A Posteriori Analysis of Analytical Models for Heap Leaching Using Uncertainty and Global Sensitivity Analyses

Mellado, Mario E.; Cisternas, Luís A.; Lucay, Freddy A.; Gálvez, Edelmira D.; Sepúlveda, Felipe D.

doi:10.3390/min8020044

Cited by 25 publications

(21 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically, model verification, calibration, and corroboration are not applied in mineral processing, but they must be considered in future model development. The interested readers can see the model developed by Mellado et al [165] for heap leaching, which has been validated, verified, and corroborated [166][167][168].…”

Section: Uncertainty and Sensitivity Analysesmentioning

confidence: 66%

Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

2019

Self Cite

View full text Add to dashboard Cite

Multiphase systems are important in minerals processing, and usually include solid–solid and solid–fluid systems, such as in wet grinding, flotation, dewatering, and magnetic separation, among several other unit operations. In this paper, the current trends in the process system engineering tasks of modeling, design, and optimization in multiphase systems, are analyzed. Different scales of size and time are included, and therefore, the analysis includes modeling at the molecular level (molecular dynamic modeling) and unit operation level (e.g., computational fluid dynamic, CFD), and the application of optimization for the design of a plant. New strategies for the modeling, design, and optimization of multiphase systems are also included, with a strong focus on the application of artificial intelligence (AI) and the combination of experimentation and modeling with response surface methodology (RSM). The integration of different modeling techniques such as CFD with discrete element simulation (DEM) and response surface methodology (RSM) with artificial neural networks (ANN) is included. The paper finishes with tools to study the uncertainty, both epistemic and stochastic, based on uncertainty and global sensitivity analyses, which is present in all mineral processing operations. It is shown that all of these areas are very active and can help in the understanding, operation, design, and optimization of mineral processing that involves multiphase systems. Future needs, such as meso-scale modeling, are highlighted.

show abstract

Section: Uncertainty and Sensitivity Analysesmentioning

confidence: 66%

Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…The following is an analytical model for heap leaching developed by Mellado et al [29][30][31], using a system of first order equations:…”

Section: Mathematical Modeling Of Heap Leachingmentioning

confidence: 99%

Optimization of the Heap Leaching Process through Changes in Modes of Operation and Discrete Event Simulation

et al. 2019

View full text Add to dashboard Cite

The importance of mine planning is often underestimated. Nonetheless, it is essential in achieving high performance by identifying the potential value of mineral resources and providing an optimal, practical, and realistic strategy for extraction, which considers the greatest quantity of options, materials, and scenarios. Conventional mine planning is based on a mostly deterministic approach, ignoring part of the uncertainty presented in the input data, such as the mineralogical composition of the feed. This work develops a methodology to optimize the mineral recovery of the heap leaching phase by addressing the mineralogical variation of the feed, by alternating the mode of operation depending on the type of ore in the feed. The operational changes considered in the analysis include the leaching of oxide ores by adding only sulfuric acid (H2SO4) as reagent and adding chloride in the case of sulfide ores (secondary sulfides). The incorporation of uncertainty allows the creation of models that maximize the productivity, while confronting the geological uncertainty, as the extraction program progresses. The model seeks to increase the expected recovery from leaching, considering a set of equiprobable geological scenarios. The modeling and simulation of this productive phase is developed through a discrete event simulation (DES) framework. The results of the simulation indicate the potential to address the dynamics of feed variation through the implementation of alternating modes of operation.

show abstract

“…Design and operation variables present this type of uncertainty. When the input variables have stochastic uncertainties, the normal distribution is generally used to represent this type of uncertainty [33].…”

Section: Uncertainty Analysismentioning

confidence: 99%

A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks

Saldaña

González

Jeldres

et al. 2019

Metals

View full text Add to dashboard Cite

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.

show abstract

A Posteriori Analysis of Analytical Models for Heap Leaching Using Uncertainty and Global Sensitivity Analyses

Cited by 25 publications

References 44 publications

Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

Optimization of the Heap Leaching Process through Changes in Modes of Operation and Discrete Event Simulation

A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks

Contact Info

Product

Resources

About