煤矿安全2024，Vol.55Issue(9)：12-21,10.DOI:10.13347/j.cnki.mkaq.20240431
基于ISSA-GM-BP的回采工作面瓦斯涌出量及其回风瓦斯体积分数预测
Prediction of mining face gas emission and gas volume fraction in mining face return air roadway based on ISSA-GM-BP
摘要
Abstract
Coal mine gas accidents are highly destructive and have a wide range of hazards.Predicting the amount of gas emitted from the mining face and the volume fraction of gas in the return air roadway can provide important basis for formulating gas con-trol measures and preventing gas accidents.To solve the problem of mining face gas emission and gas volume fraction in mining face return air being affected by multiple factors and difficult to accurately predict due to large data fluctuations,we introduce the spar-row search algorithm(SSA)based on grey prediction and BP neural network,and establish an ISSA-GM-BP model for predicting the mining face gas emission and gas volume fraction in mining face return air roadway.This model utilizes Chebyshev chaotic map-ping,dynamic inertia weight,and Lévy flight strategy algorithm to improve SSA.In grey prediction,a dynamic grey GM(1,1,)model is established by introducing dynamic generation coefficients and combined with BP neural network.The combined model is then optimized by improving SSA.Use this model to predict the mining face gas emission and gas volume fraction in mining face re-turn air roadway,and compare and analyze the prediction results with SSA-BP neural network and BP neural network.The results showed that in terms of mining face gas emission and gas volume fraction in mining face return air roadway,the average relative er-rors between the prediction results of the ISSA-GM-BP model and the measured values were 2.95%and 2.65%,respectively.The av-erage relative errors of the SSA-BP neural network were 9.50%and 8.00%,respectively.The average relative errors of the BP neur-al network were 12.49%and 9.76%,respectively.The determination coefficients of the ISSA-GM-BP model were 0.960 9 and 0.958 7,respectively.The predicted values fully conform to the trend of actual mining face gas emission and gas volume fraction in min-ing face return air roadway,and have significant advantages in prediction accuracy and adaptability.关键词
矿山安全/瓦斯涌出量预测/灰色理论/BP神经网络/麻雀搜索算法Key words
mine safety/gas emission prediction/grey theory/BP neural network/sparrow search algorithm分类
矿业与冶金引用本文复制引用
焦辈男,撒占友,韩炳南,刘杰,卢守青,王昊..基于ISSA-GM-BP的回采工作面瓦斯涌出量及其回风瓦斯体积分数预测[J].煤矿安全,2024,55(9):12-21,10.基金项目
国家自然科学基金资助项目(51974169) （51974169）
山东省自然科学基金资助项目(ZR2018PEE001) （ZR2018PEE001）
中国科学院战略性先导科技专项课题资助项目(XDA05030100) （XDA05030100）
