Prediction Model of Gas Quantity Emitted from Coal Face Based on PCA-GA-BP Neural Network and Its Application

J.W. Qiu, Z.G. Liu, L. Zhou, R.X. Qin

Abstract


Gas has always been a serious hidden danger in coal mining. The quantity of gas emitted from the coal face is affected by
many factors. To overcome the difficulty in accurately predicting the quantity of emission, a novel predictive model (PCA-GABP)
based on principal component analysis (PCA), genetic algorithm (GA) and back propagation (BP) neural network was
proposed. The model was tested and applied in different coal seams at Panbei Coal Mine in Huainan, China, involving sixteen
training samples and four predicting samples. Results showed that: Gas emission quantity was significantly correlated with
burial depth, gas content in the mining layer, gas content in the adjacent layer, and layer spacing. The correlations among
these variables exceeded 60%. Linear regression analysis using the optimized model was affected by sample size and
discreteness. The correlation coefficient (R) and maximum relative error (MRE) of the PCA-GA-BP model were 0.9988 and
3.02%, respectively. The MRE of the optimized model was 70.2% and 53.2% smaller than that of the BP and GA-BP models,
respectively. The conclusions obtained in the study provide technical support for the prediction of gas quantity emitted from
coal face, and the proposed method can be used in other engineering fields.


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