基于机器学习的烟气脱硫环保设施状态监测方法研究

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中图分类号：X773 文献标识码：A 文章编号：1008-9500（2025）05-0247-03

DOI： 10.3969/j.issn.1008-9500.2025.05.075

Abstract： Asan eficient data-driven technology，machine learningcanextract potential patterns from alarge amountof historical dataandachieveaccurate prediction and fault warning of the status offluegas desulfurization environmental protection facilities.Based on machine learning technology，this paper firstanalyzes the key monitoring requirements in the operation ofdesulfurization facilities，focusingonfacilityconditions，operating eficiencyandfault warning，and then elaborates in detail on the machinelearning based monitoring methodforfluegas desulfurization facilities，including condition analysis，monitoring of desulfurization tower operating eficiency，and fault prediction.Research has shown that machine learning modelscan monitorfacilitystatus inreal-time，identify potential faults inatimelymanner，and providedata support forfault warning，significantly improving theoperational eficiencyandfault response capabilityof desulfurization facilities.

Keywords： machine learning; flue gas desulfurization; status monitoring

随着全球环保法规的日益严格，烟气脱硫技术在工业生产中的应用已成为确保环境污染物合规排放的关键措施之一。（剩余2660字）