This study investigated the influence of high-energy milling and leaching conditions on copper extraction from an industrial copper concentrate obtained from the Balkhash copper smelter. The concentrate was subjected to high-energy milling in a planetary ball mill for 15, 30, 45, and 60 min, followed by leaching in ammonium hydroxide and citric acid solutions. The structural, morphological, and physical changes induced by milling were evaluated using X-ray diffraction (XRD), scanning electron microscopy (SEM), particle-size distribution (PSD), and Brunauer–Emmett–Teller (BET) specific surface area analysis. XRD analysis showed that the principal mineral phases, including chalcopyrite, quartz, copper sulfides, magnetite, and lead sulfate, remained detectable after milling, whereas changes in diffraction peak shape and relative intensity indicated modification of the structural state of the concentrate. SEM observations demonstrated progressive disruption and fragmentation of the initial coarse particles. Quantitative particle-size analysis confirmed substantial particle refinement, with the D50 and D90 values decreasing from 5.030 and 57.365 μm for the untreated concentrate to 1.193 and 5.101 μm, respectively, after 30 min of milling. The BET specific surface area increased from 0.5086 m2/g for the untreated concentrate to a maximum of 1.7801 m2/g after 30 min, followed by a non-monotonic variation at longer milling times. Under the selected leaching conditions, copper extraction from the untreated concentrate reached 67% in ammonium hydroxide solution and 77% in citric acid solution. High-energy milling further increased copper extraction to 81% after 45 min in ammonium hydroxide and to 86% after 60 min in citric acid. The combined XRD, SEM, PSD, and BET results demonstrate that high-energy milling substantially modified the structural, morphological, particle-size, and accessible surface characteristics of the concentrate. However, the different milling durations corresponding to the maximum BET surface area and maximum copper extraction indicate that enhanced leaching performance cannot be attributed to surface-area enlargement alone and is associated with the combined effects of particle fragmentation, accessible surface-area changes, and milling-induced structural modification.
This research examines transforming contaminated mining waste into mineralogically stable material using density separation and high-temperature settling. Samples with elevated metal concentrations were collected from century-old landfill sites, separated from overburden and organic material, and subjected to density separation and thermal treatment at 1300 and 1400 °C. This process significantly reduced heavy metal content, lowering cumulative lead, cadmium, and arsenic concentrations from 2440 g/t to below 60 g/t, and efficiently removed volatile elements such as arsenic and zinc. Sulfur content decreased from 1.67 wt% to 0.21 wt% after treatment at 1400 °C, while oxygen content increased from 31.9 wt% to 36.29 wt% and 34.2 wt% after treatment at 1300 °C and 1400 °C, respectively. Mineralogical, differential thermal and chemical analyses confirmed the thermal stability of the resulting material up to at least 350 °C, with fayalite and magnetite as the predominant minerals. Final concentrations of heavy metals were below EU natural soil background levels, supporting the material’s safe and sustainable reuse in environmental applications. By focusing on remediation of metal-rich hotspots, this approach enables practical reclamation of mining waste areas and supports the valorization of legacy copper slag deposits, advancing environmental restoration and circular economy goals.
Calcined iron ore production is of strategic importance; however, it is associated with considerable environmental burdens due to its high carbon footprint. In this study, the environmental impacts of calcined iron ore production were evaluated using a cradle-to-gate life cycle assessment (LCA) approach. The assessment was conducted using the ReCiPe 2016 Midpoint (H) V1.13 methodology, the Ecoinvent 3.12 database, and SimaPro 10.5.0.3 software. In addition, four alternative scenarios were developed to investigate potential strategies for reducing environmental impacts. The results identified the calcination stage as the dominant environmental hotspot across most impact categories. Calcination accounted for 98.8% of the total global warming potential, 99.1% of the terrestrial acidification potential, and 99.3% of the fine particulate matter formation potential. Scenario analysis revealed that technological interventions focusing on a single parameter were unable to simultaneously reduce all environmental impacts and, in some cases, introduced additional environmental trade-offs. Scenario 4, which involved the substitution of the kiln fuel with natural gas, achieved substantial reductions in air quality-related impact categories. Nevertheless, due to the predominance of process-related emissions, the reduction in global warming potential was limited to 15.77%. Scenario 2, incorporating photovoltaic-based electrification, reduced dependence on fossil energy sources but resulted in increased ecotoxicity impacts associated with the photovoltaic panel supply chain. Based on these findings, a hybrid strategy combining natural gas utilization in the calcination process with the electrification of open-pit mining and crushing–screening operations is proposed as a promising pathway for improving the environmental performance of calcined iron ore production. Overall, the proposed hybrid system could reduce emissions by 145 kg CO2 eq per tonne of product (19.5%), strengthening the long-term environmental and economic performance of calcined iron ore production.
The global mining sector is evolving rapidly, making it increasingly important to monitor its activities and associated emissions in support of sustainable development. In this paper, we use nitrogen oxide (NOx) emissions derived from satellite observations as an indicator of mining activity. Altogether, we characterize changes in nine open-pit mining sites in Mongolia and Inner Mongolia, China. The analyzed mines represent a range of minerals, including copper, rare earths, coal, and gold. When available, we relate the satellite-based NOx emission estimates to reported production data and discuss each mining site individually. Overall, the satellite-derived emissions capture the major temporal changes in mining activity indicated by production data and satellite imagery. One notable finding is the substantial increase in NOx emissions from the Mongolian Tavan Tolgoi and Nariin Sukhait/Ovoot Tolgoi coal mines after 2022, coinciding with increased coal production, of which up to 95% is exported to China. More broadly, the results demonstrate that mining activity can be tracked using open-access satellite observations, particularly in regions where other sources of information are limited. As an example, this approach could support systematic monitoring of future electrification in open-pit mining operations.
Fuzzy-based methods have become increasingly relevant in rock engineering because of their ability to represent uncertainty, imprecision, and gradual transitions inherent in geological and geotechnical information. This study presents a comprehensive bibliometric and science mapping analysis of fuzzy logic applications in rock engineering from 2000 to 2025, based on 1538 articles. The analysis examines the evolution, conceptual and intellectual structure, thematic development, and geographical distribution of the field. The findings reveal a marked expansion of research activity, with artificial neural networks, blasting, fuzzy logic, ANFIS, ground vibration, machine learning, and rock-property estimation forming major components of the research landscape. Predictive applications are particularly concentrated on rock-property estimation and blasting-related responses, while fuzzy multi-criteria approaches constitute an important direction in risk assessment and engineering decision-making. Temporal and thematic patterns indicate an increasing integration of fuzzy reasoning with machine learning, optimization, and other data-driven techniques, reflecting a transition toward hybrid computational frameworks. The intellectual structure further demonstrates strong connections between rock mechanics, engineering geology, and computational intelligence. Despite this methodological diversification, comparatively limited attention to several engineering-critical outcomes, including flyrock, backbreak, and blast damage, indicates opportunities for further investigation. By identifying the principal applications, methodological transitions, knowledge foundations, and research gaps of the field, this study provides rock engineering researchers and practitioners with a structured basis for directing future research and developing uncertainty-aware decision-support approaches.
