No abstract

Copper mining activity is going through big changes due to increasing technological development in the area and the influence of industry 4.0. These changes, produced by technological context and more controls (e.g., environmental controls), are also becoming visible in Chilean mining. New regulations from the Chilean government and changes in the copper mining industry (such as a trend to underground mining) are fostering the search for better results in typical processes such as leaching. This paper describes an experience using artificial intelligence techniques, particularly random forest, to develop predictive models for copper recovery by leaching, using data from an enterprise present in northern Chile for more than 20 years. Two models, one of them with actual operational data and another one with data generated in a controlled environment (piling) are presented. Well-classified values of 98.90% for operational data and 98.72% for pile/piling data were obtained. The methodology devised for the study can be transferred to piling columns or piles with other characteristics, though the operation must focus on copper leaching. It can even be transferred to other leaching processes using another type of mineral, with proper adjustments.

This work evaluates the potential of Adesmia atacamensis, an endemic species from the North of Chile, in phytoremediation processes of copper mining tailings. The selection of this species was based on the fact that its presence was found in 4 copper mining companies that are close to each other, its endemic quality, and its great capacity to adapt to the adverse climatic conditions of the sector of the project, characterized by having a semi-arid climate. In the experiment, the concentrations of 5 metals of environmental connotation for the country's mining were measured: Cu, Fe, Cd, Pb, and Zn; however, given the small concentrations in Cd, these could not be measured by the team. The applied experimental design quantified the variation of bioconcentration and translocation factors (BCF and TF) for the following treatments: (1) tailing control without amendment (T0), (2) tailing plus 4% CaCO 3 + 3% vermicompost (VC), and (3) tailing + 8% CaCO 3 + 6% VC. In addition, for treatments T1 and T2 (T0 was not considered as it is the control treatment), the following levels of mycorrhiza were considered: 0, 10, 15, and 20 g m −2. The Baker and Brooks criteria and the BCF were used to evaluate the species as hyperaccumulators. Regarding the first criterion, high concentrations of copper were found in the shoots (shoots) of the specimens, which generally exceeded 1000 mg kg −1 , with an average of 1513 mg kg −1 , which allowed classifying the species as a copper hyperaccumulator; however, when compared with the BCF criterion, given that all the values were less than 1, they indicated that the species was an excluder of all the evaluated metals. Given the high concentrations of metals in the tailings, in this work, the plant has been considered as a hyperaccumulator of copper. Another indicator was the TF, which, for all experiments, resulted in a value greater than 1 for Fe, Pb, and Zn, which shows that A atacamensis translocates effectively these metals from the roots toward the aerial part and, therefore, presents the potential to accumulate metals in the aerial part. Regarding the treatments carried out, no significant impact was detected.

No abstract

High-speed machining is a technique that maintains a high interest in the manufacture of metal parts for the excellent results it provides, both in surface finish and in economic benefits. In the industry, the tendency is to incorporate data management and analysis techniques to generate information that helps improve the surface roughness results in machining. A good alternative to improve the surface quality results in the manufacture of metal parts is using predictive models of the surface roughness. In this document, we present work done with experimental data obtained from two high-speed machining (HSM) machines with different types of tools and cutting conditions, conducted under an experimental design with interest in three of factors commonly studied to generate surface roughness models: tool characteristics, cutting conditions, and characteristics of the machined material. Steel and aluminum alloys were used in the experimentation. The results are contrasted with prior experiences that use the same experimental design but with different soft computing techniques and they are also contrasted with the results of similar previous works. Our results show accuracies ranging from 61.54% to 88.51% on the datasets, which are competitive results when compared with the other approaches. We also find the axial cut-depth is the most influential feature for the slots datasets and the hardness and diameter of the cutting tool are the most influential features for the geometries datasets.