西蒙矿区深部开采煤自燃特性及预测方法研究  

Characteristics and prediction methods of coal spontaneous combustion for deep coal mining in the Ximeng mining area

作　　者：马砺 高文博[1,2] 拓龙龙 张鹏宇 郑州 郭睿智 MA Li;GAO Wenbo;TUO Longlong;ZHANG Pengyu;ZHENG Zhou;GUO Ruizhi(College of Safety Science and Engineering,Xi’an University of Science and Technology,Xi’an 710054,China;Key Laboratory of Coal Fire Disaster Prevention and Control,Xi’an University of Science and Technology,Xi’an 710054,China;Inner Mongolia Haosheng Coal Industry Co.,Ltd.,Ordos 017212,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]西安科技大学煤火灾害防治重点实验室,陕西西安710054 [3]内蒙古昊盛煤业有限公司,内蒙古鄂尔多斯017212

出　　处：《煤田地质与勘探》2025年第2期33-43,共11页Coal Geology & Exploration

基　　金：国家自然科学基金项目(52174206);新疆维吾尔自治区重点研发项目(2022B01034-2);中国博士后科学基金项目(2024MD753976)。

摘　　要：【目的】鄂尔多斯盆地西蒙矿区深部开采煤层受地应力高、涌水量大、漏风严重等复杂环境条件影响,煤自燃危险性增强,自燃预测难度大。【方法】选取该矿区营盘壕和石拉乌素煤矿的煤样开展自燃程序升温实验,测定不同含水率、不同硫质量分数条件下的煤自燃特征参数,结合工业分析等煤质参数建立预测数据库,采用冠豪猪优化算法(CPO)对随机森林(RF)超参数进行优化,建立CPO-RF模型预测煤自燃程度。【结果和讨论】结果表明:营盘壕和石拉乌素矿井煤样氧化升温过程中的气体浓度、耗氧速率变化规律相似,CO为主要指标气体,初现温度约30℃,气体产生量随着硫质量分数的增加而增大,随着水分质量分数的增加则呈现先减后增的动态变化规律,煤自燃临界温度为67.5~70.5℃,干裂温度为113.5~115.4℃。通过CPO算法高效的全局搜索能力自动寻得RF模型的最优树深度与树个数,避免了设置不当导致的局部最优解,增强了其泛化性与鲁棒性;所构建的CPO-RF模型能够有效提高煤自燃预测的精度,测试集预测温度与真实值重合度良好,平均绝对误差和均方根偏差分别为0.762℃和1.014,决定系数达到0.9994。CPO-RF模型所预测结果与煤自燃特征温度对比,能够实现煤自燃危险性的高效判别,据此可以采取针对性的防灭火方法,研究结果可为矿区深部开采煤自燃预防提供参考。[Objective]The mining of deep coal seams in the Ximeng mining area within the Ordos Basin is subjected to complex environmental conditions like high in-situ stress,large water inflow,and severe air leakage,which lead to the encountered with elevated risks and difficult prediction of coal spontaneous combustion and posing challenges in predicting spontaneous combustion.[Methods]Coal samples from the Yingpanhao and Shilawusu coal mines in the Ximeng mining area were selected for temperature-programmed spontaneous combustion experiments to determine the characteristic parameters of coal spontaneous combustion under different moisture contents and sulfur mass fractions.Based on these parameters,as well as with coal quality parameters from proximate analysis,a prediction database was established.Then,the hyperparameters of the random forest(RF)model were optimized using the crested porcupine optimizer(CPO)algorithm.Accordingly,the CPO-RF model was constructed to predict the degree of coal spontaneous combustion.[Results and Conclusions]The results indicate that the coal samples from the Yingpanhao and Shilawusu coal mines showed similar laws of variations in gas concentrations and oxygen consumption rates during oxidative heat-ing.CO was identified as the dominant indicator gas,appearing initially at a temperature of about 30℃.The amount of gas produced increased with the sulfur mass fraction.However,as the moisture mass fraction increased,it decreased ini-tially and then increased.The coal spontaneous combustion manifested critical temperatures ranging from 67.5℃to 70.5℃and dry cracking temperatures from 113.5℃to 115.4℃.The optimal tree depth and tree count of the RF model were automatically identified using the efficient global search capability of the CPO algorithm,avoiding local optimal solu-tions caused by improper settings and thus enhancing the generalization and robustness of the model.The constructed CPO-RF model significantly improved the prediction accuracy of coal spontaneous combustion.As a resu

关 键 词：西蒙矿区 深部开采 自燃特性 随机森林 CPO优化 煤温预测 

分 类 号：TD752.2[矿业工程—矿井通风与安全]