Jiaosheng Yang

Extensive bedding planes have a great influence on the mechanical properties of coal. In order to study the mechanism of the effects of bedding angles on the mechanical properties and failure characteristics of coal in the Shanxi Baode coal mine, uniaxial and triaxial compression tests and numerical simulations were conducted. The strength deterioration and microstructural changes in the samples were then analyzed with discrete element method (DEM) numerical simulation. The experimental results reveal that the power function strength criterion has good applicability to the strength characteristics of this coal. It was also found that the bedding angles have a great influence on the mechanical properties of coal. The properties of peak strength at different bedding angles roughly showed a U-shaped changing trend. The maximum strength occurred at a bedding angle of 0°, whereas the minimum strength occurred at a bedding angle of 60°. The numerical simulation and test results prove that the forms of failure of different bedding coal samples are complicated and are mainly represented by tensile and shear failures.

The accurate determination of methane adsorption isotherms in coals is crucial for both the evaluation of underground coalbed methane (CBM) reserves and design of development strategies for enhancing CBM recovery. However, the experimental measurement of high-pressure methane adsorption isotherms is extremely tedious and time-consuming. This paper proposed the use of an ensemble machine learning (ML) method, namely the gradient boosting decision tree (GBDT), in order to accurately estimate methane adsorption isotherms based on coal properties in the Qinshui basin, China. The GBDT method was trained to correlate the adsorption amount with coal properties (ash, fixed carbon, moisture, vitrinite, and vitrinite reflectance) and experimental conditions (pressure, equilibrium moisture, and temperature). The results show that the estimated adsorption amounts agree well with the experimental ones, which prove the accuracy and robustness of the GBDT method. A comparison of the GBDT with two commonly used ML methods, namely the artificial neural network (ANN) and support vector machine (SVM), confirms the superiority of GBDT in terms of generalization capability and robustness. Furthermore, relative importance scanning and univariate analysis based on the constructed GBDT model were conducted, which showed that the fixed carbon and ash contents are primary factors that significantly affect the adsorption isotherms for the coal samples in this study.