基于多因素耦合作用机制的煤与瓦斯突出危险等级预测  

Risk Level Prediction of Coal and Gas Outburst Based on Multi-factor Coupling Mechanism

作　　者：王直 余伟健 WANG Zhi;YU Weijian(School of Resources,Environment and Safety Engineering,Hunan University of Science and Technology,Xiangtan 411201,China;Hunan Provincial Key Laboratory of Safe Mining Techniques of Coal Mines,Hunan University of Science and Technology,Xiangtan 411201,China)

机构地区：[1]湖南科技大学资源环境与安全工程学院,湖南湘潭411201 [2]湖南科技大学湖南省煤矿安全开采技术重点实验室,湖南湘潭411201

出　　处：《矿业工程研究》2021年第4期46-52,共7页Mineral Engineering Research

基　　金：国家自然科学基金资助项目(51974117;52174076);湖南省自然科学基金资助项目(2020JJ4027)。

摘　　要：以现有的发生煤与瓦斯突出矿井资料为基础,根据煤与瓦斯突出的影响因素确定预测指标并建立交互作用矩阵,采用BP神经网络对矩阵进行编码,基于岩石工程系统理论建立煤与瓦斯突出危险等级预测模型.通过预测模型和实际突出危险等级对比,得出结论:应用BP神经网络对交互作用矩阵编码可有效地减少人为因素对结果的影响;煤与瓦斯突出危险等级预测模型准确率可达80%,满足精度要求,能有效预测煤与瓦斯突出危险性等级.Based on the existing coal and gas outburst mine data, predictive index of coal and gas outburst influence factors are determined, interaction matrix is established, BP neural network is used for coding matrix, and coal and gas outburst risk level prediction model is established based on the theory of rock engineering system. By comparing the prediction model with the actual outburst risk level, it is concluded that the BP neural network on the interaction matrix coding can effectively reduce the influence of human factors on the results;the coal and gas outburst risk level prediction model can reach 80% accuracy, meet the accuracy requirements, and can effectively predict the coal and gas outburst risk level.

关 键 词：煤与瓦斯突出 岩石工程系统 危险等级预测 BP神经网络 

分 类 号：TD713[矿业工程—矿井通风与安全]