中国资源综合利用2025，Vol.43Issue(6)：249-251,3.DOI:10.3969/j.issn.1008-9500.2025.06.073
LSTM神经网络在脱硫除尘排放预测中的应用研究
Study on the Application of LSTM Neural Network in Desulfurization and Dust Removal Emission Prediction
卫健 1邹斌华1
作者信息
- 1. 国家电投集团江西电力有限公司分宜发电厂,江西 新余 336615
- 折叠
摘要
Abstract
Long Short-Term Memory(LSTM)neural networks have powerful processing capabilities for time series data and have received widespread attention in industrial prediction applications.As an important issue in the field of environmental protection,the prediction of desulfurization and dust removal emissions requires high requirements for data integrity and model adaptability.Based on the superior characteristics of LSTM neural network,this paper explores the process design,physical and chemical characteristic analysis,and feasibility evaluation of desulfurization and dust removal emission prediction,and proposes strategies to optimize data quality,design robust models,and improve algorithms,providing reference for improving emission prediction accuracy and promoting the development of environmental protection technology.关键词
长短期记忆(LSTM)神经网络/脱硫除尘/排放预测Key words
Long Short-Term Memory(LSTM)neural network/desulfurization and dust removal/emission prediction分类
资源环境引用本文复制引用
卫健,邹斌华..LSTM神经网络在脱硫除尘排放预测中的应用研究[J].中国资源综合利用,2025,43(6):249-251,3.
