Reactive Distillation: Advanced Control Using Neural Networks

152 Pages·2022·51.775 MB·English

by  Vandana Sakhre, Uday Pratap Singh

VandanaSakhre,UdayPratapSingh
ReactiveDistillation
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Vandana Sakhre, Uday Pratap Singh
Reactive
Distillation
Advanced Control using Neural Networks
Authors
Dr.VandanaSakhre
DepartmentofChemicalEngineering
ManipalAcademyofHigherEducation
Dubai
UnitedArabEmirates
UdayPratapSingh
ShriMataVaishnoDeviUniversity
Sub-PostOffice
NetworkCentre,Katra182320
JammuandKashmir
India
ISBN978-3-11-065614-5
e-ISBN(PDF)978-3-11-065626-8
e-ISBN(EPUB)978-3-11-065641-1
LibraryofCongressControlNumber:2022931265
BibliographicinformationpublishedbytheDeutscheNationalbibliothek
TheDeutscheNationalbibliothekliststhispublicationintheDeutscheNationalbibliografie;
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Contents
Abouttheauthor XI
Chapter1
Introduction 1
1.1 Distillationprocess 1
1.1.1 Continuousreactivedistillationprocess 1
1.1.2 Reactivedividedwall(RDW)distillationprocess 2
1.2 Industrialperspectiveofreactivedistillation 3
1.2.1 Esterification 3
1.2.2 Transesterification 5
1.2.3 Etherification 5
1.2.4 TAEEsynthesis 6
1.3 Measurementsinprocessindustry 6
1.3.1 Hardwaremeasurement 7
1.3.2 Typesofhardwaremeasurements 7
1.3.3 Importanceofhardwaremeasurement 8
1.3.4 Difficultyinmeasurement 9
1.3.5 Problemsassociatedwithmeasurement 9
1.3.6 Suggestion/improvementinhardwaresensor 9
1.4 Softsensors 10
References 11
Chapter2
Synthesisinreactivedistillationcolumn 13
2.1 Experimentalsynthesis 13
2.1.1 Synthesisofbiodieselinareactivedistillationcolumn 15
2.1.2 Synthesisoftert-amylethylether(TAEE) 18
2.1.3 Synthesisofmethylacetate 19
2.1.4 Ethylacetatesynthesis 24
2.1.5 Propylacetate 25
2.1.6 Methyltert-butylether(MTBE) 27
2.1.7 ETBEsynthesisinpilotRD 28
2.2 DualRDintegrationtechnique 30
2.3 Membrane-assistedreactivedividedwall(MRDW)distillation
process 34
References 35
VIII Contents
Chapter3
Modelingofreactivedistillationcolumn 37
3.1 Modelingofreactivedistillationcolumn 37
3.1.1 Materialandenergybalancearoundcondenser 37
3.1.2 Balancearoundreactivezone 38
3.1.3 Componentbalancearoundreactivezone 39
3.1.4 Energybalancearoundreactionzone 39
3.1.5 Balancearoundreboiler 40
3.1.6 Componentbalancearoundreboiler 40
3.1.7 Energybalancearoundreboiler 41
3.2 Nonequilibriummodeling 41
3.3 Dynamicbalancesforthecolumn 43
3.3.1 Refluxdrum 44
3.3.2 Rectifyingandstrippingzones 44
3.3.3 Reactivezone 44
3.3.4 Feedtrays 45
3.3.5 Columnbase 45
3.3.6 Energybalance 45
3.3.7 Condenserheatduty 46
3.3.8 Reboilerheatduty 46
3.3.9 Columnpressure 46
3.3.10 Temperaturemeasurement 46
3.3.11 Vapor–liquidequilibrium 47
3.3.12 Molecularweight 47
3.3.13 Catalystloading 48
3.4 Modelingofmembrane-assistedreactivedividedwall(MRDW)
column 48
3.4.1 Materialbalance 49
3.4.2 Componentbalance 49
3.4.3 Masstransferbalance 50
3.5 Simulationsoftware(AspenPlus) 50
3.5.1 Chemicalcomponents 50
3.5.2 Operatingconditions 51
3.5.3 Nonrandomtwo-liquidmodels(NRTL) 51
3.5.4 OptimizationusingsensitivityanalysistoolofAspenPlus 52
References 54
Chapter4
Introductiontoartificialneuralnetworks 57
4.1 Whyneuralnetworks 57
4.1.1 Historyofneuralnetworks 57
4.2 Introductiontobiologicalneurons 58
Contents IX
4.3 Introductiontoartificialneuralnetworks 59
4.4 Architectureofneuralnetwork 60
4.5 Differencebetweencomputersandneuralnetworks 62
4.6 Propertiesofneuralnetwork 62
4.7 Importantparametersforneuralnetwork 63
4.8 Advantagesanddisadvantagesofneuralnetwork 63
Advantages 63
Disadvantages 64
4.9 Applicationsofneuralnetwork 64
4.10 Learningalgorithmsforneuralnetwork 64
4.11 Classificationandtypesofneuralnetwork 65
4.11.1 Perceptron 65
4.11.2 ADALINE(adaptivelinearneuronelement) 66
4.11.3 MADALINE(manyadaptivelinearneuronelement) 66
4.11.4 Feedforwardneuralnetwork(FFNN) 67
4.11.5 Adaptiveresonancetheory(ART) 69
4.11.6 Backpropagationnetwork(BPN) 69
4.11.7 Recurrentneuralnetwork(RNN) 70
4.11.8 BoltzmannandCauchymachines 70
4.11.9 Counterpropagationnetwork 71
4.11.10 Hopfieldnetwork 71
4.11.11 Self-organizingfeaturemap(SOFM) 73
4.11.12 Pulse-coupledneuralnetwork(PCNN) 73
4.11.13 Bidirectionalassociativememory 75
4.11.14 Neocognitron 75
4.12 Latestdevelopmentstothefamilyofneuralnetworks 75
4.12.1 Generativeadversarialnetworks 75
4.12.2 Neuro-fuzzysystem(NFS) 75
4.12.3 Deepneuralnetwork(DNN) 76
4.12.4 Extremelearningmachine(ELM) 77
References 78
Chapter5
Applicationofartificialneuralnetwork 81
5.1 Background 81
5.1.1 Artificialneuralnetwork(ANN) 81
5.2 Adaptivelinearneuron(ADALINE) 82
5.3 Backpropagationneuralnetwork 85
5.3.1 Initialweights 85
5.3.2 Learningrate 86
5.3.3 Momentumfactor 86
5.3.4 Trainingdata 86
X Contents
5.3.5 Numberofhidden-layerneurons 86
5.4 IncrementalmodetrainingofBPNN 86
5.5 PseudocodeofBPNN 89
5.6 Teaching–learning-basedoptimization 90
5.6.1 TLBOalgorithm 90
5.7 Teaching–learning-basedoptimizedneuralnetwork
(TLBO-NN) 92
Reference 92
Chapter6
Advancedcontrolofreactivedistillationcolumn 95
6.1 Softsensorsforreactivedistillationprocess 95
6.2 Inferentialcontrolofreactivedistillation 96
6.2.1 Single-loopcontrolstrategy 94
6.2.2 Cascadecontrolstrategy 94
6.2.3 Disturbancetracking 97
6.2.4 Casestudy 98
6.2.5 Disturbancerejectionandsetpointtracking 98
6.2.6 Validationofsoftsensor 104
6.2.7 SimulationstudyofdualRDofbiodieselandTAEE 108
6.2.8 Simulationofmembrane-assistedRD 112
6.3 Intelligentcontrolofmethylacetatereactivedistillation 114
6.3.1 Controlstructureofmethylacetatereactivedistillation 115
6.3.2 Identificationofcontroltrays 116
6.3.3 PIDcontroller 118
6.4 Fuzzylearning-basedcounterpropagationnetwork(FCPN) 118
6.4.1 FCPNfornonlineardynamicalsystem 119
6.4.2 FCPNformethylacetatereactivedistillationprocess 121
6.4.3 Experimentalvalidation 128
References 133
Index 137