The global problem of water scarcity is exacerbated by the continued contamination of potable water sources. This preliminary study investigates the potential of a hazardous industrial jarosite waste to adsorb As(V) and Cr(III) from contaminated waters. The results showed that this mining waste effectively adsorbed both As(V) and Cr(III), demonstrating its potential as a low-cost and sustainable solution for water remediation along with the use of a hazardous waste that also contaminates. The adsorption process was optimized, and the effects of various parameters on the adsorption capacity were investigated. The findings of this study suggest that the use of toxic mining residues in porous concrete could provide a promising approach for the removal of toxic heavy metals from polluted water sources, contributing to the development of more sustainable and environmentally friendly water treatment technologies. A maximum adsorption of 90.6% of As(V) and 96.3% of Cr(III) was achieved, and it was verified that the industrial jarosite initially contained about 0.44% As, which was later leached during decomposition; again, the industrial jarosite was able to re-adsorb both As(V) and Cr(III).

The mining process has always generated residues that often still contain valuable metals. This work focuses on the re-processing of these residues by evaluating and comparing two reagents that can extract valuable silver from tailings. A preliminary kinetic study of silver leaching was conducted in both cyanide and thiosulfate media, and a comparative analysis was performed to determine the optimal leaching conditions. The mining residue was found to contain 83 g/ton of Ag and 0.28 g/ton of Au. This study, carried out for both cyanide and thiosulfate, involved determining the effects of the reagent concentration, temperature, and pH. For the reagent concentration, apparent reaction orders of n = 0.0531 and n = 0.41 were determined for cyanide and thiosulfate, respectively. The apparent activation energies were E_a = 21.7 kJ/mol for cyanide and E_a = 23.13 kJ/mol for thiosulfate, indicating mixed-control kinetics in both systems. Under the conditions studied, maximum silver recoveries of 92% and 93% were achieved using cyanide at 323 K and thiosulfate at 328 K, respectively. Notably, pH had no significant effect on the cyanide system, whereas for thiosulfate, the reaction order was n = 3.27. The results show similar behavior in both systems, but the thiosulfate system is more attractive due to its non-toxic nature and lower cost.

Mining operations for the extraction and processing of useful geological materials are common anthropogenic activities that generate enormous quantities of waste that can alter the natural balance. In this way, soil, water and air are contaminated with waste that contains heavy metals, acids and many other compounds that are toxic to the environment and health. Minimizing and eradicating these pollutants is an arduous but necessary task. That is why, in the present work, the physical, chemical and mineralogical characterization of metallurgical mining waste was carried out using analytical techniques. Tests and trials were also carried out to assess the degree of toxicity, corrosiveness and chemical capacity to generate acid mine drainage. In general, it was found that this waste does not contain active toxic elements or elements that could be corrosive. However, due to the residual content of sulfide minerals, it can generate acid mine drainage, which could contaminate groundwater. Likewise, it can be seen that due to the content of heavy metals and minerals such as quartz and feldspars, this waste can be reprocessed to recover metals and subsequently separate, by flotation, the associated silica and feldspar, which can be used in the glass and cement industry.

SAG grinding mills represent critical energy-intensive operations in copper concentrators, accounting for 30%–50% of total plant energy consumption. The accurate prediction of mill power draw and production rate under varying operational conditions is essential for real-time control, production planning, and energy management. This study presents a comprehensive comparison of ML algorithms for modeling Production and Power in a Chilean copper mining industry. Deterministic and stochastic models were fitted and validated using industrial data from a Chilean copper operation. More representative models were re-estimated and subsequently evaluated under different operating regimes to examine their predictive performance under aggregated conditions of the feeding variables. This procedure allowed for the identification of the modeling approaches that provide the most robust performance across varying operational regimes. The results show that XGB achieved the best predictive performance, with test RMSE and R² values of 87.98 and 97.35% for SAG Production, and 431.11 and 95.11% for SAG Power, respectively. Stochastic approaches provided complementary uncertainty quantification, supporting risk-informed decision making under variable operating conditions. The analysis by operational regime indicates that XGB presents better fit in the Thick hydraulic regime, for both responses’ variables, which could be explained why a dense pulp operation provides more predictable grinding dynamics. The comparative analysis reveals trade-offs between model complexity, interpretability, computational requirements, and predictive performance, offering practical guidance for selecting appropriate modeling frameworks based on specific operational objectives and data availability in mineral processing applications.