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Electrodeposition01:08

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Electrodeposition is a technique used to separate an analyte from interferents by electrochemical processes. Here, the analyte is a metal ion that can be deposited on an electrode immersed in the sample solution. The electrochemical setup consists of an anode and a cathode. When an electric current is applied to the setup, oxidation occurs at the anode. At the cathode, which consists of a large metal surface, metal ions undergo reduction and deposit onto the surface.
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Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Predicting copper recovery from flotation using machine learning and laboratory-generated data.

Chaos (Woodbury, N.Y.)·2025
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A Comparative Study on Supervised Machine Learning Algorithms for Copper Recovery Quality Prediction in a Leaching

Victor Flores1, Claudio Leiva2

  • 1Department of Computer and Systems Engineering, Universidad Católica del Norte, Antofagasta 1270709, Chile.

Sensors (Basel, Switzerland)
|April 3, 2021
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Summary
This summary is machine-generated.

This study compares three artificial intelligence models for predicting copper recovery in mining. Random Forest, Support Vector Machine, and Artificial Neural Network models were evaluated, showing competitive results for improved copper production.

Keywords:
artificial intelligencecomputers and information processingdata analysisdata processingknowledge engineeringmachine learning

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Area of Science:

  • * Mining Engineering
  • * Data Science
  • * Chemical Engineering

Background:

  • * Artificial intelligence (AI) is increasingly adopted in the copper mining industry to enhance production processes.
  • * Existing research often focuses on general mining applications or product quality prediction, with limited comparative studies on copper recovery prediction using machine learning.
  • * This study addresses the gap by providing a detailed comparison of AI techniques specifically for copper recovery prediction in leaching operations.

Purpose of the Study:

  • * To compare the performance of three machine learning algorithms: Random Forest, Support Vector Machine, and Artificial Neural Network.
  • * To develop and validate predictive models for copper recovery from leaching processes using real-world mining data.
  • * To identify the most effective AI model for accurate copper recovery prediction in the context of Northern Chilean mining operations.

Main Methods:

  • * Utilized four datasets from mining operations in Northern Chile.
  • * Developed predictive models using Random Forest, Support Vector Machine, and Artificial Neural Network algorithms.
  • * Validated model performance using accuracy (acc), precision (p), recall (r), and Matthew's correlation coefficient (mcc), including dataset preparation and threshold value refinement.

Main Results:

  • * Achieved a precision rate exceeding 98.50% for the best-performing model.
  • * Identified the model demonstrating the best correlation between predicted and actual copper recovery values.
  • * Reported mean performance values: accuracy = 0.943, precision = 88.47, recall = 0.995, and MCC = 0.232.

Conclusions:

  • * The developed AI models offer highly competitive performance for copper recovery prediction compared to existing methods.
  • * The study provides valuable insights into the effectiveness of different machine learning techniques for optimizing copper leaching processes.
  • * Findings support the integration of AI for enhanced decision-making and efficiency in the copper mining sector.