Copper is one of the most demanded minerals by the global industrial sector, with approximately 20 million tons mined worldwide each year. Silver, another important technical and precious metal, sees production around 26 kt/y. The general trend of declining average grades in these deposits has made mining low-grade ores a reality for many mines worldwide. The sensor-based sorting has emerged as a significant pre-concentration solution for these cases. This study investigates the applicability of this technique to copper ore samples from the Cerro do Andrade deposit, located in Caçapava do Sul, southern Brazil. The primary product of interest is copper (Cu), with silver (Ag) as a by-product. Pre-concentration tests are ongoing at the UFRGS Mineral Processing Laboratory (LAPROM) using a dual-energy X-ray transmission (DE-XRT) sensor sorter. Were analyzed 32 ore samples (64-16 mm size fraction). Relative density histograms and false-color images were generated. This data, along with Cu and Ag grades, was assessed in Excel to estimate recoveries (metallurgical and mass), concentration factors, and Cu and Ag grades in tailings fractions. Some scenarios of tailings generation and reuse were also explored. The analyzed samples had an average of 0.83% Cu and 7.31 g/t Ag. Pre-concentration simulations yielded Cu grades in the product ranging from 0.9% to 1.0% and Ag grades of 7.8 to 8.8 g/t in the Range A. Waste grades varied from 0.02-0.20% Cu and 0.7-2.2 g/t Ag. Range B exhibited more stable Cu and Ag grades in the product (around 0.9% Cu and 11 g/t Ag). Mass recoveries ranged from 92-77% in the Range A and reached 70% in the Range B. Metallurgical recoveries remained high: 99-95% Cu in the Range A and above 94% in the Range B. Silver recoveries were also promising (99-93% in Range A, 90% in Range B). Considering a feed of 1,000 kt/y, estimated ROM mass after pre-concentration ranged from 833-675 kt/y of product and 167-325 kt/y of coarse tailings. Currently, these preliminary results hold great promise, demonstrating the potential for achieving significant outcomes through the implementation of sensor-based sorting pre-concentration in the Andrade Project. 

ESG criteria have increasingly been used by investors to measure sustainability levels for investment in a company or business. In the mining sector, most, if not all, of the available commercial software used for decision-making support, does not include sustainability indicators such as carbon footprint, water consumption, social license, and other factors commonly associated with ESG practices. In this context, the present work presents the current development state of an open-use, cloud-based computational tool called MAFMINE ESG, which aims to incorporate environmental, social, and governance (ESG) sustainability indices to the usual technical-economical parameters used into the preliminary evaluation of mining projects.

The MAFMINE ESG consists of the expansion version of “MAFMINE 3”, an already existing tool developed for the economic evaluation of mining projects (available at https://www.mafmine.com.br/v3/). The core of MAFMINE ESG consists of using parametric models supported by a relatively simple set of inputs (process targets and technical coefficients specified by the user), providing preliminary estimates of sustainability indicators as model outputs. These indicators are quantitative indices associated with one of the following ESG model parameters: emissions, water management, land use, social conflicts, automation and digitalization, and governance. For example, the following indicators are associated with the "water management" parameter: total water withdrawn, affected water sources, % of water reused/recycled, and quality and destination of effluents. The parameterization of indices is established through regression analysis, within specific validation ranges, using available databases for each parameter, such as the historical series report databases from the Intergovernmental Panel on Climate Change (IPCC) for emissions and the Global Reporting Initiative (GRI) for water management.

In addition to presenting the general structure of the software under development, this paper aims to discuss the challenges associated with selecting the indexing factors linked to each index to compare project scenarios considering the realities of different countries together with a preliminary simulation for the case of a base metals mining venture.

In the mining industry, one of the main concerns is estimating costs in order to carry out projects efficiently and profitably. MAFMINE is a tool that provides quick and effective results for decision-making. My research was carried out using two parametric equation modeling methodologies, mainly manuals were studied (Bureau of Mines Cost Estimating System Handbook and Costs and Cost Estimation of T. Alan O'Hara and Stanley C. Suboleski), this work focuses on the area of mineral processing, the estimates for operations in processing plant. The parametric equations proposed and added in the plant area make it possible to estimate the processing costs of various circuits, which are fundamental for future plant facility design. These equations also make it possible to estimate the cost of implementations within mineral processing, which in turn allows the most appropriate option to be selected based on the characteristics of each ore. Each methodology has its own advantages and disadvantages, so it was necessary to select appropriate standardization factors. Once the cost estimation tool had been updated with the new parametric equations obtained, it was applied to specific case studies. The results obtained demonstrate the tool's reliability. In conclusion, the study of parametric equation modeling methodologies has made it possible to update a cost estimation tool in the area of mineral processing plants. The inclusion of more parametric equations to estimate mineral processing costs will enable better decisions to be made.