Hybrid neural network first-principles approach to process modeling

126 Pages·1999·5.6 MB·English

by  Gupta, Sanjay

HYBRIDNEURALNETWORKFIRST-PRINCIPLESAPPROACH
TOPROCESSMODELING
By
SANJAYGUPTA
ADISSERTATIONPRESENTEDTOTHEGRADUATESCHOOL
OFTHEUNIVERSITYOFFLORIDAINPARTIALFULFILLMENT
OFTHEREQUIREMENTSFORTHEDEGREEOF
DOCTOROFPHILLOSOPHY
UNIVERSITYOFFLORIDA
1999
Thisdissertation
isdedicated
to
myparents
ACKNOWLEDGMENTS
IwouldliketotakethisopportunitytothankmyadvisorDr.SpyrosA. Svoronos
for his continuing guidance, encouragement and support throughout the course ofmy
Ph.D. Henotonlyguidedmetolearnnewtechniques,hewasalsohelpfulinshowingme
therightcourseinsomeoftheproblemsinmypersonallife.
IwishtothankDr.HassanEl-Shallforhisvaluableinputsinthechemistryaspect
ofthis project. I would also liketothank my othercommittee members, Dr. Richard
Dickinson, Dr. Oscar Crisalle, and Dr. Ben Koopman, for kindly reviewing my
dissertationandservingonmycommittee.
The friendship and assistance of my colleagues, Pi-Hsin Liu, Robert Bozic,
RajeshSharma,Dr.Cheng,Dr Nagui,RachelWorthen,andLavAgarwal,willalwaysbe
valued.
My respect formy parents, brother, and sister for having stood by me and for
givingmemoralsupportalwayskeptmemotivatedtocompletethiswork.
1 1
TABLEOFCONTENTS
page
ACKNOWLEDGMENTS
iii
LISTOFFIGURES vi
ABSTRACT ix
CHAPTERS
INTRODUCTION
1 1
2ONE-LEVELHYBRIDMODEL 6
2.1 Introduction 6
2.2First-PrinciplesModel 10
2.2.1BoundaryConditions 13
2.2.2CalculationofRecoveryandGrade 16
2.2.3ModelParameters 17
2.3TheHybridModel 22
2.4MaterialsandMethods 23
2.4. ExperimentalSetupandProcedure 23
2.4.2ExperimentalConditions 27
2.4.3NeuralNetworkandTraining 27
2.5ResultsandDiscussions 28
2.6Conclusions 39
3TWO-LEVELHYBRIDMODEL 40
3. Introduction 40
3.2First-PrinciplesModel 44
3.3CalculationofModelParameters 50
3.4TheHybridModel 52
3.5MaterialsandMethods 54
3.5.1ExperimentalSetupandProcedure 54
3.5.2NeuralNetworkandTraining 56
3.6ResultsandDiscussions 58
3.7Conclusions 72
1 1
4OPTIMIZATIONPERFORMANCEMEASURESANDFUTUREWORK 73
4. ThePerformanceMeasures 74
4.1. Selectivity 74
4.1.2SeparationEfficiency 75
4.1.3EconomicPerformanceMeasure 75
4.2TheOptimizationAlgorithm 79
4.3InitialScatteredExperiments 81
44ResultsandDiscussions 82
4.5FutureWork 86
APPENDICES
A CODEFORTHEFIRSTPRINCIPLESMODELFORONELEVEL 90
B CODEFORTHEFIRSTPRINCIPLESMODELFORTWOLEVELS 99
REFERENCES 108
BIOGRAPHICALSKETCH Ill
1
LISTOFFIGURES
Figure page
2. Flotationrateconstantsforphosphateandganguearecalculatedby
usingaone-dimensionalsearchtoinvertthetirst-principlesmodel 19
2.2 Recovery'ofphosphate(%)asafunctionofflotationrateconstantfor
phosphate(kp) 20
2.2 Recoveryofgangueasafunctionofflotationrateconstantfor
gangue(kg) 21
2.4 Overallstructureofthehybridmodel 23
2.5 Aschematicdiagramoftheexperimentalsetup 25
2.6 PerformanceofNNI:Modelversusexperimentalflotationrate
constantforphosphate(kp) 29
2.7 PerformanceofNNII:Modelversusexperimentalflotationrate
constantforgangue(kg) 31
2.8 PerformanceofNNIII:Modelversusexperimentalairholdupfor
frotherCP-100 32
2.9 Performanceoftheoverallhybridmodel:Predictedversus
experimentalrecovery(%)forcoarsefeedsizedistribution 33
2.10 Performanceoftheoverallhybridmodel:Predictedversus
experimentalgrade(%BPL)forcoarsefeedsizedistribution 34
2.11 Performanceoftheoverallhybridmodel:Predictedversus
experimentalrecovery(%)forfinefeedsizedistribution 35
2.12 Performanceoftheoverallhybridmodel:Predictedversus
experimentalgrade(%BPL)fortinefeedsizedistribution 36
2 13 Performanceoftheoverallhybridmodel:Predictedversus
experimentalrecovery(%)forunsizedfeedsizedistribution 37
2 14 Performanceoftheoverallhybridmodel:Predictedversus
experimentalgrade(%>BPL)forunsizedfeedsizedistribution 38
3.1 Schematicdiagramofcolumnforphosphateflotation 45
3.2 Overallstructureofthehybridmodel 53
3.3 PerformanceofNKIII:Modelbubblediameterversusbubble
diameterinferredfromexperimentaldatawhenCP-100wasthe
frother 59
3.4 PerformanceofNNII1:Modelbubblediameterversusbubble
diameterinferredfromexperimentaldatawhenF-507wasthe
frother 60
35 PerformanceofNNIII:Modelbubblediameterversusbubble
diameterinferredfromexperimentaldatawhenOB-535wasthe
frother 61
3.6 PerformanceofNNIII:Modelbubblediameterversusbubble
diameterinferredfromexperimentaldatawhenF-579wasthe
frother 62
3.7 PerformanceofNNIV:Modelversusexperimentalairholdupfor
frotherCP-100 63
3.8 PerformanceofNNIV Modelversusexperimentalairholdupfor
frotherF-507 64
3.9 PerformanceofNNIV Modelversusexperimentalairholdupfor
frotherOB-535 65
3.10 PerformanceofNNIV:Modelversusexperimentalairholdupfor
frotherF-579 66
3.11 PerformanceofNNI:Modelversusexperimentalflotationrate
constantforphosphate(kp) 67
3.12 PerformanceofNNII:Modelversusexperimentalflotationrate
constantforgangue(k.) 69
3.13 Performanceoftheoverallhybridmodel Predictedversus
experimentalrecovery(%)forthefourfrothers 70
3.14 Performanceoftheoverallhybridmodel:Predictedversus
experimentalgrade(%BPL)forthefourfrothers 71
4.1 Valueofphosphaterockasafunctionof%BPL 78
4.2 Therun-to-runoptimizationalgorithm 80
4.3 Neuralnetworkversusexperimentalflotationrateconstantfor
phosphate(kp) 84
4.4 Neuralnetworkversusexperimentalflotationrateconstantfor
gangue(kg) 85
4.5 ModelversusexperimentalairholdupforfrotherF-507 87
AbstractofDissertationPresentedtotheGraduateSchool
OftheUniversityofFloridainPartialFulfillmentofthe
RequirementsforDoctorofPhilosophy
HYBRIDNEURALNETWORKFIRST-PRINCIPLES.APPROACH
TOPROCESSMODELING
By
SanjayGupta
May1999
Chairman:Dr.SpyrosA.Svoronos
Cochairman:Dr HassanEl-Shall
MajorDepartment:ChemicalEngineering
A hybrid model for a flotation column is presented which combines a first-
principlesmodelwithartificialneuralnetworks. Thefirst-principlesmodelisderivedby
making material balances on both phosphate and silica particles in the slurry phase.
Neuralnetworksareusedtorelatethemodelparameterswithoperatingvariablessuchas
particle size, superficial air velocity, frother concentration, collector and extender
concentration,andpH. One-levelandtwo-levelhybridmodelingstructuresarecompared
anditisshownthatthetwo-level structureofferssignificantadvantagesovertheother.
Finally,asequentialrun-torunoptimizationalgorithmisdevelopedwhichcombinesthe
hybrid modelwithanoptimizationtechnique. Thealgorithmguidesthechangesinthe
manipulatedvariablesaftereachexperimenttodeterminetheoptimalcolumnconditions.
Designed experiments were performed in a lab scale column to generate data forthe
initialtrainingoftheneuralnetworks.