LSTM神经网络在脱硫除尘排放预测中的应用研究

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中图分类号：X321；TP18 文献标识码：A 文章编号：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

脱硫除尘技术作为控制工业排放的重要手段，在政策推动下不断发展。（剩余4474字）