Table Of ContentADVANCES 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 con-
straints. 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, dis-
cusses 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 produc-
tion),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
