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dc.contributor.authorSaldaña Pino, Manuel es_ES
dc.contributor.authorGonzález Vázquez, Javier es_ES
dc.contributor.authorJeldres Valenzuela, Ricardo Iván es_ES
dc.contributor.authorVillegas, Ángelo Manuel es_ES
dc.contributor.authorCastillo, Jonathan es_ES
dc.contributor.authorQuezada, Gonzalo R. es_ES
dc.contributor.authorToro Villarroel, Norman Rodrigo es_ES
dc.date.accessioned2021-04-20T09:32:23Z
dc.date.available2021-04-20T09:32:23Z
dc.date.issued2019-11-07
dc.identifier.citationSaldaña M, González J, Jeldres RI, Villegas Á, Castillo J, Quezada G, Toro N. A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks. Metals. 2019; 9(11):1198. https://doi.org/10.3390/met9111198es_ES
dc.identifier.issn2075-4701
dc.description.abstractMultivariate analytical models are quite successful in explaining one or more response variables, based on one or more independent variables. However, they do not reflect the connections of conditional dependence between the variables that explain the model. Otherwise, due to their qualitative and quantitative nature, Bayesian networks allow us to easily visualize the probabilistic relationships between variables of interest, as well as make inferences as a prediction of specific evidence (partial or impartial), diagnosis and decision-making. The current work develops stochastic modeling of the leaching phase in piles by generating a Bayesian network that describes the ore recovery with independent variables, after analyzing the uncertainty of the response to the sensitization of the input variables. These models allow us to recognize the relations of dependence and causality between the sampled variables and can estimate the output against the lack of evidence. The network setting shows that the variables that have the most significant impact on recovery are the time, the heap height and the superficial velocity of the leaching flow, while the validation is given by the low measurements of the error statistics and the normality test of residuals. Finally, probabilistic networks are unique tools to determine and internalize the risk or uncertainty present in the input variables, due to their ability to generate estimates of recovery based upon partial knowledge of the operational variables.es_ES
dc.description.sponsorshipThis research received no external funding. Gonzalo Quezada and Ricardo Jeldres thank the Centro CRHIAM Project Conicyt/Fondap/15130015es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.urihttps://www.mdpi.com/2075-4701/9/11/1198es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.titleA stochastic model approach for copper heap leaching through bayesian networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subject.otherIngeniería Cartográfica, Geodesia y Fotogrametriaes_ES
dc.subjectBayesian networkses_ES
dc.subjectUncertainty analysises_ES
dc.subjectStochastic process modellinges_ES
dc.subjectHeap leachinges_ES
dc.identifier.urihttp://hdl.handle.net/10317/9314
dc.peerreviewSies_ES
dc.identifier.doi10.3390/met9111198
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.subject.unesco2506 Geologíaes_ES
dc.subject.unesco3305.06 Ingeniería Civiles_ES
dc.contributor.convenianteUniversidad Politécnica de Cartagenaes_ES
dc.contributor.convenianteUniversidad Católica del Nortees_ES


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