Applying Softcomputing for Copper Recovery in Leaching Process

Leiva, Claudio; Flores, Víctor; Salgado, Felipe; Poblete, Diego; Acuña, Claudio

doi:10.1155/2017/6459582

Cited by 13 publications

(35 citation statements)

References 16 publications

(16 reference statements)

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“…The experience of the operators is essential for designing optimal scheduling of the expert system to gather the best operational practices of different work shifts. It is worth mentioning that the system can improve over time, either with new operational maneuvers or with additional schedules, similar to what has been seen in three different studies (References [16][17][18][19]). The contribution of this work is that it applied methodological techniques for the development of the knowledge base in an industrial application of a multivariate and multiparametric process.…”

Section: Introductionsupporting

confidence: 71%

Design and Evaluation of an Expert System in a Crushing Plant

et al. 2018

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This document presents a proposal for designing an expert system in the Gabriela Mistral Division’s crushing plant belonging to Codelco (Chile) with the objective of maximizing stacked tonnage, allowing the improvement of operational variables that directly interact with the crushing process. In addition, this study considers the impact that occurs in both the process and operational continuity regarding the standardization of the system. In the first stage, a survey and analysis of historic operation data was carried out, which allowed the definition of benchmarking indicators. Subsequently, both modalities of operation were compared, monitoring processed tonnage and detentions related to operational failures. As a result, significant differences were observed in the performance of the critical line operating with expert control, with a 55% reduction in the detentions referred to operational failures. Added to this is the benefit of low cost and improved quality as the control provides an analysis of the variables in reduced time intervals, which is superior to human control.

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Section: Introductionsupporting

confidence: 71%

Design and Evaluation of an Expert System in a Crushing Plant

et al. 2018

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“…The performance of heap leaching depends on many input variables (operational and design), which means its optimization is complex [27]. The materials are leached with various chemical solutions that extract valuable minerals.…”

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

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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.

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“…Recent studies such as [ 10 ] report predictive copper recovery models with 95% accuracy, utilizing artificial neural networks and parameters widely used in industry, such as “Monoclass granulometry”, “Irrigation rates”, “Total acid added”, “Pile high”, “Total copper grade”, “CO 3 grade”, “Leaching ratio”, “Operation day”, and “Soluble copper grade stacked”.…”

Section: Introductionmentioning

confidence: 99%

“…Research on predictive copper models or copper recovery models considers this issue from different perspectives. Recent works reported models using different data mining techniques [ 1 , 4 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Supervised Machine Learning Algorithms for Copper Recovery Quality Prediction in a Leaching Process

Flores

Leiva

2021

Sensors

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The copper mining industry is increasingly using artificial intelligence methods to improve copper production processes. Recent studies reveal the use of algorithms, such as Artificial Neural Network, Support Vector Machine, and Random Forest, among others, to develop models for predicting product quality. Other studies compare the predictive models developed with these machine learning algorithms in the mining industry as a whole. However, not many copper mining studies published compare the results of machine learning techniques for copper recovery prediction. This study makes a detailed comparison between three models for predicting copper recovery by leaching, using four datasets resulting from mining operations in Northern Chile. The algorithms used for developing the models were Random Forest, Support Vector Machine, and Artificial Neural Network. To validate these models, four indicators or values of merit were used: accuracy (acc), precision (p), recall (r), and Matthew’s correlation coefficient (mcc). This paper describes the dataset preparation and the refinement of the threshold values used for the predictive variable most influential on the class (the copper recovery). Results show both a precision over 98.50% and also the model with the best behavior between the predicted and the real values. Finally, the obtained models have the following mean values: acc = 0.943, p = 88.47, r = 0.995, and mcc = 0.232. These values are highly competitive when compared with those obtained in similar studies using other approaches in the context.

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Applying Softcomputing for Copper Recovery in Leaching Process

Cited by 13 publications

References 16 publications

Design and Evaluation of an Expert System in a Crushing Plant

Design and Evaluation of an Expert System in a Crushing Plant

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

A Comparative Study on Supervised Machine Learning Algorithms for Copper Recovery Quality Prediction in a Leaching Process

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