安林煤矿炮放面矿压显现规律及顶煤冒放性预测研究
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摘要
安林煤矿为国有地方矿井,近年来一直采用炮采放顶煤采煤法,开采二1缓倾斜不稳定煤层。受岩浆岩侵入的影响,井田内不同区域二1煤层厚度和煤层上部侵入的岩浆岩厚度变化较大,对采场矿压显现规律和顶煤冒放性造成一定影响,影响采面的安全高效生产。论文采用实验室试验、现场观测和数值计算等研究方法,研究了炮采放顶煤工作面矿压显现规律、矿压显现参数,以及超前支承压力的影响因素等,建立了神经网络模型用来预测顶煤冒放性。首先测定安林煤矿二1煤层围岩物理力学性质,在此基础上,利用正交试验和数值计算方法,研究了煤层厚度及其上部岩浆岩厚度、煤层埋藏深度和侧压系数,这四个因素与工作面前方支承压力之间的关系,总结出超前支承压力与这4个因素之间的变化关系、各因素对支承压力影响的大小,得出了超前支承压力最大和最小时煤层赋存的地质条件。然后在现场矿压观测的基础上,分析并得出炮采放顶煤工作面在正常开采期间矿压显现规律和矿压显现参数,以及顶煤顶板运移规律。得出了炮放面矿压显现明显,沿工作面倾斜方向顶板来压不一致,炮采放顶煤采煤法适合二1煤层地质条件;炮放面顶煤分层垮落,顶煤垮落角为54.8°,直接顶垮落角为53.3°。引入了顶煤冒放性预测指数,收集了目前顶煤冒放性标准样本,建立并成功训练了顶煤冒放性预测BP神经网络模型。结合正交试验方法,研究得出顶煤冒放性预测规律与实际规律相符。最后利用该预测模型,预测出安林煤矿炮放面顶煤冒放性属于一般类型。研究结论对于提高炮放面采出率和安全生产能力具有重要的现实意义。
Anlin Coal Mine is a State-owned local coal mine, which has always used blasting mining with sublevel caving in instability coal seam No. 21 in recent years. Because coal seam was intruded by magmatic rock, the thickness of coal seam and magmatic rock both varied largely in different area of the minefield. Consequently, strata pressure behavior and top coal caving-ability were affected by them, and safely efficient production was also affected. With laboratory tests, field observations and numerical computation, thesis studied strata pressure behaviors, their parameters at coal face, influencing factors on movement abutment pressure and the predictive model for top coal caving-ability. Firstly, physical-mechanical parameters of surrounding rock in coal seam No. 21, were tested. Based on these and used numerical calculation and orthogonal design, thesis studied the relationship between above four factors and movement abutment pressure. The four factors were that thicknesses of coal seam and magmatic rock over coal seam, buried depth of coal seam and coefficient of horizontal pressure. Then thesis concluded primary and secondary orders of the four factors in light of affecting movement abutment pressure and the geological conditions in the context of the maximum and minimum movement abutment pressure. Secondly, based on field measurement of strata pressure, thesis obtained conclusions that movement rule of top coal, strata pressure behaviors and the parameters during the normal development of coal face. Strata pressure behaviors were obvious, roof weighting being differently along the dip direction of coal face, the mining with sublevel caving by individual hydraulic props was fitted for geological conditions of coal seam No.21, top coals fell hierarchically, their caving angle was 54.8o, and roof caving angle was 53.3o. Lastly, based on the induction of predictive index for top coal caving-ability and training samples, it is artificial neural network predictive models for top coal caving-ability, which was set up and trained. Then combined with orthogonal design, thesis made out that the law predicted by artificial neural network model was consistent with actual one. Using artificial neural network model, caving-ability of top coal at the coal face in Anlin Coal Mine belonged to normal type. These conclusions had crucial significances on advancing recovery ratio and safe production capacity at coal face in Anlin Coal Mine.
Anlin Coal Mine is a State-owned local coal mine, which has always used blasting mining with sublevel caving in instability coal seam No. 21 in recent years. Because coal seam was intruded by magmatic rock, the thickness of coal seam and magmatic rock both varied largely in different area of the minefield. Consequently, strata pressure behavior and top coal caving-ability were affected by them, and safely efficient production was also affected. With laboratory tests, field observations and numerical computation, thesis studied strata pressure behaviors, their parameters at coal face, influencing factors on movement abutment pressure and the predictive model for top coal caving-ability. Firstly, physical-mechanical parameters of surrounding rock in coal seam No. 21, were tested. Based on these and used numerical calculation and orthogonal design, thesis studied the relationship between above four factors and movement abutment pressure. The four factors were that thicknesses of coal seam and magmatic rock over coal seam, buried depth of coal seam and coefficient of horizontal pressure. Then thesis concluded primary and secondary orders of the four factors in light of affecting movement abutment pressure and the geological conditions in the context of the maximum and minimum movement abutment pressure. Secondly, based on field measurement of strata pressure, thesis obtained conclusions that movement rule of top coal, strata pressure behaviors and the parameters during the normal development of coal face. Strata pressure behaviors were obvious, roof weighting being differently along the dip direction of coal face, the mining with sublevel caving by individual hydraulic props was fitted for geological conditions of coal seam No.21, top coals fell hierarchically, their caving angle was 54.8o, and roof caving angle was 53.3o. Lastly, based on the induction of predictive index for top coal caving-ability and training samples, it is artificial neural network predictive models for top coal caving-ability, which was set up and trained. Then combined with orthogonal design, thesis made out that the law predicted by artificial neural network model was consistent with actual one. Using artificial neural network model, caving-ability of top coal at the coal face in Anlin Coal Mine belonged to normal type. These conclusions had crucial significances on advancing recovery ratio and safe production capacity at coal face in Anlin Coal Mine.
引文
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