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OALib Journal期刊
ISSN: 2333-9721
费用：99美元
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bpneuralnetworkbasedonlinepredictionofsteamturbineexhaustdryness
, PP. 43-47
Keywords: exhaustdryness,bpneuralnetwork,onlineprediction
Abstract:
inlargescalecondensingturbineunit,theexhauststatusalwaysliesinwetsteamarea.duetothelackofeffectivemeasuringmethod,theexhaustdrynessofthesteamturbineisdifficulttoobtaindirectly,whichhasbeenthedifficultprobleminonlineeconomicanalysisforthermalpowerunits.bytakingann1000-25/600/600ultra-supercriticalsteamturbineasanexample,thenonlinearmappingabilityofbpneuralnetworkwasusedtoestablishamodelwhichcanreflecttherelationshipbetweenexhaustdrynessandunitloadandexhaustpressure.afterlearningandtrainingundersometypicalconditions,thismodelwasusedforexhaustdrynessonlinecalculationunderfullcondition.theresultsshowthefinalerrorofthetrainingsamplesandverifyingsampleswerecontrolledwithin-0.0061and-0.0010,whichsatisfiestheaccuracyrequirementforengineeringcalculation,indicatingtheestablishedbpneuralnetworkcanbeusedinexhaustdrynessprediction.
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