Search Results (3,339)

Oversized rock fragments (boulders) produced during bench blasting adversely affect the efficiency of mining downstream processes such as loading, hauling, and crushing, thus leading to regularly requiring costly secondary breakage and the use of mechanized rock breakers. This study presents a probabilistic framework for forecasting boulder size in surface mining operations by employing Gaussian Process Regression (GPR), benchmarked against the Kuznetsov–Cunningham–Ouchterlony (KCO) empirical fragmentation model and a Multivariate Regression Analysis (MVRA) equation. The research study has analyzed blasting datasets, comprising Geological Strength Index (GSI), number of holes (NH), hole depth (HD), maximum charge per delay (MCPD), total explosive mass (TEM), and boulder size determined by Split-Desktop image analysis. Eight Gaussian Process Regression kernels—squared exponential, rational quadratic, matern with ν = 3/2, and matern with ν = 5/2, both with and without automatic relevance determination (ARD)—were assessed. The GPR model with the ARD matern 3/2 kernel attained superior validation performance of R² = 0.9016 and RMSE = 4.2482, outperforming the KCO and MVRA models, which displayed significant prediction errors for boulder size. In addition, the sensitivity analysis results demonstrated that GSI and HD were the most influential parameters on boulder size, followed by NH, MCPD, and TEM, accordingly. The findings indicate that GPR, especially when employing ARD matern kernels, precisely estimates the boulder size, and thus can serve as a viable method for optimizing blast design and facilitate efficient boulder management in surface mining operations. Full article

To address the limited environmental perception capability and the difficulty of achieving consistent and efficient representation of the workspace feasible domain caused by high dust concentration, uneven illumination, and enclosed spaces in underground coal mines, this paper proposes a digital spatial construction and representation method for underground environments by integrating RGB-D depth vision with ORB-SLAM3. First, a ChArUco calibration board with embedded ArUco markers is adopted to perform high-precision calibration of the RGB-D camera, improving the reliability of geometric parameters under weak-texture and non-uniform lighting conditions. On this basis, a “dense–sparse cooperative” OAK-DenseMapper Pro module is further developed; the module improves point-cloud generation using a mathematical projection model, and combines enhanced stereo matching with multi-stage depth filtering to achieve high-quality dense point-cloud reconstruction from RGB-D observations. The dense point cloud is then converted into a probabilistic octree occupancy map, where voxel-wise incremental updates are performed for observed space while unknown regions are retained, enabling a memory-efficient and scalable 3D feasible-space representation. Experiments are conducted in multiple representative coal-mine tunnel scenarios; compared with the original ORB-SLAM3, the number of points in dense mapping increases by approximately 38% on average; in trajectory evaluation on the TUM dataset, the root mean square error, mean error, and median error of the absolute pose error are reduced by 7.7%, 7.1%, and 10%, respectively; after converting the dense point cloud to an octree, the map memory footprint is only about 0.5% of the original point cloud, with a single conversion time of approximately 0.75 s. The experimental results demonstrate that, while ensuring accuracy, the proposed method achieves real-time, efficient, and consistent representation of the 3D feasible domain in complex underground environments, providing a reliable digital spatial foundation for path planning, safe obstacle avoidance, and autonomous operation. Full article

As a critical component of scraper conveyors, the middle trough operates under harsh conditions for extended periods, making it prone to failure and thus reducing the overall service life of the equipment. To address this issue and extend its service life, this study incorporated different amounts of CeO₂ into Cr₃C₂/Fe-based composite coatings. It investigated the effects of CeO₂ on the coating’s phase composition, microstructural evolution, wear resistance and corrosion resistance. Results show that CeO₂ addition did not alter the coating’s phase composition. The composition remained α-Fe, M₂₃C₆ (M: Fe, Cr) and vanadium carbides. However, CeO₂ promoted the transformation from columnar grains to equiaxed grains and refined the grains. With increasing CeO₂ content, the composite coating’s mechanical properties gradually improved. The Ce2 coating exhibited the highest microhardness (923.08 HV_0.5), the lowest friction coefficient (0.31) and the lowest wear rate (0.00217 mm³/N·m). Its dominant wear mechanisms were abrasive wear and mild adhesive wear. In 3.5% NaCl solution, the Ce2 coating showed the highest corrosion potential (−0.82 V) and the lowest corrosion current density (2.04 × 10⁻⁶ A/cm²), indicating excellent corrosion resistance. This study provides theoretical support for preparing high-performance Cr₃C₂/Fe-based composite coatings. It clarifies the key mechanism by which CeO₂ regulates coating properties. The developed composite coating has broad application potential due to its excellent combined wear and corrosion resistance. It can be widely used for surface strengthening of vulnerable components in mining machinery such as scraper conveyors, offering important theoretical and technical support for improving the service life of scraper conveyor middle troughs. Full article

The aim of the research is to assess the technical, environmental, and legal suitability of mining areas for dual purposes: both mineral exploitation and solar energy production. A comprehensive analysis of the Croatian legislative framework in the areas of mining, energy, spatial planning, and environmental protection was carried out. Spatial, environmental, and technical suitability were assessed using Geographic Information Systems (GIS) in combination with a Multi-Criteria Decision-Making Model (MCDM) applying the TOPSIS method. A total of 565 exploitation fields were analyzed based on twenty technical, environmental, and legal parameters. Three installation variants (Variant 1, Variant 2, Variant 3) correspond to different phases of mining operations, and three energy use models (Model 1, Model 2, Model 3) were proposed. The results confirm that exploitation fields represent a valuable but underutilized resource for the development of renewable energy sources, i.e., solar power plants installation. Full article

Methane (CH₄) is one of the most important greenhouse gases, and substantially impacts climate change. Over a 20-year period, its global warming potential (GWP) is approximately 80 times higher than that of carbon dioxide (CO₂). One of the significant sources of methane emissions is the hard coal mining industry, particularly regarding the release of methane with mine ventilation air. Methane released from coal seams during mining operations and discharged into the atmosphere through exhaust shafts is referred to as VAM (Ventilation Air Methane). In the context of the European Union’s climate policy, activities aimed at reducing and utilizing VAM emissions are gaining increasing importance. One initiative supporting the development of such solutions is the research project ProVAM (Reduction of Ventilation Air Methane Emissions in the Coal Mining Transformation Process), implemented by a consortium of scientific and industrial institutions from EU member states. The project focuses on developing guidelines and selecting technologies dedicated to the utilization of VAM. This article presents a methodology for assessing parameters associated with VAM emissions and provides a characterization of the selected mine exhaust shafts analyzed within the ProVAM project. Key technical factors affecting the feasibility of using oxidation technologies to reduce methane emissions from hard coal mining are identified. Full article

To address the technical challenge where traditional high-pressure, large-flow emulsion pump stations cannot adapt to the drastic flow rate changes in hydraulic supports due to the fixed displacement of their quantitative pumps—leading to frequent system unloading, severe impacts, and damage—this study proposes an intelligent flow control method based on the digital flow distribution principle for actively perceiving and matching support demands. Building on this method, a compact, electro-hydraulically separated prototype with stepless flow regulation was developed. The system integrates high-speed switching solenoid valves, a piston push rod, a plunger pump, sensors, and a controller. By monitoring piston position in real time, the controller employs an optimized combined regulation strategy that integrates adjustable duty cycles across single, dual, and multiple cycles. This dynamically adjusts the switching timing of the pilot solenoid valve, thereby precisely controlling the closure of the inlet valve. As a result, part of the fluid can return to the suction line during the compression phase, fundamentally achieving accurate and smooth matching between the pump output flow and support demand, while significantly reducing system fluctuations and impacts. This research adopts a combined approach of co-simulation and experimental validation to deeply investigate the dynamic coupling relationship between the piston’s extreme position and delayed valve closure. It further establishes a comprehensive dynamic coupling model covering the response of the pilot valve, actuator motion, and backflow control characteristics. By analyzing key parameters such as reset spring stiffness, piston cylinder diameter, and actuator load, the system reliability is optimized. Evaluation of the backflow strategy and delay phase verifies the effectiveness of the multi-mode composite regulation strategy based on digital displacement pump technology, which extends the effective flow range of the pump to 20–100% of its rated flow. Experimental results show that the system achieves a flow regulation range of 83% under load and 57% without load, with energy efficiency improved by 15–20% due to a significant reduction in overflow losses. Compared with traditional unloading methods, this approach demonstrates markedly higher control precision and stability, with substantial reductions in both flow root mean square error (53.4 L/min vs. 357.2 L/min) and fluctuation amplitude (±3.5 L/min vs. ±12.8 L/min). The system can intelligently respond to support conditions, providing high pressure with small flow during the lowering stage and low pressure with large flow during the lifting stage, effectively achieving on-demand and precise supply of dynamic flow and pressure. The proposed “demand feedforward–flow coordination” control architecture, the innovative electro-hydraulically separated structure, and the multi-cycle optimized regulation strategy collectively provide a practical and feasible solution for upgrading the fluid supply system in fully mechanized mining faces toward fast response, high energy efficiency, and intelligent operation. Full article

Mining conveyor belts are critical components in bulk material transportation, but their operational safety is frequently threatened by diverse damages such as blocks, cracks, foreign objects, and holes. Existing detection methods, including traditional computer vision and conventional neural networks, struggle to balance accuracy and efficiency in harsh mining environments—marked by high levels of dust, uneven lighting, and extreme scale variability (5–300 pixels). Our study proposes WTConv-YOLO, an improved model based on YOLOv11, integrating two core modules: (1) wavelet transform convolution (WTConv), which achieves a logarithmically expanding receptive field with linearly growing parameters, allowing for the concurrent capture of high-frequency local details and low-frequency global context; (2) Scale-based Dynamic Loss (SD Loss), which dynamically adjusts bounding box similarity and localization loss weights according to target scale, mitigating IoU fluctuation interference and enhancing small-target detection stability. Experiments on the Mining Industrial Conveyor Belt Dataset show that WTConv-YOLOv11 achieves a mean Average Precision (mAP@0.5) of 73.8%—a 3.5% improvement over the baseline YOLOv11. A Python-based software system is developed for end-to-end detection. This work provides a practical solution for reliable conveyor belt damage detection in mining scenarios. Full article

To address the complex multi-objective optimization problem of “cost–risk–recovery–dilution” in sublevel caving without bottom pillars under uncertainty, this study develops an operational GOOSE-based framework (OGOOSE) integrated with robust ε-constraint modeling. Methodologically, OGOOSE adopts three synergistic mechanisms: Opposition-Based Learning (OBL) for enhanced initial solution quality and spatial coverage symmetry, an Adaptive Inertia Weight (AIW) mechanism to maintain a symmetrical balance between exploration and exploitation, and a Boundary Reflection Mechanism (BRM) to ensure engineering feasibility. For modeling, an “ellipsoid-plane” geometric surrogate is employed, where the ellipsoid’s structural symmetry serves as the ideal baseline, while the Mean-CVaR criterion quantifies the asymmetry of operational risk (negative tail) under uncertainty. Taking robust cost (C) as the primary objective, the four-objective problem is decomposed via the ϵ-constraint method to enforce a balanced Pareto trade-off. Results demonstrate that OGOOSE significantly outperforms GOOSE, WOA, and HHO on CEC2017 benchmarks, achieving the lowest Friedman rank. In the engineering case study, it attains an average dilution rate of 28.95% (the lowest among comparators) without increasing unit cost or compromising recovery, demonstrating stable operational symmetry across economic and quality indicators. Sensitivity analysis of the ε-thresholds identifies an optimal “knee-point” that establishes a manageable balance between risk control (εR) and dilution limits (εP). OGOOSE effectively balances accuracy, stability, and interpretability, providing a robust tool for stabilizing complex mining systems against inherent operational asymmetry. Full article

To enhance intelligent decision-making for tunneling operations in complex geological conditions, this study proposes a high-precision prediction method for TBM cutterhead torque using engineering data from the west return-air roadway of the Shoushan No. 1 Mine in Pingdingshan, Henan (China). A multisource dataset integrating geological exploration data, TBM electro-hydraulic parameters, and surrounding rock–TBM interaction indicators was constructed and preprocessed through outlier removal, interpolation restoration, and Savitzky–Golay filtering to extract high-quality steady-state features. To capture the mechanical properties of composite strata, the equivalent strength parameter of composite strata and an integrity-classification index were introduced as key predictors. Based on these inputs, a hybrid TCN–BiLSTM–Logarithmic Attention model was developed to jointly extract local temporal patterns, model global dependencies, and emphasize critical operating responses. Testing results show that the proposed model consistently outperforms TCN, BiLSTM, and TCN-BiLSTM baselines under intact, transitional, and fractured rock conditions. It achieves an RMSE (19.85) and MAPE (3.72%) in intact strata, while in fractured strata RMSE (29.55) and MAPE (10.82%) are reduced by 23.5% and 22.7% relative to TCN. Performance in transitional strata is likewise superior. Overall, the TCN–BiLSTM–Logarithmic Attention model demonstrates the highest prediction accuracy across intact, transitional, and fractured strata; effectively captures the mechanical characteristics of composite formations; and achieves robust and high-precision prediction of TBM cutterhead torque in complex geological environments. Full article

Cemented tailings backfill (CTB) is widely used in mining operations due to its operational simplicity, reliable performance, and environmental benefits. However, the poor consolidation of fine tailings with ordinary Portland cement (OPC) remains a critical challenge, leading to excessive backfill costs. This study addresses the utilization of modified manganese slag (MMS) as a supplementary cementitious material (SCM) for fine tailings from an iron mine in Anhui, China. Sodium silicate (Na₂SiO₃) modification coupled with melt-water quenching was implemented to activate the pozzolanic reactivity of manganese slag (MS) through glassy structure alteration. The MMS underwent comprehensive characterization via physicochemical analysis, X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) to elucidate its physicochemical attributes, mineralogical composition, and glassy phase architecture. The unconfined compressive strength (UCS) of the CTB samples prepared with MMS, OPC, tailings, and water (T-MMS) was systematically evaluated at curing ages of 7, 28, and 60 days. The results demonstrate that MMS predominantly consists of SiO₂, Al₂O₃, CaO, and MnO, exhibiting a high specific surface area and extensive vitrification. Na₂SiO₃ modification induced depolymerization of the highly polymerized Q⁴ network into less-polymerized Q² chain structures, thereby enhancing the pozzolanic reactivity of MMS. This structural depolymerization facilitated formation of stable gel products with low calcium–silicon ratios, conferring upon the T-MMS10 sample a 60-day strength of 3.85 MPa, representing a 94.4% enhancement over the T-OPC. Scanning electron microscopy–energy dispersive spectroscopy (SEM-EDS) analysis revealed that Na₂SiO₃ modification precipitated extensive calcium silicate hydrate (C-S-H) gel formation and pore refinement, forming a dense networked framework that superseded the porous microstructure of the control sample. Additionally, the elevated zeta potential for T-MMS10 engendered electrostatic repulsion, while the aluminosilicate gel provided imparted lubrication, collectively improving the flowability of the composite slurry exhibiting a 26.40 cm slump, which satisfies the requirements for pipeline transportation in backfill operations. Full article

Controllable shock wave (CSW) technology offers a promising approach for improving roof cavability and safety in underground mining, yet its field-scale mechanisms remain insufficiently clarified. This study develops and validates an optimized CSW pre-cracking procedure for hard top coal at the Liuxiang Coal Mine. A series of CSW-induced fracturing experiments were conducted across multiple boreholes under real operating conditions, and the causal relationships between loading parameters, induced fracture propagation, and mining performance were systematically evaluated. Segmented water injection leak detection was used to quantify fracture development in the No. 3 coal seam. The results demonstrate that CSW significantly enhances top-coal cavability: the proportion of large coal blocks was reduced by approximately 25%, and the average roof pressure step distance decreased from the baseline of 16.12–20.03 m to 13.12–13.82 m. These improvements indicate more efficient energy release, a more stable roof structure, and safer working conditions. Overall, this study provides a technically verified and operationally optimized CSW procedure, highlighting its strong potential to support safer and more sustainable hard top-coal mining. Full article

Accurate detection of pressure-relief boreholes is crucial for evaluating drilling quality and monitoring safety in coal mine roadways. Nevertheless, the highly challenging underground environment—characterized by insufficient lighting, severe dust and water mist disturbances, and frequent occlusions—poses substantial difficulties for current object detection approaches, particularly in identifying small-scale and low-visibility targets. To effectively tackle these issues, a lightweight and robust detection framework, referred to as YOLO-DFBL, is developed using the YOLOv11n architecture. The proposed approach incorporates a DualConv-based lightweight convolution module to optimize the efficiency of feature extraction, a Frequency Spectrum Dynamic Aggregation (FSDA) module for noise-robust enhancement, and a Biformer (Bi-level Routing Transformer)-based routing attention mechanism for improved long-range dependency modeling. In addition, a Lightweight Shared Convolution Head (LSCH) is incorporated to effectively decrease the overall model complexity. Experimental results on a real coal mine roadway dataset demonstrate that YOLO-DFBL achieves an mAP@50:95 of 78.9%, with a compact model size of 1.94 M parameters, a computational complexity of 4.7 GFLOPs, and an inference speed of 157.3 FPS, demonstrating superior accuracy–efficiency trade-offs compared with representative lightweight YOLO variants and classical detectors. Field experiments under challenging low-illumination and occlusion environments confirm the robustness of the proposed approach in real mining scenarios. The developed method enables reliable visual perception for underground drilling equipment and facilitates safer and more intelligent operations in coal mine engineering. Full article

Waste deposits from the Iberian Pyrite Belt that are rich in pyrite are a valuable secondary resource for getting back sulphide minerals and important metals that go with them. This study assessed the efficacy of a Multi-Gravity Separator (MGS) in concentrating pyrite and related polymetallic minerals from sulphide waste material sourced from the Alonso mining district (Huelva, Spain). Bench-scale MGS tests were done on two particle size fractions (−500 µm and −50 µm) to see how the speed of the drum rotation, the angle of the tilt, and the flow rate of the wash water affected the separation efficiency. Mineral Liberation Analysis (MLA) showed that both size fractions had about 65.8 wt% pyrite, but the −50 µm fraction was much more liberated. Under the best operating conditions, the MGS was able to recover about 58% of the pyrite from the −500 µm fraction and about 64% from the −50 µm fraction. The mass recoveries were about 38% and 42%, respectively. There was also a better recovery of related metals like Co, Cu, Zn, and Mn, especially for the finer fraction. This shows the improvement of the liberation and stratification behaviour. The results show that MGS is a good way to pre-concentrate fine-grained pyrite-rich waste. The performance is heavily influenced by the size distribution of the particles and the operating parameters. These results suggest that improvements in gravity separation may offer a long-term pathway for the recycling of sulphide mine waste within a circular economy. Full article

In the mining industry, where equipment breakdowns cause expensive unplanned downtime, operational continuity is paramount. Internet of Things (IoT) technologies have the potential to make predictions; however, most solutions lack a holistic view and mapping of complex system interdependencies. This study presents a comprehensive predictive maintenance (PdM) framework specifically designed for continuous-operation mining environments, with a primary focus on Semi-Autogenous Grinding (SAG) mills. By combining exploratory data analysis, advanced feature engineering, classical machine learning (Gradient Boosting Classifier), and deep learning (LSTM with multiple time-window configurations), the system achieves real-time anomaly detection, root-cause explanation, and failure forecasting up to 48 h in advance (average lead time: 17 h). A four-layer digital twin architecture integrated with Streamlit enables actionable alerts classified as emergency, planned, or preventive interventions. Applied to a one-year dataset comprising 99,854 hourly records from an industrial SAG mill, the hybrid model prevented an estimated 219.5 h of unplanned downtime, yielding substantial economic benefits. The proposed solution is deliberately designed for high adaptability across multiple equipment types and industrial sectors beyond mining. Full article

Routine chemical assays, which are more readily available than direct mineralogical analyses, offer a rapid and cost-efficient approach of estimating mineral grades for geometallurgical modelling. This paper addresses the prediction of ore minerology from chemical assays for lithium-bearing pegmatites by implementing and comparing two element-to-mineral conversion (EMC) approaches: (1) mass balance techniques using two calculation variants and (2) machine learning methods (MLM). Both routines of the mass balance approach achieved satisfactory R² values exceeding 0.8, although calculation routine 1 was unable to automatically differentiate between the two lithium-bearing phases (spodumene and cookeite). Of the eight algorithms applied for the MLM approach, the top three performing models achieved R² values greater than 0.6 for both training and testing datasets, with slightly lower error evaluation metrics compared to the mass balance approach. Based on data accuracy requirements across the Mine Value Chain, the mass balance approach is suitable for the feasibility and operational stages, while the MLM approach meets the minimum data accuracy requirements of the scoping and pre-feasibility stages. However, it should be noted that the mass balance approach is limited to deposits with simple mineral assemblages while the MLM approach can handle deposits with greater elemental overlap among minerals. Full article