NO_x浓度分布在线监测系统研发及应用
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摘要
选择性催化还原(selective catalytic reduction,SCR)烟气流场和NO_x浓度分布不均匀,导致现有NO_x在线监测系统单点测量结果代表性差和多点混合测量结果无法满足喷氨的分区调整控制,且难以为精准喷氨提供可靠的数据支持,极大制约SCR在线优化水平的提高,为此研发了烟气NO_x浓度分布在线监测系统。首先依据喷氨区布置由多组网格化取样探头组成的取样分区,以巡回检测的方式实现SCR出口双侧烟道全截面的NO浓度分布在线监测;继而同步得到各测点截面NO与O_2混合浓度值、截面浓度相对标准偏差、浓度分布云图及NO、O_2、CO浓度测量值等信息;最后将该系统所测数据应用于SCR运行在线优化,而且结合喷氨调节阀的自动控制,进一步实现喷氨的动态分区智能控制,提升SCR运行在线优化水平,严格控制氨逃逸,降低空预器堵塞、引风机电耗增加甚至腐蚀等风险。该系统已在某电厂700 MW机组上进行成功应用。
SCR flue gas field and uneven distribution of NO_x concentration has caused poor representativeness of single-point measurement result of current NO_(x )online monitoring system and failure of multi-point mixed measurement result in satisfying partition adjustment control for ammonia injection, which can not provide reliable data support for accurate ammonia injection and greatly restricts improvement of SCR online optimization level. Therefore, an online monitoring system for NO_x concentration distribution is developed. Firstly, according to ammonia injection partition, sampling zones consisting of multigroup of gridding sampling probes are arranged and a way of cyclic detection is used to realize online monitoring of NO_x concentration distribution on the total cross-section of flue at both sides of SCR outlet. Then information such as mixed concentration value of NO and O_(2 )on the cross-section of each measurement point, relative standard deviation of cross-section concentration, concentration distribution cloud map, measurement values of concentration of NO, O_(2 )andCO and so on is synchronously displayed. Finally the measured data of the system is applied for online optimization of SCR. Meanwhile, by automatic control of ammonia control valve, it is able to realize intelligent control of dynamic partition for ammonia injection, promote online optimization of SCR operation, strictly controll ammonia escape and reduce risks of blocking of the air pre-heater, power consumption increase of the induced draft fan even corrosion. This system has been successfully used in a 700 MW unit in one power plant.
SCR flue gas field and uneven distribution of NO_x concentration has caused poor representativeness of single-point measurement result of current NO_(x )online monitoring system and failure of multi-point mixed measurement result in satisfying partition adjustment control for ammonia injection, which can not provide reliable data support for accurate ammonia injection and greatly restricts improvement of SCR online optimization level. Therefore, an online monitoring system for NO_x concentration distribution is developed. Firstly, according to ammonia injection partition, sampling zones consisting of multigroup of gridding sampling probes are arranged and a way of cyclic detection is used to realize online monitoring of NO_x concentration distribution on the total cross-section of flue at both sides of SCR outlet. Then information such as mixed concentration value of NO and O_(2 )on the cross-section of each measurement point, relative standard deviation of cross-section concentration, concentration distribution cloud map, measurement values of concentration of NO, O_(2 )andCO and so on is synchronously displayed. Finally the measured data of the system is applied for online optimization of SCR. Meanwhile, by automatic control of ammonia control valve, it is able to realize intelligent control of dynamic partition for ammonia injection, promote online optimization of SCR operation, strictly controll ammonia escape and reduce risks of blocking of the air pre-heater, power consumption increase of the induced draft fan even corrosion. This system has been successfully used in a 700 MW unit in one power plant.
引文
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[2] 高文松,王刚,危日光,等.600 MW机组SCR脱硝系统运行特性[J].广东电力,2017,30(11):28-32.GAO Wensong,WANG Gang,WEI Riguang,et al.Operation characterristic of SCR denitration system of 600 MW unit[J].Guangdong Electric Power,2017,30(11):28-32.
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[5] 魏宏鸽,张杨,杜振,等.燃煤机组超低排放改造对机组能耗增加的影响分析及节能优化措施探讨[J].发电与空调,2017,38(6):14-17,33.WEI Hongge,ZHANG Yang,DU Zhen,et al.Analysis of coal-fired unit ultra low emission retrofit’s influence on unit’s energy consumption increasing and its energy-saving,optimization methods [J].Power Generation & Air Condition,2017,38(6):14-17,33.
[6] 潘建强,尤良洲.某1 000 MW燃煤机组超低排放改造分析[J].华电技术,2018,40(1):59-61,80.PAN Jianqiang,YOU Liangzhou.Analysis of ultra-low emission transformation of a 1 000 MW coal-fired unit[J].Huadian Technology,2018,40(1):59-61,80.
[7] ZHAO D,LIN D,ZHANG Z.Flow field simulation and optimization design of SCR of 660 MW power unit based on CFD study[C]//IOP Conference Series:Earth and Environmental Science.Piscataway:IEEE Press,2018,170(4):42-31.
[8] LIU X,TAN H,WANG Y,et al.Low NOx combustion and SCR flow field optimization in a low volatile coal fired boiler[J].Journal of Environmental Management,2018,220(11):30-35.
[9] ZHENG Q,ZHANG G,CHE K,et al.Optimization research on the concentration field of NO in selective catalytic reduction flue gas denitration system[C]//AIP Conference Proceedings.Piscataway:IEEE Press,2017,1864(1):020167.
[10] 李帅英,武宝会,牛国平,等.600 MW燃煤机组大气污染物超低排放技术应用[J].热力发电,2016,45(12):25-29.LI Shuaiying,WU Baohui,NIU Guoping,et al.Application of ultra-low emission technology in a 600 MW coal-fired unit[J].Thermal PowerGeneration,2016,45(12):25-29.
[11] 王丰吉,王东,冯前伟.超低排放形势下CFB锅炉低氮燃烧和SNCR联合脱硝提效研究[J].发电与空调,2017,38(5):6-10.WANG Fengji,WANG Dong,FENG Qianwei.CFB boiler under ultra-low emission situation [J].Power Generation & Air Condition,2017,38(5):6-10.
[12] 孟炜,李清毅,胡达清,等.百万千瓦燃煤机组烟气超低排放设计及应用[J].热能动力工程,2016,31(1):111-116,139.MENG Wei,LI Qingyi,HU Daqing,et al.Design and applications of the ultra low emissions of flue gases from a 1 000 MW coal-fired boiler unit [J].Journal of Engineering for Thermal Energy and Power,2016,31(1):111-116,139.
[13] 倪吴忠,徐继先,王成,等.燃煤发电机组超低排放改造及其运行稳定性研究[J].环境污染与防治,2016,38(8):73-75.NI Wuzhong,XU Jixian,WANG Cheng,et al.Transformation of coal-fired power generation unit for ultra-low emission and research on its operational stability[J].Environmental Pollution & Control,2016,38(8):73-75.
[14] 李博,赵锦洋,吕俊复.燃煤电厂超低排放技术方案应用[J].中国电力,2016,49(8):144-147,153.LI Bo,ZHAO Jinyang,Lü Junfu,et al.Application of ultra-low emission technical roadmaps of coal-fired power plants[J].Electric Power,2016,49(8):144-147,153.
[15] 陈阳,邵亮,唐兆芳.百万千瓦燃煤机组超低排放改造方案的研究[J].环境工程,2015,33(增刊1):1026-1029.CHEN Yang,SHAO Liang,TANG Zhaofang,et al.Study on ultra low emission reform scheme of 1 000 MW coal fired unit [J].Environmental Engineering,2015,33(S1):1026-1029.
[16] 成新兴,武宝会,周彦军,等.燃煤电厂超低排放改造方案及其经济性分析[J].热力发电,2017,46(11):103-108.CHENG Xinxing,WU Baohui,ZHOU Yanjun,et al.Ultra-low emission reform scheme for coal-fired power plants and its economic analysis[J].Thermal Power Generation,2017,46(11):103-108.
[17] 李瞳.基于SCR反应器NOx均匀度测试的脱硝系统超低排放改造[J].环境工程学报,2018,12(4):1112-1119.LI Tong.Ultra-low emission reconstruct of denitrification system based on NOx uniformity test of SCR reactor [J].Chinese Journal of Environmental Engineering,2018,12(4):1112-1119.
[18] ZHANG L,DONG X,HOU F,et al.Study on optimization experiment of SCR denitrification technologies in a coal-fired power plant[C]//IOP Conference Series:Earth and Environmental Science.Piscataway:IEEE Press,2018,108(5):052100.
[19] 潘栋,李淑宏,景云辉,等.超低排放电站锅炉SCR脱硝装置的故障诊断及运行优化研究[J].中国电力,2017,50(3):41-45,64.PAN Dong,LI Shuhong,JING Yunhui,et al.Study on fault diagnosis and operation optimization for power plant SCR with ultra-low emission[J].Electric Power,2017,50(3):41-45,64.
[20] 徐亦淳,翟德双.1 000 MW机组降低锅炉NOx排放技术研究与应用[J].中国电力,2015,48(12):85-90.XU Yichun,ZHAI Deshuang.Study and application of NOx emission reduction technology for 1 000 MW boilers [J].Electric Power,2015,48(12):85-90.
[21] 郭凯旋,牛玉广.基于NOx浓度场实时检测的喷氨优化应用研究[J].热能动力工程,2017,32(5):88-94,139.GUO Kaixuan,NIU Yuguang.Optimization of ammonia injection based on real-time detection of NOx concentration field [J].Journal of Engineeing for Thermal Energy and Power,2017,32(5):88-94,139.
[22] 周长城,金保昇,沈凯,等.负荷随动烟气选择性催化还原脱硝控制策略优化研究及工程应用[J].环境污染与防治,2018,40(7):794-799,818.ZHOU Changcheng,JIN Baosheng,SHEN Kai,et al.Study on optimization and engineering application of selective catalyst reduction denitrification control strategy for loading fluctuation [J].Environmental Pollution & Control,2018,40(7):794-799,818.
[23] 廖永进,范军辉,杨维结,等.基于RBF神经网络的SCR脱硝系统喷氨优化[J].动力工程学报,2017,37(11):931-937.LIAO Yongjin,FAN Junhui,YANG Weijie,et al.Ammonia spraying optimization of an SCR denitrification system based on RBF neural network [J].Journal of Chinese Society of Power Engineering,2017,37(11):931-937.
[24] 屠士凤,章卫军,叶国满.智能优化控制降低SCR对空气预热器的影响[J].中国电力,2014,47(9):29-34.TU Shifeng,ZHANG Weijun,YE Guoman.Analysis and measures on reducing impacts of SCR flue gas denitrification system on air preheater [J].Electric Power,2014,47(9):29-34.
[25] 何鹏晟,王秋生,刘凤明.火力发电厂脱硝烟气监测取样网格化改造[J].自动化应用,2015(9):20-21,23.HE Pengsheng,WANG Qiusheng,LIU Fengming.Gridding reformation of sampling on denitrification flue gas monitoring in thermal power plants[J].Automation Application,2015(9):20-21,23.
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