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.