风力排渣钻孔施工过程中，钻杆（钻头）高速旋转与煤体摩擦产生高温，加之压风中O₂的不断输入，引起煤体氧化阴燃。由于钻孔通风和蓄热环境造成的无可见光缓慢燃烧，通常导致煤温度上升并产生大量CO，一旦孔内CO大量产生，易引发施钻人员伤亡事故，严重威胁到煤矿的安全生产和施钻人员生命安全。因此，研究钻孔作业诱发煤体阴燃的温度演变规律及CO生成特性是监测和防治该类灾害的基础。为此，本研究聚焦钻孔诱发煤阴燃CO超限问题，开展系统性研究：自主搭建煤层钻孔相似模拟实验平台，研究不同条件（钻机转速、进钻速度、煤体应力）对煤体升温特性的影响，探究CO生成规律；通过傅里叶红外光谱（FTIR）实验，分析钻后煤屑关键活性基团变化规律，从微观角度阐明钻孔作业时CO产生机理；基于鲸鱼优化（WOA）与最小二乘支持向量机（LSSVM）算法，构建煤阴燃风险预测模型，实现以钻头转速、进钻速度、煤体应力、进钻时长等多参数的煤体温度及CO浓度预测。该研究对煤矿井下监测系统优化和煤矿安全高效生产有着重要的现实意义。主要成果如下：

（1）自主搭建煤层钻孔相似模拟实验平台，掌握不同钻机转速（990、690、390 r/min）与进钻速度（1.5、1.0、0.5 cm/s）及煤体应力（原煤、1#型煤-85MPa围压压制、2#型煤-70MPa围压压制）等工况条件下CO浓度和煤体温度的变化规律。结果表明：钻机转速增大会提高CO生成速率和煤温升高速率，且CO涌出量随转速增加呈指数增长；进钻速度加快使CO产生速率上升，但因排渣效率提高导致最大浓度降低。同时，原煤的CO生成速率与温升速率显著高于型煤，且1#型煤明显高于2#型煤。通过线性回归分析发现，CO平均生成速率与煤体升温速率呈显著线性正相关（R²=0.727，p<0.01）。

（2）采用FTIR实验，分析煤层钻进后煤屑表面活性官能团的特性，揭示煤层钻孔过程中CO生成机理。结果表明：钻进过程中煤分子结构发生显著变化，硫键、醚键的碳氧键及游离氢氧键含量减少，而活性碳氧键（如醇、酚、醛类）及碳氢键（甲基、亚甲基）、链式碳碳键明显增加。煤表面含氧官能团（醇羟基、羧基、醛基、醚键、过氧键）显著增多，这些含氧官能团为CO的生成提供氧源。结合化学键解离能分析，CO生成存在双重路径：初期由机械破碎导致化学键断裂释放CO；后期因钻头摩擦生热，促使高活性含氧官能团热分解产生CO。

（3）采用鲸鱼优化算法（WOA）优化最小二乘支持向量机（LSSVM）参数，构建钻孔作业中煤阴燃风险预测模型。针对LSSVM模型精度受核函数宽度和惩罚因子影响问题，WOA算法全局寻优这两个参数，解决了参数选择的难题。实验验证表明：在煤体温度预测方面，WOA-LSSVM的平均绝对误差（MAE）较单一LSSVM降低56.3%，9种工况下的误差降幅达56.3%~92%，决定系数R²均接近1，显示出极强的拟合能力；CO浓度预测中，R²值稳定在0.99834~0.99994区间，预测精度显著提升。

During the construction of wind slag discharge drilling operations, the high-speed rotation of the drill pipe and friction with the coal body generate high temperatures. The continuous supply of O₂ from the compressed air promotes oxidation and smoldering of the coal. Due to the drilling ventilation and a heat-retaining environment, this slow combustion occurs without visible light, leading to a rise in coal temperature and the production of large amounts of CO. Once a high concentration of CO accumulates in the hole, it can easily endanger drilling personnel, posing a serious threat to coal mine safety and worker safety. Studying the temperature evolution and CO generation characteristics of coal smoldering induced by drilling operations is essential for monitoring and preventing such disasters. This study focuses on the problem of CO overlimit during coal smoldering induced by drilling and conducts systematic research. An independently built simulated experiment platform for coal seam drilling was used to investigate the effects of various conditions (drilling speed, feed rate, coal stress) on coal heating characteristics and to analyze CO generation patterns. Fourier transform infrared spectroscopy (FTIR) was employed to analyze changes in key active groups of coal cuttings after drilling, clarifying the CO production mechanism at a microscopic level. Based on the whale optimization algorithm (WOA) and least squares support vector machine (LSSVM), a coal smoldering risk prediction model was developed to forecast coal temperature and CO concentration using parameters such as bit speed, drilling speed, coal stress, and drilling time. This research has important practical significance for optimizing coal mine monitoring systems and ensuring safe, efficient production. The main results are as follows:

(1) A simulated coal seam drilling platform was constructed, and variations in CO concentration and coal temperature under different conditions (e.g., drilling speed, feed rate, coal stress) were analyzed. Results show that higher drilling speeds increase CO formation rates and coal temperature rise rates, with CO emissions growing exponentially. Increasing the feed rate raises the CO production rate, but the maximum concentration decreases due to improved slag discharge efficiency. Raw coal exhibits significantly higher CO generation and temperature rise rates than briquette coal, with 1# briquette outpacing 2# briquette. Linear regression revealed a strong positive correlation (R²= 0.727, p < 0.01) between average CO formation rate and coal heating rate.

(2) FTIR analysis of active functional groups on coal cuttings revealed the CO formation mechanism during drilling. The molecular structure of coal undergoes significant changes: the content of carbon-oxygen bonds, free hydrogen-oxygen bonds, sulfur bonds, and ether bonds decreases, while active carbon-oxygen bonds, carbon-hydrogen bonds, and chain carbon-carbon bonds increase markedly. Oxygen-containing functional groups on the coal surface increase, providing oxygen sources for CO formation. Combined with chemical bond dissociation energy analysis, CO formation follows a dual pathway: initial CO release results from mechanical bond breaking, while later-stage CO is produced by thermal decomposition of highly active oxygen-containing groups due to drill bit friction.

(3) The whale optimization algorithm (WOA) was applied to optimize LSSVM parameters, constructing a coal smoldering risk prediction model. To address the impact of kernel function width and penalty factor on LSSVM accuracy, WOA globally optimized these parameters, resolving selection issues. Results show that for coal temperature prediction, WOA-LSSVM achieves a 56.3% lower mean absolute error (MAE) than standalone LSSVM, with errors reduced by 56.3 ~ 92% across nine conditions. The coefficient of determination (R²) approaches 1, demonstrating strong fitting. For CO concentration prediction, R² ranges from 0.99834 ~ 0.99994, indicating significantly improved accuracy.

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