煤矿瓦斯爆炸风险评估研究综述及进展

doi:10.16265/j.cnki.issn1003-3033.2025.02.0963

摘要/Abstract

摘要：

瓦斯爆炸灾害是煤矿重特大事故中占比最高、致灾最严重的灾害,为了阐述瓦斯爆炸风险评估的研究进展,首先识别瓦斯爆炸的风险因素,然后剖析现有风险评估方法存在的不足,整理相关文献得出以下结论:煤矿瓦斯爆炸风险源识别方法及其评估方法存在主观性较强的问题,且风险因素还存在瓦斯来源与变化的不确定性、点火源的未知、通风与控风的不确定性,而应用基于数学理论的客观赋权方法与评估方法,能提高赋权及评估结果的准确性,但计算复杂性限制其广泛应用;虽然计算机模型可使评估煤矿瓦斯爆炸风险结果更加精确,但需要解决数据收集与深度学习的扩展融合问题;未来煤矿瓦斯爆炸风险评估可向多源数据融合技术方向发展,深度挖掘前兆预警信息,建立信息深度感知、数据挖掘的灾害信息智能化模型,以便动态评估煤矿瓦斯爆炸风险。

关键词: 煤矿瓦斯爆炸, 风险评估, 不确定性, 风险指标, 点火源

Abstract:

Gas explosion disaster is the most serious coal mine accidents. In order to summarize the research progress of gas explosion risk assessment, firstly, the risk factors of gas explosion were identified. Then the shortcomings of existing risk assessment methods were analyzed, and the following conclusions were drawn by sorting out relevant literature. The analysis shows that there are subjective problems in identification method and evaluation method of coal mine gas explosion risk sources. There are also some problems with risk factors, such as the uncertainty of gas source and change, the unknown ignition source, the uncertainty of ventilation and air control. The application of objective weighting method and evaluation method based on mathematical theory can improve the accuracy of weighting and evaluation results, but the computational complexity limits its wide application. Although the application of computer models has made the assessment of coal mine gas explosion risk more accurate, it is necessary to solve the problem of expanding the integration of data collection and deep learning. Based on the current research status and existing problems, the future risk assessment of coal mine gas explosion can develop in the direction of multi-source data fusion technology, deeply mining precursory warning information, establishing intelligent models of disaster information based on information depth perception and data mining, and realizing dynamic risk assessment of coal mine gas explosion.

Key words: coal mine gas explosion, risk assessment, uncertainty, risk indicators, ignition sources

中图分类号:

X932爆炸安全与防火、防爆

李敏, 王丹, 贺珊, 施式亮, 王德明, 鲁义. 煤矿瓦斯爆炸风险评估研究综述及进展[J]. 中国安全科学学报, 2025, 35(2): 127-136.

LI Min, WANG Dan, HE Shan, SHI Shiliang, WANG Deming, LU Yi. Research review and progress of coal mine gas explosion risk assessment[J]. China Safety Science Journal, 2025, 35(2): 127-136.

图/表 6

图1

图2

表1

各风险识别方法及其优缺点汇总

方法名称	方法内容	优点	缺点
德尔菲	通过匿名的方式集合专家的智慧、经验与知识等对模糊不确定性的问题发表意见,并经过多次反馈、归纳、统计而解决问题	低成本集思广益	易忽视少数人的意见;存在组织者主观影响
频率统计	根据收集到的相关事故案例,按照出现频率来统计产生瓦斯爆炸的事故原因	使用简单	难以获取大量样本
文本挖掘	从大量文本事故案例中发现知识,抽取隐含的、未知的、潜在有用的模式的过程	有效挖掘文本信息	难以获取大量样本,样本预处理工作量大
“5W”分析法	从人员(Who)、地点(Where)、时间(When)、内容(What)和原因(Why)等5方面思考事故的传播过程	使用简单	时间耗费成本较大;资料繁琐
灾害系统理论和风险理论	建立瓦斯风险的3级评价指标体系,一级为瓦斯灾害风险目标层;二级指标包括孕险环境的稳定性、致险因子的危险性、承险体的脆弱性;三级指标选取能反应二级指标特征的具体的、有代表性的、能量化的指标	快速提取风险源	对研究者自身素质依赖过强
事故树	从结果到原因描绘事故发生的有向逻辑树	可简洁、形象地表示事故之间的逻辑关系	难以适用于复杂庞大的系统,对人员专业性要求较高
鱼刺图	又称“因果图”,利用“头脑风暴法”,找出各特性要因,按相互关联性整理而成的图形来表示	层次分明,易于理解,使用简单	难以适用于复杂庞大的系统,对人员专业性要求较高
Bow-tie	主要包括事故(顶级事件)、事故原因(危险源)、事故后果,以及安全屏障,通过事故树分析探究事故原因,事件树分析研究事故后果,最后借鉴瑞士乳酪模型加入2道安全屏障	简单、易操作	难以适用于复杂庞大的系统,对人员专业性要求较高
24Model模型	将原因分为组织内部原因和外部原因,其内部原因分布在组织与个人2个层面上。组织层面上的原因分为安全文化、安全管理体系,个人层面上的原因分为习惯性行为和一次性行为与物态	层次分明,易于理解,使用简单	不考虑外部原因
HHM	可以用来识别一个项目中多个方面、维度和层级之间的内在不同特性,找出其中的关联性并加以分析	识别出的项目风险更全面系统,且该模型具有延展性	工作量大,成本较高,技术团队要求高

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表2

图3

表3

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优化算法	方法模型	内容	特点	不足
PSO-SVM^[30]	SVM	有监督学习方法的二类分类器,通过找到最大边距超平面将样本分类	可引入核技巧处理非线性分类;可处理高维数据问题;所需样本量少,泛化能力强	对缺失数据很敏感;难以找到合适的核函数
FOA-SVM^[31]
信息增益(Information Gain,IG)-SVM^[32]
PSO-BP^[33]	BP神经网络	由输入层、隐含层、输出层构成,包含信号的正向传播和误差的反向传播2个学习过程	具有可扩展性;预测精度较高;可处理非线性问题	易出现过拟合问题;训练时间较长
GA-BP^[34]	BP神经网络	由输入层、隐含层、输出层构成,包含信号的正向传播和误差的反向传播2个学习过程	具有可扩展性;预测精度较高;可处理非线性问题	易出现过拟合问题;训练时间较长
Adam-LSTM^[35]	LSTM神经网络	由遗忘门、输入门、细胞状态、输出门构成,通过门结构的控制来判断信息是否有用	改善了RNN中存在的长期依赖问题以及可能出现的梯度消失问题	不能彻底解决RNN的梯度问题;计算量较大、费时
人工化学反应优化(Artificial Chemical Reaction Optimization,ACROA)-ELM^[36]	ELM	其结构与传统的神经网络相似,不同在于可随机初始化隐含层的权重,然后利用最小二乘法一次性解析出输出层的权重值	训练速度快、泛化能力强、自动特征选择	结果不稳定;表现非线性能力较差
改进乌鸦算法(Improved Crow Search Algorithm,ICSA)-ELM^[37]	ELM	其结构与传统的神经网络相似,不同在于可随机初始化隐含层的权重,然后利用最小二乘法一次性解析出输出层的权重值	训练速度快、泛化能力强、自动特征选择	结果不稳定;表现非线性能力较差

赋权方法分类	方法名称	方法内容	优点	缺点
主观赋权法	德尔菲法	由每位专家先独立地对考核指标设置权重,然后对每个考核指标的权重取平均值,作为最终权重	简单实用	主观性较强
主观赋权法	AHP	由专家根据1~9标度建立判断矩阵确定各指标权重	具有系统性、简洁实用的特点,所需定量数据较少	主观性较强;指标过多时,难以检验判断矩阵的一致性
客观赋权法	熵权法	根据各指标数据的分散程度,利用信息熵计算出各指标的熵权,从而反映出不同指标在综合评估中所起作用的大小	可信度高	对样本依赖度大
	PCA	将样本数据矩阵转置,依据客观数据之间的关系,用PCA的方法计算各项指标的得分,继而归一化处理,作为各项指标的权重系数^[39]	降维效果显著;计算效率高	计算过程较复杂;受限于样本量与变量个数
	CRITIC法	用标准差表示评价指标的对比强度以及利用相关系数表示指标之间的冲突性来综合衡量指标的客观权重	客观性较强	处理较多影响因素时存在局限性
组合赋权法	博弈论偏序集	博弈优化得出最佳平衡权重,并由此确定指标权重排序转化为偏序关系	保证了权重排序的稳定性;降低了样本量的要求;赋权结果准确性较高	计算过程较复杂
组合赋权法	G1-CRITIC-博弈论	G1法确定主观常权,CRITIC法确定客观常权,最后利用博弈论确定最优综合常权	赋权结果准确性较高	计算过程较复杂