LSTM神經(jīng)網(wǎng)絡(luò)在脫硫除塵排放預(yù)測(cè)中的應(yīng)用研究
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中圖分類號(hào)：X321；TP18 文獻(xiàn)標(biāo)識(shí)碼：A 文章編號(hào)：1008-9500(2025)06-0249-03
DOI: 10.3969/j.issn.1008-9500.2025.06.073
Study on the Application of LSTM Neural Network in Desulfurization and Dust Removal Emission Prediction
WEIJian,ZOUBinhua (FenyiPowerPlantof SPICJiangxi Electric Power Co.,Ltd.,Xinyu336615,China)
Abstract:Long Short-Term Memory(LSTM) neural networks have powerful processing capabilities for time series dataand havereceived widespreadatention in industrial predictionapplications.Asan important issue inthe fieldof environmental protection,the predictionof desulfurizationanddust removal emisions requires high requirements for dataintegrityand model adaptability.BasedonthesuperiorcharacteristicsofLSTMneural network,this paper explores theprocess design,physicalandchemicalcharacteristicanalysis,andfeasibility evaluationof desulfurizationanddust removalemisionprediction,andproposesstrategies tooptimizedataqualitydesignobustmodels,andimprovealgoims, providing referenceforimproving emissionpredictionaccuracyand promotingthedevelopmentof environmentalprotection technology.
KeyWords:Long Short-Term Memory (LSTM) neural network;desulfurizationanddustremoval; emisionprediction
脫硫除塵技術(shù)作為控制工業(yè)排放的重要手段，在政策推動(dòng)下不斷發(fā)展。（剩余4474字）