Environment
Marco Antonio Cotrina Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Juan Antonio Vega-Gonzalez; Moises Bartolome Guia-Pianto
Abstract
The Quiulacocha tailings deposit in central Peru, containing 70 Mt of historical mine waste, presents both environmental risks and opportunities for secondary metal recovery. This study applies data-driven machine learning techniques to estimate the remaining silver resources using 927 one-meter composites ...
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The Quiulacocha tailings deposit in central Peru, containing 70 Mt of historical mine waste, presents both environmental risks and opportunities for secondary metal recovery. This study applies data-driven machine learning techniques to estimate the remaining silver resources using 927 one-meter composites from 40 vertical drillholes. Three supervised learning models—Random Forest (RF), k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost)—were trained using spatial coordinates (X, Y, Z) as the sole input features. Model validation was performed using leave-one-out cross-validation (LOOCV), and results were benchmarked against ordinary kriging (OK). Among the models, RF delivered the highest predictive performance (mean error = 0.53 g/t, RMSE = 7.21 g/t, R = 0.82), outperforming OK (R = 0.63, RMSE = 10.47 g/t). Block model predictions indicated higher silver content from machine learning models: 1,532.86 t (RF), 1,542.16 t (XGBoost), and 1,492.09 t (KNN), compared to 1,463.73 t from OK. Additionally, XGBoost maintained superior grade-tonnage relationships under elevated cutoff thresholds, highlighting its potential to delineate high-grade subdomains within the deposit. These findings confirm the value of machine learning in resource estimation under conditions of low spatial continuity, such as tailings, where material mixing and irregular deposition patterns limit correlation across space.
Environment
Ayodele Owolabi; Sunday Olabisi Daramola
Abstract
Nigeria is abundantly blessed with solid mineral resources such as copper, gold, and tantalite, which are essential for the economic growth of the country. The extraction of these mineral resources comes with the generation of huge amount of waste. This study examines the possibility of utilizing some ...
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Nigeria is abundantly blessed with solid mineral resources such as copper, gold, and tantalite, which are essential for the economic growth of the country. The extraction of these mineral resources comes with the generation of huge amount of waste. This study examines the possibility of utilizing some mine wastes from Jos, Nigeria, in embankment construction by subjecting them to relevant laboratory geotechnical experiments. The results indicates that the overburden materials contain clay-sized fraction ranging 5-20%, while the sand fraction ranged 42-82%, which is an indication of the predominance of sand size particles. On the other hand, the clay-sized particles in the tailings range 5-21%, while the sand fractions range 65-80%. The overburden materials recorded liquid limit values ranging 26-48% and plasticity index ranging 6.3-21%, while the liquid limit and plasticity index of the tailings range 23-32.8% and 6.2-11.6%, respectively. The maximum dry density (MDD) and optimum moisture content (OMC) of the overburden materials vary 1.84-1.98 mg/m3 and 1.4-17.2%, respectively, with an average of 1.89 mg/cm3 and 16%. On the other hand, the tailings recorded MDD ranging 1.88-2.06 mg/m3 with their OMC ranging 14.4-16% with an average 14.86%. The soaked California bearing ratio (CBR) of the overburden materials range 27-32%, while that of tailings ranges 25-32%. The geotechnical evaluation of the overburden materials and tailings reveals that most of the materials are suitable for embankment construction. However, the high linear shrinkage of some wastes renders them unsuitable.
S. Safari Sinegani; A. Abedi; H. Asghari; A. A. Safari Sinegani
Abstract
Phytoremediation is a technology that uses plants for the remediation of the contaminated soils, sediments, tailings, and groundwaters. In this work, the ability of TrifoliumAlexanderium for the phytoremediation of the tailings soil in the Anjir-Tange coal washing plant was investigated. For this purpose, ...
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Phytoremediation is a technology that uses plants for the remediation of the contaminated soils, sediments, tailings, and groundwaters. In this work, the ability of TrifoliumAlexanderium for the phytoremediation of the tailings soil in the Anjir-Tange coal washing plant was investigated. For this purpose, Trifolium sp. was cultivated in three soils consisting of the tailings dam, an agricultural soil, and a mixed soil. The concentrations of Fe, Cr, Cd, and P, and the factorsTF (translocation factor), BCF (bio-concentration factor), and BAF (bio-accumulation factor) were measured in the soils and plants after the harvest of Trifolium sp. The results obtained showed that BCFs in the agricultural soil, tailings dam, and mixed soil were 10.4, 12.24, and 7.23, respectively. These results also showed that TrifoliumAlexanderiumwas able to accumulate Cd in the root tissues and stabilize it, and thus it can be regarded as an appropriate species for the stabilization of the Cd ions in the contaminants and soils.The results obtained suggest that this plant can be a good candidate for use in the revegetation and phytostabilization of the Cd-contaminated lands in the region.
