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Selected Lab Publications with Abstracts
Click on a title to
view the abstract
"Intuitive Robust Stability Metric for PID Control of Self Regulating Processes"
isa08.pdf
Jeffrey Arbogast, Brett Beaugregard and D. J. Cooper, ISA Transactions,
47, 420 (2008)
"Improve Control of Liquid Level Loops"
cep08.pdf
Robert Rice and D. J. Cooper, Chemical Engineering Progress,
104, 54 (2008)
"Graphical Technique for Modeling Integrating (Non-Self Regulating) Processes without Steady-State Process Data"
cec07.pdf
Jeffrey Arbogast, Robert Rice and D. J. Cooper, Chemical Engineering
Communications,
194, 1566 (2007)
"Extension of IMC Tuning Correlations for Non-Self Regulating (Integrating) Processes"
isat07.pdf
Jeffrey Arbogast and D. J. Cooper, ISA Transactions,
46, 303 (2007)
"Opening the Black Box: Demystifying Performance Assessment Techniques"
isa05.pdf
Rachelle Jyringi (Howard), Robert Rice and D. J. Cooper, Proc. ISA Expo 2005,
459, TP05ISA164 (2005)
"Tutorial: Cascade vs. Feed Forward for Improved Disturbance Rejection"
isa04-1.pdf
D. J. Cooper, Robert Rice and Jeffrey Arbogast, Proc. ISA Expo 2004,
454, TP04ISA055 (2004)
"A Rule Based Design Methodology for the Control of Non Self-Regulating
Processes"
isa04-2.pdf
Robert Rice and D. J. Cooper, Proc. ISA Expo 2004,
454, TP04ISA076 (2004)
"Tuning Guidelines for a Dynamic Matrix Controller for Integrating
(Non-Self Regulating) Processes"
iecr03.pdf
Danielle Dougherty and D. J. Cooper, Industrial and Engineering Chemistry Research,
42, 1739 (2003)
"Building Multivariable Process Control Intuition Using Control Station"
cee03.pdf
D. J. Cooper, Danielle Dougherty and Robert Rice, Chemical Engineering Education,
37, 100 (2003)
"A Practical Multiple Model Adaptive Strategy for Multivariable Model
Predictive Control"
cep03b.pdf
Danielle Dougherty and D. J. Cooper, Control Engineering Practice,
11, 649 (2003)
"A Practical Multiple Model Adaptive Strategy for Single-Loop MPC"
cep03a.pdf
Danielle Dougherty and D. J. Cooper, Control Engineering Practice,
11, 141 (2003)
"Design and Tuning of PID Controllers for Integrating
(Non-Self Regulating) Processes"
isa02.pdf
Robert Rice and D. J. Cooper, Proc. ISA 2002 Annual Meeting,
424, P057, Chicago, IL (2002)
"A Training Simulator for Computer-Aided Process Control Education"
cee00.pdf
D. J. Cooper and Danielle Dougherty, Chemical Engineering Education,
34, 252 (2000)
"Enhancing Process Control Education with
the Control Station Training Simulator"
caee99.pdf
D. J. Cooper and Danielle Dougherty, Computer Applications in Engineering Education,
7, 203 (1999)
"A Tuning Strategy for Unconstrained
Multivariable Model Predictive Controllers"
iecr98.pdf
Rahul Shridhar and D. J. Cooper, Industrial and Engineering Chemistry Research,
37, 4003 (1998)
"A Novel Tuning Strategy for Multivariable
Model Predictive Control"
isa98.pdf
Rahul Shridhar and D. J. Cooper, ISA Transactions,
36, 273 (1998)
"A Tuning Strategy for Unconstrained SISO
Model Predictive Control"
iecr97.pdf
Rahul Shridhar and D. J. Cooper, Industrial and Engineering Chemistry Research,
36, 729 (1997)
"Automated Rule-Based Model Parameter
Estimation and Controller Design"
isa97.pdf
Carlos Velazquez-Figueroa and D. J. Cooper, Proc. ISA Tech97 Annual Conf., ISA
Publications (1997)
"Pattern-Based Closed-Loop Quality Control
of the Injection Molding Process"
pes97.pdf
Suzanne L. B. Woll and D. J. Cooper, Polymer Engineering and Science,
37,801 (1997)
"A Dynamic Injection Molding Process Model
for Simulating Mold Cavity Pressure Patterns"
Suzanne L. B. Woll and D. J. Cooper, Polymer Plastics Technology and Engineering,
36, 809 (1997)
"On-line Pattern-Based Part Quality
Monitoring of the Injection Molding Process"
pes96.pdf
Suzanne L. B. Woll, B. Souder and D. J. Cooper, Polymer Engineering and Science,
36, 1477 (1996)
"A Unified Excitation and Performance
Diagnostic Adaptive Control Framework"
aiche95.pdf
Ralph F. Hinde, Jr. and D. J. Cooper, AIChE Journal,
41, 110 (1995)
"A Neural Network Strategy For
Disturbance Pattern Classification & Adaptive Multivariable Control"
cce95.pdf
Lawrence Megan and D. J. Cooper, Computers and Chemical Engineering,
19, 171 (1995)
"Pattern Recognition Adaptive
Control of 2-Input/2-Output Systems Using ART2-A Neural Networks"
iecr94.pdf
Lawrence Megan and D. J. Cooper, Industrial and Engineering Chemistry Research,
33, 1510 (1994)
"A Pattern Based Approach to Excitation
Diagnostics for Adaptive Process Control"
ces94.pdf
Ralph F. Hinde, Jr. and D. J. Cooper, Chemical Engineering Science,
49, 1403 (1994)
"Using Pattern Recognition in Controller
Adaptation and Performance Evaluation"
acc93.pdf
Ralph F. Hinde, Jr. and D. J. Cooper, Proc. 1993 American Control Conf.,
IEEE Publications, NJ, 74 (1993)
"Neural Network Based Adaptive Control Via
Temporal Pattern Recognition"
Lawrence Megan and D. J. Cooper, Canadian Journal of Chemical Engineering, 70,
1208 (1992)
"Comparing Two Neural Networks For Pattern
Based Adaptive Process Control"
aiche92.pdf
D. J. Cooper, Lawrence Megan and Ralph F. Hinde, Jr., AIChE Journal,
38, 41 (1992)
"Modeling Combustion Efficiency in a
Circulating Fluid Bed Liquid Waste Incinerator"
D. W. Sevon and D. J. Cooper, Chemical Engineering Science, 46, 2983
(1991)
"Intuitive Robust Stability Metric for PID Control of Self Regulating Processes"
Jeffrey Arbogast, Brett Beaugregard and D. J. Cooper,
ISA Transactions, 47, 420 (2008)
Published methods establish how plant-model mismatch in the process gain and
dead time impacts closed-loop stability. However, these methods assume no
plant-model mismatch in the process time constant. The work presented here
proposes the robust stability factor metric, RSF, to examine the effect of
plant-model mismatch in the process gain, dead time, and time constant. The RSF
is presented in two forms: an equation form and a visual form displayed on
robustness plots derived from the Bode and Nyquist stability criteria. This
understanding of robust stability is reinforced through visual examples of how
closed-loop performance changes with various levels of plant-model mismatch. One
example shows how plant-model mismatch in the time constant can impact
closed-loop stability as much as plant-model mismatch in the gain and/or dead
time. Theoretical discussion shows that the impact is greater for small dead
time to time constant ratios. As the closed-loop time constant used in Internal
Model Control (IMC) tuning decreases, the impact becomes significant for a
larger range of dead time to time constant ratios. To complete the presentation,
the RSF is used to compare the robust stability of IMC-PI tuning to other PI,
PID, and PID with Filter tuning correlations.
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"Improve Control of Liquid Level Loops"
Robert Rice and D. J. Cooper,
Chemical Engineering Progress, 104, 54 (2008)
Since a large majority of processes are self-regulating, practitioners can sometimes be challenged
when tuning a controller for an integrating process. The principal characteristic that makes a
process self regulating is that it naturally seeks a steady state operating level if the controller
output and disturbance variables are held constant for a sufficient period of time. Integrating
(non-self regulating) processes can be remarkably challenging to control. After exploring the
distinctive behaviors of such processes, this work details proven methods for identifying
processes with integrating behavior. It then proceed to discuss best practices for designing
and tuning controllers from the PID family of algorithms to achieve desired performance.
Return to Top of Title List
"Graphical Technique for Modeling Integrating (Non-Self Regulating) Processes without Steady-State Process Data"
Jeffrey Arbogast, Robert Rice and D. J. Cooper,
Chemical Engineering Communications, 194, 1566 (2007)
Model fitting techniques for controller tuning that require the process to be initially at
steady state cannot generally be used with integrating (non-self regulating) processes.
To address this issue, a graphical model fitting technique is detailed and demonstrated for
determination of First Order plus Dead Time Integrating model parameters from integrating process
response plots. The resulting model parameters can be used directly in a range of tuning
correlations designed specifically for integrating processes. The advantage of this technique
is that it only requires two periods of constant manipulated and disturbance variables sustained
just long enough for the process variable to respond and establish a clear slope. This is an
important benefit because integrating processes generally cannot be maintained at an initial
steady state as required when using techniques published for self regulating processes. The
result is an industry-friendly method. The method is demonstrated for level control in a pumped
tank, a classical challenge in industrial practice. Both a simulation and a bench-scale
experimental system are used in the demonstration studies.
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"Extension of IMC Tuning Correlations for Non-Self Regulating (Integrating) Processes"
Jeffrey Arbogast and D. J. Cooper,
ISA Transactions, 46, 303 (2007)
The filter term of a PID with Filter controller reduces the impact of measurement noise on the derivative action
of the controller. This impact is quantified by the controller output travel defined as the total movement of
the controller output per unit time. Decreasing controller output travel is important to reduce wear in the
final control element. Internal Model Control (IMC) tuning correlations are widely published for PI, PID, and
PID with Filter controllers for self regulating processes. For non-self regulating (or integrating) processes,
IMC tuning correlations are published for PI and PID controllers but not for PID with Filter controllers.
The important contribution of this work is that it completes the set of IMC tuning correlations with an extension
to the PID with Filter controller for non-self regulating processes. Other published correlations (not based upon
the IMC framework) for PID with Filter controllers fix the filter time constant at one-tenth the derivative time
regardless of the model of the process. In contrast, the novel IMC correlations presented in this paper calculate
a filter time constant based upon the model of the process and the user’s choice for the closed-loop time constant.
The set point tracking and disturbance rejection performance of the proposed IMC tunings is demonstrated using
simulation studies and a bench-scale experimental system. The proposed IMC tunings are shown to perform as well
as various PID correlations (with and without a filter term) while requiring considerably less controller action.
Return to Top of Title List
"Opening the Black Box: Demystifying Performance Assessment Techniques"
Rachelle Jyringi, Robert Rice and D. J. Cooper,
Proc. ISA Expo 2005, 459, TP05ISA164 (2005)
Real-time performance monitoring to identify poorly or under-performing loops has become an integral
part of preventative maintenance. While some control software packages display performance metrics,
it is important to understand the theory, purpose, and limitations since each metric signifies very
specific information about the nature of the process. This paper reviews performance measures from
simple statistics through complicated model-based performance criteria. By understanding the underlying
concepts of the various techniques, readers will gain knowledge of how to use and implement each of the
performance criteria. Basic algorithms for computing performance measures are presented using example
data sets. A discussion with tips and suggestions provides guidance for interpreting the results.
Return to Top of Title List
"Tutorial: Cascade vs. Feed Forward for Improved Disturbance Rejection"
D. J. Cooper, Robert Rice and Jeffrey Arbogast,
Proc. ISA Expo 2004, 454, TP04ISA055 (2004)
The most popular architectures for improved regulatory performance are cascade control
and feed forward with feedback trim. Both architectures trade off additional complexity
in the form of instrumentation and engineering time in return for a controller better
able to reject the impact of disturbances on the measured process variable. Neither
architecture benefits nor detracts from set point tracking performance. This paper
compares and contrasts the two architectures. A comparative example is presented
using a jacketed reactor simulation.
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"A Rule Based Design Methodology for the Control of Non Self-Regulating
Processes"
Robert Rice and D. J. Cooper,
Proc. ISA Expo 2004, 454, TP04ISA076 (2004)
Non self-regulating (integrating) processes move in an unbounded manner when perturbed in open loop
by a bounded manipulated or disturbance variable. It is not uncommon for some temperature, level,
and pressure control loops to display this type of behavior. Integrating processes are surprisingly
challenging to control and can move to extreme and even dangerous levels if left unregulated.
An additional challenge is that the controllers and tuning methods proven for self regulating
processes can yield poor and often unstable performance when applied to integrating processes.
A rule based methodology for controller selection and design for non self-regulating processes
is developed and documented. This work fills the gaps of previous research by providing a completely
characterized set of controller design strategies encompassing a wide range of non self-regulating
processes and control objectives. The rule structure developed guides the decision making pathways
through the various design options.
The fundamental approach taken is built upon model based design methods. For a model based control
approach to be beneficial, its design must take into account an accurate representation of the
process dynamics. In this work existing model based control strategies for self regulating processes,
including IMC based PID Control, DMC/MPC, Smith Predictors, Feed Forward and Cascade control structures,
are modified to work with non self-regulating processes and are incorporated into the rule
based methodology. This modification can take the form of an enhanced tuning parameter correlation,
or a complete re-design of the control structure.
The techniques discussed in this paper will provide control engineers and technicians a simple
recipe based approach to tuning a wide class of controllers for non self-regulating processes.
These procedures are simple to implement and use, require minimal time and effort, require
minimal knowledge of first principle equations, do not require sophisticated analysis tools,
and are reliable for a broad class of integrating processes.
Return to Top of Title List
"Tuning Guidelines of a Dynamic Matrix Controller for Integrating
(Non-Self Regulating) Processes"
Danielle Dougherty and D. J. Cooper
Industrial and Engineering Chemistry Research, 42, 1739 (2003)
Designing a multivariable Dynamic Matrix Controller (DMC) for integrating processes is
challenging because of the number of tuning parameters that affect closed loop performance.
These tuning parameters required to implement DMC include: the sample time; the prediction,
model and control horizons; the controlled variable weights; and the move suppression coefficients.
The move suppression coefficients are used as the key tuning parameters to obtain desirable DMC
performance. This paper derives and demonstrates expressions for computing the complete set
of tuning parameters for integrating processes. A novel contribution of this work is the
derivation of an analytical expression for computing the move suppression coefficients based
on the process model and the other DMC design parameters. The tuning rules are demonstrated
on simulated processes including a constrained multivariable process simulation that displays
integrating characteristics.
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"Building Multivariable Process Control Intuition Using Control Station"
D. J. Cooper, Danielle Dougherty and Robert Rice
Chemical Engineering Education, 37, 100 (2003)
Control Station is used by hundreds of companies and academic institutions around the world for
process control education and training. The software provides a host of case studies students can
use for hands-on exploration and study. Control Station provides a real world
environment where students can manipulate process and controller parameters to
"learn by doing" as they experience the challenges of process control. This paper
discusses how Control Station can be used to explore and learn about a range of issues associated
with multivariable process interaction and control.
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"A Practical Multiple Model Adaptive Strategy for Multivariable Model
Predictive Control"
Danielle Dougherty and D. J. Cooper
Control Engineering Practice, 11, 649 (2003)
Model predictive control (MPC) has become the leading form of advanced multivariable control in
the chemical process industry. The objective of this work is to introduce a multiple model
adaptive control strategy for multivariable Dynamic Matrix Control (DMC). The novelty of the
strategy lies in several subtle but significant details. One contribution is that the method
combines the output of multiple linear DMC controllers, each with their own step response model
describing process dynamics at a specific level of operation. The final output forwarded to the
controller is an interpolation of the individual controller outputs weighted based on the current
value of the measured process variable. Another contribution is that the approach does not
introduce additional computational complexity, but rather, relies on traditional DMC design
methods. This makes it readily available to the industrial practitioner.
Return to Top of Title List
"A Practical Multiple Model Adaptive Strategy for Single-Loop MPC"
Danielle Dougherty and D. J. Cooper
Control Engineering Practice, 11, 141 (2003)
This paper details a multiple model adaptive control strategy for Model Predictive Control (MPC).
The internal model of the process employed in MPC is linear, but most chemical processes are
nonlinear. Hence, the performance of MPC will degrade as the operating level moves away from
the original design level of operation. To maintain performance of the controller over a wide
range of operating levels, a multiple model adaptive control strategy for Dynamic Matrix
Control (DMC), which is the process industry’s standard for MPC, is presented. The method of
approach is to design multiple linear DMC controllers. The tuning parameters for the linear
controllers are obtained using novel analytical expressions. The controller output of the
adaptive DMC controller is a weighted average of the multiple linear DMC controllers. The
capabilities of the multiple model adaptive strategy for DMC are investigated through computer
simulations and an experimental system. The work provides an adaptive DMC strategy that is
simple to implement and use, requires minimal computation for updating model parameters, relies
on the linear control knowledge of plant personnel, and is reliable for a broad class of process
applications.
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"Design and Tuning of PID Controllers for Integrating (Non-Self Regulating)
Processes"
Robert Rice and D. J. Cooper
Proc. ISA 2002 Annual Meeting, 43, 424 (2002)
This work explores an easy to use and broadly applicable method for tuning PID controllers for
integrating processes. Details are presented on the requirements for collecting closed loop
dynamic process test data near the design level of operation, the fitting of an integrating
dynamic model form to this test data and correlations for computing controller tuning values
based on the parameters from the resulting model fit. The method presented is applicable to
PID control algorithms in both the interacting and non-interacting derivative forms. The
work builds on the work of Chien and Fruehauf [8] and their use of the internal model
control (IMC) structure to derive tuning correlations for integrating processes. One novel
contribution of this work is the extension of the tuning correlations to include the PID
with derivative filter forms. The design and tuning method is demonstrated on process
simulations for both set point tracking and disturbance rejection. Results show that
the methods described here compare favorably with other more computationally intensive approaches.
Return to Top of Title List
"A Training Simulator for Computer-Aided Process Control Education"
D. J. Cooper and Danielle Dougherty
Chemical Engineering Education, 43, 252 (2000)
A training simulator offers an alluring method for providing students with the significant hands-on
practice critical to learning process control. The proper tool can provide virtual experience much
the way airplane and power plant simulators do in those fields. It can give students a broad range of
focused engineering applications of theory in an efficient, safe and economical fashion. And it can
work as an instructional companion as it provides interactive challenges that track along with classroom
lectures. Process control is a subject area well suited to exploit the benefits of a training simulator.
Modern control installations are computer based, so a video display is the natural window through
which the subject is practiced. With color graphic animation and interactive challenges, a training
simulator can offer experiences that literally rival those of the real world. These experiences can
be obtained risk free and at minimal cost, enabling students to feel comfortable exploring nonstandard
solutions at their desk. If properly designed as a pedagogical tool with case studies organized to present
incremental challenges, we believe learning can be enormously enhanced for process control with such a
training simulator. This paper presents example lessons drawn from the Control Station process control
software for education and training.
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"Enhancing Process Control Education with the Control
Station Training Simulator"
D. J. Cooper and Danielle Dougherty
Computer Applications in Engineering Education, 7, 203 (1999)
A process control training simulator can enhance learning by integrating the theoretical
abstraction of textbooks with the tactile nature of the lab and plant. The primary
objective of a training simulator is education. It can motivate, help with visualization,
and provide hands-on practice and experience. This paper explores the use and benefits of
the Control Station training simulator for process control education. Examples presented
illustrate how the standard curriculum can be enhanced with a series of hands-on exercises
and study projects.
Return to Top of Title List
"A Tuning Strategy for Unconstrained Multivariable Model
Predictive Controllers,"
Rahul Shridhar and D. J. Cooper
Industrial and Engineering Chemistry Research, 37, 4003 (1998)
Move suppression coefficients serve a dual purpose in the Model Predictive Controller
(MPC) architecture. These include suppressing aggressive control action and conditioning
the system matrix prior to inversion. The work presented here exploits this dual effect in
deriving an analytical expression that computes appropriate move suppression coefficients
as a function of process model parameters, other MPC design parameters and partitioned
block condition numbers of the system matrix. The development is based upon an approximate
mosaic Hankel matrix structure of the multivariable system matrix. The primary
contribution of this work is the derivation of the analytical expression for computing
move suppression coefficients and its demonstration in an overall MPC tuning strategy
(Table 1). The examples presented show that the move suppression coefficient remains
properly scaled as the other MPC design parameters and process characteristics change to
produce a consistent closed loop performance. This tuning method is applicable to
multivariable processes, including non-square systems.
Return to Top of Title List
"A Novel Tuning Strategy for Multivariable Model Predictive
Control,"
Rahul Shridhar and D. J. Cooper
ISA Transactions, 36, 273 (1998)
Model predictive control (MPC) has established itself as the most popular form of advanced
multivariable control in the chemical process industry. However, the benefits of this
technology cannot be realized unless the controller can be operated with desirable
performance for an extended period of time. The objective of this work is to present an
easy-to-use and reliable tuning strategy that enables the control practitioner to maintain
multivariable MPC at peak performance with minimal effort. A novel analytical expression
that computes the move suppression coefficients, guidelines to select the additional
adjustable parameters, and their demonstration in an overall tuning strategy are some of
the significant contributions of this work. The compact form for the expression that
computes the move suppression coefficients is derived as a function of a first order plus
dead time (FOPDT) model approximation of the process dynamics. With tuning parameters
computed, MPC is then implemented in the classical fashion using an internal model
formulated from step response coefficients of the actual process. Just as a FOPDT model
approximation has proved a valuable tool in tuning rules such as Cohen-Coon, ITAE and IAE
for PID implementations, the tuning strategy presented here is significant because it
offers an analogous approach for multivariable MPC.
Return to Top of Title List
"A Tuning Strategy for Unconstrained SISO Model Predictive
Control,"
Rahul Shridhar and D. J. Cooper
Industrial and Engineering Chemistry Research, 36, 729 (1997)
This paper presents an easy-to-use and reliable tuning strategy for unconstrained SISO
Dynamic Matrix Control (DMC) and lays a foundation for extension to multivariable systems.
The tuning strategy achieves set point tracking with minimal overshoot and modest
manipulated input move sizes and is applicable to a broad class of open loop stable
processes. The derivation of an analytical expression for the move suppression coefficient
and its demonstration in a DMC tuning strategy is one of the significant contributions of
this work. The compact form for the analytical expression for the move suppression
coefficient is achieved by employing a first order plus dead time (FOPDT) model
approximation of the process dynamics. With tuning parameters computed, DMC is then
implemented in the classical fashion using a dynamic matrix formulated from step response
coefficients of the actual process.
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"Automated Rule-Based Model Parameter Estimation and
Controller Design"
Carlos Velazquez-Figueroa and D. J. Cooper
Proc. ISA Tech97 Annual Conf. Anaheim, CA, ISA Publications (1997)
Proper design of automatic process controllers is key to the efficient operation of an
industrial process. The design of such controllers follows a specific procedure comprised
of three steps. These steps include generation of proper dynamic data, regression of a low
order linear dynamic model, and use of the resulting model to complete the controller
design. The objective of this work is to provide the practitioner with guidelines for
generating dynamic data appropriate for controller design. These guidelines are obtained
by studying the impact of design variables on a sum of squared errors (SSE) regression
surface. An additional objective is to automate the model parameter estimation and
controller design using a rule-based approach. The rule-based system automatically
computes initial values of the model parameters to start the estimation procedure. It also
incorporates penalty functions that ensure the physical meaning of the model parameters.
Finally, it employs the model parameters in appropriate algorithms to estimate controller
tuning values.
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"Pattern-Based Closed-Loop Quality Control for the Injection
Molding Process"
Suzanne L. B. Woll and D. J. Cooper
Polymer Engineering and Science, 37, 801 (1997)
The basis for a novel pattern-based closed-loop control strategy for the injection molding
process is presented. The strategy uses artificial neural networks (ANNs) embedded within
a cascade design to analyze sensor patterns, identify process character and control part
quality. The platform for this work, the injection molding process, is an industrially
significant, cyclic manufacturing operation. Final part quality of this process is a
non-linear function of many machine and polymer variables. Part quality control of this
process is currently attained via single-input single-output machine controls supervised
by human operators. Presented is a method that employs ANN technology to improve upon this
approach and provide the basis for closed-loop part quality control. In the cascade
design, machine controller set points of an inner loop are updated based on ANN analysis
of mold cavity pressure patterns. The controller action maintains the desired pressure
pattern set point of the outer loop associated with desired part quality. Control strategy
details are provided along with set point tracking demonstrations that support feasibility
of this pattern-based approach.
Return to Top of Title List
"A Dynamic Injection Molding Process Model for Simulating
Mold Cavity Pressure Patterns"
Suzanne L. B. Woll and D. J. Cooper
Polymer Plastics Technology and Engineering, 36, 809 (1997)
The quality of injection molded parts is typically controlled in the plant using
statistical techniques that involve measuring parts as well as monitoring processing
parameters. Part quality is also controlled by machine operators who adjust processing
conditions in response to trends in process behavior. To achieve direct on-line monitoring
and automatic control of part quality, a multivariable, nonlinear process model must be
developed that relates process behavior to machine controllable parameters. Presented in
this work are the details of such a model derived from first principles and proven
correlations. Since recent work has shown that complete mold cavity pressure patterns are
good indicators of part quality, the focus of this lumped parameter model is to simulate
mold cavity pressure patterns observed during the filling, packing and cooling stages of
the process given machine set points for barrel temperature and holding pressure. The
model is validated experimentally using a production injection molding machine and
parameter sensitivity case studies are presented.
Return to Top of Title List
"On-line Pattern-Based Part Quality Monitoring of the
Injection Molding Process"
Suzanne L. B. Woll, B. Souder and D. J. Cooper
Polymer Engineering and Science, 36, 1477 (1996)
The quality of injection molded parts is often monitored in the plant using techniques
that focus on the statistical analysis of discrete data and, in particular, peak values.
This paper presents an alternative on- line technique for part quality monitoring that
focuses on the analysis of complete data patterns. Specifically, this paper discusses the
application of artificial neural networks (ANNs) as part quality monitoring tools. The
method of approach is to train a back propagation network (BPN) to associate part quality
with the corresponding data pattern produced during injection. In Part I of this work, the
data pattern consists of a series of discrete values and the part quality measure is
defined as part weight. In Part II, the data pattern is the measurement profile observed
from a pressure sensor placed in the mold cavity and the part quality measure is defined
as part length. Results show that ANNs are successful in predicting part quality based on
data patterns when an entire sensor profile is analyzed. Furthermore, demonstrations show
that the approach is superior in predicting part quality when compared to statistical
techniques now widely practiced by the injection molding process industry.
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"A Unified Excitation and Performance Diagnostic Adaptive
Control Framework"
Ralph F. Hinde, Jr., and D. J. Cooper
AIChE Journal, 41, 110 (1995)
Model based controllers contain two elements that must be adjusted to maintain desired
performance: parameters of the process model and a tuning parameters in the controller
design equation. A unified framework is presented where vector quantizing networks are
used in pattern-based methods for diagnosing process excitation and controller
performance. Excitation diagnostics analyze sufficiently excited dynamic process data for
model updating. Performance diagnostics analyze set point response data and determine
appropriate updates to the tuning parameter. Supervisory adaptation logic enables these
two adaptive mechanisms to work together to maintain model accuracy and desired controller
performance. The method is general to a number of model based control algorithms and
process model forms. Demonstrations employ a first order plus dead time model form as well
as both PI and DMC algorithms for set point tracking and disturbance rejection in a
simulated and a bench scale application.
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"A Neural Network Strategy For Disturbance Pattern
Classification and Adaptive Multivariable Control"
Lawrence Megan and D. J. Cooper
Computers and Chemical Engineering, 19, 171 (1995)
This paper presents a neural network approach to adaptive control through pattern
recognition techniques, extending previously published results on single-input/single-
output systems to two-input/two-output systems. Two vector-quantizing neural networks are
used to analyze both the input and output patterns resulting from a perturbation to the
process. The results of these analyses are then used to update the model gain of the first
order plus dead time model that describes each input/output pair. This work focuses
primarily on making model adaptations following load disturbances as opposed to set point
changes, as load disturbances present by far the greatest adaptation challenge to chemical
process applications. The results are compared to a more traditional modeling technique,
batchwise model regression, with respect to both accuracy and computational load. The
adaptive strategy is demonstrated using a variety of disturbances on two challenging
multivariable process simulations.
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"Pattern Recognition Based Adaptive Control Of
Two-Input/Two-Output Systems Using ART2-A Neural Networks"
Lawrence Megan and D. J. Cooper
Industrial and Engineering Chemistry Research, 33, 1510 (1994)
This paper details an applied investigation of pattern recognition based adaptive control
for two-input/two-output systems. Two ART2-A neural networks perform a concurrent analysis
of controller error and manipulated input patterns resulting from a set point change or an
unmeasured disturbance to the system. This information is then used to adapt the models
that describe each input/output relationship. The adaptive strategy is demonstrated on two
challenging processes: a pilot scale continuous distillation column and a simulation of
the Shell Fundamental Control Problem. The distillation column demonstrates the
applicability of the adaptive strategy to both set point changes and disturbances in a
challenging real-world process, while the Shell problem demonstrates the ability of the
strategy to handle irregular disturbance dynamics.
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"A Pattern Based Approach to Excitation Diagnostics for
Adaptive Process Control"
Ralph F. Hinde, Jr. and D. J. Cooper
Chemical Engineering Science, 49, 1403 (1994)
To maintain desired controller performance in the presence of process nonlinearity and
nonstationarity, linear model based control strategies become dependent upon the regular
updating of a process model. This paper explores the use of a passive adaptive algorithm
which updates the process model in closed loop by taking advantage of naturally occurring
dynamic events rather than by injecting perturbations into the system to create dynamic
events. Such closed loop identification is possible, but it requires that these events
contain process information that is not masked by measurement noise or unmeasured
disturbances. Presented here is pattern-based excitation diagnostic tool (EDT) that
determines when sufficient excitation exists for model updating. The EDT consist of vector
quantizing neural networks (VQNs) similar to the ART2-A and a decision maker that is a
simple set of rules. The VQNs are trained to recognize local dynamic behavior in the
recent histories of each process variable. The decision maker uses the outputs from the
VQNs to diagnose when sufficient dynamics exist for model updating. Details of the EDT are
presented along with several challenging demonstrations on both simulated and real
single-input single-output processes.
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"Using Pattern Recognition in Controller Adaptation and
Performance Evaluation"
Ralph F. Hinde, Jr. and D. J. Cooper
Proceedings of the 1993 American Control Conference, IEEE Publications, NJ, 74
(1993)
This work presents pattern recognition based methods for controller adaptation and
performance evaluation. These methods comprise a passive model-based adaptive control
algorithm that is simple to use, easy to understand, stable, and fairly robust in a wide
variety of applications. Controller adaptation in this work uses excitation diagnostics to
initiate batch-wise regression of a process model to dynamic closed-loop process data. The
process model is then employed in model-based controller tuning relations to update the
controller's character. Controller performance evaluation is used to determine appropriate
adjustments to the tuning relations such that an accurate process model will produce
desired controller performance. These adaptive techniques are implemented using vector
quantizing neural networks as efficient pattern recognition tools. The adaptive algorithm
is presented in a structure that allows for the implementation of these advanced
techniques without requiring the replacement of an existing feedback controller. This is
demonstrated using a simulated nonlinear third order process and an IMC tuned PI
controller with Smith Predictor.
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"Neural Network Based Adaptive Control Via
Temporal Pattern Recognition"
Lawrence Megan and D. J. Cooper
Canadian Journal of Chemical Engineering, 70, 1208 (1992)
This paper presents a neural network approach to pattern recognition based adaptive
control. Two interconnected back propagation networks are trained to translate error
patterns resulting from sustained set point changes into predictions of mismatch between
current internal model parameters, model gain and model time constant, and those which
restore desired performance. The network predictions are then used to update a model based
PI controller. The strategy is demonstrated on two simulations and a pilot scale process
which are undergoing severe changes in model gain and time constant. The strategy compares
favorably against a more traditional rule based pattern recognition approach.
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"Comparing Two Neural Networks For Pattern Based Adaptive
Process Control"
D. J. Cooper, Lawrence Megan and Ralph F. Hinde, Jr.
AIChE Journal, 38, 41 (1992)
An adaptation strategy based on an analysis of the patterns exhibited in the recent
history of the controller error and manipulated input variable is presented. The strategy
is a two parameter adaptation, where the gain and time constant of the controller's
internal model are adjusted to make the closed loop error response match a desired or
target pattern. Both a back propagation network and a vector quantizing network (VQN) are
compared as pattern analysis tools. This strategy is established for a number of model
based controllers and is demonstrated here using the generalized predictive control
algorithm. Details of this set point tracking strategy are presented along with
demonstrations on both simulated and real single loop processes that experience
significant changes in process gain and time constant. Results show both networks to be
equally capable at pattern recognition with the VQNs ease of training and implicit ability
to assess the accuracy of the pattern match as deciding factors in network selection.
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"Modeling Combustion Efficiency in a Circulating Fluid Bed
Liquid Waste Incinerator"
Douglass W. Sevon and D. J. Cooper
Chemical Engineering Science, 46, 2983 (1991)
An incinerator's combustion efficiency (CE) indicates the effectiveness of the incinerator
to completely oxidize waste. Circulating fluidized beds (CFB's) show promise as a viable
hazardous waste incineration process, but to fulfill this promise, an understanding of the
interaction between CE and important operating parameters is still needed. To gain this
understanding, an experimental and a theoretical investigation of CFB incineration was
performed. A CFB incinerator was constructed to study the destruction of an organic liquid
waste. An experimental program on incineration was conducted with propanol as the
simulated waste. Operating data on the dependence of CE as a function of major operating
parameters such as excess air, average particle size, and primary air/total air ratio for
this facility is presented. A theoretical process model specific to CFB incineration of an
organic liquid waste is developed, and the numerical implementation of the model is
presented. Model constants are fitted with experimental data so the process model
specifically describes the CFB pilot plant. Process sensitivity to major operating
parameters is investigated in series of simulations. Design configurations slightly
different from the experimental CFB such as the column height and preheat temperature of
combustion air are also studied with the model.
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