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.

Aiming at the high temperature-pollution coupling problem caused by complex diagonal ventilation in the large-scale mining of dense stope groups of deep well in metal mines, a four-dimensional analysis framework of "orthogonal test and control variable method, Fluent numerical simulation, Ventsim ventilation solution and multiple linear regression" was constructed, which revealed the key role of the selective closing of the wind door in the cross-cut in the reconstruction of the wind path. The results show that the importance of the influence of various factors on the temperature field of the ore drawing roadway is as follows: the surrounding rock temperature>the inlet wind velocity of the 2^# cross-cut>the inlet wind temperature>the inlet wind velocity of the 0^# cross-cut. The optimization scheme of closing the wind door of 0^# cross-cut roadway and constructing 2^# inlet and 0^# outlet through the wind path is proposed. The scheme increases the wind volume of the ore drawing roadway from 10.8 m³/s to 30.7 m³/s, increases the regional wind speed from 0.1 m/s to 0.6 m/s, and reduces the average temperature in the roadway to 26.03 ℃. The optimization scheme effectively solves the problem of local crosswind, improves the efficiency of pollutant discharge, and improves the underground working environment. It provides a theoretical basis and engineering practice example for the regulation of "precise minimally invasive surgery" for the ventilation system of dense stope groups in similar deep wells.

Roadways are prone to deformation and failure under the influence of high in-situ stress as mining progresses to deep underground areas. Cracks in shotcrete, mesh fragmentation and weathering spalling of surrounding rock occur in the heavy haulage roadway of a crushing chamber in an ultra-deep metal mine. In-situ stress and rock mechanical parameters were measured by the hydraulic fracturing method and rock mechanics tests. Deformation characteristics of the roadway were analyzed via deformation monitoring, borehole photography and ground pressure monitoring, and the failure mechanism of surrounding rock was explored based on monitoring data. A combined support scheme was proposed, consisting of 30 mm thick initial shotcreting, 5 m long anchor cable for active support, steel mesh, U29 steel arch for passive support and 120 mm thick secondary shotcreting. Engineering practice shows that the bearing capacity of the surrounding rock is enhanced after reinforcement, no deformation and failure are observed, and the surrounding rock maintains a relatively stable state.

Aiming at the problems that the traditional engineering analogy method is easily affected by subjective evaluation, and that the simple numerical simulation method involves complex modeling and heavy workload in the optimization of stope structure parameters, an artificial intelligence stope parameter optimization method based on DBO-MLP-SVM is proposed. The Dung Beetle Optimizer (DBO) was used to optimize the performance of Multilayer Perceptron (MLP) and the Support Vector Machine (SVM). The two were fused to overcome the defect that MLP is easy to fall into a local optimum, and the relevant mine stope parameters and stope stability classification data were collected to train the proposed model. Taking a limestone mine in southwest China as an example, the stability classification under different pillar widths was obtained by substituting the model, and the stope displacement, pillar deformation and surface settlement were analyzed and verified by FLAC3D numerical simulation. The results show that the DBO-MLP-SVM model predicts that the stope is stable when the pillar width is greater than 8 m, and the optimal stope structure parameter is pillar width×spacing = 8 m×8 m, which is consistent with the numerical simulation results. Compared with the traditional engineering analogy method, the production capacity of the stope after intelligent collaborative optimization increased by 51.80%, and the loss rate and dilution rate changed little, which effectively balances production efficiency and safety. This method provides an efficient and accurate technical approach for the optimization of stope structure parameters, which can serve as a reference for the optimization of mine stope parameters.

The extraction of overlain resources shall be conducted on the premise of service shaft safety. Taking an iron mine in Anhui Province as the research background, the disturbance influence exerted by the extraction of overlain resources on No.2 service shaft was studied. Mine geological surveys and analyses on physical and mechanical parameters of rock mass were carried out. Two mining schemes were determined by means of the empirical analogy method. Numerical simulations of the two mining schemes were performed via multi-software collaboration. The stability of the service shaft after the extraction of overlain resources was analyzed, and the feasible scheme was selected. Finally, the stress state around the service shaft under horizontal stress disturbance was analyzed by adopting the stress concentration principle of circular holes in thin plates. The research results show that both the two-step mining and skip-one-mine-one schemes for overlain resources are feasible. From the perspective of improving resource utilization, the scheme of mining rooms in the first step and extracting pillars in the second step with timely filling is preferred. The stress around the service shaft induced by mining disturbance is lower than twice the in-situ stress, and the shaft remains in a stable state. The research results provide technical support for the safe extraction of overlain resources around service shafts.

After a certain iron mine in Henan Province was mined by sublevel caving method, due to the lack of forced caving measures, the thickness of the upper overburden strata became insufficient as the stope advanced to depth, resulting in the collapse of the goaf roof and the remaining point pillars from the original room-and-pillar method in the -40 m sublevel, which seriously threatened the safe mining of the stope. Using Mathews stability graph method and numerical simulation, the stability of the goaf was analyzed and studied. It was found that there was a large area of caving risk in the roof of the goaf. Therefore, it was suggested that the -72 m sublevel stop using the caving mining method, that the area above -72 m should be sealed off, and the area above -90 m should be mined by the filling mining method instead. Considering the mining conditions of ore body and the production capacity requirements of the mine, three technically feasible mining schemes were proposed: the sublevel open stoping with subsequent backfill method, the panel mechanized upward horizontal cut-and-fill stoping method, and the upward horizontal cut-and-fill stoping method perpendicular to the orebody strike. Taking into account the technical and economic indicators and mining safety, it was recommended that the mine adopt the panel mechanized upward horizontal cut-and-fill stoping method. After the mine transitioned from the caving mining method to the filling mining method, the ore dilution rate decreased to 10%, the loss rate decreased to 8%, and the development-to-production ratio increased to 7.47 m/kt. The annual output met the mine's design and production requirements, and within the remaining service life, the mine economy benefit could increase by 49.476 7 million yuan.

Ore and rock are distributed alternately in Las Bambas Copper Mine, with varying hardness and diverse lithology across zones. Single blasting parameters are hardly applicable to the complex geological conditions. To achieve proper ore fragmentation and reduce waste rock dilution during blasting, research was conducted on ore-rock separation controlled blasting technology and fragmentation prediction for open-pit bench blasting. Rock mass blastability was classified based on the Rock Mass Index (RMI). In-situ retention blasting and cast blasting were proposed to distinguish between ore and rock, thereby achieving zoned accumulation of broken materials. Schemes of multi-point initiation with differentiated borehole pattern parameters and dense empty hole blasting were designed. Blasting data of Ferrobamba mining area were used for training based on XGBoost model, and ore blasting fragmentation of 25 sample groups was predicted and verified through field tests. The results show that with the multi-point initiation scheme using differentiated borehole pattern parameters, a hole depth of 16.5 m, borehole pattern parameters of 9.0 m×8.0 m for rock area and 6.5 m×5.5 m for ore area, distinct ore-rock boundary grooves are formed after blasting, and ore and rock accumulate separately. The characteristic parameter D₈₀ of blasting fragmentation distribution is 127 mm, while the predicted D₈₀ value is 124 mm with an error of only about 2.36%. The proportion of fragments sized smaller than 25.6 mm reaches 36.7%, which satisfies the requirements of the concentrator.

To address the prevalent engineering challenges of safety production monitoring and early warning in non-coal mines, such as the fragmentation of multi-source heterogeneous data, lagging risk identification, and insufficient regulatory collaboration, a "One Map" system for safety production early warning in non-coal mines based on multi-source data fusion technology was constructed. Relying on core technologies including multi-source data fusion, real-time status monitoring, and safety risk four-color map visualization, the system designed a dual-track architecture that couples government supervision terminal and enterprise supervision terminal collaboratively. The government terminal focused on macro-level compliance supervision and emergency linkage command, while the enterprise terminal emphasized micro-level closed-loop rectification of risks and hidden dangers. Through a data sharing and collaboration mechanism, a new integrated closed-loop supervision model was formed. The engineering application results of this "One Map" collaborative early warning system in three mining enterprises in Lu'an City show that the system improves the safety supervision efficiency at the government terminal by approximately 22% and achieves a risk assessment accuracy of over 90%. At the enterprise supervision terminal, the hidden danger handling time is reduced by 30.5%, and the accident rate is decreased by 15%. The data utilization rate at the multi-source data collaborative application terminal is increased by 20%. The research provides an effective technical implementation path for intelligent safety supervision in non-coal mines and holds reference value for promoting the digital transformation of the industry.

Intelligent early warning of mine safety risk is the key technical means to ensure mine safety production. In the face of challenges such as complex mining environment, strong multi-factor coupling of disasters, and large amount of multi-source heterogeneous data, systematic research was carried out on the typical application scenarios and fusion path of mathematical methods. Starting from three typical application scenarios: dynamic prediction of disaster risk, real-time perception and intelligent control of human-machine-environment total factors, and emergency decision-making optimization of accident chain traceability and simulation, the evolution rules of mine safety early warning technology "from single to multiple, from traditional to intelligent" "from discrete monitoring to closed-loop contro" and "from experience-driven to intelligent assistance" were sorted out. On this basis, the mathematical methods supporting the above application scenarios were summarized, including basic statistics and spatio-temporal analysis, machine learning and deep learning, complex system modeling, mechanism-data hybrid driving model and digital twin model. The collaborative framework of application scenarios and mathematical methods was constructed, and the selection and combination of mathematical methods in different application scenarios were clarified. The research results can provide technical reference and method support for the transformation of mine safety risk early warning to "active prevention and control".

Aiming at the stability control of surrounding rock in gently inclined orebody stopes and safety risks of surface waste dumps under the coupling effect of surface load and mining disturbance, mechanical parameters of key rock strata were measured via indoor rock mechanics tests. Combined with physical similarity simulation adopting Digital Image Correlation (DIC) technology and three-dimensional numerical simulation, fracture evolution features as well as response laws of stress and displacement fields of stope surrounding rock under dual disturbance were systematically analyzed. Test results show that mining disturbance induces stress redistribution in surrounding rock. Superimposed surface load increases the maximum vertical displacement of stope roof directly beneath the waste dump by 12.86%, and a subsidence center consistent with the theoretical boundary of mining pressure relief arch forms on the surface. Tensile stress concentrates in the roof, floor and upper surrounding rock of the stope, while compressive stress concentrates in inter-level roof and floor pillars. Backfill mining weakens stress concentration and deformation transmission, reducing the compressive stress on inter-level roof and floor pillars by 61.67% and roof tensile stress by 10%-15%. It effectively alleviates the unloading effect and improves the stability of stope-surface system. Field monitoring verifies the effectiveness of the adopted stability control measures, providing a scientific basis for safe mining of similar surface structure-stope coupling systems.

The distribution law of in-situ stress at working faces is crucial for the safe production of metal mines. Aiming at the complex problem of in-situ stress distribution at working faces under fault structure conditions in a spodumene ore body, the stress distribution law was explored by combining numerical simulation and field measurement. Based on measured data, the initial state of underground engineering under the real in-situ stress field was simulated using the rapid stress boundary method. The results show that stress concentration exists at the upper part of the hanging wall and the lower part of the footwall of each fault, and stress distribution at other positions presents obvious hierarchical characteristics. The inversion data of numerical simulation show that abrupt changes occur in stress contours on both sides of the faults. Specifically, the stress rises from 7 MPa to 20 MPa in areas with high fault density. The measured stress values are consistent with numerical simulation results in fault-free zones. It indicates that the rapid stress boundary method can accurately reflect the actual stress state of underground rock mass and help compensate for the limitation that measuring points must avoid adverse structural planes such as faults.

To address the frequent occurrence of surrounding rock instability such as roof fall and rib spalling in roadways with weak interlayers at a working face of Wengfu Phosphate Mine, field investigations and FLAC3D numerical simulations were conducted, and the surrounding rock control effects of conventional bolt-mesh-shotcrete support and the composite support technology of "bolt-mesh-shotcrete+steel arch+advanced support" were systematically compared. The research results show that the weak interlayer contains 3.9% kaolinite, and expansion and softening after water immersion are the main causes of surrounding rock deterioration. The vertical displacement of the roadway is reduced from -8.33 mm to -6.67 mm by the composite support technology, with a decrease of 19.93%. The horizontal displacements in X and Y directions are decreased from 2.61 mm and -1.64 mm to 1.27 mm and -0.58 mm, with reductions of 51.34% and 64.63% respectively. The stress concentration is remarkably alleviated, and the total number of failure units is lowered by 13.82%. It is shown by field application that the average monthly accident rate is reduced from 0.6 times to 0.27 times, with a decrease of 55.0%. The large deformation of surrounding rock under multi-thin interbed conditions is effectively controlled by the composite support technology. A theoretical basis and key technical paradigm are provided for the safe excavation of roadways containing dense weak interlayers.

The engineering disturbance in the process of mining often leads to a variety of fractures in the rock, which affects its mechanical properties. Using indoor uniaxial compression test, combined with XTDIC and acoustic emission monitoring system, the effects of fracture position, fracture dip angle and rock combination on the mechanical behavior and failure mode of layered composite rock samples with single fracture were investigated. The results show that with the increase of fracture dip angle, the peak stress of fracture rock samples gradually increases from 13.19-25.34 MPa to 33.89-42.99 MPa, the elastic modulus shows a non-monotonic change trend of increasing first and then decreasing, the deterioration coefficient gradually decreases from 0.32-0.66 to 0.06-0.20, and the strength deterioration effect of fracture on sandstone is more significant than that of limestone. The fracture position dominates the evolution of the failure mode. When the fracture is located in the upper part of the rock sample, the tensile failure is dominant. When the fracture is located in the middle of the rock sample, the failure mode is regulated by the fracture dip angle. Under the low fracture dip angle, the tensile failure is still dominant, and under the high fracture dip angle, it is transformed into tensile-shear mixed failure. The analysis of acoustic emission parameters (RA/AF value) shows that when the fracture dip angle is low, the priority cracks are mainly tensile cracks, accounting for 81.69%-88.41%. With the increase of fracture dip angle, the proportion of shear cracks increases, and the failure mode gradually transitions from tensile failure to multi-tensile and less-shear failure, and finally forms a tensile-shear mixed failure with tensile cracks accounting for 56.33%-62.73%.

To address the problem that low-energy acoustic emission (AE) signals account for an excessively high proportion during the fracture process of water-soaked coal-rock, high-energy critical information to be easily masked, resulting in insufficient accuracy in failure precursor identification, synchronous uniaxial compression and acoustic emission tests were conducted on coal-rock specimens under natural conditions and after 120 h of water-soaked. Based on the energy contribution rate and damage stress, a novel AE signal screening method was developed to extract dominant signals during coal-rock failure and investigate their precursory characteristics. The results show that water-soaked significantly decreases the uniaxial compressive strength of coal-rock. Compared with specimens under natural conditions, the average uniaxial compressive strength of water-soaked coal-rock decreases by 14.79%. Cracks exhibite multidirectional propagation behavior, thereby aggravating the final failure of the coal-rock. The cumulative AE count of coal-rock specimens under natural conditions reach 4×10⁶, while that of water-soaked specimens decrease to 2×10⁶. Moreover, the evolution of the cumulative AE count shows good agreement with the stress-strain behavior of the coal-rock. The AE dominant signal extraction method based on the energy contribution rate and damage-stress time window effectively identifies dominant signals during the failure process of water-soaked coal-rock. Compared with coal-rock under natural conditions, the extracted dominant signals exhibite lower energy thresholds but higher signal proportions. Furthermore, the extracted dominant signals show strong correspondence with the failure precursors of water-soaked coal-rock. These findings provide a theoretical basis for engineering stability prediction.

To study the adsorption and diffusion laws of CH₄ and CO₂ in the coal molecular structure model, the adsorption and diffusion processes of CH₄ and CO₂ in the coal molecular structure model under different temperatures, pressures and moisture contents were analyzed using the grand canonical ensemble Monte Carlo method and molecular dynamics simulation method. The results show that with increasing temperature or moisture content, the adsorption capacity of CH₄ and CO₂ decreases to a certain extent, while the adsorption and diffusion capacity of CO₂ is better than that of CH₄. With increasing pressure, the adsorption capacity of the two gases increases, but it tends to saturate, exhibiting typical saturated adsorption characteristics. The Langmuir fitting results show that the increase of temperature or moisture content leads to the decrease of adsorption parameters a and b, and the parameter value of CO₂ is always higher than that of CH₄, indicating that CO₂ and coal show higher adsorption affinity. The increase of temperature can significantly enhance the kinetic energy of the two gas molecules and reduce their local enrichment in the coal molecular structure model, thus improving the diffusion performance. The mean square displacement of CO₂ at each temperature is greater than that of CH₄, indicating that the diffusion ability of CO₂ is stronger and the spatial distribution is more uniform. With increasing moisture content, the average mass density of the two gases in the coal molecular structure model decreases. Although the presence of water molecules does not change the overall trend of the gas adsorption curve, the adsorption saturation point of the gas is significantly reduced. The high moisture content condition (6.55%) will significantly inhibit the gas diffusion behavior. Water molecules fill the pores and interact with coal molecules, resulting in restricted gas diffusion paths, weakening the degree of gas migration, and limiting the gas diffusion rate. The diffusion coefficients of CH₄ and CO₂ decrease to 0.063×10^-8 m²/s and 1.33×10^-8 m²/s, respectively. The research results provide a theoretical basis for improving coalbed methane recovery rate and optimizing CO₂ sequestration strategy.

As a key equipment in the underground coal mining transportation system, the scraper conveyor's bearings operate for extended periods under extreme conditions such as high temperature, high impact loads, dust pollution, and continuous operation, resulting in a high risk of failure. Existing fault diagnosis methods still face numerous challenges in feature extraction and classification accuracy when dealing with complex working conditions and multiple failure modes. To address this, a bearing fault diagnosis method based on a dual-branch convolutional neural network was proposed. Firstly, one-dimensional vibration signals were converted into two-dimensional time-frequency images using continuous wavelet transform to enhance the time-frequency feature representation capability of the input data. Subsequently, a dual-branch convolutional neural network architecture was constructed, in which one branch employed a dilated convolutional residual module to strengthen local feature extraction, while the other branch introduced a global attention mechanism to optimize global feature learning, and feature fusion was applied to improve the model's diagnostic capability. Finally, experimental validation was conducted on the CWRU and PU bearing datasets. The results show that the proposed method can achieve fault diagnosis accuracy rates of 99.78% and 97.94% on the two datasets, respectively, and outperforms comparison models such as DCRB-CNN, GAM-CNN and GAM-ResNet in overall performance, demonstrating significant diagnostic capability and strong generalization ability. Moreover, bearing fault diagnosis experiments were carried out using real-world coal mine field data, further validating the method's practicality and potential for engineering deployment under strong noise and non-stationary operating conditions. The research results provide an efficient and reliable deep learning solution for the intelligent fault diagnosis of complex coal mining equipment such as scraper conveyors.

To address the issues of low efficiency and insufficient accuracy in traditional mining equipment anomaly detection, a mining equipment anomaly detection model integrating bidirectional long short memory network and an improved subtraction average optimizer was proposed. Attention mechanism was introduced to achieve dynamic weighting of key features. Convolutional neural network was combined to extract local temporal features, and guided regularized random forest was employed to perform feature selection, thereby reducing data dimensionality and suppressing noise interference. The improved subtraction average optimizer introduced chaotic mapping initialization and golden-sine step-size adaptive adjustment strategy, which significantly enhanced the convergence speed and training stability of the model. The results show that on the mining equipment multisensor dataset and the mining machinery fault simulation dataset, the accuracy of the proposed model reaches 94.17% and 95.48%, respectively, and its comprehensive performance surpasses that of existing mainstream detection models. In ablation experiments, when all key modules are integrated, the accuracy, precision, recall, and F₁ score of the model reach 98.25%, 97.62%, 98.05% and 97.84%, respectively. In field tests on key equipment such as belt conveyors and crushers at a large open-pit mine, the detection accuracy of the model increases to 97.32%, with an average detection time of only 0.39 s. This indicates that the model can detect abnormal states of mining equipment efficiently and accurately, providing reliable technical support for ensuring mining production safety.

To address the problems that existing foreign object detection algorithms for underground mine conveyor belts mainly model cross-channel dependencies through channel dimensionality reduction, resulting in high computational overhead and limited detection accuracy, a foreign object detection method based on attention-scale fusion and cross-spatial learning, termed ES-YOLO, was proposed. Firstly, an Efficient Multi-Scale Attention (EMA) module was introduced into the backbone network. By reshaping channels into the batch dimension and grouping channel dimensions into multiple sub-features, spatial semantic features were evenly distributed across different feature groups. Subsequently, a Scale Sequence Feature Fusion (SSFF) module was introduced to improve the network's capability for multi-scale feature extraction. By horizontally stacking feature maps at different scales and applying 3D convolution to capture scale-sequence features, the module effectively integrated high-dimensional semantic information from deep feature maps with detailed information from shallow feature maps. Furthermore, a Dimension-Aware Selective Integration (DASI) module was introduced to adaptively assign weights to high-dimensional and low-dimensional feature maps, thereby improving the model's ability to detect small objects. Experimental results demonstrate that, compared with the baseline YOLOv8n model, the proposed method increases the mean average precision by 1.5 percentage points to 89.7%. The class-wise mean average precision for large ore blocks improve by 4.1 percentage points, while the model parameters are reduced by 18%. Compared with mainstream models, the proposed method has significant advantages in detection accuracy and lightweight, making it more suitable for real-time foreign object detection in underground conveyor belts in mines.

Aiming at the problems of insufficient accuracy and weak environmental adaptability of traditional coal quantity detection devices due to the dynamic change of coal volume and the harsh working conditions such as high dust and high water mist in coal mines, a coal quantity detection method based on LiDAR and visual image fusion for extreme working conditions was proposed. Firstly, the joint calibration of LiDAR and visual camera was carried out, the mapping relationship was established and the coordinate system was unified. Then, the sampling data was obtained by a specific strategy, and the coal region was identified by Poisson reconstruction algorithm, spatial filtering strategy and structural similarity index. Finally, the depth image completion network was used to repair the depth map, and the coal amount was calculated based on the repair results. The results show that the maximum detection error of the fusion method of LiDAR and visual camera is only 2.90%, and the error decreases further with the increase of coal volume. Compared with mainstream depth completion algorithms such as ENet, the proposed algorithm achieves the best core accuracy index and meets the industrial real-time requirements. This method is basically consistent with the fluctuation of the detection curve of the contact electronic belt scale, which verifies its reliability. This method effectively overcomes the limitations of a single sensor, significantly improves the accuracy of coal quantity detection, and can operate stably in harsh environments. It provides a new technical solution for the accurate detection of coal quantity of belt conveyor and holds great significance for promoting the intelligent and efficient production of coal mines.

Aiming at the problems of poor scheduling scheme, low utilization rate and high cost of unmanned trucks in large open-pit coal mines, an optimization method based on digital twin simulation was proposed, and a digital twin system framework for unmanned trucks was constructed. The digital twin simulation model was built by AnyLogic software to realize the real-time mapping of physical and virtual space. A mathematical model aiming at minimizing transportation cost, minimizing total transportation time and maximizing truck utilization was established, and a Quantum Enhanced Particle Swarm Optimization (QEPSO) algorithm was designed to solve the model. The verification results of an open-pit coal mine in Xinjiang show that compared with the traditional particle swarm optimization algorithm, the improved particle swarm optimization algorithm and the genetic algorithm, the maximum completion time of the QEPSO algorithm is reduced by 5.84%-11.34%, the total transportation cost is reduced by 5.12%-10.49%, and the truck utilization rate is increased by 4.32%-9.14%, with a faster convergence speed and better stability. The digital twin scheduling scheme shortens the task completion time by 73 min compared with the traditional scheme, effectively improves the transportation efficiency. The study provides a feasible technical path and method support for the intelligent scheduling of unmanned trucks in open-pit coal mines.

Production scheduling of underground metal mines is confronted with severe challenges caused by multi-source uncertainties, such as geological conditions, equipment status, and safety risks, while traditional deterministic scheduling models struggle to guarantee system stability and operational efficiency. To address this issue, a Monte Carlo simulation-based scheduling optimization model(MC-MOO) for underground metal mines was proposed. Firstly, probability distribution models were established for multi-source uncertainty factors. Subsequently, Monte Carlo scenario simulation was integrated with the NSGA-Ⅱ multi-objective optimization algorithm to develop a closed-loop framework of "probabilistic modeling-scenario simulation-multi-objective optimization", enabling the comprehensive optimization of scheduling cost, production stability, and equipment load balancing. The case study results show that compared with the traditional deterministic scheduling models, the total scheduling cost of the MC-MOO model is reduced by 5.97%, the average monthly output is increased by 16.53%, the standard deviation of output is reduced by 41.82%, and the equipment load balance is increased by 23.61%. It provides an effective modeling and solving paradigm for mine intelligent scheduling under uncertain environment.

Based on the practical engineering background of separation of drilling and blasting operations in an iron mine, research was carried out on prominent problems induced by the separation of drilling and blasting operations, including difficulties in production organization and coordination, lagged inter-process information transmission, information silos in blasting operations, frequent operational risks, and high production costs. Using blasting accident case analysis and fault-tree-based risk assessment method for blasting operations, an intelligent management and control scheme for blasting operations in multiple working faces was established. Based on the Vue+Django framework, an intelligent blasting operation management and control system featuring real-time interaction, process task allocation, precise management of blasting materials, and multi-level personnel control was designed and developed. Results show that, under weak-signal conditions, the system maintains an information transmission delay of approximately 250 ms, a packet loss rate of 0.3%, and a reconnection time of less than 3 s. Under a concurrency level of 1 000 users, the system maintains an average response time of less than 1 s and a transaction success rate exceeding 98%. Following field application, the risk frequency of blasting operations is significantly reduced, considerable costs are saved, and blasting efficiency is improved, thereby effectively solving the coordination problems between drilling and blasting operations. The obtained results provide a feasible technical approach for the intelligent upgrading of mine blasting operations.

Point cloud data of mine roadways acquired by three-dimensional laser scanning often contain noise interference. Existing filtering methods struggle to balance point cloud data simplification with the accurate preservation of roadway geometric features. In contrast, adaptive filtering can dynamically adjust filtering parameters based on the characteristics of the original data, thereby providing a high-quality data foundation for subsequent roadway deformation monitoring. An adaptive progressive point cloud filtering algorithm was proposed, which integrated an improved adaptive voxel filter and a layer-constrained dynamic radius filter to achieve precise point cloud denoising. Specifically, the improved voxel filter can adaptively adjust voxel grid sizes according to the geometric complexity of the roadway, effectively simplifying the point cloud while better preserving its geometric features. The layered constraint dynamic radius filter optimized the dynamic radius parameters through a layered constraint model, enabling the accurate elimination of residual noise. Experimental results show that, compared with traditional voxel filtering, the improved voxel filter, and their conventional combinations, the proposed algorithm achieves a point cloud simplification rate exceeding 92% while controlling the change rates of point cloud volume and surface area within 3%. It can provide reliable data preprocessing technical support for mine roadway deformation monitoring.

To address the frequent accidents caused by uneven illumination, complex backgrounds, and human rule-breaking operations in underground coal mines, a personnel behavior recognition method based on the fusion and optimization of improved Retinex and YOLOv5 was proposed. Firstly, an improved multi-scale Retinex algorithm was employed to enhance underground images. Through multi-scale fusion and color restoration techniques, the interference of low illumination, coal dust, and water mist on the images was eliminated, thereby improving image clarity and color fidelity. Subsequently, the enhanced image data were input into YOLOv5 network to construct a target detection model based on the fusion and optimization of Retinex and YOLOv5, which was adapted to detection scenarios with dense equipment and complex backgrounds in underground mines. The experimental results show that the average accuracy of this method for recognizing helmet-wearing behavior and on-site operational violation postures of underground personnel reaches over 95% and 85%, respectively. This method can achieve the precise identification and judgment of rule-breaking behavior by underground personnel, providing technical support for intelligent safety supervision in coal mines.

Based on the tailings-free mine concept, beneficiation and comprehensive utilization of tailings resources were studied for a magmatic hydrothermal gold mine in Hunan. A tailings-free mine technical system was established, adopting the process of gold recovery by a gravity-flotation combined process, quartz comprehensive recovery from gold tailings, and full-volume backfill of residual tailings. The technical route is as follows: gold is efficiently recovered via a vertical centrifuge gravity-flotation combined process, and a gold concentrate with an Au grade of 81.69 g/t and a recovery rate of 97.06% is obtained. Quartz is recovered from gold tailings by the desliming-high intensity magnetic separation-reverse flotation process, and a quartz concentrate with an SiO₂ grade of 98.86% and a recovery rate of 65.37% is obtained. The residual tailings with a yield of 38.23% are all applied to underground backfill. The process innovation effectively solves the problem of tailings disposal, realizes full-component resource utilization of ore and zero tailings discharge, and provides a technical reference for the green development of similar mines.

Aiming at the difficulty of foam size extraction in flotation process, a flotation foam size extraction method based on improved YOLOv11n-seg was proposed. Firstly, the Cross-Scale Feature Fusion Module (CCFM) was introduced to optimize the neck structure and enhance the adaptability of the model to multi-scale foam and the ability of small target detection. Secondly, the Mixed Local Channel Attention (MLCA) mechanism was embedded, and the channel information, spatial information and local information were integrated to improve the expression ability of the network. Finally, the Unified-IoU loss function was introduced to enable the model to dynamically shift attention from the low-quality prediction box to the high-quality prediction box, balancing training speed and detection accuracy. The experimental results show that compared with the traditional watershed algorithm and Mask R-CNN based on deep learning, the proposed method has significant advantages in segmentation accuracy and real-time performance. Compared with the benchmark model YOLOv11n-seg, the optimized model improves the accuracy of mAP@ 0.5 and mAP@ 0.5∶0.95 by 1.5% and 5.3%, respectively, the recall rate increases by 5.1%, the amount of computation decreases by 11.8%, and the number of parameters decreases by 32.1%. This method has a faster detection speed and higher detection accuracy, which can meet the needs of accurate extraction of flotation foam size.

To achieve the efficient exploitation of refractory bismuth-bearing polymetallic ores and the comprehensive recovery of bismuth resources, the technical challenges associated with high-carbon and high-calcium bismuth middlings were addressed, including strong interference from high-carbon and high-calcium gangue, a complex flotation pulp environment, and the inhibition of bismuthinite floatability caused by residual reagents from prior separation stages. An innovative combined impurity removal process of "reagent removal pretreatment-flotation bismuth extraction-acid leaching purification" was proposed. Process mineralogy analysis and bismuth recovery experiments were systematically conducted, and the process parameters were systematically optimized. The research results show that after conditioning with hydrochloric acid and reagent removal treatment with activated carbon, the high-carbon and high-calcium bismuth middlings undergo flotation bismuth extraction using sodium silicate and lime as depressants, butyl xanthate as the collector, and No.2 oil as the frother. Subsequently, acid leaching is performed with hydrochloric acid at a mass concentration of 18% under conditions of a leaching temperature of 30 ℃ and a liquid-solid ratio of 2∶1 for 8 min. Ultimately, a qualified bismuth concentrate with a yield of 17.07%, a bismuth grade of 19.91%, and a bismuth recovery of 74.75% can be obtained. This process precisely overcomes the key technical bottleneck in the recovery of high-carbon and high-calcium bismuth middlings. The reagent removal pretreatment with activated carbon accurately eliminates the inhibitory effect of residual reagents. Subsequently, the flotation process achieves efficient separation and enrichment of bismuth minerals from gangue minerals such as calcium carbonate. Finally, acid leaching realizes the selective removal of residual impurities, thereby significantly enhancing the quality of the bismuth concentrate and ultimately achieving efficient impurity removal and purification of these high-carbon and high-calcium bismuth middlings.

In a medium-grade siliceous-calcareous phosphate ore from the Yichang region of Hubei Province, the fluorapatite is the target mineral and potash feldspar, dolomite, and quartz are the main gangue minerals, and the P₂O₅ grade of the raw ore is 23.27%. To enhance its utilization efficiency of the ore, a double reverse flotation process was adopted, and systematic optimization tests were conducted on grinding fineness, dosages of collectors (H-1, KM-6), and auxiliary reagents (phosphoric acid, HF-959). The flotation mechanism was revealed through measurements of contact angle, Zeta potential, and infrared spectroscopy. The results show that under the grinding fineness of 75 μm accounting for 81%, a closed-circuit flotation flowsheet consisting of one roughing and two sweeping for desilication followed by one roughing and one sweeping for demineralization, a high-quality phosphate concentrate with P₂O₅ grade of 32.20% and recovery rate of 81.20% can be obtained. The Al₂O₃ and MgO contents in the concentrate are reduced to 2.02% (a decrease of 53.35%) and 0.62% (a decrease of 60.26%), respectively, effectively achieving the separation of apatite from potash feldspar, dolomite, and other gangue minerals. Research indicates that the collector H-1 primarily adsorbs physically on the surface of potash feldspar, selectively enhancing its hydrophobicity, which is the core mechanism for the efficient removal of potash feldspar.

To elucidate the electrochemical mechanism of the interaction between butyl xanthate (KBX) and gold minerals under alkaline conditions, and to provide theoretical support for the optimization of alkaline flotation processes for gold-bearing sulfide ores, unidirectional voltammetry, Tafel polarization curve method, and galvanostatic step technique were employed to systematically investigate the interfacial electrochemical behavior of a gold electrode in butyl xanthate solution at pH=9.18. The results indicate that the oxidation reaction of KBX on the gold electrode exhibits mixed electrochemical-mass transfer control characteristics. The oxidation product is the dimer X₂, and the forward reaction rate constant is 1.524 cm/s. The interaction between the gold electrode and the KBX system intensifies with increasing collector concentration. The chemisorption of butyl xanthate on the gold surface constitutes the critical rate-controlling step of the reaction. Process parameters such as grinding fineness and stirring intensity significantly affect the interaction efficiency between the collector and the mineral. The exchange current density for the reduction on the gold electrode surface, measured by the galvanostatic step technique, is 0.075 A/m², which is in high agreement with the corrosion current density obtained from the Tafel method, thereby verifying the reliability of the results.

As global mining is transforming towards green and sustainable development, open-pit mines are generally confronted with a severe challenge that existing tailings storage facilities encroach on the stope boundary during slope expansion and deepening. To address the problem of "tailings encroachment", a new integrated model for full-cycle tailings management featuring "hydraulic-mechanical collaboration and multi-objective optimization of resources, space and safety" was proposed based on a tailings management project of a copper-cobalt mine in Africa. A theoretical framework integrating "objective coordination, technical coordination and management coordination" was established by this model. Its core lies in adopting the spatiotemporal coupling technology of hydraulic mining with large cutter suction dredgers and mechanical excavation, so as to realize efficient recovery of tailings resources and safe release of stope space simultaneously. An integrated "space-air-ground" monitoring network was deployed in the project, and a proactive control strategy of "phased water impoundment and dynamic early warning" was innovatively adopted. The safety of operations adjacent to the high and steep stope slope is guaranteed by these measures. During construction, the maximum horizontal displacement of the north-side seepage cut-off dam is maintained at 67.4 mm. Practice shows that approximately 8 320 kt of tailings are treated and over 4.2×10⁶ m³ of stope space are released by this model. Distinct economic, safety and environmental benefits are achieved, and a replicable and systematic technical solution is provided for this common problem in the mining industry.

The mining of building material quarries generally suffers from problems such as the use of high-quality resources for low-end applications and the indiscriminate mixing of superior and inferior materials, resulting in inconsistent product quality, diminished value, and severe resource waste. To address this, taking granite as the research object, a multi-dimensional comprehensive evaluation system for the optimal use of granite was constructed. This system comprised four first-level indicators, namely appearance quality/chemical performance, physical and mechanical properties, economic benefits, and ecological benefits, along with 19 second-level indicators. Concurrently, a decision model for the optimal use of granite was established, integrating multi-dimensional factors including physical and chemical properties, economic benefits, and environmental impact. To verify the practicability and reliability of the model, Yunfu granite, Luotian granite, and Golden Ma granite were selected as case studies. The results determined that the optimal uses for Yunfu granite, Luotian granite, and Golden Ma granite are natural stone for plazas (roads or pavements), natural stone slabs for furniture, and pebbles and crushed stone for construction, respectively. Further comparative analyses of the calculation results were conducted using the VIKOR method and the grey correlation degree method, which confirmed the feasibility of the AHP-EWM-TOPSIS decision model. Meanwhile, sensitivity analysis verified that the proposed AHP-EWM-TOPSIS decision model possesses good ranking stability. The research results can provide a scientific basis for the optimal use planning of granite resources. The constructed decision model and the developed decision analysis tools have broad application prospects and can effectively support the optimized allocation of granite products.

Under the background of ecological civilization development, mine ecological restoration has evolved from single-purpose environmental remediation toward the transformation of ecological products value. Focusing on Jiangxi Province, a typical southern hilly and mountainous region, a multi-case comparative analysis method was adopted to systematically examine the synergistic pathways between mine ecological restoration and ecological product value realization. Representative abandoned mine restoration projects in Jiangxi Province were selected to comparatively analyze policy instrument application, market-oriented operational mechanisms, multi-stakeholder collaboration modes, and value realization performance, thereby revealing both common characteristics and differences among synergistic pathways. The results indicate that Jiangxi Province has transformed abandoned mines into ecological products such as cultivated land, carbon sinks, and cultural tourism resources through innovative "ecological restoration+" diversified models, while establishing a full-chain value realization mechanism integrating restoration, transformation and value appreciation. Policy support, market-oriented operation, and multi-stakeholder collaboration constitute the three core elements underpinning the efficient functioning of this mechanism. However, constraints related to funding and technology, the absence of clear responsibility and accountability mechanisms, immature market systems, and insufficient policy coordination remain the major obstacles. The obtained results provide replicable and transferable practical pathways for promoting the coordinated enhancement of mine ecological restoration and ecological product value realization in southern hilly and mountainous regions.

To effectively trigger miners' proactive behavior and solve the problem of insufficient internal safety motivation of miners caused by the overemphasis on punishment rather than appreciation and the overemphasis on control rather than communication in mine safety management, and based on cognitive-affective system theory and self-determination theory, a moderated chain mediation model was constructed by introducing in-group identification and family-like exchange as mediating variables and face consciousness as moderating variables. Based on the questionnaire survey, 510 valid data points were collected, and the empirical research method was used to test the hypothesis of the model. The results show that workplace appreciation has a promoting effect on miners' proactive behavior, and the direct effect value is 0.134. In-group identification and family-like exchange play an independent mediating role and a chain mediating role between workplace appreciation and miners' proactive behavior, respectively. Face consciousness positively moderates the relationship between workplace appreciation and in-group identification and family-like exchange, and further positively moderates the indirect mediating effect of in-group identification and family-like exchange. The research results can provide a reference for the implementation of "people-oriented" safety appreciation management in mining enterprises.