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Due to the problems related to the numerous factors affecting the spontaneous combustion of goaf coal, such as sudden, uncertain, and dynamic changes, and the fact that the weight of the indexes in the prediction model of the spontaneous combustion risk is difficult to determine, an improved Criteria Importance Through Inter-criteria Correlation (CRITIC) modified Technique for Order of Preference by Similarity to Ideal Solution G2-(TOPSIS) dynamic prediction model of goaf spontaneous combustion was developed. An optimal decision-making model was established by introducing the Euclidean distance function, and the function-driven type G2 weighting method was modified using the differential-driven type weighting method of the CRITIC. In addition, the comprehensive weights of each index were obtained. An update factor was introduced to obtain the dynamic weight, and the primary-secondary relationship of the risk factors affecting the spontaneous combustion of goaf was evaluated. Based on the G2 weighting method, which approximates the driving function principle of the ideal solution ranking method (TOPSIS), a G2-TOPSIS goaf spontaneous combustion risk assessment model was established. The degree of closeness was analyzed and the risk grade of the goaf spontaneous combustion was finally predicted. The sub-model was applied to the goaf of working face 1303 in the Jinniu Coal Mine. It was concluded that the air leakage duration was the dominant factor inducing the risk of the spontaneous combustion of the goaf. The risk grade of spontaneous combustion of the goaf is Class III, and the predicted results are consistent with the actual situation. The improved CRITIC-G2-TOPSIS dynamic model was demonstrated to be scientific in predicting the goaf spontaneous combustion risk, and these research results have important popularization and application value.

To study the importance of the adsorption mechanism of methane (CH4) and carbon dioxide (CO2) in coal for coalbed methane development, we aimed to reveal the influence mechanism of adsorption pressure, temperature, gas properties, water content, and other factors on gas molecular adsorption behavior from the molecular level. In this study, we selected the nonsticky coal in Chicheng Coal Mine as the research object. Based on the coal macromolecular model, we used the molecular dynamics (MD) and Monte Carlo (GCMC) methods to simulate and analyze the conditions of different pressure, temperature, and water content. The change rule and microscopic mechanism of the adsorption amount, equal adsorption heat, and interaction energy of CO2 and CH4 gas molecules in the coal macromolecular structure model establish a theoretical foundation for revealing the adsorption characteristics of coalbed methane in coal and provide technical support for further improving coalbed methane extraction.

To investigate the impact of the oxidation temperature and variations in airflow conditions on coal spontaneous combustion characteristics, pre-oxidized coal samples were prepared using a programmed temperature rise method. Synchronous thermal analysis experiments and Fourier transform infrared spectroscopy were conducted to explore changes in the thermal effects and functional group content of the coal samples, respectively. The results indicate that variations in pre-oxidation conditions primarily in fluence the activation temperature and maximum weight loss temperature of the coal samples, while exerting a lesser impact on the critical temperature and ignition point. Variations in air volume conditions predominantly affect the content of Ar-C-O- and -CH 2 & -CH 3 in the oxygen-containing functional group region. The trend of the average activation energy within a conversion rate range of 0.2 to 0.6 of pre-oxidized coal samples changing with the increased of pre-oxidation temperature under the air flow conditions of 25mL/min and 50mL/min is consistent, but opposite to that under the air flow conditions of 100mL/min and 200mL/min. Compared to raw coal, under an airflow rate of 50 mL/min and when oxidized to 110°C, the coal sample exhibits an increase in the content of OH…OH, accompanied by reductions in the critical temperature, activation temperature, ignition point, and maximum weight loss temperature to varying degrees, thereby rendering it more susceptible to oxidative spontaneous combustion.

To realize the scientific judgment of spontaneous combustion risk in the coal mine, the spontaneous combustion influence factors were analyzed from the three aspects of coal spontaneous combustion tendency, air leakage, and oxygen supply, heat storage and heat dissipation. And the basis for the evaluation of t spontaneous combustion grade was constructed. Combination ordered weighted averaging (COWA) calculation was introduced to optimizes G1 subjective weighting, and a COWA modified G1 combined weighting cloud model was proposed to identify the spontaneous combustion risk in the coal mine. Finally, the rationality of the model was verified with actual cases. The research results show that the spontaneous combustion risk level in the Lingquan coal mine is relatively safe, which is consistent with the actual situation. And the spontaneous combustion tendency of coal is the leading factor affecting spontaneous combustion risk.

In response to the issue that the self-ignition hazard of coal secondary oxidation under diverse air flow conditions has not been systematically investigated, this study carried out secondary oxidation experiments on lignite under four distinct air flow conditions. By measuring indicators such as the oxygen consumption rate, exothermic intensity, oxygen consumption activation energy, and limiting self-ignition parameters of the coal samples during the experiment, the characteristics of the secondary oxidation hazard of coal under different air flow conditions were deeply explored. The experimental findings indicate that within the air flow range of 25–100 mL/min during the entire oxidation process (40–170°C) and in the first oxidation stage (40–90°C) at 200 mL/min air flow, the oxygen consumption rate and exothermic intensity of the coal samples were significantly higher than those in the primary oxidation process; however, in the second and third oxidation stages (100–170°C) at 200 mL/min air flow, the opposite characteristics were manifested. Additionally, as the air flow decreased, the differences in oxygen consumption rate and exothermic intensity between primary and secondary oxidation gradually diminished. Regarding the oxygen consumption activation energy, the primary oxidation process exhibited lower values in the first and second stages, but in the third stage, the secondary oxidation process at 25 mL/min and 100 mL/min air flow had lower oxygen consumption activation energy, while at 50 mL/min and 200 mL/min air flow, the primary oxidation process had lower oxygen consumption activation energy. The study also discovered that the increase in air flow and the accumulation of oxidation times would significantly enhance the possibility of coal self-ignition and its sustainability.

Coal and gas outbursts pose significant risks to underground mining operations, and accurate and reliable prediction is crucial for improving mine safety. Traditional machine learning models struggle to balance prediction accuracy and interpretability, particularly in cases of limited data or complex geological conditions. To address this challenge, this study proposes a prediction model based on Physics-Informed Neural Networks (PINN), which integrates physical monotonicity constraints with data-driven learning to ensure that the predictions align with physical laws. Using actual data from a coal mine, this study compares the performance of the PINN model with traditional machine learning models, including Random Forest (RF), Support Vector Machine (SVM), and Backpropagation Neural Network (BPNN). The results show that the PINN model achieves a coefficient of determination (R2) of 0.966 and a root mean square error (RMSE) of 6.452, outperforming the traditional models in both prediction accuracy and generalization ability. Furthermore, interpretability is significantly enhanced by incorporating known physical behaviors and monotonicity constraints. The proposed PINN-based prediction framework provides a more reliable and theoretically grounded approach to assessing coal and gas outburst risks. Integrating it into mining safety management systems can significantly improve early warning mechanisms and risk mitigation strategies.

In order to accurately assess the risk level of coal and gas outbursts, this study proposes an evaluation method based on an improved CUOWGA-weighted TOPSIS model. The primary challenge faced in evaluating the risk of coal and gas outbursts is the subjectivity of the evaluation indicators, which may lead to unreliable outcomes. To address this issue, a coal and gas outburst evaluation indicator system comprising three key factors—geological conditions, coal seam gas content, and the physical properties of coal and rock—was constructed based on an extensive review of the literature. By introducing an innovative fuzzy semantic quantification operator and a normalized decision matrix, the computation process of the CUOWGA operator is optimized to minimize subjective bias and appropriately allocate weights to the evaluation indicators. By combining the optimized CUOWGA method with TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), the risk level of coal and gas outbursts was assessed. A case study conducted at Duanshi Coal Mine demonstrated that the risk level of coal and gas outbursts at this mine is classified as Level II, which is consistent with the actual conditions observed in the mining area. These results validate that the evaluation method based on the ICUOWGA-weighted TOPSIS model can effectively assess the risk level of coal and gas outbursts, thereby proving the feasibility of the approach.

Based on the production conditions of the 10103 excavation working face of the Baozigou coal mine, this paper analyzes the potential sources of H 2 S and the expected emission concentrations of H 2 S in the working face. Considering the previous engineering practice for controlling H 2 S disasters in coal mine working faces, numerical simulations were conducted to investigate air flow and H 2 S migration and diffusion in the tunnel in the excavation working face. The migration and distribution of H 2 S in the coal seam mining face were studied, and the effects of outlet wind speed, duct location, and duct diameter on the H 2 S concentration distribution were explored. The higher the outlet wind speed, the more conducive to the emission of H 2 S gas, but too high a wind speed will be detrimental to the concentrated extraction and purification absorption of H 2 S; the closer the outlet position of the air duct is to the end of the working surface, the lower the H 2 S concentration in the vortex area at the corner; the air duct If the diameter is too small, the harmful gases released from hard-to-break coal cannot be entrained and taken away. When the diameter of the air duct is too large, the entrainment volume during the jet process will be expanded. To verify the field distribution of H 2 S concentration at the bottom, middle, and top of the boring machine, a CD4-type portable H 2 S instrument was used to analyze the distribution of H 2 S near the excavation working face.

In order to study the importance of the diffusion mechanism of CH4 and CO2 in coal for the development of coalbed methane, the aim of this paper is to reveal the influence mechanism of pressure, temperature, water content and other factors on the molecular diffusion behavior of gas at the molecular level. In this paper, non-sticky coal in Chicheng Coal Mine is taken as the research object. Based on the molecular dynamics method (MD) and Monte Carlo (GCMC) method, the diffusion characteristics and microscopic mechanism of CH4 and CO2 in coal under different pressures (100 kPa–10 MPa), temperatures (293.15–313.15 K) and water contents (1–5%) were analyzed in order to lay a theoretical foundation for revealing the diffusion characteristics of CBM in coal, and provide technical support for further improving CBM extraction. The results show that high temperature is conducive to gas diffusion, while high pressure and water are not conducive to gas diffusion in the coal macromolecular model.

On the basis of the jet theory of airflow fields and the gas–solid two-phase flow theory, we studied the law of dust migration in a simulated dusting space. We used the control variable method and numerical simulation software to explore the airflow field and dust concentration distribution on the working surface of the dusting under different inlet wind speeds and different attached blades of the double-walled annular air curtain. We determined the speed of the inlet of the annular air curtain to be 30 m/s. When the angle of the attached blade was 30°, the dust concentration of the driver and other workers was controlled below 100 mg/m 3 , which produced the best dust control effect is the best. Using real data, we built a similar test platform to test the airflow field and dust concentration. Through data measurement and analysis, we proved that a dust control system with a double-wall attached-ring air curtain formed a circulating airflow field that could shield dust and effectively reduce dust concentration in the simulated space. The dust removal efficiency of total dust and exhaled dust reached 98.5% and 97.5%, respectively. We compared the test data and simulation results and concluded that the double-wall attached-ring air curtain could effectively ensure the safety of mine production and provide a better underground working environment for operators.