Abandoned lead and zinc (Pb-Zn) mines around the world produce large amounts of acid mine drainage (AMD) containing Pb(II), which is toxic and accumulates in the environment and in living organisms. Bentonite-steel slag composite particles (BSC) are a new type of acid mine drainage (AMD) treatment material that can remove heavy metal ions and reduce acidity. To date, there have been no reports on the treatment of Pb(II)-containing AMD using BSC. Therefore, the effects of pH, reaction time, temperature, and Pb(II) concentration on the adsorption of Pb(II) onto BSC were studied. Moreover, the BSC before and after the reaction, as well as the precipitation after the reaction, were characterized by scanning electron microscopy and X-ray diffraction analyses. The effect of pH on the adsorption process is similar to that of the formation of soluble and insoluble hydrolysates of Pb(II) on pH. The adsorption mechanism includes ion exchange, complexation, precipitation, and synergistic adsorption–coagulation effect. Adsorption kinetics are best-fit with the pseudo-second order kinetics model ( $R^2$ > 0.98). Furthermore, the total adsorption rate is controlled by liquid film diffusion and in-particle diffusion, the liquid film diffusion rate being higher than the in-particle diffusion rate. The isothermal adsorption of Pb(II) onto BSC fit well with Langmuir and Brunauer Emmett Teller (BET) isotherms ( $R^2$ > 0.995), and both single layer adsorption and local multilayer adsorption were observed. Thermodynamic analysis revealed that the adsorption process is spontaneous and endothermic, and that the degree of freedom increases with time. In summary, this study provides a theoretical basis for the use of BSC in treating AMD containing Pb(II).

Novel multifunctional adsorbent bentonite–steel slag composite particles (BSC) were developed for highly efficient and synergistic treatment of heavy metal ions in acid mine drainage (AMD). Single-factor experiments were performed to examine the influence of different parameters on the adsorption effect, alkalinity release quantity, and loss rate of the composite particles. Based on these results, an L9(4³) orthogonal experiment was carried out, and the optimum levels and order of the factors were determined by range analysis. Finally, the optimum preparation process of the composite particles was determined: a bentonite–steel slag proportion of 5:5, Na₂CO₃ content of 5%, aging time of 12 h, calcination particle size of 2 mm, calcination temperature of 500 °C, and calcination time of 60 min. The isothermal adsorption of optimum BSC fit well with Langmuir and Brunauer–Emmett–Teller (BET) isotherms ( $R^2{R}^2$ > 0.997). A synergistic adsorption–coagulation effect occurs, leading to the appearance of multiple layers locally on the surface of BSC, which satisfies the BET model. To understand the preparation mechanism of the BSC, bentonite, steel slag, uncalcined BSC, and the optimum BSC were characterized using scanning electron microscopy (SEM), Brunauer–Emmett–Teller (BET) surface area analysis, X-ray powder diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR). The results indicate that calcination led to an increase in the average pore radius, total pore volume, and specific surface area (S_BET) in the optimum BSC; numerous pores were present on its layered surface. Although the layer spacing increased after calcination, the structure of the dioctahedra remained unchanged. Exchangeable Na⁺, montmorillonite, and alkaline components were present between the optimum BSC layers. Water and impurities were removed after calcination. The BSC not only released an alkalinity-neutralising acid but also induced a synergistic adsorption–coagulation effect that removed heavy metal ions. It is an excellent multifunctional protective material for the mining environment, that can treat AMD-containing heavy metal ions.

Exogenous fires in underground coal mines are characterized by low illumination, smoke occlusion, heavy dust loading and pseudo fire sources, which jointly degrade image quality and cause missed and false alarms in visual detection. To achieve accurate and real-time early warning under such conditions, this paper proposes a mine exogenous fire detection algorithm based on an improved YOLOv9m, termed PPL-YOLO-F-C. First, a lightweight PP-LCNet backbone is embedded into YOLOv9m to reduce the number of parameters and GFLOPs while maintaining multi-scale feature representation suitable for deployment on resource-constrained edge devices. Second, a Fully Connected Attention (FCAttention) module is introduced to perform fine-grained frequency–channel calibration, enhancing discriminative flame and smoke features and suppressing low-frequency background clutter and non-flame textures. Third, the original upsampling operators in the neck are replaced by the CARAFE content-aware dynamic upsampler to recover blurred flame contours and tenuous smoke edges and to strengthen small-object perception. In addition, an MPDIoU-based bounding-box regression loss is adopted to improve geometric sensitivity and localization accuracy for small fire spots. Experiments on a self-constructed mine fire image dataset comprising 3000 samples show that the proposed PPL-YOLO-F-C model achieves a precision of 97.36%, a recall of 84.91%, mAP@50 of 96.49% and mAP@50:95 of 76.6%, outperforming Faster R-CNN, YOLOv5m, YOLOv7 and YOLOv8m while using fewer parameters and lower computational cost. The results demonstrate that the proposed algorithm provides a robust and efficient solution for real-time exogenous fire detection and edge deployment in complex underground mine environments.