Metallurgical Copper Recovery Prediction Using Conditional Quantile Regression Based on a Copula Model
dc.contributor.author | Hernández, Heber | |
dc.contributor.author | Díaz Viera, Martín Alberto | |
dc.contributor.author | Alberdi Celaya, Elisabete ![]() | |
dc.contributor.author | Oyarbide Zubillaga, Aitor | |
dc.contributor.author | Goti Elordi, Aitor | |
dc.date.accessioned | 2024-08-01T07:10:45Z | |
dc.date.available | 2024-08-01T07:10:45Z | |
dc.date.issued | 2024-07-01 | |
dc.identifier.citation | Minerals 14(7) : (2024) // Article ID 691 | es_ES |
dc.identifier.issn | 2075-163X | |
dc.identifier.uri | http://hdl.handle.net/10810/69107 | |
dc.description.abstract | This article proposes a novel methodology for estimating metallurgical copper recovery, a critical feature in mining project evaluations. The complexity of modeling this nonadditive variable using geostatistical methods due to low sampling density, strong heterotopic relationships with other measurements, and nonlinearity is highlighted. As an alternative, a copula-based conditional quantile regression method is proposed, which does not rely on linearity or additivity assumptions and can fit any statistical distribution. The proposed methodology was evaluated using geochemical log data and metallurgical testing from a simulated block model of a porphyry copper deposit. A highly heterotopic sample was prepared for copper recovery, sampled at 10% with respect to other variables. A copula-based nonparametric dependence model was constructed from the sample data using a kernel smoothing method, followed by the application of a conditional quantile regression for the estimation of copper recovery with chalcocite content as secondary variable, which turned out to be the most related. The accuracy of the method was evaluated using the remaining 90% of the data not included in the model. The new methodology was compared to cokriging placed under the same conditions, using performance metrics RMSE, MAE, MAPE, and R2. The results show that the proposed methodology reproduces the spatial variability of the secondary variable without the need for a variogram model and improves all evaluation metrics compared to the geostatistical method. | es_ES |
dc.description.sponsorship | Work funded by project SILENCE—European Commission—Research Program of the Research Funds for Coal and Steel—Prj. No.: 101112516. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/es/ | |
dc.subject | metallurgical copper recovery | es_ES |
dc.subject | copula model | es_ES |
dc.subject | conditional quantile regression | es_ES |
dc.subject | kernel smoothing | es_ES |
dc.subject | collocated cokriging | es_ES |
dc.title | Metallurgical Copper Recovery Prediction Using Conditional Quantile Regression Based on a Copula Model | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2024-07-26T12:29:28Z | |
dc.rights.holder | © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2075-163X/14/7/691 | es_ES |
dc.identifier.doi | 10.3390/min14070691 | |
dc.departamentoes | Matemática aplicada | |
dc.departamentoeu | Matematika aplikatua |
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Except where otherwise noted, this item's license is described as © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).