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基于CEMS数据的超低排放燃煤机组大气污染物排放特性分析 被引量:2

Analysis of Air Pollutant Emission Characteristics of Ultra-low Emission Coal-Fired Units Based on CEMS Data
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摘要 以54台超低排放燃煤机组为研究对象,对比分析了主要大气污染物(颗粒物、SO_(2)、NO_(x))的年排放达标率、年排放浓度、排放性能等排放特征。在对环保设施投运和故障进行调查的基础上,分析了排放超标和故障的原因。结果显示:参与调查的燃煤机组中颗粒物、SO_(2)、NO_(x)排放年度达标率分别为99.981%、99.962%、99.893%,表明能够稳定实现超低排放;颗粒物、SO_(2)、NO_(x)排放绩效均值分别为11.11、68.16、140.26 mg/(kW·h),满足且优于国家标准要求;启停时段颗粒物、SO_(2)、NO_(x)排放超标时长占比分别为54%、64%、57%,说明机组启停过程中颗粒物、SO_(2)、NO_(x)排放浓度难以控制,消除启停机期间制约环保设施正常投运的影响因素是后续主要研究方向。 Taking 54 ultra-low emission coal-fired units as the research object,the annual emission compliance rate,annual emission concentration,emission performance and other emission characteristics of major air pollutants(particulate matter,SO_(2),NO_(x))were compared and analyzed.The causes of excessive emissions and failures were analyzed on the basis of the survey of the commissioning and failures of environmental protection facilities.As shown by the results,the annual up-to-standard emission rates of particulate matter,SO_(2),and NO_(x)in the coal-fired units participating in the survey were 99.981%,99.962%,and 99.893%;The average emission performance of particulate matter,SO_(2),and NO_(x)were 11.11,68.16,and 140.26 mg/(kW·h),respectively,which meet and exceed the requirements of national standards;The proportions of the emission of particulate matter,SO_(2),and NO_(x)exceeding the standard during the start-stop period were 54%,64%,and 57%,respectively,indicating that the emission concentrations of particulate matter,SO_(2),and NO_(x)during the start-stop process of the units were difficult to control.Therefore,eliminating the influencing factors that restrict the normal operation of environmental protection facilities during start-stop period is the research direction of subsequent technical public relations.
作者 曲立涛 齐晓辉 王德鑫 于洪海 QU Litao;QI Xiaohui;WANG Dexin;YU Honghai(Huadian Power Science Research Institute Co.,Ltd.,Shenyang 110000,China)
出处 《中国电力》 CSCD 北大核心 2023年第2期171-178,共8页 Electric Power
基金 华电集团有限公司2021年科技项目(基于大数据分析的北方地区燃煤机组深度调峰策略研究,CHDKJ21-02-179)。
关键词 超低排放 大气污染物 排放浓度 排放绩效 ultra low emission atmospheric pollutant emission concentration emission performance
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