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 Permanent Link:
 http://ufdc.ufl.edu/AA00000383/00012
Material Information
 Title:
 Chemical engineering education
 Alternate Title:
 CEE
 Abbreviated Title:
 Chem. eng. educ.
 Creator:
 American Society for Engineering Education  Chemical Engineering Division
 Publisher:
 Chemical Engineering Division, American Society for Engineering Education
 Publication Date:
 September 1963
 Frequency:
 Quarterly[1962]
Annual[ FORMER 19601961]
 Language:
 English
 Physical Description:
 v. : ill. ; 2228 cm.
Subjects
 Subjects / Keywords:
 Chemical engineering  Study and teaching  Periodicals ( lcsh )
Notes
 Citation/Reference:
 Chemical abstracts
 Additional Physical Form:
 Also issued online.
 Dates or Sequential Designation:
 1960June 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., 19651967; Gainesville, Fla., 1968
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 University of Florida
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 All applicable rights reserved by the source institution and holding location.
 Resource Identifier:
 01151209 ( OCLC )
70013732 ( LCCN ) 00092479 ( ISSN )
 Classification:
 TP165 .C18 ( lcc )
660/.2/071 ( ddc )

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CHEMICAL
ENGINEERING
EDUCATION
THE AMEPICAN SOCIETYY nnr I~ t 1 IIJ
September 1963
CHEMICAL FNSINEERING EDUCATTION
Septem!,r 1963
Chemical Engineering Division
,aerican Society for Engineering Education
CONTENTS
jnStream Computers, An Example and Some Generalities,
by C. G. Laspe 1
Optimization Theory in the Chemical Engineering Curriculum,
by Douglass J. Wilde 9
Fundamental Problems in Heterogeneous Catalysis,
by Max Peters 14
Planning Experiments for Engineering Kinetics Data,
by H. M. Hulburt 23
Joseph J. MN
George Burne
J. B. West
Chemical Engineering Division
American Society for Engineering Education
Officers 196364
martin (Michigan) Chairm
it (Iowa State) Vice C
(Oklahoma State) Secret
Ian
chairman
aryTreasurer
CHEMICAL ENGINEERING EDUCATION R Journal of the Chemical
Engineering Division, American Society for Engineering Education.
Published Quarterly, in March, June, September and December, by
Albert H. Cooper, Editor.
Publication Office; University of Connecticut
P.O. Box 445, Storrs, Connecticut
Subscription Price, $2.00 per year.
ONSTREAM COMPUTERS 
AN EXAMPLE A IND SOME EIERLITIES
C. G. Laspe
ThompsonRamoWooldridge Products Co.
Beverly Hills, Calif.
There are many excellent articles (1,2,3,4,5,6,8) giving the general
reasons for and the philosophy behind the use of digital computers in the
controlling of production processes. Some of the pioneering efforts by
Stout and Laspe (1,2,3), as early as 1957, formed the basis for many of the
digital control systems in use today. Subsequent papers by Roberts (6),
Stout (6), Brandon (5), Madigan (12,13,14), Freilich (19), and Leape (15)
presenting the results of actual case studies have bolstered the technical
literature on computer control systems. It is not the intent of the present
paper to dwell heavily upon the objectives of computer systems, nor upon
their design, for these aspects of the problem have received adequate atten
tion in the aforementioned papers.
It is the purpose of this paper to present the broad aspects of on
stream computer control and especially as these aspects affect the curricu
la of chemical engineering. In attempting to accomplish this objective, I
would like to present a few generalizations, followed by a survey of the ex
tent to which computers are used in online control. Next, as an example,
the computer control of an ammonia plant will be discussed to illustrate the
various branches of engineering and science required in its design. Finally,
the most important point, as touching upon this present session, will be a
discussion of those fundamental things which a student in an engineering
school should learn to understand the use of online control computers.
In order to orient our thinking along the lines of onstream control
computers, a few generalities should be considered. What are the ingredients
that go into making up a computer controlled process? Once these individual
ingredients are recognized, then we are in a better position to determine
the engineering talents required for the design, construction and operation
of such a system.
Let us briefly review a few fundamentals. All manufacturing processes
are designed and intended to be operated to produce a profit. This profit
results from the creation, by a combination of physical and chemical trans
formations, of a product or products whose value exceeds the cost of the
raw materials and their processing. A major goal of management in plant oper
ation is the maximization of this profit. This statement may be considered
as the process objective. Therefore, the purpose of computer control is to
provide the latest and the most efficient means of reaching this process ob
jective. It is realized that, since physical and chemical transformation are
involved, many variables influence the realization of the process objective.
When one or more of these variables are beyond the control of the operator,
such variables are classed as disturbance variables. Compensation for these
disturbances is the prime justification for any control scheme. For without
disturbances, control would not be required.
When there is but a single disturbance variable involved in the opera
tion of a plant, then it is possible to derive a unique solution to the con
trol problem. In other words, for any given value of the disturbance varia
ble, there is only one set of control variables which will meet the desired
objective. In this instance, simple relationships may be found which will
relate the manipulated variables to the disturbance variables then in effect.
On the other hand, when two or more variables are beyond the control of the
operator an interesting situation arises. In this case there are generally
two or more feasible solutions. Of these feasible solutions, one will prob
ably yield the greatest economic return and, therefore, is the desired opti
mum solution. It is in these areas where computer control may be justified.
Recently, an excellent article by Elliott and Longmire (21) gives the dollar
incentives for computer control. The results of their studies on six differ
ent production processes is presented.
The March issue of Control Engineering presented a survey of onstream
control computers. At that time the score card showed 35 closed loop com
puting control installations, either online or scheduled to be online by
early 1962. Of these 35 installations, nearly half were to be used in either
chemical or petroleum processes. In September of 1961 Freilich (19) presented
another survey of process control computers in use. Freilich shows a total of
63 process control computers, of which 20 are used in the chemical and petro
leum industries. The latest figures available from the May 1962 issue of
Control Engineering (20) show a total of 159 control computer sales, of whichh
43 are ins allied In the chemical and petroleum fields.
1
2 CHEMICAL ENGINEERING EDUCATION September 1963
Although the preceding statistics are both encouraging and interesting,
they do not tell the complete story. Table I summarizes the known instal
lations of digital computers in process control or those units known to be on
order. In the petroleum field, several installations have been reported on
catalytic cracking plants and on crude distillation units. Single installa
tions have been reported for catalytic polymerization, alkylation, and ther
mal cracking. In the chemical field ethylene and ammonia appear to be good
candidates for computer control by reporting several installations on each.
In addition other computer controlled chemical processes include vinyl chlor
ide, styrene, acrylonitrile, acetaldehyde, ethylene oxide, and the exotic
"alfol" plant of the Continental Oil Company. As can be seen from a study of
this list, the gamut of applicable processes is limited only by economic
necessity and the imagination of the system designer.
From the above list we have chosen as the working example to be dis
cussed here today, the computer controlled ammonia plant of Monsanto Chemical
Company at Luling, Louisiana. A fairly complete description of this particu
lar installation has already been given by Eisenhardt and Williams (17) in
the November 1960 issue of Control Engineering.
For purposes of description, the ammonia process can be conveniently
divided into three separate operations. The first of these is concerned
chiefly with the preparation of raw synthesis gas. The second section is
concerned with purification and compression, and the third and final section
is the synthesis unit itself. In the gas preparation area three chemical re
action stages are involved. The primary reformer, the secondary reformer,
and the CO converter. The feed to the primary reformer consists of natural
gas and steam which in the presence of a catalyst reacts to produce hydrogen,
carbon monoxide, and some carbon dioxide. External heat is applied to this
unit from a reformer furnace burning natural gas. Essentially 90% of the in
coming gas is converted. Steam reforming is the principal reaction involved,
although the water gas or CO conversion reaction accounts for some of the hy
drogen produced. The variables over which control can be exercised are the
flow rates of the natural gas, the fuel gas and the process steam.
The secondary reformer serves two specific functions. Firstly, to pro
vide additional reaction volume for continuation of the reforming and CO con
version reactions initiated in the primary, secondarily, to serve as the in
jection point at which nitrogen is introduced into the system. Atmospheric
air is used as the source of nitrogen. In the secondary reformer the oxygen
from the air which has been introduced reacts with some of the methane and
hydrogen in the feed to form water plus CO and C02. The only independent
variable over which control can be exercised is the flow of process air.
Note that at this particular point in the process, essentially all of the
natural gas has been converted into product gases. The residual methane con
tent is in the order of 0.3 of one percent. There is also an appreciable a
mount of carbon monoxide. The effluent from the secondary reformer flows
directly to the CO converter.
The sole purpose of the CO converter is to produce additional hydrogen
from the incoming carbon monoxide by means of the water gas reaction. rd
ditional water is injected at this point in the form of low pressure steam.
Because of fundamental thermodynamic and kinetic considerations, the carbon
monoxide is not completely consumed. The exit concentration is in the order
of three percent. At this point in the process the hydrogen to nitrogen ra
tio is fixed and remains constant throughout the remainder of the operations.
At this particular point in the process, carbon dioxide represents approxi
mately 1i% of the entire raw synthesis gas. This along with the carbon mon
oxide must be removed before the synthesis gas can be charged to the final
ammonia synthesis reaction stage.
Carbon dioxide is removed from this raw gas stream by passing it
through a standard Girbotol unit. Monoethanol amine is used as the absor
bent.
A compression plant consists of several parallel reciprocating com
pressors. Each compressor is equipped with five stages of compression. The
gas pressure is boosted from approximately 20 pounds per square inch at the
inlet to 5,500 pounds per square inch gage at the outlet. The entire gas
stream, however, does not pass through all five stages. At the outlet of
the fourth stage, the process gas is diverted to the high pressure purifica
tion unit. Themain function of this unit is to remove carbon monoxide,
plus any residual carbon dioxide. Copper format is used as the absorbent
for CO. The residual C02 is removed by a final caustic wash. After removal
of impurities, the purified synthesis gas is then directed to the last stage
of compression from whence the gas flows to the synthesis unit. n analysis
n4 the exit gas shows an essentially pure hydrogen, nitrogen mixture in the
io of approximately 3 to I.
CHEMICAL ENGINEERING EDUCATION
Table 1
SURVEY OF PROCESS COMPUTER APPLICATIONS
CompanyLocation Computer
1. Allied Chemicnl Corp. RW300
South Point, Ohio
2. American Oil Company IBM1710
Whiting, Indiana
3. B.A.S.F. RW300
Ludwigshafen, Germany
4. B.F. Goodrich Chemical RW300
Calvert City, Kentucky
5. Celanese Corporation
Bay City, Texas H290
Bishop, Texas RW300
6. Continental Oil Company RW300
Lake Charles, La.
7. Dow Chemical Company GE312
Midland, Michigan
8. Dupont
Beaumont,Texas (2) ISI609
Florence, S.C. ISI609
Circleville, Ohio ISI609
Gibbstown, N.J. IBM1710
9. DXSunray RW300
Tulsa, Oklahoma
10. Gulf Oil Company RW300
Philadelphia, Pa.
11. Imperial Chemical Ind. Ferranti
England
12. Monsanto Chemical Co.
Luling, Louisiana RW300
Chocolate Bayou, Texas(4) H290
13. OwensCorning Fiberglass ISI609
Aiken, S. Carolina
14. Petroleum Chemicals,Inc. RW330
Lake Charles, La.
15. Phillips Chemical Co.
Borger, Texas Recomp II
Bartlesville, Oklahoma TRW330
16. Shell Development Company PB250
Emeryville, Calif.
17. Sun Oil Company IBM1710
Marcus Hook, Pa.
18. Standard Oil Co.(N.J.)
Linden, N.J. LGP30
Baton Rouge, La. LGP30
19. Standard Oil Co.(Calif.)
El Segundo, Calif. IBM1710
Richmond, Calif. Recomp
20. Tennessee Eastman GE312
Kingsport, Tennessee
21. Texaco RW300
Port Arthur, Texas
22. Tidewater Oil Company ISI609
Delaware City, Delaware
23. Union Carbide Corp.
Charleston, W. Va. RW300
Seadrift, Texas RW300
Seadrift, Texas Daystrom
24. Universal Oil Products Daystrom
Des Plaines, Illinois
Delivery
1961
1961
1961
1959
1962
1962
1961
1960
1960
1961
1961
1961
1961
1959
1962
1960
1963
1959
1962
1961
1959
1961
1959
1960
1961
1958
Process
Ammonia
Crude distillation
Chemical process
Vinyl chloride and
acrylonitrile
2Ethly hexanol
Vapor phase oxidation
Alfol process"
Styrene
Chemical process
Chemical process
Chemical Process
Acrylonitrile
pilot plant
Crude distillation
Catalytic cracking
Soda Ash
Ammonia
Chemical process
Logger
Ethylene
Portable Logger
Portable logger
Logger
Catalytic cracking
logger
Pilot plant logger
Catalytic cracking
logger
Catalytic cracking
Product run
calculations
Chemical process
Catalytic
polymerization
Naphthalene
Pilot plant logger
Chemical process
Ethylene
Pilot plant logger
September 1963
CHEMICAL ENGINEERING EDUCATION
Table II
CRITERIA FOR JUSTIFYING MONSANTO'S
COMPUTER CONTROL SYSTEM
1. Maintain maximum gas flow in spite of changing weather and process
conditions.
2. Maintain an optimum hydrogentoniyrogen ratio.
3. Maintain an optimum methane concentration at the shift converter exit
unless in conflict with 1 or 2.
4. Maintain maximum shift efficiency if not in conflict with objectives 1,
2, or 3.
5. Maintain objectives 2, 3, and 4 under reduced flow conditions.
6. Reduce raw materials flow immediately and safely to compensate for any
loss of compression.
7. Log out all important process variables.
8. Provide the plant operator with messages in case of abnormal process or
instrument conditions.
9. Provide failsafe features such that instrument or computer malfunctions
are detected, alarmed, and prevented from affecting the process.
.0. Control the fuel and air to the reformer furnace.
.1. Maintain a specified steamtodry gas ratio at the exits of the secondary
reformer and the CO converter.
The synthesis plant feed is combined with a recycle stream to form the
feed to the synthesis reactors. Because of the low conversion per pass, (sp
proximately 12%) a high recycle ratio is required. Ammonia is recovered in
the reactor effluent gases by condensation. In order to prevent excessive
buildup of inerts in the system, purging or venting is required.
Now let us turn our attention to some of the factors involved in making
this particular plant a good candidate for computer control. As has been
pointed out by Eisenhardt and Williams (17).
"While there may be a tendency to overdesign some plant equipment
an enginecompressor system is usually conservatively sized be
cause it represents a major fraction of the capital cost of an
ammonia plant. The engine compressor system is thus likely to
be one ofthe first units to bottleneck the plant as production
increases. At Luling the highest possible production rate and
therefore the maximum economic return is obtained by operating
the compressor system at maximum possible capacity."
Due to the very definite influence of ambient conditions, particularly
temperature, upon internal combustion engine compressor efficiencies, the
allowable horsepower to be expended by the compressor is not a fixed or
arbitary constant. In fact the compressor capacity varies not only as the
ambient temperature changes, but also as the mechanical condition of the
compressors themselves are changed. In the case of a production limited
plant, such as the one we are now discussing, it can readily be seen that
the maximum plant throughput is never fixed, but varies in accordance with
compression capacities. The purpose of the computer control system now be
comes apparent. Its main function is to keep the plant running at maximum
capacity by determining the maximum as limited by the capacity in the com
pression section. Once knowing the maximum allowable flow of purified syn
thesis gas, the computer can then go about its business of setting the many
flow controllers in the reforming section. It is not enough that the com
pressors be fully loaded at all times. The synthesis gas must also have the
proper quality as measured by the hydrogen and nitrogen ratio. Since ech
of the processes in the reforming area, namely, the primary reformer, the
secondary reformer, and the shift converter involve chemical conversions,
complicated kinetic and thermodynamic equations must be solved in arriving
at the proper flow settings. To keep the plant properly balanced as well ,s
keeping the compression section fully loaded then becomes the major job of
the control computer. Table II lists the criteria for justifying Monsanto's
computer control system. This table was taken directly from Eisenhardtts
and Williams' article.
A question which immediately comes to mind is whether or not the pre
dicted economic gains have been fully realized. Of course, basic economic
figures are considered as proprietary information. However, in a qualitative
way we can answer in the affirmative as Mr. Eisenhardt and Illiams have said
September 1963
September 1963 CHEMICAL ENGINEERING EDUCATION 5
"Immediately after placing the computer on control, the gains
in controlability became evident. When the process is not on
computer control, the operator makes minor changes in control
ler setpoints trying to hold process temperatures within lim
its and maintain gas composition as required by the synthesis
loop. Superimposed on top of these minor changes are larger
step changes in throughput which are required to compensate
for those uncontrolled variables affecting the plant capacity.
At best these larger changes are made only .several times a
shift. Under computer control, however, the plant throughput
is adjusted every eight minutes to obtain maximum possible pro
duction as uncontrolled variables changed. Qualitatively,, one
can observe from the computer log sheet the steadying of gas
compositions and temperatures under computer control as com
pared with the irregular control obtained by even the best op
erator."
This now brings us to a consideration of the topic of engineering fund
amentals as related to an understanding of computer control processes. The
design of a computer controlled process requires the systems engineering ap
proach. This means that the person in charge of the overall project must
view the project in its entirety. He should not be burdened with the many
small details that go into the system design. But on the other hand, he
should be fully aware of the many fundamental engineering sciences which are
involved in such a project. In this sense the system engineering approach
may be synonymous with the common core approach in chemical engineering ed
ucation. In Table III are listed some of the fundamentals required of chem
ical engineering students for understanding of digital computer control pro
cesses.
Topping the list of required fundamentals is an understanding of eco
nomics. Since computer control is a tool to assist management in meeting
the process objectives, which is normally the maximization of operating
profit, the importance of a thorough understanding of economics can hardly
be overemphasized. Methods for pricing intermediate products, an analysis
of profit from incremental production, and a working knowledge of payout
criteria for capital investment are especially important.
The basic curricula of chemical engineering will permit a reasonable
understanding of the workings of most of chemical processes. However, in
the computer control design area perhaps a little more emphasis should be
placed upon chemical thermodynamics, chemical kinetics, and stoichiometry.
For these are the basic sciences involved in deriving the predictive mathe
matical models used in the control computer.
Mechanical engineering enters into the design of computer control sys
tems firstly, in the determination of the operating characteristics of the
mechanical equipment involved. For example, in the ammonia plant system pre
viously discussed, compressor capacity calculations were made. In addition
to these, certain mechanical equipment limitations had to be evaluated. These
limitations, which in computer parlance are called constraints, oftentimes
determine or limit the area in which the process variables may be operated.
In the field of electrical engineering, as applied to the design of com
puter control systems, a main consideration is the communication between the
computer proper and the process itself. A working knowledge of the basic
AC/DC theory, transmission lines, impedance matching, and the filtering of
electrical noise will go a long way in the understanding of the electrical
requirements of computer control systems. It is not necessary to become too
deeply involved in the computer circuitry itself. However, to deepen the ap
preciation for the entire control system, some instruction in this area would
be profitable.
The ultimate success of any computer control system depends very heav
ily upon the ingenuity and mathematical ability of the process analysts.
Most computer control systems operate on the basis of mathematical models
which simulate or represent the operation of the existing plant. These
models may be derived from fundamental theoretical considerations or possi
bly from regression analyses of plant data. In either event, considerable
mathematical skill must be exercised in obtaining an accurate Pnd represen
tative set of equations. Once the mathematical model has been developed,
the job is only half done. From here various optimizing techniques must be
explored in order that the model may be used most efficiently in reaching
the process objective.
CHEMICAL ENGINEERING EDUCATION September 1963
Table III
FUNDAMENTALS REQUIRED OF CHEMICAL ENGINEERS FOR
UNDERSTANDING OF DIGITAL COMPUTER CONTROLLED PROCESSES
I. Economics
A. Process objectives
B. Investment and payout criteria
C. General knowledge of market prices
D. Methods for pricing intermediate products
E. Analysis of incremental profit
F. General knowledge of utility costs, etc.
II. Chemical Engineering
A. Unit operations
B. Chemical thermodynamics
C. Chemical Kinetics
D. Stoichiometry
III. Mechanical Engineering
A. Operating characteristics of mechanical equipment
B. Constraints imposed thereupon
IV. Electrical Engineering
A. Basic 4C & DC theory
B. Transaission lines
C. Im dance matching
D. Filtering of noise
V. Mathematics
A. Methods of correlation analysis
1. Regression techniques
2. Curve fitting
B. Optimising techniques
1. Maximization by calculus
2. Gradient methods
3. Linear programming
4. Nonlinear programming
5. Dynamic programming
6. Calculus of variations
VI. Control System Theory
A. Linear feedback systems
B. Nonlinear feedback systems
C. Sampled aata systems
D. Laplace transforms
E. Z transforms
VII. Instrumentation
A. Hardware
1. Types
a. Pneumatic
b. Electric
c. H draulic
d. Other
2. Measurement equipment
a. Temperature
b. pressure
c. Flow
d. etc.
3. Analytical instruments
a. Chromatographs
b. Infrared
c. Physical properties
d. etc.
4. Controllers
a. Proportional
b. Derivative
c. Integral
d. Other
B. Methods of interconnection
C. Reliability and accuracy
VIII Computer Fundamentals
A. Types and characteristics
1. Digital
2. Analog
3. DDA
B. Applicability of computers
C. Basic understanding theory of operation
D. Programming
a. Flow charting
b. Coding
c. Machine language
d. Instructions
e. Routines and subroutines
September 1963 CHEMICAL ENGINEERING EDUCATION 7
As a part of the applied mathematics curricula, considerable attention
should be paid to control system theory. Here such subjects as linear feed
back systems, nonlinear feedback systems, sampled data systems, Laplace
transforms, and Z transforms should be studied. Since the onstream compu
ter is connected, as it were, to a live process, attention must be paid to
the process dynamics. All computer systems must recognize these dynamics.
Obviously, control actions must not be taken too frequently that the plant
is always in a state of jitters, nor must they be taken too infrequently or
else the full benefit of computer control will not be realized.
The study of instrumentation is essential for a complete understand
ing of the computer control process. By instrumentation we think of the
hardware involved the sensing elements, the transducers, and the control
equipment. It is these items that allows the computer to recognize or
sense the state of the process. It is also these items that allows the com
puter to take corrective action upon the process. In a sense the primary
measuring elements represent the sensors of a living organism. The trans
mission lines are the nerves. The control valves and controllers are the
muscles, while the computer controlled systems possess the same attribute
via the local feedback control loops.
Finally, some instruction should be given in computer fundamentals.
The differences between the characteristics of digital, analog and the DDA
computers should be carefully noted. A basic understanding of the theory
of operation of these computers is profitable, though not essential. Some
programming instruction should also be given with exercises in flow charting,
coding, and in the use of routines and subroutines.
In closing I might say that it is impossible for any single man to
understand completely all the workings of a digital computer control system.
The design of such a system is the work of a team of experts. And yet, this
complexity does not preclude its use as an effective and efficient produc
tion tool. Let me give you an example. There are not many people that com
pletely understand the entire working of a television set or even, for that
matter, of the automobile in which we drive to and from work. And yet,
there are millions of these machines in everyday use. By the same token the
digital computer controlled process, though its design is complex, its oper
ation can be made simple enough for a single operator to comprehend. The
efficiency of this production tool, this manmachineprocess combination
will be increased in the future through continued research efforts in all the
areas that have gone into its design.
BIBLIOGRAPHY
General
1. Stout,T.M., and Laspe, C.G. I.E.C. Vol. 49, July 1957.
2. Laspe, C.G.,"Digital Computers in Refinery Process Control' Ref.Eng. Sept. 1957.
3. Stout, T.M., "System Considerations in Computer Control of Semicontinuous
Processes", Proceedings AIEE Computer Control Systems Conference,Oct. 1957.
F. Manildi, J.F., "Modern Developments in Control",Automation, July 1959.
5. Brandon, D.B., "Let A Digital Computer Run Your Alkylation Plant",I.E.C.
52, No. 10.
6. Roberts, S.M. and Stout, T.M., "Some Applications of Computer Control in the
Iron and Steel Industry", Iron and Steel, March 1960.
7. Mears, F.C., "Organisation of the Computer Control Project", ISA Preprint
139LA61.
8. Buster, A.A., "Application of Controlling Computers to Fractionating Units",
C.E.P. Symposium Series, Vol. 57, 1961.
9. Stout, T.M., "Evaluating Control System Poyout from Process Data", Control
Engineering, Feb. 1960.
10. Grabbe, E., "Digital Computer Control Systems an Annotated Bibliography",
lst Congress International Federation of Automatic Control, Moscow,June 1960.
11. Roberts, S.M.,"Computer Control of Chemical Processes with Stochastic
Properties", Proceedings 5th International Instrument and Measurement
Conference, Stockholm, Sweden, September 1960.
12. Madigan, J.M., "Online Computer Control of A Chemical Process", 6th Annual
Data Processing Conference, Amer. Management Assoc., New York, March 1960.
13. Madigan, J.M., "How B.F. Goodrich Applies Computer Control", Chicago A.I.Ch.E.
Feb. 23, 1960.
14. Madigan, J.M., "Computer Controlled Processing", C.E.P., May 1960.
15. Laspe, C.G. & Roberts, S.M., "Online Computer Control of Thermal Cracking ,
I.E.C.,53, May 1961.
16. Adams, '.F., "Closed Loop Control of an 85,000 Bbl Crude Oil Unit",
Oil & Gas Journal, May 21, 1962
17, Eisenhardt, R.D. & Williams, T.J., "Closed Loop Computer Control at Luling",
Control Engineering, Nov. 1960.
18. "Computer Control Case History TexacO", Paper submitted to Award Committee
for 1961 Kirkpatrick Award Sponsored by Chemical Engineering Magazine.
Survey Type References
19. Freilich, A., "Whatts Doing in Computer Control", I.S.A. Jour., Sept. 1961.
20. "Industry Accepts Computing Control", Control Engineering editorial ,May 1962.
. Economic
21. Eliot, T.Q., & Longmire, D.R.,"Dollar Incentives for Computer Control",
Chem. Eng., Jan. 8, 1962.
CHEMICAL ENGINEERING EDUCATION
September 1963
OPTIMIZATION THEORY IN THE CHEMICAL ENGINEERING CURRICULUM
Douglass J. Wilde
Department of Chemical Engineering
University of Texas
Introduction
This article is intended to acquaint teachers of chemical engineering
with the theory of optimization, which has developed so rapidly in eleven
years that it is now finding its way into the practice, literature, and
curricula of our profession. Optimization theory is composed of technical
methods for computing the greatest (or least) value of some criterion of
value or effectiveness measuring the performance of a system being studied.
Since optimization involves, roughly speaking, .finding the best way to do
things, it has obvious applications in the chemical industry, where small
changes in efficiency can spell the difference between success and failure.
Today as always many important decisions can be made simply by choosing
some measure of effectiveness and then optimizing it.
To justify the inclusion of this new material into the already crowded
curriculum we cannot be content with describing the technical content of
optimization theory. We must also show why the profession needs it and how
it might be fit into existing graduate and undergraduate programs. More
over, we must assess the pedagogical value of optimization theory in devel
oping engineering judgment, scientific objectivity, and intellectual cre
ativity in our students.
To begin we shall advance two arguments suggesting the chemical en
gineers' need for optimization theory. The first of these will be specu
lative, analyzing the role of optimization in engineering decisionmaking.
The second will be historical, showing how our very lack of attention to
optimization theory has created demands for operations analysts and manage
ment scientists to solve problems which could well be handled by engineers.
In the heart of the article we shall combine a review of optimiza
tion theory with description of a threehour, one semester course already
given to chemical engineering seniors having no special preparation. This
resume will include references to recent developments of pedagogical in
terest. As each phase of the theory is discussed, its most important in
dustrial applications will be mentioned so that the "why" may be unfolded
at the same time as the "what" and "how". It is quite possible that a pro
fessor may not wish to offer an entire course in optimization theory, pre
ferring instead to incorporate parts of the theory into various existing
courses. Indeed, one would hope that eventually optimization theory would
be absorbed into the curriculum in this way. To facilitate such gradual
integration we shall indicate where each of the three main parts of op
timization theory may be pertinent to such existing courses as plant de
sign, kinetics, control, economics, and mathematics. Thus optimization
theory may be introduced suddenly or gradually depending on local circum
stances.
Finally we shall show how study of optimization theory gives a stu
dent a set of rules potentially valuable to him in making complex decisions.
The rules and procedures are of course worthwhile in themselves, but equally
important is the fact that their study reinforces the student's confidence
in the rational scientific approach to problems. Optimization theory gives
typicod trail engineeg in the analysis of functions of many variables (via multidthit such
mensional geometry) and in the use of precise, logical reasoning. Moreover,
perimental data to den ke s mdge alone; ultimately the Information
the very novelty of the mathematics and the newness of the theory ant, repeat
stimulants to the creativity and imagination of the students.
The Need or optimization Theory
Let us examine two arguments tending to justify the need of the chem
ical engineering profession for optimization theory. First consider the
typical engineering project. Theoretical principles are combined with ex
perimental data to describe the system under study. It is rare thnt such
a study is made for the sake of knowledge alone; ultimately the information
is to be used for making some sort of decision  build a new plant, replace
a heater, or change a catalyst. Without optimization theory, such decIsions
must often be made impetuously, or at best, after lbortious case studies,
despite the good engineering that went'into the study itself. Such a situ
ation is intellectually (and often economically) unsatisfying.
10 CHEMICAL ENGINEERING EDUCATION September 1963
Secondly, consider the rapid growth of the new profession of "opera
tions research" or "management science", defined by most of their practi
tioners as "the scientific preparation of decisions". This sounds auspio
iously like engineering, and on examination of their methods for making de
oisions, we find three steps: (1) rational (preferably mathematical) de
scription of the system, (2) choice of a measure of effectiveness, and (3)
optimization of that measure. Now in most Industrial problems, rational
description is precisely the job ofthe engineer, while the choice of a
measure of effectiveness is either obvious or impossible. Thus the only
difference between industrial operations research and engineering is usual
ly that the former profession has better optimization techniques. We sub
mit then that the rise of operations research has been due not only to the
ability and imagination of its own pioneers, who contributed much to the
theory of optimization, but also to the failure of the engineers to study
optimization problems. Our loss has been their gain.
Optimization Theory
Before 1951, optimization had hardly been studied at all since the
development of the calculus of variations two centuries earlier and today
most engineers know only one method for finding an optimum  the differ
ential calculus. By this method one expresses the criterion of effective
ness as a function of the independent variables, equates the first deriva
tions to zero and then solves the resulting equations. But in industrial
problems it is rarely possible to perform all these steps, and even when
it is, the "solution" is often unattainable because of physical restric
tions on the process. We shall distinguish three branches of optimization
theory here, classifying them according to the very obstacles preventing
their solution by the differential calculus. The three types of problems
are: (1) experimental problems in which the measure of effectiveness is
unknown and must be determined by direct experiment, (2) feasibility pro
blems in which the apparent optimum lies outside the physical constraints
on the system, and (3) interaction problems in which there are so many var
iables that the problem must be decomposed and solved in pieces.
Each type of problem can be covered in one semesterhour of undergrad
uate work, either all at once in a single three hour course or as parts of
other existing courses. There are optimization problems which do not fit
into these three categories, but we are limiting ourselves here to material
that can be taught to a senior engineering student in one semester and be
of use to him when he graduates.
After each type of problem is described, its historical development
will be traced and references of either research or pedagogical interest
cited. Then applications will be mentioned and finally, possible locations
in the curriculum will be suggested.
Experimental problems
In an experimental problem one knows almost nothing about the depend
ence of the measure of effectiveness on the independent variables, and the
only way to obtain information about this dependence is to take measure
ments. Kiefer (1.) has described a highly efficient way to carry out the
search when there is but one independent variable and no experimental er
ror, as for example in the calculation of the optimal number of stages in
a distillation column or evaporator. A description of this Fibonacci search
procedure in engineering terms is given in (2.) It is interesting perhaps
that with this technique one could find the best case out of a possible
twenty after only seven case studies.
Unfortunately the elegant Fibonacci technique cannot be extended to sit
uations with more than one independent variable, and in 1951, the year of
revival of interest in optimization, Box and Wilson (3.) suggested their
method of steepest ascent for multivariable problems. Recently newer ap
proaches to this problem have been advanced  the geometric techniques of
Buehler, Shah, and Kempthorne (4.) and the author (5.), as well as the log
ical methods of Hooke and Jeeves (6.) Pnd Mugele (7.).
The presence of experimental error requires different methods, known
in general as stochastic approximation procedures. Dvoretzky (8.) has gen
eralized the early methods of Robbins and Munro (9.) and Kiefer and Wolf
owitz (10.), an acceleration technique has been proposed by Kasten (11.),
and multivariable extensions have been developed by Blum (12.). Some of
these procedures have been reviewed from the chemical engineers' point of
view by Lapidus et. al. (13.).
September 1963 CHEMICAL ENGINEERING EDUCATION 11
These methods are applicable to design and operating problems involv
ing either complicated computations or significant measurement error. The
Fibonacci technique could conceivably fit into a plant design or economics
course, or even into the exposition of staged unit operations. Mltivaria
ble procedures are more appropriate in plant design courses, and the In
sight they give into multidimensional geometry could well suit them for in
clusion in an advanced mathematics course. Stochastic approximation, since
it depends on some probability theory, would be appropriate in an engineer
ing statistics or probability course. In our experience the theory of ex
perimental search for an optimum has been extremely stimulating to stu
dents, who seem to be inspired by it to surprisingly original contributions.
The author is presently completing a monograph on experimental opti
mization, reviewing and explaining all these developments, hopefully in
language that an engineering senior can understand. Engineering professors
can obtain a free preliminary draft of this material by writing the author,
who would be grateful for suggestions and corrections.
Feasibility Problems
When, as is often the case in the industrial world, the ranges of var
iation of the independent variables are limited, it is sometimes physically
impossible to attain the conditions where the first derivatives of the ef
ficiency criterion all vanish. Such restrictions give rise to feasibility
problems because only feasible conditions, those respecting all the con
straints, can be considered. The technical term "mathematical programming"
(not to be confused with the "programming" of computers) is often applied
to such problems. The year 1951 also marks the beginning of the theory of
mathematical programming. In that year Dantzig published his "simplex meth
od" for solving the linear case. Since that time literally hundreds of
articles have appeared on applications of the simplex method, and many petro
leum companies have justified the installation of large electronic computers
on the improvements in refinery scheduling and product blending made possi
ble by mathematical programming.
It is traditional in operations research curricula to spend a great
deal of time on mathematical programming, especially the linear case, which
is the simplest. Much of this time is consumed in introducing the student
to matrix algebra. While matrix algebra is interesting in its own right,
we have found that one can profitably develop mathematical programming with
out it and save considerable time. This is achieved by treating feasibility
problems as simple extensions of the classical optimization problem solvable
by the differential calculus. Since engineering students are more adept
at manipulating derivatives than matrices, this approach has proven quite
successful, and it has been possible to take a class through linear and
quadratic programming, as well as the decomposition principle to be dis
cussed later, in only six weeks. This differential approach, which we think
has great pedagogical value, is illustrated in (2.) end justified theoret
ically in (14.)
Discussion of feasibility problems is appropriate in any economics or
design course. The subject may also be used in applied mathematics courses
as an application of matrix theory; Lapidus has used this approach in his
new book (15.) With the differential approach, mathematical programming
can be covered in any engineering calculus course, almost as an exercise in
partial differentiation.
Interaction Problems
Sometimes the criterion of effectiveness depends on so many factors
that it is impractical or impossible to find the optimum by classical meth
ods. Often such problems are generated by the interaction of smaller sys
tems with each other. In such cases it is occasionally possible to decom
pose the large problem into smaller ones, solve the subproblems, and re
combine these suboptimal systems in such a way that the interactions are
properly taken into account. This exploitation of the structure of a sys
tem is advantageous because the number of calculations tends to increase as
the cube of the number of variables. Thus doubling the number of variable s
will ordinarily increase the computation load by a factor of eight. It the
problem can be split in two, however, the number of calculations will only
double or triple.
12 CHEMICAL ENGINEERING EDUCATION September 1963
Bellman (16.) has shown how to decompose a series of decisions, each
depending on the one preceding, by the method he calls "dynamic programming.'
This technique, which might also be called "serial optimization", has many
applications to such long range planning problems as capital investment, pro
duction scheduling, and maintenance planning. Application of dynamic pro
gramming to the design of chemical reactors has been described in Aris' re
cent monograph (17.) Nemmhauser has given a very clear example of design of
a straightline chemical plant by dynamic programming (18.) The conventional
exposition of this subject using functional equations is often confusing to
students, and we have found the block diagram approach of reference (2.) to
be helpful in the classroom.
The solar systemsatellite structure of many multiplant scheduling
problems lends itself to analysis by Dantzig and Wolfe's decomposition prin
ciple when all the equations are linear (19.) A numerical example of the
application of this principle to centralized planning is available (20.)
This example illustrates the power and clarity of the differential approach
mentioned earlier, and senior students have had little difficulty absorbing
this material, considered quite abstruse by many operations analysts.
References (2), (16), and (17) give many applications of dynamic pro
gramming, whose ability to handle timedependent problems makes the process
dynamics and control course an attractive place for its introduction. Aris'
work suggests that the kinetics and reactor design course would also be suit
able. Again, plant design and economics offerings can be used to introduce
serial optimization techniques. Related to dynamic programming is Pontry
agin's maximum principle (21). The decomposition principle should be dis
cussed as an extension of linear programming rather than as a separate topic
for the differential formulation makes this extension relatively painless.
Pedagogical Values
In describing the outline of a course in optimization theory we have
indicated how it is a good vehicle for developing mathematical maturity and
respect for the scientific method on engineering students. But aside from
the technical material, the decision rules themselves can build sound en
gineering judgment in the student that will help him make up his mind intel
ligently even when there is no time for detailed and rigorous analysis.
Study of the onevariable experimental optimization problem gives insight
into the important minimaxconcept and the somewhat startling concept of ran
domization. Analysis of multivariable problems unearths some rather dis
turbing facts about graphical reasoning and the paradoxes that can arise
from failing to realize that engineers often work in nonEuclidean space (2.)
Linear programming shows that it is sometimes economical to give a customer
higher quality than he asks for at no increase in price. The classic "law
of diminishing returns" is illustrated quite clearly in the study of quadra
tic programming. Anyone's point of view is affected by insight into the
farsighted philosophy of dynamic programming, which begins by analyzing the
last rather than the first decision in a sequence. Perhpas the most sur
prsing decision rule of all comes from study of the decomposition principle,
which shows that a central planning board should ask branch managers for non
optimal production plans. This is particularly significant because few or
gahizations presently operate this way, at least intentionally.
Concluding Summary
In this review we hope we have given information upon which chemical
engineering professors can decide why and how they might introduce optimi
zation theory, or parts of it, to their students. The demands of industry
have made this necessary; research has made it possible; and pedagogical
advances have made it practical. The rest is up to the profession itself.
September 1963 CHEMICAL ENGINEERING EDUCATION 13
REFERENCES
1. Kiefer, J., "Sequential Minimax Search for a Maximum", Proo. Amer.
Math. Soc, (1953), pp. 502506.
2. Wilde, D.J., "Optimization Methods", a chapter in Advances in Chemical
En ineerin g, Vol. III (T.B. Drew, J.W. Hoopers, Jr., and T. Vermeulen,
eds.) Academic Press, New York 1962.
3. Box, G.E.P., and Wilson, K.B., "The Experimental Attainment of Op
timum Conditions", J. Roy, Stat. Soc., BB (1951) pp. 120
4. Shah, p.V., Buehler, R.J., and Kempthorne, 0., "The Method of Parallel
Tangents (PARTAN) for Finding an Optimum", Iowa State Univ. Statistics
Lab. Technical Report No. 2, Ames Iowa (April 1961)
5. Wilde, D.J., "The Contour Tangent Optimization Method", to appear in
AIChE Journal.
6. Hooke, R., and Jeeves, T.A., "Direct Search Solution of Numerical and
Statistical Problems", J. Assoc. Computing Mpch. 8, 2 (April 1961),
pp. 212229.
7. Mugele, R.A. "A Monlinear Digital Optimizing Program for Process Con
trol Systems", ProcWestern Joint Computer Conference (Spring 1962).
8. Dvoretzky, A., "On Stochastic Approximation", Proc. 3rd Berkeley Symp,
Math. Stat. Prob. (J.Neyman, ed.), Berkeley, U. or California Press
119kb), pp. 3955.
9. Robbins, H.,dnd Munro, S., "A Stochastic Approximation Method", Ann.
Math. Stat., 23 (1951), . 400407.
10. Kiefer, J., and Wolfowitz, J., "Stochastic Estimation of the Maximum
of a Regression Function", Ann. Math. Stat., 23 (1952) pp. 4626 .
11. Kesten, H., "Accelerated Stochastic Approximation", Ann. Mpth. Stat.
29, (1958), pp. 4159.
12. Blum, J.R., "Multidimensional Stochastic Approximation Methods", Ann.
Math. Stat., 25 (1954), pp. 73744.
13. Lapidus, L, Stillman, R., Shapiro, S., and Shapiro, E., "Strategy for
Optimizing Chemical Engineering Systems", PIChE Journal (Summer 1961).
14. Wilde, D.J., "Differential Calculus in Nonlinear Programming", to ap
pear in Operations Research.
15. Lapidus, L. Digital Computation for Chemical Engineers, McGrawHill,
New York (1962).
16. Bellman, R., Dynamic Programming, Princeton U. Press, Princeton (1957)
17. Aria, R., Optimal Design of Chemical Reactors  Studies in Dynamic
Programming. Academic Press, New York (1961).
18. Nenmhauser, G., Ph.D. dissertation Northwestern University
19. Dantzig, G.B., and Wolfe, P., "A Decomposition Principle for Linear
Programs", Operations Research, 8_(Jan. 1960.)
20. Wilde, D.J., "Production Planning of Large Systems by the Decomposition
Principle", to be presented at the AIChE annual meeting, Chicago,
Dec. 1962.
21. Chang, S.S.L., Synthesis of Optimal Control Systems, McGrawHill,
New York (1961).
FUNDAMENTAL PROBLEMS IN HETEROGENEOUS CATALYSIS
Max. S. Peters
University of Colorado
Boulder, Colorado
At the Chemical Engineering Teachers Summer School in 1939, K.M.
Watson presented a paper in which he discussed chemical reaction kinetics
for engineers and made an appeal for including more of this type of train
ing in the undergraduate chemical engineering program. At the next Chem
ical Engineering Teachers School, held in 1948, Watson once again talked on
the subject, but this time his emphasis on necessary training was in the
area of kinetics of catalytic reactions (25, 26). Since that time, kinet
ics courses have been included in most of the undergraduate and graduate
chemical engineering curricula in the United States. With the increased
emphasis on the chemical aspects of chemical engineering, teaching and aca
demic research in the area of chemical engineering kinetics will become
even more essential as a component of any adequate undergraduate and grad
uate program.
The area of heterogeneous catalysis presents one of the most chal
lenging opportunities for new approaches from the viewpoints of both re
search and teaching. The teaching of heterogeneous catalysis for under
graduate or graduate engineers offers an ideal opportunity to impress on
the students the importance of recognizing the practical limits of purely
theoretical analyses while simultaneously emphasizing the necessity for
understanding the fundamental theoretical concepts. The literature is
full of examples of controversial claims in catalytic kinetics based on
putting too much emphasis on a given theory without adequate evaluation of
other possible interpretations. One example is the critical article by
Weller on the engineers' use of the LangmuirHinshelwood approach when a
power series of the Freundlich type would be equally applicable (29). A
rebuttal to the Weller article based on an analysis of real and ideal sur
faces is presented by Boudart (3). Another example is the critical analysis
of leastsquares determinations of rate constants presented by Chow (10).
The controversial nature of many of the currently applied techniques
of interpretation in heterogeneous catalysis illustrates vividly the need
for more research. The teacher of the subject is dealing with a field
which cannot be presented as ancient history with all problems solved. In
stead, the teaching can be made extremely interesting by bringing out the
fact that many of the past engineering techniques in heterogeneous catalysis
are questionable. If the course is taught correctly, perhaps the students
can catch some of the excitement of being part of an important engineering
area which is ripe for the development of new ideas and new approaches.
The recent survey by Hougen on Engineering Aspects of Catalysis emphasizes
these points (15).
In the past, there has been some tendency to teach chemical engineer
ing kinetics strictly from the viewpoint of design techniques, leaving the
fundamentals to the area of chemical kinetics. This approach is complete
ly inadequate for heterogeneous catalysis because of the many assumptions
required for normal design applications. Consequently, to give the engi
neer a background for intelligent understanding of the design aspects of
heterogeneous catalysis, it is essential to discuss the basic considera
tions and, from these, go through the assumptions necessary to give a
workable final design equation. In this paper some of the basic consid
erations in heterogeneous catalysis normally passed over in undergraduate
engineering courses will be discussed.
Surface Characteristics
An obvious starting point for the teaching of heterogeneous cataly
sis is to present an analysis of surface characteristics for solids with,
an initial approach through discussion of physical adsorption and chemisorp
tion. Because the energy of activation for physical adsorption is small,
the activation energies for reactions involving physically adsorbed mole
cules cannot be far different than for the homogeneous reaction. Conse
quently, physical adsorption does not play an important direct role in
catalysis, but it does become important in considering fractions of surface
area available on a solid catalyst.
Conventionally, the engineering approach to teaching about physical
adsorption and chemisorption is to immediately develop the Langmuir adsorp
tion isotherm for a unimolecular adsorbed layer in a form such as
g= h (1)
September 1963 CHEMICAL ENGINEERING EDUCATION 15
where 9 represents the fraction of the surface covered by an adsorbed mono
molecular layer at equilibrium, h is the adsorption equilibrium constant,
and p represents the partial pressure of the gas adsorbed. From this point,
with a few brief comments about multilayer adsorption, the Brunauer, Emmett,
and Teller equation for estimation surface area is normally presented in
the following form (7):
p 1 (cl)p
V(POP) V5 OVp( 2)
where c is a constant for the system and temperature, po is the saturation
vapor pressure for the gas at the temperature used, and vm is the volume of
the gas adsorbed to cover the surface completely with a monomolecular layer
Here is represented the type of engineering approach many educators
find objectionable. If it is worth presenting Fquation 2 at all, then it
certainly is necessary to present the limitations on this Equation. This
starting point would be a good spot to impress the students with the need
for understanding any equations they may use, and, despite the time it
might take, a relatively complete derivation of Equation 2 could get an en
gineering course in heterogeneous catalysis off to good start.
The development of Equation 2 is based on the existence of multimol
ecular layers of physically adsorbed molecules with a derivation similar
to that of the Langmuir isotherm for unimolecular layers. At equilibrium,
the rate of condensation on the bare surface must equal the rate of evap
oration of molecules in the first layer. Similarly, for each succeeding
layer, the rate of evaporation must equal the rate of condensation on the
preceding layer. Therefore,
aipso = bisieEi/RT (3)
a2psi b2s2eE2/RT (4)
ai pii = bisi5eEi/RT
where inca b are constants, s represents the surface area covered only
by the subscriptindicated layers of molecules, ind 3 represents the heat
of adsorption for the indicated layer. Thus, the total surface "rea of the
catalyst and the total volume adsorbed are, respectively:
n
A =f si (6)
i m 0
V is. (7)
A i: 0
where n represents the number of layers.
As indicated in Equations 3, 4, and 3, t:e heat of assorption is sn
volved exponentially in each of the equilibrium expressions "or the rite.
The assumption is made that, for ll layers except the ;rst. the e.:t c
adsorption is constant and equal to the :he:t of evapor ton. It ai e 
assumed that the ratio of b/a is constant for all 1d ees xc pt the t.
In other words, the Van der 1,aals forces of physical dsc'pton re involve
only in the first layer of molecules on the sur" ae.
With these assumptions the summation express'ns n 'e 'eve.
rectly to give a value of V/Vm as follows:
b2 .b bi (.1
a2 a3 ai
E2 = E3 Ei = Ev !
From Equations 3, 4, and 5
2 J1
si 11 ( ) s ()) l = c (, C "
gale
where J= geEv/RT rnd ( ':v)/R
Therefore, n i
cso i = 1 (
Vm so cso n (E )i
J
16 CHEMICAL ENGINEERING EDUCATION September 1963
i= 1 1
Because i (P)1 converges to p/J and ( ) converges
j (1op/) 2 verges
to ,P/J the summations represented in Equation 11 re such that the
equation reduces to n 1
y cp/J 1 (n 1) (p/J) n (p/J)
Vm 1p/J 1 (c 1) (p/J) c (p/J)n 1 (12)
When p equals the saturation pressure of the gas, po, complete con
densation can occur and V must approach infinity. This can only occur if
J is equal to po. For the case where p po or for the case where n is a
large number, Equation 12 can be rearranged to give Equation 2, and a com
plete derivation of Equation 2 showing all assumptions has been presented.
The question immediately arises as to the desirability of presenting
this much detail in an undergraduate chemical engineering course on heter
ogeneous catalysis. Actually, the derivation as presented here is relative
ly simple and the insight it gives the students into the limitations of the
final equation plus the added understanding of the types of forces involved
in condensation and adsorption make the time spent on such a derivation
worth while. With this background, students making the standard plots of
p/V(pop) versus P/Po to determine catalyst surface area will have a real
understanding of what they are doing. The value of the constant c now has
physical significance and could be used to give an approximation ofEiEv
since gal/bi is normally close to unity.
Langmuir Adsorption Isotherm
The Langmuir adsorption isotherm, as presented in Equation 1, can be
developed directly from Equation 3 as follows to give a physical meaning
to the constant h:
5o s oal aEe/RT (13)
soo 1 P al eEp
where h al e
In catalytic studies, use is regularly made of this relationship to
develop a model for the reaction. An ideal surface is assumed so that h,
and consequently the heat of adsorption Ei, remain constant. For any real
surface, there is no valid reason to assume Ei will remain constant inde
pendent of the amount adsorbed. This explains one reason why experimental
values of h obtained from reaction rate data often do not agree with the
oretically equivalent adsorption constants obtained from direct adsorption
measurements.
Despite these limitations, chemical engineers have been highly suc
cessful in using the Langmuir adsorption isotherm as an approach for cor
relation of rate data. Because of this success, some novices are convinced
that the relationships are completely sound theoretically and attempt to
develop detailed mechanisms on this basis. This approach has been attacked
by Weller who proposed that real and ideal surfaces were so different that
the practical engineer was wasting his time in attempting to use the Lang
muirHinshelwood approach (29). He proposed instead the far simpler
Freundlich type of expression for expressing reaction rate. For example,
for the gas reaction A + B C catalyzed by a solid, the Freundlich
powerseries type of rate equation would be
rate k(PA) (pB)m (p) ..... ()
while the LsngmuirHinshelwood result might be in the form of
rate k ApB 2 (15)
(1 K;A pB Kcpc)
September 1963 CHEMICAL ENGINEERING EDUCATION 17
The mathematics of handling Equation 14 is far simpler than for
Equation 15; however, a specific reaction mechanism can be applied for
Equation 15 while no mechanism can be given for Equation 14. Weller op
poses the use of the I engmuirHinshelwood approach on the grounds that real
and ideal surfaces are not similar and the results obtained tend to be mis
leading. Boudart has responded to this by proposing that discrepancies in
the constancy of the adsorption energy can be accounted for by approximating
a temperature dependence of this energy as
Ei = Eoi zT (16)
where Eoi and z are constants (3).
The type of approximation represented by Equation (14) would be an
excellent method to use for initial analysis of reaction rate data. How
ever, for any general application of kinetic results, a mechanism must be
satisfied. Accordingly, the information obtained by a preliminary analysis
of the Equation 14 type should be used to attempt to predict a mechanism,
and this should then be confirmed by a detailed analysis of the type repre
sented by Equation 15 plus recognition of the factors introduced by surface
nonideality.
The overzealous ambition of chemical engineers to prove a mechanism
on the basis of an incompletely understood theory has caused many ridicu
lous claims to be made in the literature. This illustrates the need for
careful development of the theory combined with discussion of its limitation
in the teaching of heterogeneous catalysis.
The preceding analysis hPs been concerned almost completely with the
thermodynamics of adsorption as affected by the heat of adsorption with no
attention being given to the concept of entropy relationships. Because gas
molecules are free to move in three dimensions and ndsorbed molecules are
restricted to no more than two dimensions of motion, a reduction of entropy
must occur on adsorption as pointed out by Brunauer (6). Entropy calcula
tions can give information on whether the adsorbed molecule is free to move
about the surface or is localized with no free rotation. Such information
is important in considerations of catalytic kinetics, and variation in en
tropies of adsorption can be used, at least partially, to explain changes
in heats of adsorption. The concept of entropy has only recently been giv
en serious consideration as being important in supplementing the heat of
adsorption type of analysis for thermodynamics of surface processes (5, 19)
ActiveSite Theory
Despite the limitations of the simple adsorption theory with its as
sumption of ideal surfaces, this concept has been extended by chemical en
gineers to include models whereby catalytic surface reactions take place by
means of molecules or atoms adsorbed on socalled activee sites" on the sur
face. Thus, the reaction could occur between an adsorbed reactant molecule
and a gasphase molecule or between two adjacently adsorbed molecules. In
presenting this simple model, authors have recognized the limitations of
the theory and have consequently tended to present derivations that in
clude incorrect statements which are eventually corrected by the empireel
nature of the results. For teaching purposes, errors of this type, even
though not important in the final analysis, should be avoided.
One example of an error is presented in F classic pioneer public t o
by Hougen and Watson (16) which was corrected in their later work. '*,
was the statement that the dualsite surface corcentratlon of v cent s'tea
adjacent to sites containing adsorbed material A is
CV 5 1/2 SC CV (17)
where S represents the number of equidistant active sites cent tc an
average active site and the subscripts ,. V, 'nd I stand Ic :,'terrls
vacant sites, and total sites. The error in Equ tion 17 1ies :a the 1/2
which should be unity. A similar error was aI .. :..v"th (2l) t
would be more accurate to use CTCV in p',ce 0 f CT.
Any argument tiat these two errors are vaL!' 1 n 'e i:t.ad ty Sn
extremely simple mathematical analysis ir. 4,h n a sua.t ca' t r. r ie a tIe
total possible dual sites on an ideal surface contain. r, orli vucant ste.'
(Cv) and adsorbed sites (Ct). For rdjscent similar sites, tLe duelsite
concentration must be onehall of the total number of possible .djaccat.
sites times the fraction of total sites occupied by the m teail1. in
case, the onehalf is necessary because each pair of' adj cent inoleco,
18 CHEMICAL ENGINEERING EDUCATION September 1963
counted twice. If the adjacent sites are not occupied by similar materials,
the onehalf should not be included. For this case, therefore,
Total dual sites = SCT = Sites VV Sites pV Sites AA (18)
r
SCT BCvC SCACV SCA (19)
2 20T CT 20T (19)
SC S(C CA) 2 SC2 (20)
2 T 20 T
The identity in Equation 20 can only occur if Equation 17 is incor
rect and if CT is used in the denominator. Obviously, these two errors as
discussed are not important but, for teaching purposes, analysis of this
situation and discussion of past errors of this type serve to stimulate
class interest and give the students more insight into the type of problems
involved in trying to specify surface characteristics.
Solid State Analysis and Electrochemical Potential
In recent years, hope has been raised that catalytic activity might
be explained by a new and quantitative approach involving electronic chem
ical potential and based on the principles of solidstate physics. Un
fortunately, this approach has not yet produced any significant results,
and it is doubtful if it should have more then passing mention in an un
dergraduate course on chemical engineering kinetics.
In this approach, the assumption is made that chemisorption is ac
companied by a transfer of electrons between the adsorbing solid surface
and the adsorbent. The adsorbent acts either as a donor or acceptor depend
ing upon the direction of electron transfer. Because of the high density
of electrons on a metal surface, exact quantitative analysis of the charge
transfer process is difficult. However, the surfaces of semiconductors
have a far lower concentration of electrons or free carriers than metals
and, consequently, are much more satisfactory for theoretical analyses of
the chargetransfer process and the resulting energylevel changes (4, 23) .
The Fermi level has been proposed as one means for explaining cata
lytic activity. At equilibrium conditions, there is a probability (desig
nated as the FermiDirac function) that a given quantum state of energy will
be occupied by an electron. The energy at which this probability is one
half is designated as the Fermi level (20). The Fermi level of a semicon
ductor may increase or decrease depending on the concentration of impuri
ties and the temperature. Semiconductors are characterized by densities of
electrons (n) and of socalled holes (p). Thus an ntype semiconductor has
a conductivity due to excess electrons and is a donor while a ptype semi
conductor is an acceptor.
In applying the electrochemical potential theories to catalytic kinet
ics, one can consider that a gas molecule can be adsorbed on a surface in
different forms. For example, isopropyl alcohol might be adsorbed on
chromium oxide catalyst as a donor through a hydrogen atom, as an acceptor
through a hydroxyl group, or with no net charge transfer. If the most re
active group were the form adsorbed with no net charge transfer, then a max
imum would be expected in the reaction rate if the catalyst were changed
gradually from a p to an ntype semiconductor. If the catalyst is initial
ly a ptype material it might be changed to an ntype by increasing the
pressure of hydrogen in the gas since the hydrogen would release electrons
to the solid catalyst. In other words, there would be a shift in the Fermi
level and a maximum catalyst activity would be expected at one Fermi level.
Figure 1 presents results on the rate of dehydrogenation of isopropyl
alcohol catalyzed by chromium oxide from experiments carried out at the
Institute of Physical Chemistry of Madrid (13, 14). Under normal conditions
a regular decrease in rate with increase in hydrogen partial pressure would
be expected as indicated by the dashed line in Figure 1. The experimental
results are indicated by the solid line. One possible explanation for this
apparent discrepancy could be based on the analysis presented in the pre
ceding paragraph wherein there is a Fermi level shift through the level
corresponding to maximum activity as the solid catalyst changes from a
ptype to ntype semiconductor.
Sentnkr AA N*luGINEERING EDUCUATIOJl 19
Rate \
PN1
Figure 1. Rate of Dehydrogenation of Isopropanol Catalyzed
by Chromium Oxide at Constant Total Pressure with Varying
Partial Pressures of Hydrogen in Isoproppnol (13).
Heat and Mass Transfer 0
For the design of catalytic reactors, basically three relationships
are needed as follows:
(e) The design equation.
(b) The rate expression.
(c) An energy balance.
In the simplest possible case of a plugflow reactor operated adiabatically
the design can be accomplished with
(a) The Design Equation: Fdx rdW 21)
(b) The Rate Expression: r = f (x,T) M22)
(c) The Energy Balance: ( H) Fdx = FioCpdT (23)
where x represents the fraction of reactant converted, F is the feed rate
of reactant, r is the reaction rate based on an amount of catalyst W,AH
is the heat of reaction, opis the heat capacity, and subscript i indicates
the individual components.
Heat and mass transfer considerations must be taken into account
when the plugflow assumption is not valid. The same general approach is
used for both heat and mass transfer; so this discussion will be limited
to mass transfer and development of the appropriate design equations. The
conventional approach is to correct for radial and longitudinal diffusivity
by using an effective diffusivity for each representing the actual pecked
bed as being replaced by a fictitious homogeneous material with the ap
propriate diffusivity. Radial bulk flow is neglected. With these assump
tions, the following equations can be developed by material balances:
(a) For the case of radial diffusion only with constant De/u,
J (uC) De 1 J(uC) J (uC) r B= C (24)
 u r Jr2 B
(b) For the case of longitudinal diffusion only with constant
DL/U,
DL J2(uC) J(uC) rC = C2)
u jZ7 JZ C J (2)
(c) For the case of both longitudinal diffusion and radial diffusion
J(uC) De 1 J(uC) J2 (uC) DIL 2 (uC) r
JZ u r =T 7 r j, B 5 (JZ)
JC
In vector notation, the preceding equation reduces to
div(uG) div D Grad (uC) r B = (27)
where u is velocity, C is concentration, r is radius, Z is reactor length
eB is bulk catalyst density, 9 is time, and the Dis represent diffuslv'
eS ptember 1963
CIM L E
20 CHEMICAL ENGINEERING EDUCATION September 1963
Development of the preceding design equations along with the corres
ponding energy balances are straightforward and should be presented in un
dergraduate courses on heterogeneous catalysis. However, the important
engineering function comes in applying these equation, and the solution of
the equations can become quite involved. An example of the problems in
volved in selecting boundary conditions along with the need for clear think
ing is presented in the following for the case of Equation 25.
For a firstorder irreversible reaction, r = kC, and steady state,
Equation 25 becomes a secondorder, linear, ordinary differential equation
with a solution in the form of C (Const) e mZ where m is a constant.
Smith presents limits as follows to give a very simple solution (22):
Boundary conditions proposed by Smith: C= Co at Z. 0
C= 0 at Z oa
Solution is C = eZ where 1 1 k1 D (26)
71r
u=
Danckwerts and others (11, 27) have proposed much more reasonable
limits based on a concentration gradient existing at the entrance to the
reactor. The resulting solution is far more complex and more realistic
than that shown by Equation 28.
Boundary conditions proposed by Danckwerts and Damkohler:
C Co DL dC at Z = 0
dC at Z I L
The resulting solution is
uZ ua (LZ) usa
C e2 fl (ZL)
to e 2(1 a')eL Z(la)e (29)
ua'L ua L
(1 a ') e (1a')2 e
where a' = 1 4kDL B
A rather detailed discussion of the disadvantages of oversimplifying,
as illustrated by Smith's boundary conditions, can stir up much interest
among the students and will simultaneously serve to illustrate the need for
the students to think on their own rather than merely blindly accept any
thing they see in print. The fact that both Equations 28 and 29 reduce to
the basic plugflow expression as DL approaches zero can be used to illus
trate the fact that one proof of a given theory is not always sufficient to
establish its validity.
Computer Solutions
We have now reached the point where use of computers for problem so
lution should be completely acceptable for undergraduates in Chemical En
gineering. The kinetics course is an ideal place for including outside
problemsafor solution on the computer, and at least one problem of this
type should be part of the course. A simple example is given in the fol
lowing of a typical problem in kinetics which can readily be solved on a
small analog computer of a type similar to the pace TR10.
For the case of the following consecutive reactions carried out
isothermally
A k B k2 C
the rate equations for a constantvolume reactor are
dCA klCA (30)
d k / (31)
dCB k1CtA k2CB (31)
Hr k2C
September 1963 CHEMICAL ENGINEERING EDUCATION 21
Analytical solutions of the first order linear differential equations
are ek9
Ca = CAo (32)
CB klCAo (ek1 k2) (33)
k2kI
CC CAo CACB (34)
where Cls represent concentration and subscript o represents initial con
centration of pure A at zero time.
From Equations 30, 31, 34, plots of either CA, Cg. or CC versus 9
could be obtained directly with a small analog computer by use of the un
scaled diagram shown in Figure 2. A typical concentrationtime plot tht
would result on the xy plotter from the computer is also shown in Figure
2.
This simple example can be used effectively with undergraduates to
familiarize them with the use of the analog computer. It is particularly
appropriate because the students can easily celoulate analytically from
Equations 32, 33, 34 the concentrationtime values for direct conporison to
the computer results.
Volts Figure 2.
Analog Computer
CA Potentiometer Diagram
S For Solving
IC CA A" B C
YAxis and
k, Resultant XT Plot
Integrator From Computer
Inverter
IC C13
k
S CC Axis
Summer
6 CA de Ic
S Volts XAxis
Volts
R A A B ka.C
Con.. C\
9, Time
03
" CHEMICAL ENGINEERING EDUCATION September 1963
If the students have the background and facilities for solving prob
lems on the digital computer, programs are available for solution of Lrng
muirHinshelwood types of rate expressions as presented in Equation 15
(12, 38). Other programs are available for the digital computer which
could e uaed for problems relating to operational characteristics of iso
therm.l tubular flow reactors (2), isothermal batch chemical reactors(17).
or solution of the Brunauer, Emmett, and Teller Equation 2 (8).
Conclusion
The subjects discussed in this paper represent some of the special
problems in heterogeneous catalysis that would be appropri te for presenta
tion in an undergraduate course. Obviously, there are many standard sub
jects, all of which also involve fundamental problems, which should be in
cluded. Among these would be analyses of the various resistances involved
in the catalytic kinetic processes, experimental techniques, interpreta
tion of ex perimental results, types of reactors including special prob
lems of construction and operation, optimization techniques, poisoning ef
fects, and many others.
Intraparticle transport is another fundamental problem which has re
ceived inadequate attention in many chemical engineering courses on kinet
ics. Fore diffusion and catalytic effectiveness are often completely
neglected even though these are important factors in as much as perhaps
eighty per cent of all catalytic processes. The work of Thiele (24),
Wheeler (30), Aris (1), Weisz (28), and Hougen (15) are significant in show
ing the advances being made in this area, and an excellent summary of the
current situation on this fundamental problem is presented by Carberry (9).
BIBLIOGRAPHY
1. Aris, R., Chem. Eng. Sci., 6, 262 (1957).
2. Bailie, R.r. and. Fpn, "Computer Program Abstract 041Operational
Characteristics of Isothermal Tublar Flow reactors," Chem. Eng. Prog.,
56, No. 2, 92 (1960).
3. I oudart, M., A.I.Ch.E. Journal, 2, 62 (1959).
Boudart, M., .p ., 7,= ,3556 (1952).
Boudart, M., "T l face Chemistry of Metals and Semiconductors," 409,
Edited by H. C. Getos, John Wiley and Sons, New York, 1960.
6. Brunauer, S., "Physical Adsorption," Princeton University Press, 1943.
7. Brunauer, S., P. H. Emmett, and E. Teller, J. Am. Chem. Soc., 60, 309(1938).
8. Brunauer, Emmett, and Teller Equation CompuTerPFogram Aistracr027, to be
published in Chem. Eng. Prog. (1962).
9. Carberry, J. 77"Transpor henomena and Heterogeneous Catalysis," Forth
coming publication.
10. Chow, C. H., Ind. Eng. Chem., 50, 799 (1958).
11. Danokwerts, P. V. ,Chem. _ng. !ci., 2, 1 (1953).
12. Dricokmann, W. L., "Computer Program Abstract 033 Polynomial Equation
Pitting," Chem. Eng. Prog., 56, No. 8, 86 (1960).
13. Garcia de TlaBania, J F.., and G. K. Orlandini, Technical Note No. 1,
AP61 (514)1330, Instituto de Quimica Fisica, C.S.I.C., Madrid, Nov. 1958.
14. GaroiaMoliner, F., "Trapping in Semiconductors," Personal communication,
Dept. of Physics, Univ. of Ill., Urbana, Ill., Feb., 1962.
15. Hougen, 0. A., Ind. Ag. Chem., 53, 509 (1961).
16. Hougen, 0. A., 3 KTR. Watson,Tnd. Eng. Chem., 35, 529 (1943).
17. Jeng, B. J., and T.T. Fun, "Computer Program Abstract 056 .Design of Iso
thermal Batch Reactors," Chem. Eng. Prog., 56, No. 6, 90 (1960).
18. Kaufman, D. J., and C. VW."Vo, computerr Program Abstract 060 Solution
of Simultaneous Linear Equations," Chem. Eng. Progr., $6, No. 9, 78 (1960).
19. Scholten, J. J. F., and P. ZweiteringTTrans. ar. oc., 53, 1363 (1957).
20. Shockley, W., "Electrons and Holes in SemiconducorTT" D.Van Nostrand Co.,
1950.
21. Smith, J. M., "Chemical Engineering Kinetics," P. 243, McGrawHill Book
Co., Inc., New York, 1956.
22. Ibid., page 365.
23. Stone, p. S., "Chemistry of the solid State," 367, Edited by W. E. Garner,
Butterworths Scientific Publications, London (1955).
24. Thiele, E. W., Ind. Eng. Chem., 31, 916 (1939).
25. Watson, K. M., '"RCemical eacltion'Kinetics for Chemical Engineers, Collected
Papers on the Teaching of Chemical Engineering," Proc. of Oh. Eng. Div. of
A.S.E.E., 2nd Ch. Eng. Summer School, page 175, Publ. by A.I.Ch.E., 1940.
26. Watson, K. M., "Kinetics of Catalytic Reactions," Proc. of Ch. Eng. Div.
of A.S.E.E., 3rd Ch. Eng. Summer School, 1948.
27. Wehner, J. F., and R. H. Wilhelm, Chem. Eng. Sci., 6, 89 (1956).
28. Weisz, P. B. and C. D. Prater, "AdVanesTn Cal78yss," vol. VI, 1954.
29. Weller, S., A.I.Ch.E. Journal, 2, 59 (1959).
Wheeler, A., Cataysis Vl. YI., P.R. Emmett, Editor, Reinhold Co.,
:iew York, 1955.
PLANNING EXPERIMENTS FOR ENGINEERING KINETIC DATA
H. M. Hulburt
American Cyanamid Company
Central Research Division
Stamford, Connecticut
Since chemical kinetics is not as yet a predictive science, one of the
tasks commonly faced by the engineer in process development is the accumulation
of rate data to be used in reactor design. This paper will discuss some of the
principles and techniques which can be used to establish useful design data for
complex reactions of obscure mechanism which occur under illdefined conditions
and yield incompletely characterized products  in other words, the usual case
of practical interest.
It is a truism so obvious it is usually not taught in physical chemistry
courses that one should first establish the stoichiometry of the reaction he is
studying. Yet in process development this is often difficult. Analytical
methods for the mixtures being produced may be timeconsuming or unavailable
and expensive to develop. There is a strong temptation to determine the prin
cipal product and most troublesome byproduct and ignore the rest. Gross mater
ial balances can often be made on the basis of elemental analyses without de
tailed knowledge of molecular composition. Yet these incomplete analyses oftep
fail to give adequate kinetic data. When reactions are not well understood, it
is not unusual to discover the appearance of a new product when conditions are
changed. If the design has been based on data taken in ignorance of this pro
duct and operation extrapolated beyond the pilot plant range, serious trouble
can ensue.
The first step, therefore, is a qualitative survey of the reaction
stoichiometry over as wide a range of conditions as possible. The objective
of this survey is to establish the main features of the reaction mechanism.
For design purposes, the molecular mechanism will never need to be known in de
tail, but enough of its properties must be determined to formulate a kinetic
model of the reaction for the range of conditions of design interest. The more
nearly this model reflects the actual mechanism, the more confidently can it be
applied over a wide range of conditions. Nevertheless, at some stage, the en
gineer must be satisfied to work with the data he has, recognizing that he has
not established a mechanism fully.
Is the principal reaction product the ultimate product of reaction; or
T6es it disappear in side or subsequent reactions under some conditions? Are
the byproducts formed as or from intermediates en route to the main products,
or are they formed by independent reaction routes? Do some or all of the pro
ducts reach equilibrium or steadystate concentrations which are insensitive to
residence time? Is there a phase separation in the course of the reaction?
Not every autoclave that is loaded with a homogeneous solution and delivers a
homogeneous product solution has had homogeneous contents throughout the course
of the run. Are mass or heat transfer rates comparable to or slower than the
chemical reaction rates? These are all questions that can receive qualitative
answers by comparing the results of a few wellplanned runs.
Consider a hypothetical example in which Qacid is made by catalytic con
version of electamine and carbon dioxide in a fluid bed. Under reaction con
ditions, Qacid is volatile in an atmosphere of carbon dioxide but it is found
that organic matter accumulates on the catalyst to a degree depending on the
temperature and feed ratio of electamine to CO2. Some undesirable electamnnic
acid is found both in the product vapor and in the organic residue on the cpt
alyst which, however, is largely unidentified material. The acid could be
formed by carboxylation of the amine, but there Is no evidence for the Pctual
mechanism.
First experiments might be to vary the gas residence time in the con
verter, determining the spacetime yield and purity of the elpctmilne product.
For experimental convenience in these survey runs, the process Is not run in
continuous steadystate. Instead, electamine is added to the catalyst to a
predetermined loading at a temperature below that nt which Qacid is formed.
The temperature is then raised and Qacid stripped off in a stream of C0, suf
ficient to fluidize the bed. The concentration of acid in the product'strer
is followed during the stripping process. it was found that the rounds per
hour of product recovered is directly proportional to the CCa flow rate, other
conditions being fixed, but is nearly indsperdeent of t'e electanine remaining
on the catalyst. This was established by a set of runs at two temperatures In
which C02 rate and initial electamine loading were varied.
23
A2 CHEMICAL ENGINEERING EDUCATION September 1963
At each temperature, the partial pressure of Qacid in the offgas was
nearly constant throughout each run. However, with high initial loading of
ele ar n, this partial pressure was less than with low initial loading. With
oth,.' conditions comparable, the partial pressure of Qacid was higher at high
er temperature. Because of initial transients during the stripping runs as the
temperature was being raised, the earliest steady data could be obtained only
after 20% to 40% of the Qacid had been stripped off. These results give a
strong presumption that Qacid is being formed in vaporsolid equilibrium with
the organic matter on the catalyst, since as much conversion was obtained in a
runas in runs with twice the residence time. However, runs which differ in
initial loading gave somewhat different apparent vapor pressures. Hence, we
must conclude that there is additional complexity in the mechanism. An addi
tional complication in a fluid bed is the possibility of poor contacting of
vapor reactant with catalyst when large "bubbles" can form. Since this by
passing increases with gas velocity, a lower yield at high gas velocity would
be expected from this cause alone if it were operative, even though the dense
phase reaches equilibrium.
Pour more runs under strictly continuous steady operation should settle
the qualitative nature of the mechanism. In these, temperature and feed com
position are fixed but feed rate and bed height are varied as follows:
Run
V (cu.ft./hr.)
H (ft.)
*C (hr.)
1 2
Q 2Q
L L
AL/Q AL/2Q,
Conversion of electamine to Qacid product is the measured response. The
following logic ensues:
Sequence Compare If Then
A 1. Runs 1 and 2 Same conversion Equilibrium is att
B 1. Runs 1 and 2 Different con Either kinetic con
version trol or bypassing
B 2. Runs 1 and 3 (Same conversion fNo bypassing
Different con tBypassing occurs
version
From this logic, Run 4 appears superfluous. However, adding it gives
a 2 x 2 factorial experiment in bedheight and residence time.
gained
1
S L 2
AL/2Q 2 4
AL/Q 1 3
In this balanced design the following effects are measurable.
Kinetics: K = YI Y2 +Y3 Y4
Bed Height: H = Y4 Y2 + Y3 Y1
Bypassing: B = YI Y2 Y3 +Y4
The bypassing effect appears as an interaction between the two main effects
and the techniques of statistical analysis can be used to get the most out
of the data. At the cost of an extra run, considerable additional confidence
can be obtained in the conclusions.
In this example, we see the diagnostic value of a few wellchosen runs.
Even more insight can be gained by abandoning the pilot reactor and studying
.he reaction in an altogether different configuration. Electamine and carbon
dioxide might be loaded into a pressure cell adapted to an ultraviolet spec
trometer. The product Qacid vapor as well as the byproduct, electaminic acid
be followed readily by its UV absorption. In this small batch reactor,
3
2Q
2L
AL/Q
4
4Q
2L
AL/2Q
September 1963 CHEMICAL ENGINEERING EDUCATION 25
the approach to steady vapor pressure of Qacid could be followed conveniently
at times close to the onset of reaction and at a series of temperatures. By
interrupting the run at a given time and analyzing the catalyst and its organ
ic contents, it is possible to associate the composition of organic solids
with the progress of the main reaction.
In this way, the puzzling dependence of rate on feed composition can be
resolved. In the case on which this hypothetical example is based, it was
found that a nonvolatile dimeric product of electamine forms rapidly on the
catalyst with evolution of C02. In a second step, dimer reacts with C02 and
forms a solid, "preQ", which rapidly develops a steady vapor pressure of
Qacid. At the same time dimer reacts slowly with C02 to form the byproduct
electaminic acid. Thus in the initial runs in which the loading of electamine
was low, little dimer was formed and the conversion to Qacid proceeded rapid
ly, being governed .by the rate of evaporation of preQ. In the runs in which
the loading of electamine was high, dimer formed rapidly and the conversion
of dimer to Qacid was slower, being governed by the rate of conversion of
dimer to preQ.
Thus a kinetic model might be written:
E 4 Dimer + C02 (1)
Dimer + C02 + PreQ (2)
Dimer + C02  Electaminic acid (3)
PreQ 4 Qacid vapor (4)
Reaction 1 is supposed reversible but not instantaneous. Reaction 2 is
irreversible and comparable in speed to reaction 1. Reaction 3 is irrever
sible and slow. Reaction 4 is reversible and very rapid.
Thus far, although rates have been measured, no use has been made of
their quantitative magnitudes. The arguments have depended upon relative
values, the shapes of timeconcentration curves and similar qualitative data.
As a result, however, a reaction model has been reached in terms of which
rate data can be quantitatively analyzed.
In many cases, such a model is previously known or is sufficiently prob
able that one can proceed to the quantitative phase with few preliminaries.
In such a case, however, the experimental design should permit testing the fit
of the data to the model as well as evaluation of the rate constants and acti
vation energies.
When a model has been proposed as the basis for further kinetic study,
the methods of statistical experimental design can greatly reduce the number
of runs required to determine the rate constants and reaction orders. These
methods are now quite readily available in the literature 3 and it is not pro
posed to discuss them in detail here. They must be used with insight, however,
and are no substitute for thought. Some examples will illustrate the power
and some of the precautions necessary in this approach.
Srini Vasan and the writer4 studied the kinetics of the watergas shift
reaction over a commercial iron oxide catalyst. The literature and previous
experience suggested a kinetic model, due to Temkin: 7
R dp = kPoa PH20b (5)
 \PPH A
Although the reaction is reversible, conditions were chosen to minimize the
extent of backreaction. Taking logarithms,
log R = log A E + a log pCO + b log PH20 b log p2 (6)
RT
This is a linear form in I/T and the log p. standard methods for the de
swm and analysis of experiments permit the determination of best values for
the parameters E/R, log A, a, and b as well as a test of the goodnessoffit
of Equation (6) if fairly general conditions on the errors of measurement are
satisfied. At no increased labor, the model could be generalized to include
all of the possible components with arbitrary exponents:
a b c d
R = kPcO PH20 PH2 PCO2
26 CHEMICAL ENGINEERING EDUCATION September 1963
The geometrical interpretation of the experimental design is quite help
ful. We may think of R as a function of l/T and the log pi which can be
plotted in ndimensional Euclidean space with the independent variables as
coordinate axes. We seek a mathematical representation of the hypersurface
log R(1/T, log Pi). If Equation (6) is such a representation, log R will be
a plane parallel to the axis of log PC02. Furthermore, its intercepts on the
on the log PH20 and log PH2 axis will be equal but opposite in sign. If Equa
tion (7) holds, the log R surface will still be planar, but there will be no
a prior constraints on its orientation. The experimental design problem is
now to test whether log R is indeed a plane, and, if so, to find its equation.
With five factors, a 5plane in sixdimensional space is determined by five
points. By determining log R at additional points, the deviation from plan
arity can be tested. In the example cited, sixteen points were determined
and it was decisively shown that Equation (6) could not represent the data
within the reproducibility of experiments.
If the log R surface is not planar, what shape is it? Standard methods
now exist to determine the best quadratic approximation to log R. It is
tempting to use this approximation, since the experiments already done to
test the planar hypothesis are the core of the design which determines the
quadratic approximation. Before succumbing to temptation, however, one should
considerMia objectives carefully. Equation (6) or (7) has a theoretical in
terpretation in that a molecular mechanism can be formulated which leads to It.
Of course, the possibility of deriving a kinetic equation from a mechanics
does not necessarily make it valid. However, the general quadratic form is
not derivable from any mechanism and hence cannot possibly be valid except S
an empirical interpolation formula. If the design studies which are contem
plated will never go outside the range of the data taken, then an interp cs
tion formula may be a sufficient representation of the kinetics. However, if
extrapolation is necessary, much greater confidence can be had in kinetic 
els based on the best mechanism which can be proposed.
A second consideration is often important, however. Complex mechanisms
involve many parameters. The general Langmuir mechanism 5 for the catalytic
irreversible reaction A + B Products has the form
kpA P5 (8)
C +KA pA+ KB PB7
Each of the parameters k, KA, and KB is exponentially temperature dependent:
kj = Aj eEa/RT (9)
There are thus nine parameters to determine. Usually they will differ wide
ly in magnitude, but theory will not predict in advance which ones may turn
out to be negligible.
It is a fact of the imperfect world that the more parameters that must
be determined, the more difficult it usually is to estimate them. When the
models are linear in the parameters, experimental designs may be found which
will, in theory, allow good estimates to be obtained. The upper limit on
precision is determined largely by the magnitude of the experimental error,
the number of runs to be made and the experimental range of the variables.
In practice, however, the operable region may be such that balanced designs
are made impossible by interdependence of the supposedly independent variables
That is, the process may not remain operable unless a change in one variable
is compensated for in part by a change in another. In this way correlations
tend to creep in to reduce the precision of the estimates as the number of
parameters and variables increase. The only partial counter measures are (a)
the difficult course of attempting to reduce the magnitude of the experimental
errors or (b) an increase in the range of the variables. Chemical processes,
however, always have finite restrictions on the operable range of the design
variables. The workable temperature range is finite; permissable feed com
positions may be limited by phase changes or explosion limits.
September 1963 CHEMICAL ENGINEERING EDUCATION 27
When podels are non linear in the parameters, the same difficulties
exist except that correlations between estimated parameters are more apt to
exist. First the optimum theoretical designs in an unhampered experimental
region whi d would maximize the precision of the estimates are usually ex
tremely difficult to find mathematically and secondly, the nature of the
function itself may make a high dependence of the estimates unavoidable.
Hence in any actual case, there will be a maximum number of kinetic parameters
which can be determined with precision from experimental data. The more care
ful and precise the data, the larger this number becomes, but it rarely exceeds
five or six. One must, therefore, scrutinize complex theoretical mechanisms
to discover which of the many parameters are likely to be buried in the exper
imental error.
The quadratic empirical surface can be of great help in this process. 1,6
Thus, if Equation (8) is rewritten and expanded into the form of Equation (7),
it will be discovered that KA and Kg occur only in quadratic terms involving
I/T and the log p and log pB' respectively. Hence, if the empirical quadratic
surface lacks terms in 1/T x log PA and log p, x log p one can be confident
that PA can be omitted from the denominator of Equation (8) without worsening
the fit. In fact, KA could not be determined with precision from the data
that fixed the quadratic surface. In this way, complex mechanisms can be
rationally simplified without overstepping the limits of reliability of the
data.
One may find, of course, that the data can be more simply represented by
a new choice of coordinates. Thus Equation (8) is more naturally represented
in the form
(l/R)l/n 1 KA KB (10)
k1 P/n B/n k pa/n1 /n k1 pa/n b/n1
This suggests 1/pA and 1/pB as better variables than log PA and log PB for
testing this mechanism. In this space, the original experimental points which
are wellspaced in log Pi may be poorly placed to give the best determination
of the R1/n surface. New data may be required to determine the parameters of
Equation (10) with precision.
The availability of highspeed computing capacity modifies some of these
possibilities. It may no longer be necessary to linearize the rate expres
,sion for computational reasons. Techniques for nonlinear estimation2 permit
working directly with the proposed model. However, the number of parameters
which can be determined simultaneously is limited as before and most theoret
ical mechanisms must be simplified to make nonlinear estimation feasible.
In some cases the ultimate design problem may be able to accommodate an
integral kinetic form rather than a differential one. If, for instance, it
is clear that a batch reactor will be used with no internal concentration
gradients, only the total volume or residence time will be required for the
design basis. Rather than design a differential reactor or differentiate in
tegral data, one may then propose a kinetic model in which time is an explicit
factor. Our previous remarks about the maximum complexity of a useful model
are especially pertinent.
These points are illustrated in the case of a study undertaken recently
in connection with the purification steps of a commercial process. A minor
impurity is removed by precipitation with aqueous ammonia. Complication
arises out of the base catalyzed hydrolysis of the principal product, which
represents a loss. The kinetic study was designed to locate the conditions
under which yield at required purity could be maximized. However, since in
design it might become necessary to modify some of the conditions, a kinetic
model valid over a range of conditions was required. The change in concentra
tion of the impurity is small and small hydrolysis of the principal product
is expected, even though a large single stage integral converter is ultimately
envisioned.
As is often the case, some older data were available for which the ex
perimental precision was only moderate. New data in both small and large
reactors were obtained to test the assumed independence of yields on reactor
size. In all, four sets of data were available:
1. New small reactor data
2. Old small reactor data
3. New large reactor data
4. Old large reactor data
28 CHEMICAL ENGINEERING EDUCATION September 1963
Four independent variables had been studied:
1. Temperature (T)
2. Weight per cent product initially in the impure mixture (P)
3. Weight per cent ammonia initially (N)
4. Reaction time (9)
The response in each run was the precipitate produced, measured as y, per
cent of the initial product p.
Since it was desired to make use of the unplanned older data, an ortho
gonal factorial design or central composite second order design was not pos
sible. As a preliminary survey, a full quadratic model in four variables was
fitted by least squares to four groups of data:
a. Sets 1 and 2
b. Sets 1, 2 and 3
c. Sets 1, 3 and 4
d. Sets 1, 2, 3, and 4
An additional block variable was added for data in sets 3 and 4 to test the
effect of reactor size. This effect proved to be nonsignificant and compar
ison of residual mean squares of each group of data. showed no significant
difference between groups. Hence all of the data was used in the final anal
ysis.
A full quadratic model in four variables has fifteen coefficients, four
of which only serve to locate the origin with respect to which linear terms
vanish. By rotating axes about this origin, the six crossterms can be elim
inated, leaving only the four squared terms and the constant. This reduction
to canonical form is done by proper choice of four new independent linear
combinations of the independent variables. The results of this analysis are:
y .65362 + .49654 z2 .09111 z2 .02671 zi .00083 z2 (11)
where
zI = .95990 TI .27634 P' + .02143 N' + .04214 9' (12)
z2 = .08732 T' + .37390 P' + .92333 N' .00672 9' (13)
z3 = .26183 T' + .688518 P' .38296 N' + .03520 9' (14)
z4 = .04916 T' + .01703 P' .01892 N' .99847 9' (15)
Here the primes refer to scaled values of the independent variables.
For example,
T' = (T(C.) 141.85)/27 (16)
From Equation (11) we see that z1 is by far the most important term.
By Equation (12), zI is nearly independent of N and 9 and measures T and p
most strongly. Reaction time, 9, is almost identical with z4. Hence we
feel justified in treating its small effect independently of the other var
iables. Since many of the twentyone coefficients are smell and probably
nonsignificant, one suspects that there are many alternatives to the quad
ratic form which fit the data as well.
Therefore, a reasonable mechanism was postulated as a guide to a simpler
kinetic expression. Suppose the hydrolysis to be catalyzed by hydroxyl ion.
Then one might have
p + OH  POH (kl) (17)
NH3 + H20 NH4+ OH" (k2) (18)
dfPOH_7 = kl J .7j _ff (19)
d9
September 1963 CHEMICAL ENGINEERING EDUCATION 29
ZU7 : K2 LH37 = K2 No 517 ffOHJ (20)
i _7 ZH7 ofH3j
P_7 Po ZPOH (21)
Y : JOH:7 / Po (22)
whence
d dL0H ' = klk2 1 ( HE:7/No) (y Por o) (y) 23)
d@ P0 d 12
[fHl7/No + (y Po/lo)
since y remains much less than unity throughout all the tests, and
1/2 1/2
SZB o/H o = K2 /No (24)
which is also small, Equation (23) can be simplified to
SklK2N(1y Po/No)
Po 7 (25)
: kK2No klK2 (26)
'hen y is very small, the first term dominates and the Initial conversion
should be given approximately by
y = (k, K2No/Po)1/2 0 (27)
This suggests a slightly generalized empirical kinetic expression
log (y C) = log A g p log P + n log N5+ ( log 9 (28)
T
A preliminary estimate of the coefficients with their confidence limits gave
the data in Case I of Table I. This confirms our earlier conclusion thet 9
could be treated independently as shown in Equetion (27). The data were the2
refitted fixing o( = p n = 0.5, their theoretical values in Equation (27)
This gave Casell in Table I.
Table I
Case I Case II
5% Cony.
Coefficient Limits Coefficient 95% Conf.
Limits
in A + 16.64 4.75 + 16.16
n + 8257.87 1889.65 +7648.17 192t.25
p 0.562 t 0.344 (0.50) 0
n + 0.261 t 0.567 (+0.50) 0
W + 0.978 t 0.279 (*0.50) 0
There is no significant difference in the fit between Equation 28 with
four constants (Case I) and Equation 11 with fifteen.
Note that while n and p are not significantly different from their
theoretical values, v( is significantly larger than 0.5. Nevertheless, the
fit forced with 0.5 (Case II) is not tremendously worse.
30 CHEMICAL ENGINEERING EDUCATION September 1963
Examination of the residuals shows that the fit is poorest at low No.
In fact, some hydrolysis occurs even in the absence of added ammonia. The
hypothetical mechanism does not allow for this. The empirical models based
on it force the fit by averaging up the reaction order with respect to am
monia. A more realistic model might arise by adding a term to Equation (26)
which is proportional to 1y. This, when integrated, leads' to
y = Cel + c2No ln ll No y (29)
where Cl, c2 and 03 might each have exponential temperature dependence. The
difficulty of fitting Equation (29) is much increased by its nonlinear form.
For small y, Equation (29) reduces to Equation (28). Hence it will be essen
tial to use the nonlinear form if any improvement is to be expected.
In this example, an empirical quadratic form has again given insight in
to what mechanistic terms should be retained in formulating a kinetic model
with fewer constants to determine. It should be clear that there are a large
number of kinetic models that will represent a given set of data. Unless
these data are of very high precision, the fact that the engineer has found
one such set lends very little support to the corresponding mechanistic in
terpretation. However, qualitative features can be discerned and more sensi
tive experimers are suggested by the analysis which may test the mechanistic
assumptions in a less equivocal way. Nevertheless, the mechanistically in
spired empirical kinetic form will usually be simpler and reliable over a
wider range of variables than a pure linear or quadratic form in the original
experimental variables. Thus the requirements of engineering data for design
purposes can be met without sacrificing the best theoretical knowledge avail
able.
REFERENCES
1. Box, G. E. P. and P. V. Youle, Biometrics, 11, 287323 (1955).
2. Box, G. E. P. and G. A. Coutie, Proc. Inst. Elec. Eng., 103B, Suppl.
1, 100107 (1956).
3. Davies, 0. L., Ed., "Design and Analysis of Industrial Experiments,"
Oliver and Boyd, London (1954).
4. Hulburt, H. M. and Srini Vasan, C. D., A.I.Ch.E. Journal, 7, 143147
(1961).
5. Laidler, K.J., "Chemical Kinetics," McGrawHill, N.Y. (1950).
6. Pinchbbeck, P. H., Chem. Eng. Soi., 6, 105 (1957).
7. Temkin, M. I. and Kul'kova, N. V., Zhur. Fis. KhIm., 23, 695713 (1949).

Full Text 
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' C H E MI C A L E N GI N EERING DIVISIO N THE AM ER I CAN S OC IETY 1' 0R E~Gl~E F. Hll\' C: I: l )L C : A r1():; S eptember 1 963
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! r CHSMICA.L ENJINEERING ED:.TC A'r1 :. )tl Sept.err,1)E r l';l63 Ch eIT L ical E n ginee rin g Division .' 1 r1e~:. c an Scci.E t y 1'or Eng ineerin g E ducati o n CO 1'1'I'El{T S )::1 S tream Compute r s, l\n Examp le and Some Ge ner a li ties, by c G. ~aspe 1 Op timization Theory in th e Chemical Engineering Cu rric ulum, by Douglass J. Wilde 9 Funda mental Problems in F.e tero ge neous Ca tal ysis, by Max Peters 1 4 P lanning Experiments for Engineering Kinetics D ata, by H. M. Hulburt 23 C hemical Engineering D ivision Ame rican Society for Engineering Ed ucation Joseph J. Martin George Burnet J.B. West Officers 196364 {Michigan) (Iowa State) (Oklahoma State) Chairman Vice Chairman SecretaryTreasurer CHEMICAL ENGINEERING EDUCATION @, Journal of the Chemical Engineering Division, American Society for En g ineering Education. Published Q uarterly; in March, June, September and December, by Albert H. Cooper, Editor. Publication Office; University of Connecticut P.O. Box 445, Storrs, Connecticut S ubscription Price, $2 .00 per year. f
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ON STREAM COMPUTERS AN EXAMPLE AND SOME GENERALITIES C. G. Laspe ThompsonRamoWooldridge Products co. Beverly Hills,Calif. There are many excellent articles (1,2,3,4,5,6,8) g1T1ng the general reasobs for and the philosophy behind the use ot digital computers in the controlling of production processes. Some ot the pioneering efforts by Stout and Laspe (1,2,3), as early as 1957, formed the basis for many of the digital control systems in use today. Subsequent papers by Roberts (6) Stout (6), Brandon (5), MRdigan (12,13,14), Freilich (19), and Laspe (15) presenting the results of actual case studies haYe bolstered the technical literature on computer control systems. It is not the intent of the present paper to dwell ~eavily upon the objectives of computer systems, nor upon their design, for these aspects of the pr oblem have received adequate attention in the aforementioned papers. It is the purpose of this paper to present the broad aspects of on stream computer control and especially as these aspects affect the curricu la of chemical engineering. In attempting to accomplish this objeotiTe, I would like to present a few generalizations, followed by a survey or the ex tent to which computers are used in online control. Next, as an example, the computer control of an ammonia plant will be discussed to illustrate the various branches of engineering and science required in its design. Finally, the mo st important point, as touching upon this present ~ession, will be a: discussion of those fundamental things which a student in an engineering school should learn to understand the use of online control computers. In order to orient our thinking along the lines of onstream control computers, a few generalities should be considered. What are the ingredients that go into making up a computer controlled process? Once these individual ingredients are recognized, then we are in a better position to determine the engineering talents required for the design, construction and operation of such a system. Let us briefly reTiew a few fundamentals. All manufacturing processes are designed and intended to be operated to produce a profit. This profit results from the c~eation, by a combination or physical and chemical trans formations, of a product or products whose Talue exceeds the cost of the raw materials and their processing. A major goal of management in plant oper ation is the maximization of this profit. Thia statement may be considered as the process objectiTe. Therefore, the purpose of computer control .is to proTide the latest and the most efficient means ot reaching this process ob jectiTe. It ls realized that, since physical and chemical transformation are inTolTed, many Tariables influence the realization of the process objective. When one or more of these variables are beyond the control of the operator, such Tariables are classed as disturbance variables. Compensation for these d1st\il\bancea is the prime justification for any control scheme. For without disturbances, control would not be required. When there is but a single disturbance variable inTolved in the opera tion of a plant, then it is possible to derive a unique solution to the con trol problem. In other words, for any given value of the disturbance varia ble, there is only one set of control variables which will meet the desired objective. In this instance, simple relationships may be found which will relate the manipulated variables to the disturbance variables then in effect. on the other hand, when two or more variables are beyond the control of the 1 0 perator an interesting situation arises. In this case there are generally two or more feasible solutions. Of these feasible solutions, ~ne will p~obably ~ield the greatest economic return and, therefore, is the desired opti mum solution. It is in these areas where computer control may be justified. Recently, an excellent article by Elliott and Longmire (21) g1Tes the dollar_ incentiyes for computer control. The results of their studies on six differ ent production processes is presented The MRrch issue of control Enginee~ing presented a survey of onstream control computers. At that time tfie score card showed 35 ciosed loop com puting control installations, either online or scheduled to be online by early 1962. Of these 35 installations, nearly half were to be used in either chemical or petroleum processes. In September ~f 1961 Freilich (19) presented another survey of process control computers in use. Freilich shows a total of 63 process control computers, of which 20 are used in the chemical and petro. leum industries The latest figures aTailable from the May 1962 issue of control Enfinee;i~ ( 20) show a total or 159 control computer sales, ot :1h ich 43 are Ins ailed n the ch~~cal and ~e~roleum fields. l
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2 CHEMICAL ENGINEERING EDUCATION September 1963 Although the preceding statistics are both encouraging and 1nterestin they do not tell the complete story. Table r summarizes the known instalg, lstions of digital computers in process control or those units known to be on order. In the petroleum field, several installations have been reported on catalytic crac~ing plants and on crude distillation units. Single installa tions have been reported for catalytic polymerization, alkylation, and ther mal cracking. In the chemical field ethylene and ammonia appear to be good candidates for computer control by reporting several installations on each. In addition other computer controlled chemical processes include vinyl chlor ide, styrene, acrylonitrile, acetaldehyde, ethylene oxide, and the exotic "alfol" plant of the Continental Oil Company. A s can be .. seen from a study of this list, the gamut of applicable processes is limited only by economic necessity and the imagination of the system designer. From the above list we have chosen as the working example to be dis cussed here today, the computer controlled ammonia plant of Monsanto Chemical Company at Luling, Louisiana. A fairly complete description of this particu lar installation has already been given by Eisenhardt and Williams (17) in the November 1960 issue of Contr~l Engineering. For purposes of description, the ammonia process can be conveniently divided into three separate operations. The first of these is concerned chiefly with the preparation of raw synthesis gas. The second section is concerned with purification and compression, and the third and final section is the synthesis unit itself. In the gas preparation area three chemical re action stages are involved. The primary reformer, the secondary reformer, and the CO converter. The feed to the primary reformer consists of natural gas and steam which in the presence of a catalyst reacts to produce hydrogen, carbon monoxide, and some carbon dioxide. E xternal heat is a pplied to this unit from a reformer furnace burning natural gas. Essentially 90 % of the in coming gas is convert~d. Steam reforming is the principal reaction involved, although the water gas or CO conversion reaction accounts for some of the hv. u drogen produced. The variables over which control can be exercised a re the flow rates of the natural gas, the fuel gas and the p~ocess steam. The secondary reformer serves two specific functions. Firstly, to pro vide additional reaction volume for continuation of the reforming and CO con version reactions initiated in the primary, secondarily, to serve as the in jection point at which nitrogen is introduced into the system. Atmospheric air is used as the source of nitrogen. In the secondary reformer the oxygen from the air which has been introduced reacts with some of the meth a ne and hydrogen in the feed to form water plus CO a nd CO2. The only independent variable over which control can be exercised is the flow of process ai r. Note that at this particular point in the process, essenti a lly a ll of the natural gas has been converted into product ga ses. The residual methane con tent is in the order of 0.3 of one percent. There is a lso a n a ppreciable a mount of carbon monoxide. The effluent from the second a ry reformer flows directly to the CO converter. The sole purpose of the CO converter is to produce additional hydrogen from the incoming carbon monoxide by means of the water ga s re ~c tion. pd dition a l water is injected at this point in the form of low pressure steam. Because of fundamental thermod~amic and kinetic considerations, the carbon monoxide is not completely consumed. The exit concentration is in the order of three percent. A t this point in the process the hydrogen to nitrogen ra tio is fixed a nd remains constant throu ghout the rem aind er of the operations. At this particular point in the process, carbon dioxide represents a pproxi mately 15 % of the entire raw synthesis ga s. This a long with the carbon m on oxide must be removed before the synthesis ga s can be charged to the fin a l ammonia synthesis reaction stage. carbon dioxide is removed from this raw ga s stream by passing it through a standard Girbotol unit. Monoethanol amine is used a s the absor bent. A compression plant consists of several parallel recipr o c a ting com pressors. Each compressor is equipped with five sta g es of compression. The gas pressure is boosted from app roximately 20 pounds per square in?h a t the inlet to 5,500 pounds per square inch gage at the outlet. The entire gas stream, however, does not pass throu gh all five stages: At the outlet o! the fourth stage, the process ga s is diverted to the high pressure purl f1ca t1on unit. The main function of this unit is to remove c a rbon monoxide, plus any residual carbon dioxide. Copper formate i s used as the ab sor bent for co. The residual CO2 is removed by a final caustic w a sh. A fter remov a l of impurities the purified synthesis gP. s is then directed to the last st age of compressio~ from whence the gas flows to the s ynthesis unit. A n a~alysis of the exit gas shows an essentially pure hydro g en, nitrogen mixt u re 1n the ~ io of approximately 3 to 1.
PAGE 7
September 1963 CHEMICAL ENGINEERING EDUCATION Table 1 SURVEY OF PROCESS COMPUTER APPLICATIONS CompanyLocation 1. Allied Chemic~l Corp. South Point, Ohio 2. A merican 01 1 Company Whiting, Indiana 3, B.A.S.F. Ludwi g shafen, Germany 4. B.F. Goodrich Che mical C~lvert C it y Kentucky 5. Celanese Co rporation Bpy City, Tex a s Bishop, Texas 6. Continental Oi l Company LAke Charles, LA ?. Dow Chemical Company Midland, Michigan 8. Dupont Beaumont,Texas (2) Florence, s.c. Circleville Ohio Gibbs town, N.J. 9. DXSunray 10. 11. Tulsa, Oklahoma Gulf 011 Company Philadelphia, Pa. Imperial Chemical Ind. England 12. 13. 14. 16 Monsanto Chemical Co. Luling, Louisiana Ch ocolate Bayou, Texas(4) Owenscorning Fiberglass Aiken, s. CProlina Petroleum Chemicals,Inc. Lake Charles, La. Phillips Chemical Co. Borger, Texas Bartlesville, Oklahoma Shell Development Company Emeryville, Calif. 17. 18. 19. 20. 22. 23. 24. Sun Oil Company Marcus Hook, Pa. Standard 011 Co.(N.J.) Linden, N .. J. Baton Rouge, La. Standard 011 Co.(Calif.) El Segundo, Calif. Richmond, Calif. Tennessee Eastman Kingsport, Tennessee Texaco Port Arthur, Texas Tidewater 011 Company Delaware City, Delaware Union Carbide corp. Charleston, W. Va. Seadrift, Texas Seadrift, Texas Universal 011 Products Des Plaines, Illinois Computer RW300 IBM171 0 RW300 R 1 d300 H290 RW300 RW300 GE312 ISI609 ISI609 ISI609 IBM1710 RW300 RW30 0 Ferranti RW300 H290 ISI609 RW330 Recomp II TRW330 PB250 IBM1710 LGP30 LGP30 IBM1710 Recomp GE312 RW300 ISI609 RW300 RW300 Daystr9m Daystrom Delivery 1 96 1 1 96 1 1961 1959 1962 1962 1961 1960 1960 1961 1961 1961 1961 1959 1962 ;I.960 1963 1959 1962 1961 1959 1961 1959 1960 1961 1958 Process Annnonia Crude distillation Chemicel process Vinyl chloride and acrylonitrile 2:Ethly hexanol 3 Vapor phase oxidation Alfol process _ Styrene Chemical process Chemical process Chemical Process Acrylonltrile pilot plant Crude distillation Catalytic cracking Soda Ash Ammonia Chemical process Logger Ethylene Portable Logger Portable logger Logger Catalytic cracking logger Pilot plant logger Catalytic cracking logger Catalytic cracking Product run calculations Chemical prooes s Catalytic ~olymeri zation Naphthalene Pilot plant logger Chemical process Ethylene Pilot plant logger
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4 CHEMICAL ENGINEERING EDUCATION Table II CRITERIA FOR JUSTIFYING MONSANTO'S COMPUTER CONTR O L S Y S TEM September 1963 1. Meintain maximum gas flow in spite of changing weather and process oonditions. 2. Maintain an optimum hydro g entoniyro g en ratio. 3. MAintain an optimum methane concentration at the s h ift converter ex i t unless in conflict with 1 or 2 4Meintain maximum shift efficiency if not in conflict wit h objectives 1, 2, or 3. 5. Maintain objectives 2, 3, and 4 under reduced flow cond i tions. 6. Reduce raw matertals flow immediately and safely to compensate for an y loss of compression. ?. Log out all important process variables. 8. Provide the plant operator with messa g es in case of a bnorm a l proc e ss or instrument conditions. 9. Provide failsafe features such t h at instrument or computer malfun c t i ons are detected, alarmed, and prevented from affecting the process. ~ o Comtrol t h e fuel and air to the reformer furnace 1. Maintain a specified steamtodry g as ratio at t h e exits of t h e s e c onda r y reformer and the co converter. The synthesis plant feed is com b ine d with a r e c y cle stream to fo rm t he feed to the s~thesis reactors. B ec a use of the low conversi o n per p a ss, ( s p proxim a tely 1 2% ) a hi g h recycle ratio is required. A mmon ia is rec o vered in the reactor effluent gases by condens a tion. I n o rder to p reve n t ex c ess i v e buildup of inerts in the system, pur g in g or v en t i n g is re quired. No w let us turn our a ttenti o n to s o m e o f t h e f ac tors i nv o lv ed in ma~i n E this p ar t i cul a r plant a good c a ndid a te f or co mputer c o ntrol. A S h as be e n pointed out by E i senhardt and W illiams (17 ) "While there may be a tendency to over d es ig n some plant equipm ent an en g inecompressor system is us ua lly c onserv a tively s i z ed be cause it represents a major fract i on of the capital cost o f a n a mmon ia plant. The en g ine compr e ssor system is t h us likely t o be one ofthe first units to bottlene ck the pl a nt s p r odu c tf o n incre a ses. At L ulin g t h e highe st possi b l e prod u ction r a te a n d t h ere f ore t h e maximum econ o mi c r et urn i s obtai ne d by o p er a t ing th e compressor system a t ma xim ur.1 po ss ib l e ca p aci t y ." D ue to the very definite influen c e of ambi ent co ndi ti ons, p arti c u l arly temper a ture, upo n intern a l c o mb u stion e n gi ne com pr e ss or e ff i c ie nc ie s, the allow a ble hors ep ower to b e ex p ended by t h e compre ss o r i s no t a fix ed o r a rbitary const a nt. In f a ct t h e com p ress o r capa city v ar i e s not on ly a s the a m b ient temperature chan g es, b ut also a s t he me cha n i cal c o n dit i on of the conpressors themselves are ch a n g e d In t he c a s e of a pro d u c tion li mit e d plant, such as the one we a re n o w d is cu s sing it ca n readi l y be s e e n that the maximum plant throu gh p u t is n ever f i x ed b u t var i es i n accordance w ith compression capacities. The p ur pos e o f t he compu te r contro l s y st em no w be comes apparent. Its m a in functi o n is t o keep th e p l ant r un n i n g a t m a ximum capacity by determinin g t h e m a x i murn a s lim it e d by t h e capac it y in th e c om pression section. O nce k nowin g t h e m a x i m um a llow abl e f l o w of purified syn thesis gas, the computer can then g o abou t its bu s i ness of se tting t h e many flow controllers in the reform i n g se c t ion I t i s n ot enou g h thFt t he com pressors be fully lo a ded a t a ll t i mes. T he s ynt~ es i s ga s m~ s t al~o h a v e the proper quality a s measured by t h e hy dr oge n and ni tr ogen r at10 Since e ~ ch of the processes in the reformin g a re a n a m e l y tbe primary r efor~er, the second a ry reformer, a nd the sh i ft c on v e r t er inv o lv e chem:cal conversions, complic a ted kinetic n nd thermodyn a m i c eq uatio ns mu s t be s o lv ed in a rri v in g at the proper flow settin g s. To keep t h e p l an t p r ope rl y b a l a nced a s we l l ~s keepin g the compression sect i on full y l oad e d ~ hen become s th ~ m~jor job of the control computer. T R ble II lists t he cr~teri a fo r just 1fy1ng Monsanto s computer control system. This t ab le w a s taken direc t ly f r om E i s enhardt t s and Williams' a rticle. A questi o n whi ch i mm ediP.t el y come s to mind is whethe r or ~o t t he pre d i cted econo mi c gai ns ha ve been fu ll y r ea l ized O f cour s e b~s1c econ?mic{ fi g ur e s a re c o ns i d e red a s pr o p rie t ary i nf orma ~i on Ho w e v e r, 1~ a qual1tat.ve wa y we can a nswer in t h e affi r ma t i v e a s M r. Eisenha r d t and Il l iam s ha v e s aid
PAGE 9
September 1963 CHEMICAL ENGINEERING EDUCATION "Immediately after placing the computer on control, the gains in controlability became evident. When the process is not on computer control, the operator makes minor changes in control ler setpointe trying to hold process temperatures within lim its and maintain gas composition as required by the synthesis loop. S uperimposed on top of these minor changes are larger step changes in throughput which are required to compensate for those uncontrolled variables affecting the plant capacity. At best these larger changes are made only several times a shift. Under computer control, however, the plant throughput il!I adjusted every eight minute : s to obtain maximum possible pro duction ae uncontrolled variables changed. Qual1tat1Tel~, one can observe from the computer log sheet the steadying of gas compositions and temperatures under computer control as com pared with the irregular control obtained by even the best op erator." 5 \ This now brings us to a consideration or the topic of engineering fund amentals as related to an understanding of computer control processes. The design of a computer controlled process requires the systems engineering ap proach. Thil!I means that the person in charge of the overall project must view the project in its entirety. He should not be burdened with the many small details that go into the system design. But on the other hand, he should be ~ully aware of the many fundamental engineering sciences which are involved in such a project. In this sense the system engineering approach may be synonymous with the common core approach in chemical engineering ed ucation. In Table III are listed some or the fundamentals required of chem ical engineering students for understanding of digital computer control processes. Topping the list of required fundamentals 1~ an understanding of eco nomics. Since computer control is a tool t o assist manap;ement in meeting the process objectives, which is normally the maximization Qf operating profit, the importance of a thorough understanding of economics can hardly be overemphasized. Methods for pricing i ntermediate products, an analysis of profit from incremental production, and a working knowledge of payout criteria for capital investment are especially important. The basic curricula of chemical engineering will p~rmit a reasonable understanding of the workings of most of chemical processes. However, in the computer control design area perhaps a little more emphasis should be placed upon chemical thermodynamics, chemical kinetics, and stoichiometry. For these a.re the basic s ciences involved in deriving the predictive mathe matical models used in the control computer. Mechanical engineering enters into the design of computer control sys tems firstly, in the determination of the operating characteristics of the mechanical equipment involved. For example, in the ammonia plant system pre Tiously discussed, compressor capacity calculations were made. In addition to these, certain mechanical equipment limitations had to be evaluated. ThesE limitations, which in computer pa~lance are called constraints, oftentimes determine or limit the area in which the process variables may be operated. In the field of electrical engineering, as applied to the design of com puter control systems, a main consideration is the co1nmunication between the computer proper and the process 1 tself. A working knowledge or the basic AC/DC theory, transmission lines, impedance matching, and the filtering of electrical noise will go a long way in the understanding of the electrical requirements of computer control systems. It is not necessary to become too deeply involved in the computer circuitry itself. However, to deepen the ap preciation for the entire control l!lystem, some instruction in this area would be profitable. The ultimate success of any computer control system depends very heav ily upon the ingenuity and mathematical ability of the process analysts. Most computer control systems op~rat~ on the basis of mathematical models which simulate or represent the operation of the existing plant. These models may be derived from fundamental theoreti cal conside :t'ations or po ssi bly from regression analyses or plant data. In either event, considerable mathematical skill must be exercised in obtaining an accurate Pnd represen tative set of equations. Once the mathematical model has been developed, the job is only half done. From here various optimizing techniques must be e~plored in order that the model may be used most efficiently in reach i ng the process objective
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6 .. CHEMICAL EHOINEER1BG EDUCA'l'IOB September 1963 Table IU FUNDAMENTALS REQUIRED OF CREMI CAL ElfGINEERS FOR UNDERSTANDING OP DIGITAL OOMPUl'ER CONTROLLED PROCESSES I Economics A Process objectives B. Investment and payout criteria c. General knowlege of market prices D. Methods for pricing intermediate products E. Analysis of incremental profit F. General knowledge of utility costs, etc. II. Chemical Engineering A Unit operations B. Chemical thermodynamics c. Chemical Kinetics D. stoich1omet17 III. Mechanical Engi~ering A. Operating characteristics or mechanical equipment B. Constraints imposed thereupon IV, Electrical Engineering A. Basic AC & DC theory B, Transission lines c. Impedance matching o. Filtering or noise v. Mathematics A Methods of correiation analysis l. Regression techniques 2, curve fitting B. Optimizing techniques l. Maximization by calculus 2. Gradient methods 3. Linear programpii~g 4, Nonlinear programmin g ;. Dynam~c programming 6. Celculus of variations VI, Control System Theory A Linear feedback systems B, Nonlinear feedback systems c. s~mpled Gata systems D. Laplace transforms E. ztransforms VII. Instrumentation A Hardware l. Types a. Pneumatic b. Electric c. Hydraulic d. O ther 2. Measurement equipment a. Temperature b. pressure c. Flow d. etc. 3, Analytical instruments a. Chromatograp h s b. Infrared c. Physical properties d. etc. 4, controllers a. Jlroportional b. Derivative c. Integral d, Other B, Methods of interconnection c. Reliability and accuracy VIII computer Fundamentals A Types and characteristics l. Digital 2. Analog 3. DDA B. Applicability of computers C. Ba1t1 ~ understanding theo17 of operation D. Programming a. Flow charting b. Coding c. Machine language d. Instructions e. Routines and subroutines
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September 1963 CHEMICAL ENGINEERING EDUCATION 7 As a part or the applied mathematics curricula, considerable attention should be paid to control system theory. Here such subjects as linear feed back systems, nonlinear feedback systems, sampled data systems, Laplace transforms, and Z transforms should be studied. Since the onstream compu ter is connected, as it were, to a live process, attention must be paid to the process dynamics. All computer systems must recognize these dynamics. Obviously, control actions must not be taken too frequently that the plant is always in a state of jitters, nor must ~hey be taken too inrrequently or else the full benefit of computer control will not be realized. The study of instrumentation is essential for a complete understand ing of the computer control process. By instrumentation we think of the hardware involved the sensing elements, t~e transducers, and the control equipment. It is these items that allows the computer to recognize or sense the state of the process. It is also these items that allows the com puter to take corrective action upon the process. In a sense the primary measuring elements represent the sensors of a living organism. The trans mission lines are the nerves. The control valves and controllers are the muscles, while the computer controlled systems possess the same attribute via the local feedback control loops. Finally, some instruction should be given in computer fundamentals. The differences between the characteristics of digital, analog and the DDA computers should be carefully noted. A basic understanding of the theory of operation of these computers is profitable, though not essential. Some programming instruction should al ~o be given with exercises in flow charting, codin&, and in the u~e of routines and subroutines. In closing I might say that it is impossible for any single man to understand completely all the workings of a digital computer control system. The design of such a system is the work of a team of experts. And yet, this complexity does not preclude its use as an effective and eff 1c 1ent produc tion tool. Let me give you an example. There are not many people that com ple~ely understand the entire working of a television set or even, for that matter, of the automobile in which we drive to and from work. And yet, there are millions ~f these machines in everyday use. By the same token the digital computer controlled process, though its design is complex, its oper ation can be made simple enough for a single operator to comprehend. The efficiency of this production tool, this manmachine~process combination will be increased 1n the _. t'uture through continued research efforts in all the areas that have gone into its design. ..
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8 ) CHEMICAL ENGINEERING EDUCATION BIBLIOGRAPHY aeneral September 1963 1. Stout,T.M., and Laspe, C.G., I.E.C. Vol. 49, July 1957. 2. Laspe, C.G. ,"Digital Computers in Refinery Process Control~ Ref.Eng. Sept. 1957. J. Stout, T.M., "System Considerations in Computer Control of Semicontinuous Processes", Proceedings AIEE Computer Control Systems Conference,Oct. 1957. 4. Manildi, J.F., "Modern Developments in Control",Automation, July 1959. 5 Brandon, :O. B., "Let A Digital Computer Run Your Alkylation Plant" ,I.E. C. 52, No. 10. 6. Roberts, S.M. and stout, T.M., "Some Applications of Computer control in the Iron and Steel Industry", Iron and Steel, March 1960. 7. Mears, F.c., "Organiaation of the Computer Control Project", ISA Preprint 139LA61. 8. Buster, A.A., "Application of Controllin g Computers to FrP.ctionating Units", C.E.P. Symposium Series, Vol. 57, 1961. 9. Stout, T.M., "Evaluating Control System p,,yout from Process Data", Control Engineering, Feb. 1960. 10. Grabbe, E., "Digital Computer Control Systems an Annotated Bibliography", 1st Congress International Federation of Automat ic Control, Moscow,June 1960. 11. Roberts, S.M.,"Computer Control of Chemical Processes with Stochastic Properties", Proceedings 5th International Instrument and Measurement Conference, Stockholm, Sweden, September 1960. Annual 1960. \ 12. Madigan, J.M., "Online Computer Control of A Chero.ical Process", 6th Data Processing Conference, ftmer. Management A ssoc., New York, March 13. Madi g an, J.M., ''How B.F. Goodrich Applies Computer Control", Chicago A. I. Ch. E. : Feb. 23, 1960. 14. Madigan, J.M., "Computer Controlled Processin g ", C.E.P., May 1960. 15. Laspe, C.G. & Roberts, S.M., "Online Computer Control of Thermal Crackin g ", I E C 5 3 MR. y 19 61 16. Adams, n:F., ''Closed Loop Control of an 85,000 B b l Crude Oil Unit", 011 & Gas Journal, MAY 21, 1962 17, Eisenhardt, R.D. & Williams, T.J. "Closed Loop Computer Control at Luling"., Control Engineering, Nov. 1960. 18. "Computer Control Case History Texaco", Paper submitted to Award Committee for 1961 Kirkpatrick Award Sponsored by Chemical Engineering Magazine. Survey Type References 19. Freilich, A., "What's Doing in Computer Control", I.S.A. Jour., Sept. 1961, 20. "Industry Accepts Computing Control", control Engineering editorial,May 1962. ~ l Econo~c 21. Eliot, T .Q., & Longmire, D.R. "Dollar Incentives for Computer Control", Chem. Eng., Jan. 8, 1962.
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OPTIMIZATION THEORY IN THE CHEMICAL ENG~NEERING CURRICULUM = 5 Douglass J. Wilde Department or Chemical Engineering University or Texas Introduction Thie article is intended to acquaint teachers or chemical engineering with the theory of optimization, which has developed so rapidly in eleven years that it is now finding its way into the practice, literature, and curricula of our profession. Optimization theory is composed of technical methods for computing the greatest (or least) value of some criterion or value or effectiveness measuring the performance of a system being studied. Since optimization involves, roughly epeaking, finding the best way to do things, it has obvious applications in the chemical industry, where small changes in efficiency can spell the differenc e between success and failure. Today as always many important decisions can be made simply by choosing some measure of effectiveness and then optimizing it .,, To justify t h e inclusion of this new material into the already crowded curriculum we cannot be content with describing the technical content of optimization theory. We must also show why the profession needs it and how it might be fit into existing graduate and undergraduate programs. More over, we must assees the pedagogical value of optimization theory in devel oping engineering judgment, scientific objectivity, and intellectual cre ativity in our students. To begin we shall advance two arguments suggesting the chemical engineers' need for optimization theory. The first of these will be specu lative, analyzing the role of optimization in engineering decisionmaking. The second will be historical, showing how our very lack of attention to optimization theory has created demands for operations analysts and manage ment scientists to solve probleme which could well be handled by engineers. In the heart of the article we shall combine a review of optimiza tion theory with description of a threehol:J.r, one semester course already given to chemical engineering seniors having no special ~reparation. This resume will include references to recent developments of pedagogical in terest As each phase or the theory ls discussed, its most important in dustrial applications will be mentioned so that the "why" may be unfolded at the same time as t h e "what" and "how". It is quite possible that a pro fessor may not wish to offer an entire course in optimization theory, pre ferring instead to incorporate parts of the theory into various existing courses. Inde ed, one would hope that eventually optimization theory would be absorbed into t h e curriculum in this way. To facilitate such gradual integration we shall indicate where each of the three main parts of optimization theory may be pertinent to such existing courees ae pla~t de sign, kinetics, control, economics, and mathematics. Thus optimization theory may be introduced suddenly or gradually depending on local circumstances. Finall we shall show how study of optimiz a tion theory g ives a stu dent a set ~f rules potentially valuable to him i n makin g complex decisions. he rules and procedures are of course worthwhile in th~mselves, but equallJ im ortant is the fact that their study reinforces the student 1 s confidence 1 pthe rational scientific approach to problems. O ptimi za tion theory g ives ~od trainin in the analysis of functions of many variables (v ~ a multidig i 1 ~metry) and in the use of precise, lo g ical reasonin g Moreover, n~!elty of the mathematics and the newness of t h e theory are great stimulants to the creativity a nd imagination of the students. The Need For Optimization Theory Let u s examine two arguments tending to justify ~~:m ical engineering profeseion for optimization theory. a re combined with ex typical engineering project. Theo~~;;c~~d~~i~~;~l~s It is rare th ,; t such perimental data to describ~ th ~ ~owledge a lone; ~ltimately t h e inform a tion a study is made for the sa e o f decision bu ild a new pl a nt, replace is to be used for making s~metsor!i~hout opt imi z a tion theory, s u ch decisione a heater, or change a cata y~ i best a fter laborious case studies, muet often be m a de impetu~us iha~rw:ntint~ the study itself. Such a situ despite the glolod tenglinlyeer( often econom i cally ) u nsatisfyin g ation ls inte ec ua 9
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1c, CHEMICAL ENGINEERING EDUCATION September 1963 secondly,"conaider the rapid growth of the new pro:f'eaaion of "operationa reaea~ch or "management science", defined by moat of their practi tioners aa the ac1ent1f1c preparation of dec111ona". Thia sounds suspic iously like engineering, and on examination ot their methods for making de ciaions, we find three ateps: (1) rational (preferably mathematical) de scription of the system, ( 2) choice of a meaaure of ettectiYenesa and ( 3) optimization of that measure. Now in moat industrial problems, r~tional description is precisely the job ofthe engineer, while the choice or a measure or errectiTeneaa ia either obvious or impossible. Thus the only difference between industrial operations research and engineering is usual ly that the former profession has better optimization te~bniquea. we sub mit then that the rise of operations research bas been due not only to the ability and imagination of its own pioneers, who contributed much to the theory of optimization, but also to the failure of the engineers to study optimization problems. Our loss has been their gain. Optimization Theory Before 1951, optimization had hardly been studied at all since the deyelopment of the calculus or variations two centuries earlier and today most engineers know only one method for rinding an optimum the differ ential calculus. By this method one expresses the criterion of effectiyeneaa as a function of the independent Yariables, equates the first deriYa tions to zero and then solyea the resulting equations. But in industrial problems it la rarelr, possible to perform all these steps, and eyen when it is, the "solution' is often unattainable because of physical restric tions on the proce~s. We shall distinguish three branches of optimization theory here, classifying them according to the Tery obstacles preyenting their solution by the differential calculus. The three types of problems are: (1) experimental problems in which the measure of errectiYeneas is unlmown and must be deterin1ned by direct experiment, (2) teasibilitz pro blems in which the apparent optimum lies Qutside the physical constraints on the system, and (3) interaction problems in which there are so many var iables that the problem must be decomposed and solved in pieces. Each type of problem can be covered in one semesterhour of undergrad ua:te work, either all at once in a single three hour course or as parts of ot~er existing couraea. There are optimization problems which do not fit into these three categories, but we are limiting ourselves here to material that can be ta11gh.t to a senior engineering student in one semester and be of uae to him when he graduates. After each type of problem is described, its historical development will be traced and references of either research or pedagogical interest cited. Then applications will be mentioned and finally, possible locations in the curriculum will be suggeated. E!J>erimental problems In an experimental problem one knows almost nothing about the depend ence or the measure of etfectiYeneaa on the independent Yariablea, and the only way to obtain information about this dependence is to take measure ments. Kieter (1.) haa described a highly efficient way to carry out the search when there is but one independent Yariable and no experimental er ror, aa tor example in the calculation of the optimal number of stages in a distillation column or eYaporator. A description of this Fibonacci search procedure in engineering terms is given in (2.) It is interest!ng perhaps that with this technique one could find the beat case out of a possible twenty atter only seyen case studies. Unflortuntel7 the elegant Fibonacci technique cannot be extended to sit uations with more than one independent Yariable, and in 1951, the year of r,YiTal of interest in optimization, Box and Wilson (3.) suggested their method of steepest ascent tor multiTariable problems. Recently newer ap proaches to this problem haTe been adyanced the geometric techniques ot Buehler, Shah, and Kempthorne (4.) and the author (5.), as well as the log ical methods or Hooke and Jeeyea (6.) end Mugele (7.). The presence of experimental e rror ;requires different methods, known in general as stochastic approximation procedures. Dyoretzky (8.) has gen eralized the early methods or Robbins and Munro (9.) and Kiefer and Wolt owitz (10.), an acceleration technique ha a been proposed by Keaten (11.), and multivariable extensions haYe been deyeloped by Blum (12.). Some of the se procedures have been reviewed from the ~bemical engineers point of view by Lapidus et. al. (1).).
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September 1963 CHEMI CAL ENGINEERING EDUCATION ll These methods are applicable to design and operating problems 1nYOlT ing either complicated computations or significant measurement error. The Fibonacci technique could conceiTably fit into a plant design or economics course, or even into the exposition of staged unit operations. M~tiYar1a ble procedures are more appropriate in plant design courses, and the in sight they give into multidimensional geometry could well suit them for in clusion in an advanced methematics course. Stochastic approximation, since it depends on some probability theory, would be appropriate in an engineer ing statistics or probability course. In our experience the theory or ex perimental search for an optimum has been extremely stimulating to stu dents, who seem to be inspired by it to surprisingly original contributions. The author is presently completing a monograph on experimental opti mization, reviewing and explaining all these developments, hopefully in language that an engineering senior can understand. Engineering professors can obtain a free preliminary draft of this material by writing the author, who would be grateful for suggestions and corrections. Feasibility Problems When, as is often the case in the industrial world, the ranges of var iation or the independent variables are limited, it is sometimes physically impossible to attain the conditions where the first derivatives of the ef ficiency criterion all vanish. Such restrictions give rise to feasibility problems because only feasible conditions, those respecting all the con straints, can be considered. The technical term "mathematic a l programming" (not to be confused with the "programming" of computers) is often applied to such problems. The year 1951 also marks the beginnin g of the theory of mathematical programming. J:n that year Dantzig published his "simplex meth od" for solving the linear case. Since that time literally hundreds or articles have a ppeared on applications of the simplex method, a nd many petro leum companies have justified the installation of large electronic computers on the improvements in refinery scheduling and product blendin g made poss i ble by mathematical programming. It is traditional in operations research curricula to spend a great deal of time on mathematical programming, especially the linear c a se, which is the simplest. Much of this time is consumed in introducin g the student to matrix algebra. While matrix algebra is interestin g in its own ri g ht, we have found that one can profitably develop mathematical pro g ramm i n g with out it and save considerable time. This is a ch i eved b y tre a tin g fe a sibil i ty problems as simple extensions of the classica l opti mi zation pro b l e m s o lva b l e by the differential calculus. S ince en g ineerin g students a re mo re ade pt a t manipulating derivatives than matrices, this approach h a s p ro ven qu i te successful, and it has been possible to take a class throu gh linear a n d quadratic programming, as well as the decomposition principle to be dis cussed later, in only six weeks. This differential a ppro a c h which we th i nk has great pedagogical value, is illustrated in (2.) end justified theoret ically in { 14.) Discussion of feasibility problems is appropriate in any economics or design course. The subject may also be used in applied mathematics courses as an application of matrix theory; Lapidus has used this a pproach in his new book (15.) With the differential appro a ch, mathematical pro g rammin g can be covered in any engineering calculus co u rse, a l m ost a s a n ex e rcise in partial differentiation. Interaction Problems sometimes the criterion of e ffecti veness depends on so many :factors that it is impractical or impossible to find the optimum by cl a ss i c a l methods. Often such problems are generated by the interaction of smaller sys tems with each other. In such cases it is occasionally possible to decom pose the large problem into smaller ones, solve the subproblems, and re combine these suboptimal systems in such a way that the interactions are properly taken into account. This exploitation of thE! s : ~ructure o:f a sys tem is advantageous because the number of calculations tends to increase as the cube of the number of variables. Thus doubling the number of variables will ordinarily increase the computation load by a f actor of ei g ht. It the problem can be split in two, however, the number of calculations will only ~ouble or triple.
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12 UHEMICAL ENGINEERING EDUCATION September 1963 Be llman (16.) has shown how to decompose a aeries of decisions, each depending on the one preceding, by the method he calla "dynamic programming.' This technique, which might also be called "aerial optimization", has many applications to such long range planning problems as capital 1nyeatment, pro duction scheduling, and maintenance planning. Application of dynamic pro gramming to the design of chemical reactors has been described in Arla' re cent monograph (17.) Nemmhauser has given a very clear example or design of a straightline chemical plant by dynamic programming (18.) The conyentional exposition of this subject using functional equations is often confusing to students, and we have found the block diagram approach of reference (2.) to be helpful in the classroom. The solar system .. aatell1te structure of many multiplant scheduling problems lends itself to analysis by Dantzig and Wolfe's decomposition prin ciple when all the equations are li~ear (19.) A numerical example of the application of this principle to centralized planning is available (20.) Thia example illustratea the power and clarity of the differential approach mentioned earlier, and aenior students have had little difficulty absorbing this material, considered quite abstruse by many operations analysts. References ( 2), ( 16), and ( 1 7) gl ~e many applications or dynamic pro gramming, whose ability to handle time~dependent problems makes the process dynamics and control course an attractive place for its introduction. Aris' work suggests that the kinetics and reactor design course would also be suit able. Again, plant design and economics offerings can be used to introduce aerial optimization teQhniques. Related to dynamic programming is Pontry agins maximum principle (21}. The decomposition principle should be dis cussed as an extension of linear progra.mmlng rather than aa a separate topic for the differential formulation makes this extension relatiyely painless. Pedagogical Values In describing the outline of a course in optimization theory we have indicated how it is a good vehicle for deTeloping mathematical maturity and respect for the scientific method on engineering students. But aside from the technical material, the decision rules themselves can build sound en gineering judgment in the student that will help him make up his mind intel ligently eTen when there is no time for detailed and rigorous analysis. Study of' the one var1able ti'Xperimental optimization problem gives insight into the important min1nmxconcept and the somewhat startling concept of ran domization. Analysis of multivariable problems unearths some rather dis turbing facts about graphical reasoning and the paradoxes that can arise trom failing to realize that engineers often work in nonEuclidean space (2.) Linear programming shows that it is sometimes economical to give a customer higher quality than he asks for at no increase in price. The classic "law ot diminishing returns" is illustrated quite clearly in the study of quadra tic programmlng. Anyonets point of Tiew is affected by insight into the t araighted philosophy of dJ'llamic programming, which begins by analyzing the last rather than the first decision in a sequence. Perhpas the most sur prising decision rule of all comes from study of the decomposition principle, which shows that a central planning board should ask branch managers for non optimal production plans. Thia is particularly significant because few or ganizations presently operate this way, at least intentionally. Conclu~ing Surmnari: 1 n this reTiew we hope wo have given information upon which chemical engineering professors can decide why and how they might introduce optimi zation theory, or parts of it, to their students. The demands of industry haTe made thi~ necessary; research has made it possible; and pedagogical adTaneea haTe made it practical. The rest ia up to the proteaaion itaelt
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. September 1963 CHEMICAL ENGIBEERING EDUCATION REFERENCES 13 1. Kiefer, J., "Sequential Minimax Search tor a Maximum", Proc. Amer. Math. Soc~ 2 (19S3), pp. 502So6. 2. Wilde, D. J., "Optim.1 zation Jietp.ods ", a chapter in Advances in Chemical Eag1nee r1ng, Vol. III (T.B. Drew, J.W. Hoopera, Jr., and T. Vermeulen, e s.j Academic Presa, New York 1962. 3. Box, G.E.P., and Wilson, K.B., "The Experimental Attainment of Op timum Conditions", J. Roy, Stat Soc., BB (1951) pp. 120 4. Sh~, 13.v., Buehler, R.J., alld Kempthorne, o ., "The Method or parallel Tangents (PARTA?f) tor Finding ap Optimum", Iowa State Univ. Statistics Lab. Technical Report No. 2, Ames Iowa (April 1961). 5. Wilde, D.J "The Contour Tpng.ent Optimization Method"., to appear 1n AIChE Journal. ,; 6. Hooke, R., and Jeeves, T.A., "Direct Search Solution of Numerical and Statistical Problems", J. Assoc. Computing M P ch. ~, 2 (April 1961}, pp. 212229 7. Mugele, R.A. "A Monlinear Digital Optimizing Pro g ram for Process con trol Systems", ProoWestern Joint Computer Conf~rence (Spring 1962). 8. Dvoretzky, A., "On stochastic Approxima.tio~", Proc. 3rd Berkelez Symp, Math. Stat. Prob. (J.Neyman, ed.), Berkeley, u. or dalltorfila Press 11956), pp. 3955. 9. Robbins, H.,an<;l Munro, s., "A Stochastic Approximation Method", Ann. Mnth. Stat., 23 (1951), . 400407 10. Kiefer, J., and Wolfowitz, J., "Stochastic Estimation of the Maximum of a Regression Function"., Ann. Math. Stat., 23 ( 1952} pp. 4626 11. Kesten, H., "Accelerateq Stochastic Approximation", Ann. Mrth. Stat. 29, ( 1958), PP 4 159 12. 13. 14. Blum, J.R., "Multidimensional Stochastic Approximation Methods", Ann. Math. Stat., 25 (1954), pp. 73744. Lapidus, L, Stillman, R., S hapiro, S., and Shapiro, E ., "Strate g y for Optimizing Chemi~al Engineering Systems", A IChE Journal (Summer 1961). Wilde, D.J., "Differential Calculus in Nonlinear Progr a mmin g ", to ap pear in Operations Research. Lapidus, L. Difital Computation for Che m ical Engineers, M cGr e wHill, New York (1962. 16. Beilman, R., Dzn~ic programming, Princeton U Press, Princeton (1957) 17. 18 19. 20. 21. Aris, R., o timal Desi n of Chemical Re a ctors Studies in D 1 v,o,amic prograimnlng. ca em c ress, New or Nenunhauser, G., Ph.D. dissertation Nort h western University Dantzig, G.B., and Wolfe, P., "A Decomposition Prinoiple for Linear Programs", Opera~ions Research, 8 (Jan. 1960.) Wilde, D.J., "Produc:t1Qn PlEUU1ing or Lar g e. Sy~teDUl by the Decomposition Principle", to be presented at the AIChE a nnual meeting, Chicago, Dec. 1962. Chang, s.s.L., Synthesis of Optimal Con~rol Systems, McGrawHill, New York (1961), :
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FUNDAMENTAL PROBLEMS IN HETEROGENEOUS CATALYSIS Max. s Peters Uni versit y of Colorado Bo ulder, Col or ado At the Chemica l Eng ineerin g Teac~ers Summer S chool in 1 939, K.M. W ats on presented a paper in which he discussed chem ica l re a ction kinetics for engineers a n d made an appea l for inc ludin g more of this type of tr ain in g in the undergraduate chemical engineering program. At the n ext Chem ical Engineering Te ache rs Schoo l, held in 19 48 Watson once again talked on the subject, but t his time his emphasis on necessary training w a s in t he a re a of kin~tics of catalytic reactions (25 26) Since t h a t time kin et ics courses ha ve been included in m os t of the undergraduate and g r~du a te chemical engineering curricula in t he Uni ted Sta tes. With the increased emph as is on tr 1 e chemical a sp ect s of chemical e n g ineerin g teac h in g a nd aca demic res ea rch in t he a r ea of chemic a l engineering kinetics will become even more essential as a c omponent of a n y ad equ a te undergraduate a nd g rad uate program. The a re a of heterogeneous c a t a lysi~ presents o ne of the most chal lengin g opportunities for n ew a ppro ac hes f rom the viewpoints of bo:th re search and te aching The te aching of heterogeneous catalysis for under g r edua~e or g r ad u ate en gi neers offers an idea l opportunity to impress on the students t he importance of reco g nizin g the practical limits of purely theoretical an a lyses while simultaneously emphasizing the necess ity for understandin g t he funda ment a l theoret ica l concepts. The liter a ture is f ull of examples of controversial claims in cat alytic kine.tics based on putting too much emphRsis on a given theory w ithout ade quate evaluation of other po ss ib le in terpretati on s. On e exampl e i s the critical article by \fel ler on the en g ineer s use of the LangmuirHinshelwood a ppro ach when a power s eries of the Freund lich type w o ul d be equally ap plic able ( 29) A rebuttal to the Wel ler ~ rticle based on an ana lysis of real and idea l sur faces is presented by Boudar t (3). Another example is the critical analysi s of leastsquares determina t ions of rate con st ants presented by Chow (10) T he controversial nature of many of the currently app lied te chniques o f interpretation in heterogeneous catalysis illustrates vividly the n eed for more research. The te acher of the subject is dealing with a field wh ich cannot be presented a s ~ncient history with a ll problems solved. In~ stead, the teaching can be m ad e extremely interesting by brin g in g out the fact th a t many of the p a st en gi neerin g techniques in heterogeneous ca talysis are q uestionable. If t he c ow;se is t a u gh t correct).y, perhaps the students can catch some of the excitement o f being part of a n important engineeri ng area which is ripe for th e development of new ideas and new approaches. The recent survey by Hougen on Engineering A spects of Cata l y s i s emphasizes these Points (15). In the past, there has been some tendency to teach chemical engineer ing kinetics strictly from the viewpoint of design techniques, le a vin g the fundamentals to the a rea of chemical ki netics. This approach is complete ly inadequate for heterogeneous c ata lysis because of the many assumptions required for normal desi gn applications. C onsequently, to g ive the en gi neer a back g round for intelligent understanding of the design a spects of heterogeneous catalysis, it is essential to discuss the basic considera, tions and, from these, go throug h the assumptions necessary to give a workable final design equation. In this paper s9me of the basic consid erations in heterogeneous catalysis normally passed over in undergraduate engineering courses will be discuss ed. Surface Char acteristics Nl obvious starting poi~t for t ? e te a chin g of hetero g eneous cataly sis is to present a n analysis of surface characteristics for solids with a n initial approach through discussion of physical adsorpt ion and chemisorp tion. Because the energy of activation for physical adsorption is small, the act i vation ener g ies for re a ctions involving physically adsorbed mole cules cannot be far different than for the homogeneous reaction. Conse quently, physical adsorption does not play an important direct role in catalysis, but it does become important in considering fractions of surface area available on a solid catalyst. Conventionally, the e n g ineering approach to teaching about physical adso rption a n d chemisorption is to immediately develop the Langmuir adsorp tion isotherm for a unimolecul a r adsorbed layer in a form such as Q = lhp ( 1) 14
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September 1963 CHEMICAL ENGINEERING EDUCATION 15 where Q represents the fraction of the surface covered by an adsorbed mono molecular layer at equilibrium, his the adsorption equilibrium constant, and p represents the partial pressure of the gas adsorbed. From this point, with a few brief comments about multilayer adsorption, the Brunauer, Emmett, and Teller equation for estimation surface area is normally presented in the followin g form (7): p D 1 V(p 0 P1 V C m + (cl)p cVmPo ( 2) where c is a constant for the system and tempera tu re, p 0 is the saturation vapor pressure for the gas at the temperatu re used, And vm is the volume of the gas Adsorbed to cover the sur face completely with a monomolecular layer Here is represented the type of engineerin g approach many educators find objection a ble. If it is worth presentin g E qu a tion 2 at a ll, then it certainly is necessary to present the limitatiors on this E quation. This startin g point would be a good spot to impress t h e students with the need for understandin g any equations they may use, a nd, despite the time it mi g ht take, a relatively complete derivation of E quation 2 could g et a n en g ineerin g course in heterogeneous c a t a lysis off ton. good start The development of Equation 2 is based on the existenqe of multimol ec u lar layers of physically a dsorbed molecules wit h a derivation similar to th a t of the Lan g muir isotherm ~ or unimolecul a r layers. ~ t equili b r iu m, the r a te o f ccn d e.nsation on the ba re sur f ace must equ a l the rate of ev a or a tion of m o l e c u les in the first l a yer. S imil a rl y for e a c h succee d in g layer, t h e rate of evapor a tion m st equal t l 1e r ri t e of con de ns a tio n o n t h e prece d in g l ay er. T~ ere f or e = b ,s.eEi/ R T 1 1 = b 2 s 2 eE2/RT Ei/RT : bi Si e ( 3) ( 4) ( 5) wherA ~nd i) are co nst an ts, s re p res e nts t he su r fa ce s r eq co v e r ed on l y b y ti1 e su b sct i p tin d i ca ted layers o f mo l e cule s '.l nd Z re p r ese nts t rie ~1c1:.it o f ad s o r p ti o n fo r th e indicated l a yer. Th us, t l1 e total ~ urfec e ~ re a of the catalvst a n d t h e total v o lurne a dsor b ed a re, res p ect i ve ly : n A =fi V Vn A l S, l where n repr e sents the num b er of layers. ( t,) ( 7 ) As indic a ted in E q u a ti o ns 3 4 a n d 5 t : 1e h eat uf s : 1so r pti()n is i;: v o lv ed exponenti a lly i n ea ch of t he e qui l ibri1 1m expressions .:o r ti 1 : ri,te T he assu.,iption is ro a o.e t ha t, :'o r s ll l A.ye r s except the f'! r st the :,e~ t of' a dso rption is const a r:t a n d e qu:1 t o t~e l1e:.~ t of ev.r1p~ r ~t~ :: : ~t 1s :i; rt: .:.e:r 9.ssumed that the r a t io o f b /a i s co ns ... sn t fo r a l l l r.:, er .... t:1'?. : :rst In other words, t h e van der 1 :'_ a a ls for ce s o~ phy ~ jc~ i l ::dscr1 J t~on ,, r~ .n v o~v~ only in the first l~ y er o f m olecu les on the s u r ~ ce With these rectly to g ive 8 b2 a2 E2 a s s ..unpt ions the s '..L. "ll rn ~ ti o n v a lue o f v / v ~ !1 S f'ollc,t: .s: b b3 l = g a3 ai E3 Ei T" ,v Frorn E qu a t i ons 3 4 and 5 2 i p S 1 ~(E) Sj 2 (~ ) s1 l J J e: < p r ess: cn s (' .,J r. _.,, .. i ( ~ ') c, s l C j where J : geEvt RT Therefore, rnd c ( !.~ 1 I: ,; ) / RT n 1 = 1 cso 1 ( i: \ ( C ,) \ 1 ( 1 ) I 't I l I .
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16 CHEMICAL ENGINEERING EDUCATION September 1963 i = 1 Because converges to p~J 2 ( 1p J) and converges to P _J J' the s11nnnations equation reduces to represented in Equation 11 re such that the V V m cp/J ip/J 1 { n 1) 1 (c 1) n n 1 (p/J) n (p/J) (p/J) C (p/J)n 1 ( 12) When p equ a ls tl1e s a tration pressure of the gas, Po, complete con densation can occur and V must approach infinity. This can on ly occur 1!' J is equal to p 0 For ._ t i1 ecase wp.e~e p Po or for the case wh,ere n is .Ii large nurr11:>er, Equ a ti ~ ti1 2 c~n b~ rearranged to give Equation 2, and a com plete derivation of ~ quation : 2 ~howing all ass\llllptions has been pr esented. .. . The questiqn : i~d 'i. f. te : ly a rises as to the d~sirabili ty ot presenting this much detail in a~ und,~ng~d,iia; e chemical engineering course on heter ogeneous catalysis, A ctua.J.ley t}! deriv~tion a s pre sented here is relative ly simple and the ins : gh t i~ g!y~s the stl,ldents into tl1e Umi t~tlons of the final &Qillation plus the added ~d~rstanding of the types of fo rc ~ es invol.ved in cond~n.sati :' on and; ~ ds9rip~ .Lo~ tfl.ajte tl:ie t~me spent on suoh. a de ~ ivatioh wo/rth while. With/~hts back g round, students makin g the standard plots of p V(p 0 p) vers~ P Po to determine c a talyst surface area will have a real unders1ianding of what they are doin g T1'8 value of the const a nt c now has physical slgnificanc~ and c 0'4d be used to give nn approximation ofE1Ev since ga1/b1 is noruw..lly clos~ t9 unity. Langmt1ir Adsor ption Isotherm The Langmuir adsorption isotherm, as presented in Equation l, can be developed directly f~Qm Equation 3 as follows to give a physical meanin g to the const : ant tl.: s1 soal E 1 /RT hE 9 e p ( 13) b 1 1 hp So s1 s a1 E 1 /RT So e P O where h a1 E1/RT oi l e Ip catalytic studies, use is regularly made of this relationship to develop a model for the reaction. An ideal surface is assumed so that h, and consequently the heat of adsorption Ei, remain constant. For any real surface, there is no valid reason to assume Ei will remain consiant inde pendent of the amount adsorbed. This explains one reas~n why e~perimental values of h obtained from reaction rate data often do not agree with the oretically equivalent adsorption constants obtained from direct adsorption measurements. Despite these limitations, chemical engineers have been highly suc cessful in using the Langmuir adsorption isotherm as an approach for cor relation of rate data. Because of' this success, some novices are convinced that the relationships are completely sound theoretically and attempt to develop detailed mechanisms on this basis. This approach has been attacked by Weller who proposed th at real and ideal surfaces were so different thnt the practical engineer was wasting his time in attempting to use the Lang muirHi~shelwood approach (29). He proposed instead the far simpler Freundlich type of expression !'or expressing reaction rate. For example, for the gas reaction A J B C catalyzed by a solid, the Freundlich powerseries type of rate equation would be j m o rate k(pA) (pB) (p 0 ) (14) while the LenginuirHinshelwood result might be in the form of rate= kpApB ( 15)
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September 1963 CHEMICAL ENGINEERING EDUCATION 17 The mathematics of handling Equation 14 is far simpler than for Eqaation 15; however, a specific reaction mechanism can be a pplied for Equation 15 while no mechanism can be given for Equation 1 4 w 11 poses the use of the IengmuirHinshelwood approach on the g ;oun~s ~~~p;eal and ideal surfaces are not similar and the results o b tained tend to be mis leading. Boudart has responded to this by proposin g th a t discrepancies in the constancy of the adsorption energy can be accounted for b y approximating a temperature dependence of this energy a s Ei = Eoi ,zT where Eoi and z are constants ( 3) ( 16) The type of approximation represented by Equa tion (14) would be an excellent method to use for initial analysis of reaction rate data. How ever, for any general application of kinetic results, a mechanism must be satisfied. Accordingly, the information obtained by a preliminary analysis of the Equation 14 type should be used to attempt to predict a mech~nism, And this should then be confirmed by a detailed analysis of the type repre sented by Equation 15 plus reco g nition of the factors introduced by surface nonideality. The overzealous ambition of chemical en g ineers to pro ve a mech a nism on the basis of an incompletely understood th eory b a s c au sed many ridicu lous claims to be made in the liter ature. Th is illustr at es the need for careful development of the theory combined with discussion of its limitat i on in t h e teaching of heterogeneous catalysisa The preceding analysis hp s been concerned almost completely with t h e thermodynamics of edsorpti on as affected by the heat of adsorption with no attention being given to the ~ oncept of entropy relationships. Be c au se gas molecules ~re free to move in three d i mensions ~nd ~d sorbed molecules a re restricted to no more than two dimensions of motion, a reduction o f e n tropy must occur on ad s orption a s pointed out by B runauer (6). Entropy c a lcul tions can g ive i nformation on whether the adsorbed molecule is free to move about t h e surf ac e or is localized wit h no free rotation. Such information is import a nt in considerations of ca tglytic kinetics, and v ~ ri ation in en tropies of adsorption can be used, at le a st parti a lly, t o expl ain changes in heats of ad sorption. The concept of entropy has only recen t l y been gi en serious consideration as bein g import a nt in suppl eme ntin g t he heat of adsorption type of R nalysis for ther n1odynami cs of surf a ce processes (5 19) Active S ite Theory Despite the limitations of the simple ad s o rption theory with its a ssumption of ideal surfaces, this concept has been extended by chemical en g ineers to include models whereby catalytic surface reactions t ake p l ace by me ans of molecules or atoms adsorbed on socalled Pctive sites" on the su.:i face. Thus, the reaction could occur between an adsorbed re acta nt molecule and a gas phase molecule or be tween two a d j ~c ently adsorbed molecu l es In presenting this simple model, au thors have reco g nized t he limit ations of the theory and have consequently ten ded to present deriv ations that elude incorrect statements which a re eventu a ll y corrected by the emp1r1ce~ nature of the results. For teac hing pu rp o s es erro rs of this type even though not important in the fin a l a nalysis, s hou l d b e a vo ided o ne example of an error ls presented i n A c l a ss ic pioneer p~ bl!?:t:o~ by Hougen e nd ~ atson (16) which w a s corrected in thei r l ~te r work ~ 0.s was the statement that the dualsite surf a ce concent r at i on cf' v ,:i cant. s t1:~ adjacent to sites containin g a dsor bed materi a l A is c c 1/2 sc cv AV f:. ~ ( 17) where S represents the num "!:> er of equidistant a ct ive sjtes ~~ c e,it. tc a :. average a ctive site and the su b scripts .\. ,~' 1 nd T_s t R n d f?r ci: :'~er~= 1 ls :i vacant sites, and tot a l sites. The error 1 n E9u:3t101: ~7 l .1 :5 ~11_ t. ie ~ !(.,. which should be unity. A simllr.r error was ,nf;JF.; :, :r ~:,=, tl1 (2]) 1 : r w~uld be more a ccurate to use CT CV in p : E C6 0f C 7 Any l.ir g ur.ient ttat tl. 1e se tw c e 1 7 ar ;_.v ~~ l ~ < 1 c.' .~ :: t'; t:l:~1lr, 11 ~:~d ~ '. :' ;1r'. extremely simple mathem a ti ca l a nal ys J.s 1 r 1 vJtl..1C.l1 a st r11,: 1t. tJcr 1 j 1 1 ,1::.L e t,., t. ,i f. total possible dual sites en &n ideal su r fRce c~P t 2J~~r:L only v~csnt s. te s (Cv) and ad sorbed sites (C.r), For r,d j E1cent s irn1la1 SJt.es t r. e dual s i te concentration must be onehalf cf the total nllITll:. er of poss it. 1: :..i, dj a:e. r i t .. sites times the fraction of tot el sltes cccupie d by .t ~ : e mate ~ c1 l ~r. "" ~ r case, the onehal1' is necessarj beca use e ac h p1:1ir of c, dJ i ., cer. .. molec1 1 l I
PAGE 22
18 CHEMICAL ENGINEERING EDUCATION September 1963 counted twice. If the adjacent sites are not occupied by similar materials, the onehalf should not be included. For this case, therefore, Total dual sites: SCT = Sites VV Sites A V Sites AA 2 SCT BCvCv SCACV SCA.CA 2 2CT CT 2CT SCT S(CV C ) 2 2 SCT A 2 '2c"T 2CT ( 18) ( 19) ( 20) The identity in Equation 20 can only occur if Equation 17 is incor rect and if CT is used in the denominator. Obviously, these two errors as discussed s re not important but, for teaching purposes, a nalysis of this situation and discussion of past errors of this type serve to stimult ate class interest and give the students more insight into the type of problems involved in trying to specify SUI;'face characteristics. Solid State Analysis and Electrochemical Potential In recent years, hope has been raised thct catalytic activity might be explained ~ya new and quantitative approach involving electronic chem ical potential and based on the principles of solidstate physics. Un fortunately, this approach has not yet produced any significant results and it is doubtful if it should have more then passing mention in an un~ dergradua te course on chemical en g ineering kinetics. In this approach, the assumption is m ade that chemisorption is ac companied by a transfer of electrons between the adsorbin g solid surface and the a dsorbent. The adsorbent acts either as a donor or acceptor depend ing upon the direction of electron tr a nsfer. Because of the high density of electrons on a metal surface, exact quantitative analysis of the charge transfer process is difficult. However, the surfaces of semiconductors have a far lower concen cration of electrons or free carriers than metals ~nd, consequently, are much more satisfactory for theoretical analyses of the chargetransfer process and the resultin g energylevel changes (4, 23) The Fermi level has been proposed as one means for explainin g cata lytic activity. At equilibrium conditions, there is a probability (desig nated as the FermiDirac function) that a given quantum state of energy will be occupied by an electron. The energy at which this probability is one half is designated as the Fermi level (20). The Fermi level of a semicon duc tor may 1 ncrease or decrease depending on the concentration of impuri ties and the temperature. Semiconductors are ch~racterized by densities of electrons (n) snd of socalled holes (p). Thus an ntype semiconductor has a conductivity due to excess electrons and is a donor while a ptype semi conductor is en acceptor. In applying the electrochemical potential theories to catalytic kinet ics, one can consider that a gas molecule can be adsorbed on a surface in different forms. For example, isopropyl alcohol might be adsorbed on chromium oxide catalyst as a donor through a hydrogen atom, as an acceptor through a hydroxyl group, or with no net charge transfer. If the most re active group were the form adsorbed with no net charge transfer, then a max imum would be expected in the reaction rate if the catalyst were changed gradually from a pto an ntype semiconductor. If the catalyst is initial ly a ptype material it might be changed to en ntype by increasing the pressure of hydrogen in the gas since the hydrogen would release electrons to the solid catalyst. In other words, there would be a shift in the Fermi level and a maximum catalyst activity would be expected at one Fermi level Figure 1 presents results on the rate of dehydrogenation of isopropyl alcohol catalyzed by chromium oxide from experiments carried out at the Institute of Physical Chemistry of Madrid (13, 14). Under normal conditions a regular decrease in rate with increase in hydrogen partial pressure would be expected as indicated by the dashed line in Figure 1. The experimental results are indicated by the solid line. One possible explanation for this apparent discrepancy could be based on the analysis presented in the pre ceding paragraph wherein there is a Fermi level shift through the level corresponding to maximum activity as the solid ca'talyst changes from a ptype tontype semiconductor.
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September 1963 CHEMICAL ENGINEERING EDUCATION Rate f o"==i> _______________ PHz ... .... Figre 1. Rate of Dehydrogenation of Isopropanol catalyzed by Chromium Oxide at Constant Total Pressure with Varying P~rtial Pressures of Hydrogen in Isoprop~nol (13). Heat and M11Ss Tr8nsfer For the design of catalytic reacto~s, basically three relationships are needed as follows: ( ,, ) ( b) (c) The design equation. The rate expression An energy balance. 19 In the simplest possible case of a plugflow reactor operated adiabatically the design can be accomplished with ( a) (b) ( C) The Design Equation: The Rate Expression: The Energy Balance: Fdx rdW r = (x,T) ( H) Fdx : (21) ( 22) ( 23) where x represents the fraction of re ac tant converted, Fis the feed rate of reactant, r is the reaction rate based on an amount of catalyst W,AH is the heat of reaction, cpis the heat capacity, and subscript i indicat es the individual components. Heat and mass transfer considerations must be taken into acc ount when the plugflow assumption is not valid. The same g ener a l a ppro ach is used for both heat and mass transfer; so this discussion will be limited to mass transfer a nd development of the appropri at e desi g n equations. The conventional approach is to correct for radial a nd lo ng itu dina l diffus1vity by using an effective diffusivity for each representin g the actu a l p a cked bed as being replaced by a fictitious homogeneous material with the ap propriate diffusivity. Radial bulk f lo w is ne g lected. With t hes e assum p tions, the following equations can be developed by materi a l balances : (a) For the case of radial diffusion only wit h constant De/u, J (uc) De JZ u (b) For the case of DL/u, J 2 (uC) DL u JZ2 ( c) For the case of J(uc) De 1 JZ u r JC j; 1 r J(uC) Jr lon g itudin al J(uC) r J Z C (uC) Jr2 diffusion J C JQ r on l y both longitudin a l diffus ion Z ( \J. C ) J2 (uC) D1 J2 Jr Jr2 u In vector notation, the precedin g equation re duces to div(uG) div ~ Grad (, uC ) r B = B JC JQ (24) wit h constant ( 25) and r adia l di1'1'u si or ( t.:.C ) r (~:) JZ2 B ( 27) where u is velocity, c is concentr Rt ion, r i s ra diu s, Z is re ccto r leng t ~ : eB is bulk catalyst density, Q is t i me, r.nd the D 's repr esent d1ffusi v 1t
PAGE 24
20 CHEMICAL ENGINEERING EDUCATION September 1963 Development of the preceding design equations ~long with the corres ponding ener gy balances are strai gh tforward and should be presented in un dergraduate courses o~ heterogeneous catalysis. However, the important engin eerin g function comes in applying these equation, end the solution of the equations can become quite involved. An example of the problems in volved in selecting boundary conditions along with the need for clear think ing is presented in the following for the case of Equation 25. For a firstorder irreversible reaction, r: kc, and steady atate, Equation 25 becomes a secondorder, linear, ordinary differential equation with a solution in the form of C {Const) e m z where mis a constant. Smith presents limits as follows to give a very simple solution {22): Boundary conditions proposed by Smith: C: C 0 at Z= O C: 0 at Z oo Solution is C Z 'IT = e where 0 I: u '2Di: 1u2 ( 28) Danckwerts and others {11, 27) have proposed much more reasonable limits based on a concentration gradient existin g at the entrance to the reactor. The resulting solution is far more complex and more realistic than that shown by E quation 28. Boundary conditions p roposed by oanckwerts and Damkohler: C: C 0 Dr, dC at Z = 0 u dZ dC 0 at Z L aZ The resulting uz C 8 2 I5r: 1"0 where a 1 = solution is ua 2(1 a')e 20 L 2 (1 a ) e 1 4knr, B u2 (LZ) ua ( ZL) Z{la 1 )e 20 L ua L 2Dr., 2 ( 1a ) e I ua L 2D L (29) A rather detailed discussion of the disadvantages of oversimplifying, as illustrated by Smith's boundary conditions, can stir up much interest among the students and will simultaneously serve to illustrate the need for the students to think on their own rather than merely blindly accept any~ thing they see in print. The fact that both Equations 28 and 29 reduce to the basic plugflow expression as app~oaches ze ro can be used to illus trate the fact that one prodf of a given theory is not alw ays sufficient to establish its validity. Computer Solutions we have now reached the point where use of computers for problem so lution should be completely acc eptable for undergraduates in Chemical En gineeri~. The kinetics course is an ideal place for including outside problems t for solution on the computer, and at least one problem of this cype should be part of the course. A simple example is given in the fol lowing of a typical problem in kinetics which can readily be solved on a small analog computer of a type similar to the Poe TR10. For the case of the following consecutive reactions carried out isothermally A k B 2 C the rate ~quations for a constantvolume reactor are dCA : klCA dQ dCB dQ (30) / ( 31)
PAGE 25
. CHEMICAL ENGIN........,,EERING EDUCATION 21 September 1963 Analytical are solutions ek1Q CAo of the first order 11near differential equations CB: klCAo (ekl9 ek2Q) k2k1 CC = CA o C A CB where c s represent concentration and centration of pure A at zero time. (32) ( 33) ( 34) subscript o represents initial conFrom Equa tions 30, 31, 34, plots of either CA, Ca. or Cc versus Q could be obtained di~ectly with a small analog computer by use of the un scaled diagram shown in Figure 2. A typical concentrationtime plot th .. t would result on the xy plotter from the computer i s also shown in Figure 2 This s imp le example can be used effectively with u ndergraduates to familiarize them with the use of the an a lo g computer. It is part icu l a rl y appropria te because the students c1:1n easily c a lculate anal yticall y from Equations 32, 33, 34 the concentr tiontjme v a lues for c iirect cornp n rlso !'l t o the computer results. Volts Figure 2. ... CA Potentiometer Analog Computer Diagram 0 IC CA I'... Integrator / "Inverter IC CB > / ........... Cc V Sunmler ... CA I 0 v olts Volts Relative Concn. 'I, 0 > ... k1 ... I ka d9 y Axie l. Ax~ !I lAxia IC For Solving A \c & :> B k,a > C and Resultant XY Plot From Computer 1. I ~8 ~il"XAxis A k.t > B ka > C C B A 9, Time
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22 CHEMICAL ENGINEERING EDUCATION September 1963 If the students ha~,e the background and facilities for solving prob lems on the di gi tal computer, programs are available for solution of L~ng muirH i ns he lwood types of rate expressions as presented in Equation 15 {12, 1 8 ). O the r programs are available for the digital computer which coul d e used fc) r problems relating to operational characteristics of ise ther m & l tubular flow reactors (2), isothermal batch chemicai reactors(17). or so lu t i on of the Brunauer, Emmett, and Teller Equation 2 (8). Con6lusion The subjects discussed in this paper represent some of the special problems in heterogeneous c a talysis that would be appropr1 te for presenta tion in an undergraduate course. Obviously, there are many standard sub jects, all of which a lso involve fundamental problems, which s hould be in cluded. Among these would be analyse~ of t he various resistances involved in the catalytic kinetic processes, experimental techniques, interpreta tion of experimental results, types of reactors including special prob lems of construction and oper a ti o n, optimization techniques, poisonin g ef fects, and many others. ) Intraparticle transport is a nother fundament~l problem which has re ceived inadequat~ at tention in many chemical engineering courses on kinet ics. Pore diff usion and catalytic effectiveness are often completely neglected even thou g h these are important factors in as much as perhaps eighty per cent of all catalytic processes. The work of Thiele (24), Wheeler (30 ), Aris (l) Weisz (28), and Hougen (15) a re signif:icant in show ing the advances being made in thi~ area, and an excellent summary of the current situation on this fundamental problem is presented by Carb erry (9). 1. 2. 3. 4. 5. 6. 7. 8~ 10. 11. 12. 13. 14. 1.5. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 21 2 8 29 ,~ ~ "; BIBLIOGRAPHY > Aris, R., Chem. ff sci., 6, 262 (1957). Bailie, R. C. an T. Fpn; "Computer Pro~ram Abstract 0410perational Characteristics of Isothermal Tublar Flow e actors," Chem. Eng. Pr,og., 56, No. 2, 92 (1960). ~udart, M., A.I .c h .E. Journal, 2, 6 2 (1959). Boudart, M., J. A .C.s., 74, 1531,3556 (1952). Boudapt, M. "The surf ace Chemistry of Metals and Semiconductors, 11 409, Edited by H. c. Getos, John Wiley and Sons, New York, 1960. Brunauer, s., "Physical Adsorption," Princeton University Press, 1943. Brunauer, s., p H. Emmett, and E. Teller, J. Am. Chem. soc., 60, 309(1938) Brunauer, Ermnett, and Teller Equation Computer Program Absracro27, to be published in Chem. Eng. Pto,. (1962). Carberry, J. J. ''Transpor henomena and Heterogeneous Catalysis," Forthcoming publication. Chow, C. H., Ind. En~. Chem., 50, 79 9 (1958). D~ckwerts, P. v., c em. Eng. Sci., 2, 1 (1953). Drickmann, w. L., 11 computer Program lbstract 033 Polynomial Equation Fitting, 11 Chem. Eng. Prog., 56, No. 8, 86 (1960). Garcia de !a Banoa, J. F and G. K. orlandini, Technical Note No. 1, AF61 (.514)1330, Institute de Quimica Fisica, C~s.I.c., Madrid, Nov. 1958 GaroiaMoliner, F., ''Trapping in Semiconductors," Personal communication, Dept. of Physics, Univ. of Ill., Urbana, Ill., Feb., 1962. Hougen, o. A, Ind~ Eng. Chem., .53, 509 (1961). Hougen, ~ O. A., and K. M. Watson,Ynd En;g. Chem., 35, 529 ( 1943). Jeng, B J., and T.T. Fun, "Computer Program Abstract 058 . Design o~ Isothermal Batch Reactors," Chem:. :wgs Ptog., 56, No. 6, 90 ( 1960). Kauf~an, n. J., and 0. w. Woo, ompu er Program Abstract 060 Solution of Simultaneous Linear Equations," Chem. Eng. Progr., 56, No. 9, 78 (1960). Scholten, J J. F., and P. zweitering, Trans. F'Ar. Soc., 53, 1363 (19.57). Shockley, w. "Electrons and Holes in semiconductors," D. van Nostr~d Co., 19.50. Smith, J.M., "Chemical Engineering Kinetics," P. 243, McGrawHill Book co., Inc. New York, 19.56. Ibid. page 36.5 stone 1 F. s., "Chemistry of the solid State," 367, Edited by w. E. G,,rner, Butterworths Scientific Publications, London (1955). Thiele, E. W., Ind. ~ng. Chem., 31, 916 ( 1939) Watson, K. M., "Chem cal Reaction Kinetics for Chemical Engineer~, Collected Papers on the Teaching of Chemical Engineering," Proc. of Oh. Eng. Div. of ASE E 2nd Ch. Eng. SUJt11ner School, page 175, publ. by A.I.Ch.E., 1940. 11 f Ch En Di wa tson, K. M ''Kinetics of catalytic Reactions, Proc. o g. v. of A.S.E.E., )rd Ch. Eng. Summer School, 1948. Wehner, J. F., and R.H. Wilhelm, Chem. E\_6 Sci., 6, ~9 (19.56). Weisz, p. B. and c. D. Prater, "Advances n catalysis, vol. VI, 1954. Weller, S., A.I.Ch.E. Journal,~ 59 (1959). Whe e ler A "Catalysis 11 Vol. II., P.H. Emmett, Edi tor, Reinhold Co., l'l ew York, 19.55.
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PLANNING EXPERIMENTS FOR ENGif.lEERING KINETIC DATA H. Mr Hulburt American Cyanam id Company Central Research Division Stamford, Connecticut Since chemical kinetics is not as yet a predictive science, one of the tasks comm~nly faced by the engineer in process development is the accumulation of rate data to be used in reactor design This paper will discuss some of the principles and techniques which can be used to establish useful design data for complex reactions of obscure mechanism which occur under illdefined conditions and yield incompletely characterized products in other words t he usual case of practical interest. It i~ a truism so obvious it is usually not taught in physical chemistry courses that one should first establish the stoichiometry of the reaction he is studying. Yet in process dev~lopment this is often difficult. Analytical methods for the mixtures being produced may be timeconsuming or unavailable and expensive to develop. ~here is a strong temptation to determine the prin cipal product and most troul;>lesome byproduct and ignore the rest. Gros s mater ial ba.la.nces can often be made on the basis of elemental analyses without de tailed lmowledge of molecular composition. Yet these incomplete a nalyses ofte~ fa il to give adequate kinetic data. \/he~ reactions are not well un d erstood, it is not unusual to discover the appearance of a new product when conditions are changed. If the design has been based on date taken in ignorance of this pro duct and operation extrapo l ated beyond the pilot plant r a n g e, serious trouble can ensue. The first step, therefore, i s a qualitative survey of the re a ction stoichiometr y over as wide a range of conditions as possib l e The objective of this survey is to establish tl'1e main .' features of the reaction mechanism For design purposes, the molecular mechanism will never need to be kno\1n in de tail, but enough of its properties must be determined to formulate a kinetic model of the reaction for the range of conditions of design interest The more nearly this model reflects the actual mechanism, the more confidently can it be applied over a wide range of conditions. Neve rt heless, at some stage, the en gineer must be sati~fied to work with the data he has, reco gniz in g that he has not established a mechanism fully. Is the principal reaction product the ultimate product of reaction; or aces it disappear in side or subsequent reactions un d er so1ne conditions? Are the byproducts formed as or from intermediates en route to the main products, or are they formed by independent reaction routes ? Do some o r all of the pro duct s reach equilibrium or steadystate concentrations which are i nsensit i ve to residence ~ime? Is there a ph a se separ a tion in the cc,urse of the reactj on? Not every autoclave that is loaded with a homogeneous solutj on e nd d el i vers 8 homogeneous product solution has had homogeneous contents throughout t he co urse of the run. A re mass or heat transfer r ates CO[l'lparable t o or slov,er th~n the chemical reaction rates? These are a ll questions that can receive qua l ita t ive ans wers by comparing the results of a few wellplgnne d runs Consider a hypothetical example in w hich ~ acid is made by catalytic con version of electamine and carbon dioxide in a fluid bed Lnde r re ~ ctton con ditions, Q acid is volatile in an atmosphere of c~rb~n 1 iiox3de but it is found that organic matter accumulates on the catalyst to e degree depending on the temperature a nd fee d ratio of electamine to co 2 .Some tlndesiral)le electa.minic acid is found both in the product vapor and in the org~njc residue on the cat alyst, which, however, is l a rgely unidentified material T h e R eid cou l d oe formed 'by carboxylation of the ami ne, b1..1t there j s no evider.ce for the ectu .8 1 mechanism. First experiments might be to v a ry tl 1 e e; o.s residence ti?TJe in ti1e con verter determining the sp a cetime yield and purity of tne elect?mlne product For experimental convenience in these survey r uns the process is not run in continuous ste ady state. Instead, elect a mine is e dded to t ~e C!=t t~ lyst to ~ predetermined loadin g at 0 temperature ~elow tl'1 ~ t '='t wl 1 3 ch ~ acid is formed The temperature is then raised a nd Q ~ c1d st:ipped ~fr jn stre Rm of CO? suf ficient to fluidize the bed. The concent r8 t j on of ~ ~cid 1n th e product stre f. ~ is followed during the strippin g process. It \.Jas "foun c l, tl1at tl1.e !)Ot 1n ds per hour of product recovere d is. directly propcrt i onP. l to ~ f 1e C C 2 flow rnte, 1 o~h:r conditions being fixed, but 1 s neB l:> ly indepe r.c1. ent of t 1 e 1; lee tn.r.iine remA .i nJ ne, on the catalyst. This was est abli s hed by a set of runs ~t two temperAtures ln which CO2 rate and initial elect~mine lo adin ~ we1e ,, ri ried 23
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'24 r :~ CHEMICAL ENGINEERING EDUCATION September 1963 At each temperature, the p artial pressure of Qa c id in the offgas was n~arl y c onsta nt throughout each run. However, with high initial loading of ele ami_ n .:3 ., t i i s partial pressure was less than with low initial lo ading. With oth b~ c ondi t ions comparable, the partial pressure of Qacid was higher at high er temperature. Because of initial transients during the stripping runs as the temperature was being raise d the earliest steady data could be obtained only after 20% to 40 % of the Qacid had been stripped off. These results give a strong presumption that Q ac id is being formed in vaporsolid equilibrium wit h the organic matter on the catalyst, since as much conversion was obtaine d in a run .a s in runs with twice the resi dence time. However, runs which differ in init ia l loading gave somewhat diffe r ent apparent vapor pressures Hence, we must conclude that there is add itional complexity in the mechanism. An addi tional complication in a fluid bed is the possibility of poor contacting of vapor reactant with catalyst when lar ge "bubbles" can fom. Since this by .. pass ing inc re ases with gas velocity, a lower yield at high gas velocity wou ld be expected from this c ause a l one if it were operative, e ven though the den se phase reaches eq uilibrium. Four more runs under strictly continuous steady oper a t ion should settle the quali ta ti ve nature of the mecha ni sm. In these, temper ature and feed com poai t i on are fixed but feed rate and bed height are varie d as follows: Run V ( cu. ft./hr ) H (ft.) (hr.) l Q L p L/Q, 2 2Q L AL/2Q, 3 2Q 2L AL/Q 4 4Q 2L AL/ 2Q Conversion of electamine following lo gic ensues: to Q acid product is the measured response. The Sequence Compar~ A l Runs 1 and 2 B 1. Runs 1 and 2 B 2. Runs 1 and 3 If Same conversion Dif f e rent con version 'Same conversion Diffe rent conversion Then Equilibrium is attained Either kinetic con trol or bypassin g { No bypassing Bypassing occurs From this logic, Run 4 appears superfluous. However, addin~ 1 t gives a 2 x 2 factorial exper i ment in bedheight and residence time. H L 2L A L/2 Q 2 4 A L/ Q 1 3 In this balanced design the following effects are measurable. Kinetics: K Y1 Y2 + Y3 Y4 Bed Height: H Y4 Y2 + Y3 Y1 Bypassing: B: Y1 Y2 Y3 +Y4 The bypassing effect appears as an inte~action between the two main effects and the techniques of statistical analysis can be used to get the mo st out of the data. At the cost of an extra run, considerable additional confidence can be obtained in the conclusions. In this example, we see the diagnostic value or a few wellchosen l'UilS. Even more insight can be gained by abandoning the pilot reactor and studying ::, he r eac tion in an altogether different configuration. Electamine and carbon di oxide might be loaded into a pressure cell adapted to an ultraviolet spec tr ometer. The product Qacid vapor as well as the byproduct, electaminic aoid ; '1 :.:! ~ e _f_ollow e d rea~ily ~ y its UY ap~0!6Pt..l ~n. In thi.J, Sl!lall 'QaJIJ;h r~ ~Q ~9 r, .,
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September 1963 CHEMICAL ENGINEERING EDUCATION 25 the approach to steady vapor pressure of Qacid could be followed convenientiy at times close to the onset of reaction and at a series of temperatures By interrupting the run at a given time a nd analyzing the catalyst and its 1 organ ic conte nts, it is possible to a ssociate the composition of organic solids with the pro g ress of the main reaction. In this way, the puzzl ing dependence of r a te on feed composition can be resolved. In the case on which this hypothetical example is based it was found that a nonvolatile di meric product of electamine forms rapidly on the catalyst with evolution of CO2. In a second step, dimer reacts with co 2 and forms a solid, npreQ. 11 which rapidly develops a steady vapor pressure of Q acld. At the same time dimer reacts slowly with co 2 to form the byproduct electaminic acid Thus in the initial runs in which the lo ading of electamine was low, little dimer was formed and the conversion to Qacid proceeded rapid ly, bein g governed : by the rate bf evaporation of preQ. In the runs in which the loading of electamine was high dimer formed rapidly and the conversion of dimer to Q aci d was slower, being governed by the rate of conversion of dimer to preQ. Thus a kinetic model might be written: E l=~ ==~> Dimer + CO2 Dimer+ CO2 > PreQ Dimer+ CO2 > Electaminic acid PreQ 4 > Q acid vapor ( 1) ( 2) ( 3) ( 4) Reaction 1 1s supposed reversible but not instantaneous. Rea ction 2 is irreversible and comparable in speed to reaction 1. Reaction 3 is irrever sible and slow. Reaction 4 is reversible and very rapid Thus far, although rates have been measured, no use has been made of their quantitative magnitudes. The arguments have depended upon relative values, the shapes of timeconcentration curves and similar qualitative data. AS a result, however, a reaction model has been reached in terms of whic h r~te data can be quantitatively analyzed. In many cas~s, such a model is previously known or 1s sufficiently prob able that one can proceed to the quantitative phase with few preliminaries. In such a case, however, the experimental design should permit testin g the fit of the data to the model as well as evaluation of the rate constants and acti vation energies. When a model has been proposed as the basis for further kinetic study, the methods of statistical experimental design can greatly reduce the number of runs required to determine the rate constants and reaction orders. These methods are now quite readily available in the literature 3 and it is not pro posed to discuss them in de tail here. They must be used with insight, however, and are no substitute for thought. Some examples will illustrate the power and some of the precautions necessary in this approach. Srini Vasan and the writer4 studied the kinetics of the watergas shift reaction over a commercial iron oxide catalyst. The literature and previous experience suggested a kinetic model, due to Temkin: 7 (5) Although the reaction is extent of backreaction. reversible, conditions were chosen to minimize the Ta~ing logarithms, log R : log A + a log Pco + b log PH20 RT b log % 2 ( 6) This is a linear form in 1/T and the log pi. Standard methods for the de alp and analysis of experiments permit the determination of best values for the parameters E/R, log A, a, and bas well as a test of the goodnessoffit of Equation (6) if fairly general conditions on the errors of measurement are satisfied. At no increased labor, the model could be generalized to include all of the possible component s with arbitrary exponents: a b c d R = kpco PH 2 o PH 2 Pco 2
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26 CHEMICAL ENGI~ERIWG EDUCATION September 1963 The geometrioal interpretation ot the experimental design is quite help~ ful. We may think or Ras a functi~n of 1/T and the log Pi which can be plotted i:1n dimensional Euclidean space with the independent variables as coordinate axes. We seek a mathematiQal represe ntation of the hypersurface log R(l/T, log Pi> If ~quation (6) is such a representation log R will be a plane paJ'allel to the a xis of log Pc o 2 Furthermore, its i~tercepts on the on the log PH 2 o and log PH 2 axi s will be equal but opposite in sign. If Equa tion (7) holds, the log R surface will still be planar, but there will be no !, priori constraints on its orientation. The experimental design problem is now to test whether log R is indeed a plane, ana, if so, to find its equation. With fiv~ factors, a 5plane in sixdimensional space is determined by five points. By determining log Rat additional points, the deviation from plan arity can be tested. In the example cited, sixteen points were determined and it was decisively shown that Equation (6) could not represent the d~ta within the reproducibility of experiment~. If the log R surf ace is not planar, what shape i s it? Standa rd methods now exist to determine the best quadratic approximation to log R It is tempting to use this approximation, since the experiments already done to test the planar hypothesis are the core of the design which determines the quad~atic approximation. Before succumbing to temptation, however, one !hou+d considerh1sobjectives carefully. Equation ( 6) or (7) has a theoret~c~l in terpretation in that a molecular mechanism can be formul a ted which lea d s to .!.t.. Of course, the possibility of de;riv~ng a kinetic equation from a mech13.nt9:n does not necessarily make it valid. However, the genera l quadratic for:1 ~s not derivable from any mechanism and hence cannot possibly be v a l id exce~t ~'! an empirica1 interpolation formula. If the design studies which are co:. t:::1~ plat$d will never go outside the range of the data taken, t he n an inter~cl~ tion formula may be a sufficient representation of the kinetics. Howeve~, ~: extrapolation is neces sary, much greater confidence can be had in kinetic ::.: els based on the best mechanism which can be proposed. A second consideration is often important, however. 5 co mplex mechani s::s involve many parameters. The general Langmuir mechanism for the catalyt~~ irreversible reaction A+ B Products has the form R kpi p~ ( 8) Each of the parameters k, KA, and KB is exponentially temperature dependent: There are thus nine parameters to determine ly in magnitude, but theory will not predict out to be negligible. ( 9) U sually they will differ widein advance which on~s may tu rn It is a fact of the imperfect world that the more parameters that must be determined the more difficult it usua lly is to estimate them. When the models are li~ear in the parameters, experimental designs ~ay be found which will, in theory, allow good e stimates to be obtained. The upper limit on precision is determined largely by the magnitude of the experimental error, the number of runs to be made and the expe rime nta l range of the variables. In practice however the operable region may be su ch that balanced designs are made impossible by interdepen dence of the supposedly independent variables That is the process may not remain operable unless a change in one variable is oomp~nsated for in part by a change in another In this way correlations tend to creep in to reduce the pre cision of the estimates a s the number of parameters and variables increase. The only partial counter mea sures are (a) the difficult course of attempting to reduce the magnitude of the experimental errors or (b) an increase in the range of the variables. Chemi cal processes, however always have .finite restrictions on the' operable range of the des ign ,,. variabl~s. The workable temperature range is finite; permissable feed composi tions may be lim1 ted by phase ch ane;e s or explosion limits
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September 1963 CHEMICAL ENGINEEHIHG EDUCATION 27 When ~odels are non linear in the parameters, the same difficulties exist except that correlations between estimated p a rameters are more apt to exist. F~rst the opti mum t heore tical designs in an unhampered experimental region whi~h would maximize tne precision of the estimates are usually ex tremely diffi c ult to find mathematically and secondly, the nature of the function itself may make a high dependence of the estimates unavoidable. He?ce in any actual_case there will be a maximum number of kinetic parameters which can be determined with precision from experimental data. The more care ful and p~ecise the da ta, the l arger this number becomes, but it rarely exceeds fi ve or six. One must therefore, scr\1tinize complex theoretical mechanisms to diseover which of the many paramete rs are likely to be buried in the exper imental error. The quadratic empirical surface ca.n be of great help in this process. 1,6 Thus! if Equation ( 8) is rewritten and expanded into the form of Equation ( 7), it will be disco vere d th at KA and KB occur only in quadratic terms involving 1/T and the log PA and l og pB, respe ctive ly. Hence, if the emp ~ 1:r1c '. a1. quadratic surface lacks terms in 1/T x lo g pA and log Pr, x lo g p, one can be confid ent that PA can be omitted from the denominator ot Equatio~ (8) without worseni ng the fit. In fact, KA could not be determined with precision from the da ta that fixed the quadratic surface. In this way, complex mechanisms can be rationally simplified without oversteppin g the limits of reli abi lity of the da t a One may find of course, a new choice of coordinates. in the form that the data can be more simply represented by Th u s Equa ti on (8) is more natural ly represented (1/R)l/n 1 ( a/n ~l k p PBb/n 1 /' kl a/n b/n1 PA PB ( 1 0 ) This suggests 1/pA and 1 /pB as better var iab les than l og pA a n d log pB for testin g this mechanism~ In this space, the original experimental points which are wel~sBaced in lo g Pi may be poorly placed to give the best determination of the Rl;n surface. New data may be required to determine the parameters of Equation (10) with precision. The availability of highspeed computing capacity modifies some o f these possibilities. It may no lon ge r be necessary to linearize the rate expres sion for computational reasons Techniques for nonlinear estimation2 permit working directly with the proposed model. Howeve r, the number of parameters which can pe determined simultaneously is limit ed as before and most theoret ical mechanisms must be simplified to make nonlinear estimation feasib le. In some cases the ultimate design problem may be ab le to accommodate an integral kinetic form rather than a differential one. If, for instance; it is clear that a batch reactor will be used with no internal concentration gradients, only the total volume or residence time will be required for the design basis. Rather than design a differential reactor or differentiate in tegral data, one may then propose a kinetic model in which time is an exp l icit factor. Our previous remarks about the maximum complexity of a useful model are eapeoially pertinent. These points are illustrated in the case of a study undertaken recently in connection with the purification steps of a commercial process. A minor impurity is removed by precipitation with aqueous annnonia. Com plication arises out of the base catalyzed hydrolysis of the principal product, which represents a loss. The kinetic study was designed to locate the conditions under which yi~ld at required purity could be maximized. However, since in design it might become necessary to modify some of the conditions, a kinetic model valid over a range of conditions was required. The change in concentra tion of the 1mpur1 ty i s small and small hydrolysis of the principal product 1s expected, even though a large single stage integral converter is ultimat e1y envisioned. As is often the case, some older data were available for wbich the ex perimental precision was only moderate. New data in both small and large reactors were obtained to test the assumed independence of yields on reactor size. In all~ four sets of data were available: l. New small reactor data 2. Old small reactor data 3. New large reactor data 4. Old large reactor data
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28 CHEMICAL DGIBEERIBG EDUCATION Septembe~ 1963 F o ur independent variables had been studied: 1. Temperature (T) 2. Weight per cent product initially in the impure mixture (P) 3. Weight per cent ammonia initially (N) 4. Reaction time(&) The response in each run was the precipitate produced, measured as y, per cent of the initial product P. Since it was desired to make use of the unplanned older data, an ortho gonal factorial design or central composite second order design was not pos sible. As a preliminary survey, a full quadratic model in four variables was fitted by least squares to four groups of data: a. Sets 1 and 2 b. Sets 1, 2 and 3 c. Sets 1, 3 and 4 d. Sets 1, 2, 3, and 4 An additional block variable was added for data in sets 3 arid 4 to test the ~ffect of reactor size. This effect proved to be nonsignificant and compar ison of residual mean squares of each group of da~a showed no significant difference between gr oups. Hence all of the data was used in the final analysis. A full quadratic mode l in four variables has fifteen coefficients 1 four of which only serve to locate the origin with respect to which linear terms vanish By rotating axes about this origin, the six crossterms can be elim inated1 leavingonl3the four squared terms and the constant. This reduction to canonical form is done by proper choice of four new independent linear combinations of the independent variables. The results of this analysis are: .. y .65362 + .49854 zf .09111 z~ .0267l ~ z~ .00083 z~ (11) where z1 z2 Z) 0 .95990 .08732 .26183 T' T' T' .27634 pt+ .02143 NI+ .04214 QI + .37390 p, + .92333 N' .0 0 672 QI + 88518 P' 38 2 96 N' + .03520 ~' z4 = .04916 + .01703 P' .0189 2 N' 998 47 Qt ( 1 2 ) ( 13) ( 14 ) ( 1 5 ) Here the primes refer to scaled v a lues of the independent variables. For example, Tl = (T( 0 c.) 141.85)/27 ( 16) From Equation ( 11) we see th a t z 1 is by fa r the most i mp ortant term. By Equation (1 2 ) zi is nearly independent of N a nd Q a n d measures T an d P most strongly. Reaction time, 9, is a lmost identical with z4. Hence we feel justified in treating its small effect independently of the other var iables. since many of the twentyone coefficients are sm a 11 a nd probably nonsignificant, one suspects that there are many a ltern e tives to t h e quR d ratic form which fit the data as well. Therefore, a reasonable mechanism was postulated a s a g uide to a simpler kinetic expression. suppose the hydrolysis to be catalyzed by hydroxyl ion. Then one might have __ ... ,, POH __ _,. NH4+ + OHP ..f0H NH3 + H20 d['"POHJ dQ ( 17) ( 18) {19)
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September 1963 CHEMICAL EBGIBEERlBG EDUCATION 29 fferlJ = K2 LffH.17 = K2 No ffiH:7/_'FoHJ ,. whence = 1 d9 P 0 + f.NHO ~H::_7 fJoHJ. "PJ : Po "POH'J y : ffOHJ / P 0 dfPOH_7 : aO l( f.crri.:J /No) ( Y P o/lio) DHJ/No + (y Po/lio) (1y) ( 20) ( 21) ( 22) ( 23) since y remains initially much less than unity throughout all the tests, and 1/2 1/2 : K2 /No l.Jhich is also sme 11, Equation ( 23) can be simplifie d to k1K~o(ly po/No) Po y ( 24) ( 2.5) ( 26) 1!hen y is very small., the first term dominates a nd the in:ttial conversion should be siven approximately by 1/2 y: (k, K2N 0 /P 0 ) Q (27) This suggests a slightly eeneralized empirical kinetic expression log ( y C) : log A p lo g P + n log N + o( log ( 28) T A preliminary estimate of the coefficients with their confidence limits ga ve the data in Case I of Table I. This confirms our earlier conclusion th~t Q c6uld be trE a ted independently a s shown in Equatio n ( 2 7). The data we re the1 refitted fixing c( = p = n = 0 .5, the i r theor e t i cE1 l v a lues in 3 quation ( 2. 7 ) This gave C aseII in T ab le I. Table I Case I 95% c'onf. Coefficient Limits ln A + 16.64 t 4.15 t1 + 8257. 87 t 1889.65 p o.562 t 0.344 n + 0.261 t o.567 0( + 0.978 .+ 0 .279 Case II Coefficient + 16.16 + 7648.17 ( 0 .50) {+0.5 0) (+0.50) 9.57& Conf. 1 i mi ts + ~ 116 1 92 2.5 t t. 0 + 0 +0 There is no significant difference in the fit between E quation 28 with four constants (Case I) and Equation 11 with fifteen. Note that while n and pare not significantly different from their theoretical values, o( is significantly l a rger than 0.,5. Nevertheless, the fit forced with a(: o .5 (Case II) is not tremendously worse.
PAGE 34
30 CHEMICAL ENGINEERING EDUCATION September 1963 Examination or the res i duals shows that the fit is poorest at low No. In fact, some hydrolysis occurs even in the absence of added ammonia. The hypothetical mechanism do e s not a llow for th i s. The empirical models based on it force the fit by averaging up the reaction order with respect to am monia. A more realistic model might arise by a dding a term to Equation ( 26) which is proportional to 1y, This, when integrated, leade r. to ( 29) where ci, 02 and c3 might each have exponential temperature dependence. The difficulty of fitting Equation (29) is much increased by its nonlinear form. For small y, Equation (29) reduces to Equation (28). Hence it will be essen tial to use the nonlinear form if any improvement is to be expected. In this example, an empirical quadrat i c form has ag a in given insi g ht in to what mech a nistic terms should be retained in formulating a kinetic model with fewer co n stants to determine. It should be clear that there a re a large number of kinetic models that will repres~nt a given set of d a ta. Unless these data are of very high precis i on, the fact that the eng i neer has found one such set lends very little support to the corresponding mechanistic in terpretation. However, qualitative features can be discerned and more sensi tive experimem are suggested by the analysis which may test the mechanistic assumptions in a less equivocal way. Neverthele ss, the mechanistically in spired empirical kinetic form will usually be simpler and reliable over a wider range of variables than a pure linear or quadratic form in the ori g inal experiment~l variables. Thus the requirements of engineering data for design purposes can be met without sacrificing the best theoretical knowledge avail able ; REFERENCES l. Box, G. E. P. and P. V. Youle, Biometrics, 11, 287323 (1955). 2. Box, o. E. P. and G. A Coutie, 1, 100107 (1956). Proc. Inst. Elec. Eng., 103B, I Suppl. 3. Davies, o. L., Ed., ''Design anc;l Analysis of Industrial Experiments," Oliv er and Boyd, Lond on (1954), 4. Hulburt, H. M. (1961). and Srini Vasan, : C. D., A.I.Ch.E. Journal, 7, 143147 .,. 5. Laidler, K.J., "Chemical Kinetics," McGrawHill, N.Y. (1950). 6. Pinchbeck, P. H., Chem Eng. Soi., f!, 105 ( 1957) 7. Temkin, M. I. and Kulkova, N. V., Zhur. Fiz. Kh"=m., 23, 695713 (1949)
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