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  3. Predicting Copper Recovery From Flotation Using Machine Learning And Laboratory-generated Data.
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  3. Predicting Copper Recovery From Flotation Using Machine Learning And Laboratory-generated Data.

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Predicting copper recovery from flotation using machine learning and laboratory-generated data.

José Benítez1, Víctor Flores1, Sergio Curilef2

  • 1Departamento de Ingeniería de Sistemas y Computación, Universidad Católica del Norte, Avenida Angamos 0610, Antofagasta 1240000, Chile.

Chaos (Woodbury, N.Y.)
|September 12, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning models, particularly Artificial Neural Networks (ANNs), can significantly improve copper recovery in flotation processes. This advancement aids in sustainable mining by optimizing efficiency and reducing costs.

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Area of Science:

  • Metallurgical Engineering
  • Data Science
  • Sustainable Mining

Background:

  • Declining high-grade copper ore necessitates advanced extraction technologies.
  • Flotation processes are crucial for copper recovery but are sensitive to operational parameters.
  • Optimization is key for efficient, cost-effective, and environmentally sound copper extraction.

Purpose of the Study:

  • To investigate the application of Machine Learning (ML) techniques for optimizing copper flotation.
  • To evaluate the predictive performance of four ML algorithms: Random Forest, Support Vector Machine, K-means clustering, and Artificial Neural Networks (ANNs).
  • To enhance copper recovery efficiency and support sustainable mining practices.

Main Methods:

  • Experimental data from a laboratory-scale flotation system was utilized.
  • Four ML algorithms were trained and validated to predict copper recovery.
  • Model performance was assessed using metrics including prediction accuracy and probability selection measures.

    • Main Results:

    • Artificial Neural Networks (ANNs) demonstrated the highest prediction accuracy at 98.69%.
    • ANNs effectively modeled complex nonlinear interactions among critical flotation process variables.
    • Disequilibrium and entropic measures validated the ANN model's robust performance.

    Conclusions:

    • ML, especially ANNs, shows significant potential for optimizing copper flotation processes.
    • Accurate prediction of copper recovery can lead to enhanced efficiency and reduced operational costs.
    • These findings contribute to the sustainable development of copper extraction technologies.