Abstract
In order to predict the residual gas content in coal seam in front of roadway advancing face accurately and rapidly, an improved prediction method based on both drilling cuttings indices and bat algorithm optimizing extreme learning machine (BA-ELM) was proposed. The test indices of outburst prevention measures (drilling cuttings indices, residual gas content in coal seam) during roadway advancing in Yuecheng coal mine were first analyzed. Then, the correlation between drilling cuttings indices and residual gas content was established, as well as the neural network prediction model based on BA-ELM. Finally, the prediction result of the proposed method was compared with that of back-propagation (BP), support vector machine (SVM), and extreme learning machine (ELM) to verify the accuracy. The results show that the average absolute error, the average absolute percentage error, and the determination coefficient of the proposed prediction method of residual gas content in coal seam are 0.069, 0.012, and 0.981, respectively. This method has higher accuracy than other methods and can effectively reveal the nonlinear relationship between drilling cuttings indices and residual gas content. It has prospective application in the prediction of residual gas content in coal seam.
Highlights
Coal and gas outburst, a kind of mine dynamic disasters, are characterized by the sudden ejection of large masses of coals or volumes of gases in a short time
E residual gas content is an important parameter for gas outburst prediction
The grey theory, artificial neural network, and other machine learning algorithms were applied to predict coal seam gas content. e prediction method is determined through the comparative analysis of prediction results [15,16,17]. e above static-factor-based methods for the residual gas content prediction have achieved good results in the field application
Summary
A kind of mine dynamic disasters, are characterized by the sudden ejection of large masses of coals or volumes of gases in a short time. E above static-factor-based methods for the residual gas content prediction have achieved good results in the field application. These prediction methods have limitations due to the complex and dynamic geological conditions and material properties of coal seams. Some methods have been proposed to predict the residual gas content in coal seam ahead of roadway advancing face after gas predrainage. The outburst risk prediction data of coal roadway advancing face in Yuecheng coal mine were taken as samples, including K1 value, S value, and residual gas content. E results provide an insight into the relationship between drilling cuttings indices and residual gas content in the coal seam and provided a theoretical guidance for the rapid prediction of residual gas content in front of coal roadway advancing face The outburst risk prediction data of coal roadway advancing face in Yuecheng coal mine were taken as samples, including K1 value, S value, and residual gas content. e relationship between K1 value, S value (prediction input), and the field measured residual gas content (prediction output) was established by applying the BAELM neural network. e results provide an insight into the relationship between drilling cuttings indices and residual gas content in the coal seam and provided a theoretical guidance for the rapid prediction of residual gas content in front of coal roadway advancing face
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