巨厚煤层分层开采覆岩导水裂隙带高度演化及其预测研究
Study on the height evolution and prediction of water conducting fracture zones in overlying strata during layered mining of thick coal seams
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摘要: 以往多针对单一煤层开采导水裂隙带高度研究,但鲜见对于巨厚煤层开采覆岩导水裂缝带发育高度预测工作,本文以新疆准南煤田硫磺沟煤矿(9-15)工作面为研究区,定量评价了巨厚煤层在综放分层开采条件下覆岩裂隙场的发育特征和演化规律,采用机器学习方法,构建了基于粒子群优化算法的支持向量机回归(PSO-SVR)的导水裂隙带高度预测模型。研究表明:巨厚煤层工作面分层综放开采其裂隙演化总体呈现为4个阶段:升维阶段、降维阶段、稳定阶段和波动阶段。其中,受采动影响顶板覆岩破断垮落,分形维数快速上升。而上覆岩层压实,分形维数逐渐降低。此外,PSO-SVR模型相关系数R大于0.95,且平均绝对误差、平均偏差和均方根误差较小,且模型预测值与实测值绝对误差为12.52m,相对误差为4.86%,表明PSO-SVR模型能够有效、准确地进行巨厚煤层开采导水裂隙带高度预测。Abstract: Previous studies have focused on the height of water-conducting fracture zones in single coal seam mining, but there has been little research on predicting the height of water-conducting fracture zones in overburden rock during mining of extremely thick coal seams. This article takes the working face (9-15) of the Luanhuagou Coal Mine in the southern Xinjiang coalfield as the research area, quantitatively evaluates the development characteristics and evolution laws of the overburden rock fracture field under the condition of fully mechanized top-coal caving mining in extremely thick coal seams, and uses machine learning methods to construct a water-conducting fracture zone height prediction model based on particle swarm optimization algorithm support vector regression (PSO-SVR).Research shows that the overall evolution of fractures in the layered fully mechanized top-coal caving mining of a thick coal seam working face generally presents four stages: the rising dimension stage, the decreasing dimension stage, the stable stage, and the fluctuating stage.Among them, the fractal dimension rises rapidly due to the breakage and collapse of the roof overburden affected by mining.However, the fractal dimension of the overlying rock gradually decreases due to compaction.In addition, the correlation coefficient R of the PSO-SVR model is greater than 0.95, and the average absolute error, average deviation, and root mean square error are small. The absolute error between the model prediction value and the measured value is 12.52 m, and the relative error is 4.86%. This indicates that the PSO-SVR model can effectively and accurately predict the height of the water-conducting fracture zone in the mining of thick coal seams.
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