Geometallurgy: A tool for optimizing the value of deposits and generating more efficient mining operations
Mining operations are conditioned to geometallurgical attributes that are inherently variable due to the natural heterogeneity of the deposits. In view of this, geometallurgy as an emerging discipline provides a fundamental support to evaluate the uncertainty in primary and response variables, howev...
| Autores: | , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | Ecuador |
| Recursos: | Universidad Central del Ecuador |
| Repositorio: | Revista FIGEMPA: Investigación y Desarrollo |
| Idioma: | español |
| OAI Identifier: | oai:revistadigital.uce.edu.ec:article/8384 |
| Acesso em linha: | https://revistadigital.uce.edu.ec/index.php/RevFIG/article/view/8384 |
| Access Level: | acceso abierto |
| Palavra-chave: | Geometalurgia minería yacimiento geoestadística aprendizaje automático mineralogía Geometallurgy mining deposit geostatistics machine learning mineralogy |
| Resumo: | Mining operations are conditioned to geometallurgical attributes that are inherently variable due to the natural heterogeneity of the deposits. In view of this, geometallurgy as an emerging discipline provides a fundamental support to evaluate the uncertainty in primary and response variables, however, for its use it is necessary to know the theoretical bases that allow its applicability. Consequently, the objective of this research consisted in elaborating a literature review and to present the fundamentals that support geometallurgy together with case studies where this innovative discipline has been successfully used. For this purpose, the methodology was to use a search strategy in the Scopus database considering key words, using Boolean operators, and among the articles found, the most relevant ones were chosen and a bibliometric analysis was carried out using the VOSviewer software; in addition, complementary information was collected in the indicated database taking into account the focus of this research and conference papers, books and NI 43 - 101 report were also included, all of them in english language. The results show that the effect and proper understanding of geometallurgical variables in the sampling, domain definition and subsequent modeling is fundamental for an adequate mapping of the variability in the ore behavior during processing. Finally, it is concluded that the use of mineralogy to determine species that interfere in the treatment, geostatistics and particularly cokriging for the prediction of copper mass in the feed and concentrate from which the recovery can be determined and machine learning as a tool to elaborate the geometallurgical modeling, are techniques that allow optimizing the value of the deposit and manage the mining operations in a more efficient way. |
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