Coal gas outburst predication and control based on RBF neural network

Coal gas outburst is very dangerous to mining workers, so the coal gas prediction and control in digging workplace are very important for safety production. In this paper, a novel the radial basis function (RBF) neural network gas prediction model is established to forecast the gas rush in coal workplace. The factors of producing gas are studied and the sample data are acquired from digging field, the input and output characteristics of RBF predicting model is analyzed, the fresh air flow press distribution in digging workplace is introduced and the control principle and schedule are described. The theory analysis and experiment results show that the proposed coal gas prediction and control model is feasible, and the predicting precise is enough for engineering application.