MLP-Based Model for Estimation of Methane Seam Pressure

Abstract

One of the principal indicators of the methane hazard in coal mines is gas pressure. This parameter directly affects the methane content in the seam as well as the rate of its release resulting from mining operations. Because of limitations in the existing methods for methane seam pressure measuring, primarily technical difficulties associated with direct measurement and the time-consuming nature of indirect measurement, this parameter is often disregarded in the coal and gas outburst forecasts. To overcome the above-mentioned difficulties, an attempt was made to estimate the methane seam pressure with the use of artificial neural networks. Two MLP-based models were developed to estimate the average and maximum methane seam pressure values, respectively. The analyses demonstrated high correlation between the values indicated by the neural models and the reference values determined on the basis of sorption isotherms. According to the adopted fit criterion, the prediction errors for the best fit were 2.59% and 3.04% for the average and maximum seam pressure values, respectively. The obtained determination coefficients (exceeding the value of 0.99) confirmed the very good predictive abilities of the models. These results imply a great potential for practical application of the proposed method.

Keywords

• gas seam pressure
• artificial neural networks (ANN)
• multilayer perceptron (MLP)
• geological hazard