Dynamic loads provided by a Split Hopkinson pressure bar are applied in the impact failure experiment on coal with an impact velocity of 4.174–17.652

CH₄ is the second-largest greenhouse gas and has a significant impact on global warming. China has the largest amount of anthropogenic coal mine methane (CMM) emissions in the world, with coal mining emissions (or gas emissions) accounting for 90% of total energy industry emissions. The results of CH₄ emission inventories from previous studies vary widely, with differences in the spatial and temporal dimensions of gas emission factors of belowground mining being the main points of disagreement. Affected by the policies of “eliminating backward production capacity” and “transferring energy base to the northwest”, China’s coal production layout has changed greatly in the past ten years, but the closely related CH₄ emission factors have not been dynamically adjusted. This paper investigated 23 major coal producing provinces in China, obtained CH₄ emission data from coal mining, calculated CH₄ emission factors in line with current production conditions, and studied the reduction measures of coal mine gas emission. According to the CH₄ emission data of China’s coal mines in 2018, 15.8 Tg of methane is released per year in the coal mining industry in China, and 11.8 Tg after deducting recycling. Shanxi Province’s CH₄ emissions are much higher than those of other provinces, accounting for 35.5% of the country’s total emissions. The weighted CH₄ emission factor of coal mining in China is 6.77 m³/t, of which Chongqing is the highest at approximately 60.9 m³/t. Compared with the predicted value of the IPCC, the growth trend of CCM has slowed significantly, and the CH₄ utilization rate has gradually increased. This change may be aided by China’s coal industry’s policy to resolve excess capacity by closing many high-gas and gas outburst coal mines. In addition, the improvement of coal mine gas extraction and utilization technology has also produced a relatively significant effect. This paper determines the distribution of methane emissions and emission sources in China’s coal mining industry, which is useful in formulating CCM emission reduction targets and adopting more efficient measures.

Gas distribution ahead of the coal tunnel is the key risk factor controlling coal and gas outburst. Based on an analysis of the influence of the stress state on the porosity of the coal seam, an improved quartet structure generation set (QSGS) random porous media generation tool is used to reconstruct the coal structure. Adopting the reconstructed pore structure of the coal as the boundary of the flow field, the Lattice Boltzmann method (LBM) is used to simulate the gas dynamic distribution law ahead of the coal tunnel. The gas pressure of the coal seam decreases obviously in the fracture zone only, and the change in gas pressure is very small in the stress concentration zone and ahead of the coal seam. This research is of significance for gas drainage and the prevention of coal and gas outburst hazards in the working face during coal tunneling.

In this work, we test the ability of the Ornstein-Zernike equation in the Percus-Yevick approximation (OZPY) to generate interaction potentials from pair correlation functions (PCFs) of monatomic and diatomic Lennard-Jones fluids. The PCFs are generated by solving OZPY equation (monatomic fluid) and molecular-dynamics (MD) simulations (diatomic fluid). Since the interaction potentials are inputs in the OZPY method and the MD simulation, the extraction of the potential from the PCFs using OZPY is a test of self-consistency. This test is necessary if the procedure is to be used to generate coarse-grained (CG) potentials from PCFs. We find that the procedure is completely self-consistent for the monatomic fluid in the whole range of densities studied (reduced density up to 0.55, under reduced temperature of 2.0). In the diatomic case, we find that the procedure is generally self-consistent under both low and high densities, although there is a systematic deviation at high densities. The method is able to reproduce the two parameters ($\epsilon${} and $\sigma${}) of the input Lennard-Jones potential model to within about 1%. This CG potential generating procedure can be straightforwardly extended to more complicated molecules.