Scenario deduction and node analysis of gas leakage accidents based on DBN

doi:10.16265/j.cnki.issn1003-3033.2022.S1.0893

Abstract

Abstract:

In order to quickly and effectively respond to gas leakage accidents of hazardous chemicals in chemical parks considering their characteristics of complex paths and high development dynamics in the process of evolution, a deduction model was constructed based on ″11·28″ major deflagration accident in Zhangjiakou. Firstly, according to dynamic Bayesian method, a scenario evolution model of disaster chain based on scenario state, external environment and emergency measures was constructed, and scenario state probability was calculated by using joint probability formula to achieve dynamic deduction of the accidents. Then, key nodes in scenario deduction were explored based on complex network topology and using comprehensive importance formula. Finally, targeted chain breaking measures were proposed based on data reference. The results show that the scenario deduction model based on dynamic Bayesian can play a predictive role in the evolution path of accidents, and deduction results provide a more scientific and effective basis for pre-disaster prediction, emergency response in disaster and post-disaster recovery.

Key words: dynamic Bayesian network (DBN), gas leakage, scenario deduction, key node, disaster chain, importance

YUE Wenjing, DU Lijing, CHEN Xianfeng, YUAN Bihe, YANG Jiaqi, WANG Pengcheng. Scenario deduction and node analysis of gas leakage accidents based on DBN[J]. China Safety Science Journal, 2022, 32(S1): 165-170.

Figures/Tables 7

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References 16

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情景状态节点S	外部环境E	应急措施A
气柜压力剧增S₁	气柜卡顿、倾斜E₁	检修气柜A₁
情景消失S₂	—	—
气柜持续扰动,环形水封局部减薄S₃	回流阀开速过快,气体不断进入E₂	快速开启回流阀A₂
降压完成,未发生泄漏S₄	气体未冲破环形水封E₃	监测报警A₃ 采取正确措施A₄
情景消失S₅	—	—
泄漏出的氯乙烯气体蔓延S₆	报警器延迟报警,紧急处理能力差E₄	检查处置A₅
泄漏被控制S₇	未蔓延至点火源处E₅	紧急停车A₆ 紧急疏散A₇
未发生燃爆事故,情景消失S₈	—	—
泄漏气体遇到XL-1型箱型高温炉,发生爆炸S₉	—	—

节点	先验概率	条件概率
S₁	P(A₁=Y)=0.90	P(S₁=Y\|A₁=Y,E₁=Y) = 0. 84
	P(A₁=N)=0.10	P(S₁=Y\|A₁=Y,E₁=N) = 0. 05
	P(E₁=Y)=0.38	P(S₁=Y\|A₁=N,E₁=Y) = 0. 95
	P(E₁=N)=0.62	P(S₁=Y\|A₁=N,E₁=N) = 0. 08
S₂	—	P(S₂=Y\|S₁=Y) = 0.33
		P(S₂=Y\|S₁=N) = 0.67
		P(S₂=Y\|S₁=N) = 0.67

节点	度	加权度	紧密中心性	接近中心性	中介中心性	特征向量中心性
S₄	5	3.20	1	1	9	0.37
S₇	5	3.39	1	1	12	0.68
S₆	5	3.96	0.75	0.83	24	0.36
E₃	1	0.75	0.67	0.75	0	0
A₃	1	0.87	0.67	0.75	0	0
A₄	1	0.52	0.67	0.75	0	0
E₅	1	0.71	0.67	0.75	0	0
A₆	1	0.83	0.67	0.75	0	0
A₇	1	0.82	0.67	0.75	0	0
S₃	5	2.75	0.54	0.64	30	0.13
E₄	1	0.72	0.50	0.58	0	0
A₅	1	0.68	0.50	0.58	0	0
S₁	4	1.69	0.42	0.53	16	0.02
A₂	1	0.28	0.39	0.46	0	0
E₂	1	0.34	0.39	0.46	0	0
A₁	1	0.05	0.32	0.40	0	0
E₁	1	0.95	0.32	0.40	0	0
S₂	1	0.33	0	0	0	0.11
S₅	1	0.16	0	0	0	0.69
S₈	1	0.21	0	0	0	1
S₉	1	0.87	0	0	0	0.64

情景节点	重要度	环境节点	重要度	应急节点	重要度
S₇	2.631 9	E₅	0.632 9	A₇	0.632 9
S₆	2.591 4	E₃	0.632 9	A₆	0.632 9
S₃	2.406 1	E₄	0.498 3	A₄	0.632 9
S₄	2.086 5	E₂	0.408 2	A₃	0.632 9
S₁	1.444 7	E₁	0.355 7	A₅	0.498 3
S₈	1.391 2	—	—	A₂	0.408 2
S₅	0.984 4	—	—	A₁	0.355 7
S₉	0.918 8	—	—	—	—
S₂	0.223 1	—	—	—	—