摘要
为实现煤自燃的高效预测预警,提出了一种改进鲸鱼优化算法结合BP神经网络(MSWOA-BP)的煤自燃温度预测模型。根据煤自燃升温实验进行了模型算法的有效性验证,进一步对比分析了粒子群优化(PSO-BP)模型、灰狼优化(GWO-BP)模型及标准鲸鱼优化(WOA-BP)模型的预测效果和性能,结果显示,MSWOA-BP、GWO-BP、WOA-BP和PSO-BP模型预测结果训练阶段平均百分比误差(MAPE)为1.7359%、2.6518%、6.1655%、6.5701%,测试阶段MAPE为3.0393%、6.0723%、6.7341%、7.6035%,表明MSWOA-BP预测模型具有更高的预测精度和稳定性。应用MSWOA-BP模型进行煤矿现场的温度预测,得到预测温度与现场实测温度的相对误差为2.3%~12.1%,实现了煤矿井下温度的快速预测,可为实现煤自燃的高效预测预警提供一种新方法。
In order to realize the efficient prediction and early warning of coal spontaneous combustion,an improved whale optimization algorithm combined with BP neural network(MSWOA-BP)for coal spontaneous combustion temperature prediction model was proposed,and the effectiveness of the model algorithm was verified based on coal spontaneous combustion heating experiments.The predict effects and performance of gray wolf optimization(GWO-BP)model,standard whale optimization(WOA-BP)neural network model and particle swarm optimization(PSO-BP)model were compared and analyzed.It shows that the average percentage error(MAPE)of the four models are 1.7359%,2.6518%,6.1655%and 6.5701%in the training stage,while during the testing phase they are 3.0393%,6.0723%,6.7341%,and 7.6035%.This indicates that the MSWOA-BP prediction model has higher prediction accuracy and stability.MSWOA-BP was applied to predict the temperature at the coal mine site.Comparing the predicted results with the measured temperature,the relative error range is within 2.3%to 12.1%,which realizes the rapid prediction of underground coal mine temperature.It provides a new method for achieving efficient prediction and warning of coal spontaneous combustion.
作者
孔彪
朱思想
胡相明
杨涛
赵旭帅
斐达特
万姝含
KONG Biao;ZHU Sixiang;HU Xiangming;YANG Tao;ZHAO Xushuai;FEI Date;WAN Shuhan(School of Safety and Environmental Engineering,Shandong University of Science and Technology,Qingdao 266590,China;Mine Safety College,North China Institute of Science and Technology,Langfang 065201,China;Jining No.2 Coal Mine,Yanzhou Coal Mining Company Group Co.,Ltd.,Jining 272000,China)
出处
《矿业安全与环保》
CAS
北大核心
2023年第5期30-36,共7页
Mining Safety & Environmental Protection
基金
国家自然科学基金面上项目(52374219)
山东省自然科学基金项目(ZR2023ME115)。
关键词
煤自燃
预测预报
指标气体
煤温
改进鲸鱼优化算法
预测模型
coal spontaneous combustion
forecasting
indicator gas
coal temperature
improved whale optimization algorithm
prediction model
