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ADVANCES IN CHEMICAL ENGINEERING Editor-in-Chief GUY B. MARIN Department of Chemical Engineering, Ghent University, Ghent, Belgium Editorial Board DAVID H. WEST Research and Development, The Dow Chemical Company, Freeport, Texas, U.S.A. JINGHAI LI Institute of Process Engineering, Chinese Academy of Sciences, Beijing, P.R. China SHANKAR NARASIMHAN Department of Chemical Engineering, Indian Institute of Technology, Chennai, India AcademicPressisanimprintofElsevier 525BStreet,Suite1900,SanDiego,CA92101–4495,USA 225WymanStreet,Waltham,MA02451,USA 32,JamestownRoad,LondonNW17BY,UK TheBoulevard,LangfordLane,Kidlington,Oxford,OX51GB,UK Radarweg29,POBox211,1000AEAmsterdam,TheNetherlands Firstedition2013 Copyright©2013ElsevierInc.Allrightsreserved Nopartofthispublicationmaybereproduced,storedinaretrievalsystemor transmittedinanyformorbyanymeanselectronic,mechanical,photocopying,recording orotherwisewithoutthepriorwrittenpermissionofthepublisher PermissionsmaybesoughtdirectlyfromElsevier’sScience&TechnologyRights DepartmentinOxford,UK:phone(þ44)(0)1865843830;fax(þ44)(0)1865853333; email:permissions@elsevier.com.Alternativelyyoucansubmityourrequestonlineby visitingtheElsevierwebsiteathttp://elsevier.com/locate/permissions,andselecting ObtainingpermissiontouseElseviermaterial Notice Noresponsibilityisassumedbythepublisherforanyinjuryand/ordamagetopersons orpropertyasamatterofproductsliability,negligenceorotherwise,orfromanyuseor operationofanymethods,products,instructionsorideascontainedinthematerial herein.Becauseofrapidadvancesinthemedicalsciences,inparticular,independent verificationofdiagnosesanddrugdosagesshouldbemade ISBN:978-0-12-396524-0 ISSN:0065-2377 ForinformationonallAcademicPresspublications visitourwebsiteatwww.store.elsevier.com PrintedandboundinUnitedStatesinAmerica 13 14 15 16 11 10 9 8 7 6 5 4 3 2 1 CONTRIBUTORS DominiqueBonvin Laboratoired’Automatique,EcolePolytechniqueFe´de´raledeLausanne,EPFL,Lausanne, Switzerland Gre´goryFrancois Laboratoired’Automatique,EcolePolytechniqueFe´de´raledeLausanne,EPFL,Lausanne, Switzerland SanjeevGarg DepartmentofChemicalEngineering,IndianInstituteofTechnology,Kanpur, UttarPradesh,India SantoshK.Gupta DepartmentofChemicalEngineering,IndianInstituteofTechnology,Kanpur, UttarPradesh,andUniversityofPetroleumandEnergyStudies(UPES),Dehradun, Uttarakhand,India WolfgangMarquardt AachenerVerfahrenstechnik-ProcessSystemsEngineering,RWTHAachenUniversity, Aachen,Germany AdelMhamdi AachenerVerfahrenstechnik-ProcessSystemsEngineering,RWTHAachenUniversity, Aachen,Germany SiddharthaMukhopadhyay BhabhaAtomicResearchCentre,ControlInstrumentationDivision,Mumbai,India ArunK.Tangirala DepartmentofChemicalEngineering,IITMadras,Chennai,TamilNadu,India AkhilanandP.Tiwari BhabhaAtomicResearchCentre,ReactorControlDivision,Mumbai,India vii PREFACE ThisissueofAdvancesinChemicalEngineeringhasfourarticlesonthetheme “Control and Optimization of Process Systems.” Systems engineering is a verypowerfulapproachtoanalyzebehaviorofprocessesinchemicalplants. It helps understand the intricacies of the interactions between the different variables using a macro- and a holistic perspective. It provides valuable insightsintooptimizingandcontrollingtheperformanceofsystems.Chem- ical engineering systems arecharacterized by uncertainty arising from poor knowledgeofprocessesanddisturbancesinsystems.Thismakesoptimizing and controlling their behavior a challenge. The four chapters cover a broad spectrum of topics. While they have been written by researchers working in the areas for several years, the emphasisoneachchapterhasbeenonluciditytoenablethegraduatestudent beginninghis/hercareertodevelopaninterestinthesubject.Themotiva- tionhasbeentoexplainthingsclearlyandatthesametimeintroducehim/ hertocutting-edgeresearchinthesubjectsothatthestudent’sinterestcan be kindled and he/she can feel confident of pursuing a research career in that area. Chapter1,byFrancoisandBonvin,presentsrecentdevelopmentsinthe fieldofprocessoptimization.Oneofthechallengesinsystemsengineeringis anincompleteknowledgeofthesystem.Thisresultsinthemodelofthesys- tem being different from that of the plant which it should emulate. In the presenceofprocessdisturbancesorplant-modelmismatch,theclassicalopti- mization techniques may not be applicable since they may violate constraints. One way to overcome this is to be conservative. However, this canresultinasuboptimalperformance.Thisproblemofconstraintviolation canbeeliminatedbyusinginformationfromprocessmeasurements.Differ- entmethodsofmeasurement-basedoptimizationtechniquesarediscussedin the chapter. The principles of using measurement for optimization are appliedtofourdifferentproblems.Thesearesolvedusingsomeofthepro- posed real-time optimization schemes. Mathematical models of systems can be developed based on purely sta- tistical techniques. These usually involve a large number of parameters which are estimated using regression techniques. However, this approach doesnotcapturethephysicsoftheprocess.Hence,itsextensionstodifferent conditionsmayresultininaccuratepredictions.Thisproblemisalsotrueof ix x Preface many physical models which contain parameters whose estimates are unknown.Thesemultiparameterestimationproblemsarenotonlycompu- tationally intensive but may also yield solutions which are physically not realistic.Chapter2,byMhamdiandMarquardt,discussesanoveltechnique ofastep-by-stepprocesstoaddressthisproblem.Thisisbasedonthephysics prevailingin asystemandis computationallyelegant. Herethecomplexity of the problem is increased gradually and the information learnt at each step is used in the next step. Applications of this method to examples in pool boiling, falling films, and reaction diffusion systems are discussed in this chapter. Waveletshavebeengainingprominenceasapowerfultoolformorethan threedecadesnow.Theyhaveapplicationsinthefieldsofsignalprocessing, estimation, pattern recognition, andprocesssystemsengineering. Wavelets offeramultiscaleframeworkforsignalandsystemanalysis.Herethesignals are decomposed into components at different resolutions. Standard tech- niquesarethenappliedtoeachofthesecomponents.Intheareaofprocess systems engineering, wavelets are used for signal compression, estimation, and system identification. Chapter 3, by Tangirala et al., aims to provide an introduction of wavelet transforms to the engineer using an informal approach.Itdiscussesapplicationsincontrollerloopperformancemonitor- ingandmultiscaleidentification.Theabovearediscussedwithexamplesand casestudies.Itwillbeveryusefultograduatestudentsandresearchersinthe areas of multiresolution signal processing and also in systems theory and modeling. In several problems, the need for optimizing more than one objective function simultaneously arises. A typical characteristic could be to define these criteria by using weighting functions and combining the different objective functions into a single objective function. However, a more apt approachistotreatthedifferentobjectivefunctionsaselementsofavector anddeterminetheoptimalsolution.Geneticalgorithms(GAs)constitutean evolutionary optimization technique. Chapter 4, by Gupta and Garg, discusses the applications of GA to several chemical engineering problems. These applications include industrial reactors and heat exchangers. One of the drawbacks of GA is that it is computationally intensive and hence isslow.Thischapterhighlightscertainmodificationsofthealgorithmwhich overcomesthislimitationofGA.Thebiomimeticoriginoftheseadaptations provides an interesting avenue for researchers to develop further modifica- tions of GA. Preface xi Alltheabovecontributionshaveaheavydoseofmathematicsandshow different perspectives to address similar problems. Personally and professionally, it has been a great pleasure for me to be working with all the authors and the editorial team of Elsevier. S. PUSHPAVANAM CHAPTER ONE Measurement-Based Real-Time Optimization of Chemical Processes Grégory Francois, Dominique Bonvin Laboratoired’Automatique,EcolePolytechniqueFe´de´raledeLausanne,EPFL,Lausanne,Switzerland Contents 1. Introduction 2 2. ImprovedOperationofChemicalProcesses 3 2.1 Needforimprovedoperationinchemicalproduction 3 2.2 Fourrepresentativeapplicationchallenges 5 3. Optimization-RelevantFeaturesofChemicalProcesses 7 3.1 Presenceofuncertainty 7 3.2 Presenceofconstraints 8 3.3 Continuousversusbatchoperation 9 3.4 Repetitivenatureofbatchprocesses 9 4. Model-BasedOptimization 9 4.1 StaticoptimizationandKKTconditions 10 4.2 DynamicoptimizationandPMPconditions 11 4.3 Effectofplant-modelmismatch 14 5. Measurement-BasedOptimization 15 5.1 Classificationofmeasurement-basedoptimizationschemes 16 5.2 Implementationaspects 17 5.3 Two-stepapproach 18 5.4 Modifier-adaptationapproach 23 5.5 Self-optimizingapproaches 26 6. CaseStudies 28 6.1 Scale-upinspecialtychemistry 28 6.2 Solidoxidefuelcellstack 32 6.3 Gradetransitionforpolyethylenereactors 37 6.4 Industrialbatchpolymerizationprocess 43 7. Conclusions 48 Acknowledgment 49 References 49 AdvancesinChemicalEngineering,Volume43 #2013ElsevierInc. 1 ISSN0065-2377 Allrightsreserved. http://dx.doi.org/10.1016/B978-0-12-396524-0.00001-5 2 GrégoryFrancoisandDominiqueBonvin Abstract Thischapterpresentsrecentdevelopmentsinthefieldofprocessoptimization.Inthe presenceofuncertaintyintheformofplant-modelmismatchandprocessdisturbances, thestandard model-based optimization techniques might not achieve optimality for therealprocessor,worse,theymightviolatesomeoftheprocessconstraints.Toavoid constraints violations, a potentially large amount of conservatism is generally intro- duced, thus leading to suboptimal performance. Fortunately, process measurements can be used to reduce this suboptimality, while guaranteeing satisfaction ofprocess constraints. Measurement-based optimization schemes can be classified depending onthewaymeasurementsareusedtocompensatetheeffectofuncertainty.Threeclas- ses of measurement-based real-time optimization (RTO) methods are discussed and compared.Finally,fourrepresentativeapplicationproblemsarepresentedandsolved usingsomeoftheproposedRTOschemes. 1. INTRODUCTION Processoptimizationisthemethodofchoiceforimprovingtheperfor- mance of chemical processes while enforcing the satisfaction of operating constraints. Long considered as an appealing tool but only applicable to academic problems, optimization has now become a viable technology (Boyd and Vandenberghe, 2004; Rotava and Zanin, 2005). Still, one of the strengthsofoptimization,thatis,itsinherentmathematicalrigor,canalsobe perceived as a weakness, as it is sometimes difficult to find an appropriate mathematicalformulationtosolveone’sspecificproblem.Furthermore,even whenprocessmodelsareavailable,thepresenceofplant-modelmismatchand process disturbances makes the direct use of model-based optimal inputs hazardous. In the past 20 years, the field of “measurement-based optimization” (MBO)hasemergedtohelpovercometheaforementionedmodelingdifficul- ties.MBOintegratesseveralmethodsandtoolsfromsensingtechnologyand controltheoryintotheoptimizationframework.Thisway,processoptimiza- tiondoesnotrelyexclusivelyonthe(possiblyinaccurate)processmodelbut also on process information stemming from measurements. The first widely available MBO approach was the two-step approach that adapts the model parameters on the basis of the deviations between predicted and measured outputs,andusestheupdatedprocessmodeltorecomputetheoptimalinputs (Marlin and Hrymak, 1997; Zhang et al., 2002). Though this approach has becomeastandardinindustry,ithasrecentlybeenshownthat,inthepresence Measurement-BasedReal-TimeOptimizationofChemicalProcesses 3 ofplant-modelmismatch,thismethodisveryunlikelytodrivetheprocessto optimality(Chachuatetal.,2009).Morerecently,alternativestothetwo-step approach were developed. The modifier approach (Marchetti et al., 2009) also proposestosolveamodel-basedoptimizationproblembutusingafixedplant model.Correctionforuncertaintyismadeviatheadditionofmodifierterms to the cost and theconstraintfunctions of the optimization problem. Asthe modifiers include information on the deviations between the predicted and the plant necessary conditions of optimality (NCOs), this approach is prone to reach the process optimum upon convergence. Another field has also emerged, for which numerical optimization is not used on-line. With the so-called self-optimizing approaches (Ariyur and Krstic, 2003; Franc¸ois et al., 2005;Skogestad,2000;SrinivasanandBonvin,2007),theoptimizationprob- lem is recast as a control problem that uses measurements to enforce certain optimality features of the real plant. This chapter reviews these three classes of MBO techniques for both steady-state and dynamic optimization problems. The techniques are moti- vated and illustrated by four industrial problems that can be addressed via processoptimization:(i)thescale-upofoptimaloperationfromthelaboratory to production, (ii) the steady-state optimization of continuous production, (iii) the optimal transition between grades in the production of polymers, and (iv) the dynamic optimization of repeated batch processes. Thechapterisorganizedasfollows.Theneedforimprovedoperationin the chemical industry is addressed, together with the presentation of four application problems. The next section discusses the features of chemical processesthatarerelevanttooptimization.Then,thebasicelementsofstatic anddynamicoptimizationarepresented,followedbyanin-depthexposure ofMBOandthethreeaforementionedclassesoftechniques.Then,thefour case studies are presented, followed by conclusions. 2. IMPROVED OPERATION OF CHEMICAL PROCESSES 2.1. Need forimprovedoperation inchemical production In a world of growing competition, every tool or method that leads to the reductionofproductioncostsortheincreaseofbenefitsisvaluable.Fromthis pointofview,thechemicalindustryisnodifferent.Asaconsequenceofthis increasing competition, the structure of the chemical industry has progres- sivelymovedfromthemanufacturingofbasicchemicalstoamuchmoreseg- mented market including basic chemicals, life sciences, specialty chemicals andconsumerproducts(Choudaryetal.,2000).Thissegmentationinterms 4 GrégoryFrancoisandDominiqueBonvin ofthenatureoftheproductsimpactsthestructuralorganizationofthecom- panies(Bonvinetal.,2006),theinteractionbetweenthesuppliersandthecus- tomers,butalso,ontheprocessengineeringside,thenatureandthecapacity oftheproductionunits,aswellasthecriterionforassessingtheproduction performance.Thissegmentationisbrieflydescribednext. 1. “Basicchemicals”aregenerallyproducedbylargecompaniesandsoldtoa large number of customers. As profit is generally ensured by the high- volume production (small margins but propagated over a large production),onekeyforcompetitivenessliesintheabilityoffollowingthemar- ketfluctuationssoastoproducetherightproduct,attherightquality,at the right instant. Basic chemicals, also referred to as “commodities,” encompass a wide range a products or intermediates such as monomers, large-volumepolymers(PE,polyethylene;PS,polystyrene;PP,polypro- pylene;PVC,polyvinylchloride;etc),inorganicchemically(salt,chlorine, caustic soda, etc.) or fertilizers. 2. Active compounds used in consumer goods and industrial products are referredtoas“finechemicals.”Theobjectiveoffine-chemicalscompa- niesistypicallytoachievetherequiredqualitiesoftheproducts,asgiven by the customers (Bonvin et al., 2001). Hence, the key to being com- petitive is generally to provide the same quality as the competitors at a lower price or to propose a higher quality at a lower or equal price. Examplesoffinechemicalsincludeadvancedintermediates,drugs,pes- ticides, active ingredients, vitamins, flavors, and fragrances. 3. “Performancechemicals”correspondtothefamilyofcompounds,which areproducedtoachievewell-definedrequirements.Adhesives,electro- chemicals, food additives, mining chemicals, pharmaceuticals, specialty polymers,andwatertreatmentchemicalsaregoodrepresentativesofthis classofproducts.Asthenameimplies,thesechemicalsarecriticaltothe performanceoftheendproductsinwhichtheyareused.Here,thecom- petitiveness of performance-chemicals companies relies highly on their ability to achieve these requirements. 4. Since “specialty chemicals” encompass a wide range of products, this segment consists of a large number of small companies, more so than other segments of the chemical industry (Bonvin et al., 2001). In fact, many specialty chemicals are based on a single product line, for which the company has developed a leading technology position. Whilebasicchemicalsaretypicallyproducedathighvolumesincontinuous operation,finechemicals,performancechemicalsandspecialtychemicalsare morewidelyproducedinbatchreactors,thatis,low-volume,discontinuous