基于全连接的长短期记忆网络实现采空区CO多步预测  

Multistep prediction of CO in the extraction zone based on a fully connected long short-term memory network

作　　者：罗振敏 张利冬 宋泽阳 LUO Zhenmin;ZHANG Lidong;SONG Zeyang(School of Safety Science and Engineering,Xian University of Science and Technology,Xian 710054,China;Shaanxi Engineering Research Center for Industrial Process Safety and Emergency Rescue,Xian 710054,China)

机构地区：[1]西安科技大学安全科学与工程学院,西安710054 [2]陕西省工业过程安全与应急救援工程技术研究中心,西安710054

出　　处：《清华大学学报（自然科学版）》2024年第6期940-952,共13页Journal of Tsinghua University(Science and Technology)

基　　金：国家自然科学基金项目(52174200)。

摘　　要：煤自燃是煤矿的主要自然灾害之一。煤自燃的物理化学过程十分复杂,且影响因素众多,给煤自燃危险性的预测带来很大的挑战。利用深度学习理论与方法加强对煤自燃危险性预测技术的研究,有助于提升煤矿安全生产智能化管控水平。该研究运用循环神经网络(RNN)、长短期记忆(LSTM)网络和门控循环单元(GRU)3种算法,建立了采空区CO动态序列预测模型。对数据集进行特征变量分布检验以及数据归一化处理,降低了变量依赖性。在模型构建过程中,添加了全连接层和Dropout类以避免模型出现过拟合,通过均方误差确定模型的选代次数,引入了平均绝对误差、均方根误差和确定系数3个模型性能检验指标,分析优化了模型的参数,检验了模型性能。研究结果表明:RNN、LSTM和GRU模型均能实现对CO体积分数的动态预测,且误差小于1%;在同一序列数据下,LSTM模型预测精度最高,其次是RNN模型和GRU模型。[Objective]Spontaneous coal combustion is one of the major natural disasters in coal mining;thus,accurate prediction of the risk of spontaneous coal combustion is crucial to prevent and control coal fire disasters.However,the complexity of the physicochemical process of spontaneous coal combustion and its various influencing factors poses a challenge to the risk prediction of spontaneous coal combustion.Strengthening research on spontaneous coal combustion hazard prediction technology using deep learning is crucial for improving the intelligent control level of coal mine safety production.[Methods]In this study,CO volume fraction was chosen as the index for spontaneous coal combustion evaluation.A dataset was constructed,and the field observation data were visualized.Next,the dataset was tested for the distribution of eigenvariables,normalized for the distribution of eigenvariables,and normalized for the dataset using kernel density estimation,logarithmic transformation,and maximum-minimum normalization.Finally,three algorithms,namely recurrent neural network(RNN),long short-term memory(LSTM)network,and gated recurrent unit(GRU),were applied to the data mining of spontaneous coal combustion feature information,and a dynamic sequence prediction model of spontaneous coal combustion CO volume fraction was established.During the model construction process,the full connectivity layer and Dropout class were added to prevent overfitting,and the mean square error and three model performance test indicators were introduced to analyze and optimize the model parameters and test the model performance.[Results]The results were presented as follows:(1)The CO volume fraction sequence dataset was established based on the field data of the Dafosi Coal Mine,the model generalization capability was enhanced,and the training time of the model was shortened by preprocessing the dataset.(2)The RNN,LSTM,and GRU models achieved the dynamic prediction of CO with an error of less than 1%.(3)The optimal parameters of the three models were d

关 键 词：煤自燃 CO体积分数预测 长短期记忆(LSTM)网络 深度学习 

分 类 号：X936[环境科学与工程—安全科学]