Geometallurgical model of the characterization of clays for flotation efficiency using machine learning methodology

• Autores: Castro Andrade, Julio Alejandro, Calderón Celis, Julia Marilú
• Formato: artículo
• Fecha de Publicación: 2021
• Institución: Universidad Nacional Mayor de San Marcos
• Repositorio: Revistas - Universidad Nacional Mayor de San Marcos
• Lenguaje: español
• OAI Identifier: oai:ojs.csi.unmsm:article/21706
• Enlace del recurso: https://revistasinvestigacion.unmsm.edu.pe/index.php/iigeo/article/view/21706
• Nivel de acceso: acceso abierto
• Materia: Mineralogy; x-ray diffraction; near infrared spectroscopy; chemometrics; machine learning; cross validation; froth flotation; Mineralogía; difracción de rayos x; espectroscopia del infrarrojo cercano; quimiometría; validación cruzada; flotación

The geometallurgical model is the result of the integration of the disciplines of geology, mining and metallurgy in order to add value and reduce risk in a process such as the flotation efficiency of chalcopyrite. The objective of this study was to calibrate models to quantify the content of clays and gangas of a hydrothermal deposit of the porphyry copper-gold type by NIR spectroscopy. The selected methodology was to design a chemometric model based on 173 diamond drill composites to which X-ray diffraction tests and near infrared spectroscopy were performed, validating the results with a cross-validation through a machine learning methodology. The creation of the models was carried out by means of a regularized non-linear regression by the Ridge method. Low linearity models were obtained for calcite and plagioclase minerals, with R2 values (0.51 and 0.78, respectively). The regression model presents a linearity for smectite, quartz, orthoclase and muscovite minerals showed a high R2 (0.95, 0.93, 0.64 and 0.59, respectively). The results found for the content of clays and gangue indicate that X-ray diffraction analyzes can be largely replaced by spectral models. In the case of calcite and plagioclase, it would be convenient to carry out a characterization campaign, in order to improve the model and to be able to replace the x-ray diffraction analyzes for these species, which will allow to generate a geometallurgical model in a quick and easy way. efficient with a semi-quantitative method.