To solve the problems of gas predrainage in deep seams with “three softs” and low‐air permeability, hydraulic punching pressure relief antireflection technology is proposed on the basis of the research background of gas predrainage technology in Lugou Mine to alleviate technical problems, such as low gas drainage efficiency, in this mine. Through the analysis of the mechanism of hydraulic punching and coal breaking, combined with FLAC3D software, a hydraulic punching pressure relief antireflection model is established. Then, the fracture radii of coal rock are simulated and calculated. The results show that, under hydraulic punching with a water pressure of 10 MPa and coal outputs of 3 m³, 6 m³, 9 m³, and 12 m³, the fracture radii of coal and rock are 3.4 m, 4.8 m, 5.5 m, and 5.9 m, respectively. Using the software to fit the relationship between coal output V and hydraulic punching fracture radius R under the same water pressure, R = 2.32479 V^0.3839 is obtained. The field test is carried out in the bottom drainage roadway of 32141 in Lugou Mine. The application effect is as follows: the gas concentration of hydraulic punching with a coal output of 3 m³ is twice that of ordinary drilling, and the coal output of hydraulic punching with a coal output of 6 m³ is four times that of ordinary drilling. The extraction concentration is four times that of ordinary drilling, and the extraction concentration of hydraulic punching with a coal output of 9 m³ is 6.4 times that of ordinary drilling. Combining the results of the numerical simulation and taking into account the actual construction situation on site, the coal output of water jetting from the borehole is 9 m³, and the fracture radius is 5.5 m. This outcome means that the effective half radius is 5.5 m, and the borehole spacing is 7.7 m. These values are the construction parameters for large‐scale applications. This proposal provides effective technology and equipment for gas drainage in the deep three‐soft coal seam. Consequently, it has promotion and reference significance for gas drainage in coal seam of the same geological type.

The gas drainage effect is one of the important elements in the study of gas drainage in coal mines. It is critical to establish an effective evaluation model of the gas drainage effect for coal mines because the result of gas drainage is directly related to the safety of the coal mines. Through the research related to the safety evaluation in the existing coal mining process, we discovered that there are few studies on the evaluation of the impact of deep and soft gas drainage, and the evaluation methods are not sufficiently effective to resolve the complex problems arising in the process of gas drainage. This paper took “three soft” coal seams in the Lugou Coal Mine as the research object and constructed the evaluation index system on the basis of thoroughly analyzing the factors of coal seam drainage. We then employed a combination weighting method to attain the optimal weight by organically integrating the Analysis Hierarchical Process subjective weighting method and the Criteria Importance Through Interaction Correlation objective weighting method and utilized the cloud model to compute the numerical characteristic value of the evaluation index. In the end, this method obtained an evaluation result of the gas drainage effect evaluation. The evaluation result grade is good. Additional analysis was performed according to the evaluation factors, and corresponding improvement measures were proposed. This is of great importance in promoting safe production and improving the efficiency of gas drainage.

The stability of deep “three‐soft” coal seam roof has always been a key issue in coal mining. There are a lot of factors affecting the stability of deep three‐soft coal seam outburst roof. However, there is currently no definite method able to draw an accurate assessment conclusion on roof stability. In order to accurately determine the main influencing factors of the stability of deep three‐soft coal seam outburst roof and reduce the loss of coal production, this paper performed three‐soft coal seam risk identification on Lugou Mine based on the introduction of the fuzzy analytic hierarchy process theory. 23 main risk factors were identified. Then, it established a hierarchical structure model of coal seam roof stability in accordance with experts’ opinions. The analytic hierarchy process was used to calculate the weights of indicators at all levels. Next, the paper used the fuzzy comprehensive evaluation method and expert scoring to evaluate various risk factors in the indicator system, as well as the overall safety level. The results showed that the deep three‐soft coal seam stability of Lugou Mine ranks the third hazard level. The main risk and harmful factors include safety awareness, safety monitoring system, roof weakness, ventilation system, fire‐fighting system, and rock bolt quality. In response to the evaluation results, this paper formulated corresponding control measure in terms of ventilation risk, safety monitoring risks, construction personnel risks, and fire protection risk to reduce losses in the mining process, providing a new evaluation method for the stability assessment of deep outburst coal seam roof.

Deep phosphate mining operations face complex, dynamic working conditions characterized by the superimposed disturbances of high temperature, high stress, and high strain. The occurrence of rockburst disasters demonstrates a clear pattern of dynamic evolution. Traditional rockburst risk assessment methods mostly adopt a static analysis approach, making it difficult to accurately grasp the dynamic characteristics of the entire process of a rockburst from inception and development to occurrence, and also making it hard to meet the practical work requirements of deep phosphate mining safety management. To address this engineering problem, this study constructs a superimposed analysis model for the risk of underground rockburst accidents in deep phosphate drilling based on a dynamic fault tree, and strives to tackle the complex dynamic issues in rockburst risk analysis and prediction. This model retains the technical advantages of traditional fault tree logical reasoning, integrates the time-series analysis function of dynamic fault trees, and organizes and describes various risk factors of deep phosphate rockbursts, as well as the concurrent, selective, and time-overlapping correlations among each factor. Finally, by introducing dynamic logic gates such as priority gates and standby gates, combined with the quantitative representation of rockburst risk stacking effects, it achieves dynamic risk assessment and accurate prediction of rockburst disasters. The model construction strictly follows the core processes of top event definition, hierarchical decomposition of risk factors, and dynamic logic structure construction, and organically integrates risk stacking theory with the dynamic fault tree method, forming an emergency rockburst risk prediction system that can provide technical support for reducing the probability of deep phosphate rockburst accidents. The rock fracture risk superposition model developed in this study aims to provide a tool for risk identification and spatial superposition analysis in deep phosphate mining, minimizing disturbances to the mine’s ecological environment, and offering theoretical support and technical methods for safe and green mining, sustainable development, and high-quality exploitation of deep phosphate resources.

Non-coal mines constitute a fundamental pillar of the global energy transition, providing essential raw materials across various sectors. Accidents in these facilities cause severe direct damage, including personal injuries and economic losses, while triggering broader systemic impacts, such as supply chain disruptions of essential energy materials. Taking China as an example, roof fall and rib spalling accidents account for 30% to 40% of all safety incidents in non-coal mines. Consequently, investigating the occurrence and evolutionary process of roof fall and rib spalling risks in these environments holds significant importance. To identify the fundamental factors of the accident, the 24Model and the fault tree are employed in this research. Expert elicitation and fuzzy set theory are used to determine the occurrence probability of each basic event. To overcome the limitations of traditional static risk assessment, a dynamic Bayesian network is introduced to capture the time-varying characteristics of risk factors, evaluating the overall accident probability over a 52-week period, equivalent to approximately one year. In this study, 30 basic events and 18 intermediate events were identified. Quantitative results show that effectively controlling the critical events (X26, X4, X20, X27, X2) reduces the accident probability by 56.39%. Furthermore, the dynamic Bayesian network analysis demonstrates that under the specific assumption of continuous risk accumulation across five dimensions (human, material, management, cultural, and environmental factors) without the prompt implementation of targeted interventions, the occurrence probability of the accident reaches 0.95023 after 52 weeks. The results demonstrate that this model surpasses static models by effectively identifying the critical causal factors of the accident and evaluating their occurrence probabilities through systematic causation analysis and dynamic accident evolution. This approach facilitates precise accident early warning, offering a practical reference for relevant enterprises and personnel involved in roof safety management.