中国资源综合利用2025，Vol.43Issue(5)：247-249,3.DOI:10.3969/j.issn.1008-9500.2025.05.075
基于机器学习的烟气脱硫环保设施状态监测方法研究
Research on the State Monitoring Method of Flue Gas Desulfurization Environmental Protection Facilities Based on Machine Learning
邹伟 1陈建国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
As an efficient data-driven technology,machine learning can extract potential patterns from a large amount of historical data and achieve accurate prediction and fault warning of the status of flue gas desulfurization environmental protection facilities.Based on machine learning technology,this paper first analyzes the key monitoring requirements in the operation of desulfurization facilities,focusing on facility conditions,operating efficiency and fault warning,and then elaborates in detail on the machine learning based monitoring method for flue gas desulfurization facilities,including condition analysis,monitoring of desulfurization tower operating efficiency,and fault prediction.Research has shown that machine learning models can monitor facility status in real-time,identify potential faults in a timely manner,and provide data support for fault warning,significantly improving the operational efficiency and fault response capability of desulfurization facilities.关键词
机器学习/烟气脱硫/状态监测Key words
machine learning/flue gas desulfurization/status monitoring分类
资源环境引用本文复制引用
邹伟,陈建国..基于机器学习的烟气脱硫环保设施状态监测方法研究[J].中国资源综合利用,2025,43(5):247-249,3.
