Simulation and Optimization in Process Engineering: The Benefit of Mathematical Methods in Applications of the Chemical Industry

419 Pages·2022·21.206 MB·English

by  Michael Bortz, Norbert Asprion

Simulation and Optimization
in Process Engineering
Simulation
and Optimization
in Process Engineering
The Benefit of Mathematical Methods
in Applications of the Chemical Industry
Edited by
Prof. Dr. Michael Bortz
Dr. Norbert Asprion
Elsevier
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Contents
Contributors xiii
Preface xvii
1. Prediction and correlation of physical properties
including transport and interfacial properties
with the PC-SAFTequation of state
Jonas Mairhofer andJoachim Gross
1. ModelequationsofPC-SAFT 3
2. Parameterization 8
2.1 Pure-componentparameters 8
2.2 Binaryinteractionparameters 9
3. Group-contributionmethodsforPC-SAFT 14
4. Transportproperties 16
5. Interfacialproperties 18
References 21
2. Don’t search—Solve! Process optimization modeling
with IDAES
LorenzT.Biegler,DavidC.Miller,andChineduO.Okoli
1. Introduction 33
1.1 Optimizationevolutionfromsystematicsearch
todirectsolution 35
2. Solutionalgorithmsandoptimizationmodels 37
3. Advancedoptimizationfordifferential-algebraic
applications 39
3.1 Complexityofdynamicoptimizationstrategies 40
4. TheIDAESoptimizationmodelingsoftwareplatform 41
5. Carboncaptureoptimizationcasestudy 45
5.1 Optimizationproblemformulation 48
5.2 Probleminitializationandimplementation 49
6. Conclusionsandfutureperspectives 51
Acknowledgments 53
Disclaimer 53
References 54
v
vi Contents
3. Thinking multicriteria—A jackknife when it comes to
optimization
NorbertAsprion andMichaelBortz
1. Introduction 57
1.1 Shortaccountonmulticriteriaoptimization 57
2. Processdesign 60
2.1 Continuousdesignvariables 61
2.2 Discretealternatives 62
2.3 Theimpactofuncertainties 64
2.4 Extensiontooptimalcontrol 68
3. Modeladjustment,modelcomparisonandmodel-based
designofexperiments 69
4. Decisionsupport 71
Acknowledgments 73
References 73
4. Integrated modeling and energetic optimization of
the steelmaking process in electric arc furnaces: An
industrial application
Jesu´s D. Herna´ndez,LucaOnofri,and SebastianEngell
1. Introduction 77
2. Electricarcfurnaceprocessmodel 79
2.1 HybridEAFprocessmodel 79
3. Dynamicoptimizationofthemeltingprofiles 87
3.1 Problemstatement 87
3.2 Ageneralformulationofthedynamicoptimization
problem 88
3.3 Formulationofthedynamicoptimizationproblem
oftheEAFprocess 88
4. Solutionusingcontrolvectorparametrization 89
4.1 Numericalsolutionofthemodel 89
4.2 Terminationconditions 91
4.3 Modelvalidationandparameterestimation 91
4.4 Numericalsolutionoftheoptimizationproblem 94
4.5 Batchtimeconstraint 94
5. Resultsanddiscussions 95
5.1 Numericalcasestudy 95
5.2 Resultsfortherealindustrialprocess 97
6. Conclusions 98
References 99
5. Solvent recovery by batch distillation—Applicationof
multivariate sensitivity studies to high dimensional
multiobjective optimization problems
JanC.Scho€neberger, DanielStaak, andJu€rgenRarey
1. Introduction 101
Contents vii
1.1 Separationofacetoneandmethanol 102
1.2 Continuousseparationprocesses 102
1.3 Batchprocessesforseparation 103
2. Problemdefinition 103
2.1 Productspecificationsandconstraints 103
2.2 Descriptionoftheplant 103
3. Literaturereview 106
4. Methodology 109
4.1 Heuristicsfortheselectionofasuitablemultipurpose
plant 109
4.2 Toolforrunningflowsheetsimulations 110
4.3 Algorithmsforoptimizingflowsheet
simulations 110
4.4 Toolforrunningmultivariatesensitivity
studies 111
5. Setupoftheflowsheetsimulation 111
5.1 Thermodynamicmodels 111
5.2 Screeningmodel 112
5.3 Low-fidelitymodel 115
5.4 High-fidelitymodel 115
6. Results 117
6.1 Screeningmodel 117
6.2 Low-fidelitymodel 121
6.3 High-fidelitymodel 131
6.4 Economicevaluation 137
7. Summary 139
References 140
6. Modeling and optimizing dynamic networks:
Applications in process engineering and energy
supply
JanMohring,Jochen Schmid,Jarosław Wlazło,
Raoul Heese,Thomas Gerlach,Thomas Kochenburger,
andMichael Bortz
1. Introduction 143
2. AD-Net 144
3. Applicationsinenergysupply 146
3.1 Powertransmission 146
3.2 Districtheating 146
4. Applicationsinbatchdistillation 147
4.1 Forwardsimulation 150
4.2 Parameteridentification 151
4.3 Optimalcontrol 154
5. Conclusion 158
Acknowledgment 159
References 159
viii Contents
7. Theuseofdigitaltwinstoovercomelow-redundancy
problems in process data reconciliation
FilippoBisotti, AndreaGaleazzi, FrancescoGallo,
andFlavioManenti
1. Introduction 161
2. Datareconciliation 163
2.1 Variableclassification 163
2.2 Steady-statedatareconciliation(DR) 163
2.3 Grosserrordetection 165
2.4 Grosserroreffectandhowtohandle 165
2.5 Grosserrordetection:Statisticalmethods 166
2.6 GEstatisticaldetectionalgorithms 167
2.7 Numericalmethodforlow-redundantsystem 167
3. Clevermeanandclevervariance(cmandcv) 168
4. Medianandmad 170
4.1 Dynamicdatareconciliation 171
4.2 Movingtime-windowapproach 172
4.3 SolutionofDDRwithorthogonalmatrix 173
4.4 Implementationandtheroleofdigitaltwin 175
5. Industrialcasestudy:ItelyumRegenerationamine
washingunit 177
5.1 Processdescription 177
5.2 Assumptions 179
6. Results 180
6.1 Steady-statedatareconciliationresultsdiscussion 180
6.2 Grosserrordetectionresultsdiscussion 186
6.3 Dynamicdatareconciliationcasestudy:Aminetank
dynamics 189
7. Conclusions 195
7.1 Steady-statedatareconciliation 195
7.2 Dynamicdatareconciliation(DDR) 196
Acknowledgments 198
References 198
8. Real-time optimization of batch processes via
optimizing feedback control
Dominique Bonvin,Gre(cid:2)gory Franc¸ois,Gianluca Rizzi,
andMichael Amrhein
1. Introduction 201
2. Representationofbatchprocesses 203
2.1 Distinguishingfeatures 203
2.2 Mathematicalmodels 203
2.3 Staticviewofabatchprocess 204
3. Numericaloptimizationofbatchprocesses 205
3.1 Problemformulation:Dynamicoptimization 206
3.2 Reformulationofadynamicoptimizationproblemasa
staticoptimizationproblem 206
Contents ix
3.3 Batch-to-batchsolution:Staticoptimization 207
3.4 Effectofplant-modelmismatch 208
4. Feedback-basedoptimizationofuncertainbatch
processes 209
4.1 Offlineactivity:Determinethefeedbackstructure 209
4.2 Real-timeactivities:Implementfeedbackcontrol 211
5. Illustrativeexample:Batchdistillationcolumn 213
5.1 Industrialbatchdistillationcolumn 213
5.2 Processmodel 215
5.3 Inputparameterizationoftheimpurityfraction 216
5.4 Controldesignandperformance 218
6. Conclusions 221
References 222
9. On economic operation of switchable chlor-alkali
electrolysis for demand-side management
Kosan Roh,LuisaC.Bre(cid:2)e,KarenPerrey,Andreas Bulan,
andAlexanderMitsos
1. Introduction 226
2. Operationalmodeswitchingofchlor-alkalielectrolysis 227
3. Mathematicalformulationforoptimalsizingandoperation
ofswitchablechlor-alkalielectrolysis 230
3.1 Operationalmodetransition 230
3.2 Massbalance 231
3.3 Powerdemand 231
3.4 Rampingconstraints 232
3.5 Costfunction 233
4. Casestudy 233
4.1 Optimaloperationalbehaviorofswitchablechlor-alkali
electrolysis 234
4.2 Comparisonofswitchablechlor-alkalielectrolysis
tootherflexibilityoptions 234
4.3 Simultaneousoptimizationofplantoversizingand
operation 238
5. Conclusion 238
Acknowledgments 240
References 240
10. Optimal experiment design for dynamic processes
SatyajeetBhonsale, PhilippeNimmegeers,
SimenAkkermans,Dries Telen, IoannaStamati,
FilipLogist, andJanF.M.VanImpe
1. Introduction 243
2. Optimalexperimentdesignformodelstructure
discrimination 246
2.1 OED/SDinpractice 248
x Contents
3. Optimalexperimentdesignforparameterestimation 251
3.1 Computingparametervariance-covariancematrix 252
3.2 OED/PEasanoptimalcontrolproblem 254
3.3 OED/PEinpractice 257
4. Advanceddevelopmentsinoptimalexperimentdesign 257
4.1 Robustoptimalexperimentdesignforparameter
estimation 257
4.2 Multicriterionoptimalexperimentdesign 263
5. Conclusions 268
References 269
11. Characterization of reactions and growth in
automated continuous flow and bioreactor
platforms—From linear DoE to model-based
approaches
Tilman Barz,Julian Kager,ChristophHerwig,
Peter Neubauer,MarianoNicolasCruzBournazou,
andFedericoGalvanin
1. Introduction 273
2. Miniaturizedplatformsandapplications 275
2.1 Continuous-flowmicroreactorplatformsinsynthetic
chemistry 275
2.2 Bioreactorplatformswithautomaticliquidhandling 277
2.3 ApplicationsofDoE,self-optimization,andmbOED—A
bibliographicalreview 283
2.4 Summary 297
3. Specialaspectsandchallenges 298
3.1 Staticvsdynamicexperimentalconditions 298
3.2 SequentialplanningandupdatinginmbOED 301
3.3 Parameteridentifiability 303
3.4 Bayesianstatistics 304
3.5 Mathematicalmodeling,softwareandalgorithms 306
4. Industryview 310
4.1 mbOEDsoftware,flexibility,usability,andrequired
expertknowledge 311
5. Discussionandconclusions 312
References 313
12. Product development in a multicriteria context
Philipp Su€ss, GregorFoltin,Melanie Heidgen,
DavidHajnal,JorgeDiaz, HergenSchultze,
JochenGattermayer,andStefanLehner
1. Introduction 321
2. Modelfitting 322
2.1 Generatingthedata:Designofexperiments 323
3. Multicriteriaoptimizationanddecision-making 326