The synergistic utilization of multiple solid waste is an effective means of achieving green filling and resource utilization of solid waste in mines. In this paper, the synergistic effects of solid waste granulated blast furnace slag (GS) and carbide slag (CS) as cementitious materials (GCCM) are investigated, along with their preliminary feasibility in combination with coal gangue (CG) and furnace bottom slag (FBS) for the preparation of backfill materials. The synergistic hydration mechanism, mechanical properties, working performance of GCCM and GBC were studied, and the environmental impact and cost-effectiveness of GBC were evaluated. The results indicate that when the molar ratio of n(CaO)/n(SiO₂ + Al₂O₃) is 0.635 in GCCM, CS has a significant alkaline excitation effect on GS. In addition, the hydration products mainly composed of C-(A)-S-H gel, AFm and hydrotalcite. However, when the concentration of OH^- is high in GCCM, Ca(OH)₂ preferentially precipitates, leading to a reduction in the amount of main hydration product C-(A)-S-H gel. Moreover, GBC has better competitive strength performance than ordinary Portland cement (OPC). The optimal compressive strength of GBC at 28 days is 19.699 MPa, with a standard slump is 228 mm, fully meeting the strength and transportation requirements for mine filling. The heavy metal leaching rate at 28 days meets the requirements of GB 8978, demonstrating potential for carbon reduction and cost savings. The full-solid waste GBC developed in this study can replace cement as a backfill material, which is of significant importance for achieving green mining, the synergistic utilization of solid waste resources, reducing filling costs, and enhancing environmental benefits.

To improve the accuracy of gas outburst early warning, this paper proposes a gas outburst risk warning model based on XGBoost–GR–stacking. The statistic is based on gas outburst data from 26 mines and establishes a data generation model based on XGBoost. The obtained virtual datasets are analyzed through visualization analysis and ROC curve analysis with respect to the original data. If the augmented data has an ROC area under the curve of 1, it indicates good predictive performance of the augmented data. Grey correlation analysis is used to calculate the grey correlation degrees between each indicator and the “gas emission”. The indicator groups with correlation degrees greater than 0.670 are selected as the main control factor groups based on the sorting of correlation degrees. In this study, SVM, RF, XGBoost, and GBDT are selected as the original models for stacking. The original data and virtual data with correlation degrees greater than 0.670 are used as inputs for SVM, RF, XGBoost, GBDT, and stacking fusion models. The results show that the stacking fusion model has an MAE, MSE, and R2 of 0.031, 0.031, and 0.981. Comparing the actual and predicted values for each model, the stacking fusion model achieves the highest accuracy in gas outburst prediction and the best model fitting effect.

To solve the bottleneck problem of low deep coal seam permeability and difficult coalbed methane (CBM) mining. Combining hydraulic splitting technology and directional drilling technology, a directional hydraulic splitting enhancement method of deep CBM mining was proposed. The selection equation for the directional hydraulic splitting of deep coalbed was constructed. The numerical simulation reveals the variation in coal fractures around different split angles. The split angle under the maximum coal damage effect was obtained. It was found that the combined effect of the double crack damage disturbance region led to reciprocal stress fluctuations during crack development and, eventually, the formation of a zigzag fracture. The larger the splitting angle, the larger the fissure development length and the larger the coal-damaged area. A double crack takes 25% less time to complete propagation than a single crack. When the splitting angle is 90°, the disturbed area occupies 2/3 of the area around the borehole, and the overall fracturing effect is the best. In the application process, the new directional hydraulic splitting technology can increase CBM mining by 5.08%, greatly improve CBM mining efficiency, and reduce the coal mining risk, which is of great significance to the project.

A numerical simulation method combining the detailed chemical reaction mechanism of methane deflagration with an approximate real tunnel structure was proposed to confirm whether the unevenness of the tunnel wall during a coal mine gas explosion can be ignored. The approximate real tunnel model and smooth wall tunnel model were developed using 3D modeling methods. The propagation and attenuation processes of shock waves in the two tunnel models, as well as the different dynamic responses of the two tunnel walls, were compared and analyzed. Research results show that the non-uniformity of the tunnel wall decreases the shock wave overpressure and propagation velocity. The peak overpressure reduction value of the shock wave reaches 81.91 kPa, and the shock wave overpressure reaches its peak at an extended maximum time of 7.4 ms. The stress distribution on the approximate real tunnel wall is discontinuous, the propagation speed of stress waves in the bend tunnel is slower, and the duration of high load is relatively low. The displacement of the approximate real tunnel after gas explosion is lower than that of tunnels with smooth walls, and the displacement of most measuring points on the tunnel on the right is only 1/3–1/2 that of the smooth tunnel.

The analysis of geomagnetic suitability is the basis and premise of geomagnetic matching navigation and positioning. A geomagnetic suitability evaluation model using mixed sampling and an improved back propagation neural network (BPNN) based on the gray wolf optimization (GWO) algorithm by incorporating the dimension learning-based hunting (DLH) search strategy algorithm was proposed in this paper to accurately assess the geomagnetic suitability. Compared with the traditional geomagnetic suitability evaluation model, its generalization ability and accuracy were better improved. Firstly, the key indicators and matching labels used for geomagnetic suitability evaluation were analyzed, and an evaluation system was established. Then, a mixed sampling method based on the synthetic minority over-sampling technique (SMOTE) and Tomek Links was employed to extend the original dataset and construct a new dataset. Next, the dataset was divided into a training set and a test set, according to 7:3. The geomagnetic standard deviation, kurtosis coefficient, skewness coefficient, geomagnetic information entropy, geomagnetic roughness, variance of geomagnetic roughness, and correlation coefficient were used as input indicators and put into the DLH-GWO-BPNN model for model training with matching labels as output. Accuracy, recall, the ROC curve, and the AUC value were taken as evaluation indexes. Finally, PSO (Particle Swarm Optimization)-BPNN, WOA (Whale Optimization Algorithm)-BPNN, GA (Genetic Algorithm)-BPNN, and GWO-BPNN algorithms were selected as compared methods to verify the predictable ability of the DLH-GWO-BPNN. The accuracy ranking of the five models on the test set was as follows: PSO-BPNN (80.95 %) = WOA-BPNN (80.95%) < GA-BPNN (85.71%) = GWO-BPNN (85.71%) < DLH-GWO-BPNN (95.24%). The results indicate that the DLH-GWO-BPNN model can be used as a reliable method for underground geomagnetic suitability research, which can be applied to the research of geomagnetic matching navigation.

The automatic ventilation door in mining operations is a crucial component for ensuring production safety and maintaining ventilation system stability. However, the primary power element of this equipment—the cylinder—often lacks effective monitoring, which can compromise operational reliability. To address this gap, this study proposes a Weibull life prediction method, integrating Bayesian inference and Monte Carlo simulation, aiming at anticipating changes in cylinder reliability. This proactive approach supports timely maintenance to prevent. Given the unknown shape and scale parameters of the Weibull distribution, Bayesian methodology is applied, alongside accelerated life testing principles, to analyze the life characteristics of cylinder. By deriving the posterior distribution function of Weibull parameters, Monte Carlo simulation is employed to estimate these parameters across various operational conditions. This method reveals how life characteristics relate to environmental factors such as temperature. Following the constant-failure-mechanism assumption used in accelerated life testing, the characteristic parameters of cylinder characteristic parameters under standard operating conditions are predicted. Results show that this method is effective for life prediction using truncated small-sample data, overcoming the limitations of conventional approaches. Its applicability is proven in the life assessment of automatic ventilation doors, offering a robust tool for reliability. A reliability evaluation system for mine emergency control equipment is developed. This system provides real-time assessments and visualizations of equipment reliability, enhancing maintenance and management practices essential for mining operations.