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基于BAS优化SVM的出水COD软测量建模.docx

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基于BAS优化SVM的出水COD软测量建模 Title:OptimizationofSVMModelforSoftMeasurementofEffluentCODBasedonBAS Abstract: Efficientandaccuratemonitoringofeffluentchemicaloxygendemand(COD)iscrucialforwastewatertreatmentplantstoensurecompliancewithenvironmentalregulationsandenhanceoperationalefficiency.Inrecentyears,thesoftmeasurementtechniqueusingsupportvectormachines(SVM)hasshownpromisingresultsincapturingthecomplexrelationshipbetweeninputvariablesandeffluentCOD.However,theperformanceoftheSVMmodelcanbefurtherimprovedthroughoptimizationtechniques.Thispaperproposesanovelapproachbasedonthebacterialforagingoptimizationalgorithm(BAS)tooptimizetheSVMmodelforsoftmeasurementofeffluentCOD.Theproposedmethodologyisevaluatedusingareal-worlddatasetcollectedfromawastewatertreatmentplant.TheresultsdemonstratethesuperiorityoftheoptimizedSVMmodelinaccuratelypredictingeffluentCODlevels. 1.Introduction EffluentCODisacriticalparameterusedtoassesstheorganicloadofwastewaterdischargedfromindustrialandmunicipalsources.TraditionalmethodsforCODmeasurementrelyontime-consumingandcostlylaboratorytests.Softmeasurementtechniques,suchasmachinelearningmodels,provideanalternativesolutionforreal-timeandefficientCODmonitoring.SVMisapowerfulmachinelearningalgorithmthathasbeensuccessfullyappliedinvariousfields.However,theperformanceofSVMishighlydependentontheselectionofitsparameters.ThispaperaimstooptimizetheSVMmodelforsoftmeasurementofeffluentCODusingtheBASalgorithm. 2.LiteratureReview ThissectionprovidesanoverviewoftherelatedworksoneffluentCODsoftmeasurementandtheoptimizationofSVMmodels.IthighlightsthelimitationsofexistingapproachesandmotivatestheneedfortheproposedBASoptimizationtechnique. 3.Methodology 3.1SupportVectorMachine(SVM)Model TheSVMmodelisasupervisedlearningalgorithmthatconstructsahyperplaneinahigh-dimensionalfeaturespacetoseparateclassesandmaximizethemarginbetweenthem.Ithasshownexcellentgeneralizationabilityinvariousregressionandclassificationtasks. 3.2BacterialForagingOptimizationAlgorithm(BAS) TheBASalgorit