基于输入参数优化选择的BP神经网络预测垃圾焚烧酸性气体原始排放浓度研究-豆柴文库

您所在位置：网站首页 / 基于输入参数优化选择的BP神经网络预测垃圾焚烧酸性气体原始排放浓度研究.docx / 文档详情

在线预览结束，喜欢就下载吧，查找使用更方便

5 金币

下载文档

/ 2

下载提示

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

文本预览

基于输入参数优化选择的BP神经网络预测垃圾焚烧酸性气体原始排放浓度研究
Title:ResearchonOptimizedSelectionofBPNeuralNetworkforPredictingOriginalEmissionConcentrationofAcidicGasesfromWasteIncineration
Abstract:
Theemissionofacidicgasesfromwasteincinerationposessignificantenvironmentalandhealthrisks.Accuratepredictionoftheoriginalemissionconcentrationofthesegasesisvitalforeffectivecontrolandmanagement.ThisstudyfocusesonutilizingaBackpropagation(BP)neuralnetworktopredicttheconcentrationofacidicgasesemittedfromwasteincinerationbasedoninputparameters.TheobjectiveistooptimizetheselectionoftheBPneuralnetworktoimprovetheaccuracyoftheprediction.
Introduction:
Wasteincinerationiswidelyadoptedasaneffectivemethodtodisposeofmunicipalsolidwaste.However,theemissionofacidgasesduringthisprocessisknowntocontributetoairpollutionandacidrainformation.Monitoringandcontrollingtheconcentrationofthesegasesiscrucialforminimizingenvironmentalandhealthimpacts.Traditionalmonitoringmethodsrequiresignificantresourcesandtime,whilecomputationalmethodslikeneuralnetworksofferamoreefficientandaccurateapproachtopredictingemissionconcentrations.
Methodology:
ThestudyproposestheuseofaBPneuralnetworktopredicttheoriginalemissionconcentrationofacidicgasesfromwasteincineration.TheBPneuralnetworkisawidelyusedcomputationalmodelknownforitsabilitytoapproximatecomplexrelationshipsbetweeninputandoutputvariables.Theinputparametersconsideredinthisstudyincludewastecomposition,incinerationtemperature,residencetime,andthetypeofwasteincinerated.Theoutputvariableistheconcentrationofacidicgasesemitted.
TooptimizetheselectionoftheBPneuralnetwork,aseriesofstepsareundertaken.Firstly,acomprehensivedatasetofwasteincinerationprocessesiscollected,includinginputparametersandcorrespondingemissionconcentrations.Thisdatasetisthendividedintotraining,validation,andtestingdatasets.ThetrainingdatasetisusedtotraintheBPneuralnetworkandadjustitsweightsandbiases.Thevalidationdatasetisutilizedtopreventoverfittingofthenetwork,andthetestingdatasetevaluatesthefinalpredictiveperfo