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Determination and prediction on 'three zones' of coal spontaneous combustion in a gob of fully mechanized caving face

机译：综放工作面采空区煤自燃“三带”的确定与预测。

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摘要

The precise division into "three zones" of coal spontaneous combustion in the gob plays a key role for coal fire fighting. This paper presents three-dimensional distribution maps and contour plots for the gases and temperature in the gob by the method of griddata interpolation according to the data (O-2, CO, CO2, CH4, and temperature) acquired from in-situ test, and the variation of gases and temperature. It is proposed to comprehensively divide "three zones" by using O-2 concentration of 5-18 vol%, the appearance and disappearance of CO, and the heating rate K = 0 degrees C/m. The gas explosion conditions were considered to determine the danger zone of coal spontaneous combustion. The minimum mining speed was calculated to be 4.8 m/day based on the division of the "three zones" in the gob in order to prevent spontaneous combustion phenomenon. Particle swarm optimization (PSO) was employed to optimize the parameters of support vector regression (SVR); the PSO-SVR model was established to predict the temperature of coal spontaneous combustion based on the gases' concentration in the gob and distance from the measuring points to the working face. Prediction results and performance of PSO-SVR model were compared with standard SVR, back propagation neural network (BPNN), and multiple linear regression (MLR). The results indicated that PSO-SVR model had greater prediction accuracy and generalization ability, which can predict the temperature of coal spontaneous combustion in the gob.
机译：采空区中煤炭自燃的精确划分为“三个区域”，对煤炭灭火起着关键作用。根据现场测试获得的数据（O-2，CO，CO2，CH4和温度），通过网格数据插值方法，给出了采空区中气体和温度的三维分布图和等高线图，以及气体和温度的变化。建议通过使用5-18％（体积）的O-2浓度，CO的出现和消失以及加热速率K = 0摄氏度/米来全面划分“三个区域”。考虑气体爆炸条件来确定煤自燃的危险区域。为了防止自燃现象，根据采空区中“三个区域”的划分，最小采矿速度计算为4.8 m /天。采用粒子群算法（PSO）对支持向量回归（SVR）参数进行优化。建立了PSO-SVR模型，以根据炉料中的气体浓度以及从测量点到工作面的距离来预测煤的自燃温度。将PSO-SVR模型的预测结果和性能与标准SVR，反向传播神经网络（BPNN）和多元线性回归（MLR）进行了比较。结果表明，PSO-SVR模型具有较高的预测精度和泛化能力，可以预测采空区煤自燃温度。

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  • 来源
    《Fuel》 |2018年第1期|458-470|共13页
  • 作者

    Deng Jun; Lei Changkui; Xiao Yang; Cao Kai; Ma Li; Wang Weifeng; Bin Laiwang;

  • 作者单位

    Xian Univ Sci & Technol, Sch Safety Sci & Engn, Xian 710054, Shaanxi, Peoples R China|Shaanxi Key Lab Prevent & Control Coal Fire, Xian 710054, Shaanxi, Peoples R China;

    Xian Univ Sci & Technol, Sch Safety Sci & Engn, Xian 710054, Shaanxi, Peoples R China;

    Xian Univ Sci & Technol, Sch Safety Sci & Engn, Xian 710054, Shaanxi, Peoples R China|Shaanxi Key Lab Prevent & Control Coal Fire, Xian 710054, Shaanxi, Peoples R China;

    China Univ Min & Technol, Ventilat & Fire Prevent Inst, Xuzhou 221008, Peoples R China|Xuzhou Anyun Min Technol Co Ltd, Xuzhou 221008, Peoples R China;

    Xian Univ Sci & Technol, Sch Safety Sci & Engn, Xian 710054, Shaanxi, Peoples R China|Shaanxi Key Lab Prevent & Control Coal Fire, Xian 710054, Shaanxi, Peoples R China;

    Xian Univ Sci & Technol, Sch Safety Sci & Engn, Xian 710054, Shaanxi, Peoples R China|Shaanxi Key Lab Prevent & Control Coal Fire, Xian 710054, Shaanxi, Peoples R China;

    Natl Yunlin Univ Sci & Technol, Grad Sch Engn Sci & Technol, Touliu 64002, Yunlin, Taiwan;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Spontaneous combustion; Coal explosion; Minimum mining speed; Support vector regression; Modelling;

    机译：自燃煤炭爆炸最小开采速度支持向量回归建模;

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