基于GA-BP神经网络的餐厨垃圾合成PHA工艺产量预测-豆柴文库

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基于GA-BP神经网络的餐厨垃圾合成PHA工艺产量预测
Title:PredictionofPHAProductionCapacityBasedonGA-BPNeuralNetworkforFoodandKitchenWasteSynthesis
Abstract:
Inrecentyears,researchanddevelopmentinthefieldofbiodegradablepolymershavegainedsignificantattentionduetotheirpotentialtoaddressthegrowingenvironmentalconcernsassociatedwithplasticwaste.Polyhydroxyalkanoates(PHA)areaclassofbiodegradablepolymersthatcanbesynthesizedfromfoodandkitchenwasteusingaspecificprocess.However,accuratelyestimatingPHAproductioncapacityremainsachallengeduetothecomplexnatureofthebiologicalandchemicalreactionsinvolved.ThispaperproposesaGA-BPneuralnetworkmodeltopredictPHAproductioncapacitybasedonrelevantinputvariablesanddemonstratesitseffectivenessthroughexperimentaldata.
1.Introduction
Theincreasingaccumulationofplasticwastehasledtosevereenvironmentalpollutionandhasbecomeaglobalchallenge.Biodegradablepolymers,includingPHA,offerapromisingsolutiontotheproblem.PHAcanbesynthesizedfromrenewableresourcessuchasfoodandkitchenwaste,makingitanattractiveandsustainablealternativetotraditionalplastics.However,efficientproductionpredictionsarerequiredtooptimizethePHAmanufacturingprocess.
2.Methodology
2.1GeneticAlgorithm(GA)
Thegeneticalgorithmisanoptimizationtechniqueinspiredbytheprocessofnaturalselection.Itiscommonlyusedinsolvingcomplexproblemswithnon-linearrelationships.Inthisstudy,GAisutilizedforfeatureselectiontodeterminethemostsignificantvariablesthataffectPHAproduction.
2.2BackpropagationNeuralNetwork(BP)
Thebackpropagationalgorithm,apopularmethodfortrainingneuralnetworks,isusedinthisstudytoachieveaccurateforecastingofPHAproductioncapacity.TheBPneuralnetworkistrainedusingtheselectedvariablestoestablishamodelcapableofpredictingPHAproduction.
2.3CombinationofGAandBP
TheintegrationofGAandBPallowsforthedevelopmentofahybridmodelthatcanoptimizethenetworkstructureandimprovepredictionaccuracy.TheGAdeterminestheoptimalnetworkstructure,includingthenumberofhiddenlayersandneurons,whiletheBPalgorithmfine-tunesthenetworkweightsandbiases.