煤田地质与勘探2025，Vol.53Issue(2)：33-43,11.DOI:10.12363/issn.1001-1986.24.10.0656
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
摘要
Abstract
[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 pre-dicting spontaneous combustion.[Methods]Coal samples from the Yingpanhao and Shilawusu coal mines in the Xi-meng 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 op-timizer(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 result,the pre-dicted temperatures based on the test set coincided well with the actual values,with a mean absolute error of 0.762℃,a root mean square deviation of 1.014,and a coefficient of determination of 0.999 4.The comparison between the pre-dicted results of the CPO-RF model and the characteristic temperatures of coal spontaneous combustion enabled the effi-cient discrimination of the risks of coal spontaneous combustion.Based on this,targeted fire prevention and extinguish-ing methods can be adopted.The results of this study serve as a reference for preventing coal spontaneous combustion for deep coal mining in mining areas.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Key words
Ximeng mining areas/deep mining/spontaneous combustion characteristics/random forest(RF)/CPO op-timization/coal temperature prediction分类
矿山工程引用本文复制引用
马砺,高文博,拓龙龙,张鹏宇,郑州,郭睿智..西蒙矿区深部开采煤自燃特性及预测方法研究[J].煤田地质与勘探,2025,53(2):33-43,11.基金项目
国家自然科学基金项目(52174206) （52174206）
新疆维吾尔自治区重点研发项目(2022B01034-2) （2022B01034-2）
中国博士后科学基金项目(2024MD753976) （2024MD753976）
