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Study on Dynamic Prediction Model of Gas Emission in Tunneling Working Face.
- Source :
- Combustion Science & Technology; 2022, Vol. 194 Issue 3, p506-522, 17p
- Publication Year :
- 2022
-
Abstract
- The section of appropriate prediction index is of great importance for coal and gas outburst prediction. The quantity of gas emission is a key factor directly relating to the outburst risk of tunneling working face in the coal roadways. Therefore, accurately predicting the quantity of gas emission is necessary and critical to prevent and control outbursts. In this paper, using the sphere diffusion equation of coal particle gas and radial unsteady flow equations of coal seam gas to analysis gas flow of fallen coal and coal wall, and a dynamic prediction model of gas emission is established including key factors, research shows that: (1) in tunneling working face, the change rule of gas emission of the new model, in which mechanical state, physical properties of coal seam, roadway tunneling parameters, and gas adsorption parameters are considered, is the same as that of the conventional index, which indicates the feasibility of the new model; (2) The new model shows that the gas emission is positively correlated with the gas pressure, driving speed and permeability coefficient of coal seam, and negatively correlated with the uniaxial compressive strength of coal mass; (3) By comparing the old prediction model of gas emission, the predicted value of the new model is closer to the measured value, fluctuating within a smaller range, and has a higher accuracy after taking the gas emission of coal particle into account. In addition, the multiple characteristics of the coal body in front of the working face are comprehensively considered. The research results offer practical significance for improving gas prevention and control of tunneling working face. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00102202
- Volume :
- 194
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Combustion Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 154827874
- Full Text :
- https://doi.org/10.1080/00102202.2020.1772246