have different numbers of molecules.
e) None of the above statements are true.

Question 3.1 tested misconception #3:

3.1 Consider the following 50 m3 vessels, each contain-
ing gas with the given composition:
Vessel A B
Volume 50 m3 50 m3
Composition 25% CO, 100% CO,
(by volume) 25% CH4
50% H2
A and B are at the same temperature and pressure.
Which one of the following statements is true?
a) A contains more molecules than B.
b) B contains more molecules than A.
c) A and B contain the same number of molecules.
d) None of the above statements is true.

This test was specifically designed so that no numerical
calculations were required in answering the questions (as
can be seen in the sample questions above). Despite this, it
was interesting to observe during the administration of the
test that many of the students tried to use numerical calcula-
tions to find the answers. Even though calculators were not
permitted, many students covered their question papers with
The results of the test confirmed that misconceptions were
widespread in the class: 38% of the students showed evi-
dence of misconception #1 (tested by one question); 28%
showed misconception #2 (this was averaged over five ques-

Fall 1999

tions); and 27% showed misconception #3 (averaged over
two questions).'21 When the same test was administered again
after the intervention activities (described in the next sec-
tion), it revealed a significant increase in understanding.

We designed a series of intervention activities to address
the misconceptions that had been identified during the inter-
views and the conceptual test. At the same time, we took the
opportunity to deal with other related questions, albeit in a
less focused manner.
The major objective in designing these activities was to
give students a concrete visual or experiential point of refer-
ence for their understanding. This approach was based on
recommendations in the literature concerning the general
use of tangible objects in helping learners develop appropri-
ate mental representations of chemical systems[3-51 as well as
more specific recommendations regarding their use in devel-
oping the mole concept.6'71 Where it was not possible to use
concrete objects, we used thought experiments instead.
The activities were designed to follow one another. Stu-
dents performed them in groups of three and were encour-
aged to discuss their findings with one another and with the
tutors. Multiple sets of apparatus were available so that five
groups could perform them simultaneously. A class of ninety
students could then be handled in six batches over the course
of one afternoon. The activities presented here are a refine-
ment of the original set of five activities described by Case
and Fraser.121
Activity #1
The purpose of this activity is to help students see the
difference between the different kinds of moles they will
need to work with as chemical engineers (gmol, lbmol, and
kmol). They measure out 1 gmol, 1 lbmol, and 1 kmol of
water, using a scale, and then are asked a series of questions
to help consolidate what they have observed. The activity
ends with an inspection of a display of bottles, each contain-
ing 1 gmol of a different substance, to provoke thinking
about moles, molar mass, density, form, etc.
Activity #2
This activity is aimed at helping students make sense of
gas volumes and mixtures of gases. The first task involves a
box containing a mixture of squash balls and ping-pong
balls, which have approximately the same diameter but sig-
nificantly different masses. This provides an analogy for a
mixture of different gases, such as oxygen and hydrogen,


that occupy the same volume but have different masses.
The second task helps them visualize the volume occupied
by 1 gmol of any gas at STP using a 22.4-litre Perspex box.
They are also asked to calculate the masses of two different
gases that would fill this box at STP. This is followed by a
third task in which they calculate the mass of air occupying
the room where they are working and then are asked what
would happen to the mass of air in the room if its composi-
tion were different.

Activity #3
This activity is a thought experiment in which the students
are asked, first, to calculate the kinetic energy of 1 kmol of
each of three different gases, using their molar masses and
average velocities. This leads to a discussion of why dif-
ferent gases all occupy the same volume at STP, using
the assumption that this is related to their having the
same kinetic energy.
The second task here involves determining the volume
occupied by the actual molecules (from their molecular di-
ameter) and hence the fractions of both water vapor and
liquid water that are empty space. This is to help clarify the
difference between liquids and gases.

Activity #4
The final activity simulates chemical reactions using nuts
and bolts. The concept of moles is further reinforced by
getting students to weigh out a large number of each of the
reactantss" on the basis of an average mass per nut or bolt (in
the same way that coins are "counted" by weight at a bank).
One bolt is "reacted" first with one nut, and then with two
nuts. The difference between
reacting numbers of each "re-
actant" and masses of each

"reactant" is emphasized by
the last two tasks in which
they weigh out equal masses
of nuts and bolts and then
"react" them.

When the students were
tested again to gauge if their
understanding had improved,
there was a significant in-
crease in their level of under-
standing. Figure 1 compares
the pre- and post-test scores
of all the students. It shows
that those who could improve
the most had done so, and
that the average improvement
is half the maximum possible.

This figure also shows that there was no difference between
the students who were interviewed and those who were not,
which indicates that it was the intervention rather than being
sensitized to the issues, that made the difference.
Table 1 gives a complete question-by-question analysis of
the pre- and post-test results, arranged according to whether
the questions covered the misconceptions being directly tack-
led or not. The shift of students from wrong to right answers,

Course tutor, holding the "mole" box.

Figure 1.

Chemical Engineering Education

20 ------- --------------
S+ Interviewed
S10 A A Not Interviewed
8 "- .. -- -- MaximumChange
S...... Average Change
6 '' .
a a I
5 10 15 20 25
Pre-test Score

as well as from right to wrong, is also shown. The shift from
right to wrong is taken as a measure of the randomness of
answering; this was consistent across the different groups of
questions and averaged 6.4% over all the questions.
Table 1 shows clearly that the intervention had a much
greater impact on the three misconceptions directly tackled
(net shift from wrong to right of 17.1) than on the other
issues tackled (net shift of 5.8). This is to be expected, given
the clearer focus on them in the design of the intervention.
When the data in Table 1 is analyzed according to the

Question-by-Question Analysis of Results
("W" to "R" means Wrong to Right: "R" to "W" means Right to Wrong)

Pre- Post-
Test Test Shift
Misc. %cor. %cor. W to R R to W Net

Misconceptions Tackled in Intervention

1.2 Ibmol 1 64% 76% 19 9 10
1.5 kmol/gmol 2 76% 88% 16 7 9
1.9 Avogadro's number 2 51% 90% 34 3 31
1.10 kmol/gmol gas 2 69% 80% 16 7 9
2.1 moles and molecules (same) 2 65% 91% 22 1 21
2.6 Avogadro's number 2 44% 85% 36 3 33
2.8 moles and gas volume 2 56% 83% 26 5 21
3.1 diff. gases at same conditions 3 63% 71% 15 8 7
3.6 gas mixture composition 3 59% 75% 20 7 13
Average 61% 82% 22.7 5.6 17.1
Other Issues Tackled in Intervention

1.1 kmol 61% 98% 31 2 29
1.3 m mass -kmol 68% 53% 10 22 -12
1.8 molecule and components 68% 75% 10 4 6
2.2 moles and mass (same) 89% 93% 8 5 3
2.3 moles and molecules (diff) 76% 84% 9 3 6
2.4 moles and mass (diff) 83% 85% 12 10 2
2.5 does P affect Ibmol 63% 73% 16 8 8
2.7 kmol of diff. substances 66% 68% 12 11 1
2.9 reaction stoichiometry 93% 91% 5 6 -1
3.2 mass of gas mixture/pure gas 61% 78% 15 2 13
3.3 gas mixture composition 70% 61% 5 12 -7
3.4 gas mixture composition 56% 75% 20 5 15
3.5 pure gas cf mixture 74% 90% 18 5 13
Average 71% 79% 13.2 7.3 5.8
Not tackled in Intervention
1.6 unit conv 98% 93% 1 5 -4
1.7 unit conv 75% 83% 10 4 6
Average 86% 88% 5.5 4.5 1.0
OverallAverage 69% 81% 16.1 6.4 9.7

Error in Pre-Test
1.4 m mass lb mol

46% 98% 42

etI m Topic

three misconceptions (see Case and Fraser'21 for details of
this), misconception #1 dropped from 38% to 22%, miscon-
ception #2 from 28% to 9%, and misconception #3 from
27% to 22%. The first two changes (16% and 19%) are
significant compared to the randomness of answering (6%),
whereas the third one is not (5%). This means that mis-
conceptions #1 and #2 showed significant increases in
understanding, whereas misconception #3 did not. This
points to the effectiveness of activity #1 in addressing
misconceptions #1 and #2.
Perhaps even more important than this analysis was feed-
back over the past three years from those who teach the
subsequent mass and energy balance course. They have noted
a significant decrease in problems concerning moles.

What surprised and encouraged us was how enthusiasti-
cally all the students engaged in the activities-even the
more advanced students, who we thought might find them
trivial or boring. It appeared that none of the students had
encountered similar activities at school, indicating that their
previous experience in learning chemistry had been quite
deficient in the use of tangible objects.
Why not try out the mole test on your students? You might
be interested in the results. You may also find something in
the mole activities that would be useful to try in your class,
or use them as a springboard for developing other activities
(we found developing them to be an exciting and creative
challenge). Full copies of both the conceptual test and the
intervention activities may be obtained by contacting the
first author at

1. Fraser, D.M., "Introducing Students to Basic ChE Con-
cepts: Four Simple Experiments," Chem. Eng. Ed., 33(3),
190 (1999)
2. Case, J.M., and D.M. Fraser, "An Investigation into Chemi-
cal Engineering Students' Understanding of Moles and the
Use of Concrete Activities to Promote Conceptual Change,"
International Journal of Science Education, in press
3. Lythcott, J., "Problem Solving and Requisite Knowledge of
Chemistry," J. of Chem. Ed., 67(3), 248 (1990)
4. Raghavan, K., and R. Glaser, "Model-Based Analysis and
Reasoning in Science: The MARS Curriculum," Sci. Ed.,
79(1), 37 (1995)
5. Garnett, P., P. Garnett, and M. Hackling, "Students' Alter-
native Conceptions in Chemistry: A Review of Research and
Implications for Teaching and Learning," Studies in Sci.
Ed., 25, 69 (1995)
6. Spargo, P., "Teaching the Mole Concept," Higher Education
Diploma course notes, School of Education, University of
Cape Town (1987)
7. Staver, J.R., and A.T. Lumpe, "Two Investigations of Stu-
dents' Understanding of the Mole Concept and Its Use in
Problem Solving," J. of Res. in Sci. Teaching, 32(2), 177
(1995) U

Fall 1999

"""' '"""

SI* N *D *E X

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Fall 1999

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diagnostics and fluids visualization using optical methods.
(256) 890-6439,

Jeffrey J. Weimer Ph.D. (MIT)
Associate Professor, Joint Appointment in Chemistry
Adhesion, biomaterials surface properties, thin film growth, surface
spectroscopies, scanning probe microscopies.
(256) 890-6954,

The University of Alabama In Huntsville
An Affirmative Action/Equal Opportunity Institution
Web page:
Ph: 256.890.6810 FAX: 256.890.6839


The University of Alberta is well
known for its commitment to excel-
lence in teaching and research. The
Department of Chemical and Materi-
als Engineering has 34 professors and
over 100 graduate students. Degrees
are offered at the M.Sc. and Ph.D.
levels in Chemical Engineering, Ma-
terials Engineering, and Process
Control. All full-time graduate stu-
dents in the research programs re-
ceive a stipend to cover living ex-
penses and tuition.

For further information, contact
Graduate Program Officer WCM
Department of Chemical and Materials Engineering
University ofAlberta
Edmonton, Alberta, Canada T6G 2G6

PHONE (780) 492-5805 FAX (780) 492-2881


P. CHOI, Ph.D. (University of Waterloo)
Statistical Mechanics of Polymers Polymer Solutions and Blends
K. T. CHUANG, Ph.D. (University of Alberta)
Mass Transfer Catalysis Separation Processes Pollution Control
I. G. DALLA LANA, Ph.D. (Univ. of Minnesota) EMERITUS
Chemical Reaction Engineering Heterogeneous Catalysis
J.A.W. ELLIOTT, Ph.D. (University of Toronto)
Thermodynamics Statistical Thermodynamics Interfacial Phenomena
D. G. FISHER, Ph.D. (University of Michigan) EMERITUS
Process Dynamics and Control Real-Time Computer Applications
J.F. FORBES, Ph.D. (McMaster University)
Real-Time Optimization Control of Sheet Forming Processes
M. R. GRAY, Ph.D. (California Inst. of Tech.) DEAN OF GRADUATE STUDIES
Bioreactors Chemical Kinetics Bitumen Processing
R. E. HAYES, Ph.D. (University of Bath)
Numerical Analysis Reactor Modeling Computational Fluid Dynamics
B. HUANG, Ph.D. (University of Alberta)
Controller Performance Assessment Multivariable Control Statistics
S. M. KRESTA, Ph.D. (McMaster University)
Turbulent & Transitional Flows Multiphase Flows CFD
S. LIU, Ph.D. (University of Alberta)
Fluid-Particle Dynamics Transport Phenomena Mass Transfer
D. T. LYNCH, Ph.D. (University of Alberta) DEAN OF ENGINEERING
Catalysis Kinetic Modeling Numerical Methods Polymerization
J. H. MASLIYAH, Ph.D. (University of British Columbia)
Transport Phenomena Colloids Particle-Fluid Dynamics Oil Sands
A. E. MATHER, Ph.D. (University of Michigan)
Phase Equilibria Fluid Properties at High Pressures Thennodynamics
W. C. MCCAFFREY, Ph.D. (McGill University)
Reaction Kinetics Heavy Oil Upgrading Polymer Recycling Biotechnology
P. A. J. MEES, Ph.D. (University of Alberta)
Computational Fluid Dynamics Transport Phenomena Pulp and Paper
K. NANDAKUMAR, Ph.D. (Princeton University)
Transport Phenomena Distillation Computational Fluid Dynamics
F. D. OTTO, Ph.D. (University of Michigan) EMERITIS
Mass Transfer Gas-Liquid Reactions Separation Processes
M. RAO, Ph.D. (Rutgers University)
Al Intelligent Control Process Control
S. L. SHAH, Ph.D. (University of Alberta)
Computer Process Control System Identification Adaptive Control
S. E. WANKE, Ph.D. (University of California, Davis) CHAIR
Heterogeneous Catalysis Kinetics Polymerization
M. C. WILLIAMS, Ph.D. (University of Wisconsin)
Rheology Polymer Characterization Polymer Processing
Z. XU, Ph.D. (Virginia Polytechnic Institute and State University)
Surface Science & Engineering Mineral Processing Waste Management

Fall 1999


ROBERT ARNOLD, Professor (Caltech)
Microbiological Hazardous Waste Treatment, Metals Speciation and Toxicity
PAUL BLOWERS, Assistant Professor (Illinois, Urbana-Champaign)
Chemical Kinetics, Catalysis, Surface Phenomena
JAMES BAYGENTS, Associate Professor (Princeton)
Fluid Mechanics, Transport and Colloidal Phenomena, Bioseparations,

WENDELL ELA, Assistant Professor (Stanford)
Particle-Particle Interactions, Environmental Chemistry
JAMES FARRELL, Assistant Professor (Stanford)
Sorption/desorption of Organics in Soils
ROBERTO GUZMAN, Associate Professor (North Carolina State)
Protein Separation, Affinity Methods
ANTHONY MUSCAT, Assistant Professor (Stanford)
Kinetics, Surface Chemistry, Surface Engineering, Semiconductor Processih
KIMBERLY OGDEN, Associate Professor (Colorado)
Bioreactors, Bioremediation, Organics Removal from Soils
THOMAS W. PETERSON, Professor and Dean (CalTech)
Aerosols, Hazardous Waste Incineration, Microcontamination
ARA PHILIPOSSIAN, Adjunct Associate Professor (Tufts)
Chemical/Mechanical Polishing, Semiconductor Processing
JERKER PORATH, Research Professor (Uppsala)
Separation Science
EDUARDO SAEZ, Associate Professor (UC, Davis)
Rheology, Polymer Flows, Multiphase Reactors
FARHANG SHADMAN, Professor (Berkeley)
Reaction Engineering, Kinetics, Catalysis, Reactive Membranes,
RAYMOND A. SIERKA, Professor Emeritus (Oklahoma)
Adsorption, Oxidation, Membranes, Solar Catalyzed Detox Reactions
JOST 0. L. WENDT, Professor and Head (Johns Hopkins)
Combustion-Generated Air Pollution, Incineration, Waste
DON H. WHITE, Professor Emeritus (Iowa State) 45(
Polymers, Microbial and Enzymatic Processes
DAVID WOLF, Visiting Professor (Technion)
Fermentation, Mixing, Energy, Biomass Conversion

For further information, write to

Graduate Study Committee
Department of Chemical and
Environmental Engineering
University of Arizona
Tucson, Arizona 85721
The University of Arizona is an equal
opportunity educational institution/equal
opportunity employer.
Women and minorities are encouraged
to apply.









The Chemical and Environmental Engineering Department
at the University of Arizona offers a wide range of research
opportunities in all major areas of chemical engineering and
environmental engineering, and graduate courses are offered in
most of the research areas listed here. The department offers a fully
accredited undergraduate degree as well as MS and PhD graduate
degrees. Strong interdisciplinary programs exist in bioprocessing
and bioseparations, microcontamination in electronics manu-
facture, and environmental process modification.
Financial support is available through fellowships, government
and industrial grants and contracts, teaching and
research assistantships.
Tucson has an excellent climate and many
recreational opportunities. It is a growing modern city of
,000 that retains much of the old Southwestern atmosphere.

Chemical Engineering Education





3 0 '
Beaudoin, Stephen P., Ph.D., North Carolina State
University Transport Phenomena and Surface Science 0
concerning Pollution Prevention, Waste Minimization, and ',
Pollution Remediation *
Beckman, James R., Ph.D., University of Arizona -Crystalli- *
zation and Solar Cooling
Berman, Neil S., Ph.D., University of Texas, Austin Fluid
Dynamics and Air Pollution
Burrows, Veronica A., Ph.D., Princeton University Surface
Science, Semiconductor Processing
Garcia, Antonio A., Ph.D., U.C., Berkeley Acid-Base Interac-
tions, Biochemical Separation, Colloid Chemistry
Raupp, Gregory B., Ph.D., University of Wisconsin Semiconductor
Materials Processing, Surface Science, Catalysis
Razatos, Anna, Ph.D., University of Texas, Austin Biotechnology
Rivera, Daniel, Ph.D., Cal Tech Process Control and Design
Sater, Vernon E., Ph.D., Illinois Institute of Tech
Torrest, Robert S., Ph.D., University of Minnesota Multiphase Flow, Filtration, Flow
in Porous Media, Pollution Control

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Research in a

High Technology


Guilbeau, Eric J., Ph.D., Louisiana Tech University Biosensors, Physiological Systems, Biomaterials
He, Jiping, Ph.D., University of Maryland Biomechanics, Robotics, Computational Neuroscience, Optimal Control, System Dynamics and Control
Kipke, Daryl R., Ph.D., University of Michigan Computation Neuroscience Machine Vision, Speech Recognition, Robotics Neural Networks
Massia, Stephen, Ph.D., University of Texas Bio materials Molecular and Cellular Engineering
Panitch, Alyssa, Ph.D., University of Massachusetts Tissue Engineering
Pizziconi, Vincent B., Ph.D. Arizona State University- Artificial Organs, Biomaterials, Bioseparations
Sweeney, James D., Ph.D., Case-Western Reserve University- Rehab Engineering, Applied Neural Control
Towe, Bruce C., Ph.D., Pennsylvania State University- Bioelectric Phenomena, Biosensors, Biomedical Imaging
Yamaguchi, Gary T., Ph.D., Stanford University Biomechanics, Rehab Engineering, Computer-Aided Surgery

Adams, James, Ph.D., University of Wisconsin, Madison Atomistic Simulation of Metallic Surfaces Grain Boundaries Automobile Catalysts *
Polymer-Metal Adhesion
Alford, Terry L., Ph.D., Cornell University Electronic Materials Physical Metallurgy Electronic Thin Films Surface/Thin Film
Dey, Sandwip K., Ph.D., NYSC of Ceramics, Alfred University Ceramics, Sol-Gel Processing
Krause, Stephen L., Ph.D., University of Michigan Ordered Polymers, Electronic Materials, Electron X-ray Diffraction, Electron Microscopy
Mahajan, Subhash, Ph.D., University of Michigan Semiconductor Defects. Structural Materials Deformation
Mayer, James, Ph.D., Purdue University -Thin Film Processing Ion Bean Modification of Materials

Fall 1999 343

Robert P. Chambers University of California, Berkeley
Harry T. Cullinan Carnegie Mellon University
Christine W. Curtis Florida State University
Steve R. Duke University of Illinois
Mahmoud El-Halwagi University of California, Los Angeles
James A. Guin University of Texas, Austin
Ram B. Gupta University of Texas, Austin
Gopal A. Krishnagopalan University of Maine
Jay H. Lee California Institute of Technology
Y. Y. Lee Iowa State University
Glennon Maples Oklahoma State University
Ronald D. Neuman The Institute of Paper Chemistry
Stephen A. Perusich University of Illinois
Timothy D. Placek University of Kentucky
Christopher B. Roberts University of Notre Dame
.. R. Tarrer Pildat I', nr\
Bruce J. Talarchuk in ,uitr' r t \,t i\rll'',i

Research Areas

* Biochemical Engineering Biotechnology
Pulp and Paper Process Control
Catalysis and Reaction Engineering
Computer Aided Process Synthesis,
Optimization and Design
Environmental Chemical Engineering
Pollution Prevention Recycling
Materials Polymers Surface Science
Colloid and Interfacial Phenomena
Thermodynamics Supercritical Fluids
* Separation Electrochemical Engineering
* Fluid Dynamics and Transport Phenomena
Fuels and Energy

Deparnment o. iaal Egineeng
Auir -Unt ve rsiy, A L'36849-
mo- i (334):f4-47
S--Fax 334)-44-2063
.4- E.

Financial as,'slance is available to qualified applicants.

Auburn Uniaverity is an Equal 'Opportnity Edrational-lHnslumon





R. G. Moore, Head (Alberta)
J. Azaiez (Stanford)
H. Baheri (Saskatchewan)
L. A. Behie (Western Ontario)
C. Bellehumeur (McMaster)
P. R. Bishnoi (Alberta)
R. A. Heidemann (Washington U.)
C. Hyndman (Ecole Polytechnique)
A. A. Jeje (MIT)
A. Kantzas (Waterloo)
B. B. Maini (Univ. Washington)
A. K. Mehrotra (Calgary)
S. A. Mehta (Calgary)
B. J. Milne (Calgary)
M. Pooladi-Darvish (Alberta)
W. Y. Svrcek (Alberta)
M. A. Trebble (Calgary)
H. W. Yarranton (Alberta)
B. Young (Canterbury, NZ)
L. Zanzotto (Slovak Tech. Univ., Czechoslovakia

The Department offers graduate programs leading to the M.Sc. and Ph.D.
degrees in Chemical Engineering (full-time) and the M.Eng. degree in Chemical
Engineering, Petroleum Reservoir Engineering or Engineering for the
Environment (part-time) in the following areas:
Biochemical Engineering & Biotechnology
Biomedical Engineering
Environmental Engineering
Modeling, Simulation & Control
Petroleum Recovery & Reservoir Engineering
Polymer Processing & Rheology
Process Development
Reaction Engineering/Kinetics
Transport Phenomena
Fellowships and Research Assistantships are available to all qualified applicants.

SFor Additional Information Write *
Dr. A. K. Mehrotra Chair, Graduate Studies Committee
Department of Chemical and Petroleum Engineering
University of Calgary Calgary, Alberta, Canada T2N 1N4

The University is located in the City of Calgary, the Oil capital of Canada, the home of the world famous Calgary Stampede and the
1988 Winter Olympics. The City combines the traditions of the Old West with the sophistication of a modern urban center. Beautiful
Banff National Park is 110 km west of the City and the ski resorts of Banff, Lake Louise,and Kananaskis areas are readily accessible. In
the above photo the University Campus is shown with the Olympic Oval and the student residences in the foreground. The Engineering
complex is on the left of the picture.


wCA0 R
'-^. l

Fall 1999









. offers graduate programs leading to the
Master of Science and Doctor of Philosophy.
Both programs involve joint faculty-student
research as well as courses and seminars within
and outside the department. Students have the
opportunity to take part in the many cultural
offerings of the San Francisco Bay Area and
the recreational activities of California's north-
ern coast and mountains.

Biochemical Engineering
Electrochemical Engineering
Electronic Materials Processing
Energy Utilization
Fluid Mechanics
Kinetics and Catalysis
Polymer Science and Technology
Process Design and Development
Separation Processes
Surface and Colloid Science





















346 Chemical Engineering Education

University of California, Davis

Department of Chemical Engineering & Materials Science
Offering M.S. and Ph.D. degree programs in both Chemical Engineering and Materials Science and Engineering


David E. Block. Assistant Professor Ph.D.. University of Minnesota. 1992* Industrialfennentation, biochemical processes in phannaceutical
Roger B, Boulton, Professor Ph.D., University of Melbourne, 1976 Fermentation and reaction kinetics, crystallization
Stephanie R. Dungan, Associate Professor Ph.D.. Massachusetts Institute of Technology. 1992 Micelle transport, colloid and interfacial
science in food processing
Bruce C. Gates, Professor Ph.D.. University of Washington. Seattle. 1966 Catalysis, solid superacid catalysis, zeolite catalysts, bimetallic
catalysts, catalysis by metal clusters
Jeffery C. Gibeling, Professor Ph.D.. Stanford University. 1979 Defonnation fracture and fatigue of metals, layered composites and bone
Joanna R. Groza, Professor Ph.D., Polytechnic Institute. Bucharest, 1972 Plasma activated sintering and processing of nanostructured
Brian G. Higgins, Professor Ph.D.. University of Minnesota. 1980 Fluid mechanics and interfacialphenomena, sol gelprocessing, coatingflows
David G. Howitt, Professor Ph.D., University of California, Berkeley, 1976 Forensic and failure analysis, electron microscopy, ignition and
combustion processes in materials
Alan P. Jackman, Professor Ph.D.. University of Minnesota. 1968 Protein production in plant cell cultures, bioremediation
Marjorie L. Longo, Assistant Professor Ph.D.. University of California. Santa Barbara, 1993 Hydrophobic protein design for active control,
surfactant microstructure, and interaction of proteins and DNA with biological membranes
Benjamin J. McCoy, Professor* Ph.D.. University of Minnesota, 1967 Supercritical extraction, pollutant transport
Karen A. McDonald, Professor Ph.D., University of Maryland, College Park, 1985 Plant cell culture bioprocessing algal cell cultures
Amiya K. Mukherjee, Professor D.Phil.. University of Oxford. 1962 Superplasticity of intennetallic alloys and ceramics, high temperature
creep defonnation
Zuhair A. Munir, Professor Ph.D., University of California. Berkeley. 1963 Combustion synthesis, nmultilayer combustion systems, functionally
gradient materials
Alexandra Navrotsky, Professor Ph.D.. University of Chicago. 1967 Thernodynamics and solid state chemistry: high temperature calorimetry
Ahmet N. Palazoglu, Professor Ph.D., Rensselaer Polytechnic Institute, 1984 Process control and process design of environmentally benign
Ronald J. Phillips, Associate Professor Ph.D.. Massachusetts Institute of Technology. 1989 Transport processes in bioseparations. Newtonian
and non-Newtonian suspension mechanics
Robert L. Powell, Professor Ph.D.. Johns Hopkins University. 1978 Rheology, suspension mechanics, magnetic resonance imaging of
Subhash H. Risbud, Professor and Chair Ph.D.. University of California. Berkeley. 1976 Semiconductor quantum dots, high T superconducting
ceramics, polymer composites for optics
Dewey D.Y. Ryu, Professor Ph.D.. Massachusetts Institute of Technology. 1967 Biomolecular process engineering and recombinant bioprocess
James F. Shackelford, Professor Ph.D., University of California, Berkeley, 1971* Structure of materials, biomaterials, nondestructive testing of
engineering materials
J.M. Smith, Professor Emeritus Sc.D.. Massachusetts Institute of Technology. 1943 Chemical kinetics and reactor design
Pieter Stroeve, Professor Sc.D.. Massachusetts Institute of Technology. 1973 Membrane separations, Langimir ,:. I~ ,. colloid and
surface science
Stephen Whitaker, Professor Ph.D.. University of Delaware. 1959 Multiphase transport phenomena

The multifaceted graduate study experience in the Department
of Chemical Engineering and Materials Science allows students to
choose research projects and thesis advisers from any of our faculty
with expertise in chemical engineering and/or materials science and
Our goal is to provide the financial and academic support for
students to complete a substantive research project within 2 years
for the M.S. and 4 years for the Ph.D.


Sacamento. 17 miles
San Francisco. 72 miles
Lake Tahoe: 90 miles

Davis is a small, bike-friendly university town
located 17 miles west of Sacramento and 72 miles
northeast of San Francisco, within driving dis-
tance of a multitude of recreational activities in
Yosemite, Lake Tahoe, Monterey. and San Fran-
cisco Bay Area.

For information about our program, look up our web
site at
or contact us via e-mail at
On-line applications may be submitted via

Graduate Admission Chair
Professor Jeffery C Gibehng
Department of Chenical Engineering & Materials Science
University of California, Davis
Davis, CA 95616-5294, USA
Phone (530) 752-7952 Fax (530) 752-1031

Fall 1999



Graduate Studies in IRVINE
Chemical and Biochemical Engineering
Materials Science and Engineering
for Chemical Engineering, Engineering, and Science Majors

Offering degrees at the M.S. and Ph.D. levels. Research in frontier areas
in chemical engineering, biochemical engineering, biotechnology and materials
science and engineering. Strong physical and life science and engineering groups on campus.
Ying Chih Chang (Stanford University)
Nancy A. Da Silva (California Institute of Technology)
James C. Earthman (Stanford University)
Steven C. George (University of Washington)
Stanley B. Grant (California Institute of Technology)
Juan Hong (Purdue University)
Enrique J. Lavernia (Massachusetts Institute of Technology)
Henry C. Lim (Northwestern University)
Martha L. Mecartney (Stanford University)
Farghalli A. Mohamed (University of California, Berkeley)
Frank G. Shi (California Institute of Technology)
Vasan Venugopalan (Massachusetts Institute of Technology)

Joint Appointments:
G. Wesley Hatfield (Purdue University)
Roger H. Rangel (University of California, Berkeley)
William A. Sirignano (Princeton University)

The 1,510-acre UC Irvine campus is in Orange County, five miles from the Pacific Ocean and
40 miles south of Los Angeles. Irvine is one of the nation's fastest growing residential,
industrial, and business areas. Nearby beaches, mountain and desert area recreational
activities, and local cultural activities make Irvine a pleasant city in which to live and study.

For further information and application forms, please visit
or contact
Department of Chemical and Biochemical Engineering and Materials Science
School of Engineering University of California
Irvine, CA 92697-2575

Control and
Processing and
of Materials
and Glass
-Cei Technol-
Sol-Gel Process-
Flow -
*Water Pollution
.____ ,____


Chemical Engineering Education





* Molecular Simulations
* Thermodynamics and
Cryogenics -l
* Process Design, Dynamics, and '
* Polymer Processing and Transport-
* Kinetics, Combustion, and
* Surface and Interface Engineering
* Electrochemistry and Corrosion
* Biochemical Engineering
* Aerosol Science and
* Air Pollution Control and Environ-
mental Engineering

UCLA's Chemical Engineering Department offers a
program of teaching and research linking fundamental
engineering science and industrial practice. Our Depart-
ment has strong graduate research programs in Bioengi-
neering, Energy and Environment, Semiconductor Manu-
facturing, Molecular Engineering of Materials, and Pro-
cess Systems Engineering.
Fellowships are available for outstanding applicants in

J. P. Chang
P. D. Christofides
Y. Cohen
M. W. Deem
T. H. K. Frederking
(Prof. Emeritus)
S. K. Friedlander
R. F. Hicks
E. L. Knuth
(Prof. Emeritus)
J. C. Liao
V. Manousiouthakis
H. G. Monbouquette
K. Nobe
L. B. Robinson
(Prof. Emeritus)
S. M. Senkan
W. D. Van Vorst
(Prof. Emeritus)
V. L. Vilker
(Prof Emeritus)
A. R. Wazzan


.Fall 1999 349 Ag

Fall 1999 349

both M.S. and Ph.D. degree programs. A fellowship
includes a waiver of tuition and fees plus a stipend.
Located five miles from the Pacific Coast,
UCLA's attractive 417-acre campus extends from
Bel Air to Westwood Village. Students have access
to the highly regarded science programs and to a
variety of experiences in theatre, music, art, and
sports on campus.

University of California, Riverside
Department of Chemical and Environmental Engineering

The Graduate Program in Chemical and Environ- arlan and Rosemary Bourns College of
mental Engineering offers training leading to the
degrees of Master of Science and Doctor of Phi- engineering
losophy. All applicants are required to submit
scores from the general aptitude Graduate Record
Examination (GRE).

For more information
and application materials, write:
Graduate Advisor
Department of Chemical and
Environmental Engineering
University of California
Riverside CA 92521

Visit us at our website:

Wilfred Chen (Cal Tech) Environmental Biotechnology, Microbial Engineering, Biocatalysis

Marc Deshusses (ETH, Zurich) Environmental Biotechnology, Bioremediation, Modeling

Mark R. Matsumoto, Chair (UC Davis) Water and Wastewater Treatment, Soil Remediation

Ashok Mulchandani (McGill) Biosensors, Environmental Biotechnology

Joseph M. Norbeck (Nebraska) Advanced Vehicle Technology, Air Pollution, Renewable Fuels

Akula Venkatram (Purdue) Micrometeorology, Air Pollution Modeling

Anders O. Wistrom (UC Davis) Particulate and Colloidal Systems, Wastewater Treatment

Yushan Yan (CalTech) Advanced Materials, Zeolite Thin Films, Catalysis

The 1,200-acre Riverside campus of the University of California is conveniently located
50 miles east of Los Angeles within driving distance to most of the major cultural
and recreational offerings in Southern California. In addition, it is virtually
equidistant from the desert, the mountains, and the ocean.

Chemical Engineering Education



ERAY S. AYDIL Ph.D. (University of Houston) Microelectronics and Plasma Processing.
SANJOY BANERJEE Ph.D. (Waterloo) Environmental Fluid Dynamics, Multiphase Flows, Turbulence, Computational Fluid Dynamics.
BRADLEY F. CHMELKA Ph.D. (U.C. Berkeley) Inorganic-Organic Hybrid Materials, Zeolites and Molecular Sieves, Polymeric Solids, Liquid
Crystals, Solid-State NMR.
GLENN H. FREDRICKSON Ph.D. (Stanford) (Chair) Statistical Mechanics, Glasses, Polymers, Composites, Alloys.
JACOB ISRAELACHVILI Ph.D. (Cambridge) (Vice-Chair) Surface and Interfacial Phenomena, Adhesion, Colloidal Systems, Surface
Forces, Biomolecular Interactions, Friction.
EDWARD J. KRAMER Ph.D. (Carnegie-Mellon) Microscopic Fundamentals of Fracture of Polymers, Diffusion in Polymers, Polymer
Surfaces and Interfaces.
FRED F. LANGE Ph.D. (Penn State) Powder Processing of Composite Ceramics, Liquid Precursors for Ceramics, Superconducting Oxides.
L. GARY LEAL Ph.D. (Stanford) Fluid Mechanics, Physics and Rheology of Complex Fluids, including Polymers, Suspensions, and Emulsions.
GLENN E. LUCAS Ph.D. (M.I. T.) Mechanics of Materials, Structural Reliability.
DIMITRIOS MAROUDAS Ph.D. (M.I.T.) Theoretical and Computational Materials Science, Microstructure Evolution in Electronic and
Structural Materials.
ERIC McFARLAND Ph.D. (M.I. T.) M.D. (Harvard) Biomedical Engineering, NMR and Neutron Imaging, Transport Phenomena in Complex
Liquids, Radiation Interactions.
DUNCAN A. MELLICHAMP Ph.D. (Purdue) Computer Control, Process Dynamics, Real-Time Computing.
SAMIR MITRAGOTRI Ph.D. (M.I.T.) Drug Delivery and Biomaterials
DAVID J. PINE Ph.D. (Cornell) Polymer, Surfactant, and Colloidal Physics, Multiple Light Scattering, Photonic Crystals, Macroporous
ORVILLE C. SANDALL Ph.D. (U.C. Berkeley) Transport Phenomena, Separation Processes.
DALE E. SEBORG Ph.D. (Princeton) Process Control, Monitoring and Identification.
MATTHEW V. TIRRELL Ph.D. (U. Massachusetts) Polymers, Surfaces, Adhesion Biomaterials.
T. G. THEOFANOUS Ph.D. (Minnesota) Multiphase Flow, Risk Assessment and Management
W. HENRY WEINBERG Ph.D. (U.C. Berkeley) Surface Chemistry, Heterogeneous Catalysis, Electronic Materials, Materials Discovery
using Combinatorial Chemistry
JOSEPH A. ZASADZINSKI Ph.D. (Minnesota) Surface and Interfacial Phenomena, Biomaterials.
The Department offers M.S. and
Ph.D. degree programs Finan-
cial aid, including fellowships,
teaching assistantships, and re-
search assistantships, is avail-
One of the world's few seashore
campuses, UCSB is located on
the Pacific Coast 100 miles
northwest of Los Angeles. The
student enrollment is over
18,000. The metropolitan Santa
Barbara area has over 150,000
residents and is famous for its
mild, even climate.

For additional information
and applications, write to
Chair Graduate Admissions Committee Department of Chemical Engineering University of California Santa Barbara, CA 93106

Fall 1999 351

Chemical Engineering at the





"At the Leading Edge"

Frances H. Arnold
John F. Brady
Mark E. Davis
Richard C. Flagan


George R. Gavalas
Konstantinos P. Giapis
Julia A. Kornfield
John H. Seinfeld

David A. Tirrell
Nicholas W. Tschoegl
Zhen-Gang Wang

Aerosol Science
Applied Mathematics
Atmospheric Chemistry and Physics
Biocatalysis and Bioreactor Engineering
Chemical Vapor Deposition

Colloid Physics
Fluid Mechanics
Materials Processing
Microelectronics Processing
Microstructured Fluids
Polymer Science
Protein Engineering
Statistical Mechanics

For further information, write
Director of Graduate Studies
Chemical Engineering 210-41 California Institute of Technology Pasadena, California 91125
Also, visit us on the World Wide Web for an on-line brochure:
Chemical Engineering Education

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M.S. and Ph.D. Programs in Chemical Engineering


Stuart Adler
John Angus
Colman Brosilow
Robert Edwards
Donald Feke
Nelson Gardner
Howard Greene
Uziel Landau
Chung-Chiun Liu
J. Adin Mann
Philip Morrison
Syed Qutubuddin
Robert Savinell

Research Opportunities

* Low Pressure Growth of Diamonds
* Process Control
* Colloidal Phenomena and Microemulsions
* Electrochemical Engineering
* Biomedical Sensors
* Synthesis of Electronic Material
* Polymers and Interfacial Phenomena
* Fuel Cells
* Catalysis and Reactor Design
* Separation Processes
* Interfacial Transport and Liquid Crystals
* In Situ Diagnostics


Chemical Engineering Education

Students in the Department of Chemical Engineering are involved in state-of-the-art re-
search. Here, two students make adjustments to a component of a prototype fuel cell

For more information on
Graduate Research,
Admission, and Financial Aid,

Graduate Coordinator
Department of
Chemical Engineering
Case Western Reserve University
10900 Euclid Avenue
Cleveland, Ohio 44106-7217

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