For centuries, Chile has been a territory with significant mining activity, resulting in associated social benefits and impacts. One of the main challenges the country faces today is the presence of a great number of mine tailings containing heavy metals, such as Cu, Cr, Ni, Zn, Pb, As, Cd, and Fe, which make up a potential risk for the population. This study is intended to develop a methodology for determining tailings requiring urgent treatment in Chile, based on risks associated with heavy metals. Geochemical data from 530 Chilean tailings were compared to the Dutch norm and the Canadian and Australian soil quality guidelines for residential use. Additionally, criteria about residents and water bodies were used, considering a 2-km area of influence around tailings. To do this, QGIS (Böschacherstrasse 10a CH-8624 Grüt (Gossau ZH), Zurich, Switzerland), a geospatial tool, was used to geolocate each deposit, considering regions, communes, rivers, lakes, and populated areas. To evaluate potential ecological contamination risks, Hakanson’s methodology was used. Results revealed the presence of 12 critical tailings in Chile that require urgent treatment. From the 530 tailings evaluated, 195 are located at less than 2 km from a populated area and 154 at less than 2 km from a water body. In addition, 347 deposits require intervention: 30 on Cu, 30 on Cr, 13 on Zn, 69 on Pb, 138 on As, 1 on Cd, and 5 on Hg.

The mining industry of the last few decades recognizes that it is more profitable to simulate model using historical data and available mining process knowledge rather than draw conclusions regarding future mine exploitation based on certain conditions. The variability of the composition of copper leach piles makes it unlikely to obtain high precision simulations using traditional statistical methods; however the same data collection favors the use of softcomputing techniques to enhance the accuracy of copper recovery via leaching by way of prediction models. In this paper, a predictive modeling contrasting is made; a linear model, a quadratic model, a cubic model, and a model based on the use of an artificial neural network (ANN) are presented. The model entries were obtained from operation data and data of piloting in columns. The ANN was constructed with 9 input variables, 6 hidden layers, and a neuron in the output layer corresponding to copper leaching prediction. The validation of the models was performed with real information and these results were used by a mining company in northern Chile to improve copper mining processes.

This document presents a proposal for designing an expert system in the Gabriela Mistral Division’s crushing plant belonging to Codelco (Chile) with the objective of maximizing stacked tonnage, allowing the improvement of operational variables that directly interact with the crushing process. In addition, this study considers the impact that occurs in both the process and operational continuity regarding the standardization of the system. In the first stage, a survey and analysis of historic operation data was carried out, which allowed the definition of benchmarking indicators. Subsequently, both modalities of operation were compared, monitoring processed tonnage and detentions related to operational failures. As a result, significant differences were observed in the performance of the critical line operating with expert control, with a 55% reduction in the detentions referred to operational failures. Added to this is the benefit of low cost and improved quality as the control provides an analysis of the variables in reduced time intervals, which is superior to human control.

The method used to extract copper from its ores depends on the nature of the ore. The main process currently to separate copper from sulphide ores is the smelting process. The concentrated ore is heated strongly with silicon dioxide (silica), calcium carbonate and oxygen enriched air in a furnace or series of furnaces which is carried out using the injection of the air for oxidation the Fe and Si present in the raw material. Oxygen can be produced using several different methods. One of these methods is Air separation process, which separates atmospheric air into its primary components, typically nitrogen and oxygen, and sometimes also argon and other rare inert gases by cryogenic distillation. In this paper, simulation of air separation units (ASUs) was studied using Aspen Hysys^®. The obtained simulation and model was validated with the operational data from the Oxinor I of Air Liquide S.A Plant. The ASU was divided into subsystems to perform the simulations. Each subsystem was validated separately and later on integrated into a single simulation. An absolute error of 1% and 1.5% was achieved between the simulated and observed the process variables(s). This indicated that Aspen Hysys^® has the thermodynamic packages and required tools to perform simulations in cryogenic processes at industrial scale.

Coated bubbles arise in flotation columns and solvent extraction contactors; however, no unified correlation predicts their drag coefficient from dimensionless parameters across multiple fluid systems. This work develops an empirical power-law correlation from 2,268 observations spanning three continuous phases (water, 26 wt% NaCl, 30 wt% sucrose) and two organic phases (kerosene and sunflower oil), covering viscosity ratios from 0.34 to 58 and Reynolds numbers from 56 to 894. Drag modification by oil coating depends critically on the viscosity ratio: coated bubbles exhibit higher drag than uncoated bubbles in water and salt systems but lower drag in the sucrose system, where the coating is less viscous than the continuous phase. Three power-law correlations were developed: an uncoated bubble model (Mean Absolute Percentage Error (MAPE) = 1.2%), a coated-bubble model (MAPE = 6.9%), and a unified model (MAPE = 2%) that reduces to the uncoated bubble model when no coating is present. The Reynolds and Eötvös numbers are the dominant parameters; Morton number, coating fraction, and viscosity ratio are secondary within the tested parameter window (0.34 ≤ η ≤ 58). Validation against 25 independent external datasets (2,882 uncoated bubble and 28 coated-bubble measurements, 2,910 total) yielded a mean R² = 0.97 and a MAPE of 5–6% for the uncoated bubble subset; coated-bubble external validation, while consistent with model predictions, is based on a limited number of data points and should be interpreted as preliminary. The model is valid for 30 < Re < 1000 and 0.1 < Eo < 10, providing empirical drag closure for drift-flux and multiphase flow models applied to single-bubble systems under quiescent conditions within this parameter window.

A new technology for solvent extraction has been developed. This innovation, Hollow Drop, merges flotation with solvent extraction by using air bubbles coated with an insoluble extractant material. While the concept of coated bubbles is not new, our work focuses on advancing this idea by generating a swarm of coated bubbles to scale up the process. Experimental tests were conducted using dissolved copper solutions, SX extractants, Iodine solutions, and Kerosene, achieving copper recoveries of 70% with aqueous-to-organic (A/O) ratios of 17. These results demonstrate that the coated bubble process can operate continuously and holds promise for industrial-scale implementation.