煤与瓦斯突出多指标耦合预测模型研究及应用

Study of the multi-index coupling forecasting model of coal and gas outburst and its application

  • 摘要: 针对煤与瓦斯突出预测准确率问题,在分析煤与瓦斯突出瓦斯地质因素的基础上,构建了包含瓦斯指标、煤体指标、地应力指标3个一级指标和瓦斯压力、构造煤厚度等12个二级指标的预测指标体系,通过综合运用网络分析法和多类别距离判别法对灰色关联模型中的输入端和输出端进行研究,建立了煤与瓦斯突出多指标耦合预测模型.该模型基于对煤与瓦斯突出瓦斯地质因素的综合分析,计算预测指标权重,划分煤与瓦斯突出可能性等级,建立了对突出可能性进行判别的2个判别式.以平煤八矿为例应用该模型对8组预测样本进行了煤与瓦斯突出可能性判断,预测结果与实际符合,为矿井煤与瓦斯突出防治提供了技术支撑,证明了煤与瓦斯突出多指标耦合预测模型的准确性和适用性.

     

    Abstract: Coal and gas outburst seriously threatens the safety of underground coal mining. With the increase in coal mining depth, the geological conditions of coal mines become complicated, which adversely affects the safety of coal mining production. Prominent prediction is an important link to reduce the damage caused by outburst accidents. This study investigated the prediction model of coal and gas outburst. On the basis of the analysis of the gas geological factors of coal and gas outburst, a prediction system including 3 major indices (i.e., gas, coal, and crustal stress indices) and 12 minor indices (e.g., gas pressure and tectonic coal thickness) was established. A multi-index coupling prediction model based on the improved input and output of the gray correlation model was proposed. Network analysis and multi-class distance discriminant method were also comprehensive applied to improve the accuracy of coal and gas outburst index prediction. On the basis of the comprehensive analysis of the gas geological factors of coal and gas outburst, the model calculates the weight of the prediction index, classifies the possible grade of the outburst, and derives the discriminant formulas to discriminate the possibility of outbursts. Taking the Pingdingshan No. 8 coal mine as an example, the model estimated the probability of coal and gas outburst in eight sets of prediction samples. The predicted results are consistent with the actual conditions, provide technical support for coal mine and gas outburst prevention, and prove the accuracy and applicability of the multi-index coupling prediction model.

     

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