01-EyeOnEducation

Chemical Process Control Education and PracticeBy B. Wayne Bequette and Babatunde A. Ogunnaike

Chemical process control textbooks and courses differsignificantly from their electrical or mechanical-ori-ented brethren. It is our experience that colleagues in

electrical engineering (EE) and mechanical engineering (ME)assume that we teach the same theory in our courses andmerely have different application examples. The primarygoals of this article are to i) emphasize the distinctly chal-lenging characteristics of chemical processes, ii) present atypical process control curriculum, and iii) discuss howchemical process control courses can be revised to bettermeet the needs of a typical B.S.-level chemical engineer.

In addition to a review of material covered in a standardprocess control course, we discuss innovative approachesin process control education, including the use of case stud-ies, distributed control systems in laboratories, identifica-tion and control simulation packages, and studio-basedapproaches combining lecture, simulation, and experi-ments in the same room. We also provide perspectives onneeded developments in process control education.

Chemical Engineering CurriculaChemical engineering curricula across the United States arerelatively uniform for several reasons: departmental historyand culture, Accreditation Board of Engineering and Tech-nology (ABET) accreditation requirements, and the successof alumni in industry. Standard courses include material andenergy balances, thermodynamics, equilibrium stage sepa-rations, transport phenomena, chemical reaction engineer-ing, process dynamics and control, process design, andchemical engineering laboratory. Virtually every topic cov-ered in these chemical engineering curricula assumessteady-state process operation—the sole exception is theprocess dynamics and control course, which must thereforeaccept the burden of introducing all topics associated withthe dynamic behavior of process systems.

Characteristics of Chemical ProcessesA major difference between process control and device con-trol is the replicability of control system designs. For exam-ple, a disk drive manufacturer can perform a singleadvanced control system design for this device and use thatcontroller in thousands of units. Each chemical process con-trol system design project, on the other hand, tends to beunique. A styrene polymerization reactor in one manufac-turing plant may have feedstocks, flow patterns, and prod-uct specifications that differ significantly from a similar

reactor in another facility. Process operations managementphilosophy, plant control hardware and software, processengineering structure, sensor selection and maintenance,and the analytical laboratory vary substantially from plantto plant. These issues lead to virtually an entirely new con-trol system development for each process, a factor that sig-nificantly influences process control practice and hence,indirectly, process control education.

The common characteristics that make chemical pro-cesses so challenging to control are noted in papers pre-sented at most control research conferences and incountless research proposals. It is worth reviewing theseproblems here to understand if we are getting the majorpoints across to our undergraduates. Chemical processesare usually high order, nonlinear, with multiple inputs andoutputs; they have time delays, input constraints, and a lim-ited number of measured states. The desired properties of aproduct stream are often not directly measured, so inferen-tial control is important. Economic objectives are domi-nated by steady-state considerations. Large-scale processesare often energy integrated, causing a high degree of interac-tion between inputs and outputs of different process units.Specialty chemicals and pharmaceuticals are often pro-duced in batches, frequently with a single vessel servingmore than one function (heater, reactor, and separator, forexample). The same temperature controller may be re-quired to provide cooling under some conditions and heat-ing under others. Often robustness, rather than nominalperformance for any particular operating condition, be-comes the prime consideration.

The proportional-integral-derivative (PID) controller isdominant in the chemical process industry and will remainso for many reasons. One is that lower-level loops, such asflow control, are adequately controlled by PID action. Also,no explicit process model is required for tuning the two orthree controller parameters; many commercial PID control-lers have autotuning algorithms. Cascade control is preva-lent, since most higher level loops cascade a setpoint to aflow control loop. Feedforward and ratio control are used inwell-studied unit operations. Distributed control systems(DCSs) are the norm, although the hardware/communica-tion structure is significantly different from the systems ofthe late 1970s and 1980s. Most loops are sampled at a highfrequency relative to the process dynamics, so continuouscontrol system design procedures can easily be used.

10 IEEE Control Systems Magazine April 2001

EYE EDUCATIONon

0272-1708/01/$10.00©2001IEEE

Bequette ([email protected]) is with The Howard P. Isermann Department of Chemical Engineering, Rensselaer Polytechnic Institute, Troy,NY 12180-3590, U.S.A. Ogunnaike is with DuPont Central Research and Development, Wilmington, DE 19880-0101, U.S.A.

Authorized licensed use limited to: University of Michigan Library. Downloaded on May 19,2010 at 20:32:15 UTC from IEEE Xplore. Restrictions apply.

Cascade control is worthy of further dis-cussion, since the approach does not appearto be well known in other disciplines. An ex-ample of a double cascade control strategyto regulate temperature of a chemical reac-tor is shown in Fig. 1; the correspondingblock diagram is shown in Fig. 2. The domi-nate time constant for the flow control loopis a few seconds, the jacket temperature loopis a few minutes, while the reactor tempera-ture may be several minutes to several hours(particularly for a polymerization reactor).Notice that each control loop rejects differ-ent disturbances. The flow control loop re-jects coolant header pressure disturbancesand compensates for valve nonlinearities.The jacket temperature control loop rejectscoolant header temperature disturbances.The reactor temperature controller rejectsreactor feed, temperature, and concentra-tion disturbances and compensates forchanges in the rate of heat transfer due tofouling, etc. This approach has many of thebenefits of a state feedback strategy used in other disci-plines, without sensitivity to model uncertainty.

The most commonly used advanced control scheme ismodel predictive control (MPC). The basic idea behind MPCis illustrated for a single-input, single-output process in Fig.3. Here, an open-loop optimal control problem is solved attime step k. The least-squares objective function to be mini-mized is based on the residuals between the model predic-tions and the desired setpoint profile over a horizon of Ptime steps. The decision variables are the next M controlmoves; note that the control moves can be constrained.Only the first control move is actually implemented and thenext process measurement is obtained. The model is up-

dated and a new constrained optimization problem issolved at time step k+1. Multivariable systems with con-straints and time delays are handled naturally by MPC. MPChas been particularly successful in the petroleum refiningindustry where large-scale, interacting, constrained sys-tems are the norm. Time constants and sample times arelarge, so computation time to solve large-scale constrainedsystems is not an issue. When linear models and quadraticobjective functions are used, the optimization problem re-sults in a quadratic program (QP); there are a number of ro-bust QP codes available. For a tutorial overview of MPC, seeRawlings [1]. Again, we should stress that it is common thatflow rates are the manipulated inputs used by MPC strate-

April 2001 IEEE Control Systems Magazine 11

ReactorTemperature

Controller

TCITjspReactor Feed

CoolantReturn

JacketTemperature

Controller

TC2Fsp

FC

FlowController

F

Coolant Make-Up

Pump Reactor Product

JacketRecirculation

v

Figure 1. Double cascade control strategy to regulate temperature in a jacketed,stirred tank reactor.

TspTC1 TC2 FC FP JP RP

FlowDisturbance

CoolantTemperatureDisturbance

ReactorDisturbance

Tjsp Fsp F Tj

Measured Flow

Measured Jacket Temperature

Measured Reactor Temperature

Flow Process RecirculatingJacket Fluid

Process

ReactorProcess

+ + +− − −

+ + +

Figure 2. Block diagram for double cascade control strategy.


gies. The manipulated flow rates are set points to flow con-trollers, which remain PID.

Current Status of the Chemical ProcessDynamics and Control CourseMost chemical engineering departments in the UnitedStates offer a single course in dynamics and control, which

is most often taught during the first semester of the senioryear, although an increasing number of schools are teachingthis course during the junior year [2].

TopicsTable 1 summarizes topics covered in a typical dynamicsand control course. Contrast these with topics taught in EE

and ME systems and control courses [3], shownin Table 2. It is particularly striking thatstate-variable techniques, signal-flow graphs,and Nichols charts are rarely studied in chemi-cal engineering (ChE) courses yet widely stud-ied in EE and ME.

A few critical topics distinguish ChE from EEand ME systems and control courses and books.Dynamic models are usually not encountered inother chemical engineering courses; thus, inprocess control courses, significant time isspent on the development and analysis of dy-namic chemical process models. Almost allchemical process models based on fundamen-tal material and energy balances are nonlinear.Even simple mixing problems are bilinear, sincea manipulated input (flow rate) often multiplesa state (concentration or temperature). Morecomplex chemical reaction models include theArrhenius rate expression, where reaction rateis an exponential function of the reactor tem-perature (students learn in reaction engineer-ing courses that this can result in multiplesteady-state behavior). It is therefore importantfor chemical engineers to learn linearization be-fore they begin to analyze dynamic behavior.Contrast this with the numerous inherently lin-ear circuit and mechanical systems; EE and MEstudents can begin to learn linear dynamic be-havior before worrying about understandinglinearization.

State feedback techniques are not com-monly applied to chemical processes since fewstates are measured. Much of the focus is onclassical feedback using continuous PID con-trol. There has been a trend to incorporate moremodel-based techniques, primarily internalmodel control (IMC) and MPC.

A number of process dynamics and controltextbooks are currently available (Table 3). Itshould be noted, however, that most of the ba-sic topics covered do not differ substantiallyfrom Coughanowr and Koppel [4], the firstwidely used textbook. Stephanopoulos [5] wasthe first to present a detailed treatment of digi-tal control and to discuss plantwide control.


CurrentStep

Setpoint

y

Actual Outputs (Past)

PPredictionHorizon

Past ControlMoves

u

Max

Min

MControl Horizon

Past Future

Model Prediction

CurrentStep

Setpoint

y

Actual Outputs (Past)

P

PredictionHorizon

Past ControlMoves

u

Max

Min

M

Control Horizon

Model Predictionfrom K

New Model Prediction

tK

tK+1

Figure 3. Model predictive control schematic. Top—optimization at time stepk, Bottom—optimization at time step k+1.


Seborg et al. [6] provide the first treatment of dynamic ma-trix control and model algorithmic control, two model pre-dictive control algorithms. Luyben [7] has a strong focus onmodeling and simulation and realistic physical examples.Marlin [8] does a good job of considering the integration ofprocess design and control; it also has a MATLAB workbookavailable to instructors. The text by Ogunnaike and Ray [9]is almost encyclopedic in its coverage of process controltechniques. Luyben and Luyben [10] include a strong sec-tion on plantwide control. A forthcoming book by Bequette[11] focuses on model-based control and containsMATLAB-based modules that treat specific unit operationcontrol problems in depth. In contrast to these textbooks,which generally analyze and synthesize controllers in theLaplace or frequency domains, the text by Svrcek et al. [12]takes a time domain approach. Case study “workshops,” us-ing chemical process simulation software, are used to rein-force basic concepts.

Deshpande [13] has suggested that more attentionshould be paid to statistical process/quality control (SPC/SQC). He presents a course that adds several topics to thestandard course; it is not clear what topics should be omit-ted to fit this expanded version into the same number ofcourse hours.

SimulationMost control courses make use of a simulation packagesuch as MATLAB. Bequette [14] presents a two-course se-quence in dynamics and control that makes use of theMATLAB simulation environment for homework assign-ments and special projects. Rensselaer has since moved toa single, four-credit course covering dynamics and control.Bequette et al. [15] provide details of a case study projectin multivariable control. Students, working in teams, selecta unit operation (from a list of five) to study for the lastthird of the semester. The project begins with a literaturesearch, followed by process identification (the “process”is a SIMULINK masked block diagram) and single- input,single-output (SISO) control loop design. The groups thenstudy multiple SISO loops and decoupling, write a final re-port, and give an oral presentation. Doyle et al. [16], [17]present an integrated MATLAB-based set of modules withseveral low-order linear systems and higher- order pro-cesses such as furnaces and biochemical reactors.MATLAB-based modules focusing on process dynamicsstudies are presented in Bequette [18].

Cooper [19], [20] at the University of Connecticut has de-veloped a PC-based package called Control Station that sim-ulates the dynamic behavior of several common chemicalprocesses. Realistic problems such as noisy measurements,unmeasured and measured disturbances, and manipulatedvariable saturation are included. The package can also beused to develop models from experimental data.


Table 1. Common Chemical Process Dynamics andControl Course Topics [2].

Topic Lecture time, %

Process Dynamics and Modeling 28.1

Feedback Control and Tuning 22.1

Stability and Frequency Response Analysis 14.3

Computer Simulation 8.9

Advanced Control Techniques 8.4

Control System Hardware 7.7

Computer Control Systems 4.8

Other 5.7

Table 2. Top 8 Topics Covered in EE and MEUndergraduate Control Courses [3].

Topic, % of Departments EE ME

Nyquist stability criteria 94 80

Root-locus techniques 94 95

Routh-Hurwitz stability test 93 89

Bode plots 92 93

State-variable techniques 87 65

Signal flow graphs 84 45

Sensitivity analysis 66 56

Nichols charts 47 36

Table 3. Process ControlTextbooks.

Bequette [11]

Coughanowr [38]

Coughanowr and Koppel [4]

Erickson and Hedrick [39]

Luyben [7]

Luyben and Luyben [10]

Marlin [8]

Ogunnaike and Ray [9]

Riggs [40]

Seborg, Edgar, and Mellichamp [6]

Smith and Corripio [41]

Stephanopoulos [5]

Svrcek, Mahoney, and Young [12]


Laboratory ExperimentsSome departments have control laboratories that are asso-ciated with the control course, whereas others have con-trol experiments that are part of the unit operationslaboratory course, usually taken in the senior year. The ma-jority of control laboratories use PC-based data acquisitionand control software where a single PC is interfaced to asingle experiment. Braatz and Johnson [21] at the Univer-sity of Illinois use the Hewlett-Packard Visual EngineeringEnvironment to provide a graphical operator interface forbench-scale experiments. Holt and Pick [22] at the Univer-sity of Washington present a two-tank experiment con-trolled by a MacIntosh and Workbench software. Theirphilosophy is to use the same experimental apparatusthroughout the quarter; students conduct initial experi-ments on sensor calibration, design single-loop controllers,and finish the quarter with multiple single-loop controllerdesign and implementation.

There are some departments, however, where industrialDCS-based systems are used. Rivera et al. [23] at ArizonaState University use a Honeywell TDC 3000-based system tocontrol 11 of 12 experiments in a senior unit operations labo-ratory. Most of the experiments are bench scale. Skliar et al.[24] at the University of Utah use an Opto22 DCS to monitorand control seven laboratory experiments; eight more areplanned. Pintar et al. [25] at Michigan Tech have developed aTDC 3000-based system to control a 30-gal jacketed polymer-ization batch reactor (producing polydimethylsiloxane) anda 30-ft-high distillation column, neither of which is commonlyavailable in academic settings. Students receive extensivesafety training for this laboratory.

Experimental equipment can be expensive and not costeffective if only operated a few days each year. Henry [26]has developed a Web-based virtual laboratory where stu-dents from across the world can perform remote dynamicsand control studies on experiments at the University of Ten-

nessee-Chattanooga (http://chem.engr.utc.edu/Webres/Stations/controlslab.html).

Industrial Views on Undergraduate EducationSeveral papers authored by industrial practitioners makesuggestions on how undergraduate education can bechanged to meet the needs of practicing engineers. Downsand Doss [27] feel that the control educational paradigm hasbeen to i) start with a purely mathematical description (ab-straction), ii) develop, analyze, and evaluate theoretical de-scriptions, and iii) apply the theory to specific abstractions(e.g., “For this transfer function design a controller...”). Theycontrast this with the case study paradigm used in the medi-cal, legal, and business professions where instructors: i)present a single illustrative case, ii) abstract lessons fromthe specific to the general, and iii) iterate i) and ii) such thatthere is a gradual buildup of an overall abstract knowledgebase supported by hundreds of case studies.

Ramaker et al. [28] feel that control students should betaught using concepts that fit with the rest of the chemicalengineering education. Since the rest of the curriculum em-phasizes time domain ideas such as flow rates, residencetimes, and rate constants, frequency domain conceptsshould not be a primary focus. Contrast this with electricalengineering where many concepts are taught in the fre-quency domain.

In fact, there is as yet no true consensus perspective fromindustry. The strictly theoretical approach is clearly inade-quate because it fails to confront students with enough ofthe real-life issues routinely encountered in practice. By thesame token, the strict case-study approach is inadequate:given that there is a limitless number of actual chemical pro-cesses, no single case study can provide all the requisite in-gredients for teaching the concepts necessary to solveproblems other than those within the scope explicitly cov-ered by such an isolated case study. A balanced approach inwhich the basic principles are taught first and then illus-trated with practical case studies is probably more produc-tive in the long run.

Goals for Undergraduate Process Dynamics andControl EducationNotice that there is not enough overlap between the per-ceived challenges in the control of chemical processes andthe actual topics typically covered in an undergraduatecourse.

Since there is a limited amount of time to cover the manyimportant concepts in dynamics and control, it is impera-tive that many of these concepts be introduced in othercourses. Laplace transforms have been taught in the differ-ential equations courses for many years; a primary problemis that students often do not appreciate the connection be-tween the nth-order linear differential equations and physi-cal reality. Too much time is often spent reviewing matrix


Table 4. Suggested Topics for a Course in 2000 [30].

Dynamic simulation 2 weeks

Response characteristics 1 week

Development of discrete-time models 1 week

Analysis of discrete-time models 2 weeks

Conventional and predictive control structures 2 weeks

Optimization methods for controller design 2 weeks

Tuning of controllers/robustness 1 week

Feedforward, adaptive, multivariable 2 weeks

Digital hardware/implementation 1 week

Expert systems 1 week


algebra concepts in the dynamics and control course. Again,the linear algebra course tends to be too abstract, with littlemotivation for how eigenvalues/eigenvectors can be used tounderstand engineering problems. Dynamic models shouldbe introduced in the introductory material and energy bal-ances course; Felder and Rousseau [29], for example, in-clude a chapter on dynamic models in their populartextbook.

There is certainly a strong argument for consideringprocess systems engineering throughout the curriculum.Every chemical engineering course should have some de-sign/operation/control components; all courses shouldstill have important fundamental science in their content,but these must be accompanied by application examplesthat will motivate the students to learn the fundamentalsand applications.

A Look Back at a Look ForwardAt the dawn of a new millennium, it is appropriate to reviewideas presented a decade ago by Edgar [30], who suggestedcurricula for a course on dynamics and control in 2000; hissuggested topics are presented in Ta-ble 4. One pie-in-the-sky concept thathas not come to pass is the commonuse of nonlinear programming tech-niques; some courses do cover MPC,but usually focus on the unconstrainedform, which has an analytical solutionfor linear process models. Also, therecontinues to be a focus on continu-ous-time rather than discrete-time de-sign and analysis. An important topicnotably missing from the 2000 courseis statistical process/quality control.

A Desired Course in ChemicalProcess Dynamics and ControlDuring the past decade, there has beena major impetus in engineering educa-tion to change from a teacher-centeredlecture environment to a student-cen-tered learning environment. This hasgenerally required instructors to re-move some course content, sacrificingbreadth for depth. Since students tendto take home only a few major con-cepts from a course, we feel it is moreimportant for them to learn criticalanalysis skills rather than to solve asmattering of problems in a large num-ber of areas. A particular type of stu-dent-based learning is the studioapproach. In studio teaching, the in-

structor provides motivating mini-lectures and poses prob-lems to be discussed and solved in class. The instructorserves as the “guide on the side” rather than the “sage on thestage.” Some perceived problems with this approach, whencomputer-based tools are used for problem solving, is thatstudents are often learning how to use software rather thanhow to formulate and solve engineering problems.

The particular view at Rensselaer is to combine the mostpositive attributes of lectures, simulation-based laborato-ries, and experimental laboratories into a single course [31].Simulation-based assignments have become more commonand are used to illustrate problems that cannot be easilystudied using classical pen-and-paper analytical solutions.Although simulation-based assignments provide much in-sight into practical control system issues, nothing can takethe place of hands-on experiments. To this end, we have de-veloped a control studio that combines lectures, simula-tions, and experiments in a single classroom. We haveconstructed a classroom facility that seats 40 students andincludes 20 computer-based simulation and controlworkstations. The students face the front of the studio dur-


1. Control Valve Fresh Water2. Control Valve Salt Water3. Heater for Fresh Water4. Differential Pressure to Infer Tank Level5. Temperature Probe in CSTR

6. Inline Conductivity Probe7. Conductivity Measurement Display8. Temperature Measurement Display9. Salt Water Tank

10. Manual Valves for Trimming P∆

3

1 10

10

2

9

4

5

6

7

8

10

Figure 4. The Rensselaer prototype chemical process control experiment. Freshfeedwater, regulated with a control valve, flows into the vessel containing an electricheater (upper left). Concentrated salt water, which is regulated with a control valve, thenmixes with the heated feedwater in a small mixing tank that contains a temperatureprobe. The effluent from this tank discharges through a conductivity sensor into the sink.


ing lecture and discussion periods and swivel in their chairsto perform simulations and conduct experiments on thecountertops behind them. During the problem-solving peri-ods, the instructor and teaching assistant move around theroom answering questions and generating discussion. Mostof the problems have been solved in two-person groups;however, the space could handle a group size of three. Al-though it is conceivable that 20 copies of a single experi-ment could be used so that all groups are working on thesame problem, this is not economically attractive. It is moreattractive to have roughly five copies of an experiment;groups working with an experiment during one period maybe doing simulations or a detailed design project during thenext period.

A prototype chemical process control experiment,shown in Fig. 4, mimics the behavior of a typical chemicalprocess. Fresh feedwater, regulated with a control valve,flows into a vessel containing an electric heater. A concen-trated salt solution from a reservoir then mixes with theheated feedwater in a mixing tank that contains a tempera-ture probe. The outlet from the tank discharges through aconductivity sensor into a sink. The objective of the experi-ment is to regulate three measured process variables(level, temperature, and conductivity) at desired setpointvalues by manipulating three input variables (freshwaterflow rate, concentrated salt solution flow rate, and heaterpower) via feedback control. The experimental apparatusis benchtop scale (with a “footprint” of roughly 3 ft2), sothat it can be used in the studio classroom. The experimentwas designed to have time constants that are roughly 20-30s; the time scale is slow enough for students to observe thephysical changes, yet fast enough for a number of experi-ments to be conducted during an interactive session. Na-tional Instruments hardware and software (LabVIEW) is

used for data acquisition and control. The control inter-face shown in Fig. 5 is intuitive, with a simple process andinstrumentation diagram that closely matches the experi-mental apparatus.

We currently use two-hour sessions, twice a week, for thestudio; a third session is used for recitation and providestime for students to “catch up” on assignments. Shorter pe-riods would not allow enough time to set up and perform ex-periments, whereas significantly longer periods would bedraining for students and instructors alike.

Graduate EducationThe focus of this article has been on undergraduate educa-tion. Since most chemical engineering departments have asingle faculty member with expertise in systems and con-trol, rarely is more than one graduate-level process controlcourse taught on a frequent basis. The available selectionof graduate-level process control textbooks is limited [32]-[34]. Graduate students conducting research in processcontrol generally take several systems and control theorycourses in electrical engineering departments. Special top-ics in chemical process control are normally covered incourse notes and instructor handouts. MPC is probably themost covered special topics process control course; severalMPC textbooks/monographs are currently in preparation.Nonlinear control is probably the next most widely taughtspecial topics course. A monograph on nonlinear processcontrol has been published [35] but does not appear to bewidely used in these courses. As plantwide control begins toreceive more attention, the monograph by Luyben et al. [36]will probably be the text of choice.

It is widely recognized that graduate students need morepractical control experience, so there is a move to developexperiments for graduate control courses. An example froma graduate-level multidisciplinary control laboratory at theUniversity of Delaware is presented by Gatzke et al. [37].

SummaryThe primary purpose of this article is to provide a sum-mary of chemical process control education and practicefor our colleagues in other engineering disciplines. Wehave presented a typical process control curriculum, out-lined some of the distinctly challenging characteristics ofchemical processes, and discussed recent and ongoing de-velopments in process control education. We considercontrol education to be an area where “continuous im-provement” is important and look forward to discussionsbased on this article and education sessions at future con-trol conferences.

AcknowledgmentSupport from a Curriculum Development grant from Procter& Gamble is gratefully acknowledged.


Figure 5. LabView interface for the Rensselaer prototypeprocess control experiment. Each control loop can be placed ineither manual or automatic (PID) mode.


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[29] R.M. Felder and R.W. Rousseau, Elementary Principles of Chemical Pro-cesses, 3rd ed. New York: Wiley, 2000.

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[32] W.H. Ray, Advanced Process Control. McGraw Hill, 1981. Reprinted byButterworths, 1991.

[33] W.F. Ramirez, Process Control and Identification. New York: Academic,1994.

[34] S. Skogestad and I. Postlewaite, Multivariable Feedback Control: Analysisand Design. New York: Wiley, 1996.

[35] M.A. Henson and D.E. Seborg, Nonlinear Process Control. Upper SaddleRiver, NJ: Prentice Hall, 1997.

[36] W.L. Luyben, B.D. Tyreus, and M.L. Luyben, Plantwide Process Control.New York: McGraw Hill, 1999.

[37] E.P. Gatzke, R. Vadigepalli, E.S. Meadows, and F.J. Doyle, III, “Experienceswith an experimental project in a graduate control course,” Chem. Eng. Educ.,vol. 33, no. 4, pp. 270-27, 1999.

[38] D.R. Coughanowr, Process Systems Analysis and Control, 2nd ed. NewYork: McGraw-Hill, 1991.

[39] K.T. Erickson and J.L Hedrick, Plantwide Process Control. New York: Wiley,1999.

[40] J.B. Riggs, Chemical Process Control. Lubbock, TX: Ferret, 1999.

[41] C.A. Smith and A.B. Corripio, Principles and Practice of Automatic ProcessControl, 2nd ed. New York: Wiley, 1997.

B. Wayne Bequette is a Professor of chemical engineeringat Rensselaer Polytechnic Institute. His teaching and re-search interests are in the area of process systems and con-trol engineering. Applications of interest includebiomedical systems, pharmaceuticals, chromatography,and complex chemical processes. He is an Associate Editorof Automatica and the General Chair for the 2003 AmericanControl Conference (Denver). He is the author of ProcessDynamics: Modeling, Analysis and Simulation (New York:Prentice Hall, 1998).

Babatunde A. Ogunnaike received a B.S. in chemical engi-neering, an M.S. in statistics, and a Ph.D. in chemical engi-neering. He is currently a Research Fellow in the AdvancedControl and Modeling group, DuPont Central Research andDevelopment. He is also an Adjunct Professor in the Chemi-cal Engineering Department, University of Delaware. He is aco-author (with W. Harmon Ray) of Process Dynamics,Modeling and Control (Oxford, 1994), and he serves as an As-sociate Editor of Industrial and Engineering Chemistry Re-search. His research interests include identification andcontrol of nonlinear systems, modeling and control of poly-mer reactors and distillation columns, applied statistics,and biosystems analysis and control.



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