Comminution Energy Based on Particle Size Distribution and Crushing Mechanism During Coal and Gas Outburst

Abstract

As the intensity and depth of coal mining grow year by year, coal seam gas pressure increases and stope structures become more complex, which can easily cause coal and gas outburst. During the process of coal and gas outburst, a large amount of coal is broken and ejected, seriously threatening the safety of workers and coal mine production. Therefore, a multifunctional coal and gas outburst physical simulation test system was used to carry out three outburst tests under different gas pressures to study the particle size distributions and fragmentation characteristics of the ejected coal. The results showed that the relative intensity of outburst increased with gas pressure, but the increase rate decreased. Gas pressure also played a role in promoting the coal crushing. For the crushing product, the R–R (Rosin–Rammler) distribution model with high COD (coefficient of determination) was used to calculate the comminution energy at 0.35 MPa, while the fractal distribution model with high COD was used at 0.85 MPa and 2.00 MPa. When gas pressure increased, the basic shape of the R–R model curve remained unchanged, the probability density curve of fractal model changed from concave to nearly straight and then to convex and the basic shape of the cumulative distribution curve of fractal model remained constant. The values of α (uniformity coefficient) and x_e (characteristic particle size) impacted on the R–R model and the values of D_f (fractal dimension) and x_max (maximum particle size) impacted on the fractal model. Within a certain error range, the comminution energy could be approximated. The comminution energy increased with gas pressure, and the potential energy of crushing product decreased with the value of the n related to the crushing mechanism. There was a strong linear relationship between relative intensity of outburst and comminution coefficient. The combination of experiments and machine learning provided a new direction for outburst prediction and prevention at coal mine sites.