Prediction of Gas Emission in Coal Face with BP Neural Network Optimized by Grey Correlation
LEI Wenjie
LIU Ruitao
SU Guoshao
Abstract：An independent matrix and reference sequence were constituted according to the regularity of coal-bed gas geology in the middle of Yima coal field and by selecting the buried depth of coal seams, the mining intensity, mining sequence and the thickness of coal seam as the independent valuables and the gas emission as the objective variable, the grey correlation analysis on the influence factors of the gas emission was made. Since each influence factor of the coal-bed gas geology has a high nonlinear relation with gas emission, normalized treatment for the influence of each factor was made, a mathematical model based on the optimized neural network was set up for gas emission prediction, the convergence speed of the sample training of the model is fast, with the error less than 0. 12%,and this model was used for the gas emission prediction in deep coal seams in Gengcun coal field.
Keywords：coal-bed gas geologymultifactorgrey correlation analysisoptimized neural networkmodel sample traininggas emission prediction
Publication Date：2013-09-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 34-37,41 )