Risk Prediction of Coal and Gas Outburst Based on Abnormal Gas Concentration in Blasting Driving Face

Abstract

In order to realize dynamic, continuous, and real-time prediction of coal and gas outburst risk in real time in blasting driving face, an outburst risk prediction method based on the characteristics of gas emission after blasting is proposed. In this study, the causes of abnormal gas concentration in blasting driving face are analyzed, and the identification method of abnormal gas concentration based on weighted K-nearest neighbor (weighted KNN) is proposed. The correlation between gas emission characteristics after blasting and K1 value is analyzed, and the prediction model of outburst risk based on convolutional neural networks (CNN) is established and applied in Jinjia coal mine in China. The results show that the causes of abnormal gas concentration mainly include ventilation stop, blasting operation, sensor adjustment, and other abnormalities. The accuracy of the identification method is 86.1%. Especially, the identification accuracy of blasting operation is 92%. There are strong correlations between the growth rate, peak value, and decay rate of gas concentration after blasting and K1 value, and the maximum correlation coefficient is 0.92. Using the prediction model, 28 times of jet holes and 1 small outburst event are predicted successfully, and the efficiency of the prediction model is 76.39%. By this technology, the utilization rate of gas information is improved, and the relationship between the change characteristics of gas concentration after blasting and the risk of coal seam outburst is established, which is of significant for improving the prediction accuracy and risk management ability of coal and gas outburst.

Combined Prediction Model of Gas Concentration Based on Indicators Dynamic Optimization and Bi-LSTMs

“…Gas concentration monitoring value C c ( t c ) at the moment t c ; First order difference value of gas concentration at the moment t c , D c ( t c ) = C c ( t c ) − C c −1 ( t c −1 ); Statistical features of the gas concentration time series C l ( t ) = { C c − l ( t c − l ), …, C c −2 ( t c −2 ), C c −1 ( t c −1 ), C c ( t c )} with time window length l before moment t c , including 10 statistical features of maximum, average, root mean square, variance, standard deviation, dispersion coefficient, peak factor, skewness, kurtosis and range of C l ( t ) [ 39 ].…”

“…The gas emission situations are divided into the following two categories: gas abnormal emission and gas normal emission. Considering the influence of sample imbalance [ 41 ], missing alarm and false alarm comprehensively, the false alarm rate ( FAR ), missing alarm rate ( MAR ), and prediction efficiency ( R ) [ 39 , 42 , 43 ] are applied to objectively evaluate the prediction model for gas abnormal or normal emission. These evaluation indexes are defined as follows: where TP is the number of samples with both predicted and real values exceeding the threshold; FN is the number of samples with real values exceeding the threshold but not the predicted value, i.e., the number of missing alarm samples; FP is the number of samples with predicted value exceeds the threshold but not the real value, i.e., the number of false alarm samples; TN is the number of samples with neither predicted value, nor real value exceeds the threshold.…”

Research on a Coal Seam Gas Content Prediction Method Based on an Improved Extreme Learning Machine

“…Liming Qiu et al established a protrusion risk prediction model based on a convolutional neural network. This model explores the correlation between post-explosion gas concentration changes and coal seam protrusion risks, which is crucial in improving coal and gas outburst prediction accuracy [22]. Xiang Wu et al proposed a gas outburst prediction model based on the grey relation analysis (GRA) and adaptive PSO algorithm-optimized SVM.…”