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

摘要

Gas has always been a serious hidden danger in coal mining. The quantity of gas emitted from the coal face is affected bymany 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 wasproposed. The model was tested and applied in different coal seams at Panbei Coal Mine in Huainan, China, involving sixteentraining samples and four predicting samples. Results showed that: Gas emission quantity was significantly correlated withburial depth, gas content in the mining layer, gas content in the adjacent layer, and layer spacing. The correlations amongthese variables exceeded 60%. Linear regression analysis using the optimized model was affected by sample size anddiscreteness. The correlation coefficient (R) and maximum relative error (MRE) of the PCA-GA-BP model were 0.9988 and3.02%, 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 fromcoal face, and the proposed method can be used in other engineering fields.