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Dissolution kinetics of a low‑grade oxide‑sulfide copper ore with high silica content: Laboratory studies and statistical modeling

Hiva Farhadi, Faramarz Doulati Ardejani

2026encoppergeochemistrykineticsextractionleaching

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In this study, copper extraction from low-grade oxide-sulfide ores was investigated using a leaching method combined with response surface methodology (RSM) to optimize operational conditions and assess leaching kinetics. Given copper’s extensive industrial applications, sustainable recovery from low-grade ores is critical. Five key parameters—acid concentration, leaching time, particle size, temperature, and solids percentage—were identified as major influences on copper recovery. The results revealed that leaching time and solids percentage, along with interactions between temperature-time and temperature-solids percentage, had the most significant effects. Optimal conditions for 80% copper recovery while minimizing iron recovery below 3% included an acid concentration of 1.21 mol L−1, a leaching time of 108 min, a particle size of 438 μm, a temperature of 45 °C, and a solids percentage of 18.2%. Leaching kinetics were analyzed using shrinking core models, with the Dickinson model best describing the process, showing an activation energy of 32.63 kJ mol−1, indicative of mixed diffusion and chemical reaction control. The final kinetic model effectively predicted the influence of key parameters. These findings highlight the importance of optimizing process variables and selecting suitable kinetic models to enhance extraction efficiency, reduce costs, and improve sustainability in copper recovery.

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Cite This Work

@article{089c3d25-c9c6-49af-9f31-a6af42a2edc6,
  title={Dissolution kinetics of a low‑grade oxide‑sulfide copper ore with high silica content: Laboratory studies and statistical modeling},
  author={Hiva Farhadi and Faramarz Doulati Ardejani},
  year={2026},
  language={en}
}
TY  - JOUR
TI  - Dissolution kinetics of a low‑grade oxide‑sulfide copper ore with high silica content: Laboratory studies and statistical modeling
AU  - Hiva Farhadi
AU  - Faramarz Doulati Ardejani
PY  - 2026
LA  - en
ER  -

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