Data-Driven Synthesis of a Geometallurgical Model for a Copper Deposit
| dc.contributor.author | Mu, Yuyang | |
| dc.contributor.author | Salas, Juan Carlos | |
| dc.date.accessioned | 2025-01-20T20:09:54Z | |
| dc.date.available | 2025-01-20T20:09:54Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Geometallurgy integrates aspects of geology, metallurgy, and mine planning in order to improve decision making in mining schedules. A geometallurgical model is a 3D space that is typically synthesized from early-stage small-scale samples and is composed of several metallurgical units, or domains. This work explores the synthesis of a geometallurgical model for a copper deposit using a purely data-driven unsupervised approach. To this end, a dataset of 1112 drill samples is used, which are clustered using different methods, namely, k-means, hierarchical clustering (AGG), self-organizing maps (SOM), and DBSCAN. Two cluster validity indices (Silhouette and Calinski-Harabasz) are used to select the final model. To validate the potential of the proposed approach, a simulated economic evaluation is conducted. Results demonstrate that k-means exhibits a better performance in terms of modeling and that using the obtained geometallurgical model for mining scheduling increases the project's Net Present Value (NPV) by as much as 4%. Based on these results, the proposed methodology is an appealing alternative for generating geometallurgical models within greenfield, brownfield and ongoing operations. | |
| dc.description.funder | ANID | |
| dc.fuente.origen | WOS | |
| dc.identifier.doi | 10.3390/pr11061775 | |
| dc.identifier.eissn | 2227-9717 | |
| dc.identifier.uri | https://doi.org/10.3390/pr11061775 | |
| dc.identifier.uri | https://repositorio.uc.cl/handle/11534/92027 | |
| dc.identifier.wosid | WOS:001017694500001 | |
| dc.issue.numero | 6 | |
| dc.language.iso | en | |
| dc.revista | Processes | |
| dc.rights | acceso restringido | |
| dc.subject | geometallurgy | |
| dc.subject | machine learning | |
| dc.subject | unsupervised learning | |
| dc.subject | cluster analysis | |
| dc.subject | copper deposit | |
| dc.title | Data-Driven Synthesis of a Geometallurgical Model for a Copper Deposit | |
| dc.type | artículo | |
| dc.volumen | 11 | |
| sipa.index | WOS | |
| sipa.trazabilidad | WOS;2025-01-12 |