Search Results (924)

Ground deformations induced by underground mining significantly affect the geometric condition and serviceability of buildings located in mining areas. Conventional assessments are commonly based on ground deformation indicators, which do not necessarily reflect the actual structural response. This study presents a terrestrial laser scanning (TLS)-based methodology for assessing the three-dimensional deformation of an eleven-storey residential building located in the Legnica–Głogów Copper District (LGCD), Poland. The analysis was performed using a high-density point cloud acquired from ten scanning positions. Following registration and filtering, building geometry was reconstructed and corner positions were determined from 123 horizontal cross-sections. Horizontal displacements, tilt profiles, and rotation about the vertical axis were subsequently analysed within a local coordinate system. The results revealed pronounced spatial variability in both displacement magnitude and direction. The maximum horizontal displacement reached approximately 0.18 m, corresponding to a local tilt of 5.9 mm/m. Corner displacements at the highest common observation level ranged from 8.6 mm to 178.3 mm, indicating that the observed geometry is inconsistent with a simple rigid-body model subjected to uniform tilting. Analysis of geometric changes with height further identified an overall increase in torsional rotation with height, accompanied by local variations. Comparison of TLS-derived geometry with a theoretical mining-induced ground deformation model showed that the measured structural response does not directly reproduce the underlying ground deformation pattern. The largest discrepancies occurred along the building longitudinal axis, indicating that structural stiffness and soil–foundation–structure interaction significantly modify the transfer of ground movements to the superstructure. These results demonstrate the capability of TLS for detailed assessment of mining-affected buildings and provide quantitative insight into the relationship between ground deformation and actual structural response. Full article

Copper has emerged as a major potential bottleneck in the global energy transition. Electrification, AI-driven data centers, electric vehicles, renewable energy deployment, grid expansion, and rising defense spending are projected to increase global copper demand by approximately 50% by 2040, while supply remains constrained by declining ore grades, lengthy permitting processes, and a decade of underinvestment. According to S&P Global demand could reach 42 Mt by 2040, resulting in a supply deficit exceeding 10 Mt, despite USGS estimates of 820 Mt of global copper reserves. The challenge is therefore not primarily one of geological scarcity, but rather of reserve concentration, geopolitical access, and the pace of new discoveries and mine development. This study evaluates whether Bayesian artificial intelligence (AI) can help alleviate these constraints by combining Bayesian exploration analysis with Monte Carlo simulations of long-term copper supply and price risk. Bayesian exploration may reduce dry-hole rates by 30–50%, shorten exploration timelines by 3–4 years, and lower exploration costs by 10–15%, as suggested by KoBold Metals’ rapid advancement of the Mingomba deposit. However, AI cannot accelerate permitting, mine construction, social licensing, or geopolitical access. A stochastic price model based on 5000 Monte Carlo simulations indicates that, without AI-enabled exploration and renewed mining investment, copper prices could exceed USD 16,000/t by 2036, increasing the risk of substitution and demand destruction. By contrast, Bayesian optimization could support a more sustainable long-term price equilibrium of USD 11,000–13,000/t (constant 2026 USD). AI should therefore be viewed as a supply-side risk mitigator with potentially stabilizing macroeconomic effects, rather than as an independent stabilizer. It can improve exploration efficiency and capital allocation but cannot replace new mine development or overcome the geological, regulatory, and geopolitical constraints future copper supply. Full article

Although the mineralogical form of occurrence of critical metals is widely recognised as a key factor controlling their recovery during beneficiation, this relationship has rarely been verified directly on freshly mined ore and its primary beneficiation products from Central Asian polymetallic deposits. This raises the research question of how the mineralogical mode of occurrence of Bi, In, Cd, Co, Se, Te and Re governs their distribution between gravity and flotation products. Accordingly, the aim of this study was to establish how the mineralogical form of occurrence of these critical metals determines their distribution among gravity-concentration and flotation products, using ores from the Zhuantobe and Strezhanskoe deposits (Kazakhstan) as a case study. To this end, the mineralogical features and distribution patterns of critical metals during gravity and flotation beneficiation of polymetallic ores from these deposits were investigated by optical microscopy, X-ray diffraction, and SEM-EDS, while metal distribution among beneficiation products was determined by chemical analysis. Sphalerite, galena, pyrrhotite, and silver tellurides were identified as the main carriers of the critical metals, with bismuth occurring as an isomorphic admixture in sphalerite (3.69 wt.%) and galena (1.85 wt.%). During flotation, distribution was governed by mineralogical affinity: cadmium and indium were preferentially concentrated in the zinc concentrate (56.31% and 15.44% recovery, respectively), bismuth in the copper–lead concentrate (23.03%), and selenium and rhenium in the copper-bearing products (23.60% and 37.05%). Correlation analysis of the gravity-concentration products confirmed a close association of cadmium with sphalerite (R² = 0.9998), bismuth with galena and sphalerite (R² = 0.9674), and cobalt with iron-bearing sulfides (R² = 0.9144). These results demonstrate that the distribution of critical metals is governed primarily by their mineralogical form of occurrence rather than by bulk ore content, providing a basis for technologies for the complex processing of polymetallic ores. Full article

Preconcentration before grinding is important for reducing unnecessary downstream processing and improving ore utilization. Dual-energy X-ray transmission imaging provides paired responses of ore particles under different energy levels, which can be used for particle-level classification. However, adjacent categories, such as waste rock and low-grade copper ore, may exhibit similar transmission appearances, and discriminative cues may be distributed across both local attenuation details and global transmission patterns. In this study, we propose a dual-energy X-ray image classification method, named the Difference-Guided Cross-Level Feature Fusion Network (DGCF-Net), for three-class copper ore classification. Waste rock and copper ore samples from the Dexing Copper Mine were used to construct a three-class dual-energy X-ray image dataset. DGCF-Net incorporates response-difference cues from paired low- and high-energy images and combines local and global feature representations for ore-particle classification. Experimental results on the constructed dataset show that the proposed method achieved an Overall Accuracy of 0.9570, a Macro-F1 of 0.9664, and an AUC of 0.9953, with 3.6424 M parameters. These results indicate that the proposed method provides effective classification performance on the current dataset, particularly for categories with relatively similar image responses, while its broader practical applicability requires further validation under more realistic operating conditions. Full article

This study presents the development and validation of an intelligent error compensator based on Long Short-Term Memory (LSTM) recurrent neural networks for dynamic weighing systems in copper concentrate belt conveyors. Conventional weighing systems fail to capture nonlinear temporal dynamics, leading to measurement inaccuracies during container filling operations. The methodology comprised data acquisition from load cells, speed sensors, and inclinometers; systematic hyperparameter optimization; and evaluation using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and coefficient of determination (R²). Hyperparameter optimization identified an optimal configuration with one LSTM layer (20 units, learning rate 0.001, window size 20 steps). Evaluation on an independent test set showed that the compensator reduced MAPE from 8.5% (uncompensated system) to 3.01%, representing a 64.6% improvement, and reduced RMSE from 12.3 to 4.2 tons (65.9% improvement), with an R² of 0.95. Feature importance analysis confirmed physical consistency, with load cell voltage as the dominant predictor (42%). These results demonstrate that LSTM-based compensation significantly enhances weighing accuracy. The study provides a replicable framework for industrial metrology modernization, contributing to sustainable mining operations through material loss reduction and logistics optimization. While the proposed model has been validated offline using historical data, its deployment in the live production environment remains pending. Full article

Amidst the intensification of strategic mineral exploration, the discovery of concealed and deep-seated deposits within covered areas and the peripheries of existing mines has become a critical challenge and a central focus of contemporary research. “Dongchuan-type” (DCT) copper deposits represent a key mineralization style within the SW Yangtze Block. However, significant exploration breakthroughs have stagnated over the past three decades. Consequently, advanced exploration prediction research is urgently required to guide deep-resource evaluation. Using the Shizishan copper deposit in the Yimen area as a representative case, this study applies the theory of “metallogenic geological body prediction” to construct a tripartite model encompassing “metallogenic geological bodies, metallogenic structures and structural planes, and metallogenic characteristic markers”. The Shizishan deposit is classified as a sedimentary-reformed type, characterized by multi-episodic mineralization involving initial sedimentary processes, non-magmatic hydrothermal activity, and subsequent structural-magmatic hydrothermal overprinting. Key metallogenic geological bodies include ore-bearing stratigraphic associations, basin-margin faults, NE-trending fold–fault systems, and deep-seated concealed intrusions. Metallogenic structural planes comprise primary ore-controlling surfaces, secondary fold-related structures, and potential deep-seated magmatic intrusive interfaces. Alteration patterns display a distinct vertical zonation: shallower levels show medium-to-low temperature hydrothermal processes (silicification, carbonatization, sericitization, and chloritization), whereas deeper portions reveal high-temperature magmatic–hydrothermal indicators, specifically skarnization and intense silicification. This study proposes that the integration of “stratigraphy + structures + concealed intrusions” should serve as the primary criterion for target screening in deep exploration. By integrating geophysical and geochemical datasets into this model, three promising exploration targets were delineated within the Yimen area. These findings provide a refined theoretical framework for large-scale prospecting of DCT. Full article

Heavy metal/loid (HM) pollution of soils, primarily as a consequence of mining and ore-processing activities, poses significant risks to ecosystems and human health. Soil microbial communities play essential roles in maintaining key ecosystem functions, including nutrient cycling, carbon sequestration, and soil stability. The purpose of this study was to characterize the taxonomic composition and diversity of bacterial communities and evaluate their functional adaptation to heavy metal stress in soils affected by long-term gold–copper mining activities in Bulgaria. Ten soil samples representing a Cu pollution gradient (53–860 mg kg⁻¹) were categorized into five pollution classes. High-throughput sequencing of 16S rRNA gene amplicons revealed the dominance of the phyla Pseudomonadota (mean relative abundance 32%), Acidobacteriota (22%), and Actinomycetota (16%). At the class level, Alphaproteobacteria (18%), Terriglobia (16%), and Gammaproteobacteria (14%) were the most abundant taxa, indicating their adaptation to long-term heavy metal contamination. The genus Z2-YC6860 exhibited significant tolerance to Cu, whereas Bradyrhizobium_503372 was negatively associated with As and Zn concentrations. Functional predictions suggested enrichment of key pathways related to heavy metal resistance, including efflux systems and detoxification. The study design spans a broad Cu pollution gradient across river-associated and industrially impacted sites, providing an ecologically relevant framework for evaluating microbial responses to long-term metal stress. Full article

The Murmansk Region (the Russian Arctic) faces severe environmental degradation due to heavy metal (HM) pollution from copper–nickel smelting, resulting in vast industrial barrens with elevated concentrations of copper (Cu) and nickel (Ni). Conventional phytostabilization methods are often ineffective or costly, necessitating alternative bioremediation strategies. This study evaluates the potential of microalgae and cyanobacteria for revegetating HM-contaminated substrates. Six strains of Nostoc-like morphotypes and three green microalgae were tested for Cu²⁺ tolerance (0.5–15 mg/L). While most strains exhibited growth inhibition at ≥3 mg/L Cu²⁺, Atlanticothrix sp. KPABG-154445, isolated from Tolbachik Volcano, showed positive growth at 2 mg/L Cu²⁺ under the tested conditions and recovering metabolic activity post-exposure. In sorption experiments, non-viable biomass achieved 68% Cu²⁺ removal at 2 mg/L, outperforming actively growing cultures. A microcosm experiment using copper-spiked nepheline slime (simulating mining waste) revealed Atlanticothrix sp. KPABG-154445’s ability to colonize nutrient-poor substrates, forming biocrusts covering 42% of the surface within one month, even under Cu²⁺ contamination (10 mg/kg). These findings highlight cyanobacteria, particularly strains such as KPABG-154445, as promising agents for the bioremediation of Arctic industrial barrens, leveraging their dual capacity for heavy metal tolerance and biocrust formation. Full article

Incorporating supplementary cementitious materials (SCMs) and alternative aggregates promotes cleaner production of cement-based materials; however, optimizing their coupled effects on mechanical and environmental performance remains a key challenge. This study assesses the valorization of copper slag (CS), a massive industrial mining by-product, simultaneously as an SCM and an artificial fine aggregate (AFA) in the development of mortar mixtures with better performance and lower embodied carbon. Specifically, CS partially replaced Portland cement (PC) at 0% and 15% by volume, while natural fine aggregate (NFA) was substituted with AFA at volumetric replacement levels of 0%, 20%, 40%, and 60%. Mortar performance was systematically evaluated in the fresh state via workability and in the hardened state through water absorption, alongside short- and long-term compressive and flexural strength development. Furthermore, a cradle-to-gate life cycle assessment (LCA), expressed in terms of embodied carbon (EC) emissions, was executed to assess the environmental performance of the mixtures. The results indicate that incorporating CS as both SCM and AFA improved mortar workability by up to 30.9% compared to the reference mortar mixture (100% PC and 100% NFA). At 7 days, compressive strength decreased by 4% to 20% relative to the reference mortar. However, long-term performance improved substantially; at 330 days, mixtures with 15% CS–0% AFA and 15% CS–60% AFA achieved average compressive strengths of 42.9 MPa and 53.1 MPa, respectively, outperforming the reference mortar (42.1 MPa) by up to 26%. The cradle-to-gate embodied-carbon assessment showed that CS incorporation reduced product-stage emissions, although outcomes depended on transport distance and strength development. EC ranged from 369.8 to 438.0 kg CO₂e/m³. When transport was included, EC decreased by approximately 14%, while 330-day strength-normalized EC decreased by 29.6–32.1% relative to the reference mixture. Overall, CS-based mortars improved fresh-state properties, enhanced long-term mechanical performance, and reduced product-stage embodied carbon, demonstrating their potential for cleaner production. Full article

Artificial intelligence (AI) is increasingly being investigated to support decision-making and process improvement in copper mineral processing and extractive metallurgy; however, the available evidence remains fragmented across operational units, methods, sustainability dimensions, and geographical contexts. This systematic mapping review, complemented by qualitative cross-study synthesis, analysed publications from 2014 to 2025 retrieved from IEEE Xplore, Scopus, and Google Scholar. Of the 410 records identified before screening, 71 studies were classified according to four predefined research questions addressing copper-processing operations, AI domains, sustainability contributions, and geographical distribution. The findings show an uneven distribution of research across the processing flowsheet. Comminution and froth flotation received the greatest attention, whereas lixiviation, solvent extraction, electrowinning, and electrorefining were less represented. Machine learning was the dominant AI domain, particularly supervised approaches based on historical operational data; however, no algorithm was universally superior, as reported performance depended on the dataset, target variable, operational context, and validation procedure. Most studies focused on prediction, monitoring, fault diagnosis, and operational optimisation, while reinforcement learning, hybrid mechanistic–data-driven models, adaptive control, and sustained industrial deployment remained comparatively limited. Sustainability assessments emphasised operational and economic outcomes more frequently than social, ethical, circular-economy, and broader environmental implications. Geographical results reflected the locations assigned to the reviewed studies and industrial cases rather than regional AI maturity. The review identifies data availability, heterogeneous validation practices, model transferability, and limited evidence of sustained industrial deployment as recurring challenges. It proposes a staged research agenda to develop more reliable, transferable, and human-supervised AI applications across copper-processing operations. Full article

This study analyzed the content of polluting and toxic elements (Cr, Ni, Cu, Zn, As, Cd and Pb) in the cadastral municipalities immediately surrounding the mining and metallurgical complex Bor in Serbia. The soil is exposed to hazardous substances due to historical pollution. Soil pollution at different locations was assessed based on the measured concentration of metals and the contamination factor (Cf). The increased content of the toxic metals was determined mostly for copper and arsenic. Three types of different plants were used in order to reduce the content of toxic metals in the contaminated mining and metallurgical land. The results showed that these plants (barley Hordeum sativum, sugar grazer Sorghum bicolor, and hybrid BMR 333 Sudan grass) were capable of growing and accumulating metals in plants at copper-mined and metallurgical sites. Full article

Mining project evaluation depends on geological resource models, yet these models remain inherently uncertain because they are constructed from sparse drillhole data, indirect geophysical observations, and incomplete geological knowledge. Conventional workflows typically treat this uncertainty only partially by defining deterministic orebody wireframes and then interpolating or simulating grades within these fixed boundaries. This separation neglects the propagation of geometric uncertainty into grade continuity, resource tonnage, and economic forecasts. The challenge is particularly significant where drillholes do not fully intersect the orebody, leaving its extent at depth unconstrained and forcing boundary placement to rely on extrapolation rather than data-supported inference. To overcome this limitation, we present a sequential uncertainty quantification framework that integrates level-set implicit geological modeling within a Markov Chain Monte Carlo (MCMC) sampler with Sequential Gaussian Simulation (SGSIM) grades. Orebody geometry is represented using a signed distance function perturbed by Gaussian random fields and constrained by drillhole, outcrop, and geological interpretation data. The resulting ensemble captures plausible geometric variability, particularly in poorly constrained regions. Conditional grade simulations are then generated for each accepted geometry, producing paired realizations of geometry and grade uncertainty. Applied to the Tarmante copper deposit in Morocco, the framework demonstrates that deterministic models overestimate tonnage, whereas the joint ensemble provides realistic grade–tonnage uncertainty, enabling more reliable resource evaluation and risk-informed decision-making. Full article

Mining flotation effluents may contain residual collectors that pose risks to aquatic environments when discharged without adequate treatment. Among these compounds, sodium diethyl dithiophosphate (SDE DTP) is of particular concern due to its persistence and potential ecotoxicological effects. This study evaluated the removal of SDE DTP from aqueous media using commercial activated carbon and thermochemically modified activated carbon prepared through nitric acid oxidation and urea treatment. Batch adsorption experiments were conducted using synthetic SDE DTP solutions and a laboratory-generated flotation effluent obtained from a copper sulfide ore containing approximately 0.5 wt.% Cu. A UV–visible spectrophotometric method was applied for the quantification of SDE DTP in both systems. The effects of adsorbent dosage, solution pH, and surface modification were investigated. Adsorption performance increased with activated carbon dosage and was strongly influenced by pH. Thermochemical modification significantly enhanced adsorption performance, increasing SDE DTP removal from 40% for unmodified carbon to 93% for the modified material. The modified adsorbent also maintained high efficiency in real flotation effluent, achieving 88% removal despite competing dissolved species. These results demonstrate that thermochemically modified activated carbon is a promising functional material for treating flotation effluents contaminated with dithiophosphate collectors. Full article

During underground mining, stope stability is affected by excavation-induced stress redistribution and nonlinear degradation of rock mass strength. The conventional Mathews stability graph method employs an empirical stress factor A, which does not explicitly consider the nonlinear relationship between stress conditions and rock mass strength. In this study, the generalized Hoek–Brown criterion was introduced to define the maximum stress factor (MSF), and a modified stress factor A′ was developed by considering tensile and shear failure mechanisms. The proposed method incorporates the nonlinear stress–stability relationship of underground stopes and improves the reliability of stability assessment. A copper mine in southwest China was selected as a case study. The rock mass quality indices and stress parameters of ten representative stopes were obtained through field investigations, stope roof stress measurements, discontinuity surveys, and laboratory tests. The modified stress factor A′ was incorporated into the Mathews stability graph to account for excavation-induced stress redistribution and rock mass strength degradation. Compared with the conventional method, the modified approach generally reduced the stability numbers of the investigated stopes, with an average reduction of approximately 25.6% (excluding D1780-1, where the confinement strengthening effect resulted in a slight increase in stability number). The revised stability classifications show good consistency with the FLAC^3D simulation results and field observations, providing supporting evidence for the application of the proposed method to underground stope stability assessment in the studied mine. Full article

Rock drillability classification provides an important basis for drilling-parameter optimization, equipment selection, and improved mining efficiency. Existing drillability evaluation methods often rely on fixed empirical weights and rigid grade boundaries, making it difficult to capture fuzzy transitions between adjacent grades under multi-indicator geological conditions or to explain classification deviations for boundary samples. This study proposes a quantitative rock drillability classification method that integrates FDAHP-based subjective weighting, objective weighting, and a multidimensional cloud model. A 12-indicator evaluation system is first established by considering rock physicomechanical properties, rock-mass structural conditions, and drilling-response characteristics. FDAHP is then used to derive subjective weights from judgment matrices provided by five experts, while the entropy weight method, CRITIC method, and coefficient of variation method are used to obtain objective weights. These weights are combined into a subjective–objective weighting scheme and incorporated into a multidimensional cloud model to represent the fuzziness and randomness of drillability grade boundaries. For incomplete-indicator samples, the comprehensive weights are projected onto the available indicator subset and renormalized, avoiding forced imputation of missing indicators. Validation using 15 complete samples and seven incomplete-indicator samples from the Sungun copper mine shows that the proposed combined weighting method achieves an accuracy of 93.33% for complete samples and correctly classifies six of seven incomplete-indicator samples, with an accuracy of 85.71%. The cloud-model analysis of the misclassified sample indicates that it lies near an adjacent-grade boundary, providing an interpretable explanation for its classification uncertainty. These results suggest that the proposed method can provide interpretable quantitative drillability classification for the Sungun case study and may support preliminary field drillability assessment under incomplete-information conditions. Full article