Integrated Intelligent Energy ›› 2024, Vol. 46 ›› Issue (8): 77-85.doi: 10.3969/j.issn.2097-0706.2024.08.010

• Energy Conservation and Environmental Protection • Previous Articles    

Reliability evaluation on SCR denitrification systems in thermal power plants based on FTA

WANG Yawen1(), ZONG Shaoliang1(), CHENG Zhiyuan2(), LU Wanpeng1,*()   

  1. 1. School of Thermal Energy Engineering,Shandong Jianzhu University,Jinan 250101,China
    2. Qingdao Guorui Information Technology Company Limited, Qingdao 266100,China
  • Received:2023-06-16 Revised:2023-10-23 Published:2024-08-25
  • Contact: LU Wanpeng E-mail:2737095259@qq.com;407948350@qq.com;zhiyuan.cheng@qdgr.cn;luwp@sdjzu.edu.cn

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

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