Metallurgical copper recovery prediction using conditional quantile regression based on a copula model

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dc.contributor.authorHernández, Heber
dc.contributor.authorDíaz Viera, Martín Alberto
dc.contributor.authorAlberdi Celaya, Elisabete
dc.contributor.authorOyarbide Zubillaga, Aitor
dc.contributor.authorGoti Elordi, Aitor
dc.date.accessioned2025-03-10T08:43:12Z
dc.date.available2025-03-10T08:43:12Z
dc.date.issued2024-07
dc.date.updated2025-03-10T08:43:11Z
dc.description.abstractThis 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.en
dc.description.sponsorshipWork funded by project SILENCE—European Commission—Research Program of the Research Funds for Coal and Steel—Prj. No.: 101112516en
dc.identifier.citationHernández, H., Díaz-Viera, M. A., Alberdi, E., Oyarbide-Zubillaga, A., & Goti, A. (2024). Metallurgical Copper Recovery Prediction Using Conditional Quantile Regression Based on a Copula Model. Minerals, 14(7). https://doi.org/10.3390/MIN14070691
dc.identifier.doi10.3390/MIN14070691
dc.identifier.eissn2075-163X
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2494
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rights© 2024 by the authors
dc.subject.otherCollocated cokriging
dc.subject.otherConditional quantile regression
dc.subject.otherCopula model
dc.subject.otherKernel smoothing
dc.subject.otherMetallurgical copper recovery
dc.titleMetallurgical copper recovery prediction using conditional quantile regression based on a copula modelen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue7
oaire.citation.titleMinerals
oaire.citation.volume14
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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