主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2021, Vol. 40 ›› Issue (4): 504-509.

• • 上一篇    下一篇

基于贝叶斯网络的加热炉煤气泄漏风险评估

常一1，吴雅菊1，2   

  1. 1. 沈阳航空航天大学安全工程学院，辽宁沈阳110136；2. 东北大学资源与土木工程学院，辽宁沈阳110819）
  • 出版日期:2021-04-15 发布日期:2021-04-15
  • 通讯作者: 吴雅菊（1981-），女，沈阳航空航天大学安全工程学院讲师
  • 作者简介:常一（1999-），男，沈阳航空航天大学安全工程学院在读本科生，主要从事系统安全分析研究，辽宁省沈阳市沈北新区道义南大街37 号，110136。
  • 基金资助:
    国家重点研发计划项目（2017YFC0805100）；沈阳航空航天大学大学生创新训练计划项目（201910143120）

Risk evaluation of gas leakage in heating furnace based on Bayesian Network

CHANG Yi1，WU Ya-ju1,2   

  1. 1. School of Safety Engineering, Shenyang Aerospace University, Liaoning Shenyang 110136, China; 2. School of Resources and Civil Engineering, Northeastern University, Liaoning Shenyang 110819, China
  • Online:2021-04-15 Published:2021-04-15

摘要： 针对轧钢加热炉煤气泄漏的风险，以蝴蝶结模型为基础，建立了加热炉煤气泄漏的贝叶斯网络模型。采用模糊理论的隶属度函数、λ 截集理论、左右模糊排序法确定贝叶斯网络的先验概率，分析了影响加热炉煤气泄漏的动态因素，引入时间因素研究加热炉煤气泄漏风险随时间变化的动态特性。由实例分析得到了某加热炉煤气泄漏的风险，采用比例变化法得到敏感度分析(ROV)值曲线，从而确定重要根节点，得到了加热炉煤气泄漏风险的动态变化曲线。结果可为加热炉煤气泄漏的动态风险分析及安全管理提供借鉴和参考。

关键词: 蝴蝶结模型, 贝叶斯网络, 模糊理论, 敏感性分析, 动态风险分析

Abstract:  In order to evaluate the risk of gas leakage in heating furnace of rolling steel production, a Bayesian network model of gas leakage in heating furnace is established based on the bow-tie model.The prior probability of Bayesian network is determined by using the membership function of fuzzy theory, lambda section set theory and the left and right fuzzy sorting method, and the dynamic factors affecting gas leakage in heating furnace are analyzed. The dynamic characteristics of gas leakage risk in heating furnace with time change are studied by introducing time factor.The risk of gas leakage in a heating furnace is analyzed by an example, and the sensitivity analysis (ROV) value curve is obtained by using the proportional change method, so as to determine the important root nodes and obtain the dynamic change curve of gas leakage risk in the heating furnace. The results can provide reference for dynamic risk analysis and safety management of gas leakage in heating furnace. 

Key words: bow- tie model, Bayesian network, fuzzy theory, sensitivity analysis, dynamic risk analysis