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

Saldaña, Manuel Romero; Toro, Norman; Castillo, Jonathan; Hernández, Pía; Navarra, Alessandro

doi:10.3390/min9070421

Cited by 29 publications

(27 citation statements)

References 30 publications

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“…As a result of the sensitivity analysis of the ANN model, the individual effect produced by each added stage of an RCS circuit can be seen, that is, the sensitivity analysis of the mineral concentrate in the rougher (RR), cleaner (RC) and scavenger (RS) cells, as well as the effects of the parameters that affect the mineral concentrate within these cells, disaggregating the aggregate process, and analyzing the impact on the global mineral concentrate of the following variables: mean resident time in each of the cells (τM), gas flow rate (Gfr), bubble size (db), particle size (dp), bubble speed (Vb), turbulence dissipation rate (ε), kinematic viscosity of the fluid (ν) and the bubble (νb), particle density (ρp) and fluid density (ρf), induction time (tind) and surface tension (σ). Then, by quantifying the effect of the independent variables on the dependent variables (by the first-order and total effect index), it is expected to have a greater understanding of the stages more critical when searching for an optimum or to improve the operational results [45][46][47][48][49][50][51], such as the overall mineral concentrate at the output of the RCS circuit. Figure 5 shows that the operation of the three threads turned out to be crucial for the subsequent evaluation of the circuit presented in Figure 1.…”

Section: Effect Associated With the Degree Of Uncertainty Of The Amentioning

confidence: 99%

Global sensitivity analyses of a neural networks model for a flotation circuit

Saldaña

Ayala

Torres

et al. 2020

Hem Ind

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Modeling of flotation processes is complex due to the large number of variables involved and the lack of knowledge on the impact of operational parameters on the response(s), and given this problem, machine learning algorithms emerge as an alternative interesting when modeling dynamic processes. In this work, different artificial neural network (ANN) architectures for modeling the mineral concentrate in a rougher-cleaner-scavenger (RCS) circuit based on the main process variables are generated (variables as the recovery of the rougher, cleaner and scavenger cells, along with disaggregated variables). Analysis of the global sensitivity was performed to study the importance of the individual and joint performances of the stages of the flotation circuit, reflected by sensitivity indicators that allow to infer the impact that the stages and operational parameters produce on the dependent variables (mineral concentrate in rougher, cleaner and scavenger cells, in addition to the global concentration in the RCS circuit). It should be noted that the ANN is a useful tool for modeling dynamic systems such as flotation, while sensitivity analysis shows that the operation of the three threads turns out to be crucial for the subsequent evaluation of the circuit, while the Unbundled variables that most interact with the overall recovery are gas flow rate, bubble and particle diameters, bubble velocity, particle density, and surface tension.

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Section: Effect Associated With the Degree Of Uncertainty Of The Amentioning

confidence: 99%

Global sensitivity analyses of a neural networks model for a flotation circuit

Saldaña

Ayala

Torres

et al. 2020

Hem Ind

Self Cite

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“…According to the results obtained, a study of the kinetic models for adsorption process will be performed. The generation and/or adjustments of analytical models for the study of dynamic systems generate abstractions of the these systems [40] , identifying the variables that explain the response as optimizing the dependent variable depending on the domain or range of operation of the independent variables [41] . Kinetic adsorption models provide very important information in copper ions retention procedure in waste water.…”

Section: Kinetic Models Of Processesmentioning

confidence: 99%

Kinetic Study of Copper Retention by Kaolin Using a System of Supports Designed in 3D

Macı́as-Garcı́a

Calderón-Rios

Dı́az-Dı́ez

et al. 2020

Advanced Materials Science and Technology

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Kaolin is a clay product of the decomposition of feldspatic rocks; it is a hydrated aluminum silicate whose main component is kaolinite; its formula is 2H 2 O-Al 2 O 3-2SiO 2 and it has a wide application in various industries. In this study copper ions removal of aqueous solutions using kaolin, was investigated. Kaolin sample are texturally and chemically characterized and its electrical conductivity was established, and finally diverse kinetic models were tested to the copper ion retention process. Kinetic adsorption and electroadsorption processes of Cu (II) ions on kaolin are practically identical. The q e values for both adsorption and electroadsorption are of the order of 10.1 mg•g-1 for an equilibration time of 1200 min. This suggests that adsorption procedure is the determining factor. In view of results, we can indicate that kinetic adsorption procedure of Cu (II) ions on kaolin is adjusted to a pseudo second order kinetics. High regression coefficients are reached, greater than those of first order pseudo model, with worths above 0.99. Its explanation comes from the valence forces involved in the adsorption mechanism, due to electrons exchange between Cu (II) ions and the adsorbent. Likewise, correlation between experimental q e values, and the calculated q e , through the kinetic model are good. A more detailed observation through intraparticle diffusion model allows us to distinguish two well defined linear parts, a first one: that would correspond to the transport of ions from the dissolution to the external covering of the absorbent, with a fast ions distribution on the external kaolin covering. And a second part, which symbolize the intraparticular diffusion, as well as Cu (II) union with the kaolin internal active sites. Keywords Kaolin; copper; adsorption; kinetic 1. INTRODUCTION Kaolin is a hydrated aluminum silicate, product of the decomposition of feldspatic rocks mainly. The term

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“…Following the example of Saldaña et al [10], the generated stochastic model can be incorporated into a simulation framework that can quantify the benefits derived from the incorporation of probabilistic models in estimating the expected value of mineral recovery, or it could have the potential to include it in a system of support for decision making in the mining industry, as presented in Saldaña et al [36].…”

Section: Bayesian Network Validationmentioning

confidence: 99%

A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks

Saldaña

González

Jeldres

et al. 2019

Metals

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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.

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

Cited by 29 publications

References 30 publications

Global sensitivity analyses of a neural networks model for a flotation circuit

Global sensitivity analyses of a neural networks model for a flotation circuit

Kinetic Study of Copper Retention by Kaolin Using a System of Supports Designed in 3D

A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks

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