Coalmine Gas Concentration Forecasting Based on Chaotic Theory and Neural Network Model

Coalmine gas explosion is unique to the extremely serious type of disaster. The root cause of gas explosion accident is the Overrun of the gas concentration. Gas concentration is forecast to achieve effective prevention of gas explosion accidents. According to the non-linear of gas concentration and the predictability of the chaotic time series, gas concentration phase space was reconstructed by the Takens theory. In the first, the time delay was attained by the mutual information method. Secondly the embedding dimension was determined by GP algorithm and the chaotic time series was predicted by the BP neural network. Finally, an example is given which shows the forecast results could approximate the actual situation well, and accomplishing the forecast objection of gas concentration.