煤矿安全2025，Vol.56Issue(4)：75-81,7.DOI:10.13347/j.cnki.mkaq.20241406
基于DBO-BP神经网络的采空区煤温预测方法及应用
Prediction method and application of coal temperature in goaf based on DPO-BP neural network
摘要
Abstract
One of the key means of preventing and controlling spontaneous combustion of coal in mines is to predict the natural igni-tion temperature of coal in the goaf.In order to more accurately predict the natural ignition temperature of the coal in the mining area,coal samples from the 2#coal seam of Zaoquan Mine were collected for the spontaneous combustion characterization experi-ment.The data obtained from the experiments were collected to record the changes in the volume fractions of O2,CO,CH4,CO2,C2H4 and C2H6 gases released from the coal samples during the oxidation heating process.Taking the above marker gas volume frac-tions as input data and the corresponding oxidation temperatures of the coal samples as output data,a BP neural network-based coal spontaneous combustion prediction model was established using python software by selecting the training set and test set according to the ratio.A novel swarm intelligent dung beetle optimization algorithm was used to optimize the hyperparameters of the BP neur-al network,and the parameter-optimized DBO-BP prediction model was established,and the prediction results were compared and analyzed with the performance indexes of the particle swarm algorithm(PSO-BP),the genetic algorithm(GA-BP)and the sparrow search algorithm(SSA-BP).The results show that the mean absolute percentage errors(MAPE)in the test phase after hyper-paramet-er optimization of the BP neural network prediction models by DBO-BP,SSA-BP,GA-BP and PSO-BP are 6.97%,8.63%,7.88%and 8.18%,respectively;the regression coefficients R2 are 0.976 3,0.966 8,0.970 1,0.969 0,in which the MAPE value of DBO-BP model is the smallest and the R2 is the closest to 1.It proves that the DBO-BP prediction model has faster convergence speed,higher solution accuracy,higher prediction accuracy and robustness.Based on the DPO-BP model,the oxidation temperature of the left coal in the goaf of 150202 working face of Zaoquan Coal Mine is predicted.According to the prediction error of the model,the relative error between the predicted temperature and the field measured temperature is 6.97%.The optimized model using the DBO al-gorithm has a high accuracy in predicting the oxidation temperature of the left coal.关键词
煤自燃/标志气体/监测预警/蜣螂算法/预测模型Key words
coal spontaneous combustion/marker gas/monitoring and early warning/dung beetle algorithm/forecasting model分类
矿业与冶金引用本文复制引用
翟小伟,王博,蒋学明,侯树宏,郝乐..基于DBO-BP神经网络的采空区煤温预测方法及应用[J].煤矿安全,2025,56(4):75-81,7.基金项目
国家自然科学基金资助项目(52274229) （52274229）
陕西省教育厅科学研究计划资助项目(21JP078) （21JP078）
