Risk assessment of ventilation system in coal mines based on DS theory and Bayesian network

doi:10.16265/j.cnki.issn1003-3033.2022.08.1633

Abstract

Abstract:

In order to evaluate risk level of ventilation system in coal mines, firstly, an assessment index system was established with 16 main indicators from perspectives of human, equipment, environment and management according to characteristics of ventilation system, and related literatures and technical standards. Then, DS theory-Bayesian network model that could achieve information fusion was introduced to establish a risk assessment model. Finally, with actual investigation data from a coal mine in Henan province as an example, index weight and risk probability were calculated, and its risk level and sensitive indicators were obtained through risk reasoning and sensitivity analysis. The results show that the ventilation system is at a general risk level, and its sensitivity indicators include disrepair rate of ventilation laneway, percentage of return air resistance, average length of service and efficiency of main ventilation fan.

Key words: ventilation system in coal mine, DS theory, Bayesian network, risk assessment, indicator system, sensitivity analysis

LI Jinrong, YANG Yuzhong. Risk assessment of ventilation system in coal mines based on DS theory and Bayesian network[J]. China Safety Science Journal, 2022, 32(8): 146-153.

Figures/Tables 12

Fig.1

Fig.2

Tab.1

Classification of risk assessment index of ventilation system in coal mines

2级指标	风险等级
2级指标	1级	2级	3级	4级	5级
作业人员平均工龄/a	[0,2)	[2,4)	[4,6)	[6,10)	[10,∞)
平均每年培训时长/d	[0,2)	[2,4)	[4,6)	[6,8)	[8,∞)
作业人员"三违"率/%	[16,∞)	[12,16)	[8,12)	[4,8)	[0,4)
风量供需比/%	[1.5,∞) $⋃$ [0,1)	[1.4,1.5)	[1.3,1.4)	[1.2,1.3)	[1,1.2)
有效风量率/%	[0,60)	[60,70)	[70,80)	[80,90)	[90,100]
矿井外部漏风率/%	[15,∞)	[10,15)	[6,10)	[2,6)	[0,2)
主通风机效率/%	[0,50)	[50,65)	[65,80)	[80,95)	[95,100]
通风构筑物质量合格率/%	[0,80)	[80,85)	[85,90)	[90,95)	[95,100]
等积孔/m²	[0,0.5)	[0.5,1)	[1,1.5)	[1.5,2)	[2,∞)
回风段阻力占比/%	[60,100)	[50,60)	[40,50)	[30,40)	[0,30)
通风巷道失修率/%	[7,100)	[5,7)	[3,5)	[1,3)	[0,1)
矿井气候(合格量化分值)	[0,60)	[60,70)	[70,80)	[80,90)	[90,100]
持证上岗率/%	[0,60)	[60,70)	[70,80)	[80,90)	[90,100]
安全投入占比/%	[0,60)	[60,70)	[70,80)	[80,90)	[90,100]
应急措施完备率/%	[0,60)	[60,70)	[70,80)	[80,90)	[90,100]
管理制度完备率/%	[0,60)	[60,70)	[70,80)	[80,90)	[90,100]

Tab.1

Fig.3

Tab.2

Tab.3

Tab.4

Tab.5

Fusion results of secondary index evaluation and its weight

指标	融合评价	最终评估结果	权重
C₁₁	m({V₃})=0.994 0;m({V₄}) =0.006 0	0.498 8	0.409 4
C₁₂	m({V₅})=0.128 1;m({V₄}) =0.871 9	0.274 4	0.225 2
$C 1 3$	m({V₃})=0.725 9;m({V₄}) =0.274 1	0.445 2	0.365 4
C₂₁	m({V₃})=0.978 8;m({V₂}) =0.021 2	0.504 2	0.201 6
C₂₂	m({V₂}) =0.000 1;m({V₃})=0.999 7;m({V₄}) =0.000 2	0.500 0	0.199 9
C₂₃	m({V₃}) =0.007 5;m({V₄})=0.992 1;m({V₅}) =0.000 4	0.301 4	0.120 5
C₂₄	m({V₁}) =0.001 6;m({V₂})=0.996 8;m({V₃}) =0.001 6	0.700 0	0.279 9
C₂₅	m({V₃})=0.977;m({V₄}) =0.023	0.495 4	0.198 1
C₃₁	m({V₃})=0.176 4;m({V₄}) =0.8236	0.335 3	0.190 5
C₃₂	m({V₃}) =0.993 9;m({V₄})=0.0061	0.498 6	0.283 4
C₃₃	m({V₁}) =0.000 2;m({V₂})=0.631 9;m({V₃}) =0.367 9	0.626 5	0.356 0
C₃₄	m({V₅})=0.003 6;m({V₄}) =0.996 4	0.299 3	0.170 1
C₄₁	m({V₅})=0.539 5;m({V₄}) =0.460 5	0.192 1	0.198 2
C₄₂	m({V₃})=0.323 7;m({V₄}) =0.676 3	0.364 7	0.376 4
C₄₃	m({V₃}) =0.056 1;m({V₄})=0.943 7;m({V₅}) =0.000 2	0.311 8	0.321 8
C₄₄	m({V₅})=0.997 9;m({V₄}) =0.002 1	0.100 4	0.103 6

Tab.5

Tab.6

Tab.7

Fig.4

Fig.5

References 16

[1]	SHI Long, WANG Jinhui, ZHANG Guomin, et al. A risk assessment method to quantitatively investigate the methane explosion in underground coal mine[J]. Process Safety and Environmental Protection, 2017, 107:317-333. doi: 10.1016/j.psep.2017.02.023
[2]	LI Min, WANG Hetang, WANG Deming, et al. Risk assessment of gas explosion in coal mines based on fuzzy AHP and bayesian network[J]. Process Safety and Environmental Protection, 2020, 135:207-218. doi: 10.1016/j.psep.2020.01.003
[3]	鲁锦涛, 任利成, 戎丹, 等. 基于灰色-物元模型的煤矿瓦斯爆炸风险评估[J]. 中国安全科学学报, 2021, 31(2):99-105. doi: 10.16265/j.cnki.issn 1003-3033.2021.02.014
	LU Jintao, REN Licheng, RONG Dan, et al. Assessment of coal mine gas explosion risk based on grey-matter element model[J]. China Safety Science Journal, 2021, 31(2):99-105. doi: 10.16265/j.cnki.issn 1003-3033.2021.02.014
[4]	皮子坤, 贾宝山, 贾廷贵, 等. 基于前景理论和区间数的煤矿瓦斯爆炸风险评价[J]. 中国安全科学学报, 2017, 27(6):91-96. doi: 10.16265/j.cnki.issn1003-3033.2017.06.016
	PI Zikun, JIA Baoshan, JIA Tinggui, et al. Assessment of risk of gas explosion in coal mine based on prospect theory and interval number[J]. China Safety Science Journal, 2017, 27(6):91-96. doi: 10.16265/j.cnki.issn1003-3033.2017.06.016
[5]	苏盈盈, 刘兴华, 李景哲, 等. 矿井通风系统指标体系的约简及其安全评价[J]. 中国安全科学学报, 2013, 23(9):83-89.
	SU Yingying, LIU Xinghua, LI Jingzhe, et al. Mine ventilation system index system reduction and its safety evaluation[J]. China Safety Science Journal, 2013, 23(9):83-89.
[6]	丁厚成, 黄新杰. 基于 AHP-FCE 的煤矿通风系统可靠性评价研究[J]. 自然灾害学报, 2013, 22(3):153-159.
	DING Houcheng, HUANG Xinjie. AHP-FCE based reliability of coal mine ventilation system[J]. Journal of Natural Disaster, 2013, 22(3):153-159.
[7]	张聪聪, 吴世跃, 安省伟, 等. 煤矿通风系统可靠性评价[J]. 工矿自动化, 2015, 41(1):114-118.
	ZHANG Congcong, WU Shiyue, AN Shengwei, et al. Reliability evaluation of coal mine ventilation system[J]. Industry and Mine Automation, 2015, 41(1):114-118.
[8]	马恒, 苗倩斐, 韩宝华, 等. 矿井通风系统稳定性SD预测仿真分析[J]. 中国安全生产科学技术, 2019, 15(12):108-114.
	MA Heng, MIAO Qianfei, HAN Baohua, et al. SD prediction and simulation analysis on stability of mine ventilation system[J]. Journal of Safety Science and Technology, 2019, 15(12):108-114.
[9]	韩艳杰, 张志军, 李亚俊. 基于新型综合集成法的矿井通风系统安全评价[J]. 中国安全生产科学技术, 2014, 10(2):75-80.
	HAN Yanjie, ZHANG Zhijun, LI Yajun. Safety evaluation of mine ventilation system based on new comprehensive integration method[J]. Journal of Safety Science and Technology, 2014, 10(2):75-80.
[10]	张加国, 张庆财. 运用风险事故树分析矿井通风系统安全性[J]. 煤炭科学技术, 2020, 48(增2):169-173.
	ZHANG Jiaguo, ZHANG Qingcai. Safety analysis of mine ventilation system with risk fault tree[J]. Coal Science and Technology, 2020, 48(S2):169-173.
[11]	王文辉, 杨玉中, 孔静, 等. 基于熵权的矿井通风系统灰色综合评价[J]. 工业安全与环保, 2011, 37(12):39-41.
	WANG Wenhui, YANG Yuzhong, KONG Jing, et al. Grey comprehensive evaluation of mine ventilation system based on entropy weight[J]. Industrial Safety and Environmental Protection, 2011, 37(12):39-41.
[12]	DENG Xinyang, ZHENG Xi, SU Xiaoyan. An evidential game theory framework in multi-criteria decision[J]. Applied Mathematics and Computation, 2014, 244:783-793. doi: 10.1016/j.amc.2014.07.065
[13]	DENG Yong, SHI Wenkang, ZHU Zhenfu, et al. Combining belief functions based on distance of evidence[J]. Decision Support Systems, 2004, 38: 489-493. doi: 10.1016/j.dss.2004.04.015
[14]	张军, 涂国平. 加权平均法解决证据理论中的失效问题[J]. 微计算机信息, 2007, 23(11-3):202-203,225.
	ZHANG Jun, TU Guoping. Weighted averaging approach to conflict evidence in D-S evidence theory[J]. Microcomputer Information, 2007, 23(11-3):202-203,225.
[15]	ZHAO Dan, WANG Zhirong, SONG Zeyang. Assessment of domino effects in the coal gasification process using Fuzzy Analytic Hierarchy Process and Bayesian Network[J]. Safety Science, 2020, 130:1-10.
[16]	李贤功, 宋学锋, 张明慧, 等. 矿山安全态势预测预警研究[J]. 工矿自动化, 2021, 47(5):35-39,111.
	LI Xiangong, SONG Xuefeng, ZHANG Minghui, et al. Research on mine safety situation forecast and early warning[J]. Industry and Mine Automation, 2021, 47(5):35-39,111.

指标	专家1	专家2	专家3	专家4
C₁₁	({V₃},0.8; {V₄},0.2)	({V₃},1.0)	({V₃},0.5; {V₄},0.5)	({V₄},0.1; {V₃},0.9)
C₁₂	({V₅},0.6; {V₄},0.4)	({V₅},0.2; {V₄},0.8)	({V₄},1.0)	({V₅},1.0)
C₁₃	({V₃},1.0)	({V₃},0.5; {V₄},0.5)	({V₃},0.7; {V₄},0.3)	({V₄},0.6; {V₃},0.4)
C₂₁	({V₃},0.7; {V₂},0.3)	({V₃},0.6; {V₂},0.4)	({V₃},1.0)	({V₃},0.8; {V₂},0.2)
C₂₂	({V₃},0.8; {V₂},0.2)	({V₃},1.0)	({V₃},0.7; {V₄},0.3)	({V₃},1.0)
C₂₃	({V₄},1.0)	({V₅},0.2; {V₄},0.8)	({V₃},0.1; {V₄},0.9)	({V₃},0.8; {V₄},0.2)
C₂₄	({V₃},0.5; {V₂},0.5)	({V₂},1.0)	({V₁},0.3; {V₂},0.7)	({V₁},0.1; {V₂},0.9)
C₂₅	({V₃},1.0)	({V₃},0.7; {V₄},0.3)	({V₃},1.0)	({V₃},0.5; {V₄},0.5)
C₃₁	({V₃},0.8; {V₄},0.2)	({V₄},1.0)	({V₃},0.1; {V₄},0.9)	({V₃},0.5; {V₄},0.5)
C₃₂	({V₃},0.9; {V₄},0.1)	({V₃},1.0)	({V₃},0.8; {V₄},0.2)	({V₃},0.5; {V₄},0.5)
C₃₃	({V₂},1.0)	({V₃},0.8; {V₂},0.2)	({V₃},0.5; {V₂},0.5)	({V₁},0.1; {V₂},0.9)
C₃₄	({V₄},0.8; {V₅},0.2)	({V₄},1.0)	({V₅},0.1; {V₄},0.9)	({V₄},0.6; {V₅},0.4)
C₄₁	({V₅},0.5; {V₄},0.5)	({V₅},0.4; {V₄},0.6)	({V₄},1.0)	({V₅},0.7; {V₄},0.3)
C₄₂	({V₃},1.0)	({V₃},0.5; {V₄},0.5)	({V₃},0.5; {V₄},0.5)	({V₃},0.3; {V₄},0.7)
C₄₃	({V₃},0.4; {V₄},0.6)	({V₄},1.0)	({V₅},0.2; {V₄},0.8)	({V₃},0.5; {V₄},0.5)
C₄₄	({V₅},1.0)	({V₅},0.7; {V₄},0.3)	({V₅},0.8; {V₄},0.2)	({V₅},1.0)

指标	专家1	专家2	专家3	专家4	指标	专家1	专家2	专家3	专家4
C₁₁	0.309 8	0.235 5	0.178 9	0.275 8	C₃₁	0.157 9	0.219 3	0.280 7	0.342 1
C₁₂	0.367 9	0.301 8	0.226 5	0.103 7	C₃₂	0.272 4	0.227 6	0.317 3	0.182 7
C₁₃	0.176 0	0.274 7	0.324 0	0.225 3	C₃₃	0.235 2	0.155 6	0.346 3	0.262 9
C₂₁	0.271 5	0.228 5	0.207 1	0.292 6	C₃₄	0.292 9	0.228 5	0.271 5	0.207 1
C₂₂	0.253 6	0.262 8	0.220 8	0.262 8	C₄₁	0.299 3	0.348 6	0.151 4	0.200 7
C₂₃	0.271 7	0.304 9	0.330 2	0.093 2	C₄₂	0.142 6	0.319 5	0.319 5	0.218 4
C₂₄	0.185 7	0.256 3	0.266 5	0.291 5	C₄₃	0.280 2	0.230 6	0.258 7	0.230 6
C₂₅	0.250 0	0.299 3	0.250 0	0.200 7	C₄₄	0.250 0	0.228 5	0.271 5	0.250 0

指标	融合评价	评估结果	权重
B₁	m({V₃})=0.996 9;m({V₄})=0.002 9;m({V₅}) =0.000 2	0.499 3	0.262 0
B₂	m({V₂})=0.016 9;m({V₃})=0.974 6;m({V₄}) =0.008 5	0.5017	0.263 3
B₃	m({V₂})=0.191 5;m({V₃})=0.766;m({V₄})=0.042 5	0.529 8	0.278 1
B₄	m({V₃})=0.392 2;m({V₄})=0.588 2;m({V₅}) =0.019 6	0.374 5	0.196 6