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Bioleaching Modeling-A Review.

Manuel Saldaña1,2, Matías Jeldres2, Felipe M Galleguillos Madrid3

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Bioleaching uses microorganisms for mineral extraction, offering an eco-friendly alternative to traditional mining. This study explores modeling techniques for bioleaching processes, focusing on mineral recovery rates.

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machine learningmicroorganismsmineral bioleachingmineral processingtheoretical and empirical modeling

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Area of Science:

  • * Environmental Science
  • * Mining Engineering
  • * Bioprocess Engineering

Background:

  • * Mineral leaching is a key step in metal extraction, with bioleaching emerging as a sustainable alternative to pyrometallurgical methods.
  • * Bioleaching offers environmental benefits like reduced emissions, lower costs, and suitability for low-grade ores.
  • * Traditional mining methods often carry significant environmental liabilities.

Purpose of the Study:

  • * To present the theoretical basis for modeling bioleaching processes.
  • * To focus on the modeling of mineral recovery rates within bioleaching.
  • * To review various modeling approaches for mineral dissolution.

Main Methods:

  • * Review of conventional leaching dynamics models, including the shrinking core model.
  • * Exploration of diffusion-controlled, chemically controlled, and film diffusion-controlled oxidation processes.
  • * Introduction to statistical analysis models like surface response methodology and machine learning algorithms.

Main Results:

  • * Established models range from diffusion-based to advanced statistical and machine learning approaches.
  • * Bioleaching modeling for industrial minerals is a developed field.
  • * Bioleaching modeling for rare earth elements presents significant future growth potential.

Conclusions:

  • * Bioleaching presents a more sustainable and environmentally friendly mining approach.
  • * Modeling is crucial for optimizing bioleaching processes and mineral recovery.
  • * Further research in rare earth element bioleaching modeling is warranted.