High-altitude mountain areas severely restrict the development of the local mining economy due to complex topographic and geological conditions, fragile ecological environments, and weak traditional transportation infrastructure. To study transportation schemes for high-altitude mountain mines, the raw ore transportation of Motuo stone pots in Xizang was taken as the research object. Given its characteristics of high added value and susceptibility to breakage, three ore transportation schemes, Unmanned Aerial Vehicle (UAV) transportation, simple cableway transportation, and belt conveyor transportation, were compared and analyzed from four dimensions: technical feasibility, economy, safety, and environmental adaptability. The research results indicate that in the specific scenario of high-altitude mountain mine transportation, UAV transportation achieves point-to-point transportation by virtue of its excellent terrain adaptability, high timeliness, low cargo breakage rate, and low environmental disturbance. Due to the limited production capacity of the raw ore for Motuo stone pots, the UAV transportation scheme is superior to the simple cableway and belt conveyor transportation schemes in terms of initial infrastructure investment and unit transportation cost, demonstrating significant comprehensive advantages and making it a preferable solution for transporting the raw materials of Motuo stone pots out of Xizang. However, UAV transportation is notably affected by airspace restrictions and meteorological conditions. The airspace application cycle is long and management is strict, while the complex meteorological conditions in the region necessitate strict adherence to flight safety regulations.

Aiming at the problem that the accuracy of the existing anti-fatigue early warning systems is less than 95% and they are susceptible to interference in the harsh driving environment of ultra-high altitude mines, a fatigue detection system based on visual and electroencephalogram (EEG) features was developed. The FastICA algorithm was used to remove ocular artifacts, and the EEG energy ratio and sample entropy were extracted. YOLOX was used for face detection, combined with an improved PFLD algorithm to extract eye state, yawn and head posture features. A multi-source information fusion decision algorithm based on D-S evidence theory and SVM was designed, which effectively integrates the probabilistic classification results of visual and EEG features. The application in the ultra-high altitude mine in Las Bambas, Peru, shows that the detection accuracy of the system based on multi-source information fusion is significantly improved to 97.6%, and the key fatigue features can be reliably captured. The fusion of visual and EEG features can significantly improve the accuracy and robustness of fatigue detection in ultra-high altitude environments. The fusion algorithm of D-S evidence theory and SVM is the key to achieving 97.6% high accuracy. The system provides reliable technical support for the transportation safety of high-altitude mines, which can reduce accident risk by about 15%, and also provides a new approach for anti-fatigue research in extreme environments.

During the transport of unmanned mining trucks in open-pit mines, their perception systems are highly susceptible to harsh weather conditions such as wind-blown sand and dust. This vulnerability leads to a degradation in the detection and tracking capabilities of key targets, consequently causing adverse consequences such as missed detections, false alarms, and unintended vehicle stops. To enhance the safety and efficiency of perception technologies for unmanned operations under dust interference, an intelligent dust perception model and scheme for unmanned mining trucks were proposed by integrating deep learning algorithms, point cloud data processing techniques and multi-sensor fusion methods, followed by experimental validations. On this basis, a classification strategy and corresponding criteria for dust severity during unmanned operations were established, and the trained model was evaluated on a test dataset. The results indicate that the predicted mean Intersection over Union reaches 87.3%, with the Intersection over Union for the dust category specifically achieving 90.7%. This demonstrates that the perception model possesses high recognition precision for dust, fully satisfying the requirements of dust scene recognition for unmanned operations in mining areas. The unmanned operation dust intelligent perception scheme effectively enhances vehicle perception capabilities in open-pit unmanned operation environments, achieving a point cloud removal rate of over 95%. It significantly improves the accuracy and sensitivity of environmental perception, thereby providing a robust guarantee for the safe operation of unmanned mining trucks.

To address the insufficient prediction accuracy of the compressive strength of fiber reinforced cemented tailings backfill (FRCTB), an improved Stacking ensemble learning model based on a weighted residual mechanism was proposed, utilizing 364 sets of experimental data collected from the literature. The model constructed an ensemble framework with six heterogeneous base learners and four meta-learners, and employed the Hippopotamus Optimization Algorithm (HOA) for global optimization of the base learner parameters. The prediction results of the base learners were obtained through 5-fold cross-validation, and adaptive weights were assigned to each base learner according to their prediction errors. Residual features were extracted as inputs for the meta-learner. Six single models were set as benchmarks for horizontal prediction accuracy comparison. Finally, interpretability analysis was introduced to explore the key influencing factors. The results show that among the single models, the light gradient boosting machine achieves the best comprehensive performance, with a coefficient of determination of 0.887 0. The HOA-R-Stacking model with Support Vector Regression (SVR) as the meta-learner performs the best, achieving a coefficient of determination of 0.947 0, and its mean absolute error and root mean square error are reduced by 34.53% and 33.94%, respectively, compared with the best single model. The interpretability analysis indicates that fiber content is one of the key factors affecting the compressive strength of FRCTB. The research results provide an effective technical means for predicting the compressive strength of FRCTB, which helps to improve backfill stability and reduce the cost of mine filling.

Considering the characteristics of turbulent flow field and the influence of process parameters on particle flocculation behavior, a particle flocculation kinetic mathematical model based on local turbulent energy dissipation rate and slurry viscosity correction was established using the CFD-PBM coupling method. On the basis of test verification of flow velocity and average particle size distribution of flocs, the influence of feed volume concentration on flocculation behavior in deep cone thickener was studied. The results show that the energy dissipation rate of turbulent flow above the annular baffle in the feedwell is about 2 orders of magnitude higher than that in the lower area, and the energy dissipation rate of turbulent flow below the baffle first increases and then decreases with the increase of feed volume concentration. The average particle size of flocs first increases and then decreases with the increase of feed volume concentration, and the average particle size of flocs is larger under the condition of medium feed volume concentration (10%). Appropriately increasing the length of the feedwell can improve the flocculation effect of fine mineral particles. The research results can provide guidance for modeling turbulent flocculation kinetics and optimizing flocculation thickening process parameters.

To address the problems that existing diagnosis methods can hardly capture the long-term variation law of mine soil quality, the evaluation of reclamation effect lags behind, and the soil quality diagnosis results lack interpretability, an improved dynamic diagnosis model for mine reclamation soil quality based on Swin-Unet and Gradient-Weighted Class Activation Mapping (Grad-CAM) visualization technology was proposed (IPPSUG model). Raw soil image features were extracted by the model via Swin-Unet, and noise was suppressed while key features were highlighted with the squeeze-and-excitation network module. Meanwhile, the model structure was optimized by the pruning algorithm to reduce computational complexity. Finally, the interpretability of diagnosis results was improved by Grad-CAM. The test results show that the accuracy and precision of soil quality diagnosis of the PSSU model reach 96.11% and 97.63%, respectively. The determination coefficient of IPSSUG model for identifying available potassium content is 0.983, and the recognition rate of soil-polluted areas is 98.40%. Test data are superior to those of the comparison models, which fully demonstrates the feasibility and superiority of this model. In addition, the degree of correlation between main influencing factors and soil quality indicators was quantified by IPSSUG model, the reliability of soil quality diagnosis results was improved, and a new method was provided for research on mine reclamation soil quality diagnosis.

Process mineralogical analysis indicates that the valuable minerals in a low-grade lead-zinc sulfide ore in southern China are galena, sphalerite and associated silver minerals, while other metal sulfides are predominantly pyrrhotite and pyrite. To achieve the efficient comprehensive utilization of lead, zinc and silver resources, systematic flotation tests were conducted on the ore. Through conditional tests on grinding fineness, lead flotation and zinc flotation, the suitable grinding conditions and reagent regime were determined. A preferential lead-zinc flotation flowsheet was adopted, in which both the lead and zinc flotation circuits consist of one roughing, two cleanings and one scavenging stage. Closed-circuit test results demonstrate that at a grinding fineness of 56.6% passing -0.074 mm, a lead concentrate with a Pb grade of 65.08% and a recovery of 91.32%, alongside an Ag grade of 1 323.80 g/t and a recovery of 79.28% was obtained. Additionally, a zinc concentrates with a Zn grade of 41.70% and a recovery of 85.96% was produced. This process realizes the effective recovery of valuable elements from the low-grade Pb-Zn-Ag sulfide ore, providing a reference for the development and utilization of similar resources.

Anionic polyacrylamide (APAM) is the most widely used flocculant in the mining sand-making industry. The phenomenon of compound doping during its production is relatively common, which leads to the degradation of APAM flocculation performance. Flocculation sedimentation tests were conducted to systematically evaluate the effects of five compounds, including sodium chloride (NaCl), on the flocculation performance of APAM. The characteristics of doped APAM were characterized by using techniques such as X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and chemical titration. Cost-benefit analysis was employed to quantify the economic impact of the doping behavior. The results show that APAM achieves the optimal flocculation effect at a mass concentration of 0.1% and a dosage of 24 g/t. High compound dosages (≥30%) severely degrade the flocculation performance of APAM. Specifically, when the NaCl dosage is 50%, the median particle size of sludge flocs decreases from 95.3 μm to 41.3 μm, a reduction of 56.7%, and the settling time is extended to more than double, resulting in a significant decline in wastewater treatment efficiency. The established combined detection system integrating solid content measurement, FTIR, XRD, and chemical titration can specifically identify the doping components. The economic analysis indicates that 50% NaCl doping can reduce the production cost of APAM to 6 150 yuan/t, with a profit margin of 48.75%. The research results can provide a technical basis for the quality control of flocculants and the optimization of water treatment systems in sand-making plant.

In the context of the in-depth implementation of the "Dual Carbon" strategy goal, the synergistic development of digital transformation and green innovation has become a key pathway for the sustainable development of mining enterprises. Based on a sample of A-share listed companies in China's mining industry from 2015 to 2024, the impact and underlying mechanisms of digital transformation on the green innovation efficiency of mining enterprises were systematically investigated. Furthermore, the existence of a peer effect in digital transformation and its exogenous impact on green innovation efficiency were verified at the regional level. The results demonstrate that digital transformation significantly improves the green innovation efficiency of mining enterprises, a conclusion that remains valid after endogeneity treatments and robustness tests. Regarding the functional mechanisms, digital transformation promotes green innovation efficiency by optimizing resource allocation efficiency, enhancing information transparency, and reducing supply chain concentration. This promoting effect is particularly pronounced among upstream supply chain enterprises, state-owned enterprises, and heavily polluting enterprises. Moreover, the digital transformation of mining enterprises exhibits a distinct regional peer effect, which in turn significantly facilitates the enhancement of their green innovation efficiency.

To address the problems of flow velocity runaway and wear risk caused by high potential energy in slurry filling pipelines of ultra-deep mines, the ANSYS numerical simulation method was employed to systematically reveal the influence mechanisms of coarse aggregate type, mass concentration, flow velocity, and pipeline geometric parameters on pipeline wear. An innovative two-stage pressure regulation and wear reduction technology was proposed. The results indicate that the coarse aggregate type has a limited effect on the pipeline wear rate, with the water-quenched slag-based slurry exhibiting the lowest wear rate. The slurry mass concentration is significantly negatively correlated with the pipeline wear rate, whereas the slurry flow velocity is linearly positively correlated with the pipeline wear rate. Among the pipeline geometric parameters, the bending angle and inclination angle exert significant effects on pipeline wear, with the most severe wear occurring at the bend sections. In response, horizontal turn-back loop pipes and electric pressure regulating valves are installed at the middle and lower sections of the pipeline to regulate the slurry flow velocity in stages within the range of 0.8-3.1 m/s. Supplemented by a T-shaped buffer structure to optimize the flow pattern, this technology substantially reduces pipeline wear and system pressure fluctuations. It can reduce the wear rate of critical 90° bends by 82% compared to the pre-modification level, reduce the system pressure fluctuation amplitude by approximately 40%, decrease the incidence of slurry leakage accidents by 30%, and enhance the system operational efficiency by 25%. The research results can provide key technical support for deep mineral resource development.