EYE on EDUCATION
Chemical Process Control Education and Practice
By B. Wayne Bequette and Babatunde A. Ogunnaike
C
hemical process control textbooks and courses differ
significantly from their electrical or mechanical-oriented 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 and
merely have different application examples. The primary
goals of this article are to i) emphasize the distinctly challenging characteristics of chemical processes, ii) present a
typical process control curriculum, and iii) discuss how
chemical process control courses can be revised to better
meet the needs of a typical B.S.-level chemical engineer.
In addition to a review of material covered in a standard
process control course, we discuss innovative approaches
in process control education, including the use of case studies, distributed control systems in laboratories, identification and control simulation packages, and studio-based
approaches combining lecture, simulation, and experiments in the same room. We also provide perspectives on
needed developments in process control education.
Chemical Engineering Curricula
Chemical engineering curricula across the United States are
relatively uniform for several reasons: departmental history
and culture, Accreditation Board of Engineering and Technology (ABET) accreditation requirements, and the success
of alumni in industry. Standard courses include material and
energy balances, thermodynamics, equilibrium stage separations, transport phenomena, chemical reaction engineering, process dynamics and control, process design, and
chemical engineering laboratory. Virtually every topic covered in these chemical engineering curricula assumes
steady-state process operation—the sole exception is the
process dynamics and control course, which must therefore
accept the burden of introducing all topics associated with
the dynamic behavior of process systems.
Characteristics of Chemical Processes
A major difference between process control and device control is the replicability of control system designs. For example, a disk drive manufacturer can perform a single
advanced control system design for this device and use that
controller in thousands of units. Each chemical process control system design project, on the other hand, tends to be
unique. A styrene polymerization reactor in one manufacturing plant may have feedstocks, flow patterns, and product specifications that differ significantly from a similar
reactor in another facility. Process operations management
philosophy, plant control hardware and software, process
engineering structure, sensor selection and maintenance,
and the analytical laboratory vary substantially from plant
to plant. These issues lead to virtually an entirely new control system development for each process, a factor that significantly influences process control practice and hence,
indirectly, process control education.
The common characteristics that make chemical processes so challenging to control are noted in papers presented at most control research conferences and in
countless research proposals. It is worth reviewing these
problems here to understand if we are getting the major
points across to our undergraduates. Chemical processes
are usually high order, nonlinear, with multiple inputs and
outputs; they have time delays, input constraints, and a limited number of measured states. The desired properties of a
product stream are often not directly measured, so inferential control is important. Economic objectives are dominated by steady-state considerations. Large-scale processes
are often energy integrated, causing a high degree of interaction between inputs and outputs of different process units.
Specialty chemicals and pharmaceuticals are often produced in batches, frequently with a single vessel serving
more than one function (heater, reactor, and separator, for
example). The same temperature controller may be required to provide cooling under some conditions and heating under others. Often robustness, rather than nominal
performance for any particular operating condition, becomes the prime consideration.
The proportional-integral-derivative (PID) controller is
dominant in the chemical process industry and will remain
so for many reasons. One is that lower-level loops, such as
flow control, are adequately controlled by PID action. Also,
no explicit process model is required for tuning the two or
three controller parameters; many commercial PID controllers have autotuning algorithms. Cascade control is prevalent, since most higher level loops cascade a setpoint to a
flow control loop. Feedforward and ratio control are used in
well-studied unit operations. Distributed control systems
(DCSs) are the norm, although the hardware/communication structure is significantly different from the systems of
the late 1970s and 1980s. Most loops are sampled at a high
frequency relative to the process dynamics, so continuous
control system design procedures can easily be used.
Bequette (bequette@rpi.edu) 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.
10
0272-1708/01/$10.00©2001IEEE
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Cascade control is worthy of further disReactor
cussion, since the approach does not appear
Temperature
to be well known in other disciplines. An exController
ample of a double cascade control strategy
Tjsp
TCI
to regulate temperature of a chemical reacReactor Feed
tor is shown in Fig. 1; the corresponding
Jacket
block diagram is shown in Fig. 2. The domiTemperature
Controller
nate time constant for the flow control loop
Coolant
is a few seconds, the jacket temperature loop
TC2
Return
is a few minutes, while the reactor temperaFsp
ture may be several minutes to several hours
(particularly for a polymerization reactor).
Jacket
Recirculation
Notice that each control loop rejects differFlow
Reactor
Product
Controller
Pump
ent disturbances. The flow control loop reFC
v
jects coolant header pressure disturbances
F
and compensates for valve nonlinearities.
The jacket temperature control loop rejects
coolant header temperature disturbances.
The reactor temperature controller rejects
Coolant Make-Up
reactor feed, temperature, and concentration disturbances and compensates for Figure 1. Double cascade control strategy to regulate temperature in a jacketed,
changes in the rate of heat transfer due to stirred tank reactor.
fouling, etc. This approach has many of the
benefits of a state feedback strategy used in other disci- dated and a new constrained optimization problem is
solved at time step k+1. Multivariable systems with conplines, without sensitivity to model uncertainty.
The most commonly used advanced control scheme is straints and time delays are handled naturally by MPC. MPC
model predictive control (MPC). The basic idea behind MPC has been particularly successful in the petroleum refining
is illustrated for a single-input, single-output process in Fig. industry where large-scale, interacting, constrained sys3. Here, an open-loop optimal control problem is solved at tems are the norm. Time constants and sample times are
time step k. The least-squares objective function to be mini- large, so computation time to solve large-scale constrained
mized is based on the residuals between the model predic- systems is not an issue. When linear models and quadratic
tions and the desired setpoint profile over a horizon of P objective functions are used, the optimization problem retime steps. The decision variables are the next M control sults in a quadratic program (QP); there are a number of romoves; note that the control moves can be constrained. bust QP codes available. For a tutorial overview of MPC, see
Only the first control move is actually implemented and the Rawlings [1]. Again, we should stress that it is common that
next process measurement is obtained. The model is up- flow rates are the manipulated inputs used by MPC strateCoolant
Temperature
Disturbance
Flow
Disturbance
Tjsp
Tj
F
Fsp
+
Tsp
TC1
+
−
TC2
+
−
FC
+
−
Reactor
Disturbance
+
+
FP
JP
RP
Flow Process
Recirculating
Jacket Fluid
Process
Reactor
Process
Measured Flow
Measured Jacket Temperature
Measured Reactor Temperature
Figure 2. Block diagram for double cascade control strategy.
April 2001
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11
gies. The manipulated flow rates are set points to flow controllers, which remain PID.
Current Status of the Chemical Process
Dynamics and Control Course
is most often taught during the first semester of the senior
year, although an increasing number of schools are teaching
this course during the junior year [2].
Topics
Most chemical engineering departments in the United
States offer a single course in dynamics and control, which
Table 1 summarizes topics covered in a typical dynamics
and control course. Contrast these with topics taught in EE
and ME systems and control courses [3], shown
in Table 2. It is particularly striking that
state-variable techniques, signal-flow graphs,
Past
Future
and Nichols charts are rarely studied in chemiSetpoint
cal engineering (ChE) courses yet widely studied in EE and ME.
A few critical topics distinguish ChE from EE
y
and ME systems and control courses and books.
Model Prediction
Dynamic models are usually not encountered in
Actual Outputs (Past)
other chemical engineering courses; thus, in
process control courses, significant time is
spent on the development and analysis of dyP
tK
Prediction
namic chemical process models. Almost all
Current
Horizon
chemical process models based on fundamenStep
tal material and energy balances are nonlinear.
Max
Even simple mixing problems are bilinear, since
a manipulated input (flow rate) often multiples
u
a state (concentration or temperature). More
Min
complex chemical reaction models include the
Arrhenius rate expression, where reaction rate
M
Past Control
is an exponential function of the reactor temMoves
Control Horizon
perature (students learn in reaction engineering courses that this can result in multiple
steady-state behavior). It is therefore important
Setpoint
for chemical engineers to learn linearization before they begin to analyze dynamic behavior.
Model Prediction
Contrast this with the numerous inherently linfrom K
ear circuit and mechanical systems; EE and ME
y
New Model Prediction
students can begin to learn linear dynamic behavior before worrying about understanding
Actual Outputs (Past)
linearization.
State feedback techniques are not comP
monly applied to chemical processes since few
tK+1
Prediction
states are measured. Much of the focus is on
Horizon
Current
classical feedback using continuous PID conStep
trol. There has been a trend to incorporate more
Max
model-based techniques, primarily internal
model control (IMC) and MPC.
u
A number of process dynamics and control
Min
textbooks are currently available (Table 3). It
should be noted, however, that most of the baM
Past Control
sic topics covered do not differ substantially
Moves
Control Horizon
from Coughanowr and Koppel [4], the first
widely used textbook. Stephanopoulos [5] was
the first to present a detailed treatment of digiFigure 3. Model predictive control schematic. Top—optimization at time step
tal control and to discuss plantwide control.
k, Bottom—optimization at time step k+1.
12
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Seborg et al. [6] provide the first treatment of dynamic matrix control and model algorithmic control, two model predictive control algorithms. Luyben [7] has a strong focus on
modeling and simulation and realistic physical examples.
Marlin [8] does a good job of considering the integration of
process design and control; it also has a MATLAB workbook
available to instructors. The text by Ogunnaike and Ray [9]
is almost encyclopedic in its coverage of process control
techniques. Luyben and Luyben [10] include a strong section on plantwide control. A forthcoming book by Bequette
[11] focuses on model-based control and contains
MATLAB-based modules that treat specific unit operation
control problems in depth. In contrast to these textbooks,
which generally analyze and synthesize controllers in the
Laplace or frequency domains, the text by Svrcek et al. [12]
takes a time domain approach. Case study “workshops,” using chemical process simulation software, are used to reinforce basic concepts.
Deshpande [13] has suggested that more attention
should be paid to statistical process/quality control (SPC/
SQC). He presents a course that adds several topics to the
standard course; it is not clear what topics should be omitted to fit this expanded version into the same number of
course hours.
Table 1. Common Chemical Process Dynamics and
Control 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 ME
Undergraduate Control Courses [3].
EE
ME
Nyquist stability criteria
94
80
Root-locus techniques
94
95
Routh-Hurwitz stability test
93
89
Simulation
Bode plots
92
93
Most control courses make use of a simulation package
such as MATLAB. Bequette [14] presents a two-course sequence in dynamics and control that makes use of the
MATLAB simulation environment for homework assignments and special projects. Rensselaer has since moved to
a single, four-credit course covering dynamics and control.
Bequette et al. [15] provide details of a case study project
in multivariable control. Students, working in teams, select
a unit operation (from a list of five) to study for the last
third of the semester. The project begins with a literature
search, followed by process identification (the “process”
is a SIMULINK masked block diagram) and single- input,
single-output (SISO) control loop design. The groups then
study multiple SISO loops and decoupling, write a final report, and give an oral presentation. Doyle et al. [16], [17]
present an integrated MATLAB-based set of modules with
several low-order linear systems and higher- order processes such as furnaces and biochemical reactors.
MATLAB-based modules focusing on process dynamics
studies are presented in Bequette [18].
Cooper [19], [20] at the University of Connecticut has developed a PC-based package called Control Station that simulates the dynamic behavior of several common chemical
processes. Realistic problems such as noisy measurements,
unmeasured and measured disturbances, and manipulated
variable saturation are included. The package can also be
used to develop models from experimental data.
State-variable techniques
87
65
Signal flow graphs
84
45
Sensitivity analysis
66
56
Nichols charts
47
36
April 2001
Topic, % of Departments
Table 3. Process Control
Textbooks.
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]
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Laboratory Experiments
Some departments have control laboratories that are associated with the control course, whereas others have control experiments that are part of the unit operations
laboratory course, usually taken in the senior year. The majority of control laboratories use PC-based data acquisition
and control software where a single PC is interfaced to a
single experiment. Braatz and Johnson [21] at the University of Illinois use the Hewlett-Packard Visual Engineering
Environment to provide a graphical operator interface for
bench-scale experiments. Holt and Pick [22] at the University of Washington present a two-tank experiment controlled by a MacIntosh and Workbench software. Their
philosophy is to use the same experimental apparatus
throughout the quarter; students conduct initial experiments on sensor calibration, design single-loop controllers,
and finish the quarter with multiple single-loop controller
design and implementation.
There are some departments, however, where industrial
DCS-based systems are used. Rivera et al. [23] at Arizona
State University use a Honeywell TDC 3000-based system to
control 11 of 12 experiments in a senior unit operations laboratory. Most of the experiments are bench scale. Skliar et al.
[24] at the University of Utah use an Opto22 DCS to monitor
and control seven laboratory experiments; eight more are
planned. Pintar et al. [25] at Michigan Tech have developed a
TDC 3000-based system to control a 30-gal jacketed polymerization batch reactor (producing polydimethylsiloxane) and
a 30-ft-high distillation column, neither of which is commonly
available in academic settings. Students receive extensive
safety training for this laboratory.
Experimental equipment can be expensive and not cost
effective if only operated a few days each year. Henry [26]
has developed a Web-based virtual laboratory where students from across the world can perform remote dynamics
and control studies on experiments at the University of Ten-
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
14
nessee-Chattanooga (http://chem.engr.utc.edu/Webres/
Stations/controlslab.html).
Industrial Views on Undergraduate Education
Several papers authored by industrial practitioners make
suggestions on how undergraduate education can be
changed to meet the needs of practicing engineers. Downs
and Doss [27] feel that the control educational paradigm has
been to i) start with a purely mathematical description (abstraction), ii) develop, analyze, and evaluate theoretical descriptions, and iii) apply the theory to specific abstractions
(e.g., “For this transfer function design a controller...”). They
contrast this with the case study paradigm used in the medical, legal, and business professions where instructors: i)
present a single illustrative case, ii) abstract lessons from
the specific to the general, and iii) iterate i) and ii) such that
there is a gradual buildup of an overall abstract knowledge
base supported by hundreds of case studies.
Ramaker et al. [28] feel that control students should be
taught using concepts that fit with the rest of the chemical
engineering education. Since the rest of the curriculum emphasizes time domain ideas such as flow rates, residence
times, and rate constants, frequency domain concepts
should not be a primary focus. Contrast this with electrical
engineering where many concepts are taught in the frequency domain.
In fact, there is as yet no true consensus perspective from
industry. The strictly theoretical approach is clearly inadequate because it fails to confront students with enough of
the real-life issues routinely encountered in practice. By the
same token, the strict case-study approach is inadequate:
given that there is a limitless number of actual chemical processes, no single case study can provide all the requisite ingredients for teaching the concepts necessary to solve
problems other than those within the scope explicitly covered by such an isolated case study. A balanced approach in
which the basic principles are taught first and then illustrated with practical case studies is probably more productive in the long run.
Goals for Undergraduate Process Dynamics and
Control Education
Notice that there is not enough overlap between the perceived challenges in the control of chemical processes and
the actual topics typically covered in an undergraduate
course.
Since there is a limited amount of time to cover the many
important concepts in dynamics and control, it is imperative that many of these concepts be introduced in other
courses. Laplace transforms have been taught in the differential equations courses for many years; a primary problem
is that students often do not appreciate the connection between the nth-order linear differential equations and physical reality. Too much time is often spent reviewing matrix
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algebra concepts in the dynamics and control course. Again,
the linear algebra course tends to be too abstract, with little
motivation for how eigenvalues/eigenvectors can be used to
understand engineering problems. Dynamic models should
be introduced in the introductory material and energy balances course; Felder and Rousseau [29], for example, include a chapter on dynamic models in their popular
textbook.
There is certainly a strong argument for considering
process systems engineering throughout the curriculum.
Every chemical engineering course should have some design/operation/control components; all courses should
still have important fundamental science in their content,
but these must be accompanied by application examples
that will motivate the students to learn the fundamentals
and applications.
A Look Back at a Look Forward
At the dawn of a new millennium, it is appropriate to review
ideas presented a decade ago by Edgar [30], who suggested
curricula for a course on dynamics and control in 2000; his
suggested topics are presented in Table 4. One pie-in-the-sky concept that
has not come to pass is the common
use of nonlinear programming techniques; some courses do cover MPC,
3
but usually focus on the unconstrained
form, which has an analytical solution
for linear process models. Also, there
continues to be a focus on continuous-time rather than discrete-time design and analysis. An important topic
10
notably missing from the 2000 course
is statistical process/quality control.
A Desired Course in Chemical
Process Dynamics and Control
During the past decade, there has been
a major impetus in engineering education to change from a teacher-centered
lecture environment to a student-centered learning environment. This has
generally required instructors to remove some course content, sacrificing
breadth for depth. Since students tend
to take home only a few major concepts from a course, we feel it is more
important for them to learn critical
analysis skills rather than to solve a
smattering of problems in a large number of areas. A particular type of student-based learning is the studio
approach. In studio teaching, the in-
April 2001
structor provides motivating mini-lectures and poses problems to be discussed and solved in class. The instructor
serves as the “guide on the side” rather than the “sage on the
stage.” Some perceived problems with this approach, when
computer-based tools are used for problem solving, is that
students are often learning how to use software rather than
how to formulate and solve engineering problems.
The particular view at Rensselaer is to combine the most
positive attributes of lectures, simulation-based laboratories, and experimental laboratories into a single course [31].
Simulation-based assignments have become more common
and are used to illustrate problems that cannot be easily
studied using classical pen-and-paper analytical solutions.
Although simulation-based assignments provide much insight into practical control system issues, nothing can take
the place of hands-on experiments. To this end, we have developed a control studio that combines lectures, simulations, and experiments in a single classroom. We have
constructed a classroom facility that seats 40 students and
includes 20 computer-based simulation and control
workstations. The students face the front of the studio dur-
10
1
9
2
5
4
8
7
10
6
1. Control Valve Fresh Water
2. Control Valve Salt Water
3. Heater for Fresh Water
4. Differential Pressure to Infer Tank Level
5. Temperature Probe in CSTR
6. Inline Conductivity Probe
7. Conductivity Measurement Display
8. Temperature Measurement Display
9. Salt Water Tank
10. Manual Valves for Trimming ∆P
Figure 4. The Rensselaer prototype chemical process control experiment. Fresh
feedwater, regulated with a control valve, flows into the vessel containing an electric
heater (upper left). Concentrated salt water, which is regulated with a control valve, then
mixes with the heated feedwater in a small mixing tank that contains a temperature
probe. The effluent from this tank discharges through a conductivity sensor into the sink.
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15
used for data acquisition and control. The control interface shown in Fig. 5 is intuitive, with a simple process and
instrumentation diagram that closely matches the experimental apparatus.
We currently use two-hour sessions, twice a week, for the
studio; a third session is used for recitation and provides
time for students to “catch up” on assignments. Shorter periods would not allow enough time to set up and perform experiments, whereas significantly longer periods would be
draining for students and instructors alike.
Graduate Education
Figure 5. LabView interface for the Rensselaer prototype
process control experiment. Each control loop can be placed in
either manual or automatic (PID) mode.
ing lecture and discussion periods and swivel in their chairs
to perform simulations and conduct experiments on the
countertops behind them. During the problem-solving periods, the instructor and teaching assistant move around the
room answering questions and generating discussion. Most
of the problems have been solved in two-person groups;
however, the space could handle a group size of three. Although it is conceivable that 20 copies of a single experiment could be used so that all groups are working on the
same problem, this is not economically attractive. It is more
attractive to have roughly five copies of an experiment;
groups working with an experiment during one period may
be doing simulations or a detailed design project during the
next period.
A prototype chemical process control experiment,
shown in Fig. 4, mimics the behavior of a typical chemical
process. Fresh feedwater, regulated with a control valve,
flows into a vessel containing an electric heater. A concentrated salt solution from a reservoir then mixes with the
heated feedwater in a mixing tank that contains a temperature probe. The outlet from the tank discharges through a
conductivity sensor into a sink. The objective of the experiment is to regulate three measured process variables
(level, temperature, and conductivity) at desired setpoint
values by manipulating three input variables (freshwater
flow rate, concentrated salt solution flow rate, and heater
power) via feedback control. The experimental apparatus
is benchtop scale (with a “footprint” of roughly 3 ft2), so
that it can be used in the studio classroom. The experiment
was designed to have time constants that are roughly 20-30
s; the time scale is slow enough for students to observe the
physical changes, yet fast enough for a number of experiments to be conducted during an interactive session. National Instruments hardware and software (LabVIEW) is
16
The focus of this article has been on undergraduate education. Since most chemical engineering departments have a
single faculty member with expertise in systems and control, rarely is more than one graduate-level process control
course taught on a frequent basis. The available selection
of graduate-level process control textbooks is limited [32][34]. Graduate students conducting research in process
control generally take several systems and control theory
courses in electrical engineering departments. Special topics in chemical process control are normally covered in
course notes and instructor handouts. MPC is probably the
most covered special topics process control course; several
MPC textbooks/monographs are currently in preparation.
Nonlinear control is probably the next most widely taught
special topics course. A monograph on nonlinear process
control has been published [35] but does not appear to be
widely used in these courses. As plantwide control begins to
receive more attention, the monograph by Luyben et al. [36]
will probably be the text of choice.
It is widely recognized that graduate students need more
practical control experience, so there is a move to develop
experiments for graduate control courses. An example from
a graduate-level multidisciplinary control laboratory at the
University of Delaware is presented by Gatzke et al. [37].
Summary
The primary purpose of this article is to provide a summary of chemical process control education and practice
for our colleagues in other engineering disciplines. We
have presented a typical process control curriculum, outlined some of the distinctly challenging characteristics of
chemical processes, and discussed recent and ongoing developments in process control education. We consider
control education to be an area where “continuous improvement” is important and look forward to discussions
based on this article and education sessions at future control conferences.
Acknowledgment
Support from a Curriculum Development grant from Procter
& Gamble is gratefully acknowledged.
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B. Wayne Bequette is a Professor of chemical engineering
at Rensselaer Polytechnic Institute. His teaching and research interests are in the area of process systems and control engineering. Applications of interest include
biomedical systems, pharmaceuticals, chromatography,
and complex chemical processes. He is an Associate Editor
of Automatica and the General Chair for the 2003 American
Control Conference (Denver). He is the author of Process
Dynamics: Modeling, Analysis and Simulation (New York:
Prentice Hall, 1998).
Babatunde A. Ogunnaike received a B.S. in chemical engineering, an M.S. in statistics, and a Ph.D. in chemical engineering. He is currently a Research Fellow in the Advanced
Control and Modeling group, DuPont Central Research and
Development. He is also an Adjunct Professor in the Chemical Engineering Department, University of Delaware. He is a
co-author (with W. Harmon Ray) of Process Dynamics,
Modeling and Control (Oxford, 1994), and he serves as an Associate Editor of Industrial and Engineering Chemistry Research. His research interests include identification and
control of nonlinear systems, modeling and control of polymer reactors and distillation columns, applied statistics,
and biosystems analysis and control.
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