Mining, Volume 6, Issue 1 (March 2026) – 20 articles

Background: Bucket-wheel excavators are critical assets in surface mining operations, where structural modifications to increase productivity must be validated through rigorous stress analysis to ensure operational safety. Following modification of an ERc 1400-30/7 excavator’s bucket wheel from 18 to 20 buckets, increased operational loads necessitated experimental verification of structural integrity. Methods: A custom 10-channel strain-gauge data acquisition system with 0–10 kHz bandwidth measured stresses in cable anchoring lugs and H-type diagonal members under operational conditions at the Jilț lignite mine, Romania. Measurements were performed during both left and right bucket-wheel rotation. Finite element analysis validated experimental results. Results: Maximum equivalent stresses of 210.0 MPa and 167.1 MPa were measured in the left and right anchoring lugs, respectively, during left bucket-wheel rotation, representing 59% and 47% of material yield strength with safety factors of 1.69 and 2.12. Significant load asymmetry was observed, with left rotation inducing 220–284% higher stresses than right rotation. FEA validation showed <15% agreement with measurements. Dynamic stress amplification of 15–32% above quasi-static values was attributed to bucket–soil interaction and structural vibration. Conclusions: Despite increased operational loads, measured stresses remain below yield strength, confirming structural adequacy. Both anchoring lugs require prioritized monitoring due to elevated stress levels and load asymmetry. The validated methodology provides a framework for post-modification verification of large mining equipment. Full article

Measure-While-Drilling (MWD) data provide real-time insight into subsurface conditions and drilling performance, yet their complexity and operational noise often hinder reliable modeling. This study demonstrates the role of Exploratory Data Analysis (EDA) in developing robust machine learning (ML) models for lithology classification and penetration rate (PR) prediction in mining operations. A structured EDA workflow—comprising data integrity assessment, feature distribution analysis, correlation mapping, and depth-wise parameter profiling—was implemented to identify redundant attributes, isolate non-productive intervals, and enhance dataset consistency. Through EDA-informed normalization and feature selection, data consistency and model performance were significantly improved. Machine learning algorithms, including Decision Tree, Random Forest, and Multi-Layer Perceptron, were trained on the refined dataset. The Random Forest Classifier achieved 98.45% accuracy in lithology prediction, while the Random Forest Regressor produced the most accurate PR estimation (R² = 0.83, RMSE = 0.52). These results highlight EDA as a critical foundation for constructing physics-informed, data-driven models that enhance predictive reliability and operational efficiency in mining environments. Full article

Geometallurgical modeling is pivotal for optimizing mining projects, yet the selection of an appropriate modeling strategy often relies on empirical experience rather than a systematic methodology. This paper introduces a novel systems-theoretic framework that formalizes geometallurgical modeling as an information acquisition problem under cost and uncertainty constraints. We propose a taxonomy of four fundamental strategies (S₀–S₃) defined by their use of direct measurement, interpolation, and regression to populate the key target variable geometallurgical ore type in a spatial block model. A generalized decision algorithm is developed to select the optimal strategy by evaluating economic feasibility and predictive accuracy against system characteristics such as deposit complexity, cost structure, and internal variable correlations. The framework demonstrates that the proxy-based strategy (S₂) generally offers the most robust balance between cost and accuracy for complex deposits. This work provides a scalable and generalizable approach applicable not only to geometallurgy but also to other domains involving spatial resource characterization under uncertainty, such as environmental monitoring and petroleum engineering. Full article

The mining industry still faces major environmental and socioeconomic problems as a result of tailings dam failures, which highlights the urgent need for improved monitoring and early-warning systems. This research offers practical recommendations for improved monitoring and safer design practices, in addition to investigating the use of digital image processing (DIP) as a non-invasive technique for tracking slope deformation in tailings dam models subjected to incremental pore water pressure increases. To replicate real-world conditions as closely as possible, a scaled laboratory embankment was built using coarse and fine tailings. During controlled pore-pressure loading, more than 500 high-resolution photos were taken, recording the entire deformation sequence from initial displacement to slope failure. The images were processed using Mathematica to generate pixel-by-pixel displacement fields and vector plots, providing a detailed visualization of deformation mechanisms. The findings demonstrated that DIP accurately detects and measures surface displacement, revealing the mechanisms, direction, and intensity of deformation. This study illustrates the extensive potential of DIP for real-time monitoring by directly connecting slope instability triggered by incremental pore water pressure with visual indications of slope deformation. While the results confirm the strong potential of DIP for deformation monitoring with a minimum detectable displacement of approximately 1.0 mm under controlled laboratory conditions, its field application may be affected by scale effects, variable lighting, and environmental occlusion. The mining industry benefits greatly from the insights gained through in-depth image analysis, which promotes safer tailings dam design and management. Overall, DIP can provide a reliable, scalable foundation for real-time deformation monitoring in operational tailings dams, where continuous image-based measurements can help identify early signs of instability and support proactive risk management. Full article

The marble extractive industry heavily depends on diesel-powered equipment, particularly wheel loaders and excavators used for block handling, resulting in high energy consumption and operating costs. In this study, the potential for fuel reduction through managerial and technological transitions was evaluated using the example of the marble quarry located in the Carrara basin. The energy demand of excavators, wheel loaders, and dumpers was characterized using telemetry data gathered through an Industry 4.0 methodology. A standard elementary cycle was modeled via the program evaluation and review technique (PERT) to map productive tasks and idling periods. To ensure comparability, a specific consumption coefficient (SCC) was defined. Subsequently, a novel fuel handling index (FHI) is proposed to prioritize investments by accounting for the uncertainties and production variables typical of quarry projects. Results demonstrate that while idle management offers a 4% fuel reduction, transitioning to hybrid wheel loaders represents a more significant strategy, achieving a 12% saving among the scenarios analyzed. The full-hybrid scenario leads to a cumulative 17% reduction. This framework supports decision-making for energy efficiency in high-yield extraction sectors, mitigating the economic risk associated with technological transitions. Full article

Hydraulic fracturing is a widely used technique in the oil and gas industry and, specifically, it is used in mining for fragmentation enhancement and rockburst risk mitigation. The technique is actively being applied to cave mining environments to induce caving and improve seismic response in deep high-strength rock masses. The method has great potential in Long Hole Open Stoping mines for large-scale stress management in high-risk environments. The use of hydraulic fracturing in deep mining was explored through the development of a conceptual design for the destressing of a mining pillar. Numerical modeling was conducted to understand the effects hydraulic fracture has on stress reduction, and how fractured geometries affect these results. The results of this analysis showed that there is a strong dependence on the geometry of hydraulic fractures on the stress reduction potential of the method. The developed conceptual design showed that hydraulic fracturing can be directly integrated into mine planning as a tool to strategically manage the hazards associated with highly stress pillars. The activities associated with treatment design directly identifies when treatment should occur in the mining sequence and provides a general assessment of risk reduction that can be used directly for operational decision-making. Full article

Close-distance multi-seam mining frequently induces secondary surface deformation and subsidence. Extracting a lower coal seam beneath an existing goaf repeatedly disturbs the overburden, often leading to roof collapse and the expansion of vertical water-conducting fractures that connect the working face to aquifers. Furthermore, the overlying goaf increases the risk of water inrush into active lower workings. This study investigates the mechanisms of strata reactivation and fracturing within an overlying goaf during lower seam extraction at a mine in Northwest China. Using theoretical analysis, numerical simulation, and microseismic monitoring, the research examines the secondary fracture mechanisms of the goaf roof and the resulting water-inrush potential. Research Findings: Strata Instability: Analysis of the key sandstone strata indicates that subsidence (W) of the key rock blocks satisfies 3.17 < W₁ = 4.61 m < 18 m for the lower seam and 3.17 m < W₂ = 5.31 m < 69.6 m for the 3-1# seam. These values confirm that key rock blocks in the basic roof undergo “reactivated” instability following fracture during lower seam mining. Pressure Relief and Fluid Dynamics: Mining-induced fracture initiation and propagation trigger strata reactivation. As the distance to the center of the goaf decreases, the subsidence of the overburden increases, ultimately resulting in a “trapezoidal” bending deformation pattern. Due to secondary activation, the roof subsidence 30 m above the 221 coal seam increased from 1.89 m to 5.475 m. The layers of high-strength, medium-grained sandstone and siltstone overlying the 317 coal seam and beneath the 221 goaf serve as high-strength material for the overlying rock formations. This suppresses the development of the caving zone and fracture zone, leading to subsidence failing to reach the sum of the heights of the two coal seams (6.8 m) and only reaching a value of 5.475 m. During extraction, the stress field undergoes a distinct evolution: it transitions from an initial “regular triangular” pressure-relief zone into a tripartite “weak–strong–strong” distribution. Furthermore, fluid discharge in the overlapping zone between the 317 working face and the 221 goaf increased sequentially, displaying an “alternating” pattern of peak vector variations as the face advanced. Microseismic Activity: Monitoring within the 300–500 m range identified frequent low-energy events and high-magnitude events (10⁴ J, 10⁵ J). These findings demonstrate that secondary excavation directly impacts the aquifer, creating a significant water-inrush hazard for the active working face. Full article

The study presents the development of a backfill composite based on technogenic waste with controlled volumetric stability, ensuring complete filling of underground voids while maintaining high strength performance. The formulation incorporates beneficiation and metallurgical wastes, as well as activators, foaming agents, and reinforcing fibers. A comprehensive analysis of strength, pore structure, and fracturing was performed using CT-scanning, 3D reconstruction, and fractal analysis. It was established that fibers of different nature exert multidirectional effects on porosity and strength, with basalt fiber contributing to the formation of a hierarchically stable structure. The results obtained confirm the feasibility of producing an environmentally efficient backfill material for safe mineral resource extraction. Full article

The rupture of the B-I dam at the Córrego do Feijão mine in Brumadinho, Minas Gerais, Brazil, on 25 January 2019, prompted the implementation of environmental remediation actions. Among these actions is the need for groundwater quality monitoring in the Feijão Pit (“Cava de Feijão”) area due to the disposal of tailings from dams B-I, B-IV, and B-IVA at this site. In order to assess potential impacts on groundwater, the determination of baseline values for elements of interest was proposed for ten monitoring wells installed in and around the pit, with monitoring results from 2019 to 2024, totaling 854 samples. Due to the lack of hydrochemistry data and local hydrogeological complexity of the existing aquifers within the context of the Iron Quadrangle (IQ), it was necessary to evaluate and determine individual baseline values for each monitoring well, assessing data variability and population distribution. For this purpose, the 95–95 Upper Tolerance Limit (UTL) method was applied to establish baseline values providing a robust statistical approach that encompasses 95% of observations with a 95% confidence interval as it is a widely used standard in statistics due to its practical balance between confidence and precision. This methodology proved effective and has potential for application in groundwater monitoring in areas that may present high compositional variability due to the chemical heterogeneity of the groundwater. The baseline values obtained for the main elements of interest, which are iron (Fe) and manganese (Mn), were consistent with findings from previous studies conducted in the hydrogeological units of the study area, also demonstrating that the adopted methodology was effective in identifying representative concentrations for the region. Full article

Mining operations conducted beneath water-bearing strata pose significant risks associated with the development of water-conducting fracture zones in the overburden. The height criterion for this parameter is critical to ensuring the stability of underground mine workings and preventing the risk of water inrush incidents. The research is based on physical and numerical simulations and aims to forecast the development of the water-conducting fracture zone. The methodology is based on in situ hydrogeology data, geotechnical boreholes, physical 2D modeling of rock strata, discrete element modeling using UDEC, and finite–discrete element modeling using Prorock software. A physical model of layered rock mass is constructed to simulate unfilled excavation areas induced deformation under real polymetallic ore field conditions. Based on the results, relationships between vertical subsidence, layer curvature, inclination, and the height of the water-conducting fracture zone were obtained. Particular attention is given to the effects of tectonic discontinuities, chamber geometry, and backfilling on fracture development. A stepwise excavation sequence is simulated to reproduce field conditions and assess the evolution of stress and deformation fields in the overburden. The study reveals that the propagation of the fracture zone around a mine excavation adheres to a polynomial law, characterized by an increase in height concurrent with the expansion of the excavation. This approach enables the design of safe extraction strategies beneath aquifers or surface water bodies. The proposed framework is expected to enhance prediction accuracy and reduce uncertainties. Full article

The effects of key variables on weak-acid-dissociable (WAD) and free cyanide oxidation by ozone injection in gold mine pulp were studied at laboratory scale to find an alternative cyanide treatment. A fractional factorial analysis of five process variables (O₃/O₂ flow, reaction time, NH₄HSO₃ concentration, temperature, and pH) informed a 60-run experimental matrix, in a 1 L cylindrical reactor, with the process variables controlled during the ozone injection. The findings may inform future strategies for safer cyanide management in gold mining processes. Free cyanide is the most toxic form of cyanide. Its oxidation increases with higher O₃/O₂ concentrations, longer exposure time, and higher pH. Maintaining a pH above 7 is crucial. Lower pH values favor the dissociation of cyanide into its toxic, free form. WAD cyanide oxidation depends mainly on the O₃/O₂ concentration, exposure time, and NH₄HSO₃ concentration. Increasing O₃/O₂ and time enhanced both WAD and free cyanide oxidation, while NH₄HSO₃ concentration affected oxidation rates differently. The results show that free cyanide was significantly more oxidized (84.1413%) than WAD cyanide (67.2423%). Controlling the WAD cyanide process yields excellent free cyanide oxidation. This represents ongoing improvement at an industrial scale. This approach quantifies the extent to which process variables affect the WAD and free cyanide oxidation under controlled conditions, thereby greatly reducing environmental impact. 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

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

Water wastage management (WWM) in deep-level mines remains a critical challenge, as wastage increases operational costs and threatens sustainability. This study presents a systematic state-of-the-art review of WWM in deep-level mines. Relevant literature was critically assessed to establish current practices, identify limitations, and explore emerging solutions. Five principal approaches were identified: leak detection and repair, pressure control with fixed schedules, network optimisation, accountability measures, and smart management. While each provides benefits, significant challenges persist. Particularly, current pressure control techniques, essential for limiting leakage, rely on static demand profiles that cannot accommodate the stochastic nature of service water demand, often resulting in over- or under-supply. Smart management systems, which have proven effective for managing stochastic utilities in other industries, present a promising alternative. Enabling technologies such as sensors, automated valves, and tracking systems are already widely deployed in mining, underscoring the technical feasibility of such systems. However, no studies have yet examined their development for WWM in deep-level mines. This study recommends a framework for smart water management tailored to mining conditions and highlights three opportunities: developing real-time demand approximation methods, leveraging occupancy data for demand estimation, and integrating these models with mine water supply control infrastructure for implementation and evaluation. Full article

To address the severe geological hazards (e.g., high ground stress and rock burst) that threaten safety and efficiency in ultra-deep mining, this study develops a comprehensive microseismic monitoring system tailored for the Sishanling Iron Mine—a typical ultra-large-scale, ultra-deep mine with an extraction depth exceeding 1500 m. The system integrates high-sensitivity sensors, real-time data transmission, and intelligent processing algorithms. A scientifically designed sensor deployment plan achieves full-coverage of key mining areas, while a multi-level data processing framework encompassing signal acquisition, event detection, location inversion, and magnitude calculation enhances result accuracy. Applied in actual operations, the system effectively captures microseismic events with magnitudes from −2.14 to −1.96, achieving optimal planar and spatial positioning errors of 6.75 m and 9.66 m, respectively. It provides real-time early warning for hazards like rock burst, thereby mitigating risks and ensuring operational continuity. This work offers a practical reference for constructing microseismic systems in similar “double super” mines and enriches the theoretical and technical framework for safety monitoring in deep mining. Full article

Assessing the stress–strain state around interacting mining excavations using the finite element method (FEM) is computationally expensive for parametric studies. This study evaluates tabular machine-learning surrogate models for the rapid prediction of full stress–strain fields in fractured rock masses treated as an equivalent continuum. A dataset of 1000 parametric FEM simulations using the elastoplastic generalized Hoek–Brown constitutive model was generated to train Random Forest, LightGBM, CatBoost, and Multilayer Perceptron (MLP) models based on geometric features. The results show that the best models achieve R² scores of 0.96–0.97 for stress components and 0.99 for total displacements. LightGBM and CatBoost provide the optimal balance between accuracy and computational cost, offering speed-ups of 15 to 70 times compared to FEM. While Random Forest yields slightly higher accuracy, it is resource-intensive. Conversely, MLP is the fastest but less accurate. These findings demonstrate that data-driven surrogates can effectively replace repeated FEM simulations, enabling efficient parametric analysis and intelligent design optimization for mine workings. Full article

While multi-view visualization systems are widely used for mining vehicle teleoperation, they often impose high cognitive load and restrict operator attention. To explore a more efficient alternative, this study evaluated a merged-view interface that integrates multiple camera perspectives into a single coherent display. In a controlled experiment, 35 participants navigated a teleoperated robot along a 50 m lab-scale path representative of an underground mine under both multi-view and merged-view conditions. Task performance and eye-tracking data—including completion time, path adherence, and speed-limit violations—were collected for comparison. The merged-view system enabled 6% faster completion times, 21% higher path adherence, and 28% fewer speed-limit violations. Eye-tracking metrics indicated more efficient and distributed attention: blink rate decreased by 29%, fixation duration shortened by 18%, saccade amplitude increased by 11%, and normalized gaze-transition entropy rose by 14%, reflecting broader and more adaptive scanning. NASA-TLX scores further showed a 27% reduction in perceived workload. Regression-based sensitivity analysis revealed that gaze entropy was the strongest predictor of efficiency in the multi-view condition, while fixation duration dominated under merged-view visualization. For path adherence, blink rate was most influential in the multi-view setup, whereas fixation duration became key in merged-view operation. Overall, the results indicated that merged-view visualization improved visual attention distribution and reduced cognitive tunneling indicators in a controlled laboratory teleoperation task, offering early-stage, interface-level insights motivated by mining-relevant teleoperation challenges. Full article

Optimizing the blast energy distribution is crucial for enhancing rock fragmentation, minimizing overexcavation, and boosting profitability in mining operations. This study introduces a theoretical model to predict the blasting Energy Factor ${(\mathrm{F}}_{\mathrm{e}})$ in mining tunnels, based on the Cracking Energy ${(\mathrm{E}}_{\mathrm{g}})$ of the rock mass, derived from the deformation energy of brittle materials (Young’s modulus) and adjusted by the Rock Mass Rating (RMR). The model was validated using 42 blasting datasets from horizontal galleries at El Teniente mine, Chile. Data included geometric parameters (tunnel sections, drilling length, diameter, number of holes, meters drilled), explosive type and consumption, and geomechanical properties, particularly the RMR. Results show that as rock mass quality improves (higher RMR), both ${\mathrm{F}}_{\mathrm{e}}$ and $\%{\mathrm{E}}_{\mathrm{g}}$ increase, more competent rock masses require higher input energy to initiate and propagate cracks, and a greater portion of that energy is effectively utilized for crack formation. For instance, rock masses with an RMR of 66 exhibited an average ${\mathrm{F}}_{\mathrm{e}}$ of 7.62 MJ/m³ and $\%{\mathrm{E}}_{\mathrm{g}}$ of 4.8%, while those with an RMR of 75 showed higher values (${\mathrm{F}}_{\mathrm{e}}$ = 8.47 MJ/m³, $\%{\mathrm{E}}_{\mathrm{g}}$ = 6.4%). This confirms that less fractured rock masses require higher ${\mathrm{F}}_{\mathrm{e}}$ and $\%{\mathrm{E}}_{\mathrm{g}}$ for effective fragmentation. Lithology also plays a significant role in energy consumption. Diorite displayed the highest ${\mathrm{F}}_{\mathrm{e}}$ (8.34 MJ/m³) and higher efficiency ($\%{\mathrm{E}}_{\mathrm{g}}$ = 7.0%), whereas andesite showed lower ${\mathrm{F}}_{\mathrm{e}}$ (7.61 MJ/m³) and lower crack propagation efficiency ($\%{\mathrm{E}}_{\mathrm{g}}$ = 3.7%). Unlike traditional ${\mathrm{F}}_{\mathrm{e}}$ prediction methods, which rely solely on explosive data and excavation volume, this model integrates RMR, enabling more precise energy allocation and fostering sustainable mining practices. This approach enhances decision-making in blast design, offering a more robust framework for optimizing energy use in mining operations. Full article

The classification of mineral resources and reserves provides a structured framework for evaluating the geological, technical, and economic aspects of mineral deposits. To reduce subjectivity and enhance reliability, international reporting standards established the principles of transparency, materiality, and competence. Many operating mines are seeking alignment with these frameworks to strengthen governance and access global capital. Within this context, the Mineral Resources and Reserves Readiness Index (MR³ Index) is introduced as a tool to assess the degree of alignment of mining operations with international reporting requirements. For operating mines, a key variable in the MR³ Index is the demonstrated ability to consistently convert Inferred Mineral Resources into mine production, even without prior reclassification into Indicated or Measured categories. When supported by geological homogeneity and well-defined controls, this track record serves as a strong proxy for geological confidence and operational maturity. The methodology was applied to an underground lithium mine in Brazil, which achieved a readiness level of 95.5%. A sensitivity analysis demonstrated the robustness of the MR³ Index and showed that the final score is considerably more sensitive to the class scores than to the selection of class weights, reinforcing the importance of documentation quality and technical consistency in public reporting. Full article