HAL Id: hal-02952927
https://hal.inrae.fr/hal-02952927
Submitted on 29 Sep 2020
HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
Distributed under a Creative CommonsAttribution - NonCommercial - NoDerivatives| 4.0 International License
microfiltration of skim milk
Maëllis Belna, Amadou Ndiaye, Franck Taillandier, Louis Agabriel, Anne-Laure Marie, Geneviève Gésan-Guiziou
To cite this version:
Maëllis Belna, Amadou Ndiaye, Franck Taillandier, Louis Agabriel, Anne-Laure Marie, et al.. For- mulating multiobjective optimization of 0.1 µm microfiltration of skim milk. Food and Bioproducts Processing, Elsevier, 2020, 124, pp.244-257. �10.1016/j.fbp.2020.09.002�. �hal-02952927�
ContentslistsavailableatScienceDirect
Food and BioproductsProcessing
jo u r n al ho m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / f b p
Formulating multiobjective optimization of 0.1 m microfiltration of skim milk
MaëllisBelnaa,b,c, AmadouNdiayeb, FranckTaillandierd,∗, LouisAgabrielc, Anne-LaureMariec, GenevièveGésan-Guizioua,∗
aSTLO,INRAE,InstitutAgro,F-35042,Rennes,France
bINRAE,UniversitédeBordeaux,I2M,F-33400Talence,France
cBoccard,ResearchandDevelopment,F-35360Montauban-de-Bretagne,France
dINRAE,AixMarseilleUniv,RECOVER,France
a r t i c l e i n f o
Articlehistory:
Received23June2020
Receivedinrevisedform24August 2020
Accepted5September2020 Availableonline14September2020
Keywords:
Knowledgeacquisition Expertknowledge Knowledgerepresentation Causalmaps
Proteinfractionation Dairysector
a bs t r a c t
Optimizing0.1mmicrofiltration(MF)ofskimmilkrequiresformulatingoptimizationofa foodprocessingproblemwithconflictingobjectivesthatconsiderproductcomposition,pro- cessvariablesandoperatingconditions.FormulatingtheMFoptimizationproblemrequires knowledgefromexpertsaboutfoodprocessing,dairyproductproductionandequipment manufacturing.ThisstudyformulatedtheMFoptimizationprobleminaninnovativeman- ner:asamultiobjectiveoptimizationproblemthatconsideredtheentireMFprocess.Eleven expertswereinterviewedtoidentifytheknowledgedomainsnecessary,whichrequired36 interviewsoveratotalof14h.FormulationwasachievedfromtheMFprocesstothecom- positionsofthepermeateandretentatefractions.Fiveconflictingoptimizationobjectives, influencedby36variables,weresetuptoformulatetheproblem.Fiveofthevariableswere decisionvariablesusedtocontroltheMF,andtheother31wereintermediatecalculation variables.Thisapproachopensnewperspectivesforoptimizationoffoodprocessesthat integrateexpertknowledge.
©2020InstitutionofChemicalEngineers.PublishedbyElsevierB.V.Allrightsreserved.
1. Introduction
Crossflowmicrofiltrationofskimmilkwithaporesizeof0.1m(MF) iscommonlyusedin thedairyindustry toseparatethetwo main groupsofproteins:nativecaseinmicelles(retentate),usedtomake cheese,andserumprotein(permeate),usedmainlytoformulatefood forspecificpopulations(e.g.elderlypeople,infants).Milkproteincan befractionatedeitherwithceramicorpolymericspiralwoundmem- branes.Inordertoovercomefoulingandavoidretrofiltration,skimmilk microfiltrationoperatingwithceramicmembranesisneveroperated inconventionalfiltrationsystem(Gésanetal.,1993;Gésan-Guiziou, 2010).Microfiltrationisoperatedwitheitheruniformtransmembrane pressuresystem(UTPsystem)whichconsistsinthecirculationofthe permeateco-currenttotheretentateinordertogetapressuredropin thepermeatesidesimilartotheoneobtainedintheretentatesideand thenanhomogeneousTMPalongthefilteringpath(Sandblöm,1974),
∗ Correspondingauthors:
E-mailaddresses:[email protected](F.Taillandier),[email protected](G.Gésan-Guiziou).
orceramicmembraneswithahydraulicresistancegradient(suchas GP®andIsoflux®membranes)(GarceraandToujas,1998;Skrzypekand Burger,2010).Comparatively,microfiltrationwithpolymericSWmem- branesisperformedinconventionalmodewithTMPhighenoughto avoidretrofiltrationattheoutletofthemembrane.Eachtechnology (furthercalledUTP,GPandSW)hasitsownbenefitsanddrawbacks, whichleadstoconflictingobjectives.Forinstance,ceramicmembranes havehigherpermeationflux(ca.75−80Lh−1m−2inindustrialcondi- tions)thanpolymericmembranes(ca.25Lh−1m−2),whilepolymeric membraneshavelowerinvestmentcoststhanceramicmembranes.
Despitethedairysector’sinterestinMF,thisoperationisnotcompletely optimized.
Inthedairysector,twomainapproacheshavebeenusedtocom- pare oroptimize processesin thedairy sector.Thefirst approach focusesonthedefinitionofthebestprocessoptionseitherbyoptimiz- ingproductionscheduling(i.e.timemanagement)(Seletal.,2017)or
https://doi.org/10.1016/j.fbp.2020.09.002
0960-3085/©2020InstitutionofChemicalEngineers.PublishedbyElsevierB.V.Allrightsreserved.
Nomenclature
C concentration(gkg−1)
CD concentrationonadry-matterbasis(gkgDM−1) CI investmentcost(D)
CO cost(D)
CPR productioncost(D) DM drymatter(gkg−1)
GP membranewithgradientofpermeability(-) Jp permeationflux(Lh−1m−2)
MF skimmilkcrossflowmicrofiltrationwith0.1m poresize(-)
MT membranetechnology(-) NM numberofmodules(-) P pressure(Pa)
q quantity(g)
Qfeed feedflowrate(m3h−1) Qrec recirculationflow(m3h−1) R scoregivenbyexperts(-)
SW polymericspiralwoundmembrane(-)specific weightedaverage(-)
T filtrationtemperature(◦C) t filtrationtime(min)
TMP transmembranepressure(Pa) Tr transmissionrate(%)
UTP uniformtransmembranepressure(-) V volume(m3)
VRR volumereductionratio(-) w weightingfactor(-)
recoveryyield(-)
Subscripts
CN casein
i ithlineinparallelinonestageofthemicrofil- trationplant
in inlet
j jthmicrofiltrationmoduleonlinei
k issue
m numberofissuespercriterion
n numberofstagesofthemicrofiltrationplant
o outlet
p permeate
r retentate
SP serumprotein
bycomparingmultipleprocessscenariosonthebasisofseveralcriteria (Gésan-Guiziouetal.,2019;Deppingetal.,2017,2020).Inthesestudies, theoperatingconditionsandprocessdesignofeachunitoperations, combinedintheoverallprocess,havebeenconsideredasconstant.The secondapproachconsistsinincludingthechoiceofoperatingcondi- tionsandprocessdesigninanoptimizationprocess.Thisisthecasefor instancefortheoptimizationoftheevaporator(Madoumieretal.,2020) orheatexchanger(DekaandDatta,2017)inthedairyfield.Inthislast approach,operatingparametersanddesignparametersareoptimized todefinetheoptimalmodeofoperationanddesignoftheprocessin ordertomeetspecificrequirements.Thisapproachrequirestoknow therelationsbetweenvariablesandvariablesandoptimizationobjec- tives.Nowadaysinskimmilkmicrofiltration,theserelationsarenot explicitlydefined.
Thelackofknowledgeinmicrofiltrationaboutmechanismsthat limitprocessperformancesisarealobstacleforthedefinitionofthe relationsbetweenvariablesandvariablesandoptimizationobjectives (Jimenez-Lopezetal.,2008;TolkachandKulozik,2006;Trystram,2012).
Attheindustriallevel,thechoiceofmembranetechnologyaswellas
processingdesignandconditionsarebasedontheknow-howofoper- atorsandavailableexpertknowledge,whicharecloselyrelatedtothe historyandexperienceofeachequipmentmanufacturer.Theydonot haveenoughdatatocomparethethreefiltrationtechnologiesdefined inthisstudyintermsoffractionscompositions,operatingvariables anddesignoftheplant.Thislackofdatamakestheoptimisationof givenspecificationsimpossibleregardingthechoicesoftheoptimal membranetechnology,operatingvariablesandprocessdesign.Inthe scientificliterature,optimaloperatingconditionsareoftenidentified empiricallyinexperimentsthatrevealtheinfluenceofonevariableona groupofchosenvariables(Adamsetal.,2015;Gésan-Guiziouetal.,1999, 2000;Jørgensenetal.,2016;Tremblay-Marchandetal.,2016;Zulewska andBarbano,2013,2014).Otherstudies,suchasthatofAstudillo-Castro (2015),modelledtheMFprocess.Eachoftheseexperimentsandmod- elsassessedonlyonemembranetechnologytodetermineprocessing conditionsthatincreasetheyieldofserumproteinrecoveryintheper- meateand/orimprovepermeationflux.Toourknowledge,onlyone study(Zulewskaetal.,2009)comparedtheperformance(i.e.serum protein recoveryyieldandpermeationflux)ofallthreemembrane technologies.Thisstudyhighlightedstrongdifferencesintheseper- formancesamongthemembranetechnologies.Itsauthorssuggested thattoachieveaserumproteinrecoveryyieldwithSWtechnologysim- ilartothoseobtainedwiththeUTPandGPtechnologies,themembrane areawouldhavetobeincreased,whichincreasesthecostsoftheSW plant.Althoughmicrofiltrationstudiesareusefulbecausetheyidenti- fiedinfluentialoperatingvariablesandhelpedunderstandmembrane fouling,theydidnotoptimizeoperatingvariables,processdesignand economiccostsrespondingtogivenconflictingobjectivespecifications.
Theygenerallyconsiderasingleoptimizationobjectiveandwhenthey considerseveral,theoptimizationofalltheobjectivesisnotachieved simultaneously.Whileoptimizingmicrofiltrationmustaddressseveral conflictingobjectives,suchasmaximizingproductrecoverywhilemin- imizingcosts.Asitisnotpossibletosolveamultiobjectiveproblem bymergingincrementaloptimizations,itisnecessarytooptimizethe wholeunitoperationofmicrofiltration.
Toaddressthechallengeofoptimizinganentireprocesswithcon- flictingobjectives,weformulatedMFoptimizationasamultiobjective optimization problemthat considered conflicting objectives simul- taneously. Multiobjective optimization has three main challenges:
formulatingthemultiobjectiveproblem,modellingtheoptimization objectivesandsolvingtheproblem.Problemformulationconsistsof identifyingthedecisionproblemthroughitsobjectives,decisionvari- ablesandconstraints.Modellingtheoptimizationobjectivesconsists of formulating them as mathematical functions or computational algorithmsofdecisionvariables(i.e.“objectivefunctions”).Modelling theobjectivefunctionsrequiresestablishingthe“influencerelations”
betweendecisionvariablesandoptimizationobjectives,butalsogood understandingofthephenomenathatconnectthem.Problemsolving consistsofexploringthesolutionspacetofindParetooptimalsolutions (i.e.non-dominatedsolutions)(Reyes-SierraandCoelloCoello,2006).
Thislastchallengeisaddressedbywidevarietyofefficientmetaheuris- ticalgorithmssuchasNSGA2,MOPSOandAntColony(Colletteand Siarry,2002).However,therelevanceoftheresultsofthesealgorithms dependsonthequalityofproblemformulation.Thisstudyfocusedon formulatingMFoptimizationasamultiobjectiveoptimizationproblem.
FormulatingthemultiobjectiveoptimizationproblemofMFiscom- plex due to the large number and heterogeneity of the variables involved(e.g.ordinal,cardinal,discrete,continuous)andthelackof knowledgeaboutthephysicallawsinvolved.Descriptionofinfluence relationsamongthevariablesthemselvesandbetweenthevariables andoptimizationobjectivescanbebasedonexpertknowledge,espe- ciallywhenrelationsarenotscientificallyestablished.Twostudieson integratingexpertknowledgewhenrepresentingafoodprocesshave beenperformedinthepastseveralyears.Thefirstcombinedexpert andrheologicalknowledgeaboutFrenchbreadtopredictthestateof doughandbreadfromrawmaterialsandprocessingconditions(Ndiaye etal.,2009).Thesecondpredictedcheeseripeningfrombiochemical measurementsandsensoryobservations(Baudritetal.,2010).Toour knowledge,onlyonestudy(Hobballahetal.,2018)developedamethod tointegrateexpertknowledgeintotheformulationofamultiobjective
optimizationproblem.Thismethodwasappliedtoformulateaprelimi- narydesignofwood-basedinsulatingmaterials(Hobballahetal.,2018).
Inthisstudy,theoptimizationobjectives,initiallydefinedbypartners, concernedonlytheproductforagivenpredefinedprocess,andscien- tificknowledgewassufficienttoformulatetheoptimizationproblem successfully.TousetheHobballahetal.(2018)methodtoformulatethe optimizationofMF,itwasnecessarytoaddtoiti)establishmentofopti- mizationobjectives,ii)considerationofprocessdesignandoperating conditionsandiii)knowledgeofindustrialexperts.
Theobjectiveofthepresentstudywasthustoformulatethemul- tiobjectiveoptimizationproblemofmicrofiltrationinaholisticway.
Duetothelackofscientificknowledgeonthedifferentimpliedpro- cess/phenomena,andinordertointegrateobjectivesthatarerarely foundintheliterature,itrequirestouseknowledgefrombothsci- entificandindustrialknowledge.This paperfocusesonknowledge acquisitionandmodellinginordertobeabletoperformthemulti- objectiveoptimization.Theissueisnotonlytodefinetheobjectives butalsotographicallyrepresentknowledgeaboutmilkmicrofiltration fromscientificandindustrialinformationinordertoultimatelyenable decision-makerstomakemorerationaldecisions,couplingscientific andexpertknowledge.Itiscertainthatnowadays,decision-makersin microfiltrationprocessdesignincludeseveralcriteriaintheirdecision, buttheydosoinanunformalized,non-reproducibleway,atthesame timelimitingtheirabilityto“rationally”justifytheirchoicesandto explainthem.Thisfirstformalizationworkisasteptoprovidecomput- erizeddecisionsupport.TheformulationofMFwasperformedusing themethodofHobballahetal.(2018)inwhichthechoiceofoptimiza- tionobjectivesandexpertswasaddedasaninitialstep.TheHobballah etal.(2018)methodisrobust,becauseitcanconsiderbothMFproducts andprocesses,whichinvolvesmanyvariables.
Thepresentstudywasperformedaspartofthe“Optimal”project, whosemainobjectiveistostudyanddevelopamethodtooptimize crossflowmicrofiltrationofskimmilkwithaporesizeof0.1mto supportthedesignandperformanceoffiltration.Thisstudybrought togetherdairyproductproducers, anequipmentmanufacturerand researchersfromseveralscientificdomainsintoa“projectgroup”.
2. Materialsandmethods 2.1. Descriptionoftheprocesssystem
Inthis firstapproachoftheformulation ofthemultiobjec- tiveproblemofmicrofiltration,theassumptionsweremadein ordertospecifytheframeworkofthestudywhileconsidering industrialconstraints.Theseassumptionsimplytosetsev- eralvariablesasconstants.Notconsideringtheassumptions wouldimplytokeepthesamemethodologybuttomodifythe knowledgemodelbyaddingtherelationsbetweenthenew variables(previouslysetasconstants)withthevariablesand objectivesofthemicrofiltrationoptimizationmodel.
Inthisstudy,bovinemilkwasassumedtobestoredat4
◦C for48h, skimmed, thermized(68 ◦Cfor30 s)and then bactofugedtodecreasethebacterialcountoftheprocessed milk.Skimmilkwasthenmaintainedatthefiltrationtem- peraturefor20mintoreachmineralequilibriapriortoMF.
Characteristicsofmilkhistory(pre-treatments,storagecon- ditions)weresetasconstants.
MFcanbeperformedusingthreemainmembranetech- nologies(Table1).Themembranesconsideredinthisstudy werethoseusuallyusedinthedairyindustry:a0.1mUTP ceramictubularmembrane,Pall7P1940UTP(19channels,4 mmdiameter,1.68m2 filtrationarea);a0.1mGPceramic tubularmembrane,Pall7P1940GP(19channels,4mmdiam- eter,1.68m2 filtration area);and apolymericspiralwound membrane800kDaSW,SynderFR3A6338(41milsspacers, 15.9m2filtrationarea).Thetemperaturewassettotheusual MFtemperature:12◦CforSWand50◦CforGPandUTP.Tobe
consistentwithindustrialconstraints,MFwasassumedtobe performedatconstantpermeationflux,whichensurescon- tinuousfeedingofthenextstepsintheprocess.TheMFplant wasdescribedwithnthenumberofstages(2–5),itheithline inparallelinonestageandjthejthmoduleonlinei.Inagiven stage,eachlineihadthesamenumberofmodules.
Inthisstudy,thefollowingconfigurationsareconsidered:
UTP microfiltration system performedat50 ◦Cin continu- ousmodewithoutdiafiltration,GPmembraneperformedat 50◦CincontinuousmodewithoutdiafiltrationandSWpoly- mericmembraneperformedat12◦Cincontinuousmodewith or without diafiltration. Thediafiltration solvent is reverse osmosis water. Diafiltration increases the performance of the separation by adding a solvent, which increases the recovery of serum proteinin the permeate but in a more dilutedform.IncreasingthevolumeoftheMFpermeatefrac- tionstronglyinfluencesthedesignoftheultrafiltrationand reverse-osmosisplantswhichfollowtheMF,buttheirdesign layoutsidethescopeofthestudy.Inaddition,cleaningand disinfectionstepswereconsideredinthisstudybutnotopti- mized.Alleffluentsweresent tothe wastewatertreatment plant, andeffluenttreatmentwasconsidered tolie outside thescopeofthestudy.
Although researcherscontinue to study optimizationof cleaningprocedures,thecleaningprocedureschoseninthis studywereassumedtobeeffectiveandreproducible.Clean- inganddisinfectionproceduresforeachtypeofmembrane were definedaccordingtoindustrialstandards. Membranes wereassumedtobechemicallyandbacteriologicallycleaned, thewaterfluxwasassumedtobeconstant,anddegradation ofmembranesduetochemicalswasassumedtobenegligible throughouttheirlifetime.
Wemadecertainassumptionsaboutthecompositionof retentateand permeatefractions. Inthe retentatefraction, weconsideredthecaseinsasawhole,withoutdistinguishing caseinmicellesandfreecaseins(ca.85%and15%ofcaseins, respectively). In this study and as a first attempt, casein permeationisnotconsideredintheoptimizationapproach.
It is known that casein permeation depends on filtration temperature,concentrationfactor,membranetypeanddiafil- trationmode(ratioandsolvent).However,thereisfewdata oncaseinpermeationasfunctionoftheseparametersinthe literature(ZulewskaetBarbano,2014;Zulewskaetal.,2009;
BeckmanandBarbano,2013;HartingerandKulozik,2020)and noneofthemare relevantforthe threefiltrationtechnolo- giesconsideredinthestudy.Includingcaseinpermeationin theoptimisationapproachwouldhaverequiredacquisitionof data,whichlaybeyondthescopeofthisstudy.
Investmentcostwasestimatedfromthecostofequipment (i.e. tanks, pumps, heat-exchangers, membranes, modules, sensors,plantautomationandcleaningplant)andlabour(i.e.
engineering department,projectfollow-up,installation and commissioning, automationprogramming). Productioncost wasestimatedfromconsumptionofutilities(i.e.water,energy andchemicalproducts),maintenancecostsandtheoperator’s salary.
2.2. Problemformulationapproach
InadaptingtheHobballahetal.(2018)method,formulation ofthemultiobjectiveoptimizationproblemwasdividedinto fouriterativesteps(Fig.1):i)chooseoptimizationobjectives and experts, ii) rate the relative importance ofknowledge
Table1–Descriptionofthethreemembranetechnologiesconsideredinthisstudy.Pisthepressure(inPa),andp,r,in andoaresubscriptsforpermeate,retentate,inletandoutlet,respectively.
Membranetechnology Spiralwound (SW)
Uniformtransmembrane pressure(UTP)
Gradientof permeability(GP)
Typeofmembrane Polymeric Ceramic Ceramic
Mechanismofmembrane technology
Details Permeateunder
pressure(Pp,in= Pp,o>0Pa)
. Circulationofpermeate co-currenttothatofthe retentate.
Permeateunderpressure(Pp,in= Pp,o>0Pa).Membranewithagradientof permeabilitythatincreasesbetweenthe inletandtheoutlet.
Fig.1–Theformulationmethodappliedtothemicrofiltrationmultiobjectiveoptimizationproblem(adaptedfromHobballah etal.(2018)).
domains,iii)collectknowledgeandiv)constructandmerge causalmaps.
2.2.1. Chooseoptimizationobjectivesandexperts
Inthefirststep,theprojectgroupdeterminedtheoptimiza- tionobjectives,guidedbyscientificinterests andindustrial practicesobtainedfrom aquestion e-mailedtothe chosen expertsintheprojectgroup.Thesurveyasked,“Inyouropin- ion,whatrelevantcriterianeedtobeconsideredtooptimize theseparationofcaseinmicellesandserumproteinbymicro- filtration?”.Theprojectgroupaggregatedsimilar responses into setsand discussed each set todetermineif it was an optimizationobjective(i.e.agoaltooptimize)oradecisioncri- terion(i.e.apreferencewithintheoptimizedsolutions).Based onthelistofoptimizationobjectives,theprojectgroupchose relevantandavailableexperts.Theseexpertswereaskedto identifytheirexpertisedomain(s)(i.e.“knowledgedomains”) withintheentireMFknowledgedomain.Expertswerechosen inaniterativeprocess thatwascloselyrelatedtothe iden- tificationofknowledgedomainsinordertoensurethatthe availableexpertisecoveredtheMFknowledgedomain.
KnowledgeaboutMFcamefrom avarietyofknowledge domains,suchasprocessdesign,dairytechnologyandfood biochemistry, all ofwhich were shared byseveral experts.
TheexpertsfirstdefinedboundariesoftheMFscopetodrive knowledgeacquisition,asisusuallydoneforlifecycleassess- ment(Tillmanetal.,1994)orsoftwaredevelopment(Paetsch etal.,2003).AsrecommendedbyMilton(2007),expertsdivided theentiredomainofMFintomorespecificmanageableknowl- edgedomains:thechosenexpertswereaskedviasurveyabout theknowledgedomainsnecessarytodescribeMF.Responses fromtheseexpertsweremerged,discussedandvalidatedby theentiregroupofexperts(i.e.projectgroupandchosen).
2.2.2. Ratetherelativeimportanceofknowledgedomains Once the experts had covered the entire MF knowledge domain,theywere askedtoratetherelativeimportanceof knowledgedomainstoeachothertoorganizetheknowledge elicitation(Milton,2007).Eachknowledgedomainwasrated according totwo criteria. Thefirst was the relativeimpor- tanceofcapturingtheknowledgedomain,whichwasassessed accordingtofourissues:abilitytoachievetheprojectobjec- tive, closeness tothe subject, novelty ofthe knowledgeto thesubjectandabilitytoincreasethequalityofknowledge.
Each issue ofthis criterion was scored on a scale from 1 (slightlyimportant)to4(veryimportant).Thesecondcriterion wastheeaseofcapturingtheknowledgedomain,whichwas assessedaccordingtothreeissues:explicitness,existenceof documentsandavailabilityofexperts(Hobballahetal.,2018).
Eachissueofthiscriterionwasscoredonascalefrom1(easy) to3(difficult).
Therelativeimportance oftheknowledgedomains was ratedasfollows.First,eachexpertscoredeachissueofeach criterionfortheknowledgedomains.Fromtheissuescores, we calculated amean score per issue. Aspecific weighted average(SWA)wasthencalculatedforeachcriterionofeach knowledgedomainfromthemeanscoresperissue:
SWA=
m
k=1
Rk×wk
m (1)
withkthesubscriptoftheissue,mthenumberofissuesper criterion (4 or3),R themean scoreoftheissue andw the weightingfactoroftheissue,whichwasthesameasthose ofHobballahetal.(2018).
