Modeling of the methane gas diffusion in coal mining, a case study Parvadeh central mine in Tabas

Prediction of gas emissions is a calculation based on empirical processing in the coal mining. In this study, methane diffusion was investigated coal exploration on parameters such as coal gas content, mining, depth, etc., in two faces in the Tabas Parvadeh central coal mining. Therefore, in the beginning, the initial and final values for the studied faces were modeled based on the neural network. The neural network was calculated methane for P8/TG face, between 0.4476%-0.9921% and P8/MG face are between 0.1636%-0.3379%. The intensity of methane gas emission from the studied mine was done based on the level of coal and the amount of gas volume using MATLAB coding. Also, the type of extraction method in the mine was done on the amount of gas emission caused by mining. According to the amount and aggregate of induced fractures, the permeability shows several degrees of increase. These amounts can evacuation the gas caused by the underground extraction of the mine faster. In this study, the amount of gas emissions due to mining was calculated using coding to the content of the remaining gas matrix In this study, the amount of gas emission due to drilling was 0.04%, blasting was 0.045%, support was 0.040%, muckout was 0.06% and jiggring was 0.25%. Small errors in the measurement of gas content can lead to large errors in the estimated calculations, which were determined using the software. The correlation coefficient of input and output of the studied data was estimated as 0.984%. The modeling done for the 2 face current shows a good result which can be used to improve mine safety.