Chemical engineering education

Material Information

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


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


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-

Record Information

Source Institution:
University of Florida
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
01151209 ( OCLC )
70013732 ( LCCN )
0009-2479 ( ISSN )
TP165 .C18 ( lcc )
660/.2/071 ( ddc )

UFDC Membership

Chemical Engineering Documents


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Department of Chemical Engineering
University of Florida
Gainesville, Florida 32611

Editor: Ray Fahien (904) 392-0857
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4UAa'Sd Iectwte.

72 Semiconductor Chemical Reactor Engineer-
ing and Photovoltaic Unit Operations,
T. W. F. Russell

The Educator
54 Joe Hightower of Rice University,
Joyce Taber

Department of Chemical Engineering
58 Cornell University, Julian C. Smith and
Paul H. Steen

68 The Nature of Adjoint Variables and Their
Role in Optimal Problems, O. K. Crosser

78 The Use of Computer Graphics to Teach
Thermodynamic Phase Diagrams,
C. D. Naik, Paulette Clancy, and
Keith Gubbins

84 An Improved Design of a Simple Tubular
Reactor Experiment, A. A. Asfour

62 Classical Solution Thermodynamics: A
Retrospective View, H. C. Van Ness and
M. M. Abbott

88 The B. C. (Before Computers) and A. D. of
Equilibrium-Stage Operations,
J. D. Seader

67 Books Received
71 Positions Available
82,83 Book Reviews

CHEMICAL ENGINEERING EDUCATION is published quarterly by Chemical
Engineering Division, American Society for Engineering Education. The publication
is edited at the Chemical Engineering Department, University of Florida. Second-class
postage is paid at Gainesville, Florida, and at DeLeon Springs, Florida. Correspondence
regarding editorial matter, circulation and changes of address should be addressed
to the Editor at Gainesville, Florida 32611. Advertising rates and information are
available from the advertising representatives. Plates and other advertising material
may be sent directly to the printer: E. O. Painter Printing Co., P. O. Box 877,
DeLeon Springs, Florida 32028. Subscription rate U.S., Canada, and Mexico is $20 per
year, $15 per year mailed to members of AIChE and of the ChE Division of ASEE.
Bulk subscription rates to ChE faculty on request. Write for prices on individual
back copies. Copyright 1985 Chemical Engineering Division of 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 of the ASEE
which body assumes no responsibility for them. Defective copies replaced if notified
within 120 days.
The International Organization for Standardization has assigned the code US ISSN
0009-2479 for the identification of this periodical.


W educator

of Rice University

Rice University
Houston, TX 77251

-t 'VE BEEN DELIGHTED to be where I am," says
Dr. Joe Hightower in regard to his decision
17 years ago to become a chemical engineer and
an educator as well.
Joe Hightower, of the chemical engineering
department at Rice University in Houston, says
he started out like many other members of engi-
neering faculties: "I started as a child by taking
things apart-bicycles, motorcycles, clocks, every-
thing!" As early as the fourth grade he proceed-
ed to take his clarinet apart and to rebuild it
shortly after he began taking music lessons. Then
throughout high school, in addition to becoming
an accomplished musician as a member of the all-
state band, he made a veritable career out of re-
pairing the instruments of the other band mem-
While he was in high school Joe also decided
to study chemistry. Later he obtained his masters
and PhD in chemistry from Johns Hopkins but
couldn't decide if he wanted to do industrial re-
search or academic work. It was during a three-
year stint at the Mellon Institute that he decided
to teach. "I found that I enjoyed the interaction
with the students, the stimulation of the faculty,
and the flexibility of the job."
He found he had to make another decision,
however-whether to go into a department of
chemistry or chemical engineering. "All my edu-
cational background was in chemistry, but I had
a philosophical desire to work on things that have
very practical uses," he says.

However, chemistry departments
were moving in the direction of quantum
mechanics and other more esoteric areas while
chemical engineering was moving from unit operations
into engineering science. Thus, chemical
engineering embraced catalysis, and Joe
Hightower embraced engineering.

It happened at that time that heterogeneous
catalysis, the research area in which Joe was
interested, was a field that had been explored
primarily in chemistry departments. However,
chemistry departments were moving in the di-
rection of quantum mechanics and other more
esoteric areas while chemical engineering was
moving from unit operations into engineering
science. Thus, chemical engineering embraced
catalysis, and Joe Hightower embraced engineer-
Since then, Joe's research philosophy has been
directed toward providing new insights into how
existing catalysts work as opposed to discovering
new catalysts. "We try to ask the question 'Why?' "
he says. Using his chemical training, Joe has
worked at gaining information about the chemical
nature and concentration of active sites, the in-
fluence of solid state parameters in determining
activity and selectivity, and the mechanisms of re-
actions that occur over solids that are of interest
to the petroleum and petrochemical industries. He
and his students have extensively used isotopic

Copyright ChE Division, ASEE, 1985


tracers (both stable and radioactive) to study the
kinetics, reaction networks, rate limiting steps,
and incorporation of surface species into product
molecules. (Some of his work has been sum-
marized in an earlier issue of this journal, Vol.
XVI, No. 4, p. 148, Fall 1982). A few of the
catalyst systems that his group has studied in-
clude cracking catalysts, auto emission control
catalysts, partial oxidation catalysts, and zeolites.
But Joe's research does not stop at the gradu-
ate level. Several years ago he incorporated some
research techniques into a sophisticated under-
graduate experiment. While taking his kinetics
and reactor design course, junior level students
now investigate all the kinetic parameters for cu-
mene dealkylation over a silica-alumina cracking
catalyst, explore the reaction mechanism with
deuterium tracers and a mass spectrometer, and
determine the surface area of the material. From
their results the students are able to calculate the
true surface reaction rate constant, the concentra-
tion of active sites, the turnover frequency, and
the role of intraparticle diffusion on the kinetics
(Chem. Eng. Educ., p.118, Summer, 1969). This
experiment allows the students to determine ex-
perimentally many of the parameters that are
useful in scaling up such reactions from labora-
tory to plant size.
Joe's research has led him into other situations
which he has especially enjoyed. In the early 70's,
for example, he was chairman of several National
Academy of Science panels which were assigned
the task of assessing the feasibility of using
catalytic converters to decrease pollutants from
automobiles. "No one had ever applied catalysts
in this way, and we were assigned the task of de-
termining if these devices would in fact work,"
he says. "It was fascinating. There was a lot of
secrecy. No company would tell us directly what
they were doing, but they would tell us what they
thought the other companies were doing, and we
had to try to put together a clear picture. Once I
was asked to testify before the House of Repre-
sentatives Committee on Science and Technology
which was chaired by Representative George
Brown of California. The congressman from
Detroit would say things like, 'I want you to know
that people from my district are being put out of
jobs because of government regulation and
control.' Brown would respond, 'People in my
district in California are dying because we don't
have enough controls, and pollutants are killing
people.' How can one give an objective testimony

in an atmosphere like that!"
Involvement in professional societies has been
another rewarding part of Joe's career. He is cur-
rently chairman of the 24-member Petroleum Re-
search Fund Advisory Board, a group that is re-
sponsible for a corpus of $150 million. This year
the foundation will donate $11 million for uni-
versity research in petroleum-related areas. In
1971 he received the National Award in Petroleum
Chemistry from the American Chemical Society.

Joe finds time to enjoy simple, relaxing activities, such
as blowing glass in his lab.

As a councilor for the American Chemical So-
ciety, he represents the southeastern Texas local
section. He has been on the national research
committee and is on the national awards commit-
tee of the American Institute of Chemical Engi-
neers. He has also served as chairman of the
petroleum chemistry division of the American
Chemical Society.
Dr. Hightower has over 50 publications to his
credit and is also very much involved in presenting
short courses for industry. "Catalysis," he says,
"is a field that is not taught in many universities as
an area of specialization. Yet, 80 or 90% of all
commercially important chemical reactions are
catalytic reactions. People are trained as organic
chemists, physical chemists, or chemical engi-
neers and then learn about catalysis on the job.
This creates a great demand for the types of short
courses that we instituted at Rice years ago and
that are being continued in cooperation with other
schools such as the University of Houston."
It would appear that Joe's day would have to
last more than 24 hours in order to accomplish
his many activities. Yet, there is still another part
of his life that is as important to him as his pro-


. there is still another part of his life that is as important to him as his professional work.
In 1968 he was a leader in establishing the Human Resources Development Foundation (which) provides free
temporary housing for needy families who come for treatment to the Texas Medical Center hospitals.

fessional work. In 1968 he was a leader in es-
tablishing the Human Resources Development
Foundation. The foundation provides free tempor-
ary housing for needy families who come for
treatment to the Texas Medical Center hospitals.
The foundation started as a project at Dr. High-
tower's church and has expanded to serve over 700
families from over 38 states and 26 foreign
countries in the last 16 years. Joe is president of
the foundation whose facilities have grown from
an old army barracks into 15 beautiful apart-
ments. He heads a group of approximately 40 dedi-
cated volunteers who minister daily to the needs
of families who are under enormous stress.
"All a person needs to move in are pajamas
and a toothbrush!" Joe laughs. But his statement

Human Resources Development Foundation apartments.

is very accurate. The apartments are furnished
down to the pots and pans. A local church even
provides meat once a week for the residents. Social
workers, ministers, and even former residents
refer potential patients. Selection is made on a
first-come, first-served basis without regard to
race, creed, sex, religion, age, or level of disability.
Need is the sole criterion used to determine eligi-
bility. Residents are allowed to stay for up to
three months.
"The project is valued at over a half-million
dollars, and most of it has been given because of
something Joe has done," states Marge Norman,
Vice-President of the foundation. "Joe never hesi-
tates to go speak to a group if there is some chance
that they might have an interest in the founda-

tion. His work takes him to the far reaches of
the U.S. and overseas; on every airplane trip he
takes he makes sure his seat partner is very well
acquainted with his pet project, and it often leads
to very good things for this foundation."
Senator Orrin Hatch was one of the latest
people to hear about the foundation because of
one of Joe's "airplane contacts." Joe sat next to
a member of Senator Hatch's staff on one flight,
and later he received a letter from the senator,
who had been informed by the staff member about
the foundation. The letter commended Dr. High-
tower for his charitable work.
Continues Mrs. Norman, "Even though we
have a foundation board which is functioning very
well, without Joe I don't know if the Board would
have been as effective or if this place would have
become what it is today. He loves it so much, and
he works so hard. A couple of times each year we
have a work day when all our volunteers come to
clean, repair, and paint. All kinds of people are
represented in the workers. Joe is always the first
here and the last to leave, working at anything
that needs to be done. Even before he comes over,
he gets up before dawn and bakes bread so that at
10 a.m. we can have hot bread and butter and
coffee. We've been acquiring land to build more
apartments next door, so you can be sure he's on
the campaign trail again! We have parties for
our residents, and again, Joe is always here with
bread he has baked. He even brings his mandolin
and plays and sings. There's not much Joe can't
On any given day the facility may house people
from Florida, or from various towns in Texas,
Columbia, or Indiana. There are no class dis-
tinctions. Last winter a brain surgeon from Main-
land China was allowed to leave his country with
only $200 when he came to care for his quadri-
plegic daughter in the medical center. As a resi-
dent of the foundation's apartments, he scrubbed
floors and took a lot of good-natured ribbing when
he painted an outside door with interior paint.
Something all residents share, however, is grati-
tude to Hightower, who personally greets each
newcomer with a loaf of bread, and gratitude to
the foundation he helped establish.


Wrote one resident, "What a tremendous help
this facility has been to us. Each of us is faced
with an extremely serious medical problem, and
the expenses are staggering. To be sure, the
financial savings are important, but even more,
we have a place to call 'home' and people with
whom we can talk as friends. I'm one of the lucky
ones. Two weeks ago I had a kidney transplant,
and now I am hoping and praying that my body
will not reject it. I have been so impressed by the
consistent care and visits that I've had from the
jogger (Dr. Hightower) and his friends even
during my recovery from surgery. I hope in some
way I can repay the favors done for me. Right
now, though, I'm going to sit back and enjoy an-
other slice of the hot bread Joe Hightower has
brought me before it has time to lose its flavor!"
In his professional life also, colleagues have
only good things to say about Joe Hightower. Dr.
T. W. Leland who was department chairman when
Joe came to Rice says, "I was impressed with him
right from the start, and I had a great interest in
getting him to come to Rice. I think it's been a
first-rate choice. He's done a remarkably good job
over the years. He's an excellent teacher and has
had an active research career. He is well thought of
by his students and has perfected a graduate
course in kinetics and catalysis to a high degree.
He has been effective in giving short courses and
he is outstanding in his volunteer public service.
Personally, he is outgoing, friendly, and extreme-
ly well-organized. He has excellent rapport with
people in all walks of life, from the top of the
technical ladder in terms of ability to students not
doing too well in their courses. He's a remarkable
individual who has been a great addition to our
Joe is modest in describing his daily activi-
ties. "I just enjoy it all," he says. He gets up at
4:45 every morning to jog and share breakfast and
a Bible reading with his wife Ann, a chemical
engineer who works for the Exxon Chemical
Company. By 6:30 a.m. he is at work, doing all
the things he loves to do. "From the very be-
ginning I couldn't make up my mind about what
I wanted to do. I wanted something in both in-
dustry and the academic world. Now I have both,
and I'm grateful for that. I probably border on
getting involved in more things than I should ..
but they're all so interesting! I guess I just like
being where I can interact with people and see
them grow, whether it's at the university or
whether it's with people who are hurting." E

Christie J. Geankoplis
University of Minnesota
1983 casebound 350pages
This text takes a unified approach to basic transport
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basic equations and calculation methods, and the
differences which occur in the actual physical
Each chapter of this class-tested text is divided into
elementary sections, followed by more sophisticated
Selected Topics allowing you to expand or focus
your course according to the needs of your students
and the time limits of your course.
SI Units are used throughout, with important equa-
tions and tables in dual units. Over 150 example
problems and more than 340 homework problems
emphasize applications as well as theory.
This text offers complete coverage of more essentials
than any other book you'll find. Look for these new
topics: diffusion in solids and porous solids, bioengi-
neering transport, non-Newtonian fluids, numerical
methods for steady and unsteady-state diffusion and
conduction, design and scale-up of agitation systems,
mass transport, and an introduction to engineering

Christie J. Geankoplis
University of Minnesota
1983 casebound 650 pages
This fully-revised Second Edition includes TRANSPORT
Part One of the text, plus the unit operations (Part
Two) so essential to chemical engineers.
Second Edition offers an optimal balance of theory and
application. Geankoplis emphasizes the diversity of
practical applications in chemical, ceramic, mechanical,
civil, food process, and bioengineering. Over 220
example problems and 500 homework problems
illustrate both theory and applications.
The Second Edition features new sections on multi-
component distillation, unit operations of membrane
processes, non-Newtonian fluids, diffusion in solids,
porous solids and biosystems, freezing, freeze drying,
and sterilization of biomaterials.
For examination copies or more information
on these two titles, write to Ray Short, Engineering
Editor, Allyn and Bacon, Inc., 7 Wells Avenue, Newton,
MA 02159.

Allyn and Bacon, Inc.
College Division

Olin Hall from the west.

no department -


Cornell University
Ithaca, NY 14853

F OUNDED IN 1868 AND with a long tradition in
engineering, Cornell is almost unique in being
both private and state-supported; about half the
divisions, including engineering, are privately en-
dowed while the other half are funded by the
State of New York. An awkward arrangement,
it would seem, but it works surprisingly well.
Cornell, a medium sized university with a total
enrollment of some 18,000 students, is set on a
hill overlooking the city of Ithaca, and the waters
of Cayuga Lake, the largest of the Finger Lakes.
Ithaca is small but strongly cosmopolitan. The
setting is semi-rural; the scenery is beautiful; the
air is clean. Almost the only drawbacks are a
modicum of cold gray weather on occasion, and
some possible difficulties in travelling in and out.

Ithaca has been called "the most centrally iso-
lated city in the Northeast," but as a graduate
student from Greece recently remarked, "If it
wasn't for the weather, Ithaca would be Paradise!"
The School of Chemical Engineering has 18
faculty members, about 100 undergraduate
students (3rd and 4th years only), and over 65
graduate students. During the past twelve years
research activity and expenditures have greatly
increased, and strong research programs have
been established in fluid mechanics, polymers, sur-
face science and catalysis, thermodynamics, and
biochemical engineering. The number and quality
of MS and PhD candidates (especially PhD's)
have risen rapidly. The growth in research, how-
ever, has not reduced the traditional concern for
undergraduate and professional graduate teach-
ing. All faculty members are expected to teach
undergraduate courses, and many participate in
advanced design projects in the professional
Master's program. The school occupies its own
building (shared with a few other tenants) with


Copyright ChE Division, ASEE, 1985

a total area of some 90,000 square feet, 54,000 of
which is exclusively chemical engineering.


At Cornell, as at many institutions, chemical
engineering began in the Chemistry Department,
but its development was somewhat unusual. Very
early (before 1900) courses were offered in in-
dustrial chemistry which had a considerable
practical flavor; as taught by Fred H. "Dusty"
Rhodes in the 1920's they dealt with the principles
and practice of chemical engineering. By 1930
Dusty had established both undergraduate and
graduate programs in chemical engineering, but
because of rivalries between Chemistry and the
Engineering College the undergraduate program
had to be a 5-year-long hybrid: four years in Arts
and Sciences (leading to the degree Bachelor of
Chemistry) followed by one year in engineering
(for the degree Chemical Engineer).
In 1938 the department with its three faculty
members became part of the Engineering College
and the 5-year program led to the degree Bachelor
of Chemical Engineering. In 1942 chemical engi-
neering moved to Olin Hall, the first building
on what was to become engineering's new
quadrangle. It was at a considerable distance
from chemistry and the old ties quickly weakened.
After World War II all the undergraduate engi-
neering programs at Cornell were lengthened to
five years. This lasted until 1965, when the present
4-year BS programs, including that in chemical
engineering, were established.
Dusty Rhodes was director of the school until
he retired in 1957 and Charles C. "Chuck" Wind-
ing took over. Ken Bischoff, now at the University
of Delaware, was director from 1970 to 1975;
Julian Smith from 1975 to 1983; and Keith
Gubbins from 1983 to date.
For many years chemical engineering at
Cornell was known for its strong undergraduate
program. Rhodes felt that good teaching was the
most important thing required of a faculty mem-
ber and while ability to do research should be
considered in reviews for promotion or tenure, it
should not be a major factor. This is not to say
that there was no research or graduate work. Be-
tween 1932 and 1970 the school awarded 140 MS
and 104 PhD degrees, and many of the recipients
have had distinguished careers in industry and
academia, including John Prausnitz (Berkeley),
Ed Lightfoot (Wisconsin), and a string of past

or present heads of chemical engineering depart-
ments: Bob Coughlin (UCONN) ; Howard Greene
(Akron) ; Deran Hanesian (NJIT) ; Will Kranich
(Worcester Poly) ; Larry McIntire (Rice) ; Steve
Rosen (Toledo); Julian Smith (Cornell); Tom
Weber (SUNY Buffalo) ; and Jacques Zakin (Ohio
State). Bob Finn's pioneering work in biochemi-
cal engineering was begun in the 1950's.
Dusty's policies set a pattern for the school
which persisted until the early 1970's. By then it
was clear that the research effort had to be greatly

The Fred H. Rhodes Student Lounge, redecorated
through a gift from Joseph Coors, '40.

expanded. Beginning in 1970, new faculty mem-
bers were added who developed, or brought with
them, strong research programs in several areas.
This attracted additional research-minded faculty
and increasingly stronger graduate students.
During an 8-year period research expenditures in-
creased by a factor of six. The number of gradu-
ate students has risen to 67; more significantly,
a majority of them are now PhD candidates. And
collaboration with Chemistry and other depart-
ments of the university is once again close and

From 1976 through 1982, the annual research
expenditures, in dollars per faculty member,
climbed at a rate that was second highest and
reached a level that was fourth highest among all
chemical engineering departments in the country.*
Total sponsored research costs for 1983-84 were
over 1.5 million dollars, for an average of $94,000

*Journal of Engineering Education, March issues, 1976-


per full-time faculty member. This is especially
striking since only about a third of the faculty
was responsible for 75% of the total expenditures.
As the current younger faculty develop their pro-
grams and additional research-minded faculty re-
place retiring senior professors, the overall re-
search program should continue its strong ex-

Biochemical Engineering
Biochemical engineering research has grown
from Bob Finn's early studies of microbes and

... many of the PhD recipients have
had distinguished careers in industry and
academia, including John Prausnitz (Berkeley), Ed
Lightfoot (Wisconsin), and a string of
past or present heads of chemical
engineering departments.

microbial populations. The goal was, and is, to
develop new and more efficient biochemical con-
versions. One project seeks to find economical
ways of producing ethanol from pentose sugars,
a second to develop better treatment methods for
wastes containing pentachlorophenol (PCP), and
a third to exploit an unusual bacterium which can
rapidly ferment arabinose.
Mike Shuler shares several specific interests
with Bob Finn including the treatment of waste-
water by specialized microorganisms. Mike's di-
verse interests are tied together by a view of the
living cell as a "catalyst" waiting to be used in
chemical reactors. His research embraces studies
of plant-cell tissue culture, reactors with solid
substrates (e.g. mold growth on solid surfaces),
photobioreactors, biofilm formation, and the con-
tinuous protein production from bacteria with re-
combinant DNA. Particularly noteworthy have
been his group's experimental demonstration of
the feasibility of hollow-fiber membrane units for
entrapment of microbial populations (necessary
groundwork for the development of hollow-fiber
reactors) and the construction of a mathematical
model of the organism Escherichic coli.
Doug Clark, who joined the faculty in 1984,
brings the point of view that enzymes rather than
the whole cell can be the building blocks for bio-
chemical reactors. He is studying how the im-
mobilization, or attachment to a foreign matrix
support, affects the structure and function of an
enzyme. A related interest is the transport of bio-

logical macromolecules through porous media; this
transport is an essential step in enzyme immobili-
zation, gel permeation and affinity chromatogra-
phy, and ultrafiltration. In collaboration with Bill
Street, Doug has initiated a study of methane-
producing bacteria which live at extreme tempera-
tures and pressures in deep-sea hydrothermal

Polymers and Materials Science
For a million circuit elements to fit on a tiny
silicon chip linewidths must be on the order of a
tenth of a micron. In one technique for achieving
such precision-electron-beam lithography--the
silicon surface is covered with a polymer film
polymethyll methacrylate, for example), then ir-
radiated by an electron beam creating a pattern
of soluble polymer. The soluble polymer is washed
away leaving a precision mask and the chip is
ready for the final step, silicon modification.
Ferdinand (Rod) Rodriguez is directing an inter-
departmental program on polymers for advanced
lithography, to improve the performance of the
polymer "resist" used in the masking process. This
is a good example of Rod's research on polymeric
materials which has the broad goal of understand-
ing the processes of polymerization and gelation
(crosslinking) and degradation (chain scission)
in order to produce better materials.
Claude Cohen uses macromolecular science to
interpret the physical properties of polymer
systems and to understand the structures that
develop during industrial processing. On the
fundamental level, predictions from models of
macromolecules are used to understand rheo-
logical and light-scattering behavior, with ex-
periments to complement the theoretical work and
to test the adequacy of the models. On the applied
level, the orientation of glass fibers in composite
thermoplastics during the molding process is
being investigated. This work is in conjunction
with an interdepartmental program on injection

Surface Chemistry, Catalysis and Reactor Engineering
The surface chemistry and physics of hetero-
geneous systems which have direct technological
application is the central concern of Bob Merrill's
studies. Examples are CO oxidation on noble
metals (automobile exhaust converters), the de-
composition of hydrazine (rocket monopropel-
lant), the oxidation of aluminum (catalyst sup-


port technology, corrosion protection, and elec-
tronic insulators in microcircuitry) and hydrode-
sulfurization catalysis (sulfur removal from pe-
troleum). On the one hand, Bob's group answers
practical questions; on the other they are develop-
ing and sharpening several types of analytical
tools. These include the use of lasers in surface
chemistry, the use of synchrotron radiation
(EXAFS) to study the dynamics of gas-solid re-
actions, and the use of spectroscopy in real
catalyst systems (high-surface-area configurations
and high pressures).
Peter Harriott studies the influence of mass
transfer, heat transfer and mixing on the per-
formance of chemical reactors as well as the
kinetics of reactions in heterogeneous systems.
One project concerns the regeneration of catalysts
used in the pyrolysis and gasification of coal. An-
other examines the heat and mass transfer and
the overall kinetics in lime-slurry droplets used
in the "dry scrubbing" of SO, from flue gas; the
goal is to pin down the rate-limiting step and im-
prove the design of commercial units.
Joe Cocchetto's recent work on catalytic re-
action kinetics has concentrated on the fuel cell.
By controlling the structure of a porous electrode,
a better understanding of the interplay between
transport and reaction has been gained and tech-
niques for improving efficiency have emerged. Joe
returned to industry in early 1985.
Bob Von Berg is interested in the use of gamma
radiation in various chemical processes: ammonia
synthesis and the reaction of hydrocarbons and
liquid nitrogen. Bob has also collaborated with
Herb Wiegandt on a long-term project involving
the desalination of water by freezing, as described

Fluid Dynamics and Stability: Rheology

Bill Olbricht concentrates on problems in fluid
mechanics and rheology with applications in en-
hanced oil recovery, biomedical fluid mechanics,
and the production of semiconductor materials.
He is studying the low-Reynolds-number motion
and coalescence of immiscible drops in tubes of
various geometries (characteristic of porous
media) for critical evaluation of methods for en-
hanced oil recovery. In the biomedical area, in
conjunction with the University of Rochester
Medical School, he is modelling the motion of red
blood cells in microcapillaries to predict the dis-
tribution of these cells within tissue. A third area

Cornell's Chemical Engineering Faculty, 1984. Back
row: Shuler, Finn, Scheele, Steen, Smith, Winding.
Middle row: Zollweg (Research Associate), Cocchetto,
Harriott, Jolls (Visiting from Iowa State), Von Berg,
Rodriguez. Front row: Olbricht, Merrill, Clark, Wie-
gandt, Thorpe, Clancy, Gubbins, Streett.

of research examines the momentum, heat, and
mass transfer involved in silicon film growth by
chemical vapor deposition with the aim of pre-
dicting rates of film growth in low-pressure
deposition reactors.
Paul Steen, who joined the faculty in 1982,
studies fluid motions and their stabilities. Buoy-
ancy-driven convection patterns, generated in
fluid-saturated porous media, are examined as
prototypes of fluid motions susceptible to transi-
tions in which strong nonlinear effects are domin-
ant. This work involves the development of tools
in applied mathematics. In another area, motions
induced at fluid/fluid interfaces due to tempera-
ture gradients (thermocapillary effects) are being
investigated by experiment, with relevance to the
float-zone crystal-growth process and the break-
up of thick films.
George Scheele's study of liquid-liquid immis-
cible systems focuses on the coalescence of drop-
lets and the break-up of jets-both at relatively
high Reynolds numbers. He also has interests in
the computer simulation of chemical processes,
particularly in computer graphics.

Molecular Thermodynamics and Computer Simulation
Keith Gubbins and Bill Streett have coordinat-
ed their efforts towards understanding, predicting,
Continued on page 103.


P lecture


A Retrospective View

Rensselaer Polytechnic Institute
Troy, NY 12181

T HE PRIMARY VARIABLES of classical thermo-
dynamics for fluid systems are temperature T,
pressure P, and the molar properties volume V,
internal energy U, and entropy S. Temperature
is a primitive, having no definition in terms of
anything simpler. Pressure and molar volume are
defined directly by three other primitives: force,
mass, and length. These primitives-temperature,
force, mass, and length-are subject to direct
sensory perception, and we have little difficulty
accepting them as meaningful. Internal energy and
entropy, however, are primitives not associated
with direct detection by the senses. Nor are they
directly measurable; we have no energy meters,
no entropy meters. Energy and entropy are
mental constructs which have meaning only as
mathematical functions. Accepting this, we then
need to know what they are functions of.
We find by experiment that the molar volume
of a homogeneous phase is a function of its
temperature, pressure, and composition. Generaliz-
ing, we postulate that the molar internal energy
and entropy of a homogeneous phase are likewise
functions of temperature, pressure, and com-
position. When this is true, the first and second
laws lead to a fundamental property relation
among the primary thermodynamic variables

d(nU) = Td(nS) Pd(nV) + Zidni (1)

The ni are mole numbers of the species present,

Nor are they directly measurable;
we have no energy meters, no entropy meters.
Energy and entropy are mental constructs which have
meaning only as mathematical functions.

Copyright ChE Division, ASEE, 1985

H. C. Van Ness is Distinguished Research Professor of Chemical
Engineering at Rensselaer Polytechnic Institute, where he has been
a faculty member since 1956. He is coauthor with J. M. Smith of
Introduction to Chemical Engineering Thermodynamics and has co-
authored a number of research papers on thermodynamics with
M. M. Abbott, in addition to two books, Schaum's Outline of Theory
and Problems of Thermodynamics and (with M. W. Zemansky as a
third co-author) Basic Engineering Thermodynamics. (L)
Michael M. Abbott is Associate Professor of Chemical Engineering
at Rensselaer Polytechnic Institute, where he has been a faculty
member since 1969. Prior to that he was employed by Esso Research
and Engineering. His teaching interests are mainly in the thermal
sciences and in chemical process design. He is co-author with H. C.
Van Ness of the books Schaum's Outline of Theory and Problems of
Thermodynamics and Basic Engineering Thermodynamics (with M. W.
Zemansky). (R)

n = Ini is the total number of moles, and the
[i are chemical potentials. Written for n = 1, Eq.
(1) becomes

dU = TdS PdV + J.dx.
showing that
U = U(S,V,x)
Thus, in general, the natural independent (canoni-
cal) variables for U are entropy, volume, and
New thermodynamic properties can be defined
that are functions of alternative sets of inde-
pendent variables. In particular, the enthalpy H
and the Gibbs function G are defined as


H E U + PV (2)
G H TS (3)
nG = nU + P(nV) T(nS)
d(nG) = d(nU) + Pd(nV) + (nV)dP Td(nS) (nS)dT
Substitution for d (nU) by Eq. (1) gives

d(nG) = (nS)dT + (nV)dP + ZJ.dn. (4)

This equation is equivalent to Eq. (1), and repre-
sents an alternative fundamental property rela-
tion. Written for one mole of material, it becomes

dG = SdT + VdP + lZidxi (5)
G = G(T,P,x)
Because temperature, pressure, and composition
are subject to direct measurement and control,
the Gibbs function is a defined thermodynamic
property of great potential utility.
An equation such as Eq. (4) is too general
for direct practical application. Its value is in
storing much information. Thus, we write by in-

s = pl (6)

V (7)
F T,x
i = n (8)

where the subscript nj indicates that all mole
numbers are held constant except ni. Application
of Eqs. (6) through (8) presumes knowledge of
G as a function of T, P, and x; given this, then
Eqs. (6) and (7) yield S and V as functions of
T, P, and x. Other properties come from defining
equations; for example, by Eq. (3)

H= G + TS

Thus, if we know how G is related to its canonical
variables, we can by simple mathematical opera-
tions evaluate all the other thermodynamic
properties; given G = G (T,P,x), we can also find
S, V, ,it H, Cp, etc. as functions of temperature,
pressure, and composition.

All this is the legacy of J. W. Gibbs and in
principle nothing more is needed. An expression
giving G = G (T,P,x) is an example of a canonical
equation of state. Such an equation serves as a
generating function for the other thermodynamic
properties, and implicitly represents complete
property information.
For real-fluid mixtures, canonical equations of
state are unknown. The problem is that such an
equation must be based on experimental data. Un-
fortunately, there are no G meters and no con-
venient experimental measurements that lead
easily to values of G. Without a canonical equation
of state, we can make no direct practical use of a
fundamental property relation. The slow evolu-
tion of solution thermodynamics since Gibbs'
time has led to new formulations that relate much
more directly to experiment. Our purpose here is

The slow evolution of solution thermodynamics... has
led to new formulations that relate much more
directly to experiment. Our purpose is to
rationalize the structure of modern
solution thermodynamics.

to rationalize the structure of modern solution
In the early years of this century, G. N. Lewis
introduced several concepts basic to all subse-
quent developments: the partial property, the
fugacity, and the ideal solution.
A partial property is defined by the equation

=.- (nM) (10)
i n. (10)
1 T,P,nj

where M is the molar value of any extensive
property. The simplest interpretation of Eq. (10)
is that it apportions a mixture property among
the constituent chemical species. Thus, MR has the
characteristics of the property of species i in the
mixture. Indeed, a mathematical consequence of
Eq. (10) is the relation

M = iX

which shows that the partial properties combine
in the simplest rational way to yield the mixture
property. We see by comparison of Eq. (8) with
Eq. (10) that

Ui = G


Thus, the chemical potential is identified with
partial Gibbs function.
The fugacity is an auxiliary thermodyna
property related to the Gibbs function. Thus,
a mixture, the fugacity f is defined by the e

dG = RT d in f

(const T,x)



dGa = RT d in (xiP)

(const T)

IIIlU The actual properties of a fluid may be com-
for pared with the properties the fluid would have as
lua- an ideal gas at the same temperature, pressure,
and composition. The comparison by subtraction
(13) gives rise to residual properties. Thus, by defini-

lim = 1 (14)
For the special case of pure species i, these become


dG = RT d in fi

(const T)

lim 1

For species i as a constituent of a mixture, the
fugacity ?. is defined by the equations


MR E M. Mi (26)

Applying this concept to the Gibbs function,
we subtract Eq. (19) from Eq. (13)

d(G 6') = RT d in

dG = RT d in p

(const T,x)

(const T,x)

(const T)


lim -P 1 (18)
P-0O i

For an ideal-gas mixture one replaces V in
Eq. (5) by RT/P; then

dG' = RT d in P

(const T,x)

where the prime (') denotes an ideal-gas proper
From Gibbs' theorem for such mixtures, we I

G' = xiGi + RT xi in xi

where 0 is the fugacity coefficient, defined as

Integration of Eq. (27) gives
Integration of Eq. (27) gives

G = RT in i

(19) The integration constant vanishes, because for
P = 0, GR = 0 by assumption and In 4) = 0 by
rty. Eq. (14). For the special case of pure species i,
have this becomes


G. = RT in n

By Eqs. (6), (7), and (9), we get

For species i as a constituent of a mixture, we
subtract Eq. (24) from Eq. (17)

S' = -xiS R Axi in xi

V, = zxiv!

d(Gi GI) = RT d in
i .P
dGi = RT d n i

H' = xiH!
L 1

(const T)

(const T)

Each of these is implicit in Eq. (20). Moreover,
Eq. (8) yields

S= G = G + RT in x

dG' = dG' + RT d in xi

(const T)

By Eq. (19) written for pure species i, this be-

where i. is the fugacity coefficient of species i
in the mixture, defined as

$ -
1 X.P

Integration of Eq. (31) gives

.R = RT n i
3. i


dG. = RT d in f.
1 3.

Unlike a formulation based on a canonical equation of state, the
residual-property formulation cannot provide complete property information. One needs in addition
the heat capacities necessary for evaluation of properties for the ideal-gas state.

where again the integration constant vanishes.
An alternative form of Eq. (4) derives from
the mathematical identity

) 1 nG
d nG= d(nG) dT

Substituting for d (nG) by Eq. (4) and for G by
Eq. (3) gives

d dT + -dP dn (34)
R 2I R I T
For the ideal-gas state, Eq. (34) becomes

'd = dT + W dP + dn. (35)

With ti replaced by Gi in Eq. (34) and p' replaced
by G: in Eq. (35), we subtract these two equa-
d nG dT + nV dP + dni (36)

where the definitions of Eqs. (25) and (26) have
been invoked. This is the fundamental property
relation for residual properties. In view of Eq.
(33), it may also be written

d T = 2 dT + C dP + in i. dn (37)

Working relations for the residual properties can
now be written by inspection

H- ( ( JRT)
RT aT p,x

- (a Zn 4
I aT )Px

VR f (GRT ,x a ki

in n= ( /RT)
n Oi @ani T,P,nj

Sa(n an ,,
I 3ni T,P,n.

where the second form in each case follows from
Eq. (29).

Equation (39) may be written

d In =, dP

(const T,x)

where by definition
VR = V V' = V RT
Values of VR come directly from experimental
PVTx data, and Eq. (41) then allows calculation
of In 0; Eqs. (38) through (40) yield other
properties of interest. This close link to experi-
ment is the major reason for a residual-property
formulation of solution thermodynamics. Given
a PVT equation of state

V = V(T,P,x)

we can evaluate all residual properties. Because
of its direct relation to experiment, a PVT equa-
tion of state is far more easily developed than is a
canonical equation of state. Furthermore, the
principle of corresponding states allows the
generalization of PVT data and the development
of generalized correlations for the residual proper-
ties, thus greatly extending the usefulness of
available experimental data. Unlike a formulation
based on a canonical equation of state, the residual-
property formulation cannot provide complete
property information. One needs in addition the
heat capacities necessary for evaluation of
properties for the ideal-gas state.
In principle, PVT equations of state apply
equally to gases and to liquids. In practice, how-
ever, the accurate representation of liquid proper-
ties proves much more difficult. Thus, an alterna-
tive formulation of solution thermodynamics has
developed for liquids. The key idea is that of an
ideal solution. By definition

fid x.f (42)
1 l 1
where the superscript id denotes an ideal-solution
property. Expressions for all of the properties
of an ideal solution follow from this equation.
Integration of Eq. (17) from the state of pure
i to the state of i in solution at the same T and P



G Gi = RT n (43)

For an ideal solution, this becomes

Gi = G + RT n x (44)
and by Eq. (11)

Gid = xii + RT
Equations (6), (7), and (9) in this case yield

Sid = xiSi Rxi n xi (46)

Vid= xIVI (47)

Hid = xiHi (48)

Just as we may compare the actual properties
of a fluid with its ideal-gas-state properties, so
may we compare the actual properties of a fluid
mixture with its ideal-solution properties at the
same temperature, pressure, and composition.
Thus, we have definitions of excess properties
quite analogous to those for residual properties

ME =M Mid

-E id
M. =M. -M.
1 1 1

Equation (49) applied in turn to the properties
whose ideal-solution expressions are given by
Eqs. (45) through (48) becomes

GE = G Zx.Gi RTx.i An x. (51)

SE = S x.iSi + Rjxi in xi (52)

VE = V xiVi (53)

HE = H xiHi (54)

The excess properties are closely related to
property changes of mixing

AM = M xi.M. (55)

These quantities measure the changes that occur
when one mole of mixture is formed from the
pure constituent species by a mixing process at
constant T and P. The definition of Eq. (55)
allows Eqs. (51) through (54) to be written

GE = AG RTyx. in x.

SE = AS + Rx.i n x.



Thus, the excess properties are readily calculated
from property changes of mixing and vice
versa. Interest in property changes of mixing is
focused on AV and AH, because these quantities
can be experimentally determined by direct
Unfortunately, measurements of AV = VE and
of AH = HE for liquid mixtures do not allow
calculation of GE. For this, we need vapor/liquid-
equilibrium data, which are related to GE as
follows. Subtraction of Eq. (44) from Eq. (43)

-E id i
Gi = Gi -G = RT nf
1 1 1 x.f.
= (60)
in Yi RT

where the activity coefficient yi is defined by

Yi -(61)
In accord with Eq. (11)

RT= x RT
and by Eq. (60) this becomes
R = x.i n yi (62)

Values of yi are calculated from experimental
vapor/liquid-equilibrium measurements by the

Yi sat (63)

Here, Ii is a secondary factor of order unity
that can be readily evaluated from volumetric
data for the equilibrium phases or from correla-
tions of such data.
The fundamental property relation for the
excess properties follows from Eq. (34). For an
ideal solution, this equation is written


Summary of key equations

rnc -nH- nV i
d nj = -dT d + T dP + dn

d = dT + nR dP
RT j RT2 T

+ I in i.dni
1 1



d nGrE -nHE nVE
d dT + __T dP + L n idni (66)
RT2 RT R21

G Pi


GE G i E
RT ^i RT J in" y,

SnGid -nlid nVid
d (R -n I dT + dP

+ I- dni
RT i

With ii replaced by G. in Eq. (34) and ~d replaced
by Gi in Eq. (64), we subtract these two equa-
E nHE E 6
d = -n dT + + IdP + dn. (65)
RT2 T RT i

tions where the definitions of Eqs. (49) and (50)
have been invoked. In view of Eq. (60), this equa-
tion may also be written

n _-nHE nVE
d Ii nH dT + n dP + I n y dn (66)
RT2 RT 1 i

Equation (66) is analogous to both Eqs. (34) and
(37); analogous to Eqs. (38) through (40), we

S= T (/RT)1 (67)
RT ^ T JP,x

VE (G/RT) (68)
RT [ P J T,x

n (nGE/RT) T (69)
A i ni T,P,n


The formulation of solution thermodynamics
through excess properties derives its usefulness
from the fact that HE, VE, and yi can all be found
by experiment. This relative abundance of experi-
mental entries provides alternative measurements
that yield property data. However, the excess-
property formulation provides even less-complete
property information than the residual-property
formulation, because it tells us nothing about the
properties of the pure chemical species.
In Table 1, we bring together for comparison
the parent fundamental property relation for the
Gibbs function and the two analogous property
relations which follow from it for the residual and
excess Gibbs functions. Included as well are the
equations which relate the three mixture Gibbs
functions to their respective partial properties.
These are particular applications of Eq. (11). [

10 books received I

Fundamentals of Chemistry, Second Edition, James E.
Brady, John R. Holum; John Wiley & Sons, Inc., New
York; $34.95 (1984)
Handbook of Powder Science and Technology, Edited by
M. E. Fayed and L. Otten; Van Nostrand Reinhold, 135
West 50th Street, New York, 10020; 850 pages, $79.50
Analytical Pyrolysis: Techniques and Applications, Edited
by Kent J. Voorhees; Butterworths, 80 Montvale Ave.,
Stoneham, MA 02180; 486 pages, $69.95 (1984)
Heat and Mass Transfer in Rotating Machinery, Darryl
E. Metzger, Naim H. Afgan; Hemisphere Publishing Co.,
79 Madison Ave., New York 10016; 713 pages, $74.50
Cheaper, Safer Plants or Wealth and Safety at Work,
Trevor A. Kletz; Institution of Chemical Engineers, 165-
171 Railway Terrace, Rugby, England; (1984)
Engineering Information Resources, Margaret T. Schenk
and James K. Webster; Marcel Dekker, Inc., New York
10016; 232 pages, $24.75 (1984)
From Technical Professional to Corporate Manager; A
Guide to Career Transition, David E. Dougherty; John
Wiley & Sons, Somerset, NJ 08873; 279 pages, $19.95
Natural Product Chemistry: A Mechanistic and Biosyn-
thetic Approach to Secondary Metabolism, Kurt B. G.
Torssell; John Wiley & Sons, Inc., Somerset, NJ 08873;
401 pages, $24.95 (1984)
The Wiley Engineer's Desk Reference, Sanford I. Heisler;
John Wiley & Sons, New York 10158; 567 pages, $34.95;
Laboratory Manual of Experiments in Process Control,
Editor, Ch. Durgaprasada Rao; ChE Education Develop-
ment Center, Indian Institute of Technology, Madras 600
036 India, $20 (1984)




University of Missouri-Rolla
Rolla, MO 65401

A DJOINT VARIABLES ARE frequently arbitrarily
introduced into the textbook discussion of op-
timal or extremal theory. For example, Bryson
and Ho [1] "adjoin" them to the optimization prob-
lem, Denn [2] introduces them as a "convenience,"
and Leitman [11] regards them as a transforma-
tion to a "useful" vector space basis. Only Jackson
[10] has shown that they are desirable as a general
transformation from one set of variables which
appear naturally during the formulation of the
problem to the set of interest in the solution
search problems. Adjoint variables are the
sensitivity coefficients in optimal search problems.
Adjoint variables exist because the coefficient
matrix of every system (of describing equations)
has a transpose, and there are, therefore, two
independent solutions to the homogeneous form
of the system.
However, it was the late Professor F. M. J.
Horn who in 1958 most directly presented the
fundamental nature of the adjoint variables and
their role in optimal reactors in chemical engi-

neering [5]. The original papers [6, 7, 8] and the
more complete elaboration in his thesis were in
German, with results published in English [9] by
1967. Publications about Pontryagin's Principle
[8, 10] became the standard literature reference,
and the directness of Horn's approach became less
available for the beginning student to appreciate.
Furthermore, this appreciation or understanding
of adjoint variables makes much of Horn's later
work in optimal chemical reactors-effect of by-
passing, cyclical operation of non-linear process-
es-much easier to follow.
This demonstration makes use of the example
presented in detail in appendix 1.11 of his Thesis
[5]. One asks for the optimal temperature profile
for a plug flow reactor with several independent
chemical reactions. The set of independent chemi-
cal reactions is any set of the smallest number of
time dependent stoichiometric equations sufficient
to define all reaction compositions uniquely in
time. The proper interpretation of independent
is also clearly discussed in this thesis, although a
more formidable presentation is now available
[12]. We suppose a tubular plug flow reactor with
several chemical reactions and arbitrary kinetics



dx./dz = Vi(xlx2 ..."m, T),
1 1..x )

(i = 1 to m)

O. K. Crosser received his PhD from Rice University in 1955 and
is currently professor of chemical engineering at the University of
Missouri-Rolla. His primary interests include optimization of pre-
liminary plant design and fixed bed separations.

and presume that the objective function has the

M=M(xle,x ,...xme) = M(xe) (2)

M depends only upon the exit composition (ex-
tents) x. and a straightforward solution to the
problem would be to assume a temperature pro-
file, calculate the exit composition vector to give

Copyright ChE Division, ASEE, 1985


M, then presume another temperature profile and
continue to adapt the profile in some beneficial
way until an extreme in M was obtained. Suppose
we had two such solutions (we use x for the vector
of extents of the independent reactions and V for
the vector of reaction rates, and the super dot to
imply differentiation with respect to z)

x -+ xl = V(xl,T1) and x2 x2 = V(x2,T2) (3)

so that for sufficiently small differences between
T, and T, a first difference (perturbation) is
sufficient. Then

x2 xI = [3Vi/3x.](x2 x1) + (3Vi/ T)(T2 T1)
y = [3Vi/ax.]y + (aVi/aT) T (4a)

where y stands for the perturbation in x caused
by the perturbation T in T. We will also have
the perturbed response
m aM
M = M(x2) M(Xl) = a Yk(Ze)
k=1 ke
= (M/axie)' y (Ze) (4b)

Since both x, and x, are zero at z equal zero, y(0)
is zero. Note that the matrix, [aVi /xj] and vector,
(aVi/aT) are functions of z only, so that the
system of Eqs. (4a,b) is a set of linear differential
equations in which the coefficients are functions
only of the independent variable z. y is the re-
sponse of the system to T What we desire is to
solve Eqs. (4a,b) subject to the restriction that M
be an extreme, so that it is necessary that

dM = (aM/x. e)' dx = 0 (5)

(aM/axie)' is the transpose of the vector of
partial derivatives of M with respect to xie, that
is, x at the end of the reactor. A system like (4)
is usually solved by Variation of Parameters
(Boyce & DePrima [3] or Hochstadt [4]), finding
first the homogeneous (complementary) solutions.
The form of these solutions is more conveniently
manipulated if we use the solution matrix rather
than the solution vector (in contrast with usual
forms for systems with constant coefficients).

Y = [B] yC
C c

where B.. = 3V./ax.
ij 1 J

has the homogeneous solution matrix [Y] such

[Y] = [B][Y] (7)

and we take [Y(ze)] to be [I] the identity matrix.
Any other boundary condition on [Y] may be ob-
tained directly from this one. Using the Variation
of Parameters we suppose y = [Y]c and hope to
find the vector c to fit the inhomogeneous part,
which is the second term of Eq. (4)

y = ([Y]c) = [Y]c + [Y]c = [B][Y]c + (aVi/3T)T (8)

Substituting from Eq. (6) we have

[Y]c = (aVi/aT) T c = [Y] (V.i/aT) T (9)

and we see that the vector of the particular solu-
tions c is directly related to the temperature pro-
file, T Now, these functions depend only on z,
and we intend to keep the same inlet temperature
but to alter the shape of the profile. Therefore any
one of the particular solutions c must have the

Ck() = ck(z)dz
and since
y = [Y]c and [Y]

with ck(O) = 0

= [I] at z = z

y(ze) = c(z )

Then from Eq. 4b, using t as a dummy variable
and recalling that (DM/Dxie) does not depend on z


M dt =



S(M/axke )ck(t) dt

M = (3M/aXke)k(Ze) = (aM/3aie)'c

(Note that M is the derivative, with respect to
the independent variable of the response M to the
perturbation T .)
We now have to solve simultaneously m + 1
linear equations involving c There are m inde-
pendent chemical reactions, and Eq. (11) for
M Since these equations must be linearly de-


Thus, the differential equations for the adjoint variables and their corresponding
conditions at the end of the reactor show that the influence of the exit extents upon the value of
the objective function can be obtained for any entering conditions to the reactor by integrating their
adjoint variable differential equations from the end of the reactor to the entrance.

pendent, their determinant must be zero


(av /aT)T


i u'
or = 0
w [Y]

using vectors u and w for notational convenience,

S 0 u'
M JYJ + Y = 0
w [Y]

M/ax1 aM/ax2

(e 0 1
l(Ze) = o

3M/axi 3M/8x2

2(Z e)= 1 0


0 u'
and M=-

A(ze) =

= u = (aM/xid )

As we expand the numerator determinant of Eq.
(12) first about w, the first column (deleting the
ith row in Y) and then about u' in the first row
(deleting the jth column in Y), we will obtain
the cofactors of the elements Yj, in Y, which we
label aij and Eq. (12) can be written

i uy w'[a ]u w[Adj(Y')]u
M Y i = w'A (13)
because [aij] is the adjoint matrix of the transpose
of [Y], (a sign change occurs as the i + 1 index
in the determinant decreases to the i index for w).
The adjoint variables, A, are defined by Eq. (13)

S= [Y']-lu [Y']X u
S= (aM/xie) =
(for the extreme in M)
{[Y'IX} = 0 = [Y']x + [Y']J = [y']- 1 'l,]
[Y] = [B][Y] + [Y']X = {[B][Y]}' [Y'][B']

S= [Y']-'[Y'][B']

and A = [B']X

These are the differential equations for the
adjoint variables. The boundary conditions of
[Y] = [I] at z = Ze imply (for m = 2 for clarity)
because Iij is the unit ij cofactor from the identity

Thus, the differential equations for the adjoint
variables and their corresponding conditions at
the end of the reactor show that the influence of
the exit extents upon the value of the ob-
jective function can be obtained for any enter-
ing conditions to the reactor by integrating their
adjoint variable differential equations from the
end of the reactor to the entrance. These
functions, therefore, explain how the optimal re-
sult is affected by changing the values of the ex-
tents of reaction at any point along the reactor
such as the entrance. Since there is a direct cor-
respondence between length in a plug flow re-
actor and time, it is equally clear how the adjoint
variables apply to time optimization as well.
The adjoint variables are therefore nothing
more than the additional homogeneous solution
for the linear perturbation. Had the problem been
cast in the form of time optimal control, they
would have indicated the switching functions; in-
troduced with an Hamiltonian or Lagrange multi-
plier problem, they would have been the cor-
responding multipliers [13].
The thing to see is that all of these structures
rely essentially only on a Cramer's rule for solving
a dependent set of linear equations and that the
adjoint variables appear naturally as the added
homogeneous solutions to the transpose of the
system coefficient matrix, and they show how
temperature changes along the reactor affect the
objective function, which depends on the con-
version at the exit from the reactor.


One of the original reviewers of this article
observed that the subject of this paper is contained
in modern control theory texts. It is a pleasure to
suggest to students that Linear Systems by
Thomas Kailath (Prentice-Hall 1980) is an ex-
cellent reference with good examples and exercises.
The most directly relevant part is section 9.1 pp
598-606 and example 9.1-3 p. 605, but there are
many other items of interest throughout the entire
The University of Missouri-Rolla awarded the
Faculty Sabbatical during which this note was
written. D. W. T. Rippin and his Systems Engi-
neering Group of ETH Zurich provided the
affectionate welcome and gentle scholarly support.
Don MacElroy offered a most helpful suggestion
toward the end of the work.

Use CEE's reasonable rates to advertise. Minimum rate
% page $60; each additional column inch $25.

Chemical Engineering .. Tenure system faculty positions.
Doctorate in Chemical Engineering or closely related field.
A strong commitment to teaching and the ability to de-
velop a quality research program is expected. Preference
will be given to candidates with research interests in the
areas of Biochemical Engineering, Surface Science, Solid
State Phenomena, or Polymeric Materials. However, ap-
plicants with outstanding credentials and research interests
in other fields related to Chemical Engineering are en-
couraged to apply. Teaching and/or industrial experience
desirable but not essential. Michigan State University is
an affirmative action-equal opportunity employer and wel-
comes applications from women and minority groups. Send
applications and names of references to Chairperson,
Faculty Search Committee, Department of Chemical Engi-
neering, Michigan State University, East Lansing, Michi-
gan 48824-1226.


1. Bryson, A. E., Y. Ho, Applied Optimal Control,
Halsted Press, New York (1975) [chapter 2, and pp
47-50, pp 149-150].
2. Denn, M., Optimization by Variational Methods,
McGraw-Hill, New York (1969), [pp 102-109].
3. DiPrima, R. C., W. E. Boyce, Elementary Differential
Equations and Boundary Value Problems, John Wiley
& Sons, New York (1977).
4. Hochstadt, H., Differential Equations, Dover Press,
New York (1975).
5. Horn, R. "Optimalprobleme bei kontinuierlichen
chemischen Prozessen," Thesis, Tech. Hochsch. Wien,
Ostereich (1958).
6. Horn, F., Discussion of "Optimum Temperature Se-
quences in Reactors," K. G. Denbigh, Chem. Eng.
Sci., Special Supplement, 8, 131 (1958).
7. Horn, F., U. Troltenier, "uber den Optimalen
Temperatur verlauf im Reaktionsrohr," Chem. Ing.
Tech, 3S, 382 (1960).
8. Horn, G., "Adjungtierte Variable und Maximum
prinzip in der Theorie Chemischer Reaktoren,"
Ostereichische Chemiker-Zeitung, 6, 186 (1967).
9. Horn, F., "Mathematical Models in the Design and
Development of Chemical Reactors," Ber. Buns. Ges.,
74, 81-89, (1970) (in English).
10. Jackson, R., "Optimization of Chemical Reactors
With Respect to Flow Configuration," J. Theo. App.
Opt., 2, 240-259 (1968).
11. Leitman, G., An Introduction to Optimal Control,
McGraw-Hill, New York (1966), [p 26, Chapter 2].
12. Smith, W. R., R. W. Missen, Chemical Reaction
Equilibrium Analysis, John Wiley & Sons, New York
(1982), [Chapter 2-also see the reference by these
authors in Chem. Eng. Educ. which contains a re-
view of the literature to 1976].
13. Report: SEG/R/128(83), Systems Engineering
Group, Tech. Chem. Labor, ETH, Zurich.


I I Determinant
[ ] Matrix (square)
( ) Vector (column)
( )' Transposed vector (row)
z Length of plug flow reactor
x Extent of reaction
V Vector of reaction rates
y Perturbation in x
T Temperature
M Objective function to be optimized
m Number of independent chemical reactions
B Coefficient matrix from partial derivatives
of rates V
Y Matrix of homogeneous solutions to Eq. 4a
I Identity matrix
c Vector of particular solutions for Eq. 4a
u' Row vector (aM/axie)'
w Column vector (aVi/aT)T
X Vector of adjoint variables


Entrance to reactor
Exit from reactor
Row column indexes

i Index to independent chemical reactions
(1 to m)
S Differentiation with respect to length
-1 Inverse matrix
- Indicates perturbation value


4waEd .2eceOND


The Chemical Engineering
Division Award Lecturer for
1984 is T. W. Fraser Russell.
The 3M Company provides
financial support for this an-
nual lectureship award. The
lecture has been presented at
the University of Florida,
N the University of Michigan,
and Colorado School of Mines.
A native of Moose Jaw,
Canada, Fraser Russell re-
ceived his BSc and MSc in
chemical engineering from
the University of Alberta and
his PhD from the University
of Delaware. He joined the department at Delaware in
1964 and is presently the Allan P. Colburn Professor of
Chemical Engineering.
Prior to beginning his academic career, he spent two
years with the Research Council of Alberta, where he did
early development work with the Athabasca Tar Sands.
He later joined Union Carbide Canada as a design engineer,
where he completed the reactor and process design for all
of Union Carbide's ethylene oxide derived chemicals. His
innovative process design for these oxide derivative units
became the first multi-purpose continuous processing unit
built in Canada.
In research, Russell's efforts have been directed into
two major areas: design of gas-liquid systems; and semi-
conductor chemical reaction engineering and photovoltaic
unit operations. His research in gas-liquid system design
has resulted in over 25 publications which have been
widely used by industrial concerns and have led to improved
design of gas-liquid contractors and reactors and biological
waste treatment systems.
Russell is recognized as a respected and inspiring teach-
er and has received the University of Delaware's Excellence
in Teaching Award. His efforts in education have resulted
in the publication of two texts, Introduction to Chemical
Engineering Analysis with Morton M. Denn, and The
Structure of the Chemical Process Industries with J. Wei
and M. Swartzlander.
In the research and development of thin-film photo-
voltaic cells, his efforts have centered on the need to apply
chemical reaction engineering principles to improve the
design and operation of reactors used in making the semi-
conductor material, and to ensure that solar cells developed
at the laboratory scale can be manufactured in commercial
quantities. He carries out this research as Director of the
Institute of Energy Conversion, a laboratory with a staff
of some fifty people devoted to the development of thin-
film photovoltaic cells.

Institute of Energy Conversion
University of Delaware
Newark, DE 19716

ducing semiconductor film can be termed semi-
conductor chemical reactor engineering if the
analysis creates procedures which improve the de-
sign, operation, and product quality of laboratory
or larger scale reactors. The creation of a thin-
film semiconductor, or indeed any thin film, re-
quires an understanding of both molecular and
transport phenomena. The process is analogous to
that encountered in a typical catalytic reacting
system (Fig. 1). Molecules must reach the surface
of a substrate, adsorb on the substrate, diffuse
and/or react on the substrate to produce a film
possessing specified material and electronic
properties. In a catalytic system, the product de-
sorbs, leaving the substrate for the surrounding
fluid phase.
A variety of reactors have been employed to
move molecules or atoms to the substrate but much
can be learned by considering two general types:
Physical vapor deposition reactors
Chemical vapor deposition reactors

In a physical vapor deposition reactor the re-
quired solid or liquid phase species are placed in a
source. Energy is supplied to vaporize these species
causing molecular beams to impinge on the sub-
strate [1].
In a chemical vapor deposition reactor the
molecular species are continuously supplied in a
vapor phase which flows over the substrate. A


FIGURE 1. Surface molecular phenomena.


0 Copyright ChE Division, ASEE, 1985


quantitative understanding of transfer from the
bulk vapor to the surface is required and it may
be necessary to contend with complex reactions in
the vapor phase [2].
The performance of a reactor which produces
a semiconductor film is judged by the quality of
the film produced. Much effort is being devoted to
ascertaining film quality by measuring optical and

Electronic & Optical

Material Properties

Design & Operation
of Reacting System

FIGURE 2. Simplified logic diagram.
electronic properties but film quality is ultimately
determined by the performance of the semicon-
ductor in some type of electronic device. A success-
ful semiconductor chemical reaction and reactor
analysis should provide experimentally verified
models linking the electronic properties of the film
to the design and operation of the reactor through
a detailed understanding of the material proper-
ties of the film and the mechanism of film growth
(Fig. 2).
The logical sequence summarized in Fig. 2 has
been followed by the integrated silicon circuit com-
munity of researchers and industrial practitioners
in dealing with the key step in integrated circuit
manufacture of dopant diffusion into a film. It has
not been a trivial task and well over two decades
of effort have gone into the development of models
relating device performance to doping concentra-
tion profile and doping concentration profile to the
design and operation of the furnace [3]. Growing
a polycrystalline or amorphous film, predictably,

with the desired electronic properties is an even
more difficult task; one which remains an active
integrated circuit research area today.
If one is interested in applications which could
require millions of square meters per year of semi-
conductor film, the task of effectively predicting
film growth becomes an order of magnitude more
complex. Semiconductor films covering an area on
the order of a square meter or more are needed for
photovoltaic panels for power generation
electronic devices for thin-film displays
For these large-area applications it is necessary
to carry out research on a scale between that used
in a typical laboratory and that required for com-
mercial operations. This unit operations scale re-
search needs to have both a theoretical and experi-
mental component which builds upon the labora-
tory scale research. The position of chemical re-
actor engineering and unit operations scale experi-
mentation in the research logic is shown in Fig. 3.
I will illustrate the application of semicon-
ductor chemical reactor engineering with research
we have underway in physical vapor deposition of
CdS at both the laboratory and unit operations





FIGURE 3. Role of chemical reactor analysis.


We originally became interested in the semi-conductor research because of a
need to design larger scale reacting systems. However, the last five years of research has
taught us that the chemical engineering analysis is very useful in the laboratory scale research effort,
and indeed essential, if such research is to be done efficiently and with minimum
expense (a key issue with today's research costs).

bell jar
heater box
radiant heater
i f-foil substrate
substrate holder

S- top heat shield
S-heat shield
--source bottle
I 1 "^ -tantalum heater

|current lead
and support
base plate

-thickness monitor
FIGURE 4. Physical vapor deposition reactor.

scale and chemical vapor deposition of amorphous
silicon at the laboratory scale.


Laboratory Scale Research
A typical laboratory scale physical vapor de-
position unit is shown in Fig. 4. The rate of evapo-
ration of any material is determined by the surface
temperature of the source material. For thermal
evaporation this is a function of bottle geometry,
the material surface area, and the design of the
source heater. To make a semiconductor film, the
material of interest is placed in the source bottle,
heated to the point at which it evaporates or sub-
limes, flows out of the source bottle to the sub-


pVCp, t-pg AHR FVF -(T-TI4)A.

FIGURE 5. Model equations.

state, and then condenses on the substrate, the
temperature of which is carefully controlled.
The modeling and experimental verification of
a model describing the rate of effusion for CdS
which dissociates and sublimes has been thorough-
ly discussed by Rocheleau et al [4]. The mass and
energy balance equations written for the material
to be evaporated are shown in Fig. 5 (nomencla-
ture in [4]). These equations can be solved
numerically, given the initial dimensions of the
material in the source bottle and the appropriate
constitutive equations for the flow through the
orifice in the source bottle. Fig. 6 gives the required
equations in terms of the mass flux, r, related to

Free gcR2p 2
Molecular r= (plgc/2 ")12 ( l-P) r= -- (P2- P2 )[4( ~--) xm
(l\,/R>l) .16pL R "
Viscous r= CY[Zp gc(Pl-P2)]"2 r= -- ( p2-P22)
(Xm/R<00 ) 16pL

FIGURE 6. Constitutive flow equations.

pgq through the area available for flow. The solu-
tion method is somewhat complex and complete de-
tails are given by Rocheleau et al [4] and Roche-
leau [6]. Solving the equations yields the rate of
effusion versus charge temperature, T1. A com-
parison of model prediction (solid lines) and ex-
perimental data (horizontal bars marked with the
wall temperature, T2) are shown for two different
orifices in Fig. 7. The heat transfer from the source
bottle walls to the subliming surface is the key
issue in predicting rate of effusion from the source
Another type of experiment in which cadmium
and sulfur are used in separate source bottles can
be used to obtain information on the surface phe-
nomena (Fig. 1). An extensive set of data has been
obtained by Jackson [5] who also was able to pre-
dict the impingement rate of the molecular beam
at any point on the substrate. The impingement
rate of cadmium and sulfur on the substrate was
calculated and the corresponding rate of CdS film


growth measured. About 1000 separate pieces of
data were obtained to verify the predicted model
behavior. The model equations for each species are
shown in Fig. 8. The rate of reaction of cadmium
to the CdS comprising the film is assumed to be

r(rxt, Cd) = k(CdS) [Cde][Ss]

This expression combined with the equations
shown in Fig. 8 yields

r(rxt,Cd) = K(CdS) [8(Cd) r(i,Cd)
-r(rxt, Cd) ] [8(S) r (i, S) r (rxt, S)]

The parameter, 8, is a condensation coefficient;
r (i, Cd or S) is the rate of molecular impingement
of Cd or S; r (rxt, Cd or S) is the rate of reaction
of Cd or S; and K is a modified specific reaction
rate constant whose detailed form is given by
Jackson [5].
Comparison of the model behavior with some
of the data is shown as Fig. 9 where the rate of
deposition of CdS is plotted as a function of the

1.2 1
numbers indicate wall temperature in oK
1.0 /

.5 .8
"E orifice set 4 1271

12 1 ~orifice set 3
.2 1179



charge temperature, K

FIGURE 7. Comparison of model behavior with ex-
perimental data.

rate at which cadmium is delivered to the sub-
strate. At low values of r (i, Cd) the rate of film
growth is proportional to the rate at which cad-
mium is delivered; as the film growth becomes sur-
face-reaction dependent, the lines curve. The hori-
zontal line indicates a region in which there is not
enough sulfur to react with all the cadmium being
delivered to the substrate.
Experimental evidence indicates that photo-
voltaic-grade CdS can only be made when the rate
of film growth is controlled by the rate at which

r(r xt, Cd)=r(i, Cd)-r(e, Cd)-r(r, Cd)
r(rxt, S)=r(i, S)-r(e, S)-r(r, S)

Cadmium Sulfide
I dM(CdS)
I dM(CdS) == -r(rxt, CdS)
M,(CdS)A, dt

FIGURE 8. Component mass balances.

cadmium and sulfur react on the surface to form
CdS. Furthermore, if sulfur is not present when a
cadmium molecule arrives at the surface, the
cadmium will reflect and not adhere. We are
just beginning to try to relate these observa-
tions to film properties. This second laboratory
scale study of Jackson's is an attempt to learn more
about the semiconductor chemical reaction engi-
neering necessary for the field to progress in an
orderly fashion. A much more complicated set of
chemical equations will be considered in the section
on chemical vapor deposition.

Unit Operations Scale Research

For large area applications uniform defect-
free film with the required properties must be de-
posited over areas on the order of meters in
dimension. It may be necessary to deposit on a
moving substrate to lower costs to the level re-
quired to make a large area application like photo-
voltaics economically feasible. In this section the
cooperative research efforts in photovoltaic unit
operations between the Department of Chemical
Engineering and the Institute of Energy Con-




3.3I I I I I


67 n 22
Model Predictions
Constant Sulfur Flux Indicated x 108 mi se
220 C Substrate Temperature
0 o I

0 8.3 16.7 25 33.3 41.7 50 58.3 66.7 75
r(i, Cd) x 108 Kg-moles/m2 sec

FIGURE 9. Deposition versus incident rate of cadmium
in cadmium sulfide.


I ( ( I i I I I

83.3 91.7 ICO


The theoretical and experimental
work of Rocheleau, Rocheleau et al, and Jackson
provide the verified models of the laboratory scale
batch experiments that can be used to design
apparatus and experiments at the
unit operations scale.

version at the University of Delaware will be de-
CdS is the wide bandgap window semicon-

FIGURE 10. Thin-Film polycrystalline solar cell.

ductor for the following polycrystalline hetero-
junction cells (Fig. 10)

All of the above devices have achieved con-
version efficiencies (percentage of sun energy con-
verted to electricity) of just over 10%, although
in the case of CuS cells some ZnS had to be al-
loyed with the CdS. At this conversion efficiency,
inexpensive electrical power generation begins to
become feasible if modules containing the indi-
vidual cells can be made cheaply. A first step in
meeting this goal is to find a way to continually

Approximate Throughput and Size Specifications

Unit Ops.
Lab. Scale Scale
Exps. Expts.

Chamber (m3)


1-100 1,000-20,000 100,000-1,000,000

0.1-0.5 1-3


FIGURE 11. Unit operations scale deposition system.

deposit the CdS on a moving substrate.
The theoretical and experimental work of
Rocheleau [6], Rocheleau et al [4] and Jackson
[5] provide the verified models of the laboratory
scale batch experiments that can be used to design
apparatus and experiments at the unit operations
scale. Throughputs and chamber sizes for typical
units are shown in Table 1 for the three scales of
operation of interest. The laboratory scale ex-
periments are almost always batch experiments
on a static substrate. The unit operations and com-
mercial scale equipment for photovoltaics need
to be designed for continuous deposition on a mov-
ing substrate.
A sketch of the unit operations scale equip-
ment used at the Institute of Energy Conversion

Deposition Unit Specifications

Chamber-1.28 m diameter X 1.34 m long
Vacuum-Pump-down to 5 X 10-6 torr in 2 hours
Capacity-500 mm maximum width
250 mm roll diameter (200 m Cu foil)
Speed-1.2 to 12 cm/min
Zone-45 cm X 10 cm
Source-A proprietary design (U.S. Patent
4,325,986) providing:
Constant rate-2 micron/min
Uniformity over 20 cm wide zone
80% CdS utilization
Temperature-200 to 2250C
Throughput-0.6 to 6 m2/shift


Windup Roll

Web or /


Payoff Roll
Vacuum Chamber

SGuide Roll

- Evaporation

is shown in Fig. 11. This piece of equipment was
designed using model equations similar to those
presented as Fig. 5 and 6 and modified for a multi-
orifice geometry and the different source-substrate
geometry of the unit operations scale equipment.
It was also necessary to expand the energy balance
analysis to include radiative heat transfer between
the source and substrate. The model equations,
their behavior and their influence on the design
and operation of the unit operations scale reactor
are given by Rocheleau [6] and Griffin [7]. The
specifications determining the equipment are
shown in Table 2 and a photograph of the equip-
ment is shown as Fig. 12.

FIGURE 12. Photograph of unit operations scale deposi-
tion system.

The unit operations experimentation consisted
of controlled deposition of CdS on rolls of zinc
plated copper foil. Throughput of the foil ranged
from 180 to 600 cm2 per hour with film growth
rates ranging from 0.5 to 2 Mm/min. Substrate
temperatures were varied between 200C and
250C. At throughputs of 400 cm2/hour, up to
3000 cm2 of 25 im thick CdS was prepared in a
single run. Fig. 13 compares a cross-section of con-
tinuously deposited CdS with that of photovoltaic-
grade batch deposited CdS. X-ray diffraction con-
firms predominantly c-axis orientation for the
continuously deposited CdS. Resistivities of the
continuously deposited CdS films ranged from
about 1 to 100 ohm-cm. Resistivity of the best
laboratory CdS ranges from 1 to 10 ohm-cm.


FIGURE 13. Cross-Section of CdS film. Cross-section of
continuously-deposited CdS on left, cross-section of
batch-deposited CdS on right.

The principal means of evaluating the CdS was
to measure the photovoltaic response of cells fabri-
cated using the material from the unit operations
experiments. Results are summarized in Table 3
which shows the efficiencies of CdS/Cu2S cells
made using both laboratory scale and unit opera-
tions scale CdS. The Cu2S layer can be made using
a wet process by dipping CdS into a CuCl solution
or it can be made using a dry process in which
CuCI is evaporated onto the CdS and then allowed
to react with CuS. A quantitative description is
given by Brestovansky et al [8]. Cells made by the
dry process in the unit operations scale equipment
had both layers, CdS and CuS, continuously de-
posited on the moving substrate. Cells made by
the wet process had to have the Cu2S layer made
in a batch operation. All cells had an evaporated
gold front contact.
The efficiency figures show that the unit opera-
tions scale continuously deposited CdS is virtually
of the same photovoltaic quality as the laboratory
scale batch deposited CdS. It took some ten years
of research to achieve the efficiencies shown for
the batch deposited CdS. The continuously deposit-
ed CdS reached the efficiency shown in well under
two years of unit operations experimentation. This
could only have been achieved by drawing heavily
Continued on page 106.

Cell Efficiencies (CdS/Cu2S)


Wet Process
(CdS Only)
Dry Process

Unit Operations



Sn classroom



Singer Corporation
Silver Springs, MD 20904

Cornell University
Ithaca, NY 14853

THE TEACHING of thermodynamic phase dia-
grams poses problems which affect both the in-
structor and the students. The usual approach in
which the three-dimensional pressure-tempera-
ture-composition diagrams for binary fluid mix-
tures are represented on a two-dimensional page is
difficult for students to visualize. Traditionally, in
order to simplify this complex situation, 'cuts' at
constant pressure, temperature, or composition are
made to show a truly two-dimensional diagram de-
scribing the relationship between two of the three
independent variables. However, the inter-re-
lationship of all the variables involved is lost with
this approach, and the problem of comprehension
intensifies as the complexity of the phase behavior
increases. Construction of three-dimensional

Chandrashekhar D. Naik ob-
tained his B. Tech. degree in
chemical engineering from the
Indian Institute, India, in 1981.
He obtained an MS degree in
chemical engineering from
Cornell University in 1983 and
is currently employed at Singer
Link Simulations Systems Di-
vision in Silver Spring, Mary.
land. (L)
Paulette Clancy is current-
ly an assistant professor in
chemical engineering and as-
sociate director of the Manu-
facturing Engineering Program at Cornell University. She received
her BS degree at the University of London and a D.Phil. degree at the
University of Oxford. She held fellowships at Cornell University and
at London University before joining the faculty at Cornell in 1984.
Keith E. Gubbins is currently the Thomas R. Briggs Professor of
Engineering and director of chemical engineering at Cornell Uni-

models offers an alternative solution, but they are
difficult and time-consuming to produce and offer
no possibility for student interaction.
At Cornell an alternative to traditional ap-
proaches was sought to improve the quality of
teaching and the level of comprehension of the
students. Computer graphics offers an innovative
solution to these difficulties: present-day graphics
hardware can perform rotational transformations
of three-dimensional images almost instantaneous-
ly and allows extensive manipulation of the viewed
image by the user, making this an extremely
powerful tool eminently suited to the task at hand.
During the past two years a highly interactive
"user friendly" graphics package has been de-
veloped depicting the three-dimensional phase be-
havior of binary fluid mixtures, and it has been
used in both undergraduate and graduate courses
with great success.

The Computer Aided Design Instructional

Copyright ChE Division, ASEE, 1985

versity. He received his BS and PhD degrees at the University of
London, and was on the staff at the University of Florida from 1962-
76, when he moved to Cornell. He has held visiting appointments at
Imperial College, London, at Oxford University, and at the University
of California at Berkeley. He has co-authored two books, Applied
Statistical Mechanics (Reed and Gubbins) and Theory of Molecular
Liquids (Gray and Gubbins). (R)


FIGURE 1. An example of a typical workstation show-
ing the Evans and Sutherland vector refresh graphics
monitor with VT100 terminal, electronic tablet and

Facility (CADIF) at Cornell houses "state-of-the-
art" computer graphics equipment used solely for
teaching (and developmental work towards edu-
cation). The central computers for the facility are
Digital Equipment Corporation (DEC) VAX ma-
chines, an 11/780 and an 11/750, running the
VMS operating system, with DEC PDP 11/44 ma-
chines as post-processors. Attached to these ma-
chines are two different types of graphical display
equipment for student use: vector refresh stations
with dynamic three-dimensional capabilities and
color raster stations for applications requiring
color. In this application, the vector refresh work-
stations were used exclusively, these being the
highly sophisticated Evans and Sutherland Multi-
picture Systems. Each workstation has a digitiz-
ing tablet and electronic stylus as the primary in-
put peripheral for cursor control, with a DEC
VT100 terminal for alphanumeric input. A typi-
cal workstation is shown in Fig. 1. An electrostatic
plotter is also available for hardcopy output, a use-
ful and necessary addition allowing students to
submit a record of their progress to the instructor.
The software, which is the heart (or perhaps
more appropriately, the brains) of this application,
was written in FORTRAN making use of system
graphics software routines developed at CADIF.
The consideration of ergonomic factors to produce
a well designed application in terms of its "user-
friendliness" was considered essential to promote
ease and clarity of use of the graphics package as

well as increased flexibility. Some of the ways this
was achieved include the following points: ex-
tensive 'help' messages and prompts for required
input were made available, clear consistent
"menus" for optional choices of interactive re-
sponse by the program were produced, and the
ability to recover from mistakes or unintentional
"miskeying" was provided. It was an original
tenet of this study that students should not have
to read a computer manual before using the pro-
grams. The emphasis is thus on learning engineer-
ing principles without requiring prior expertise
in computing.

The phase equilibrium data for binary mix-
tures needed for the representation of the phase
diagram (i.e. pressures, temperatures and com-
positions) were generated using a theoretical
model. The original Redlich-Kwong equation of
state was employed for this purpose, chiefly be-
cause of the simplicity of its representation (since
only two adjustable parameters are involved) and
the reasonably realistic description of binary phase
behavior it provides. This approach was also used

It has proven to be extremely
popular with the students, and has raised the
level of comprehension of this potentially difficult
subject above that achieved previously by
using conventional means.

by Willers and Jolls [1] who produced three-
dimensional phase diagrams on a Cal Comp plotter
using the same equation of state.
The well-known Redlich-Kwong expressions
describing the conditions for vapor-liquid or liquid-
liquid equilibrium in terms of the pressures and
chemical potentials of both phases were used to
generate data points P, T, VL, VG, X1, and y1 cover-
ing a region from the higher of the pure com-
ponent triple points to a temperature above both
critical temperatures. The nonlinear equations in-
volved were solved using a multidimensional
Newton-Raphson [2] technique. Close to the criti-
cal region, however, convergence problems were
encountered which were due, we believe, to
singularities in the Jacobian matrix. These difficul-
ties were overcome by using the Marquardt [3]
method which combines the advantages of New-
ton-Raphson and Steepest Descent algorithms.
Here Argonne National Laboratory's 'MINPAK'


Computer graphics offers an innovative solution present day graphics hardware can
perform rotational transformations of three-dimensional images almost instantaneously and allows
extensive manipulation of the viewed image by the user an extremely powerful tool .

software package provided the subroutine for a
Marquardt method of solution. Solving for vapor-
liquid critical lines also provided a challenge.
Neither of the previous techniques mentioned was
able to reproduce these highly non-linear equa-
tions, and a specialized algorithm due to Deiters
[4] was employed for their solution.
Scott and Van Koynenburg [5, 6] classified the
experimentally observed types of fluid phase dia-
grams into six classes, based on the presence or
absence of three-phase lines and their connection
with the critical lines. So far we have been able
to cover the two simplest classes, I and II, although
extension of the programs to cover the other
classes is well underway. These more complex
systems will provide an even more striking visual
illustration of the advantage of using computer
graphics. In classes I and II both components have
similar critical temperatures with the vapor-liquid
critical line passing continuously between them.
In class II, however, the mixture is more non-ideal
and exhibits liquid-liquid immiscibility at low
temperatures. For this class, in addition to the
vapor-liquid region encountered for class I, two
other regions exist in the phase diagram, those of
liquid-liquid equilibrium and a three-phase liquid-

Examples of Classes I- and II- Type Behavior Available
for Display By the User



Azeo- Temp Pressure
tropet Range K+ Range Barst
N 425-590(594) 2.38-24.30(33.7)

N 425-590(596) 2.39-21.92(45.2)

N 425-615(617) 4.18-36.70(37.4)

Y- 420-530(535) 11.86-51.18(55.6)

N 80-224.5(228)

Y,Het 240-505(506)
,+ Horn


tN,Y = no, yes; + = positive or negative deviation from
Raoult's law; Het, Hom = heterogeneous, homogeneous
+The figures in parentheses are the highest values of T,
and Pc occurring along the critical line.

FIGURE 2. Three-dimensional phase diagram for a typi-
cal class I system, pentane-nonane. The solid and
dashed lines show the vapor and liquid boundaries,

FIGURE 3. P-T-x diagram for the class II system, me-
thane-tetrafluoromethane, showing the coexisting
vapor-liquid equilibria (solid lines for the vapor, dashed
for the liquid) and the region of liquid-liquid immisc-
ibility (shown as solid vertical lines). Superimposed on
the diagram (shown in bold) is a T-x cut at a pressure
of 0.03 bars.


liquid-gas line. Examples of the binary systems
chosen to illustrate the phase behavior of classes I
and II are shown in Table 1. Some of the available
systems exhibit azeotropic phenomena with either
positive or negative deviations from Raoult's Law,
and of either a heterogeneous or homogeneous
nature. Photographs depicting some of the com-

FIGURE 4. A 3-D view of another class II system, CF,, -
CH12. The original display has been rotated by 180
degrees and tilted downward so that the view is from
the high-temperature end and somewhat above the
phase diagram. The regions of vapor-liquid equilibria
(showing an azeotrope) and liquid-liquid equilibria
(solid vertical lines) are clearly visible.

SYSTEM: C5F12(1)-Pentane(2)

I0o mTrArP 9479, F VArni






FIGURE 5. Two-Dimensional x-y diagram for the system
C5F12-C5sH, derived from the three-dimensional phase
diagram at 247.26K. This diagram shows the character-
istic behavior of an azeotropic system with liquid-liquid
immiscibility, as shown by the horizontal portion of the

puter-generated phase diagrams are reproduced
in Figures 2-6 illustrating the kind of image dis-
played for the user to manipulate.


The image of the phase diagram (e.g. as in
Figs. 2-6) can be manipulated by the user by
means of an electronic tablet and stylus (pen). As
the pen is moved over (and slightly above) the
surface of the tablet, a cursor in the form of cross-
hairs moves over the display. When the pen is
pressed down onto the tablet the graphics program
is activated and performs an operation appropri-
ate to the area of screen chosen, given that such an
area is one of the several specially designated parts
of the screen called "windows" on a so-called
"menu" of options.
In this application of computer graphics the
menu contained the following list of 'entrees' for
the user to select
a) READ: Allows the user to choose different binary
systems to examine by supplying one of a given
set of data file names via the terminal.
b) ORBIT: This allows the phase diagram to be ro-
tated about its pressure and composition axes in a
continuous fashion as required.
c) PAN: Allows horizontal or vertical translation of
the phase diagram.
d) ZOOM: Provides closer examination of a chosen
area of the image by scaling the diagram up or

SYSTEM: CSF12(1)-Pentane(2)

L I :i!N.




FIGURE 6. A view of the CsF12-C5Hi2 phase diagram as
it appears in the initial orientation on the screen. Solid
and dashed lines have the same meaning as in Figure
2. A P-x-y cut is shown superimposed in bold on the
diagram at a temperature of 259.9 K.




e) STRETCH: Scales either of both of the P, T axes
relative to the composition axis for ease of viewing.
f) HELP: Summons the HELP text.
g) RESET: Voids all previous manipulations and resets
the system to the beginning of the program.
h) SNAP: Produces a hard-copy image of the screen
on a nearby plotter.
i) EGRESS: Allows the user to terminate his or her
j) T-X, P-X, P-T: Each of these windows allows a
particular highlighted "cut" of the phase diagram
to be chosen by the user as shown in Figure 3 for a
T-x cut at 0.03 bars for the system methane-carbon
tetrafluoride, and in Figure 6 for a P-x cut at
259.9K for CF12-pentane.
k) CUT: Produces a P-x, P-T or T-x "cut" displayed
alone (i.e. not superimposed on the whole phase
diagram) depending on which of these three
windows (P-X, P-T or T-X) was last active.
Multiple cuts (of P-x at different temperatures for
example) may be displayed simultaneously.
1) Produces an x-y plot at constant temperature, as
shown in Fig. 5 for the system CF,,-pentane at
247 K.
A 16mm movie lasting approximately thirteen
minutes has been prepared to illustrate the cap-
abilities of this graphics package; this was pre-
sented at the 1983 AIChE annual meeting in
Washington, D.C.


The interactive graphics package illustrating
the phase behavior of binary mixtures which has
been described in this paper has been used within
the chemical engineering curriculum at Cornell
since the fall semester of 1982. It has proven to be
extremely popular with the students, and has
raised the level of comprehension of this potential-
ly difficult subject above that achieved previously
using conventional means. The major advant-
age lies in the suitability of computer graphics as
a means of visualizing three-dimensional objects
(here the PTx phase space) ; the capability of the
hardware to perform rapid and continuous rota-
tions of the image; and, perhaps most importantly,
the opportunity to interact, manipulate and con-
trol the image observed on the screen, brought
about by flexible "user-friendly" software. All
these features combine to contribute to the success
of this technique in undergraduate instruction. O

It is a pleasure to thank the Gas Research
Institute for partial support of this work.
1. K. R. Jolls and G. P. Willers, Cryogenics, 329, June
2. J. Stoer and R. Bulirsch, Introduction to Numerical
Analysis, Springer Verlag (1980).
3. D. W. Marquardt, J. Soc. Ind. & App. Math, 11, 431
4. U. K. Deiters, Diplomarbeit, Univ. of Bochum, West
Germany (1976).
5. R. L. Scott, Ber. Buns. Phys. Chem., 76, 296 (1972).
6. R. L. Scott and P. H. Van Koynenburg, Disc. Fara.
Soc., 49, 87 (1970).

Qs book reviews

by Joseph A. Schetz
Prentice Hall, Inc., NY (1983)
Reviewed by
O. T. Hanna
University of California, Santa Barbara
This book on Boundary Layer Theory is indi-
cated by the author to be applicable for students
in mechanical, aerospace, chemical, civil, and
ocean engineering. Some people would doubt that
anyone could succeed in such a broad task. The
author's stated goals for this book include (i) pro-
viding an understandable coverage of advances in
turbulence modeling, (ii) presenting application
of large digital computers to boundary layer prob-
lems, and (iii) treating mass transfer in an inte-
grated manner with momentum and heat transfer.
It would appear that the first goal has been met
reasonably well; achievement of the second goal
is questionable, and the third goal has definitely
not been met to the satisfaction of chemical engi-
The book is generally well written and well-
organized. The coverage of laminar flows includes
chapters on integral and differential equations of
flow together with approximate integral solutions
and exact similarity solutions. Unfortunately al-
most all of this material is available in a number
of other sources and hardly any of it is more
recent than 1960. The meager coverage of mass


transfer is likely to be of little interest to chemical
engineers. Chapters 4 and 5 do contain some use-
ful discussions of numerical solutions of bound-
ary layer problems. However, there are no example
problems or computer programs.
The major contribution of this book would ap-
pear to lie in Chapters 6 through 9, on turbulence
modeling, which constitute more than half the
length of the book. This material includes a useful
historical perspective and spans the complete
range of engineering approaches in this area up
to the present time. The chronological discussion
of work in turbulence modeling beginning with
early mixing-length theory and progressing up
to algebraic and various partial differential equa-
tion models should be of interest and value to
chemical engineering. This discussion also inte-
grates well the contributions to modeling from
both experimental and theory.
In summary, the present book seems somewhat
disappointing in its treatment of laminar bound-
ary layers, but in contrast it contains material on
turbulent momentum transfer which should be of
interest to chemical engineers. In this context the
book can be recommended as a useful reference. E

by L. J. Cralley, L. V. Cralley, and J. E. Mutchler
Macmillan Publishing Company, New York,
1984: $60.00
Reviewed by
Klaus D. Timmerhaus
University of Colorado
This is the second volume of a new three-
volume series that is being prepared to
provide recognition, measurement, and control
of potential hazards normally present in various
industrial plant operations. The first volume
covered process flows while the third volume will
treat equipment selection, layout and building de-
This volume, divided into two major sections
of unit operations and product fabrication, en-
compasses a broad range of industries with au-
thoritative information contributed by specialists
from these industries. In the first section twenty-
three contributors discuss unit operations as dis-
tinct entities along an industry-wide concept.
Some of the unit operations considered include
filtration, clarification, mixing, blending, grind-

ing, and spray, vacuum, freeze and fluidized bed
drying. The second section includes thirteen con-
tributions which cover the operations and pro-
cedures for assembling parts and materials into
final products. The industries considered in this
latter survey range from such basic industries as
storage battery and tire manufacturing to the
high technology industries of semiconductor and
liquid scintillation counter manufacturing. One
may argue with the manufacturing processes that
were selected by the editors; however, the breadth
of the selected processes and the hazards associ-
ated with these processes should provide a good
introduction to the hazards associated with those
manufacturing processes that were not included.
Even though most contributors to this second
volume have adequately described each step in the
unit operations and product fabrication flow of a
specific manufacturing process and have included
a discussion of the various health hazards that
may be encountered with suggestions for their
monitoring and control, many engineering read-
ers will be disappointed by the qualitative ap-
proach taken by the contributors to this important
subject. Only a few of the chapters in the volume
have included quantitative information that
would be necessary in the design and construction
of process equipment that minimizes or eliminates
identified industrial hygiene hazards. Where such
quantitative information is included, it is general-
ly quite sketchy and incomplete forcing the design
engineer to consult other literature sources. Un-
fortunately, no guidance to such quantitative data
is included by any of the contributors. Chemical
engineers will also be somewhat disappointed in
this volume because the "unit operations" portion
of the title implies that some of the contributions
will examine the conventional unit operations as-
sociated with heat, mass and momentum transfer.
However, many of the key unit operations such
as distillation, absorption, extraction, evaporation,
heat transmission, etc. found in most typical
petroleum and chemical processing plants have not
been included.
Nevertheless, this volume does manage to bring
together a wealth of experience in a broad range
of industries and will aid engineers, managers,
and industrial hygienists to more fully recognize
potential hazards of industrial processes. This, in
turn, will permit these professionals to evaluate
such hazards and take the necessary steps to
effectively control the problem. C





University of Windsor
Windsor, Ontario, Canada N9B 3P4

ment for an undergraduate laboratory is not
a simple task. This is because the experiment will
have to meet certain criteria, viz
It is safe
It is simple and cost effective
It is instructive
Its analytical needs must be simple and easy, to meet
the time constraints of an undergraduate laboratory
Anderson [1] developed a tubular flow reactor
experiment for an undergraduate laboratory at
Princeton that utilized the system acetic an-
hydride-water. This particular experiment re-
quires relatively elaborate safety precautions.
Moreover, since the reaction is exothermic, rather
expensive temperature control equipment is re-
quired. Samples taken at the reactor inlet and
outlet are analyzed by the aniline-water method
which is relatively lengthy and subject to errors.
Hudgins and Cayrol [2] utilized the basic de-
sign of Anderson in developing a simple and
interesting experiment. They utilized the classical
reaction system of crystal violet dye neutraliza-
tion with sodium hydroxide. This system was
studied earlier by other investigators, mainly in a
batch reactor (Carsaro [3]). The two novel aspects
of the Hudgins-Cayrol experiment compared with
that of Anderson are
A colour change can be seen between the inlet and
outlet of the reactor
The temperature constraint is removed. This makes
the experiment operable at room temperature

Also, from the safety standpoint, a relatively dilute
sodium hydroxide solution (0.04 N according to
Hudgins and Cayrol) is used.
However, the experimental set-up design
given by Hudgins and Cayrol can be significantly

Copyright ChE Division, ASEE, 1985

A. A. Asfour received his B.Sc. (Hon) and M.Sc., both in chemical
engineering, from Alexandria University, Egypt, and a Ph.D. from
the University of Waterloo, Waterloo, Ontario, Canada. He joined
the research department of Imperial Oil, Sarnia, Ontario for the
period 1979-1981. In 1981 he joined the Chemical Engineering De-
partment at the University of Windsor, Windsor, Ontario, Canada. His
research interests are in the area of mass transfer in three-phase
fluidized beds and in membrane processes.

improved. The design improvements suggested in
this article should make the experiment easier to
run and control, significantly improve the repro-
ducibility of results, and expedite the process of
data collection within the time constraints of an
undergraduate laboratory.
The main objectives of this experiment are
To study the effect of residence time on conversion
in a tubular flow reactor
To compare the experimental conversions with those
obtained from plug-flow and laminar-flow reactor

As it was established by Corsaro [3], the re-
action between crystal violet dye and sodium hy-
droxide is of the first order in the concentration
of each of the reactants, i.e., the reaction is of
the second order. However, the reaction can be
made pseudo-first order if sodium hydroxide is
used in great excess with respect to crystal violet
dye. In other words
-rdye = k' [dye] (1)


The value of the rate constant, k', is needed for this experiment. Students are requested to
run a batch experiment to determine the value of k' at the same temperature of the flow experiment.. (and) to
prepare their own calibration curve of the dye concentration versus absorbance.

For the purpose of this experiment, 0.02 N sodium
hydroxide solution is used with 6.86 x 10-4 M dye
solution, i.e., the sodium hydroxide concentration
would be about 282 times that of the dye, if equal
volumes of reactants are used.
Experimental conversions are calculated, as
will be described later, and compared with theo-
retical conversions predicted from the plug-flow
model and the laminar-flow model.
For a first order reaction in a plug-flow re-
actor, the following equation applies assuming
constant density of reaction mixture:
V 1
7= =- I-n(1-x) (2)
Vo k
=- In CA (3)
k' CAo
If CA is taken as [dye]e, i.e., the dye concentration
at reactor exit and CAo as [dye]i, i.e., the dye con-
centration at reactor inlet, then one can rewrite
Eq. (3) as follows:
V 1 [dye],
T- --- n (4)
vo k [dye]e
For a first order reaction in a laminar-flow
reactor, the following equation applies assuming

no change in volume by reaction as well as no
mixing in both radial and axial directions (4)

x= 1-i 2 E ( ) + (- -1)exp(-Nn/2)
NR =k'r
V Lrr2
V, Vo
The function E (y) is defined by:

exp (-4)
E(y) = d

The function E (y) is tabulated in standard tables
as -Ei(-X).

A schematic diagram of the proposed experi-
mental set-up is given in Fig. 1. The experimental
apparatus is comprised of the following compo-


Constant Head




FIGURE 1. Schematic diagram for the experimental
set-up. M: mixer, P: pump, R: rotameter, RES: reser-
voir, S: sampling point, T: constant head tank, TFR:
tubular flow reactor.

:(RES 1) 200-L polyethylene tank for
the sodium hydroxide solution
(RES 2) 20-L polyethylene tank for
the dye solution
: (T1) 20-L polyethylene tank for the
sodium hydroxide solution
(T2) 4-L polyethylene tank for the
dye solution
:(P1) Magnet drive gear pump; Model
P/N 81152 manufactured by
Micropump Corp., Conford, Cali-
fornia. Purchased from Cole
Parmer Co.
(P2) Centrifugal pump. Cole Parmer
catalogue No. K-7004-30
: (M) Little Giant Pump. Model 2E-
38NT. Purchased from Can Lab
: (R1) Size R-6-15-A rotameter. Max
flow 450 ml/min with SS-float.
Purchased from Brooks Instru-
ment Co.
(R2) Size R-6-15-B rotameter. Max
flow 1300 ml/min with SS-float.


Purchased from Brooks Instru-
ment Co.
: 40 meters of 3/8 in. I.D. Tygon tubing,
wound on spool (made of lexan),
28 cm in diameter and 55 cm in
: needle valves to adjust flow
: Spectronic 20 (Bausch & Lomb) modi-
fied, as will be described later, to
provide continuous measure-

Two polyethylene tanks (RES 1 and RES 2)
of capacity 20 liters and 200 liters serve as reser-
voirs for the crystal violet dye and sodium hy-
droxide solutions, respectively. Two pumps (P1
and P2) are employed to pump the reactants to
two constant head tanks (T1 and T2). The over-
flows from the constant head tanks are returned
to their respective reservoirs. The underflows from
the constant head tanks go via rotameters (R1
and R2) to a small pump that acts as a mixer
(M). The reactant streams are mixed in the mixer,
M, and are pumped through the reactor. The
tubular reactor is in the form of a helical coil
wound on support. Connections are provided at
the inlet and outlet of the reactor to the flow-
through curvettes of the spectrometers.
The main advantages of the proposed experi-
mental set-up over that suggested by Hudgins
and Cayrol are

Reservoirs and constant head tanks are used. This
arrangement provides more stable rotameter opera-
tion, especially at low flow rates.
A flow-through accessory which is simpler in design
and operation than that suggested by Hudgins and
Cayrol has been used. The flow-through accessory
shown in Fig. 2 allows one to use Spectronic 20 for
continuous measurements.


Due to the limitation of the headroom in most
undergraduate laboratories, the constant head
tanks (T1 and T2) are placed about 3 meters above
the rotameters' level. This limitation makes it
only possible to attain maximum flow rate of 1300
ml/min of NaOH. The maximum flow rate of the
dye is set at about 135 ml/min.

*One of the reviewers suggested the use of polyethylene
instead of Tygon tubing, which discolors to a deep purple
making it difficult to observe gradual colour-change
along the reactor. It is believed that polyethylene is
more resistant to the dye than Tygon tubing.

Cap made ---
of blue glass



Parafilm used as
a seal

Spectronic 20

FIGURE 2. Flow-through cuvette for the Spectronic 20.

The flow rates of sodium hydroxide and the
dye solution are set such that the ratio is 9:1. One
should start at the highest possible flow rate to
expel all air bubbles from the reactor.
One should wait for slightly longer than the
residence time, for a particular flow rate, for
steady-state to be reached. The reaction mixture
is then allowed to flow through the Spectronic 20
flow-through cuvettes and the readings are re-
corded. Usually, one waits for two minutes and
takes another reading as a duplicate. Experience
has shown that the Spectronic 20 readings are
highly reproducible. Other flow rates of NaOH and
dye solution are chosen, keeping the flow rates
ratio 9:1 as before, and the Spectronic 20 read-
ings are recorded. The experiment usually lasts
for one hour provided that the solutions are pre-
pared prior to the laboratory period.

Holding Time and Conversion Data

Holding Exp.
Time Reynolds PFRM LFRM Conversion
min Number conv. conv. x

6.42 791 71.7 63.3 71.5
4.3 1181 57.1 50.3 53.3
3.23 1651 47.1 41.6 44.5
2.54 2000 39.4 34.96 36.9
2.13 2385 34.3 30.7 33.85




The value of the rate constant, k', is needed
for this experiment. Students are requested to run
a batch experiment to determine the value of k'
at the same temperature of the flow experiment.
This has proven worthwhile, since temperature
fluctuations in most undergraduate laboratories
do not allow conducting a batch experiment at the
beginning of the semester and giving the value
of k to the students to perform the required calcu-
lations. Also, students are required to prepare
their own calibration curve of the dye concentra-
tion versus absorbance. This leads to better results.

Table 1 reports the residence time and the con-
versions from the plug-flow reactor model
(PFRM), laminar-flow reactor model (LFRM)
and the experimental conversions. Fig. 3, also,
depicts the conversions against the residence
time. The data reported in Table 1 and Fig. 3 were
obtained from an experiment conducted on the
set-up available in Windsor.
It is clear from Fig. 3 that, as expected, the
experimental conversions fall between the con-


0.6 +



0.3 II I
2 3 4 5 6 7
HOLDING TIME, 7 (min.)

FIGURE 3. Comparison between conversion obtained
from experiment and those obtained from LFRM and

a special fall issue devoted to graduate education. This issue
consists 1) of articles on graduate courses and research written
by professors at various universities, and 2) of announcements
placed by ChE departments describing their graduate pro-
grams. Anyone interested in contributing to the editorial
content of the fall 1985 issue should write the editor, indicating
the subject of the contribution and the tentative date it can be
submitted. Deadline is June 15th.

versions obtained from the two theoretical models,
viz., the PFRM and LFRM.
It is worthwhile to note here that the data re-
ported by Hudgins and Cayrol indicate that the
experimental conversion curve crosses the LFRM
curve at short holding times, i.e., experimental
conversions are lower than those predicted by
LFRM, which is not possible. Such results may be
attributed to the obvious design flaws in the set-
up reported by those authors.
The change of colour of the reaction mixture
between the inlet and outlet of the reactor is due
to the conversion along the reactor. Such a visual
effect helps the students to integrate the labora-
tory experiment with what they learned in the
lecture part of the course about conversion in
tubular flow reactors. O

1. Anderson, J. B., "A Chemical Reactor Laboratory for
Undergraduate Instructions," Princeton University,
2. Hudgins, R. R., and B. Cayrol, "A Simple Tubular
Reactor Experiment," CEE, XV, 1, 26, 1981.
3. Corsaro, G., Chem. Educ., 41, 48, 1964.
4. Holland, C. D. and R. G. Anthony, Fundamentals of
Chemical Reaction Engineering, Prentice-Hall, Engle-
wood Cliffs, N.J., 1979.





= subscript symbol for initial
= subscript symbols for reactor inlet and
exit, respectively
= concentration of component A, (mole/L)
= pseudo-first order rate constant, (min-1)
= length of reactor tube, (m)
= k' = reaction number for a first order
= inside radius of reactor tube, (m)
= reaction rate, (mol/L.min)
= volumetric flow rate, (L/min)
= reactor volume, (m3)
= conversion
= V/vo = space time (min)


OnI lecture

The B. C. (Before Computers) and A. D. of


University of Utah
Salt Lake City, UT 84112 1

T HE ART OF DISTILLATION and other multi-
component, multistage separation operations
has been practiced since antiquity. Although de-
scribing equations for distillation were formu-
lated before 1900, flexible, efficient, and robust
procedures for solving the equations did not ap-
pear in the literature until after the availability
of digital computers beginning in 1951. This
paper is keyed to that date with B.C. referring to
"before computers."
In 58 B.C., Sorel developed the first theoretical
equations for simple, continuous, steady-state
distillation, but they did not find wide application
until 30 B.C., when they were adapted to a rapid
graphical construction technique for binary
systems by Ponchon and then Savarit. This was
quickly followed in 26 B.C. by a much simpler, but
restricted, graphical technique by McCabe and
Thiele. Computer methods have largely replaced
the rigorous Ponchon-Savarit Method, but the
graphical McCabe-Thiele Method is so simple and
so illustrative, it continues to be popular.
A modern version of Sorel's equations (shown
in Fig. 1) includes, in the case of a partial con-
denser, total and component material balances and
an energy balance around the top section of the
column. Phase equilibrium on each tray is ex-

The development of a separation
process can be an exciting experience when
computers and computer programs are available to
perform the tedious calculations and allow
time for more consideration of synthesis
and optimization aspects.

*Tutorial Lecture presented at 92nd ASEE Annual Con-
ference, Salt Lake City, Utah, June 22-28, 1984.

J. D. Seader has been a faculty member at the University of Utah
since 1966. Prior to that, he was employed by Chevron Research and
Rocketdyne. His principal technical interests are equilibrium-stage
operations, process synthesis, process simulation, and synthetic fuels.
He is a Director of the AIChE and was the Annual Institute Lecturer in
1983 for AIChE. He prepared the section on distillation for the sixth
edition of the Chemical Engineers' Handbook and is a Trustee of

pressed in terms of component K-values and one
mole fraction sum per stage for either vapor or
liquid is needed. Similar equations are written
for the bottom section of the column and for the
feed stage.

A degrees of freedom analysis for the equations
was first developed by Gilliland and Reed in 9
B.C. A more thorough treatment for all types of
separations and other operations was reported
by Kwauk in 5 A.D.
If the equations and variables are counted,
for a column with N stages (including the con-
denser and reboiler) to fractionate a feed with
C components, it is found that the number of
equations is N (2C + 3), while the number of vari-
ables is N(2C + 4) + C + 7. Variables include
stage temperatures, pressures, vapor and liquid
flow rates and component mole fractions; feed

Copyright ChE Division, ASEE, 1985


The nature of the equations of Sorel and the difficulty of their solution for
multicomponent systems has long been recognized. The set of equations can be large in
number. For example, with 10 components and 30 equilibrium stages, the equations number 690. Sixty
percent of the equations are nonlinear, making it impossible to solve them directly.


flow rate, composition, temperature and pressure;
reboiler and condenser duties; and number of theo-
retical stages above and below the feed. The
thermodynamic properties, K and H, are not
counted as variables because they can be written
explicitly in terms of the other variables just
mentioned. The degrees of freedom or number of
variables that must be specified equals the differ-
ence between the number of variables and the
number of equations or N + C + 7.
A simple set of specifications would include
feed flow rate, composition, temperature, and

I TVN+ t = LN + VI
IN+1 VN+1 Xl,N LN + Y1,1 V]
HVN,1 VN+ L HLN Vi V1 0c

X1, M- 1 = YIM V + X+ N LN
1HLM L-0 + O; = HVH V, V HLN LN


SF + LF-1 + VF+1 = L + V

XIF-1 Y ZI, F + X,F- LF1 YIF+ VF+
F XI,F F ,, YI,, VF
HFF + HLF-1 LF-1 + F+ VF+1 HLF LF + HV VF
XIF YI ,F+ XFF = 1 F-

FIGURE 1. Modern version of Sorel's equations.

pressure; number of trays above the feed and
below the feed; the pressure of each stage; and
the reflux flow rate, L1. This totals N + C + 6,
which is one short of the number of degrees of
freedom. From my own practical experience in
5 A.D., failure to supply the one additional
specification can result in a calculational pro-
cedure that will never converge. The additional
specification might be the distillate flow rate V1.

The nature of the equations of Sorel and the
difficulty of their solution for multicomponent
systems has long been recognized. The set of
equations can be large in number. For example
with 10 components and 30 equilibrium stages,
the equations number 690. Sixty percent of the
equations are nonlinear, making it impossible to
solve them directly. The magnitude of the values
of the variables can cover an enormous range.
For example, the mole fraction of a very volatile
component at the bottom of the column might be
very small, perhaps 10-50. The value of a total
flow rate might be 104.
Commonly used procedures for solving such
sets of equations, as discussed by Henley and
Seader, are iterative in nature, requiring start-
ing guesses for some or all of the variables.
Early procedures were complete equation-tearing
methods, suitable for manual calculations, wherein
the equations were solved one-at-a-time in a se-
quential manner. With the advent of the digital
computer, partial tearing methods appeared,
wherein small groups of equations as well as
single equations were solved at a time. Most re-
cently, with the availability of larger and faster
digital computers, very flexible simultaneous-
correction methods appeared wherein all the equa-
tions were solved simultaneously by a modified
Newton's method.
An additional characteristic of Sorel's set of
equations is sparsity. That is, no one equation
contains more than a small percentage of the
variables. For example, for the case of 10 com-
ponents and 30 stages, no equation contains even


7 percent of the variables. This sparsity is due to
the fact that each stage is only directly connected
to, at most, two adjacent stages.
The nature of this sparsity has been exploited
in the development of the above-cited methods by
seeking certain sparsity patterns which are best
observed by the use of incidence matrices. The
rows of the incidence matrix represent the differ-
ent functions or equations being solved. The
columns of the matrix represent the different vari-
ables contained in the equations. Thus, for N =
30 and C = 10, the matrix is of size 690 by 690.
If a certain equation contains a certain variable,
some non-zero entry, such as an X, is placed in
the matrix at the corresponding location. Other-




x x x
x x

5 7 1 3 8 4 2 6
6 X
8 X X
4 X X
7 X X X
5 X X X X
2 X X X X
3 X X X X X X

FIGURE 2. Incidence matrices.

wise a zero or no entry is made.
The sparsity pattern depends upon the order
in which the columns and rows of the matrix are
arranged. The arrangement shown at the left in
Fig. 2 appears to be random without pattern. How-
ever, by interchanging certain columns and rows,
the lower triangular pattern shown at the right
is obtained. Such an organized pattern, if it can be
achieved, is highly desirable because it indicates
that the equations can be solved one-at-a-time
starting with the equation for the first or top row,
solving for the only unknown, and then proceed-
ing down the rows, equation-by-equation, solving
for one unknown at-a-time, but where necessary,
using values of previously computed variables.
Since at least 10 B.C., a number of other de-
sirable sparse matrix patterns have been recog-
nized. Shown at the left in Fig. 3 is a block-
diagonal pattern. The non-zero entries are all
contained within the interior bold-lined region.
Shown in the middle is a banded matrix, where




FIGURE 3. Desirable sparse matrix patterns.

all non-zero entries are contained on the main
diagonal and a few adjacent diagonals. Shown at
the right is a blocked-and-bordered matrix. Such
organized sparse matrix patterns, when they exist,
can be found readily by computer algorithms
such as the MA-28 subroutine of the Harwell li-


For the four organized patterns just dis-
cussed, specialized sparse-pattern computer al-
gorithms have been developed to solve, in an
efficient manner, linearized forms of the nonlinear
equations that describe the system. These sparse
matrix methods strive to: (1) eliminate storage
of zero coefficients and certain repetitious nonzero
elements, (2) reduce arithmetic operations, in par-
ticular those involving zeros, and (3) maintain
sparsity during computations.
Less-organized sparse-matrix structures can
sometimes utilize organized sparse-matrix methods
in an iterative manner by employing equation-
tearing strategies. The structure shown in Fig. 4
is lower triangular, except for an additional non-


1 2 3 4 5 6 7 8 9 10 11



FIGURE 4. Application of tearing to a sparse matrix.


I N\ \ -

zero entry at column 8 in row 3. The linearized
equations cannot be solved directly, one-at-a-time,
starting with Equation 1, because when Equation
3 is reached, the value of variable 8 is not known;
thus, Equation 3 cannot be solved for variable 3.
A tearing strategy can be employed to overcome
this difficulty, but an iterative calculational loop
or cycle, shown by the dashed border, involving
Equations 3 through 8, is necessary. Variable 8
shown as a circled X, is the single tear variable
which, when given an estimated value, results in
the tearing apart of that subset of equations so
they can be solved individually in order. At Equa-
tion 8 in the cycle, variable 3 is calculated and
the value obtained is compared to the value used
in Equation 3. If the two values are sufficiently
close, the cycle is converged and variable 9 in
Equation 9 is computed, followed by solution of

Fr = 2x{l + X2 85 = 0
F1 2x12 35- 0
F2o201 2 ,* 0-35=0

1 2

2 X X


2 1

2 X

1 8 110 5,9 x 108
2 5.9 x 108 3.0 x 1034







FIGURE 5. Sensitivity of the tearing method.

Equations 10 and 11 to complete the system.
Otherwise, a new approximation for the tear
variable must be determined and another iteration
of the cycle completed.
For the tearing strategy to be successful and
efficient, it is necessary that Equation 3 not be
sensitive to the assumed value of Variable 8. If
too sensitive, it is best, if possible, to reorder the
equations and variables to obtain a less sensitive
situation. As a simple example of this sensitivity,
consider the two equations shown in Fig. 5. If x,
is the tear variable and Equation 1 is solved first,


1 2 3 4 5 6

x x
x x[



1 3 5 2 4 6


FIGURE 6. Partitioning when convergence acceleration
methods ignore interactions among the variables.

convergence is impossible to achieve from any
starting guess of the tear variable. For example,
if the initial guess for x2 is 8, the sequence ob-
tained quickly diverges, as shown. After only one
iteration, x, has increased in value to 5.9-108,
which is far from the solution.
Alternatively, in tearing strategy #2, where
the two columns of the matrix are interchanged
to make x, the tear variable, convergence is readily
achieved, as shown, from any initial guess, even
x, = 500. The solution x, = 4, x2 = 9 is obtained
in just three iterations. These two drastically
different results are obtained because Equation 1
is very sensitive to the value of x, and almost in-
sensitive to the value of x,. Thus, in this example,
x, should not be the tear variable when Equation
1 is solved before Equation 2.
In the example just considered, the guess for
x,, at the beginning of each iteration is set equal
to the value computed from Equation 2 in the
previous iteration. This procedure, called succes-
sive substitution, can be slow to converge, and,
therefore, a number of some simple and some
complex procedures have been developed to ac-
celerate convergence. These procedures are par-
ticularly useful when more than one tear variable
must be used as in Fig. 6. The example at the left
has two tear variables. Some methods, including
successive substitution, bounded Wegstein, de-
layed Wegstein, and dominant eigenvalue, ignore
interactions among the variables. When inter-
actions do not exist, it should be possible to inter-
change columns and rows of the matrix to obtain


Chemical engineering educators
need to closely examine courses on separation
processes to make sure that students are
being instructed in modern and
efficient computational tools.


1 2 3 4 5 6

1 TI (X)
2 xl
2 X 0


5 X XX
6 X X X X

FIGURE 7. Convergence acceleration methods that ac-
count for interactions among the variables.

a block diagonal structure, shown at the right
of Fig. 6, which allows separate computations of
the two individual partitions, each containing just
a single tear variable.
The more common case is when interactions
among the tear variables exist, as shown in Fig.
7, where the two tear variables are 5 and 6. The
iterative cycle includes all equations. Then, par-
titioning is not possible and, if the interactions
are strong enough, convergence acceleration by
Newton's method or a quasi-Newton method, such
as that of Broyden, may be desirable.

The nature of the sparse-matrix pattern ob-
tained from Sorel's equations and the correspond-
ing calculational procedure depends on a number
of factors, including: (1) selection of the work-
ing equations, (2) selection of the variables, (3)
degree of flexibility in the specifications, (4)
order of arrangement of the equations, (5) order
of arrangement of the variables, (6) functionality
of the physical properties, and (7) method by
which any equations are linearized.
An additional factor that influences the nature
of the sparse matrix pattern of Sorel's equations
is the type of separation operation. In simple
distillation, a single feed is separated into two
products, a distillate and a bottoms; energy re-
quired to separate the species is added in the form
of heat by a reboiler at the bottom of the column
where the temperature is highest. Also, heat is
removed by a condenser at the top of the column
where the temperature is lowest. This frequently
results in a large energy input requirement and

low overall thermodynamic efficiency, which was
of little concern (except for cryogenic and high-
temperature processes) before 22 A.D. when
energy costs were low. With recent dramatic in-
creases in energy costs, complex distillation opera-
tions (described by Seader in the 6th edition of
Perry's Chemical Engineers' Handbook) and sys-
tems are being explored that offer higher thermo-
dynamic efficiency and lower energy input re-
quirements. Complex columns and systems may
involve multiple feeds, sidestreams, intermediate
heat transfer, multiple columns that may be inter-
linked, and in some cases, all or a portion of the
energy input as shaft work.

Simple and complex distillation operations
have two things in common: (1) both rectifying
and stripping sections are provided such that a
separation can be achieved between two compon-
ents that are adjacent in volatility, and (2) the
separation is effected only by the addition and
removal of energy and not by the addition of any
mass separating agent (MSA) such as in liquid-

V1 = 50 (YT 0.25)

L "10

F = 100
X( = 0.5
X, = 0.5

(L2 = 10)
(T3 = 200'F)


FIGURE 8. Example of specifications and tear variables
for top down, bottom-up, stage-by-stage method.

liquid extraction. Sometimes, other related
multiple-stage vapor-liquid separation operations,
such as refluxed rectification, reboiled stripping,
absorption, stripping, reboiled absorption, re-
fluxed stripping, extractive distillation, and azeo-
tropic distillation, as described by Seader, may be
more suitable than distillation for the specified
task. All these separation operations can be re-
ferred to as distillation-type separations because
they have much in common with respect to calcula-
tions of thermodynamics properties, vapor-liquid
equilibrium stages, and column sizing. For calcu-


lations involving such operations, prior to digital
computers, the factors influencing the nature of
the sparse matrix pattern from Sorel's equations
were chosen so that a complete tearing method
could be employed so the equations could be solved
sequentially one-at-a-time. Many techniques were
proposed, with the stage-by-stage methods of
Lewis and Matheson in 19 B.C. and Thiele and
Geddes in 18 B.C. being the most useful. In 6
A.D., features of these two methods were com-
bined into a single method, applicable to computa-
tions with a digital computer. Specifications are
the simple set discussed previously and illustrated
in Fig. 8 by an example involving two compon-
ents, benzene and toluene, and five theoretical
stages. The tear variables (2C + N-l) in number,
and typical initial guesses for them are shown in
parentheses. These tear variables include the com-
ponent mole fractions in the distillate.
The initial guesses for the distillate composi-
tion are conveniently obtained by using a rear-
rangement of the Fenske equation with the mini-
mum number of equilibrium stages set equal to
one-half of the total number of specified equilib-
rium stages. The sum of the component flow rates
in the distillate must equal the specified total dis-
tillate flowrate and, for each component, the sum
of the flow rates in the distillate and bottoms must
equal the feed rate.
The equations used are modifications of Sorel's
equations, and include total material balances,
component material balances, bubble-points, dew-
points, energy balances, an adiabatic flash for the
feed stage, and equations for re-estimating distil-
late mole fractions.
The incidence matrix, shown in Fig. 9, is lower
triangular except for the six tear variables, which
are represented as circled X's. They appear in
vertical columns in the order L2, Va, V4, Ta, Y1,1,
and Y2,1. The latter are the assumed distillate mole
fractions. The variables across the top and the
equations corresponding to the rows are ordered
by stage number, as shown, where the stages are
numbered from the top down, with 1 as the partial
condenser and 5 as the partial reboiler. The calcu-
lations begin in the upper left corner and proceed
down the diagonal. At the upper left corner, the
first two equations, which each contain only a
single unknown, are solved directly for the bottoms
rate L,, and the top-tray vapor rate V2. All remain-
ing equations are enclosed within the dashed-line
border, which contains all six tear variables. This
large, squared region is the outer loop that con-

j 1 1 2 5

i 4 1 3 1

x x -
xxx 42-
I X X X _
x xxx x
I X X X 2 I

xx XI
1-I x I1
I xxx x x x 1
x x x x xx x xL132_
/ X X XX V5
x X x 1, 1
Xx xix Xx 11
x x

x x xx x xxLxXx 3

x l X X I X
I xx x x xx x x x x I
I xx xx xxxx

o Tear Variable
-- Border of Inner Cycle

! Nonlinear Equatlon

FIGURE 9. Incidence matrix for top-down, bottom-up,
stage-by-stage method (6 A.D.).

tains 40 equations to converge.
The matrix is 42 x 42, rather than 35 x 35
(calculated from N[2C + 3]) because the feed-
stage temperature is computed with three differ-
ent equations, and V, and the vapor and liquid
mole fractions for the feed stage are computed
with two different equations.
Calculations for the outer loop-inner cycle
begin with stage 1, the partial condenser. All cal-
culations involve just linear equations in the case
of composition-independent properties, except for
T2, which is computed iteratively from a non-
linear dew-point equation. Variables computed
from nonlinear equations are boxed. Calculations
for stage 2 are completed next. Seven equations
are involved, with the last five contained in a
dashed inner loop, containing a single tear vari-
able, L,. At this step in the calculations, the stage
above the feed stage has been completed and the
calculation steps are now transferred to stage 5,
the partial reboiler. Here, two nonlinear bubble-
point equations are encountered for T, and T4, and
another tear variable, V,, is contained in a 5 x 5
matrix. Similar calculations are made next for
stage 4. Finally, the feed stage (3) is computed
by making an adiabatic flash calculation. The last
two equations at the lower-right corner are used
to compute a new estimate of distillate composition
by comparing the feed flash conditions with those
computed from the top-down and bottom-up stage


The method works best for feeds of narrow-boiling-rang e components. Otherwise, estimates of
distillate composition may be too uncertain and cause difficulty in convergence. If feeds are wide-boiling,
the bubble and dew-point calculations become sensitive and convergence is jeopardized.

calculations, with an adjustment made to main-
tain the specified distillate rate.
In all, four inner loops are contained within
one major outer loop. Thirteen equations are in-
fluenced directly by the guesses for the distillate
composition and ten others are influenced in-
directly by the corresponding bottoms mole
fractions. Thus, although the complete tearing
method is suitable for manual calculations, the
method is relatively inefficient and limited to in-
sensitive cases of simple distillation of nearly ideal
solutions with low reflux ratios.
The method works best for feeds of narrow-
boiling-range components. Otherwise, estimates of
distillate composition may be too uncertain and
cause difficulty in convergence. If feeds are wide-
boiling, the bubble and dew-point calculations be-
come sensitive and convergence is jeopardized. In
any event, convergence may be slow, unless special
acceleration techniques are used. However, the
calculation by hand of just a few stages for a
multicomponent mixture by this method is a very
worthwhile learning experience; one not to be

Some of the limitations of the previous method
were eliminated by McNeil and Motard (23 A.D.)
in their development of a stage-by-stage algorithm
that utilizes adiabatic or percent vaporization
flash calculations. Their technique requires
[(N-1) (2C + 2) 2] tear variables, but, as shown
in Fig. 10, initial guesses for all but (N-2) of
these variables can be set to zero. The (N-2) vari-
ables are interior stage temperatures, which are
relatively easy to estimate.
If the feed is predominantly liquid, the pro-
cedure starts with an adiabatic flash at the feed
stage followed by stage-by-stage adiabatic flashes
in a downward direction until the partial reboiler
is reached, where a percent vaporization flash is
made. Subsequent adiabatic flashes are made
moving up the column until the partial condenser
is reached and another percent vaporization flash
is made. Additional sequences of flash calcula-
tions are made moving down, and then up, the
column until convergence is achieved.

The method is not particularly suitable for
manual calculations because adiabatic-flash compu-
tations are tedious. However, flash computer sub-
routines are readily available, and it is relatively
easy to construct an executive routine to apply
the method. It is, therefore, another very worth-
while learning exercise, particularly because
column startup is simulated. The method is ap-
plicable to complex distillation, and initial guesses
for the tear variables are easily obtained from
vapor pressure data. The flash calculations are
usually not sensitive, but convergence, although


F = 100
XB = 0.5
XT = 0.5

V = 50

L = 10
(T2 = 200"F)
(L2 = 0)
(T3 = 200"F)

(T4 = 200*F)

(T5 = 200'F)

FIGURE 10. Example of specifications and tear vari-
ables for equilibrium-flash method of McNeil and
Motard (23 A.D.)..

almost certain, can be very slow, particularly for
large ratios of internal traffic-to-feed flow rate.

Rather than use an equation-by-equation com-
plete tearing technique for Sorel's equations,
Amundson and Pontinen in 7 A.D., in a significant
development, showed how the equations could be
combined in a manner such that they could be
solved in the order of type of variable, rather than
by stage. However, only partial tearing was
achieved and the method involved solving C sets
of N x N simultaneous linear algebraic equations.


To do this, they used full matrix inversion, which
often led to computational difficulties. These diffi-
culties were overcome by taking advantage of the
sparse tridiagonal form of the matrices and apply-
ing Gaussian elimination or LU decomposition in


F = 100
XB = 0,5
X, = 0.5

S V1 = 50

(Ti = 170F)
= 10
(T2 = 185F)

(T3 = 200F)

(T4 = 215'F)

(T5 = 230F)


FIGURE 11. Example of specifications and tear variables
for bubble-point method of Wang and Henke (15 A.D.).

the manner of Thomas. In 15 A.D. Wang and
Henke applied the Thomas algorithm to narrow-
boiling feeds, while Burningham and Otto re-
formulated some of the equations in 16 A.D., fol-
lowing the work of Sujata in 10 A.D., to apply
them to wide-boiling feeds typical of absorbers and
strippers. The need for two such partial-tearing
methods was shown clearly in 13 A.D. by Friday
and Smith, who referred to the two procedures
as the bubble-point and sum-rates methods.
An N x N tridiagonal equation for each com-
ponent is formed by combining the component
material balance, phase equilibrium, and a total
material balance to form an equation in liquid-
phase mole fractions, stage temperatures, and
vapor flow rates. By choosing the temperatures
and vapor flow rates as tear variables, the equa-
tions become linear in the mole fractions, with no
more than three mole fractions contained in any
one equation, because one stage is connected to
no more than two adjacent stages. For each com-
ponent, the linear equations are ordered by stage.
The result is a tridiagonal matrix equation, where
the nonzero coefficients are contained only on the
three principal diagonals. The solution of the
matrix equation is easily achieved by Gaussian
elimination, as shown e.g. by Carnahan, Luther,

and Wilkes, in no more than 20 lines of FORTRAN


The bubble-point method of Wang and Henke
in 15 A.D. utilized the tridiagonal matrix al-
gorithm to obtain a computer method for solving
distillation problems for relatively narrow-boiling
feeds. The specifications for the same 5-stage, 2-
component example used above are shown in Fig.
11. The tear variables are the stage temperatures
and vapor flow rates. The distillate rate V1 and
the reflux rate L1 are specified. Initial guesses for
these tear variables are obtained with a minimum
of effort by assuming constant molar overflow, in
the manner of the McCabe-Thiele method. Esti-
mates of the stage temperatures are obtained by
linear interpolation of the distillate and bottoms
temperatures, which may be computed by dew-
point and bubble-point calculations assuming the
most perfect split of the feed components, con-
sistent with the specified distillate rate.
Wang and Henke utilized a form of Sorel's
equations that permits the solution by variable
type rather than by stage as in the first two
methods described. The equations include a total
material balance to compute liquid traffic, a com-
ponent material balance combined with phase


Border of inner | | Set of Linear Equations
cycle i

FIGURE 12. Incidence matrix for bubble-point method.


Tear Variable

O Nonlinear Equation


S ---------i

x x x
X XX x -

x x x|x
x x x|
S x xx I

x x xx x
x x x x I
x x

X X X *X X

x x

equilibrium and total material balance to compute
liquid-phase mole fractions, bubble-point equa-
tions to obtain stage temperatures and vapor-phase
mole fractions, and energy balances to compute
vapor traffic.
Although the same variables are computed,
starting from Sorel's equations, the incidence
matrix, shown in Fig. 12, is quite different from
the stage-by-stage method. The matrix is lower
triangular except for the circled tear variables and
N x N (5 x 5 in this case) block sub matrices for
each component (two in this case). The overall
size of the matrix for the example is only 35 x 35
because no variable is computed from more than
one equation. At the upper left corner, the first
two variables are the same as before and are com-
puted directly as before. All but the last two of
the remaining equations are contained in one large
loop. Then the two tridiagonal submatrices are
solved separately to obtain the liquid-phase mole
fractions. All remaining variables are computed
one-at-a-time. Stage temperatures are computed
from the nonlinear bubble-point equation. This is
followed by computation of vapor-phase mole
fractions. Energy balances give the vapor traffic
and total material balances give the liquid traffic.
The cycle is repeated until the tear variables are
converged. Then the duties of the reboiler and con-
denser are computed.
For narrow-boiling feeds, the initial estimate
of stage temperatures and vapor traffic will be


FB = 50
FT = 50

T = 12.5
(T1 = 170'F)

(T2 = 185'F)

(T3 = 200'F)

(T4 = 215"F)

(T5 = 230'F)
BB = 12.5

FIGURE 13. Example of specifications and starting
guesses for simultaneous-correction method of Naphtali
and Sandholm (20 A.D.).

quite close to the final result and convergence is
rapid using successive substitution for the tear
variables. For wider-boiling feeds, convergence is
quite sensitive to the initial estimates of T and V
and may not be rapid. In that event, use of a de-
layed Wegstein [Orbach and Crowe (20 A.D.)] or
dominant eigenvalue technique [Rosen (29 A.D.)],
rather than successive substitution, can reduce the
number of iterations required.
The bubble-point method is not suitable for
manual calculations because of the need to solve
matrix equations. However, it is easily pro-
grammed if algorithms are available for solving
single nonlinear equations and the tridiagonal
matrix equation. The method is applicable to simple
and complex distillation columns. Convergence
may not be possible if the liquid phase is highly
non-ideal. The method provides no flexibility in
specifications. The user must specify the reflux
rate or ratio and the total distillate rate; however,
these specifications almost always permit a real,
positive solution. An exception can occur where
the reflux rate is too small, such that it diminishes
to zero at some stage down from the top. The
bubble-point method can be successfully applied
to complex distillation e.g. two feeds, two side-
streams, and one intercooler. Such problems are
difficult for stage-by-stage tearing algorithms, but
are relatively easy for partial tearing algorithms
like the bubble-point method, where variables are
computed by type.


More advanced computer methods that can
handle a complete range of boiling-point of feed
components and non-ideal liquid solutions, as well
as offer more flexibility in problem specifications,
involve handling the nonlinear equations simul-
taneously without the use of tear variables.
Newton's method, and variants thereof, has long
been the popular technique. The use of a simple
two-equation manual exercise is sufficient to il-
lustrate to students the basic concept of Newton's
*method, which may then be applied with com-
puter programs to hundreds of equations when
solving a multicomponent, multistage separation
Computer methods that solve all of Sorel's dis-
tillation equations simultaneously may be referred
to as simultaneous-correction methods. Many such
methods have been proposed and the Newton-based
Naphtali-Sandholm technique of 20 A.D. is repre-


sentative of one of the better ones. The equations
and variables are ordered by stage. To reduce the
size of the matrix to be handled, component flow
rates replace mole fractions and total flow rates.
Thus, for the five-stage, two-component example,
shown in Fig. 13, the number of equations to be
solved is 25, rather than the 35 previously. The
specifications are different from previous ones
in that distillate and bottoms purities replace re-
flux and distillate rates. Such specifications should
be used with caution and the Fenske (19 A.D.)
minimum-stage equation should be checked to
make sure that the minimum number of required
stages for the specified purities is less than the
specified number of five. Theoretically, initial
guesses must be provided for all 25 unknowns, but
these guesses can be generated by the program
based on guesses of just a few temperatures and
vapor rates as shown. These guesses are called
tear variables here, but are really not tear vari-
ables in the strict sense. The initial guesses are
generated easily from the T and V guesses by
solving the tridiagonal matrix equations of the
Wang-Hanke method for the liquid-phase mole
fractions, from which the initial guesses for the
component flow rates are obtained readily from
their definition and the component material
The Naphtali-Sandholm method only involves
three types of equations, namely stage component
material balances, phase equilibrium in terms of
K-values, and stage energy balances. The form of
the equations is almost identical to the original
equations of Sorel. Because bubble-point, dew-
point and flash calculations are absent, sensitivity
problems are largely avoided.
With equations and variables ordered by stage,
the incidence matrix, shown in Fig. 14, is block
tridiagonal in shape. The blocks are 5 x 5 in this
example. The matrix is for the linearized form of
the equations, which permits the application of
Newton's method. Thus, the matrix is the
Jacobian of partial derivatives and an X entry
signifies a nonzero-derivative. The entire matrix
is iterated to convergence.
The solution of the block tridiagonal matrix
is obtained readily by modifying the previously
mentioned Thomas algorithm for a tridiagonal
matrix. The only significant changes are the re-
placement of matrix multiplication for scalar
multiplication and matrix inversion and multipli-
cation for division. If large numbers of components
are present, the (2C + 1) x (2C + 1) submatrices

may be large and time-consuming to invert. The
convergence criterion is based on the sum of the
squares or so-called square of the Euclidean norm
of the three different types of functions. Early
iterations are often damped to avoid corrections
that are too large.
Because of the block nature of the matrices in
the Naphtali-Sandholm method, it is not at all
suitable for manual calculations. Furthermore, the
computer program is rather complex. Consequent-
ly, it is best to obtain the code from one of several


1 2 3 5
x x xx
x x x
x x x x
xxxxxxx x
xxx x x xx X X


XXXX_ ___
xxx xx
xx xx
x x x x x x xx x


x x x x x
x x x x x

FIGURE 14. Incidence matrix for simultaneous-correc-
tion method.

sources, e.g. Fredenslund et al (26 A.D.). Versions
ranging from PC to Mainframe codes are avail-
able. The method is applicable to all single-column,
complex multistage operations, including those
with highly non-ideal liquid solutions. Flexibility
in specifications is provided at the top and bottom
of the column by substituting specification equa-
tions for the condenser or reboiler energy balances.
For example, specification options at the top in-
clude condenser duty, reflux rate, reflux ratio, dis-
tillate rate, component mole-fraction purity and
component distillate rate. Convergence is rapid
from good initial guesses, but may otherwise be
slow and require damped corrections. The method
can fail, particularly if initial guesses are very

Ideally, especially in practice, one would prefer


a multistage, multicomponent separation com-
puter method that would offer complete flexibility
in specifications and would always converge to
a correct solution. Newton's method and most of
its variants are known to be only locally con-
vergent. That is, the initial guesses must be within
a certain region of the variable space or con-
vergence will not be achieved. This region can be
expanded by adjusting the Jacobian in Newton's
method or employing a hybrid method such as that
of Powell (19 A.D.) or Marquardt (12 A.D.),

20 x + x2 = 17
I(8X) 3 + x2=
1 H omotopy
x2 Path

FIGURE 15. Example of regions of convergence for
some methods of solving nonlinear equations.

which combine the best features of Newton's
method with steepest descent. To achieve complete
robustness, however, it is necessary to employ a
globally convergent technique, such as differential
homotopy continuation, for which four algorithms,
in FORTRAN, have become available starting in
25 A.D. The one by Kubicek is particularly easy to
understand and apply, but is not written for sparse
The regions of convergence for a simple two-
nonlinear-equation example are shown in Fig. 15.
Newton's method will converge to X1 = 1 and
X, = 4 from an initial guess of X1 = 2, X2 = 5.
As seen, another root exists at X, = 4.07 and X2 =
0.65, which can be reached by Newton's method
from a nearby initial guess. With Newton's
method, the initial guesses must lie within the

rather narrow and confined cross-hatched regions.
With Powell's hybrid method (which is available
in the Harwell library, the IMSL library, and
MINPACK) the region of convergence is expanded
outward to the dashed lines. Both methods will
fail badly from a starting guess of 15 and 15. The
use of differential homotopy continuation gives
convergence from any starting guess, with a typi-
cal homotopy path to one of the two roots, shown
as a dash-dot line starting from (15, 15).
Many types of homotopy paths have been pro-
posed, with the linear homotopy being common.
The linear homotopy, h, is set equal to the function
to be solved, f(X), multiplied by a homotopy pa-
rameter, t, and added to a function g(X), whose
solution is known, multiplied by the function
(1 t). The calculations start from the known
solution at t = 0, where h = g and X = Xo and
move along the path of h vs. t as t is gradually in-
creased to a value of one, at which point h = f,
whose solution X* is to be determined.
Choices for the function g(X), with a known
solution, Xo, are almost unlimited. For consistency
with Newton's method, the Newton homotopy is
useful, where g(X) is set equal to f(X) f(Xo),
where Xo can be selected arbitrarily. With this
homotopy, h equals f(X) minus (1 t) times
f(Xo). Alternatively, (1 t) can be replaced by a
new homotopy parameter, X, to obtain a slightly
more compact form for the homotopy expression.
The path will then be from X = 1 to X = 0.
If the homotopy path is simple, without turns
or rapid changes in X with t, classical continuation
can be employed by selecting a sequence of values
of t at 0, t,, t2, t, etc., and 1, with X being solved
from h at each step by Newton's method using an
initial guess equal to the solution from the pre-
vious step. Thus, Newton's method is embedded
into classical continuation. This technique of using
continuation is not common though because it is
not globally convergent and can not, in general,
solve problems that fail with Newton's method
alone, which amounts to moving in one step from
t = 0 to t = 1. To be robust, one must closely
follow the homotopy path and not just continually
take steps in t with Newton corrections in X-space.
For example, classical continuation as well as
Newton's method will fail on the cubic equation,
x3 30x2 + 280x 860 = 0, because of two
singular points at about x = 7.418 and 12.582,
where the derivative of the function becomes zero.


C1 40
C2 = 30
C 3= 30

R 3

HP 30

L_. ) Stage 19

FIGURE 16. Example of specifications for an interlinked
system of Petlyuk towers.

For initial guesses of x less than about 12.6,
Newton's method fails to find the single real root
at about 15.55. The homotopy path for this cubic
function depends on xo the choice of g(x). For
g(x) = x x0 or g(x) = f(x) f(xo), the two
singularity points become turning points in the
path, and it is important that the continuation
method follow the path closely around these turn-
ing points to avoid cycling in the manner of Moses
in the Sinai.
Consider the application of the homotopy-con-
tinuation method to the Petlyuk system of two
interlinked towers shown in Fig. 16. The feed is a
ternary mixture, which is to be separated into
three products. A sloppy split is made in the pre-
fractionator, with the final three products being
produced in the second tower. Reflux and boilup
for the first tower are provided by the second
tower. The two towers in the system can be solved
by alternating back and forth between separate
iterations on the individual towers. But numerous
studies have shown that it is more efficient to con-
verge the two towers simultaneously. The stages
for the two towers are ordered as shown starting
at the top of the second tower, switching to the
first tower after stage 11, and then switching back
to the second tower after stage 15. Specifications

include interlink flows from stage 4 to stage 12
and from stage 16 to stage 15; the reflux ratio;
the middle product flow rate; and the bottoms flow
The types of equations solved are as in the
Naphtali-Sandholm method except that balances
include interlink flows and provision, if desired,
for entrainment of liquid droplets, occlusion of
vapor bubbles, and chemical reaction. Phase
equilibrium equations can include a Murphree
plate efficiency that can be specified by component
and tray location.
A FORTRAN computer code for applying
differential homotopy-continuation to such a prob-
lem was reported by Wayburn and Seader in 32
A.D. Considerable flexibility in specifications is
provided, including at any stage, total flow rates
or ratios, component flow rates or purities, and
stage temperatures or heat transfer rates. The
equations are linearized in the Newton manner
and ordered by stage to a bordered, block-diagonal
structure, which is processed by an efficient and
stable block-row-reduction algorithm. An attempt
is first made to solve the equations by Newton's
method, using a line search on the Euclidean norm
of the function residuals to determine the best
damping factor. If Newton's method fails, then
differential homotopy continuation with a linear

f(x) xf(x) = 0

IVP: df(x) dx d )
1 a x fdx dp ) -

I dx, + x (x
IC: =0 ) = 1, x =

1C: p= 0, Az 1, x = x0


Equations for differential homotopy con-

Newton homotopy is employed.
The differential form of homotopy continuation
was first proposed by Davidenko in 2 A.D. As
shown in Fig. 17, the homotopy function, f(X) -
Xf(Xo), is differentiated with respect to arc
length, p (i.e., distance along the path), to con-
vert a system of M nonlinear equations to a system


of M + 1 ordinary differential equations that
constitute an initial value problem. Because of the
extra variable, p, an additional equation is need-
ed. This is provided by the Pythagorean theorem
applied in (M + 1)-dimensional space. Fortunate-
ly, the set of differential equations is not stiff.
Rather than simply integrating the differential
equations by, say, a Runge-Kutta method, it is pre-


S2 3 4 5 6 7 8 910111213141516171819
1 BC
10 ABC
12 A BC
13 ABC
14 ABC
15 ABC
16 A ABC
17 ABC
18 ABC
19 AB


FIGURE 18a. Example of incidence matrix for homotopy-
continuation method.

ferable and more efficient to follow the homotopy
path by alternating between an Euler predictor
for the differential equations and two Newton-step
correctors for the nonlinear homotopy equations.
The Euler step moves the variables somewhat off
the path, but the Newton step corrects the vari-
ables back sufficiently close to the path. An im-
portant aspect of such a technique is the size of the
Euler step, for which a number of stepsize al-
gorithms have been proposed, as discussed by
Seader and Wayburn.
The incidence matrix for the Petlyuk column
example is in Fig. 18a, where the organization is
by stage. Each letter, A, B, or C actually repre-
sents, in this example, a nonzero 7 x 7 submatrix,
which applies to both the coefficients of the differ-
ential homotopy equations and the Jacobian
partial differentials of the nonlinear homotopy
equations. The matrix is almost block tridiagonal
with bottom and right-side borders, which contain

the non-standard specifications. The four disperse
submatrices of A and C, located above and below
the three principal diagonals represent the inter-
By moving the number 4 and 16 rows and
columns, which contain the disperse submatrices,
to the borders, the block diagonal and bordered
matrix form, shown in Fig. 18b, is obtained. Solu-
tion of the corresponding matrix is achieved block
by block, starting at the upper left-hand corner,
by a block-row reduction algorithm, which treats
the right-hand border as part of the right-hand
side vector.
The differential homotopy-continuation method
has been applied to the interlinked system shown
in Fig. 19 for a ternary aromatic system, over a
range of reflux ratios from 4.55 to 5.75. Purity
specifications of between 90 and 95 mole% are
made for each product, and a bottoms rate of 380
is specified. The program must compute the re-
quired interlink flow rates, including L, the liquid
interlink recycle from the second column back to
the first column. In some cases, Newton's method
converged, for this system, while in other cases,
the differential-homotopy-continuation method had


I 2 3 5 6 7 8 91011121314151718194 16
3 AB C
5 BC A
10 ABC
12 BC A
13 ABC
14 ABC
15 AB C
17 BC A
18 ABC
19 AB
4 -AIC C B
16 A AC

FIGURE 18b. Permuted incidence matrix for homotopy-
continuation method.


to be applied to obtain a solution.
In Fig. 20, a plot of L, the liquid interlink rate
versus the reflux ratio, shows unexpected multiple
solutions, three in number. For example, at a re-
flux ratio of 5, the specifications were achieved
with three different liquid interlink rates of about
110, 330, and 420 lbmoles/hr. Such multiple solu-
tions have long been known to exist for certain
cases of an adiabatic reaction in a CSTR reactor,
but have not been observed previously for distilla-
tion. When such solutions are close together, as for
solutions 1 and 2 at low reflux ratios, possible
control problems could arise.

Lqulid Fed

nzee (B) zo00O
Toluen (T) 200
o*Xyler. {X 400

XB = 0.95
5 o 5.75

X = 0.95
B = 380

FIGURE 19. Example that gave multiple solutions.

The continuation method is not at all suitable
for manual calculations. The computer code is
lengthy, but is applicable to all kinds of complex
multistage operations, including interlinked
columns. Except for tray numbers, complete flexi-
bility in specifications is permitted. When the
homotopy is constructed properly, convergence is
always achieved. The method is best suited for
cases where the Naphtali-Sandholm fails or can't
be applied. The method can find multiple solutions
if they exist.


Sorel's equilibrium-stage-model of almost 100
years ago has served us well in the calculation of
multicomponent, multistage separation opera-
tions. However, that model has always been sus-
pect for applications to systems of known moder-


4. 5 4. 9 5. 1 5. 3 5. 5 7

FIGURE 20. Multiple solutions to Petlyuk towers.

ate-to-low stage efficiency. For that reason, some
programs, such as the S-C method of Naphtali
and Sandholm and the differential-homotopy-con-
tinuation method of Wayburn and Seader in-
corporate a Murphree tray efficiency, which ac-
counts to some degree for mass-transfer effects.
However, the plate efficiencies must be specified,
and heat transfer effects are ignored. A better
approach is to apply a transport model to handle
non-equilibrium directly. Such a model has just
been developed by Krishnamurthy and Taylor,
who account for multicomponent mass-transfer
interactions and heat transfer. Their modeling
equations are written separately for the vapor and
liquid phases with coupling by liquid and gas mass
transfer rates, and energy transfer rates. These
transport rates are estimated from carefully
formulated mass and energy transfer coefficients,
applicable to multicomponent systems. For non-
interlinked columns, the resulting equations lead
to an incidence matrix that is similar to that of
the Naphtali-Sandholm method, for which a solu-
tion technique is well established. Krishnamurthy
and Taylor have applied their method, with good
success, to several sets of experimental data from
the operation of small laboratory columns. Data
from commercial-size columns are now being
sought to make further comparisons of predicted
and measured compositions so as to evaluate the
usefulness and applicability of this transport


The digital computer has been responsible for
sweeping changes in the manner in which multi-
stage separation operations are synthesized and






-~ ~t'

Recommended Additions to Content of Undergraduate
1. Numerical methods for
A. Linear algebraic equations
B. Sparse matrices
C. Systems of nonlinear equations
2. Application of numerical methods to
A. Complete tearing, partial tearing, and simul-
taneous correction methods for multicomponent
separation processes
3. Use of computer-aided simulation programs to
A. Analyze, correlate and predict multicomponent
thermodynamic properties
B. Solve open-ended separation process problems in-
volving energy integration
4. Second-law analysis

analyzed. Chemical engineering educators need to
closely examine courses on separation processes
to make sure that students are being instructed
in modern and efficient computational tools. Some
recommended additions to the content of under-
graduate courses are listed in Table 1, where many
of the items should prove useful in other chemical
engineering subjects as well.
Numerical methods should be stressed for
linear algebraic equations, including efficient
handling of sparse matrices, and systems of non-
linear equations. These methods should then be
applied using computers to utilize partial tearing
and simultaneous correction methods for multi-
component separation processes. However, some
manual calculations on simple examples should be
performed using complete tearing methods to help
develop a basic understanding.
More complex and open-ended separation
problems should be assigned that stress energy
integration. A second-law analysis [see Denbigh
(5 A.D.), and de Nevers and Seader (28 A.D.)] of
a process should be required, and attempts should
be made to improve the process by finding eco-
nomical means to reduce the lost work.
The development of a separation process can
be an exciting experience when computers and
computer programs are available to perform the
tedious calculations and allow time for more con-
sideration of synthesis and optimization as-
pects. Ol

Amundson, N. R., and A. J. Pontinen, Ind. Eng. Chem.,
50, 730 (1985).
Burningham, D. W., and F. D. Otto, Hydrocarbon Pro-

cessing, 46 (10), 163-170 (1967).
Carnahan, B., H. A. Luther and J. O. Wilkes, Applied
Numerical Methods, John Wiley, New York (1969).
Davidenko, D., Dokl. Akad. Nauk USSR, 88, 601 (1953).
Denbigh, K. G., Chem. Eng. Sci., 6, 1-9 (1956).
de Nevers, N., and J. D. Seader, "Mechanical Lost Work,
Thermodynamic Lost Work and Thermodynamic
Efficiencies of Processes," paper presented at the
AIChE 86th National Meeting, Houston, Texas, April
1-5, 1979.
Fenske, M. R., Ind. Eng. Chem., 24, 482-485 (1932).
Fredenslund, A., J. Gmehling, and P. Rasmussen, "Vapor-
Liquid Equilibria Using UNIFAC, A Group Contribu-
tion Method." Elsevier, Amsterdam, (1977).
Friday, J. R., and B. D. Smith, AIChE J., 10, 698 (1964).
Henley, E. J., and J. D. Seader, Equilibrium-Stage Separa-
tion Operations in Chemical Engineering, John Wiley
and Sons, New York (1981).
Krishnamurthy, R., and A. Taylor, AIChE J., 31, 449-465
Kubicek, M., "Algorithm 502," ACM Trans. on Math. Soft-
ware, 2, No. 1, 98 (1976).
Lewis, W. K., and G. L. Matheson, Ind. Eng. Chem, 24
496-498 (1932).
Marquardt, D. W., SIAM J., 11, 431-41 (1963).
McCabe, W. L., and E. W. Thiele, Ind. Eng. Chem., 17,
605-611 (1925).
McNeil, L. J., and R. L. Motard, "Multistage Equilibrium
Systems," Proceedings of GVC/AIChE Meeting at
Munich, Vol. II, C-5, 3 (1974).
Naphtali, L. M. and D. P. Sandholm, AIChE J., 17, 14
Orbach, 0., and C. M. Crowe, Can. J. Chem. Eng., 49, 509-
513 (1971).
Ponchon, M., Tech. Moderne, 13, 20, 55 (1921).
Powell, M. J. D., "A Hybrid Method for Nonlinear Equa-
tions," in "Numerical Methods," Ed. P. Rabinowitz,
Gordon and Breach, New York (1970).
Rosen, E. M., "Steady-State Chemical Process Simulation:
A State-of-the art Review," Computer Applications to
Chemical Engineering, R. G. Squires and G. V. Reklaitis,
editors, ACS Symp. Ser. No. 124 (1980).
Savarit, R., Arts et Metiers, pp. 65, 142, 178, 241, 266, 307
Seader, J. D., Section 13 of Perry's Chemical Engineers
Handbook, 6th ed., McGraw-Hill, New York (1984).
Sujata, A. D., Hydrocarbon Processing, 40, No. 12, 137
Thiele, E. W., and R. L. Geddes, Ind. Eng. Chem., 25, 289
Wang, J. C., and G. E. Henke, Hydrocarbon Processing,
45 No. 8, 155 (1966); Hydrocarbon Processing, 45
No. 9, 169 (1966).
Wayburn, T. L., and J. D. Seader, "Solutions of Systems
of Interlinked Distillation Columns by Differential
Homotopy-Continuation Methods," Proceedings of the
Second International Conference on Foundations of
Computer-Aided Process Design, June 19-24, 1983,
Snowmass, Colorado (available from CACHE Corp.,
P. O. Box 7939, Austin, Texas 78713-7939).


A, B, C, Coefficients in a tridiagonal matrix equa-






tion; submatrices of partial deriva-
tives in a block tridiagonal matrix
Bottoms product molar flow rate
An arbitrary function
Molar feed rate to a stage; mathematical
Molar enthalpy of feed to a stage
The homotopy function whose arguments
are x and t
The homotopy function whose arguments
are x and X
Molar enthalpy of vapor leaving a stage
Molar enthalpy of liquid leaving a stage
Vapor-liquid equilibrium ratio
Molar liquid flow rate leaving a stage
Middle product molar flow rate
Path length
Heat duty (R for reboiler; C for con-
Reflux ratio
Ratio of liquid drawoff to primary liquid
(liquid not withdrawn or entrained)

Continued from page 61.
and measuring the properties of liquids and
liquid mixtures using theory, computer simula-
tion, and experiment. Cornell is one of very few
institutions with strength in all three areas.
Keith guides the theory and the computer
simulation (with help from Senior Research As-
sociate Steve Thompson,) making use of recently
developed accurate theories for dense fluids of
complex molecules as well as improved computer
simulation methods and computer hardware.
Typically, highly nonideal substances (in the
thermodynamic sense) are chosen for study; sub-
stances for which traditional methods of pre-
diction fail. Examples include mixtures occurring
in coal gasification and liquefaction, hydrogen-
energy technology, synthetic fuel processing and
supercritical fluid extraction. Other research
underway or planned includes studies of ad-
sorption at gas-liquid, liquid-liquid and solid-fluid
interfaces, nucleation and droplet phenomena,
polarization in polar fluids, and surfactant effects.
Bill Streett and Senior Research Associate
John Zollweg carry out experimental studies of
dense fluids. In progress are (i) experiments in
vapor-liquid, liquid-liquid, and gas-gas equilibria
at temperatures from 70 to 500 K and pressures
to 10,000 atmospheres; (ii) equation-of-state
(PVT) measurements of pure liquids and mix-
tures at temperatures from 70 to 500 K and pres-
sures to 4,000 atmospheres; and (iii) measure-

ments of enthalpy of mixing in samples of lique-
fied gases at temperatures from 70 to 300 K and
pressures to 20 atmospheres. Bill is currently de-
veloping new experiments to measure the surface
and interfacial tensions and the velocity of sound
in fluids under pressure.
The researches of Paulette Clancy, who became
a member of the faculty in 1984, range from a
statistical mechanical study (using perturbation
theory) of multicomponent highly polar fluid
mixtures to a development of phase diagrams
(based on molecular thermodynamics) of semi-
conductor materials. In addition, she is involved
in the application of computers to chemical engi-
Herb Wiegandt's interest in desalting sea
water, using a freezing process based on direct
contact with butane, goes back to 1958. Recent
efforts, with Bob Von Berg as a partner, have
aimed at overcoming the problems associated
with washing and separating the ice crystals
which are typically very small.
Julian Smith, past Director of the School in a
period of unprecedented growth, seasoned edu-
cator and co-author of Unit Operations of Chemi-
cal Engineering (now in its fourth edition, with
Pete Harriott as co-author), has expertise in
mixing, centrifugal separation, and handling of
granular solids. He is teaching full-time and is
active in the guidance of the school.
Ray Thorpe, who has advised graduate
students in the areas of phase equilibria and
separations processes, splits his time between


S Ratio of vapor drawoff to primary vapor
(vapor not withdrawn)
t Homotopy parameter.
T Temperature; when used as a superscript
denotes matrix transpose
V Molar Vapor flow rate leaving stage
x The vector of independent variables (un-
knowns) for the distillation equations;
liquid-phase mole fraction
x The starting vector for the nonlinear
equation solver
x* The solution to the set of nonlinear equa-
X Mole fraction in liquid of a component;
y Vapor-phase mole fraction
Y Mole fraction in vapor of a component
Z Mole fraction in feed of a component

Greek Letters
X Homotopy parameter

teaching and university administration: he is di-
rector of the Division of Unclassified Students.

Research Interactions
Many research projects involve active col-
laboration with other researchers at Cornell or
elsewhere. Some are directly with other depart-
ments; some are through Cornell's numerous in-
disciplinary programs, centers, and institutes-
many industrially supported-that facilitate inter-
action among departments and with industry.
Examples are the Biotechnology Institute; the In-
jection Molding Project; COMEPP (Cornell
Manufacturing Engineering and Productivity
Program); Applied Mathematics Center; Theory
and Simulation Center (established by Ken
Wilson, Cornell's 1982 Nobel laureate in physics) ;
Materials Science Center; National Facility for
Submicron Studies. Strong ties have been es-
tablished with other departments and colleges of
Cornell, and with workers at other universities
around the world.
Paulette Clancy, Associate Director of COM-
EPP, is joined by Professor Scheele in a study
of ways to improve the interface between the
user and ASPEN software chemical process syn-
thesis and design.

Undergraduate chemical engineering enroll-
ments at Cornell were almost constant during the
twenty years before 1975, with about 40 bache-
lor's degrees awarded annually. Then, although
freshman admissions to the engineering college
were held constant, the number of students opt-
ing for chemical engineering roughly doubled, and
for nine years the number of BS degrees awarded
was between 65 and 75. After 1985, however, the
number will return to 40 or so and is expected to
stay at that level for the next several years.
The subject matter of the undergraduate pro-
gram is much the same as at other institutions.
For the first two years the students are not in
chemical engineering but are enrolled in the "com-
mon curriculum" of the engineering college. Never-
theless, their curriculum has much that is differ-
ent from that of other engineering students. In
the freshman year chemical engineers take two
semesters of chemistry, not one. Sophomores take
two semesters of physical chemistry, with labora-
tory each term-a special course taught by Chemis-
try almost exclusively for chemical engineers-

and the required introductory course in mass and
energy balances. Organic chemistry (two semes-
ters, one with laboratory) is given in the third
year, as are chemical engineering thermody-
namics, rate processes and separation processes.
The fourth year includes required courses in re-
action kinetics, process evaluation, process con-
trol, and unit operations laboratory, and a spring
term course in process design. Overall, 132 credit
hours are required for the BS degree, including
two courses in computer programming and ap-
plications, four engineering distribution courses,
and six courses in humanities and social sciences.
Ten of the required courses (32 credit hours) are
in chemical engineering subjects.
The senior laboratory course is considered the
most demanding by students and faculty alike.
Each student reports on only five experiments
during the term, but each report is thoroughly
edited for both form and content by the faculty
member in charge of that experiment and nearly
always must be extensively revised by the student
before it is accepted. The emphasis is on technical
accuracy, completeness, and clarity of expression.
Oral presentations are stressed in the senior
design course, in which each team of students
makes weekly oral presentations before two faculty
members or industrial visitors. In recent years
experienced engineers from industry have been
hired for full-time assistance in this course and
in the senior laboratory. Their contributions have
been supplemented, during short-term visits, by
those of people from Exxon, Union Carbide, and
other firms. Despite this, the laboratory and de-
sign courses demand large contributions of time
by senior faculty members, and pose the most
difficult problems for future staffing.

A Special Cooperative Program
For the past ten years the better students in
the sophomore year have been encouraged to enroll
in an unusual industrial cooperative program
which gives them practical experience without
lengthening their time at the university. Typical-
ly 15 to 20 students are accepted into the program
after company interviews exactly like those for
permanent employment. Co-op students take the
fall-term third-year courses during the summer
following their sophomore year; they work in in-
dustry during the fall and return to Cornell in
the spring; work again for the same sponsor the
following summer; and complete their senior year


in the regular sequence. Industrial assignments
are carefully monitored to insure appropriateness,
and each student is visited at the worksite by a
Cornell person at least once during the course of
the program.

This is a two-semester non-thesis master's
program leading to the degree Master of Engineer-
ing (Chemical). It requires 30 credit hours of
advanced technical work, including a substantial
design project, with emphasis on practical ap-
plications. Most of the matriculants are not from
Cornell or other U.S. schools; instead the program
is attractive to foreign students, especially from
developing countries such as the Dominican Re-
public, Guatemala, India, Kuwait, Taiwan and
Venezuela. Over the years a chemical company
in India has sent, one after another, three of its
top technical employees to this program.
Required courses for the MEng (Chemical)
degree include equipment design and selection,
numerical methods, reactor design, the design pro-
ject, and a chemical engineering elective. The
remaining credit hours can be filled by elective
courses in science or engineering or in the Gradu-
ate School of Management. The choice of subjects
for MEng design projects is much wider than in
the typical undergraduate design course, and
more initiative and originality are expected of
the students. Some of the projects are done in
close collaboration with industrial firms.

The school has always had close relations with
industry and an unusually supportive group of
alumni. Industry helps us in many ways: in the
design courses; in a "Non-resident Lecture Series"
(zero credit, but compulsory), given to seniors on
the various kinds of professional careers; in un-
restricted grants; in scholarships, fellowships, and
sponsored research. Continuing fellowship sup-
port has been provided by Amoco, Chevron, Dow,
DuPont, Exxon, Shell, Stauffer and Union Car-
bide, and recent large research projects came
from IBM, Kodak and Mobil. In 1981 the Sun
Company gave $250,000 over three years to sup-
port research initiation on ideas too new and ill-
defined to merit submission of a proposal to NSF
or other granting agencies. This unusual grant
led to a number of publications and several con-
tinuing sponsored research programs.

An advisory council, largely from industry,
was formed a few years ago. It meets in Ithaca
twice a year to review progress and help the di-
rector steer a course for the school. About half
the members are alumni. Recently expanded to
15 members, the council now includes four aca-
demic people: Andy Acrivos (Stanford), Gus Aris
(Minnesota), Gary Leal (CalTech), and Bill
Schowalter (Princeton). We don't always agree
with the council's suggestions, of course, but as
a group it has been marvelously effective in pro-
viding an "outside" viewpoint and keeping us from
being too provincial or self-satisfied.

Cornell is facing many of the same problems
that face other chemical engineering departments
around the country-faculty retirements and fu-
ture faculty development, staffing of design and
laboratory courses, the optimum use of computers
for teaching, expansion of research and the gradu-
ate program, and renovation of aging facilities.
The five professors hired right after World War II
are nearing retirement, so for the next several
years an average of one new faculty member per
year will have to be hired to keep the number
constant. Because of the loss of professors with
industrial experience one or more people with an
industrial background will probably be hired on
a non-tenure-track basis to teach design and to
supervise the laboratory courses.
A related problem is in the use of computer
software. How much emphasis should be placed
on teaching the use of ASPEN, for example?
More generally, as personal computers become
ubiquitous, what will happen to teaching methods?
Will the course in mass and energy balances, for
example, become a course in the use of available
canned programs?
The total number of graduate students in the
school, and the fraction going for a PhD rather
than an MS, should rise somewhat over the next
ten years, depending on the availability of
financial support. This will increase the need for
equipment and laboratory and office space. Rela-
tively speaking, the Chemical Engineering School
has a lot of space, but much of it is virtually un-
usable for modern research. A comprehensive
building renovation plan, made by a firm of archi-
tects, proposes a complete reallocation and rear-
rangement of available space and the conversion


of the enormous unit operations laboratory into
offices and small research labs. New electrical and
other services will be provided, along with central
air conditioning. The average faculty office will
shrink from over 400 to a more modest 200 square
feet and the offices will be grouped more closely,
to stimulate greater interaction among the oc-
cupants. The total estimated cost is some fifteen
times the original cost of the building. A fund
drive for the first stage is being launched. D

Continued from page 77.
on the batch experience and using verified mathe-
matical models to both design the equipment and
direct the experimentation.

Laboratory Scale Research
A low pressure chemical vapor deposition
(LPCVD) system for amorphous silicon is shown
in Fig. 14 and the simplified process flow diagram
as Fig. 15. Reactants, Si2H,, and material for
doping the film, PHs and B2He, in a stream of argon
are controlled by valves at the inlet to the reactor.
The tubular quartz reactor is temperature con-
trolled inside an electric furnace. System pressure
is controlled manually with a valve at the exit.
Effluent gas can be analyzed by gas chromatogra-
phy and unreacted material is decomposed in a
furnace before venting. The detailed operation of
this system is described by Bogaert [9].
This effort in amorphous silicon research, spon-

FIGURE 14. Photograph of Low Pressure Chemical
Vapor Deposition unit (LPCVD).

scored by the Department of Energy through the
Solar Energy Research Institute, is ongoing at
the present time and is far from being complete. I
am discussing it here to allow the reader to con-
trast and compare with the physical vapor depo-
sition reacting systems just described.
The chemistry is much more complex for
amorphous silicon than for CdS and not well

FIGURE 15. Simplified process flow diagram of LPCVD.

understood. The present state-of-the-art is shown

Gas Phase
Si2H, ;SiH4 + SiH2
SisHs -Si2He + SiH,
SiHloSiiH, + SiH2
SisHijsSiHo + SiH2
SieH,,4SisH,1 + SiH2
SiH1,,SiH,, + SiH,
SisHs8SiH,, + SiH2
Film Formation
SisH2-*3SiHo.os + 2SiH, + 1.88H2
SiH2 -*SiHo.os + 0.96H,
This is a preliminary set of chemical equations.
The gas phase equations are based on the results
of Ring [10], John and Purnell [11], and Bowery
and Purnell [12]. The film formation equations are
based upon our own preliminary research.
The component mass balance equations for this
tubular reactor system are given below:
Gas Phase

D4q j dCi= Ir(rxt,i) + kga (Ci-Cs)
\v;rD'j dZ


0 = kga(Ci- Ci,) -ka(yiCi)

1 dprV' = ka(yiCi)
MW, dt
Both the gas phase composition and the film
growth rate are functions of axial position. Film
growth rate (i.e., amount of amorphous silicon
deposited) at any axial position can be determined
but it has been possible to measure gas composition
only at the reactor exit. The gas and solid phase
mass balance equations are coupled through the
chemistry of film formation and the transfer from
the bulk gas to the surface.
Solution of the model equations produces the
gas phase exit composition versus reactor holding
time plots shown as Figs. 16 and 17. The solid
lines were obtained using our present "best"
estimates of the specific reaction constants. This
"best" estimate is now obtained by using the ex-
perimentally determined growth rate in the solu-
tion of the set of mass balance equations. The
agreement between data and the predicted values
is only fair but we expect to improve the model

100 I I
NT.-400*C NSSl2H6
80 P= 24 Torr SIH4
S60 S12H *

20 /S13H

0 o0 20 30 40 50 60 70
Holding Time. (sec.)
FIGURE 16. Normalized molar percentages versus hold-
ing time: Major silanes.

predictions as we learn more about the system.
This research on the chemical reactor and re-
action engineering for amorphous silicon in the
LPCVD reactor is closely coupled with studies of
the material and electronic properties of the film
and much effort has been devoted to finding the
best conditions for good photovoltaic amorphous
silicon. To date, we have been able to make a 4%
solar cell using material from the LPCVD reactor.
These efforts are described in the work of Hegedus
et al [13].

Holding Time, (sec.)

FIGURE 17. Normalized molar percentages versus hold-
ing time: Minor silanes.


Incorporating chemical reactor and reaction
engineering analysis into a semiconductor research
effort requires the researchers to achieve a quanti-
tative understanding of both the molecular phe-
nomena and the transport phenomena associated
with the creation of the semiconductor materials.
A test of this understanding is the ability to write
useful mathematical descriptions of the laboratory
scale reacting system. Mathematical descriptions
are an essential part of the analysis because they
provide the language which allows the profes-
sionals doing the research to effectively and un-
ambiguously communicate with each other. Com-
munication is easier if the models are simple and,
of course, the model predictions must be verifiable
by experiment. In fact, the model behavior should
be used to plan the experimental program because
an enlightened use of a chemical reaction and re-
actor engineering analysis will identify critical
molecular and transport phenomena problems and
direct experimental attention to them with the
proper priorities.
We originally became interested in the semi-
conductor research because of a need to design
larger scale reacting systems. However, the last
five years of research has taught us that the chemi-
cal engineering analysis is very useful in the
laboratory scale research effort, and indeed es-
sential, if such research is to be done efficiently and
with minimum expense (a key issue with today's
research costs).
It is not possible, in our view, to effectively de-
sign and operate larger scale systems without re-
action and reactor engineering analysis. In photo-


voltaic applications it is also necessary to carry out
analysis and experimentation at the unit opera-
tions scale. Those who have attempted to scale up
without following these procedures have wasted
time and money building equipment which is in-
adequate for the commercial scale processing of
solar cells.
A useful start has been made in applying
chemical engineering analysis to the deposition of
thin-film semiconductors but much effort now must
be devoted to the task of relating electronic and
optical properties to the design and operation of a
reacting system. When we have learned to do this
properly, we can begin to "tailor-make" material
with any desired property.

Semiconductor chemical reaction and reactor
research requires a team effort involving a number
of professionals. I am particularly indebted to
B. N. Baron, R. E. Rocheleau, S. C. Jackson and
R. J. Bogaert, my chemical professional colleagues
at the Institute of Energy Conversion. Their
analysis, their effective experimentation and their
discussions with me have been essential to the de-
velopment of this field. None of the research could
have been carried out without the excellent semi-
conductor material development and analysis and
device design and analysis that my other col-
leagues at the Institute of Energy Conversion do
so well. I am also in their debt for their willing-
ness to educate a chemical engineer in the art and
science of applied solid state physics.
Science and engineering research today re-
quires some considerable management talent. The
Department of Energy's photovoltaic office and the
Solar Energy Research Institute have worked very
hard to develop a rational plan for photovoltaic
research that both produces results and handles
the political pressures that arise in a budget con-
scious government. The management group within
the Institute of Energy Conversion is unique in
its capability to protect the director from ad-
ministrative detail and to allow me to put most of
my effort into technical work. I would like to thank
S. Barwick and M. Stallings for this gift. Ol

1. Thornton, J. A., Annual Rev. of Material Science, 7,
p. 239 (1977).
2. Kern, W. and V. S. Ban, Thin Film Processes, (J.
Vossen and W. Kern, editors) Academic Press, New
York (1978).

3. Dutton, R. W., "Modeling of the Silicon Integrated
Circuit Design and Manufacturing Process," IEEE
Trans. Electron Dev., 30, 9, p. 968 (1983).
4. Rocheleau, R. E., B. N. Baron and T. W. F. Russell,
"Analysis of Evaporation of Cadmium Sulfide for the
Manufacture of Solar Cells," AIChE Journal, 28, 4,
p. 656 (1982).
5. Jackson, S. C., PhD Thesis, "Engineering Analysis of
the Deposition of Cadmium-Zinc Sulfide Semicon-
ductor Film," University of Delaware (1984).
6. Rocheleau, R. E., PhD Thesis, "Design Procedures
for a Commercial Scale Thermal Evaporation System
for Depositing CdS For Solar Cell Manufacture,"
University of Delaware (1981).
7. Griffin, A. W., MChE Thesis, "Modeling and Control
of a Unit Operations Scale System to Deposit Cad-
mium Sulfide for Solar Cell Manufacture," Uni-
versity of Delaware (1982).
8. Brestovansky, D. F., B. N. Baron, R. E. Rocheleau
and T. W. F. Russell, "Analysis of the Rate of Vapor-
ization of CuCl for Solar Cell Fabrication," J. Vac. Sci.
Technol. A, 1, 1, p. 28 (1983).
9. Bogaert, R. J., PhD Thesis, "Chemical Vapor Deposi-
tion of Amorphous Silicon Films," University of
Delaware (1985).
10. Ring, M. A., "Homoatomic Rings, Chains and Macro-
molecules of Main Group Elements," Elsevier, N.Y.,
1977, Ch. 10.
11. John, P. and J. H. Purnell, Faraday Trans. I, 69, p.
1455 (1973).
12. Bowery, M. and J. H. Purnell, Proc. Roy. Soc. Lond.,
A821, p. 341 (1971).
13. Hegedus, S. S., R. E. Rocheleau and B. N. Baron,
"CVD Amorphous Silicon Solar Cells," Proceedings
of the 17th IEEE Photovoltaic Specialists Confer-
ence-Orlando, p. 239 (1984).

r (rxt,i)

concentration of species i
diameter of reactor
effective reaction rate constant
reaction rate constant
mass transfer coefficient
molecular weight
volumetric flow rate
rate of evaporation
impingement rate, species i
rate of reflection
net rate of reaction, species i
axial position in tubular reactor

8 condensation coefficient
y stoichiometric coefficient
p density
f film property
g gas phase
i molecular species
s denotes on the surface



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    Wiepndton o lonc-to,-rn p...,itin,...ivin 1 :i:,.-li,,.tionor..-awbyf,i n1,a,daed fl .W .,_,, _;,,...o s,, _,_, H;itOlb n< ht <,,.1<,ont, Wonproolem,inftoid moth.ortics a ndrhoolon'withapplic,.t ..... l n ... h o nttd oi l ...,,,..,y, biomodi<& l ~oid m,,;:h o n ko. ;t~::~ 1 ~a.~:.!:F1 :;;:~:r~:; : ~r'R':.~J~ M!i c,. IS,ho(ll,ho it. moolOtiooofn,d ;=.~~;~:~~~~,::.:.~7hi'..':' :.: c..-..w, c ,-;.,1 .... 1 ... ..., ,. ...... k .... ...,., ~ -. wi-i. ._,..,,11,,w; ,,;,, ,. ,_, lo 'f,..,. (hHH tfto;d / ftu;d;n,.rl ..,.. d uo tot I Molo
    PAGE 12

    tiD iectur e CLASSICAL SOLUT I ON THER MODYNAMICS A lt e lr O/i / H!cffo e Vi ew 11.C.l'l,)/NF.l!SANDW.K.ARIIOTT R_,..,,,.,,~,...1>,,.,;t-,, T,., vr11111 T :;.:,:..OU:! ;::.;.~":'pon'!'::;: .. r ....itho_,....,,....,.__\'. lal-enW-,vlJ ........ ,...,,a.,._,.,.,.,. i..p,1..;,;..,1t, .... or on)'lhlnlim<>lo,.l',_,.d.....,,...,...,.,.,.
    PAGE 13

    n.,,,..., .,._ol....,..,.,.___. .,.;., M ..., ,. _,__,, ._ ,th., ,.. _.,.,.h,. -'Y ,.. ....... o.. ......... ,;,.,,,1; ,. .... ......, .... o1 ....i.,,, ... ................ __ .... .. ,. ,;o.. H .. the,tn>;'1 "' wh, ,. t o u l>o< ri])tn indiol< n u mbe ,. re heldron,tant,xpo .. tha!\11 < parti IPJ\>IIO!rl iff romhine lnth impln of B<, (8) ~-ith .,.,,

    PAGE 14

    n,-.u.,.--..,...,tia11olc1n11!11U..Glbbo1-io. -n..,r ... :~re,,..;, 1 r1oo1o11....i1o,-u.. .. ,... :'.: F ... u .. ,.,..i,.1,-ol .. rt ... iooi.U1-. .. ..... ,_,.,, t-, I' ... ... ;.; .. _,.;,_,, ol. o,1 ...... th, fpdty 1 1 lo o,on,po.riaonb1""ht"""looo f;;.,..,.;to....U...i,...,.mu.Th""'b,
    PAGE 15

    ;o. b ,...i ........ ;..1,....,;..,,o1,.,1o,tM ...,.._.,, .... ., ............... ._, ...... _.,;.,..,..,;o..o.. ........ _;,;o,, ,.,,. ...... .. ............... ... ;... .,,,_.;.., .. ................ hereag a lnth<,. (3~). w "bt ro .ct t ll,,oo t'O oqua[~) =:i ~ ... !~-, ,.. \ h< dertnili ona of ~ :.,. (2~) o nd (UJ ha, beoninvokt n noh< writtenbylnoJ>tioo ~1 [ '41-'1, ~ 1 .. I, .. ,,("l:;""1, ., .. ,-1~ 1 . ,'"' ~hrethe dlorm;nHQo.(3S)througt,(40Jyi e ldother proporti ,. ofint< ,..., Thi o c 'l i nktoJ>Ori rnenti th e majo, ......,,, for a,.. idoal-proporty ~~;; ~':'at~:.,-;:;;~:~ th e m> o h a prinO,>d i nr u1 .. a l l ov, o th ,ren e r a l i ia tionofP\'Td> toa n dth e
    PAGE 16

    ; t,-<,n1a,: r .......... 1-,u.1," ., ... ........ ,11, ,, .,1.,1a Y,ioa\loN\6),(7),and(t)inllll.o-j'lfld ,,-o./ ,'", ... ,., .W ,, J..i ..... may..,.,,.,.,.,1,ooe1 ... 1p,weni. of ~ Id i lh i !S i do.t.1-p..wt,,to P!'OPorlko oo -..ywocompo,othe.., tu al peo po,1lofftu i d ,. l,lort-.. il hitoid<&l..ol u ti0<1 p roperll01al t ho oamolffllporalo,.., p ,_ure.ond_,p>oi\ioo, 11, ..._.,.1u,-. ..... ll-of,.,,...,,,_,-1w. qoito__..,._..,,_,iw..-ip,,oponi. ........ .. ..... f ..... ,.,., ..... ,., TIMH_p,-.tioo&NC-, .... \edtG ,.,...,,m,, .... ,...,.,;zj., = ::n.:= .7::::0i.~: ~.:.,,-~ 00-ro...u,_, .,.. .. ..,. -1 T -' P .'J1>odoln i tioaoti',q.(NI allowo~(ll)lll""'P{M)lobowrlttoa ........ z., .. 'l,'", .. .. Th U!1 ,t pro oe r1 1 .. a,..u di lJ<&l II -H'fliquid.,;,1,,,_ ... __ uJnJo.tloaofG'.Forlliia..,.NOd
    PAGE 17

    [~ J-, .. i" fit-, l~J-~ .. ,t .. ... .. ~l-~ .. ft.. 1 .. ,, ~ -h h .. [~J -.~ .. ...... 1 .. W i th ,., r,p\a:I .. lnT a h lt l -.. -. brin 1 1og .. rO Y ... ...... .. ... H + ,_, r .," "" .,. .. ,. ,,..., .. ,. o,. ., v .. s _, _. ..., ..,.. ,, .. ,. ....,~ ,. ... v ..... ...., ...,...,. ,, ,._.. 11 ... ) ..,.,...,,..,.,,... ,, ,,.....,., .. ,.&.-.~ ...... b y K,o< J.V Boll < >'" .. .. .. .. .... A ,. ,. ~,llA .. > .. .. .,._"9 .K ( l ... l H ,_, o _,JI_T / R _,.., ,, _,.. ,,. ,.,, .. ,...,s.c ,. 11.Af H _,,....., ... w .. 1 -. C.. ,. Y .... 00 .. ;71 J,_., 07, .,, ...,__ .r ,_ ,._ w <> -' Sof "' T ..... A X~"'' "' ti" U.0"'"""""'"' .... .. T"""R E ,< o .. (l ... ) I .' .._ R .,.~ ll '< ,..T il
    PAGE 18

    rilJl'SI classroom THE NATURE OF ADJO INT VARIAB LE S AND THEIR ROLE IN OPTIMAL PROBLEMS A ~;:!:i~~;;;:, :. .:1=:~~~i:t;;,: tim>.lorutr,11U1 ith oory.fore,ompl<.Bryson "dllo [ l]" o djol""th.,,.totheoptimiuti""p,ob. l n,f om,a. \ion too ... .. rol" veoto r _., buio. Only Ja<' [IO ] h u, ho-..n th a t \hey o,e .... ir&bl .,,a at;eno ral tr>nolormatior, from ooe oet of oriabl,. >l'hiblems. Adjoint ,ariobleo ""tho ..,..itivity-11\clontainoptimal ... T&tions) haaatran- a nd thoN>ar,, th er,for,, t>1 ~~~.:::~:'.'m oolotion, to tho homos,neoo form ll o,~:rt;'n 1 'i;~ ~i;:/ d~~~ /7 ..;. ~,:...M ,:~ fondamentolnatu,,.oftheadjoint,orlab lel,a nd theirrol
    PAGE 19

    X,tho,,p-....U..rlflm.t.rtx,.\htr thonthtooMlon...,,o,nnrontnutwllhuoual r;:.::,:: a, t dhopoto ~odlhtvIOT< to~\ tho In~ PoT\, -.,hichi,tht-ond tt nno!F.q.(4) ;.u,t.i-111 ,-1,1<-,.,,.,,.,.,,,..,, <" 10<< .. ,.,,,.;.1,i1 .. ,,..,t m IMld-u.attM..-oftl,>lllo.Tho..tort""1 :.::ro:~;"" PoTll
    PAGE 20

    ' w .. .. ,. I I 1 I u,nO (d) bo< [ ,, ]io tlle&djointl'l!.l.trixofU..tran, pooo t! /~ ..!:.~::: ~ ,::: to -; ~~I~;, ~o~~ :!!::&djoint, o rio bla ,>, a .. de ~nl by;.;,. (13) > '! 01 ll ,,., .... Tla r,\he~i/l o r,nti a leou a ti..,.ro,th e adjoint ri a bl ,o The bound TJ oondilion M !!J : J 1l,:~',~ '.:ni!~~~~:O~ ;;,;,., f!:::;;~ l w", w" I 1 ,l ., .. IW'\,"" I ... 1::.1-> W> .,>

    PAGE 21

    0ootoru.oo,q1..i....-..tt1iU:u.1tlsa ........... to ....-10.c_,..,u_,s,.,..,1,y ,._.. Kallslh (!'""taH all 1111:111) ia.., ...
    PAGE 22

    ,Q.,,.,.,J ,Peciwu, SEMICONDUCTOR CHEMICAL REACTOR ENGINEERINC .. ,~ .....,? .... ,c.... __ ,.,, ... ..., ____ .. ... i;_,t,_ ... ,,_"' c __ .,.,...._ ___ ,_ ..... """"""
    PAGE 23

    ~ND PHOTOVOLTAIC UNIT OPERA TION S ----quo n \11>-ti,,.unkoroperti .. butft l mquali\7ioulh"'"-ts ~E~.f;rfif;g~f]FiiS. r,tt.tinr<1t,-;.,...,.,,orn>an
    PAGE 24

    W ..._ =.. ... :: t t M ..,..,_ :i: 1'"h: :: ,.hh" to"lht"'tho1 tho.,.,1 _....;., ,o,l,..;, ;. ..-, u ..fvl ;..tho i,bo,o1..-, ,.. 1, ,mh-, _. -.,i .. ,.. 1,1, ... h ,_. ,.h;. to IN "'"' olll ioo ~r ...dw;,h '" "' """"' ,.... .. ( .. yiM .. .,;,h_..,' .., ,,.h,001o ). Atyp;call o l>orotoryocai,phyoicolvopord<> pooitionuiti ohown!nFiJ.4.Thoroteofe,pc, rotiooofaymote,i lio~rminodb1the o an of :.h~\=~i::.~:~~~:~~ u:;;...c:h~ ~..:~~~,;: ~-:.~::: ... <;;:~t~:= .. to he evooatod ,. ll>own in Yig. 6 (nom,o""I s~tff~:~:~~:;~j:ffi ofth o m .., nux ,,,.1 t:1 p,qth.,,.1h\he o ,..ava i lablefor llo w .ThdRoch< lu {i] SolvinK\hetti lldsU1,.teof ellu,ionv,n,..,,,h r,etemP<"""T .Aoom l"i10T1ofmodelpn,diction( oo lldli.,.)ond,x perimont a ldato(hori<0ntolbara,,..,k,dwithh ,. lltempe ,a tr,,T )ar,tho.,nfo,tdllr,.n f~::~: ::~~:~~ ; ;:;~~;:2~; d~~~-~~ ~~":.';.":"! :.::~1:,:~ hetl l
    PAGE 25

    r,'OW-1~-Nd.AbooltlOOO-rate ..... ol c1a,.,.,.,.obtolwdloa>lorwi1haomo oll11t ,"1,.i !A=' -!" :;:;-;:::,.:--..: ... '!,--=' "".._c.,,d ... _,_._nm ::=.:. ":"'. ... of.

    PAGE 26

    :;;-i::"8Uninroit1of l )t-fCv,,StCdS-...-oobot....._ Tho\MOroti<&ludoxp,ri.....,tal.-erlcol Rod,ol""'"' "d HP,rlm+n\.Oo ttheunitoporallo,,o :~~ :~':"'i!::"1~ ;:~~-~~:.:;::..::.~~:: <>POntion of TI>t 1&1;,on,..,. 0t~it_,..tiomud. ""'mlll ...,le oqw-, .. '::.-: fo,-,1-~tioo> -A outcll o( tho Hit_,.,_ o,aJo ;.,. ,...,,. --1 &I tho l...,i, ~tc ol Enoru c.,,,..""'" i>e,,M ....... ,. ,_ak _, ""'_.,_, ............. x ,_..._ ,,. __ ,.. ..-,,,1 x 1t-'-lo>_,. -___ ,.., __ ( ... c.,., _, ..... ........... ,...,,,(u.&r.,,,. ui,_...,.,..,..,., -"'"---'" :.w., .,k, .... 00'\c.>l_,lllu ... T __ _, __ ,o ... c ___ ..... ,. _.,

    PAGE 27

    lo-J'la'. 11 .Tliia....,..,....,,_. .... dalflM'dq--K>MolailllrlO -leduFi1.loacl6....i-lodf..-a,nMlti orild.-ry..,d thod1tr...,t--botnl< .-..,0111 .... 1,_.,. ._ .... ..,.,_, llu--rrlOUp&odlhoo_l"ll)" ... 111"'311 lrola10lndGri~ l 1],The ,l)i,.._11_ det,no.inin1 th< ,qu,pn,o,,t,.,.. ot.wnla T obloZondor,IL,otocniplioftht-ip. ,,_,1oot.waMl'i1.IZ. =::.:.:. --"' "" .. ... i.~ ,,._~lt0110n.tiomv.Pffl.....,tatloafoon \lnu0110lydtpooltedGdS;1hthol<>fphotO\'ol1ale11TaOU ll1l.~_,Ctilo ~!: :,;;:c'.;;\.::.,~;~1~:!::~ ':,':; p.--hydlpplnrGdSlntoCClooM!on orit .. nbomadolllinradryp-lnwhkh C..cli o ... oPontodonltlon.Alltllollallu .... Po .. tod ~fro,,l-t&ct. The-lnc1~r,,-lhoth at\hOlonfor U..bollloao4'Nvil;r

    PAGE 28

    @@classroom THE USE OF COMPUTER GRAPHICS TO TEACH THERMODYNAMIC PHASE DIAGRAMS C HANDRA$Hf:KIIAKD.NAIK SiQ.,c.,.,_,.c;.,.. S;J,,;Sprig,MIJ20904 PA U U:TIE CLANCYASO K EITH K CUBB!NS c .... .uu.;,.,.;c N lt ltoeo,NY w,~ T ;:.:::;;,;i.:::r:~~ ~~h/ ; :'x.:OT,;;d~~~::~.;,,,~I ;~.~=!~~ ,u-pooitiondiv.,.rorbin a ey~uidml<~~::.~1~0~~=~1: :.~.~:f;":1.' ";.':!'.':::.~~ i; onierto,imp!ifythl oompkol: a ituation,' t
    PAGE 29

    fl0UH1 .A o -., lool, ty,i.ol-koto.... ,.. ..._ .... ... ,.., s.....,.. _,_,_..,.,, ,,.,.., :;;::--v1100 -,,.....,,.., ~aeility(CAD F>atCo ..... llhou-~ta!-...rll..,...aNlood.._ <&lioa).T1loot11\nl....,Po11ettforU1 .. facilit10R Di,.;i,,1r..,..1-1Corpontio,,(DY.C)VAx ...... ':!'t..i:.~t~~':~r:,:,;_ti..I~":'b;:~ \"TIOOtttw,i,..lforali,llan..,.,.;elnpul .At n>i d !npo,1.-. ... ..-,,..1....,dear..........,.,"foro,,tloaol,_otimtenepll&Moqumbriu 1d&tafortilruor)"mi,.._ lurean..-dedfo,-\h<.-.p,_,,tatlonol1i>ephaoo dl .o..,-a mH. o. p..-un!&.tempe,atureo a ndrom="'.J.. -:i~=-~~h-i;~ ~=;,,:: otate..-Nffl~fo,lhU,...,-,e <"""'ofd,.aimplieityoflia...,.....i,,tio,,(ai""" only ~oUnti als ofbothpt, .... .,re"...Jto ...,.,..,.dotaPe 1N ........... ::1 !:~:::;:.~~ '==:":.i=.. vol,...i w o rt aol
    PAGE 30

    Kft,. paelu,ppr<,V l (ledlheoubrou!I,,. foro M ,q u nltl'Mlhodofoolullon.S..Mo J forpor liquld orilkal Ji ,oloo prwi ded oh o ll ,op. Ntlthetollhep<"OVlotorOl.anda,pocioli>tnmlalb'-in,.ofBoldphMOdia,. ,..._i..tom.-.... u..-., =:. "!:ii:-t= ;:.,~.::. U::~ -:..--= to..,_thtt....,oimploatol-.lM ll .olt"'-'rh uttnalo,,ofthe p ,_toiver!hoolher 1 -1,,.., 11unl>llla. l ndoU,,_,,.._liquld-_ .. --..i.. ........ 4; .. ,_, ... ,,.,.. ........ ,, ____ n., .. :w .... ---""'-....... -" "'"P'" of c1.,,. ,nd ,e r, ,.....,,.,.. Aollo~lo f.,Dl'f'lr, .... uAT .-._ ,_, ...... J : ........... ltl-UOi ... > Uf.21.>0(A>) !< ......... ...... 1.n(IU) )< I D-4 0 >("7) <.11 .... N(OI.<) r ,_, .. ,,,....,,_,.( .,_. ) ,. ........ (ffl) ... ,_,, .. ,. ..... __ ... .. .......... ) UhlO ,..,.p,.,H........ 1) tN.Yoo,,..; -.,-& ......._,_ -~lo;R"N- -:!:'r~m':.='::"'...::..~---'T, 1ouu 1 ,. 1 ,41,. .... ,.. ......... o,,,_ ._ .... .. ~--. ................. ; .. .. ;::."'t!;::"..:.'-=..i..:~ ::;;:.". ;l~~-::.. -:7.=.::

    PAGE 31

    11,,oMru li ,... ;xamplnofthbinnyoy,th;bit-rop>< phffl""'"& ~ithtr>ti~U..\lndofhru.,..d i ,. pl~fotheu .. t<>manipul t UUI INTU -',CTI ON WITI I TH GI A'" ICS The i,ru.,reofthe pl,MUlhlOllhe~,..phi<>p~rm io11<1i,otod,ndpefoms noper a tionapp,opn. ,ttoth ar>& of .. ,.,,.n,ttootn ,.;,,.nthot oehon :~~:.::t::.:~!:r...:!:,:~:~"':'.:i: "m H~ All .. .. to,... ,.~ ~, ., ......... .... ... .... .. ,..... .. ..................... .... .... OHHIT.1,-,,., ... .. ,.,,.,. .... .. ........... .. _.,, .... ....... .. ,_, ........ ,...,,.,. ,) v.,~,.,,. ..,,_ ,., .,, .,.w.,,,. ,. .. > ,oo>t,I', .. -,._ ,., b\ .. _, ...... .. ..... ... ......... .... ..

    PAGE 32

    o) ntm:N, ................. .. .. P.T .. ,. ...... .... .. -.... .............. ,,., .. ,~---......... .._.,.,_, .... .......... ..... .... ., .,,.,... ......... _,, ... ........ .... I) .. ... ... ..... ...... ,.._.-... .. .... ,., ................ ............. ..... ._ .. ,, .. _, .. -.. ... ... ..... .. ..................... .. _,,_ .... .._.. ............. .......... .. ... ..., ... c,, ... <;UT,.-.-.r., .. T.,,"<"-JN __ (..._ ... ... .. .. .... ..... ..... ........... .... ... ._ .. ... J.r.TorTX), .. lo.<.,.I,._ ........ .-( .............. ._ ....... .. .. _1_, .. _, .......... u ........... ,..., .. _.,_..,,.,.._ .. !11Klon. .. .... .. C,l,.-oo" A 16mm '"'""' luilltiNpf1hloa-npl,iN,..Up;tluowu -te<:1,1\llt1983AlCttl:...,nual._lnJin W u hinrton.D. C. Theintedp,...looob' ...i~,,_,,...lloul-.Themojor..,..,o,. 0plicolotMltabmt.,ofmpo,te.-n,pl,lcou o....,.. pf viouollj .... t1>e ... 11iePTup1,ue,_.,1,11, eu Poblll1,or111e :::!::~~..:;!~:/:1!:;d..::1:~~::i;. 1M -rtHlt., IO lni-t. m&nipula\e Mdlrol1Mi"'"l'--..dIM0i,, $ < .... IT(I"") t+OtiN book reviews l'OUN D AT I 0!'/1101' I IOUN l>Alff J .A n ( R T HOOR \" t "O R MOME l/TtlM. HE A T AN II M AS!I TR ANS l'ER ~ J-_o-\ p ,.,.i;,. Holl.ln < NY( UU) R nl< "Nll>)" O. T H Ualnhll 1 7.rC. ~f -1a.Sa a1& l 1&...,._ Thiobook.,.Boond.o.rl.or<'"'""'1lindi ""'edby\loenmot .--.nablywelicater,,on!nt.o,inlanddillo,....tiol0quo1ioN,o f llow_.......-llhopp,..,l ... telo\eft,ilooltiono :!i -:r,,,o1.:;!1a.!%::"':!':-oi=1::-!:i! ofotM>roo,,,....,n
    PAGE 33

    ,,.. .. fo,iolikolytob .,_.r t h<,..,,.. .. ,umpl< problomso,lt'l'lpulerpn>o;,._m> Them,;o,00.01rit.utiooofthi&-..,ld o ... ,tolioinCho~"6throoi,h9.ontu'Wlonn>titu te mo, o lrt1>< itri I p l a nt op,r o tlon>. The .. ,olum< ,;ov,r,dp ....,_ n., ,.... ,hilJ,olul'Mwl l l ,... 1,qulpm,n1 .. 1t1on.l,,....tandl,uild i n s doTh"t~um,.d ded l n,jo,-, olunitooe,._,;,,.,,,ndprodoctlhrin.,n """'broodron.,.ollodu1tn. i thou. tho ritohY1:. o n do.,...,.v ..: uu m .f,_ ndnu id iiedbed dryln J The..,.,,,..J...,tionineol"'ralionoondproli ha,-. i n< lu dodqu a "titoth-. ;oformatioo 1h o t ,...,.1dbo...,....rylotl>tdtolp o ndoo.o"ru'onyolth<>"'ri""to" 0,..,,1<>1 ,n_,; r, wi l lol .. 1,e_,,..,.h.,di .. ppointlumedooom o n ap tobrin1 ... ,ht,o .. l thof., ..,rien in obro& oflodu tri .. o nd "illa idon.,;n .. r,, m o no,pr,, =.~fr~;i1l::?.~::: 1 : ~!.1~~::~nt~ a t,p, to

    PAGE 34

    [!jJfiJI. laboratory AN I MPROVED DESIGN OF A SIMPLE TUBULAR REACTOR EXPERIMENT AIIDUL.l'ATTAltA.A8YOUR ~7,X:'.o:'.!.~11911 m .. ....... -..... .... _,,,~.,.................. ... .... .. ,,_,_ ......... ........ .. .... A ... .--[I J ~tobolo-;::=:".!:. ".':1~:;!:... ~.:'. IIJ'drido-nler.Thlopomc,,k, .. po,l ... t,. .. ;,. ... ;"' _,..,. ~,._...., ........ ..................... ..... ......... -pon ..... _,,..,..,.u1_, .. .. ~INpk,otokentlllo-llot...t .. 11o1 ........ 1,,...ti.,,u.. ... ;1;,... ....... .. hl,clllorolotl,tlylnl,rtllr""""bjoct10orN>R H ud,lnoodC~rol[iJtlllood!hebui<&o. 1 .. of AI'< in ""lopinr ,1 m ple ood ln1et.-tlnr0>1"'rimtn\.Thoyotill.otlthe< .... I .. I ,_.Hlll.,..tom<>fe ...... lviolc\dy0""t,.ll'" =~~,l:'::;.!:~.!i.!!'!.': :-: t.t< <>p
    PAGE 35

    forlM,o,-of\lllo.,a,er;-1.t.nNoo,iuu,, ~--ooo1o... 1111 ~ 1oJf..,. ~t~~=:::~:~: willbo-ll > odlate,.on) E (1)-/UP!.:t!~ ~ ,-/a-e43) ~~~Et,)lotaboo-lalWl.d&I....,._ lfC lo ....... l ~i.e..lM.,_,_,,,,,,,.tio,, ~'E!::',Cj~~I~'!':.= -i-1==:g."t~~ -:. i:,.1a t ::J::::... ... .. i..-..... u.. .. ,_ ..... f orftnt-r<-'ionlnawnin& ,___,hofol..,_lr-tlonappllooauumiq HG U II 1 41,plo, .... .._.. -. M ,.., 0,,1',.,_, -, HS, ..i.,t,_....,....,.,, T ....,_ 1,oo,1 ,...i, lfa _flo _, ..,.,_c_,_,..,,., .. _____ __ (.._.,.,,.. c .,., ., ,_,,_.,., .. ....... (T1> >H.,i, '",._,_,,,., .. .... ... & ..... (.,, ... ... .. "' _, ..... "" .. .. _..,.., ...... ~-, c,.._c-..._o, .. ,._ "' ''"""'~-.. -g. ... ,.,._,._ ..... ,_ "....,. c ............ ... ..... ,._, __ ,....,__ ,._._. ___ .,.,.. (11) ........ ............... .. _, .. .. ..............

    PAGE 36

    . .. _.,, .... u,. ... ---(....... ....... ........ .. -u ..... -......... ....... ___ __ T'wo~t.,.. ,:,lo,;ill-'.StodREll2) oleSpo
    PAGE 37

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    PAGE 38

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    ACKNOWLEDGME N TS Departmental Sponsors: Tfle follow i ng 150 dep,,rtments con1ribut ed to !he $upport of C HEMI C AL ENGINEERING E D UC A TI O N in 1985 with bulk $ubscriptions r ;4, .,,, ol A,.. Uol 4', >fAla .. ,., U ol ,., ol 1, -, An ,.,. Ari,_,, .. ,u ... ~. ~ ., ..... ,, .. A, ...... ,, ... <, otA .. oolo ,., Lo .. ,.,ol<, ol O,k ... Col ........ .. ........... c rr > ,., ,...,_, ,--.,,.-.,., c.rr o.,,,,.,,J A.ol C. r< onio (ll ... ) ~ ~ 1,M ..... .iR /4, s ....... u .... ...., ... ..... .... ., ... .. ,.h""'"""'"'""' )&;,l, i"""' u,.;,,..., ,., ,._,, u.-...,, .,, ,,.,. .. w. u.;._,.,,..__.,,...,,., ,. _u"' """' ......... ,. .... ... ........ ,. ...... .. u ........ ,,, ... .. u ........... s .. ... ,...,, ., .,T..._ ........ .... u.... ~-x -1 .. ,r ., ,,. ,.. ., ., .. ... ,, ., .... .. .. x, .. u ........ c-,...,,,.,x .. ....... x -,,..,i,_,,....,, u A. .. ,, ., ., AloN U ,J ,,. ~ .., ,...,,..,, ..... k1 ... 11 1o i,.,,.,.., .,_., ......... "'" '"'"" 10 OU I llA D 1 1$ II .,_ ......... lo_ ...,t,;b kN. plooM ., 1- .... ...... ~ .. C IIEMS r.: i : '.'IN II IINO I DUC AT ION ,o / C .. -"'90.,- U i .. ,oit-,offlo,,d.D-,'l lo, F

    PAGE 60

    Our na111e has been synonymous with engineering education for over 150 years Here are eight more reasons why. ECONOMIC ANALYS I S AND INVESTMENT DECIS ION S Ch i U. l k o l"' /l ~ 111 r/,,. ,, i:, S ta te: U n;,~.,,.; 1 y (0-471-6 14 55-5 ) April 198:i J23pp, PRINCI P L ES AND P RACTI C E OF AUTOMATI C PROCESS CONTROL C a rki A. S m i 1 h, Uniw,.,.iit <1/ S..... th Florida A mu ndo B Corripio, L> ~ WH ; oJ ::: ;~ ;: ;-~}~~ NU ME R l Ct\L M ET H Ol)S AN D MOOEUN G F OR C H E M I CA L E N GI NEOIS ~! ::~~ l !:ii,S.:'t::!::-' '"'S. ~u ;.,,,. M >n u o l mil.o l>I< 1"41 1 -Ul "' I) L 'IM Jl\1.,... l l\ '1ll.O D UCT1 0 N TO ~~~~OENE R G V __ ,._ .... ....... I0-+71-041Jl,91 """" ,.,._,~ NA T U RA L G A S R ESE R\"O I R .. ~,.... s ... S. ~ ul O ,.., M o n uo.lmiW>k i