基于CNN的煤岩瓦斯复合动力灾害预测  

Prediction of coal-gas compound dynamic disaster based on convolutional neural network

作　　者：王凯 李康楠[1,2] 杜锋 张翔 王衍海 周家旭 Wang Kai;Li Kangnan;Du Feng;Zhang Xiang;Wang Yanhai;Zhou Jiaxu(Key Laboratory for Precise Mining of Intergrown Energy and Resources,China University of Mining and Technology-Beijing,Beijing 100083,China;School of Emergency Management and Safety Engineering,China University of Mining and Technology-Beijing,Beijing 100083,China)

机构地区：[1]中国矿业大学(北京)共伴生能源精准开采北京市重点实验室,北京100083 [2]中国矿业大学(北京)应急管理与安全工程学院,北京100083

出　　处：《矿业科学学报》2023年第5期613-622,共10页Journal of Mining Science and Technology

基　　金：国家自然科学基金(52130409,52004291)。

摘　　要：随着我国煤矿开采逐渐进入深部区域,煤岩瓦斯复合动力灾害日益严重,对煤矿的安全生产造成极大威胁。基于某矿现场数据,采用智能预测手段对煤岩瓦斯复合动力灾害进行研究。首先,依据大数据处理流程,应用箱型图分析法(Box-plot)与多重插补法(MI)进行数据清洗,结合灰色关联度分析法(GRA)建立煤岩瓦斯复合动力灾害指标体系;然后应用主成分分析法(PCA)进行数据降维,结合深度学习中的卷积神经网络(CNN)建立基于BMGP-CNN的煤岩瓦斯复合动力灾害预测模型;运用现场案例数据将此模型与BP模型、随机森林(RF)模型、支持向量机(SVM)模型及人工神经网络(ANN)模型进行对比验证,发现BMGP-CNN模型预测结果的准确率最高,且该模型的收敛速度较快,能够在数秒内完成预测。研究结果对于煤岩瓦斯复合动力灾害的预测和防控具有重要意义。As deep mining becomes prevalent in China􀆳s coal mining industry,coal-gas compound dynamic disasters pose increasing threat to the safety production of coal mines.This paper adopts the field data of Pingmei No.8 coal mine for analysis,with the attempt to predict coal-gas compound dynamic disaster through convolutional neural network.Following the routine of the big data processing,we first employed Box-plot analysis and multiple interpolation method(MI)to clean the data.Combined with grey relation analysis(GRA),we established a coal-gas compound dynamic disaster index system.Then,principal component analysis(PCA)is used for dimensionality reduction of the data.Combined with the convolution neural network(CNN)in deep learning,we established the coal-gas compound dynamic disaster prediction model based on BMGP-CNN.The field data is used to compare and verify this model with BP,random forest(RF),support vector machine(SVM)and artificial neural network(ANN).It is found that BMGP-CNN model yields prediction results with satisfactory accuracy and quick convergence.The results offer implications for the prediction and prevention of coal-gas compound dynamic disasters.

关 键 词：煤岩瓦斯复合动力灾害 深度学习 大数据 指标体系 预测模型 

分 类 号：TD713[矿业工程—矿井通风与安全]