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Study on Intelligent Identification Method of Coal Pillar Stability in Fully Mechanized Caving Face of Thick Coal Seam

机译:厚煤层全机械塌方煤柱稳定性智能识别方法研究

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摘要

The combination of coal precise mining and information technology in the new century is one of the important directions for the future development of coal mining. Taking the fully mechanized top coal caving condition of a thick coal seam in the 90,101 working face of Baoshan Yujing Coal Mine in Shanyin City, Shanxi Province as an example, the intelligent identification method of section coal pillar stability was studied. The load transfer law of overlying strata in the upper part of coal pillar was analyzed, and the coal pillar values of each index were obtained by using an empirical formula, mean impact value-genetic algorithm-BP neural network (MIV-GA-BP) simulation experiment, and finite difference algorithm. The Delphi index evaluation system was used to calculate the optimal value of the coal pillar. The results showed that the non-contact cantilevered triangle on the two wings of the coal pillar in the goaf reduced the stress on the coal pillar; according to the width of the coal pillar at 10 m, 14 m, 16 m, and 20 m, combined with the relationship between the plastic zone and the core zone of coal pillar, and the relationship between the stress field and the ultimate strength of coal pillar, the numerical simulation value of the coal pillar was determined. The MIV (mean impact value) characteristics screened out the influencing factors of coal pillar width in the section near the horizontal fully mechanized top coal caving face order of importance; the relative error between the predicted value and the expected value of the MIV-GA-BP simulation experiment was less than 5%, which has good stability for the multi-factor nonlinear coupling prediction problem; and the optimal value of the coal pillar was 16.03 m by the intelligent identification method of the coal pillar. When the 16 m pillar was used, the surrounding rock deformation of the roadway was small, and the control effect was good. The research results provide a theoretical basis and reference for the parameter determination of similar projects.
机译:新世纪煤炭精确采矿和信息技术的结合是煤矿未来发展的重要方向之一。山西省山西省山山市宝山玉井煤矿的90,101个工作面综合机械化顶部煤矿条件为例,研究了煤柱稳定性智能识别方法。分析了煤柱上部上部的覆盖层的载荷传递定律,通过使用经验公式,平均影响值 - 遗传算法-BP神经网络(MIV-GA-BP)获得每个指标的煤柱值仿真实验和有限差分算法。 Delphi指数评估系统用于计算煤柱的最佳值。结果表明,GOF中煤柱的两个翅膀上的非接触式悬臂三角形降低了煤柱上的应力;根据煤柱的宽度,10米,14米,16米和20米,结合塑料区与煤柱芯区之间的关系,以及应力场与最终强度之间的关系煤柱,确定了煤柱的数值模拟值。 MIV(平均影值)特征筛选了煤柱宽度的影响因素,附近的横向综合煤矿面重点序号; MIV-GA-BP仿真实验的预测值和预期值之间的相对误差小于5%,这对于多因素非线性耦合预测问题具有良好的稳定性;煤柱的智能识别方法,煤柱的最佳值为16.03米。当使用16米柱时,道路的周围岩石变形很小,控制效果很好。研究结果为类似项目的参数确定提供了理论基础和参考。

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