Modeling and Control of Batch Processes: Theory and Applications

346 Pages·2019·9.78 MB·English

by  Prashant Mhaskar

Advances in Industrial Control
Prashant Mhaskar
Abhinav Garg
Brandon Corbett
Modeling and
Control of Batch
Processes
Theory and Applications
Advances in Industrial Control
Series Editors
Michael J. Grimble, Department of Electronic and Electrical Engineering,
University of Strathclyde, Glasgow, UK
Antonella Ferrara, Department of Electrical, Computer and Biomedical
Engineering, University of Pavia, Pavia, Italy
Advisory Editor
Sebastian Engell, Technische Universität Dortmund, Dortmund, Germany
Editorial Board
Graham C. Goodwin, School of Electrical Engineering and Computer Science,
University of Newcastle, Callaghan, NSW, Australia
Thomas J. Harris, Department of Chemical Engineering, Queen’s University,
Kingston, ON, Canada
Tong Heng Lee, Department of Electrical and Computer Engineering, National
University of Singapore, Singapore, Singapore
OmP.Malik,SchulichSchoolofEngineering,UniversityofCalgary,Calgary,AB,
Canada
GustafOlsson,IndustrialElectricalEngineeringandAutomation,LundInstituteof
Technology, Lund, Sweden
Ikuo Yamamoto, Graduate School of Engineering, University of Nagasaki,
Nagasaki, Japan
Editorial Advisors
Kim-Fung Man, City University Hong Kong, Kowloon, Hong Kong
Asok Ray, Pennsylvania State University, University Park, PA, USA
Advances in Industrial Control is a series of monographs and contributed titles
focusingontheapplicationsofadvancedandnovelcontrolmethodswithinapplied
settings. This series has worldwide distribution to engineers, researchers and
libraries.
The series promotes the exchange of information between academia and
industry, to which end the books all demonstrate some theoretical aspect of an
advanced or new control method and show how it can be applied either in a pilot
plant or in some real industrial situation. The books are distinguished by the
combination of the type of theory used and the type of application exemplified.
Note that “industrial” here has a very broad interpretation; it applies not merely to
the processes employed in industrial plants but to systems such as avionics and
automotivebrakesanddrivetrain.Thisseriescomplementsthetheoreticalandmore
mathematical approach of Communications and Control Engineering.
Indexed by SCOPUS and Engineering Index.
Series Editors
Professor Michael J. Grimble
Department ofElectronic and Electrical Engineering, Royal College Building, 204
George Street, Glasgow G1 1XW, United Kingdom
e-mail: [email protected]
Professor Antonella Ferrara
Department of Electrical, Computer and Biomedical Engineering, University of
Pavia, Via Ferrata 1, 27100 Pavia, Italy
e-mail: [email protected]
or the
In-house Editor
Mr. Oliver Jackson
Springer London, 4 Crinan Street, London, N1 9XW, United Kingdom
e-mail: [email protected]
Publishing Ethics
Researchersshouldconduct theirresearchfrom research proposaltopublicationin
linewithbestpracticesandcodesofconductofrelevantprofessionalbodiesand/or
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matters please see:
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More information about this series at http://www.springer.com/series/1412
Prashant Mhaskar Abhinav Garg
(cid:129)
Brandon Corbett
Modeling and Control
of Batch Processes
Theory and Applications
123
PrashantMhaskar Brandon Corbett
Department ofChemical Engineering Department ofChemical Engineering
McMaster University McMaster University
Hamilton, ON,Canada Hamilton, ON,Canada
Abhinav Garg
Department ofChemical Engineering
McMaster University
Hamilton, ON,Canada
ISSN 1430-9491 ISSN 2193-1577 (electronic)
Advances in IndustrialControl
ISBN978-3-030-04139-7 ISBN978-3-030-04140-3 (eBook)
https://doi.org/10.1007/978-3-030-04140-3
LibraryofCongressControlNumber:2018961228
©SpringerNatureSwitzerlandAG2019
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’
Series Editor s Foreword
Controlsystemsengineeringisviewedverydifferentlybyresearchersandthosethat
practice the craft. The former group develops general algorithms with a strong
underlying mathematical basis while for the latter concerns over the limits of
equipment and plant downtime often dominate. The series Advances in Industrial
Control attempts to bridge this divide and to promote an understanding of the
problems that really need a solution.
The rapid development of new control theory and technology has an impact on
all areas of control engineering and applications. There are new control theories,
actuators, sensors, communication and computing methods, and of course new
application areas. It is important that new control theory and design methods are
stimulated and driven by the needs and challenges of applications. A focus on
applications is also essential if the different aspects of the control design problem
aretoreceivesufficient attention.Infact, there isa lotof workon theanalysis and
synthesisproblemsincontrolsystemsengineeringbutmuchlessontheproblemsof
control design. The path from a control loop specification to a suitable design is
often paved with uncertainties and confusion. The series provides an opportunity
for researchers to present an exposition of new work on industrial control, raising
awareness of the benefits that can accrue, and the challenges that can arise, and
dealing with this issue of control design.
Thebatchcontroltopiccoveredbythistextisimportantinmanyindustriesand
particularly in the pharmaceuticals industry’s manufacture of healthcare products.
Theseareofhighvaluecommerciallybutalsoofimmensevaluetosociety,soitis
valuabletoexplorewhatadvancedcontroltoolscanimproveinsystemsthatdonot
have the luxury of long periods of steady-state operation. As the authors explain,
fault tolerance in batch processes is a special problem where reliability is mainly
concerned with achieving the desired end-point. Disturbances can result in the
nonlinear system being in a region where the end-point is not achievable, and this
can represent a challenging control problem.
Itisnotsurprisingthatmodelpredictivecontrol(MPC)isproposedforthistype
of application since, as shown, it has very relevant features, particularly regarding
constrainthandling.TheuseofrobustMPCisparticularlynotableinaccountingfor
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vi SeriesEditor’sForeword
uncertainties. The first-principles process models are often presented in physical
equation form, but the development of suitable models by online estimation
methods is also described including online learning features. A model predictive
quality control approach is introduced using an inferential quality model to ensure
low variance of the final tracking errors.
The authors mainly cover chemical types of application, but they also cover
electric arc furnaces: another batch operation but of a very different nature.
A multi-model control approach, which is now very popular, is discussed for this
andotherapplications.TheverytopicalsubjectofeconomicMPCisalsodiscussed
in terms of its role in the hierarchy for solving batch control problems. The
development of models is as important as control techniques, and the use of sub-
space identification methods for data-driven modeling is described. The chemical
engineering application problems are treated in some detail as expected from
authors with great expertise in the subject. This is therefore a welcome addition to
the series Advances in Industrial Control.
Glasgow, UK Michael J. Grimble
September 2018
Preface
Competitive economic conditions have compelled the manufacturing industries in
most industrialized countries to pursue improved economic margins through the
production of low-volume, higher-value-added specialty chemicals and materials,
such as advanced alloys, polymers, herbicides, insecticides, pharmaceuticals, and
biochemicals, that are manufactured predominantly in batch processes. Moreover,
startupsandshutdowns(thatarebatch-likeprocesses)areanintegralconstituentof
almost every process operation. The operation of these processes, however, has to
grapple with several challenges, such as the lack of online sensors for measuring
critical process variables, the finite duration of the process operation, the presence
of significant nonlinear dynamics (due to predominantly transient operation), and
rejecting raw material variability. Modeling and control of these batch and
batch-likeprocessesarethereforeessentialtoensuretheirsafeandreliablefunction
and to guarantee that they produce consistent and high-quality products or, in the
case of startup operation, transit smoothly to continuous operation. Batch process
operation, however, differs from operation around equilibrium points, both in the
model identification aspects and control design.
Motivated by the above considerations, this book presents methods for the
modeling and control of batch and batch-like processes with techniques ranging
from mechanistic model to data-driven models. Specifically, the book proposes:
(1)anovelbatchcontroldesignwithwell-characterizedfeasibilityproperties;(2)a
modeling approach that unites multi-model techniques and partial least squares;
(3)ageneralizationofthesubspaceidentificationapproachforbatchprocesses;and
(4) application to several detailed case studies, ranging from complex simulation
testbedtoindustrialdata.Theproposedmethodologyemploysstatisticaltoolssuch
aspartialleastsquaresandsubspaceidentificationandsynergizesthemwithnotions
from state-space-based models to provide solutions to the quality control problem
for batch processes.
The application of the proposed modeling and control tools is expected to sig-
nificantly improve the operation of batch and batch-like processes. The book
requires basic knowledge of statistical modeling, differential equations, and opti-
mization methods and is intended for researchers, graduate students, and process
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viii Preface
control engineers. Throughout the book, practical implementation issues are dis-
cussedtohelpengineersandresearchersunderstandtheapplicationofthemethods
in greater depth.
Finally, we would like to thank all the people who contributed in some way to
this project. These include former graduate students and our colleagues at
McMaster University for creating a pleasant working environment. Last but not
least, we would like to express our deepest gratitude to our families for their
dedication,encouragement,andsupportoverthecourseofthisproject.Wededicate
this book to them.
Hamilton, ON, Canada Prashant Mhaskar
September 2018 Abhinav Garg
Brandon Corbett
Contents
Part I Motivation
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Objectives and Organization of the Book . . . . . . . . . . . . . . . . . 6
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Batch Process Modeling and Control: Background. . . . . . . . . . . . . 11
2.1 Batch Process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Data-Driven Process Modeling . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.1 PLS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 Classical System Identification . . . . . . . . . . . . . . . . . . 13
2.2.3 Subspace Identification . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 Batch Control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.1 Model Predictive Control Design . . . . . . . . . . . . . . . . 16
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Part II First-Principles Model Based Control
3 Safe-Steering of Batch Processes. . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2 Preliminaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2.1 Process Description . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2.2 End-Point-Based MPC . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3 Reverse-Time Reachability Region-Based MPC . . . . . . . . . . . . 28
3.3.1 Reverse-Time Reachability Regions. . . . . . . . . . . . . . . 28
3.3.2 MPC Formulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.4 Safe-Steering of Batch Processes . . . . . . . . . . . . . . . . . . . . . . . 35
3.4.1 Problem Definition. . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.4.2 Safe-Steering to Desired End-Point Properties . . . . . . . 36
ix