基于数据挖掘的煤与瓦斯突出实时预警研究

为提高工作面煤与瓦斯突出预警的准确率与时效性,利用工作面瓦斯涌出特征与突出“三要素”的之间的变化关系建立了以地应力系数、瓦斯压力系数、乘幂系数、变动率及离散率为基础的实时预警指标体系;将K-means聚类、FOA及RF三种算法结合构建基于数据挖掘的煤与瓦斯突出实时预警模型探究实时预警指标与煤与瓦斯突出的潜在发生规律,并通过模型的智能寻优及训练输出最优预警等级;现场应用结果表明：所建预警指标敏感性较好,预警模型的运算时间为0.118s,在本次实例应用中提前4小时发出煤与瓦斯突出危险级别预警,预警等级与现场突出实际情况较吻合,且与K1值、钻粉量S具有较好的一致性,实现工作面煤与瓦斯突出实时、准确预警。

Real-time warning of coal and gas outburst based on data mining

In order to improve the accuracy and timeliness of early warning of coal and gas outburst in working face, a real-time early warning index system based on in-situ stress coefficient, gas pressure coefficient, power coefficient, variation rate and dispersion rate was established by utilizing the change relationship between gas emission characteristics and "three factors" of outburst.The k-means clustering, FOA and RF algorithms were combined to build a real time warning model of coal and gas outburst based on data mining to explore the potential occurrence rules of real time warning indexes and coal and gas outburst, and the optimal warning level was output through intelligent optimization and training of the model.Field application results show that the built early warning indicators better sensitivity, early warning model of the operation time was 0.118 s, in the application of a coal and gas outburst dangerous level 4 hours in advance warning, warning level with the outstanding theoretical results fit well with actual situation, and with the values of K1, diamond powder quantity s has good consistency, realize real-time and accurate working face of coal and gas outburst early warning.