Precise recovery of Coalbed Methane (CBM) based on transparent reconstruction of geological conditions is a branch of intelligent mining. The process of permeability reconstruction, ranging from data perception to real-time data visualization, is applicable to disaster risk warning and intelligent decision-making on gas drainage. In this study, a machine learning method integrating the Random Forest (RF) and the Genetic Algorithm (GA) was established for permeability prediction in the Xishan Coalfield based on Uniaxial Compressive Strength (UCS), effective stress, temperature and gas pressure. A total of 50 sets of data collected by a self-developed apparatus were used to generate datasets for training and validating models. Statistical measures including the coefficient of determination (R2) and Root Mean Square Error (RMSE) were selected to validate and compare the predictive performances of the single RF model and the hybrid RF– GA model. Furthermore, sensitivity studies were conducted to evaluate the importance of input parameters. The results show that, the proposed RF–GA model is robust in predicting the permeability; UCS is directly correlated to permeability, while all other inputs are inversely related to permeability; the effective stress exerts the greatest impact on permeability based on importance score, followed by the temperature (or gas pressure) and UCS. The partial dependence plots, indicative of marginal utility of each feature in permeability prediction, are in line with experimental results. Thus, the proposed hybrid model (RF–GA) is capable of predicting permeability and thus beneficial to precise CBM recovery.

Accurate prediction of the coal spontaneous combustion hazard grades is of great significance to ensure the safe production of coal mines. However, traditional coal temperature prediction models have low accuracy and do not predict the coal spontaneous combustion hazard grades. In order to accurately predict coal spontaneous combustion hazard grades, a prediction model of coal spontaneous combustion based on principal component analysis (PCA), case-based reasoning (CBR), fuzzy clustering (FM), and the snake optimization (SO) algorithm was proposed in this manuscript. Firstly, based on the change rule of the concentration of signature gases in the process of coal warming, a new method of classifying the risk of spontaneous combustion of coal was established. Secondly, MeanRadius-SMOTE was adopted to balance the data structure. The weights of the prediction indicators were calculated through PCA to enhance the prediction precision of the CBR model. Then, by employing FM in the case base, the computational cost of CBR was reduced and its computational efficiency was improved. The SO algorithm was used to determine the hyperparameters in the PCA-FM-CBR model. In addition, multiple comparative experiments were conducted to verify the superiority of the model proposed in this manuscript. The results indicated that SO-PCA-FM-CBR possesses good prediction performance and also improves computational efficiency. Finally, the authors of this manuscript adopted the Random Balance Designs—Fourier Amplitude Sensitivity Test (RBD-FAST) to explain the output of the model and analyzed the global importance of input variables. The results demonstrated that CO is the most important variable affecting the coal spontaneous combustion hazard grades.

With the increasing complexity of mine ventilation networks, the difficulty of regulating ventilation systems has significantly increased. Lagging regulatory responses are prone to causing problems such as airflow turbulence and insufficient air supply in air-required areas, which seriously threaten the safety of underground operations. To address this challenge, this paper introduces the MOBWO algorithm into the field of ventilation system air volume optimization and proposes a mine air volume optimization and regulation method based on MOBWO. This paper constructs a multi-objective air volume optimization model with the total power of ventilators and the complexity of air pressure regulation as the optimization objectives. Using indicators such as GD and IGD, it compares the performance of the MOBWO algorithm with mainstream optimization algorithms such as NSGA-II and MOPSO and verifies the practicality of the optimization method with the case of the Jinhua Palace Mine. The results show that the MOBWO algorithm has significant advantages over other algorithms in terms of convergence and distribution performance. When applied to the Jinhua Palace Mine, the air volume optimization and regulation using MOBWO can reduce the power of ventilators by 10.3–21.1% compared with that before optimization while reducing the complexity of air volume regulation and the time loss during air volume regulation. This method not only reduces the energy consumption of ventilators but also shortens the regulation timeliness of the ventilation system, which is of great significance for reducing the probability of accidents and ensuring the safety of personnel’s lives and property.

Deep coalbed methane (CBM) is widely distributed in China and is mainly commercially exploited in the Qinshui basin. The in situ stress and moisture content are key factors affecting the permeability of CH₄-containing coal samples. Therefore, considering the coupled effects of compressing and infiltrating on the gas permeability of coal could be more accurate to reveal the CH₄ gas seepage characteristics in CBM reservoirs. In this study, coal samples sourced from Tunlan coalmine were employed to conduct the triaxial loading and gas seepage tests. Several findings were concluded: (1) In this triaxial test, the effect of confining stress on the permeability of gas-containing coal samples is greater than that of axial stress. (2) The permeability versus gas pressure curve of coal presents a ‘V’ shape evolution trend, in which the minimum gas permeability was obtained at a gas pressure of 1.1MPa. (3) The gas permeability of coal samples decreased exponentially with increasing moisture content. Specifically, as the moisture content increasing from 0.18% to 3.15%, the gas permeability decreased by about 70%. These results are expected to provide a foundation for the efficient exploitation of CBM in Qinshui basin.

Underground coal mining of CH₄ gas-rich tectonic coal seams often induces methane outburst disasters. Investigating gas permeability evolution in pores of the tectonic coal is vital to understanding the mechanism of gas outburst disasters. In this study, the triaxial loading–unloading stresses induced gas permeability evolutions in the briquette tectonic coal samples, which were studied by employing the triaxial-loading–gas-seepage test system. Specifically, effects of loading paths and initial gas pressures on the gas permeability of coal samples were analyzed. The results showed the following: (1) The gas permeability evolution of coal samples was correlated with the volumetric strain change during triaxial compression scenarios. In the initial compaction and elastic deformation stages, pores and cracks in the coal were compacted, resulting in a reduction in gas permeability in the coal body. However, after the yield stage, the gas permeability could be enhanced due to sample failure. (2) The gas permeability of the tectonic coal decreased as a negative exponential function with the increase in initial gas pressure, in which the permeability was decreased by 67.32% as the initial gas pressure increased from 0.3 MPa to 1.5 MPa. (3) Coal samples underwent a period of strain development before they began to fail during confining pressure releasing. After the stress releasing-induced yield stage, the coal sample was deformed and cracked, resulting in a quickly increase in gas permeability. With a further releasing process, failure of the sample occurred, and thus induced rapidly increasing gas permeability. These obtained results could provide foundations for gas outburst prevention in mining gas-rich tectonic coal seams.