Use of Alternative Water Resources in Copper Leaching Processes in Chilean Mining Industry—A Review

Heavy metal geochemistry and toxicity assessment of water environment from Ib valley coalfield, India: Implications to contaminant source apportionment and human health risks

The present study aims to investigate the hydrogeochemical evolution of heavy metals and assesses impacts of mining activities on the groundwater resources and potential human health risks in the coal mining areas of Ib valley coalfield. In this perspective, a total of one hundred and two mine water and groundwater samples were collected from different locations. The water samples were analysed for some selected heavy metals i.e. Mn, Cu, Pb, Zn, Ni, Co, As, Se, Al, Sr, Ba, Cd, Cr, V and Fe using ICP-MS. In addition, pH and SO42- concentration were also measured following APHA procedure. The water pH in the Ib valley coalfields ranged from 3.26 to 8.18 for mine water and 5.23 to 8.52 for groundwater, indicating acidic to alkaline nature of water. Mn in mine water and Zn in groundwater environment were observed as the most dominant metals. The water hazard index (WHI) reflects that around 80% of mine water are non-toxic (WHI<5), 5% slightly toxic (510) and 15% extremely toxic (WHI>15). Relatively high pH and low concentration of dissolved metals and SO42- in groundwater as compared to mine water indicate lesser impact of mining activities. The calculated drinking water quality index (DWQI) suggests that Mn, Al, Ni and Fe in mine water and Mn, Fe, Ni and Pb in groundwater were the major objectionable metals which caused the water quality deterioration for drinking uses. Further, the non-carcinogenic health risk assessment for adult male, female and child populations identifies Co, Mn, Ni as the key elements making the water hazardous for human health. Comparatively higher ratio of ingestion rate and body weight in child population might be causing higher health risks in child population as compared to adult male and adult female population.

Using structural equation modeling to assess the genotoxic and mutagenic effects of heavy metal contamination in the freshwater ecosystems: A study involving Oreochromis niloticus in an urban river

Chemical pollutants represent a leading problem for aquatic ecosystems, as they can induce genetic, biochemical, and physiological changes in the species of these ecosystems, thus compromising their adaptability and survival. The Capibaribe River runs through the state of Pernambuco, located in Northeastern Brazil, and passes through areas of agricultural cultivation, densely populated cities, and industrial centers, primarily textiles. Despite its importance, few ecotoxicological studies have been conducted on its environment, and knowledge about pollution patterns and their effects on its biota is still being determined. The objective of this study was to evaluate the water quality and the damage supposed to be caused by pollutants on the DNA specimens of Nile tilapia (Oreochromis niloticus) obtained from seven strategic points of Capibaribe. Tilapia specimens and water were collected during the rainy and dry seasons from 2015 to 2017. The following characteristics were analyzed: physicochemical (six), metal concentration (seven), local pluviosity, micronuclei, and comet assay. The physicochemical and heavy metal analyses were exploratory, whereas the ecotoxicological analyses were hypothetical. To verify this hypothesis, we compared the groups of fish collected to the results of the micronuclei test and comet assay. We created a Structural Equation Model (SEM) to determine how each metal's micronuclei variables, damage index, pluviosity, and concentration were related. Our results demonstrated that the highest values for markers of genetic damage were detected at points with the highest heavy metal concentrations, especially iron, zinc, manganese, chromium, and cadmium. The SEM demonstrated that metals could explain the findings of the genotoxicity markers. Moreover, other pollutants, such as pesticides, should be considered, mainly where the river passes through rural areas. The results presented here demonstrate that the Capibaribe River has different degrees of contamination and confirm our hypothesis.

Low-Cost Sensors Technologies for Monitoring Sustainability and Safety Issues in Mining Activities: Advances, Gaps, and Future Directions in the Digitalization for Smart Mining

Nowadays, monitoring aspects related to sustainability and safety in mining activities worldwide are a priority, to mitigate socio-environmental impacts, promote efficient use of water, reduce carbon footprint, use renewable energies, reduce mine waste, and minimize the risks of accidents and fatalities. In this context, the implementation of sensor technologies is an attractive alternative for the mining industry in the current digitalization context. To have a digital mine, sensors are essential and form the basis of Industry 4.0, and to allow a more accelerated, reliable, and massive digital transformation, low-cost sensor technology solutions may help to achieve these goals. This article focuses on studying the state of the art of implementing low-cost sensor technologies to monitor sustainability and safety aspects in mining activities, through the review of scientific literature. The methodology applied in this article was carried out by means of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and generating science mapping. For this, a methodological procedure of three steps was implemented: (i) Bibliometric analysis as a quantitative method, (ii) Systematic review of literature as a qualitative method, and (iii) Mixed review as a method to integrate the findings found in (i) and (ii). Finally, according to the results obtained, the main advances, gaps, and future directions in the implementation of low-cost sensor technologies for use in smart mining are exposed. Digital transformation aspects for data measurement with low-cost sensors by real-time monitoring, use of wireless network systems, artificial intelligence, machine learning, digital twins, and the Internet of Things, among other technologies of the Industry 4.0 era are discussed.

Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry

Considering the continuous increase in production costs and resource optimization, more than a strategic objective has become imperative in the copper mining industry. In the search to improve the efficiency in the use of resources, the present work develops models of a semi-autogenous grinding (SAG) mill using statistical analysis and machine learning (ML) techniques (regression, decision trees, and artificial neural networks). The hypotheses studied aim to improve the process's productive indicators, such as production and energy consumption. The simulation of the digital model captures an increase in production of 4.42% as a function of mineral fragmentation, while there is potential to increase production by decreasing the mill rotational speed, which has a decrease in energy consumption of 7.62% for all linear age configurations. Considering the performance of machine learning in the adjustment of complex models such as SAG grinding, the application of these tools in the mineral processing industry has the potential to increase the efficiency of these processes, either by improving production indicators or by saving energy consumption. Finally, the incorporation of these techniques in the aggregate management of processes such as the Mine to Mill paradigm, or the development of models that consider the uncertainty of the explanatory variables, could further increase the performance of productive indicators at the industrial scale.

Two-Stage Oxidative Leaching of Low-Grade Copper-Zinc Sulfide Concentrate

Bioleaching may be effectively used to extract nonferrous metals from sulfide ores and concentrates. At the same time, some minerals are refractory and their bioleaching rate is often comparatively low that does not allow the required metal extraction rate to be achieved. In the present work, we studied the two-stage process, which included stages of biological and chemical leaching, to improve copper extraction from low grade Cu-Zn sulfide concentrate containing chalcopyrite, tennantite, pyrite, and sphalerite. Bioleaching was conducted in the continuous mode in three laboratory scale reactors connected in series. The pulp density was 10% and the residence time was 7 days. The temperature was 40 °C in the 1st reactor and 50 °C in the 2nd and 3rd reactors. Bioleaching allowed the extraction of 29.5 and 78% of Cu and Zn, respectively. The solid bioleach residue obtained was then treated for additional Cu and Zn recovery using high temperature leaching at 90 °C for 25 h. The liquid phase of the bioleaching pulp contained Fe³⁺ ions, which is the strong oxidant, and the leach solution was supplemented with NaCl. In the presence of the maximal NaCl concentration (1 M), Cu and Zn extraction reached 48 and 84%. Thus, two-stage leaching may allow to increase bioleaching efficiency and may be used to improve the bioleaching rate of refractory minerals, such as chalcopyrite.