A stochastic model approach for copper heap leaching through bayesian networks
Autor
Saldaña Pino, Manuel; González Vázquez, Javier; Jeldres Valenzuela, Ricardo Iván; Villegas, Ángelo Manuel; Castillo, Jonathan; [et al.]Área de conocimiento
Ingeniería Cartográfica, Geodesia y FotogrametriaPatrocinadores
This research received no external funding. Gonzalo Quezada and Ricardo Jeldres thank the Centro CRHIAM Project Conicyt/Fondap/15130015Realizado en/con
Universidad Politécnica de Cartagena; Universidad Católica del NorteFecha de publicación
2019-11-07Editorial
MDPICita bibliográfica
Saldaña 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/met9111198Revisión por pares
SiPalabras clave
Bayesian networksUncertainty analysis
Stochastic process modelling
Heap leaching
Resumen
Multivariate 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 ...
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