Research on Coal Spontaneous Combustion Characteristics and prediction Method of Deep Mining in XiMeng Mining Areas
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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。Abstract: The deep mining coal seams in the XiMeng mining area are affected by complex environmental conditions, such as high in-situ stress, large water inflow, and severe air leakage, which increase the risk of coal spontaneous combustion and make prediction more challenging. Coal samples from Yingpanhao and Shilawusu coal mines within the mining area were selected for temperature-programmed spontaneous combustion experiments to determine characteristic parameters of coal spontaneous combustion under different moisture and sulfur mass fraction conditions. Combined with coal quality parameters from industrial analysis, a prediction database was established, and the Crested Porcupine Optimization algorithm (CPO) was applied to optimize the hyperparameters of the random forest (RF) model, CPO-RF model was established to predict the degree of spontaneous combustion of coal. The results showed that the patterns of gas concentration and oxygen consumption rate during the oxidation heating process were similar in the Yingpanhao and Shilawusu coal samples. CO was identified as the main indicator gas, with an initial appearance temperature of about 30 ℃. The amount of gas produced increased with higher sulfur mass fraction, and initially decreased and then increased with higher moisture mass fraction. The critical temperature of coal spontaneous combustion was determined to be 67.5~70.5 ℃, and the dry cracking temperature was 113.5~115.4 ℃. Through CPO’s efficient global search capabilities, the RF model’s optimal tree depth and tree count were automatically identified, avoiding suboptimal solutions caused by improper settings, thus enhancing the model’s generalization and robustness. The constructed CPO-RF model significantly improved the accuracy of coal spontaneous combustion predictions, with high alignment between predicted and actual temperatures on the test set, a mean absolute error and root mean square deviation of 0.762 ℃ and 1.014, respectively, and a coefficient of determination reaching 0.9994. The predicted results of the CPO-RF model, when compared with the characteristic temperatures of coal spontaneous combustion, can enable efficient discrimination of the risk of coal spontaneous combustion. Based on this, targeted fire prevention and extinguishing methods can be adopted. The research findings provide a reference for the prevention of coal spontaneous combustion in deep mining in mining areas.
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