Modeling and Control of Batch Processes: Theory and Applications PDF
Preview Modeling and Control of Batch Processes: Theory and Applications
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 national and international regulatory bodies. For more details on individual ethics matters please see: https://www.springer.com/gp/authors-editors/journal-author/journal-author- helpdesk/publishing-ethics/14214 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 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland ’ 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 v 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 vii 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
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