Next Article in Journal
Pure Aluminum Structure and Mechanical Properties Modified by Al2O3 Nanoparticles and Ultrasonic Treatment
Previous Article in Journal
Influence of Vibrational Loading on Deformation Behavior of Metallic Glass: A Molecular Dynamics Study
Previous Article in Special Issue
Process-Structure-Properties-Performance Modeling for Selective Laser Melting
Open AccessArticle

A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks

1
Departamento de Ingeniería de Sistemas y Computación, Facultad de Ingeniería y Ciencias Geológicas, Universidad Católica del Norte, Antofagasta 1270709, Chile
2
Departamento de Ingeniería Metalúrgica y Minas, Facultad de Ingeniería y Ciencias Geológicas, Universidad Católica del Norte, Antofagasta 1270709, Chile
3
Departamento de Ingeniería Química y Procesos de Minerales, Facultad de Ingeniería, Universidad de Antofagasta, Antofagasta 1270300, Chile
4
Departamento de Ingeniería en Metalurgia, Universidad de Atacama, Copiapó 1531772, Chile
5
Water Research Center for Agriculture and Mining (CRHIAM), University of Concepción, Concepción 4030000, Chile
6
Department of Mining and Civil Engineering, Polytechnic University of Cartagena, 30203 Cartagena, Spain
*
Authors to whom correspondence should be addressed.
Metals 2019, 9(11), 1198; https://doi.org/10.3390/met9111198
Received: 14 October 2019 / Revised: 21 October 2019 / Accepted: 4 November 2019 / Published: 7 November 2019
(This article belongs to the Special Issue Advanced Simulation Technologies of Metallurgical Processing)
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 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.
Keywords: Bayesian networks; uncertainty analysis; stochastic process modelling; heap leaching Bayesian networks; uncertainty analysis; stochastic process modelling; heap leaching
MDPI and ACS Style

Saldaña, M.; González, J.; Jeldres, R.I.; Villegas, Á.; Castillo, J.; Quezada, G.; Toro, N. A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks. Metals 2019, 9, 1198.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop