基于BP神经网络的供热机组低压缸质量流量在线监测方法-豆柴文库

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基于BP神经网络的供热机组低压缸质量流量在线监测方法
Title:OnlineMonitoringMethodforQualityFlowRateofLow-PressureCylinderinHeatingUnitbasedonBPNeuralNetwork
Abstract:
Accurateandefficientmonitoringofthequalityflowrateinthelow-pressurecylinderofaheatingunitisessentialforensuringoptimalperformanceandenergyefficiency.Inthispaper,weproposeanovelonlinemonitoringmethodbasedontheBack-Propagation(BP)neuralnetwork.Thismethodutilizeshistoricaloperatingdatatotraintheneuralnetworktopredictthequalityflowrate,enablingthereal-timemonitoringanddetectionofanydeviationsfromnormaloperation.Theproposedmethodshowspromisingresultsintermsofaccuracyandcanbeeasilyintegratedintoexistingheatingsystems.
1.Introduction
Thelow-pressurecylinderplaysacrucialroleinheatingunits,anditsperformancedirectlyimpactstheoverallefficiencyandreliabilityofthesystem.Monitoringandmaintainingthequalityflowratewithinthedesiredrangeis,therefore,acriticaltask.Traditionalmonitoringmethods,suchasphysicalsensors,oftensufferfromlimitationssuchashighcost,limitedapplicability,anddifficultyinaccuratelycapturingdynamicchanges.Incontrast,neuralnetworkshaveshowngreatpotentialinvariousfields,includingprocessmonitoringandfaultdiagnosis.Therefore,thispaperproposesaBPneuralnetwork-basedonlinemonitoringmethodspecificallydesignedformonitoringthequalityflowrateinthelow-pressurecylinderofaheatingunit.
2.Methodology
Theproposedonlinemonitoringmethodcomprisesthreemainsteps:datapreprocessing,BPneuralnetworktraining,andonlinemonitoring.
2.1DataPreprocessing
Thequalityflowratedatacollectedfromvarioussensorsarepreprocessedtoremovenoiseandoutliers.Varioustechniques,suchasmovingaverageandKalmanfiltering,canbeemployedtoensuretheaccuracyandintegrityofthedata.
2.2BPNeuralNetworkTraining
Thepreprocesseddataaredividedintoatrainingsetandavalidationset.ThetrainingsetisusedtotraintheBPneuralnetworkwhilethevalidationsetisusedtooptimizethenetworkparametersandpreventoverfitting.TheBPneuralnetworkconsistsofaninputlayer,oneormorehiddenlayers,andanoutputlayer.Thenumberofnodesineac