基于ACO−KELM 的采空区遗煤温度预测模型研究  

Study on the temperature prediction model of residual coal in goaf based on ACO-KELM

作　　者：翟小伟 王辰[1,2,3] 郝乐 李心田 侯钦元 马腾 ZHAI Xiaowei;WANG Chen;HAO Le;LI Xintian;HOU Qinyuan;MA Teng(College of Safety Science and Engineering,Xi'an University of Science and Technology,Xi'an 710054,China;Shaanxi Province Key Laboratory of Coal Fire Disaster Prevention and Control,Xi'an University of Science and Technology,Xi'an 710054,China;Mine Emergency Rescue Innovation Team,The Youth Innovation Team of Shaanxi Universities,Xi'an 710054,China)

机构地区：[1]西安科技大学安全科学与工程学院,陕西西安710054 [2]西安科技大学陕西省煤火灾害防控重点实验室,陕西西安710054 [3]陕西高校青年创新团队矿山应急救援创新团队,陕西西安710054

出　　处：《工矿自动化》2024年第12期128-135,共8页Journal Of Mine Automation

基　　金：国家自然科学基金资助项目(52274229);陕西省教育厅青年创新团队建设科研计划项目(21JP078)。

摘　　要：现有采空区遗煤温度预测研究多侧重于温度与气体浓度之间的关系,较少考虑采空区内遗煤温度与距工作面距离及漏风风速之间的复杂非线性关系。针对该问题,提出了一种基于蚁群优化算法优化核极限学习机(ACO−KELM)的采空区遗煤温度预测模型。在葫芦素煤矿21404工作面采空区布置束管及分布式光纤,对21404工作面采空区内O_(2),CO,CO_(2)浓度和温度数据进行采集,同时结合采空区内漏风强度和距工作面水平距离构建KELM模型,通过ACO对KELM模型中的正则系数和核参数进行寻优,获得最优超参数组合,进而得到性能最优的KELM模型。与基于极限学习机(ELM)和基于随机森林(RF)算法的预测模型相比,ACO−KELM模型在测试集上的平均绝对误差为0.0701℃,均方根误差为0.0748℃,较基于ELM模型分别降低了65%和195%,较基于RF模型分别降低了53%和156%;ACO−KELM模型在测试集上的判定系数为0.9635,与训练集的判定系数仅相差0.01,说明该模型未陷入过拟合且拟合程度较高。Existing studies on the temperature prediction of residual coal in goafs have mainly focused on the relationship between temperature and gas concentration,with limited attention given to the complex nonlinear relationships between the residual coal temperature in the goaf,the distance from the working face,and the air leakage velocity.To address this gap,a prediction model based on ant colony optimization(ACO)and kernel extreme learning machine(KELM)(ACO-KELM)was proposed.In the 21404 working face goaf of Hulususu Coal Mine,beam tubes and distributed fiber optics were arranged to collect data on O_(2) concentration,CO concentration,CO_(2) concentration,and temperature within the goaf.Simultaneously,the air leakage intensity and horizontal distances from the working face were incorporated to construct the KELM model.ACO was employed to optimize the regularization coefficients and kernel parameters in the KELM model,thereby obtaining the bestperforming hyperparameter combination and generating the optimal KELM model.Compared to the prediction models based on extreme learning machine(ELM)and random forest(RF)algorithms,the ACO-KELM model achieved an average absolute error of 0.0701℃and a root mean square error(RMSE)of 0.0748℃on the test set,reducing these errors by 65%and 195%,respectively,compared to the ELM-based model,and by 53%and 156%,respectively,compared to the RF-based model.The coefficient of determination(R2)for the ACO-KELM model on the test set was 0.9635,which was only 0.01 lower than that of the training set,indicating that the model was not overfitted and demonstrated a high degree of accuracy.

关 键 词：采空区遗煤 煤自燃 遗煤温度预测 核极限学习机 蚁群算法 漏风强度 指标气体分析法 漏风风速 

分 类 号：TD752[矿业工程—矿井通风与安全]