To solve the problem of the influence of disturbance induced by deep approach mining on roadway surrounding rock stability, Sanshandao Gold Mine was taken as the engineering background. Combined with FLAC3D numerical simulation and on-site monitoring data, the failure evolution laws of surrounding rock in sublevel roadways and stope main roadways under different approach parameters and stoping sequences were compared and analyzed. According to the displacement and stress distribution characteristics of roadways, the mining schemes were optimized. The results show that under the disturbance of approach mining, the displacement of roadway roof increases nonlinearly in four stages with the advancement of mining process. The displacement of stope main roadways is generally larger than that of sublevel roadways, and the stage with the fastest growth rate of roof displacement differs among different mining schemes. Thestress concentration levelin the sidewalls of stope main roadways and sublevel roadways is higher than that in the shoulders, and the stress growth rate on the right side of sublevel roadways is significantly greater than that on the left side, exhibiting asymmetric characteristics. In addition, when upward approach mining is adopted, the stress in stope main roadways undergoes obvious staged mutations, while the stress increaseis relatively gradual under downward approach mining. When the approach width increases from 4 m to 5 m, the increments of roadway stress and displacement are relatively small. However, when the approach width increases from 5 m to 6 m, the variation of approach width exerts a remarkable influence on roadway stability. Therefore, based on a comprehensive analysis of roadway stability and mining efficiency, the downward approach mining scheme with a width of 5 m better meets the requirements of on-site safe production. The on-site monitoring results further verify the reliability of parameter optimization. The research results can provide references for the layout and stability control of deep roadways.

Aiming at the surrounding rock control problems of gob-side entry retaining under the condition of deep water-rich composite roof, the support design and stress analysis were carried out. By analyzing the influence mechanism of composite roof structure and water-rich environment on surrounding rock stability, the limitations of traditional support methods in controlling continuous deformation and floor softening were pointed out. The collaborative control technology system was proposed as follows. Firstly, the constant resistance anchor cable with a length of 11.8 m was used for three rows of vertical roof support, and the two-way shaped charge blasting roof cutting technology was combined to block the lateral stress transmission in the goaf. Secondly, "one beam and four columns" hydraulic support was set up in the dynamic pressure area to strengthen the support. Thirdly, in the drenching section, C20 concrete bedding and grouting anchor cable joint technology were used to control the water seepage softening of the bottom plate. The engineering application results show that the proposed control system reduces the time from severe deformation to stability by more than 40%, the maximum cumulative convergence of the left side of the return air chute is controlled within 940 mm, and the floor heave is significantly reduced within 660 mm. The proposed collaborative control technology can effectively deal with the complex geological conditions of water-rich composite roof, and realize the long-term stability control of gob-side entry retaining. The research results can provide a reliable technical basis for non-pillar coal mining under deep and complex geological conditions.

To address the challenge of the unclear spatiotemporal coupling mechanism of the wind temperature field in dynamic excavation roadways during the thermal hazard control of deep high-temperature mines, a three-dimensional unsteady heat transfer model of the dynamic excavation process was constructed. A dimensionless time F_ov characterizing the excavation speed and the traditional dimensionless distance X were introduced to establish a spatiotemporal binary relationship Θ(F_ov, X) for the temperature field. The spatiotemporal coupling relationship of the dimensionless temperature in the excavation roadway was obtained through iterative inversion based on the Gauss-Newton method, further revealing the nonlinear mechanism of the effect of inlet air temperature on thermal hazard regulation. The results show that the evolution of the dimensionless temperature over time and space follows an exponential-quadratic time-varying relationship (Mean Relative Error MRE=1.1%). The temperature gradient is positively correlated with distance and negatively correlated with time. The initial cooling effect of low-temperature air supply is significant, but after long-term ventilation, the air flow and surrounding rock gradually approach thermal equilibrium, and the cooling efficiency attenuates by 26.3%. Thermal hazard control projects should integrate surrounding rock thermal management with localized cooling to improve cooling sustainability. A reduction in the inlet air temperature intensifies the nonlinear variation characteristics of the air temperature, increasing the MRE of the dimensionless temperature spatiotemporal coupling model from 1.10% (30 ℃) to 1.81% (20 ℃), while still meeting engineering accuracy requirements. The air temperature response in the area near the heading face is the most sensitive. When the inlet air temperature rises from 20 ℃ to 30 ℃, the total temperature rise near the heading face area is 42.1% higher than that in the roadway exit area. Taking a heading face air temperature threshold of 26 ℃ as the constraint, a dynamically designed inlet air temperature that meets the cooling requirements was derived.

Aiming at the extraction challenges of high-grade ore bodies in deep fractured surrounding rock, the applicability of the drift filling mining method was explored using a copper-gold mine as the engineering background. Through core sampling, uniaxial compression and Brazilian splitting tests, combined with the Hoek-Brown and Mohr-Coulomb strength criteria, the key mechanical parameters of the surrounding rock and backfill were obtained. Subsequently, numerical simulations on overhand and underhand drift filling mining methods were conducted to analyze the displacement characteristics of the surrounding rock under corresponding support conditions, and scheme optimization was achieved by integrating technical and economic indicators. The results indicate that the overhand drift filling mining method is applicable to ore bodies with good to moderate rock mass stability, whereas the underhand drift filling mining method is more suitable for ore bodies featuring extremely fractured and highly unstable rock masses. For both drift filling mining methods, the total displacement of the surrounding rock remains relatively small; however, the displacement distributions differ due to variations between the roof and the floor. An economic comparison reveals that the underhand drift filling mining method incurs higher mining and filling costs (4.01 yuan/t higher than the overhand drift filling mining method), while its support costs are lower (0.313 yuan/t lower than the overhand drift filling mining method). Based on a comprehensive evaluation, the overall application of the overhand drift filling mining method is recommended, with the underhand drift filling mining method reserved exclusively for localized areas containing highly fractured surrounding rock.

Large quantities of coal gasification slag are generated during the exploitation and utilization of coal resources, and the efficient and green disposal of this slag is one of the key issues that urgently needs to be resolved in the coal sector. Through an orthogonal experimental system, the working and mechanical properties of coal gasification slag paste filling materials were systematically studied, and their compliance with current standard specifications was compared. Using range analysis, the optimal mix proportion was determined as follows: a slag-to-gypsum ratio of 2.5∶1.5, a fly ash blending ratio of 2∶2, a slurry mass concentration of 76%, and a corresponding coal gasification slag content of 11.7%. The static leaching method was adopted to conduct immersion tests on this material, and the water quality indicators of the leachate at different immersion ages were examined. The results show that the indicators of the leachate stabilize in the later stage, comply with the Class III groundwater quality standard, and meet the basic requirements for groundwater quality. The F-value method was employed for a comprehensive water quality assessment. The results indicate that the groundwater quality grade for different immersion durations is predominantly "Good". Although the water quality declines slightly with prolonged immersion time, it remains in a safe state overall. Compared with traditional paste filling materials, the cost of the coal gasification slag paste filling material is reduced by 16%, and the comprehensive performance is significantly enhanced. This provides an important basis for the engineering application of coal gasification slag paste filling technology and holds great significance for realizing the resource utilization of coal-based solid waste and the protection of groundwater quality.

In view of the problems of large water inflow and difficult grouting and water plugging in some underground mines in the copper (cobalt) metallogenic belt of Central Africa, which seriously restrict the safe mining of mineral resources, Chambishi Copper Mine was selected as the research object, and efficient plugging materials and processes suitable for this mine and similar water-rich mines were developed. Five kinds of materials, including local clay, bentonite, a chemical material, domestic ultrafine cement and tailings from Africa, were selected for laboratory tests. Multi-proportion slurries were prepared according to different mixing schemes, and the physicochemical properties (viscosity, relative density, bleeding rate and plastic strength), setting time and compressive strength of each slurry's stone body were tested. The results show that the local mud cake meets the requirements for pulping. The 7 d compressive strength of the modified clay slurry stone body is 0.11-0.69 MPa, and its viscosity increases significantly with a decrease in water-solid ratio. It is suitable for large-scale ground curtain, wellbore curtain and other projects, and can reduce material costs by about 30%. The local bentonite meets the requirements for pulping. The 7 d compressive strength of the cement-bentonite slurry stone body is 0.10-2.34 MPa. When the water-solid ratio is ≤2∶1, the setting time shortens with the increase in bentonite content, making it suitable for deep poorly injectable strata. In the deep multi-layer sand belt area, its injectability is 7 times higher than that of traditional cement slurry. The 28 d compressive strength of the ultrafine cement slurry stone body can reach up to 59.8 MPa, and its strength decreases with the increase in water-binder ratio. It is suitable for micro-fracture and backfill grouting, offering improved impermeability and high compressive strength. By adjusting the content of GROUTEX, the chemical slurry can achieve instant setting, and the compressive strength of its stone body reaches 43.65 MPa. It is suitable for sealing poorly sealed boreholes, achieving instant setting with a strength greater than 43 MPa, thereby preventing water inrush accidents caused by exposing water-rich boreholes underground. The bleeding rate of the tailings slurry is high, and the 7 d compressive strength of its stone body reaches up to 9.7 MPa; further improvement tests with different additives are therefore necessary.

To solve the problem that the cultural relic area and cultural relic group directly above a copper ore body are affected by mining blasting vibration, the dynamic response and parameter optimization of blasting in near-surface mining were systematically studied. Firstly, the relationship between the surface cultural relic area and the ore body was analyzed, and the maximum allowable blasting vibration velocity was determined by combining domestic and international safety criteria for blasting vibration. Based on the M. A. Sadovsky empirical formula, the maximum single-delay blasting explosive charge within the blast safety distance was obtained. Then, blasting simulations of the four levels at 1 945 m, 2 020 m, 2 095 m and 2 245 m closest to the surface throughout the mine's life cycle were carried out by numerical simulation, and the rationality of the maximum single-delay blasting explosive charge was verified. Finally, during blasting at the representative level underground, surface vibration tests were conducted using measuring points arranged on the surface. The results show that the maximum single-delay explosive charges at the four level are 745 kg, 390 kg, 172.5 kg and 10 kg, respectively, all of which are equal to or less than the allowable maximum single-delay explosive charge. The surface peak vibration induced by blasting velocity at the 2 245 m level is 0.5 cm/s, which is greater than that of the other three levels, but remains less than the maximum allowable vibration velocity, indicating that the blasting seismic wave has no effect on the surface structures. Based on the field blasting vibration data, an SSA-Elman neural network model for predicting Y-direction blasting vibration was constructed and compared with the traditional Elman neural network model. The predicted values of the SSA-Elman neural network model are basically consistent with the measured values, with an average MAE of 0.01, which verifies its accuracy. The research results provide a basis for the design of blasting parameters for other levels of the copper mine.

To satisfy the current demand for the construction of information systems supporting the dual prevention mechanism—specifically, hierarchical risk control and hidden danger investigation and governance—in non-coal mines, a Browser/Server (B/S) architecture-based information system tailored for non-coal underground mines was developed. In accordance with the implementation workflow of the dual prevention mechanism, the system was grounded in the "9+X risk identification method" and the "human-centric evaluation method", and adopted a business architecture comprising "one platform, two terminals, and three levels". Through data collaboration between the platform and terminals, it had realized entire-process refined and intelligent construction and management of the dual prevention mechanism in mining enterprises, which effectively promoted transparent risk control and flattened hidden danger governance. The application results in pilot mines demonstrate that the completion rate of hidden danger investigation and governance reaches 100%, and management efficiency increases by 90%. The system significantly enhances mine safety management efficacy, facilitates the implementation of the all-staff safety responsibility system, drives the digital transformation of safety management, and provides robust support for the construction of work safety standardization in non-coal mining enterprises.

In view of the threat of complex karst water disasters in deep mining of metal mines, it is urgent to develop refined curtain closure water control technology and multi-dimensional effect evaluation methods. In order to improve the efficiency of water disaster prevention and control and verify the engineering effect, ZK Iron Mine was selected as the research object. Combined with geophysical prospecting and drilling to identify the karst channel in advance, the fine grouting technology based on channel identification was implemented (dynamic control pressure of 2-4 MPa, water-cement ratio of 1∶1-0.6∶1). FEFLOW software was used to simulate and predict the change of groundwater flow field before and after curtain closure and the attenuation effect of daily mine drainage (reduced to 7 984 m³/d, water plugging efficiency of 65%). A multi-dimensional effect evaluation system integrating grouting volume analysis, hydrological monitoring, geophysical exploration, inspection hole verification and AHP quantitative evaluation was constructed. The results show that the fine grouting process effectively plugs the complex karst fracture network. The daily average drainage volume of the mining area decreased from 22 984 m³/d to 7 770 m³/d, a decrease of 15 214 m³/d, and the water plugging efficiency reached 66.2%, which is close to the simulated predicted value. The comprehensive evaluation score of AHP is 0.895 (excellent grade), among which the reduction of drainage rate (weight of 31.1%) and the contribution of water plugging efficiency are significant. This technical system provides theoretical support and a replicable technical paradigm for aquifer protection and water disaster prevention and control in similar metal mines.

During the mining process of the mine working face, the disturbance of the aquifer can easily lead to an increase in the water inflow of the working face, which threatens the safe production of the mine. Accurate prediction of the water inflow of the working face can effectively prevent water disaster accidents. In this study, the multi-feature prediction method of water inflow in working face based on deep learning model was studied. Combined with the characteristics of water inflow data and the actual situation of the site, the "double aquifer water level+maximum height of microseismic event" was selected as the characteristic data, and the multi-feature prediction model of water inflow in working face of mine based on VMD-iCHOA-GRU was established. At the same time, the prediction effect of multi-feature model was further verified under different feature combinations, single feature conditions and different working face data. The results show that the evaluation indexes MAE, RMSE and MAPE of the water inflow prediction results of the VMD-iCHOA-GRU model are 53.56 m³/d, 62.98 m³/d and 3.1%, respectively, which are better than other control models, and its prediction accuracy is the highest. The prediction accuracy of the multi-feature model is higher than that of the single-factor model and other single-feature and double-feature models in various combinations, and the stronger the correlation between the feature data and the research target, the more conducive it is to improving the prediction accuracy. The prediction effect of the model is good under different working face data, which verifies that the model has relative stability and certain scalability. The research results provide new ideas and new methods for the prediction of water inflow in mine working faces, and have scientific guiding significance and reference value for mine water prevention and control.

With the expansion of open-pit mining scale, slope stability problems have become increasingly prominent. Great significance is attached to realizing safe and efficient open-pit mining by accurately predicting slope stability and taking preventive measures in advance. A whale optimization algorithm-convolutional neural network-long short-term memory (WOA-CNN-LSTM) prediction model for slope stability was constructed based on factor analysis. Six factors, including cohesion, internal friction angle, slope angle, slope height, pore pressure ratio and unit weight, were selected as the discrimination indicators of the model. Factor analysis was adopted to eliminate the correlation among the six indicators, reduce data dimensions and extract common factors. The extracted common factors were taken as the inputs of the WOA-CNN-LSTM model, and the model was applied to slope engineering cases to verify its reliability. The research results show that the CNN-LSTM network can be optimized by iteratively updating and searching for optimal network parameters using the WOA algorithm. The problems of the CNN-LSTM model, including falling into local optimal solutions and thegradient descent affectingthe prediction accuracy of slope stability, can be effectively overcome. The prediction accuracy and generalization ability of the model are thus improved, and a new idea for slope stability prediction is provided.

To ensure the safe operation of open-pit mines, safety evaluation of their slopes needs to be carried out. A slope of a certain mine was taken as the research object. A numerical calculation model of the slope was established by the finite element method, the distribution laws of slope stress, displacement and plastic zone were analyzed, and the slope safety factor was calculated by multiple quantitative analysis methods. The results show that the maximum slope displacement, plastic zone and minimum safety factor are all close to the third-stage slope. The third-stage slope was taken as the evaluation object. Single index measurement functions were constructed by the unascertained measurement theory, index weights were determined by the entropy weight method, and the distance discriminant method was adopted as the evaluation criterion for slope safety grade. The research results show that the evaluation grade of the slope in an open-pit mine is Grade Ⅱ. The evaluation results are consistent with the numerical simulation results, which verifies that the mine slope is in a relatively stable safety state. After reinforcement and monitoring were carried out on the slope risk areas, no slope accidents occurred during the operation of the open-pit mine, which verifies the reliability and practicability of the unascertained measurement theory-finite element method for slope safety evaluation.

The southeast orebody of Chambishi Copper Mine is hosted in shallow metamorphic rock strata. The surrounding rock features well-developed joints and fissures. Under the influence of high in-situ stress and long-term mining disturbances, the roadways are prone to disasters such as roof fracturing, anchor bolt loosening, and anchor mesh collapse. To address the issues of poor support performance and insufficient adaptability of the existing split-set anchor bolts, an analysis of joint fissures and rock mechanical properties was conducted for the fractured quartzite in the hanging wall and footwall of the orebody. Based on the fracturing and deformation characteristics of typical roadways, the applicability and mechanical properties of different anchor bolt types were comprehensively considered, and resin anchor bolts were proposed to replace the original split-set anchor bolts for supporting roadways in fractured surrounding rock. The 3DEC numerical simulation method was employed to establish a typical roadway model, and the displacement control effects and surrounding rock response characteristics of the two support schemes were compared and analyzed. The results show that the quality grade of the quartzite rock mass is determined to be Class III based on the combined application of the RMR method, Q-system method, and BQ method. When resin anchor bolts with a length of 2.4 m and a spacing and row distance of 1.2 m are used to replace the original split-set anchor bolts for support, the roof subsidence decreases by 8.4%, the floor heave decreases by 4.4%, the inward convergence deformation of the sidewalls is significantly improved, and the overall stability of the roadway is enhanced compared with that achieved by split-set anchor bolts. The industrial application results show that the average pullout force of the resin anchor bolts is 174.17 kN, which is far higher than the design value of 101.47 kN. Although the initial support cost is 2.75 times that of the split-set anchor bolt support, the supplementary support rate is reduced to 2.46%, and the supplementary support cost is reduced by approximately 84%. The research results can provide technical support and a theoretical basis for support optimization in similar roadway engineering.

The tunneling working face is an area prone to frequent gas accidents during coal mining. In view of the strong nonlinear and spatiotemporal dynamic correlation characteristics of gas concentration variations, a prediction model for gas concentration at the tunneling working face, named KAN-iTransformer, was proposed, which integrates the Kolmogorov-Arnold Networks (KAN) and an improved iTransformer (Inverted Transformer). Firstly, a relevant feature dataset for gas concentration prediction was constructed through data preprocessing and Spearman correlation coefficient analysis. Secondly, based on the inverted Transformer structure, the complete variable sequence was regarded as a token, and a graph attention mechanism was introduced to explicitly model the spatial topological relationships of the sensor network, enabling effective modeling of the dynamic dependencies among sensors. Thirdly, the learnable activation functions of KAN were used to replace the traditional linear weights, enhancing the model's ability to characterize complex nonlinear relationships among multi-source monitoring parameters. Finally, a hierarchical response mechanism combining point prediction and interval estimation was proposed to enable advanced dynamic hierarchical response to gas overrun risk. The field test results show that the MSE, MAE and MAPE of the improved KAN-iTransformer model in single-step prediction are 0.000 307, 0.012 921 and 2.321 373%, respectively, significantly outperforming both the iTransformer and Transformer models. In multi-step prediction, the MSE of the improved KAN-iTransformer model is 0.000 913, which is 63.7% and 80.9% lower than those of the comparison models, respectively, demonstrating better long-term prediction performance and effectively realizing accurate prediction and dynamic early warning of gas concentration at the tunneling working face.

To address the issue of poor efficiency of dust control in open-pit blasting, a geometric model of the flow field for bench blasting in open-pit mine was established using Fluent software. The influence of flow field distribution characteristics under different wind speeds on the migration law of blasting dust was studied, aiming to determine the optimal dust capture time, water bag position, and delayed activation time for water mist dust suppression technology. The results indicate that the flow develops a back-step flow pattern at the bench, forming distinct zones of high-speed and low-speed turbulence. As wind speed increases, the influence range of the high-speed and low-speed turbulent zones expands, and the time required for the flow field to become fully developed decreases. Concurrently, a structural vortex with a height matching the bench is formed. When the wind speed is constant, the blasting dust has a significant upward vertical movement and obvious horizontal movement in a short period of time, subsequently, the vertical upward movement becomes constrained while the horizontal travel distance increases substantially under the comprehensive influences of blasting impact, gravity and stable airflow. Within the wind speed range of 2.4 m/s to 4.4 m/s, the dust demonstrates prominent vertical movement within the first 4 seconds of blast and significant horizontal movement after 6 seconds of detonation. The vertical movement distance of the dust at the 4th second after blasting is about 30 m, and the horizontal movement distance of the dust at the 6th second after blasting is 22.6-23.5 m. Consequently, the optimal dust capture time is determined to be 4-6 s after blasting, and the optimal position of the water bag is determined to be 15-24 m from the bottom line of the bench slope, and the delayed excitation time of the water bag is Δt=T_f-T_s.

To systematically reveal the multi-scale response mechanism of surrounding rock structures under cyclic thermal disturbance, a comprehensive research system encompassing macroscopic physical property evolution, mechanical properties, microstructural damage, and mesoscopic crack simulation was established, taking dense sandstone from a mining area in Shanxi as the research object. The experimental results show that the peak strength of sandstone in its natural state is 85.00 MPa, which decreases to 40.50 MPa after eight 0 ℃ cooling cycles, representing a reduction of 52.53%. The strength of sandstone exhibits a negative exponential decay pattern with increasing number of cycles at different temperatures. The elastic modulus undergoes significant degradation during the first cycle, with a reduction reaching 38.8% under the 0 ℃ condition. After eight cycles, the overall reduction in elastic modulus ranges from 39.3% to 61.1% at different temperatures. The failure mode manifests as a composite characteristic of "crack convergence-local slippage-structural loosening." Scanning electron microscopy results reveal the evolution process of "intergranular bond breakage-microcrack propagation-skeleton disintegration" induced by thermal cycling. Mesoscopic simulation based on the discrete element method further captures the crack initiation sequence and tension-shear differentiation characteristics, quantitatively demonstrating the evolutionary path of structural damage progressing from the boundary to the core. The research findings elucidate the multi-scale rock failure process induced by thermal cycling from macroscopic to mesoscopic levels, providing a theoretical basis for predicting rock mass stability and conducting multi-field coupling simulations under mine geothermal development conditions.

To investigate the energy evolution law and damage mechanisms of salt rock under different loading and unloading rates, salt rock specimens obtained from a proposed salt cavern compressed air energy storage (CAES) facility in China were subjected to uniaxial cyclic loading and unloading tests at varying rates. Mechanical testing, acoustic emission (AE) monitoring and energy analysis methods were employed to explore the mechanical response and damage evolution process. The results indicate that under uniaxial cyclic loading and unloading conditions, the salt rock exhibits significant viscoelasticity, and its compressive strength decreases as the loading and unloading rate increases. With an increasing number of loading and unloading cycles, the input work continuously increases; the elastic energy first increases and then decreases, whereas the dissipated energy increases continuously and dominates in the later stages. Acoustic emission activity demonstrates a positive correlation with the stress level. Under low loading and unloading rates, the internal behavior of the salt rock is dominated by low-energy, high-frequency micro-fracturing activities. Conversely, under high loading and unloading rates, it exhibits macroscopic crack propagation characteristics characterized by high-energy and low-frequency. The damage evolution of the salt rock can be divided into three stages: an initial slow stage, a middle stable stage, and a final accelerated stage. Furthermore, a higher loading and unloading rate results in a greater degree of damage in the salt rock under the same stress level. The findings provide a theoretical basis for optimizing the injection-production rates and evaluating the long-term stability of salt cavern energy storage facilities.

The construction of green mines is the core focus of ecological civilization construction in the mining industry. Against the backdrop of comprehensively promoting green mine construction, exploring a replicable and scalable institutionalized management model holds significant theoretical and practical implications. By using a combination of institutional reference and analogical reasoning, the core connotation of the control system for the mining completion effect of non-metallic open-pit mines was analyzed, and the construction logic, implementation pathway, and institutional advantages of this control system were systematically analyzed. The research findings indicate that the mining completion effect control system fills the gap of missing intermediate supervisory levels within the vertical management transmission system of mineral resources. By orienting toward the ultimate state of ecological restoration and land reuse, it systematically guides the entire process of mining and restoration, driving the transformation of mine governance from post-treatment to source control. This marks a fundamental paradigm shift in China's mine governance from passive response to proactive planning. Drawing on the formulation, implementation, and management approaches of regulatory detailed planning in territorial spatial planning, this study innovatively proposes the establishment of a mining completion effect control plan specifically targeting the entire process of mining and restoration. This provides a management basis and technical support for natural resources authorities to guide the compilation of various mine-related plans. Adopting a "one mine, one policy" approach to study the mining completion effects of different types of nonmetallic open-pit mines in Jining City can effectively guide the scientific allocation of mining rights and orderly resource extraction, promote the transformation of mine resource asset values, coordinate the relationship between resource development and ecological protection, and strongly propel the high-quality development of green mine construction in Jining. The mining completion effect control system can provide theoretical reference and practical lessons for the nationwide advancement of green mine construction and contribute to building a beautiful China.

Problems including land occupation, soil degradation and environmental pollution are often triggered by massive coal gangue stockpiles generated alongside coal mining. Resource utilization approaches of coal gangue as potential soil amendments and materials for ecological restoration of mining areas were systematically discussed. Coal gangue is rich in silicon-aluminum minerals, organic matter and trace elements. After being modified by microbial and hydrothermal activation technologies, the bioavailability of key nutrients such as silicon, potassium and phosphorus in coal gangue is improved, and this treatment method has both environmental friendliness and low energy consumption. The research results indicate that the pore structure of degraded soil is significantly optimized, the water and fertilizer retention capacity is enhanced, the content of soil organic matter and fertility level are raised, and positive effects on soil carbon sequestration are achieved by activated coal gangue. In terms of ecological restoration of mining areas, modified coal gangue has been successfully applied in reclamation projects of subsidence areas, and artificial soil can be prepared with it to effectively support vegetation reconstruction and soil and water conservation. Effective approaches for the high-value and large-scale utilization of coal gangue are offered by applying modified coal gangue to soil improvement and mining ecosystem restoration, and important practical significance is also shown in advancing ecological reconstruction of mining areas, ameliorating degraded soil and facilitating the realization of the "Dual Carbon" goals.

Aiming at the problem of insufficient testing and verification of mining new-energy electric drive equipment under typical working conditions such as full-load start, ramp traction, load mutation and long downhill braking, an electric control system for a mining new-energy dynamometer was designed based on PLC and host computer collaborative control. The system realizes the functions of constant-speed operation, low-speed high-torque loading, load mutation simulation, VF separation and segmented control, braking reverse drag, and fault interlocking protection. Bench tests were carried out with motors of different power ratings, and field application verification was conducted in combination with a new-energy auxiliary transport equipment at a metal mine. The results show that when the system is in constant-speed operation, the steady-state speed error is within 0.44% and the speed fluctuation is within 2.11%. When the system is under low-speed high-torque loading, the deviation between the actual average torque and the set value is within 2.22%, and the torque fluctuation is within 6.7%. Under load mutation conditions, the maximum speed of the system is reduced to 8.6%, and under braking reverse drag conditions, the peak bus voltage is lower than the protection threshold. The protection tests for emergency stop, overspeed and overload achieved an accuracy rate of 100%, with a average emergency stop response time of 0.12 s. The engineering application results show that the system provides a new means for factory inspection, maintenance review, and operational diagnosis of new-energy electric drive systems for mines.

To enhance the accuracy and stability of fault prediction for mining excavator engines under high-intensity and complex working conditions, a fault prediction model based on the Grey Wolf Optimizer (GWO) optimized Support Vector Machine (SVM) was constructed. Key operating parameters of the Weichai WP14T mining excavator engine were collected during real-time operation. Data preprocessing was conducted using outlier detection and elimination as well as data standardization. Feature selection was performed using the Pearson correlation coefficient method to construct a sample set with a reasonable structure and complete feature information. The Grey Wolf Optimizer was employed to adaptively and globally optimize the penalty factor and kernel function parameters of the Support Vector Machine, thereby enhancing the model's nonlinear learning and generalization capabilities. Fault prediction experiments were designed, and comparative verification was carried out under various typical fault conditions. The results show that the GWO-SVM model exhibits higher accuracy and stronger robustness in multi-class fault prediction. The prediction accuracy on the test set reaches 92.8%, which is 9.9 percentage points higher than that of the traditional SVM, and it maintains stable output under conditions of few-sample interference and data fluctuation.

To establish a working performance prediction model for a vertical spiral stirred mill and determine reasonable model input independent variables, this study systematically investigates the key factors influencing the mill's working performance from the grinding mechanism perspective using a univariate analysis method. Firstly, a numerical simulation model of the spiral stirred mill was established using a coupled discrete element method (DEM) and computational fluid dynamics (CFD) approach. The dynamic characteristics of the grinding media and slurry during the stirring process were analyzed, and the reliability of the simulation model was verified. Subsequently, based on the validated numerical simulation model, the influence laws of process parameters (rotational speed, media filling rate, and slurry mass concentration) and spatial structural parameters (bottom clearance, side clearance, and helix angle) on the mill's working performance (collision energy, driving torque, and energy utilization rate) were systematically analyzed, clarifying the quantitative relationships between each factor and the grinding performance indicators. Finally, a sensitivity analysis method was employed to investigate the degree of influence of each factor on the performance indicators. The research results indicate that, under the premise of satisfying the grinding effect, optimal design values exist for both the mill's spatial dimension design and process parameter selection, which can achieve a balance point between grinding performance and energy utilization rate. Spatial structural parameters exert a stronger influence degree on the mill's energy utilization rate, collision energy, and driving torque compared to process parameters. Among them, the helix angle is the critical parameter affecting the grinding performance of the tower mill and can serve as the core input variable for the mill's performance prediction model.

To reveal the particle motion mechanisms and the effects of feed parameters on the moisture distribution within the filter cake, a three-dimensional dynamic filtration numerical model of a plate-and-frame filter press was constructed based on the Eulerian multiphase model and the multi-scale characteristics of coal slime particles. The motion processes of the particle and liquid phases, alongside the particle deposition process, were systematically investigated, and the impacts of coal slime mass fraction and filter chamber thickness on the moisture distribution of the filter cake were analyzed. The results indicate that fine particles (<0.1 mm) are dominated by fluid drag forces. During the initial filtration stage (0-120 s), streamline dispersion and particle bleed-through phenomena occur, forming a dense shell on the filter cloth surface with a volume fraction approaching 80%. Conversely, coarse particles (≥0.1 mm) are primarily governed by gravitational settling, aggregating in the middle and lower sections of the filter chamber to constitute a supporting skeleton. The feed mass fraction of the coal slime significantly impacts the filter cake structure. Under a low feed mass fraction (30%), fine and coarse particles undergo stratification, leading to a "sandwich" moisture accumulation in the middle and upper parts of the filter cake. At a mass fraction of 40%, a uniform particle distribution and efficient dewatering are achieved. When the coal slime mass fraction exceeds 50%, the weakened carrying capacity of the water flow causes particles to settle synchronously, resulting in an overall dense filter cake with dispersed moisture. Furthermore, when the filter chamber thickness exceeds 38 mm, the densification of the outer cake layer combined with insufficient internal drainage pressure collectively induces the "sandwich" phenomenon at the center of the filter cake, which shifts toward the chamber axis as the chamber thickness increases. The findings provide a theoretical basis for optimizing the process parameters of pressure filtration.

To address the problems of low detection accuracy and severe missed detections of small targets for hydraulic support protection plates caused by the complex underground environments of coal mines, an improved YOLOv8n detection algorithm (CMH-YOLO) was proposed. In the CMH-YOLO algorithm, a SPDConv space-to-depth convolution module was first introduced into the shallow network to encode spatial information into the channel dimension, thereby preserving the detailed features of small targets. Subsequently, a lightweight C2f_MSA multi-scale attention module was designed, which integrated the long-range dependency modeling of large-kernel separable attention with the adaptive feature selection of efficient channel attention. This enabled the shallow network to simultaneously capture protection plate features at various distances and scales. To tackle the blurred surface features and partial occlusion of protection plates caused by the highly dusty underground environment, a C2f_DRFE dust-robust feature enhancement module was constructed. This module expanded the receptive field through multi-scale dilated convolutions to acquire information from surrounding unconcluded regions, and worked in tandem with deformable convolutions to adaptively avoid dense dust areas. Meanwhile, a BiFPN bidirectional feature pyramid network was introduced to replace the traditional PAN-FPN, which further improved the detection performance of the model. Finally, the Inner-IoU loss function was adopted, utilizing an auxiliary bounding box mechanism to enhance bounding box regression accuracy. Experimental validation was conducted on a coal mine hydraulic support dataset containing 20 045 annotated images. The results show that the mAP@0.5, mAP@0.5:0.95, precision, and recall of CMH-YOLO reach 97.9%, 81.4%, 93.8% and 94.5%, respectively. Compared with the baseline model YOLOv8n, these metrics are improved by 1.7, 1.8, 2.7, 1.6 percentage points, respectively, successfully maintaining the lightweight nature of the model while enhancing detection accuracy. The proposed algorithm effectively resolves issues such as small target recognition, multi-scale adaptation, and dust interference in extreme environments. It provides a reliable real-time monitoring solution for the safety monitoring of coal mine hydraulic supports, which holds significant engineering value for preventing roof accidents and ensuring miner safety.

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.