Research on unascertained clusters on the gas emission of the working face;

Research on the prediction of gas emission in working face based on neural network;

Stepwise regression method for predicting gas emission from coal faces

Main controlled factors of gas emission quantity in oil-gas mine;

Prediction for gas emission quantity of the working face based on LS-SVM;

Based on the different-source gas emission quantity prediction theory,the BP nerve network was applied to predict respectively the gas emission quantity from the mining coal seam,neighboring coal seam and goaf of working face.

The Finite difference method was applied to simulate the dynamic variation of gas pressure in coal seam around a developing roadway and gas emission rate on the airway surface.

With the caving mining method(comparing with the caving slicing method),the relative gas emission rate has decreased,and the increasing rate of the absolute gas emission is less than that of the output,but the gas emission rate is maximum in each production process during the drawing.

Based on the theory of mathematical statistics,the regression analysis model between relative gas emission rate(q)and the thickness(H)and breakage degree(B)of coalbeds with same level of -450 m in Xiejiaji No.

Based on the gas geology information of combined mining faces of Chenjiashan Coal Mine that contains coal and oil gas,it is researched that the main geologic and productive factors controlling gas gushing quantity of mining faces using the relating analysis method of gray system theory.