Mining, Volume 6, Issue 2 (June 2026) – 19 articles

The environmental impacts of mine dust in mining operations can be mitigated through improved prediction of its spatial distribution using dispersion models, particularly Gaussian-based air quality models. However, Gaussian-based models often predict concentrations that differ substantially from observed mine dust behavior, because dust properties and transport mechanisms vary markedly with particle size. In this study, particle-size-related mechanisms for dust dispersion behaviors were classified as dry/wet deposition, turbulent diffusivity, erosion, hygroscopicity, or agglomeration, and their effects on dust dispersion behaviors and effective simulation methods were reviewed. Currently, the most clearly established particle size influence is on deposition, especially for coarse dust emitted from mechanical mining processes. Other mechanisms, including erosion, hygroscopicity, and agglomeration, are more relevant to finer dust below 2.5 µm or in the submicron range. This study proposes that wind erosion, mainly saltation flux, can also be integrated into Gaussian dispersion models as near-ground boundary flux terms. Hygroscopic and agglomeration effects can be assessed using relative humidity and simplified particle size redistribution assumptions near dust emission sources. In particular, incorporation of agglomeration mechanisms may begin with a simple bimodal assumption: the agglomeration of PM_2.5 into PM₁₀. This can be incorporated into a modified Gaussian deposition equation. Finally, the size dependence of the turbulent diffusivity coefficient is relatively insignificant, so the diffusivity values can be regarded as constants. These findings provide a mechanistic basis for improving mine dust prediction and environmental management in open-pit mines, haul roads, tailings areas, and stockpile environments. Full article

The foundational element of any ventilation system assessment is the precise definition of the primary airflow intake. In underground mine networks, even a marginal error in primary inputs triggers a series of inaccuracies, resulting in significant volumetric errors across the entire network. This study explores the sensitivity of the iterative Hardy Cross algorithm for ventilation network analysis towards the main intake, and demonstrates that the intake overestimation error reaches 77%, creating a false sense of security. Furthermore, when utilizing the Hardy Cross approach, evaluating a model based solely on its mathematical tendency to balance is misleading; analysis of relative error evolution demonstrates that a converged network can achieve mathematical balance while remaining fundamentally uncoupled from the mine’s physical reality due to flawed input data. While technical fields currently diverge into two primary paths for airflow definition, overly simplistic approximations or specialist-dependent numerical CFD models, this study proposes a middle ground alternative. The proposed methodology relies on direct anemometric field measurements synthesized through cartographic mapping integration techniques. The suggested technique offers a detailed graphical representation of air velocity across the excavation, derived from the isovel mapping. This visualization illustrates that airflow behaviour through rock excavations is fundamentally non-uniform and dictated by wall roughness and structural irregularities. Full article

Hydrothermal alteration mapping is a critical component of porphyry copper exploration because alteration assemblages provide important vectors toward mineralization. This study presents a systematic evaluation of supervised machine learning algorithms for delineating hydrothermal alteration zones using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) short-wave infrared (SWIR) surface reflectance data (AST_07XT). The investigation focuses on the Nain region within the central Urumieh–Dokhtar Magmatic Arc (UDMA), Iran, a major metallogenic belt hosting numerous porphyry copper systems. Representative spectral endmembers corresponding to Al–OH-bearing and Mg–OH-bearing hydrothermal alteration minerals were extracted using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-dimensional visualization techniques. These endmembers were subsequently used to train and evaluate a comprehensive suite of supervised machine learning classifiers, including linear, kernel-based, tree-based, ensemble, probabilistic, boosting, and neural-network algorithms for pixel-wise hydrothermal alteration mapping. Model performance was evaluated using multiple statistical metrics, including overall accuracy (OA), average accuracy (AA), precision, recall, F1-score, Cohen’s kappa coefficient, area under the ROC curve (AUC), spatial cross-validation accuracy, uncertainty analysis, and spatial agreement analysis. Among the evaluated classifiers, SVM_Linear, SVM_RBF, LDA, and MLP achieved the highest classification performance, with overall accuracies exceeding 94% and strong spatial consistency between classified maps. The resulting alteration maps display spatially coherent distributions of Al–OH and Mg–OH minerals that are consistent with established hydrothermal alteration zoning models in porphyry–epithermal systems. The mapped hydrothermal alteration zones show strong spatial correspondence with known mineralized areas and alteration patterns within the Urumieh–Dokhtar Magmatic Arc, confirming the geological reliability of the classification results. Uncertainty analysis further indicates high model confidence across most alteration zones, with higher uncertainty values mainly restricted to transitional and spectrally heterogeneous regions. The results demonstrate that integrating ASTER SWIR imagery with supervised machine learning algorithms provides a robust, scalable, and transferable framework for regional-scale hydrothermal alteration mapping and mineral exploration in porphyry copper provinces. Full article

Accurate modeling of underground mine ventilation requires reliable estimates of roadway aerodynamic resistance. Conventional methods, based on geometric surveys or barometric pressure measurements, have notable limitations, including neglect of local losses, high time requirements, and sensitivity to environmental disturbances. This paper introduces a two-point flow-based method for determining roadway resistance directly from in situ measurements. Using basic instruments (anemometer, differential manometer, thermometer, and hygrometer), measurements are taken at two points along a straight airway. The pressure drop is calculated via the Bernoulli equation, allowing resistance to be determined without relying on geometric data or friction assumptions. This method captures both frictional and local losses inherently. Field testing in five roadway sections of a coal mine in Vietnam yielded resistance values 10–15 times higher than theoretical friction-only estimates, highlighting the importance of local losses. The equivalent cross-sectional areas back-calculated from the measured resistance using literature-based friction factors showed consistency with geometric survey data (typical deviation 3–6%), indicating internal coherence of the measurements. Full validation against independent barometric or CFD methods remains a subject of ongoing research. The method is simple, fast, minimally disruptive, and compatible with ventilation modeling tools. It provides a practical and accurate alternative for resistance estimation under real operating conditions. Full article

Slope stability in open-pit mining is not a static condition but evolves continuously as excavation progresses and geomechanical conditions change. In this study, an integrated approach combining ground-based radar monitoring, satellite-based InSAR time-series analysis, and numerical stability modeling was applied to evaluate slope behavior in a large-scale open-pit copper mine with complex geological and structural characteristics. Radar data revealed progressive and episodic deformation concentrated in specific slope sectors, while InSAR observations showed that deformation continued at lower rates after the main movement phase, providing a longer-term perspective of slope response. Stability analyses using limit equilibrium and finite element methods indicate that the slope operates close to a limit equilibrium condition, particularly under saturated scenarios where factors of safety approach critical levels and strain localization becomes more pronounced. The results show a clear link between observed deformation patterns and calculated stability conditions, with structural discontinuities and groundwater playing a dominant role in controlling slope behavior. Based on these findings, an integrated workflow is proposed that links monitoring data with stability assessment, enabling the identification of critical zones and supporting the evaluation of slope conditions during ongoing mining operations. This approach contributes to more reliable decision-making and supports safer and more sustainable open-pit mining practices. Full article

An X-ray fluorescence online analyzer was applied to the analysis of samples of known composition and concentration containing rare earth elements (REEs) and heavy metals (HMs), which were specially prepared by the authors (working samples). Reference samples were used for Th and U. The statistical parameters (detection limit, accuracy, and sensitivity) of the measurements of the spectra were calculated and a thorough assessment of the results was carried out. For large-volume samples, detection limits of 20–100 ppm for REEs and 10–140 ppm for HMs were achieved within 600 s. For thin-layer samples and similar geometries, detection limits for light and medium REEs improved to 3–20 ppm. The methodological possibilities for quantitative analysis of the REEs and HMs were examined and a rather simple approach with an easy implementation was developed. The method was tested in automatic measurements using concentrations in the range of 1000–4000 ppm, as a simulation of real-life measurements, and to determine the stability of the analyzer and the consistency of the results obtained. The results show that the online XRF analyzer can be applied for reliable detection and quantification of REEs and HMs at the ppm level. With these results, we are closer to obtaining results under conditions representative of those on real-world mining conveyor belts. Full article

Autonomous navigation is a key requirement for underground mine automation, where the choice of a suitable global path planner plays a significant role. In this study, four representative planning approaches—Dijkstra’s algorithm, A*, Rapidly exploring Random Tree (RRT*), and Particle Swarm Optimization (PSO)—were evaluated on a differential-drive mobile robot within the ROS navigation framework. The algorithms were tested in two simulated underground environments: a room-and-pillar layout with relatively open space and multiple path alternatives and a narrow tunnel scenario designed to reflect more constrained mining conditions. The results indicate that Dijkstra’s algorithm consistently produced the shortest paths with the lowest computation times, while A* showed comparable performance with slightly higher computational effort. RRT* required modifications to operate effectively in narrow tunnels and exhibited significantly longer planning times. PSO, although capable of generating near-optimal solutions in open spaces, showed limitations in constrained environments due to collision handling and path feasibility issues. Differences in replanning behavior were observed when unknown obstacles were introduced. Overall, graph-based planners such as A* and Dijkstra’s algorithm demonstrated more stable and predictable performance. Future work will focus on validating these findings in real mining environments, particularly considering wheel slippage, sensor noise, and path generation challenges in narrow tunnel conditions. Full article

While conventional numerical studies often treat excavated stopes as empty voids or as backfilled, few investigations have simulated the post-mining void as a weak granular caved rock material that evolves naturally from a hanging wall failure. This study addresses this gap by modeling the caved rock progressively, which makes the excavation representation more realistic for sublevel caving operations. This study introduces stope stability for the Zarmitan gold mine in Uzbekistan, where mining occurs at about a 500 m depth in a narrow quartz vein. A hybrid approach combining empirical and numerical methods was adopted. The Mathews stability graph method provided initial design guidance, while three-dimensional FLAC3D numerical modeling was used to simulate the mining sequence with explicit representation of caved rock behavior. A various study was conducted, which included the effects of stress ratio, stope length along strike, and pillar thickness on overall stability. The obtained results show that the stress ratio is the dominant factor controlling stope behavior. Stope length significantly affects failure extent, with shorter stopes showing better performance under similar conditions. Pillar thickness was found to improve stability and reduce tensile stresses in critical areas, though in all cases, hanging wall support remains essential. The numerical results confirm empirical predictions while providing quantitative insights into stress distributions and failure mechanisms not captured by empirical methods alone. These results provide mine operators with quantitative, site-specific design criteria, most notably that, under the measured high horizontal stress, limiting stope length to 40 m and increasing pillar thickness to 8 m substantially improves hanging wall stability, which demonstrates how a hybrid empirical-numerical methodology can directly support safer and more economic extraction in deep, narrow-vein operations. Full article

Flotation is a fundamental unit operation in mineral processing; however, achieving high selectivity while reducing the environmental impact of reagents remains a major challenge in phosphate ore beneficiation. Conventional depressants often exhibit limited selectivity and may pose environmental concerns, highlighting the need for sustainable alternatives. This study reports, for the first time, the application of starch nanostructures derived from potato pulp processing residues as a depressant in phosphate flotation, representing an innovative and eco-friendly approach. An exploratory and experimental methodology was adopted, including nanostarch synthesis via acid hydrolysis followed by centrifugation and sonication, as well as comprehensive physicochemical characterization. The primary objective was to evaluate the selective depressant performance of the nanomaterial in apatite–calcite flotation systems. The synthesized nanostructures exhibited particle diameters ranging from 179 to 443.6 nm. Microflotation tests conducted in a Hallimond tube using pure mineral samples under alkaline conditions (pH ≈ 9), at a depressant dosage of 500 mg/L and in combination with a plant-based fatty acid collector, revealed a pronounced selectivity window, resulting in an approximately 77% difference in flotation recovery between apatite and calcite. These findings demonstrate that nanostarch derived from agro-industrial residues is a promising, biodegradable, and sustainable depressant capable of enhancing selectivity in phosphate flotation. The results contribute to the advancement of greener mineral processing Technologies, although Further studies are required to elucidate the underlying interaction mechanisms. Full article

Metallurgical coal operations are a significant but poorly constrained source of methane (CH₄) in Canada. We present a multi-instrument analysis of 63 methane plume detections at Fording River Operations, British Columbia (January 2022–March 2026), using the Airborne Visible/Infrared Imaging Spectrometer—Next Generation (AVIRIS-NG; n = 39), the Earth Surface Mineral Dust Source Investigation (EMIT; n = 4) and Tanager-1 (n = 20). Of these, 41 plumes (65%) were quantified, with retrieved emission rates of 34–3622 kg CH₄ h⁻¹; 54% exceeded the 500 kg h⁻¹ super-emitter threshold. Because 73% of detections fall in September and no detections are available for 2023, results characterize the late-summer overpass window and should not be extrapolated seasonally without further coverage. The central finding is a systematic, asymmetric wind-speed disagreement between two numerical weather prediction (NWP) products that maps onto the Integrated Mass Enhancement (IME) quantification outcome. A univariate logistic regression identifies HRRR wind speed as a significant predictor of quantification success (OR = 0.29 per m s⁻¹, 95% CI [0.15, 0.56], p < 0.001; AUC = 0.80; 5-fold cross-validated AUC = 0.79 ± 0.20, fold range 0.45–1.00). Cross-validation against ERA5-Land shows that HRRR exceeds ERA5 by a mean of +0.86 m s⁻¹ (+43%) for unquantified events but shows near-zero disagreement for quantified events (–0.09 m s⁻¹, –6%). A sensitivity analysis restricted to HRRR-forced retrievals (EMIT + Tanager-1, n = 24) confirms the finding is not an artefact of mixed wind data sources (OR = 0.28, AUC = 0.83, p = 0.018). Full article

The Carajás Province, located in the southeastern Amazon, hosts some of the world’s largest high-grade iron deposits. Despite their economic importance, the processes linking lateritic weathering and iron enrichment remain incompletely understood. This study investigates the role of lateritic weathering in the evolution of the Serra Leste iron deposit through the characterization of a weathering profile and its parent rocks using drill-core samples. Analytical methods included X-ray diffraction (XRD), optical microscopy, scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS), whole-rock geochemistry, and Mössbauer spectroscopy. Jaspilites weathered into ferruginous saprolite while preserving relic banding and mineral textures. Magnetite alteration produced pseudomorphic hematite with dissolution cavities progressively infilled by goethite, indicating iron remobilization during weathering. Weathering of chloritites generated clayey saprolite enriched in kaolinite and iron oxyhydroxides, with gibbsite occurring in more advanced stages. The uppermost horizon consists of a ferroaluminous duricrust composed of massive, spherulitic, and brecciated iron oxyhydroxides associated with gibbsite. Up-profile geochemical trends are marked by decreasing SiO₂ and increasing Fe₂O₃. The mineralogical, textural, and geochemical relationships indicate that the ferroaluminous duricrust was developed through contributions from both ferruginous and clayey saprolitic systems, particularly from the latter. These results support the interpretation that lateritic weathering played an important role in iron redistribution and supergene enrichment within the Serra Leste deposit, consistent with mature Amazonian lateritic systems. Full article

Compressed CO₂ energy storage (CCES) in deep sedimentary basins offers a promising option to integrate carbon management with long-duration energy storage. However, most existing subsurface energy-storage studies focus on salt caverns or generic porous reservoirs, while the potential of evaporite-bounded carbonate reservoirs remains insufficiently explored. This study presents the first application-oriented numerical assessment of CCES in Southern Ontario. It investigates the feasibility of CCES in the Upper Silurian Salina Group beneath offshore Lake Huron, focusing on a porous A-2 carbonate interval vertically confined by B and A-2 halite caprocks. A fully coupled three-dimensional thermo-hydro-mechanical model is developed in COMSOL Multiphysics 6.3 to simulate two-phase (brine-CO₂) Darcy flow, heat transfer, and poroelastic deformation under a realistic Michigan Basin stress, pressure and geothermal regime. After an initial cushion-gas stage at 8 kg/s that establishes a caprock-parallel supercritical CO₂ wedge beneath the B-salt, 24 h injection-production cycles are imposed for two years, followed by a five-month high-resolution window. Three well completion strategies are compared: full-length, upper-only, and split (upper + lower) perforations. Results indicate that in all simulations the CO₂ plume stabilizes as a persistent gas cap beneath the B-salt, far-field pressures remain close to hydrostatic, and reservoir deformations are very small, pointing to a substantial geomechanical safety margin. Among the three completion strategies, the split completion provides the best compromise: it maintains high and relatively stable CO₂ production while avoiding the stronger lower-zone depressurisation seen in the full-length case and the more limited working volume of the upper-only case. These findings suggest that a Salina A-2 carbonate reservoir bounded by B and A-2 salts can accommodate cyclic CCES under realistic basin conditions, and that appropriately designed split completions offer a practical balance between storage utilisation and operational robustness in this setting. Full article

Blasting-induced ground vibrations, typically quantified by peak particle velocity (PPV), pose one of the most critical environmental challenges in surface mining and can damage nearby structures and disrupt surrounding ecosystems. Consequently, the development of reliable and accurate predictive models is essential for designing safe, environmentally responsible, and sustainable blasting operations. This study develops a robust predictive framework using a harmonized database of 506 blasting events, from which 386 high-quality records were retained after preprocessing to model PPV as a function of charge per delay (Q), monitoring distance (R), and rock mass rating (RMR). Several machine learning (ML) algorithms, including artificial neural networks trained using the Levenberg–Marquardt algorithm (ANN-LM), adaptive neuro-fuzzy inference systems (ANFIS), Gaussian process regression (GPR), and decision trees (DT), were evaluated alongside conventional empirical models such as the USBM, Ambraseys–Hendron, Langefors–Kihlstrom, and BIS. To further enhance predictive capability, two optimization strategies, Bayesian optimization (BO) and differential evolution (DE), were applied to the GPR model, producing optimized BO-GPR and DE-GPR variants. Model performance was assessed using the correlation coefficient (r), variance accounted for (VAF), mean absolute error (MAE), and relative root mean square error (RRMSE). Results indicate that the BO-GPR model achieved the best predictive performance during testing for both the two-input (Q, R) and three-input (Q, R, RMR) configurations, with r values of 0.97426 and 0.98381, respectively, and VAF values exceeding 94%. SHAP analysis revealed monitoring distance as the dominant attenuating factor controlling PPV. The optimized framework provides an accurate, interpretable tool for vibration prediction and precision blast design, supporting environmentally responsible, sustainable mining operations. Full article

This paper presents a comprehensive methodology for assessing supply chain transparency and resiliency using a data-driven approach. Leveraging global trade data and Harmonized System (HS) codes, the methodology maps each stage of the supply chain to enhance regulatory compliance and mitigate operational risks. Transparency is evaluated using a novel classification system that categorizes branches as fully transparent, highly transparent, moderately transparent, or non-transparent. This enables raw material traceability, Scope 3 greenhouse gas (GHG) emission estimation, and identification of high-emission nodes for targeted reductions. Resiliency is assessed through graph analytics and stress testing, incorporating metrics such as the Giant Connected Component (GCC) and probabilistic simulations to analyze vulnerabilities and develop recovery strategies. A case study on the Cr-13 Steel Drill Pipe supply chain highlights the benefits of incorporating scrap materials for sustainability, alongside challenges related to traceability due to regulatory gaps and non-transparent networks. Monte Carlo simulations identify critical nodes whose disruption significantly affects network connectivity; therefore, resiliency, and transparency. This methodology delivers actionable insights to improve supply chain resiliency, sustainability, and operational efficiency. It is scalable across industries, enabling stakeholders to optimize management strategies, align with global climate initiatives, and build resilient and transparent networks. Full article

Quantifying time-dependent rock mass degradation is critical for assessing long-term slope stability during open-pit mine closure. This study evaluates the geotechnical evolution of Paleoproterozoic arenites and argillites in the semi-arid Essakane Main Zone (Burkina Faso) over a 0–9-year atmospheric exposure period. Field characterization across 32 sampling stations included density measurements, point load testing (I_s(50)), determination of the Geological Strength Index (GSI), and petrographic analysis. The results demonstrate a time-dependent reduction in physico-mechanical properties, modeled with a high correlation (R² = 0.80–0.99). While density exhibited minor reductions, structural degradation was pronounced; the GSI decreased by 10 points for both lithologies, and I_s(50) dropped significantly, particularly in argillites (4.1 to 2.3 MPa) relative to arenites (4.0 to 3.6 MPa). Petrographic evidence indicates negligible chemical weathering and mineral neoformation. Consequently, the degradation was attributed primarily to physical processes, specifically microcracking and discontinuity deterioration driven by thermal cycling and phyllosilicate sensitivity in argillites. These empirical relationships provide essential quantitative input for numerical slope stability modeling in semi-arid mine closure scenarios. Full article

Uranium is a resource with exceptionally high energy density, releasing substantially more energy per unit mass than conventional fossil fuels. In uranium mining, in situ leaching offers significant advantages over open-pit and underground mining, including reduced environmental impact, lower operational costs, enhanced safety, and improved controllability. Within the in situ leaching framework, acid leaching faces limitations in high-carbonate ore bodies, while alkaline leaching is unsuitable for deposits rich in pyrite and other sulfide minerals due to side reactions and precipitate formation that hinder leaching efficiency. In contrast, CO₂-O₂ leaching, as a neutral leaching approach, exhibits broader applicability across diverse ore types and geological settings. Incorporating CO₂ into the leaching process also enables carbon utilization, offering a potential pathway to cleaner uranium extraction aligned with carbon reduction and sustainable energy goals. This review systematically examines the geochemical principles, as well as hydrological and transport phenomena governing CO₂-O₂ in situ leaching. Recent technological advances are summarized, including progress in reaction kinetics and leaching efficiency, leaching solution design and control, and reservoir modification. Furthermore, the techno-economic implications of CO₂-O₂ in situ leaching are critically assessed, with particular emphasis on operational cost structures and the evolution of techno-economic analysis methodologies. On this basis, key challenges and future directions are identified. This work aims to support the future large-scale and economically efficient deployment of CO₂-O₂ in situ leaching for uranium resource development. Full article

Mining practices and operating conditions are continually evolving, and the respirable fraction of coal mine dust is accordingly expected to change in composition and particle characteristics over time. Between the early 2000s and late 2010s, several regulatory and operational changes occurred in U.S. underground coal mining that could plausibly influence respirable coal mine dust (RCMD), including expanded rock-dusting practices, increased emphasis on respirable crystalline silica, and reductions in diesel emissions. This study evaluated temporal differences in RCMD by comparing samples collected in 2003–2005 and 2018–2020 using particle-level scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDX). The most consistent temporal change observed was an increase in carbonate particles, consistent with expanded rock-dusting practices. Shifts in coal- and rock-strata-derived dust were observed but were not consistent across regions, and no consistent trend toward finer particle sizes was identified. These results demonstrate the value of particle-level analysis for evaluating changes in RCMD characteristics over time. Full article

Strategic Mine Planning (SMP) creates the long-term economic baseline for mining operations, yet economic variability necessitates Dynamic Mine Planning (DMP) to rapidly stress-test those financial assumptions. Currently, this capability is hindered by fragmented software ecosystems that require manual data handoffs, slowing iteration and breaking the audit trail between market data and valuation models. While Generative AI affords an opportunity to automate these workflows, its adoption in the mining industry is stalled by concerns over data quality and the risk of uncritical acceptance of automated outputs. Addressing these challenges, this paper describes the Mine Intelligence and Decision Support (MINDS) framework. We present MINDS as a modular reference architecture that uses Large Language Model (LLM) agents to orchestrate the economic evaluation process while maintaining strict engineering oversight. The system integrates a conversational interface with a multi-agent assessment layer that acts as an adversarial review, assessing price assumptions against market intelligence before generating economic valuation scenarios. A proof-of-concept using the Marvin copper benchmark evaluates the framework, demonstrating automated request-to-report orchestration, execution stability with an average debate latency of 10.69 s and a transparent decision audit trail. These findings show that MINDS can systematize economic scenario analysis without sacrificing the governance and verification required for definitive feasibility studies. Full article