基于FTA的火电厂SCR脱硝系统可靠性评价

doi:10.3969/j.issn.2097-0706.2024.08.010

摘要/Abstract

摘要：

火电厂选择性催化还原（SCR）脱硝系统的可靠性评价分析，对脱硝系统安全运行具有现实意义。对脱硝系统故障原因进行归类后，建立以氨氮摩尔比异常为主要指标的故障树（FTA）法，采用布尔代数运算法，对故障指示器进行FTA径集分析，共得到11个最小割集，5个最小径集。分析各基本事件对顶事件产生影响的组合模式和传播路径，提出根据置信度判断对系统安全性危害的评价方法。基于某300 MW机组锅炉脱硝系统的运行大数据，对反应器内部某一具体故障进行故障概率分析，得到最小割集K7的置信度与可靠性评价方法的置信度的相对误差仅为0.694%的结论，另取20次故障事件，发现相对误差均在1%之内。结果表明，通过最小割集置信度的评价方法准确可行。

关键词: 烟气脱硝, 大数据分析, 故障树法, 置信度, 电力大数据, 选择性催化还原

Abstract:

Making reliability evaluations on selective catalytic reduction（SCR） denitrification systems in thermal power plants is significance for the safe operation of denitrification systems. Based on the classification on failure causes of denitrification systems，a fault tree analysis（FTA） taking abnormal ammonia/nitrogen molar ratio as its main indicator is applied on the evaluation. Then， Boolean algebra is used to analyze the path set of the FTA fault indicator，with a total of 11 minimum cut sets and a total of 5 minimum path sets. Considering the combination modes and propagation paths of impacts of elementary events on top events，an evaluation method for system safety hazards according to confidence degree is proposed. Based on the operational data of a denitrification system in a 300 MW unit boiler，a failure probability analysis was carried out on a specific fault of the reactor，and the conclusion was reached that the relative error between the confidence degree of the minimum cut set K7 and the confidence degree of the reliability evaluation method was only 0.694%. And the relative errors of other 20 fault events were all within 1%. The results show that the proposed evaluation method based on the minimum cut set confidence is accurate and feasible.

Key words: flue gas denitrification, big data analysis, FTA analysis, confidence, electric power big data, SCR

中图分类号:

TK228

王雅雯, 宗绍梁, 程之远, 陆万鹏. 基于FTA的火电厂SCR脱硝系统可靠性评价[J]. 综合智慧能源, 2024, 46(8): 77-85.

WANG Yawen, ZONG Shaoliang, CHENG Zhiyuan, LU Wanpeng. Reliability evaluation on SCR denitrification systems in thermal power plants based on FTA[J]. Integrated Intelligent Energy, 2024, 46(8): 77-85.

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参考文献 23

[1]	胡小夫, 王云, 郝正, 等. 燃煤电站SCR催化剂寿命在线预测研究[J]. 华电技术, 2021, 43(3): 65-69.
	HU Xiaofu, WANG Yun, HAO Zheng, et al. Research on SCR catalyst service life on-line prediction in coal-fired power plants[J]. Huadian Technology, 2021, 43(3): 65-69.
[2]	曹喜果, 张永涛, 李雅恬. 基于IQGA-GRNN模型的SCR脱硝出口NO_x质量浓度预测方法[J]. 华电技术, 2021, 43(5):9-14.
	CAO Xiguo, ZHANG Yongtao, LI Yatian. Prediction method for N O x discharged from SCR denitrification systems based on IQGA-GRNN model[J]. Huadian Technology, 2021, 43(5):9-14.
[3]	赵大周, 王明祥, 阮慧锋, 等. 分布式能源站天然气内燃机Urea-SCR系统模拟优化研究[J]. 华电技术, 2021, 43(5):45-52.
	ZHAO Dazhou, WANG Mingxiang, RUAN Huifeng, et al. Simulation and optimization for Urea-SCR system of the natural gas internal combustion engine in a distributed energy station[J]. Huadian Technology, 2021, 43(5): 45-52.
[4]	胡小夫, 汪洋, 王云, 等. 燃煤电站SCR脱硝系统运行优化[J]. 华电技术, 2019, 41(10): 12-15, 52.
	HU Xiaofu, WANG Yang, WANG Yun, et al. Operation optimization an SCR denitration system in a coal-fired power plant[J]. Huadian Technology, 2019, 41 (10): 12-15, 52.
[5]	朱春华, 黄奎, 刘海秋, 等. 基于模糊三角分布隶属函数的脱硝装备状态评价[J]. 能源与环境, 2019(3):14-15,18.
	ZHU Chunhua, HUANG Kui, LIU Haiqiu, et al. Denitration equipment status evaluation based on fuzzy triangular distribution membership function[J]. Energy and Environment, 2019(3):14-15,18.
[6]	朱懿灏, 许竞. 300MW机组空气分级低NO_x燃烧技术应用[C]// 中国电机工程学会清洁高效发电技术协作网2014年会论文集. 2014:1-10.
	ZHU Yihao, XU Jing. Application of air classification low NO_xcombustion technology for 300 MW units[C]// Chinese society for electrical engineering,proceedings of 2014 annual meeting of clean and efficient power generation technology cooperation network of Chinese society for electrical engineering, 2014:1-10.
[7]	卢晗. 燃煤电厂锅炉大气污染物减排技术费效分析及减排潜力评估[D]. 北京: 华北电力大学, 2018.
	LU Han. Cost effectiveness analysis and emission reduction potential assessment of coal-fired power plant boiler air pollutant emission reduction technology[D]. Beijing: North China Electric Power University, 2018.
[8]	柏源, 薛建明, 唐仲恺. 燃煤电厂脱硝设施性能诊断及策略研究[J]. 电力科技与环保, 2018, 34(3): 30-32.
	BAI Yuan, XUE Jianming, TANG Zhongkai. Study on performance diagnosis and strategy for denitrification facilities of coal-fired power plant[J]. Electric Power Technology and Environmental Protection, 2018, 34(3): 30-32.
[9]	陈建维. 火电厂SCR脱硝系统氨区安全风险评估与防范[J]. 电力安全技术, 2013, 15(9):8-11.
	CHEN Jianwei. Safety risk assessment and prevention of ammonia zone in SCR denitration system of thermal power plants[J]. Power Safety Technology, 2013, 15(9):8-11.
[10]	王悦, 曹雄, 朱邵涌, 等. 液氨泄漏的事故树分析[J]. 山西化工, 2012, 32(5):63-66.
	WANG Yue, CAO Xiong, ZHU Shaoyong, et al. Accident tree analysis of liquid ammonia leakage[J]. Shanxi Chemical Industry, 2012, 32 (5): 63-66.
[11]	顾建伟. 火电厂脱硝项目环境风险评价研究——以液氨储罐泄漏为例[D]. 兰州: 西北师范大学, 2013.
	GU Jianwei. The analysis of environmental risk assessment to the denitration item of heat-engine plant—A leakage case study of liquid ammonia tank[D]. Lanzhou: Northwest Normal University, 2013.
[12]	孙辽东, 龚贤忠. 脱硫脱硝液氨储罐泄漏的风险评估及控制[J]. 云南化工, 2018, 45(2):185.
	SUN Liaodong, GONG Xianzhong. Risk assessment and control of leakage from desulfurization and denitrification liquid ammonia storage tanks[J]. Yunnan Chemical Industry, 2018, 45(2): 185.
[13]	马有和. 液氨泄漏事故树分析、风险预测与风险管理[C]// 全国电力技术市场协会.第四届火电行业化学(环保)专业技术交流会论文集, 2013:636-639.
	MA Youhe. Fault tree analysis, risk prediction and risk management of liquid ammonia leakage[C]// National Electricity Technology Market Association. Proceedings of the Fourth Chemical(Environmental Protection) Professional Technical Exchange Conference in the Thermal Power Industry, 2013:636-639.
[14]	胡二邦, 彭理通, 陆雍森, 等. 环境风险评价实用技术和方法[M]. 北京: 中国环境科学出版社,1999.
[15]	罗云, 樊运晓, 马晓春. 风险分析与安全评价[M]. 北京: 化学工业出版社, 2004.
[16]	SHIN D H. Demystifying big data: Anatomy of big data developmental process[J]. Telecommunications Policy, 2020, 44(5):837-854.
[17]	宇德明. 易燃·易爆·有毒危险品储运过程定量风险评价[M]. 北京: 中国铁道出版社, 2000.
[18]	KANG J J, NIU Y G, HU B, et al. Dynamic modeling of SCR denitration systems in coal-fired power plants based on a bi-directional long short-term memory method[J]. Process Safety and Environmental Protection, 2021, 148(2): 867-878.
[19]	LI B, WANG K, ZHU B Y. Study on energy-saving operation technology of environmental protection facilities of ultra-low emission coal-fired unit[J]. Energy Reports, 2020, 6(9): 1356-1364.
[20]	木拉里·马扎甫, 陆卫东, 王悦, 等. 基于最小割集求解最小径集的方法研究[J]. 煤炭技术, 2017, 36(8):302-304.
	MULARI Mazafu, LU Weidong, WANG Yue, et al. Research on method of solving minimum path sets based on minimal cut sets[J]. Coal Technology, 2017, 36(8): 302-304.
[21]	陈玉毅. FTA故障树分析研究[D]. 北京: 北京交通大学, 2005.
	CHEN Yuyi. FTA fault tree analysis research[D]. Beijing: Beijing Jiaotong University, 2005.
[22]	石朝骥, 李自立. 基于最小割集的系统可靠性评价方法研究[J]. 机械, 2014, 41(7): 55-59.
	SHI Chaoji, LI Zili. The method research of system reliability evaluation based on MCS[J]. Mechanical, 2014, 41(7): 55-59.
[23]	唐海锋, 刘彬, 马欣欣, 等. 浅谈智慧发电大数据分类/分级研究[J]. 自动化博览, 2022, 39(3): 70-74.
	TANG Haifeng, LIU Bin, MA Xinxin, et al. Discussion on classification and grading of smart power generation big data[J]. Automation Panorama, 2022, 39(3): 70-74.

事件	F（j）	事件	F（j）	事件	F（j）
X1	0.255 6	X6	0.021 3	X11	0.000 1
X2	0.255 6	X7	0.021 3	a1	0.516 0
X3	0.021 3	X8	0.021 3	a2	0.004 7
X4	0.021 3	X9	0.005 6	a3	0.750 0
X5	0.021 3	X10	0.000 1

置信度取值范围	含义	可靠性评价
0.68~1.00	真实	严重安全隐患
0.34~0.67	不太真实	一般安全隐患
0.01~0.33	不真实	小隐患

事件代号	基本事件	测点指标
X3	锅炉负荷过高	锅炉负荷
X4	水解器氨蒸汽出口压力异常	水解器压力
X7	SCR出入口NO_x质量浓度测点不准	SCR出入口NO_x质量浓度
X9	氨蒸汽流量异常	NH₃蒸汽流量
X10	催化剂磨损堵塞	SCR出入口压差
X11	催化剂烧结	SCR出入口烟温

事件代号	基本事件	故障发生概率
X1	思想懈怠	0.21
X2	操作失误	0.55
X3	锅炉负荷过高	0.09
X4	水解器氨蒸汽出口压力异常	0.32
X5	水解器调节阀故障	0.12
X6	燃烧调节不合理	0.66
X7	SCR出入口NO_x质量浓度测点不准	0.77
X8	阀门故障	0.43
X9	氨蒸汽流量异常	0.38
X10	催化剂磨损堵塞	0.23
X11	催化剂烧结	0.46
a1	应急能力差	0.68
a2	催化剂层更换不及时	0.24
a3	设备检修不合格	0.58

隐患事件集合	严重程度
无	严重安全隐患
K₆,K₇	一般安全隐患
K₁,K₂,K₃,K₄,K₅,K₈,K₉,K₁₀,K₁₁	较小隐患