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.

Coal seam gas pressure is one of the fundamental parameters used to assess coal seam gas occurrence and is an important index in assessing the risk of gas disaster. However, the geological characteristics of coal seams become increasingly complex with increasing mining degree, thus decreasing the accuracy and success rate of direct methods for measuring gas pressure. To address such issues, we have developed a new method for direct measurement of gas pressure in water-bearing coal seams. In particular, we developed a pressure measurement device based on theoretical analysis and quantified the basic parameters of the device based on well testing. Then, we verified the applicability of our method based on comparative analysis of the results of field experiments and indirect measurements. Our results demonstrate that this new method can resolve the effects of water pressure, coal slime, and other factors on the estimation of gas pressure. The performance of this new method is considerably better than that of traditional methods. In particular, field test results demonstrate that our method can accurately and efficiently measure gas pressure in water-bearing coal seams. These results will be of great significance in the prevention and control of coal seam gas disaster.

Accurate prediction of toxic gas concentrations during conveyor-belt fires is essential for ensuring mine safety, yet the nonlinear, time-varying, and turbulent characteristics of underground environments pose significant challenges for real-time forecasting. This study proposes a Physics-Informed Neural Network–Long Short-Term Memory (PINN-LSTM) hybrid model that integrates the one-dimensional convection–diffusion equation as a physical constraint with the sequential learning capability of an LSTM. Full-scale mine tunnel combustion experiments and Fire Dynamics Simulator (FDS) numerical simulations under multiple wind speeds and distances were conducted for model training and validation. The results indicate that the proposed PINN-LSTM achieves the lowest error metrics under all test conditions. The model reduced MSE and RMSE by 70–78% and 65–73%, respectively, compared with traditional LSTM models, and by 8–12% compared with the PINN-TCN variant. The proposed PINN-LSTM achieves the lowest error under all conditions. The PINN-LSTM model has strong prediction accuracy, physical interpretability, and real-time reasoning ability, providing a reliable and physically consistent solution for intelligent gas monitoring and early warning systems in underground fire scenarios.