Optimizing heap leaching of complex sulphide ores ((Fe,Ni)9S8-CuFeS2): A review on dissolution kinetics and process improvement for nickel (Ni) and copper (Cu)

• Ore complexity and leach media influences recovery and secondary phase formation. • pH controls leaching; ((Fe,Ni) 9 S 8 ) dissolve at wide range, CuFeS 2 requires pH < 2. • ((Fe,Ni) 9 S 8 ) and CuFeS 2 dissolution involves transient intermediate phase formation. • Chloride-based systems reduce dissolution time and significantly improve metal recovery. • Machine learning and automation enables better prediction and heap process optimization. Heap leaching of polymetallic sulphide ores has become a cost-effective and scalable method for recovering base metals such as copper (Cu) and nickel (Ni). However, optimizing metal recovery remains challenging due to mineralogical heterogeneity, electrochemical reactivity, and surface passivation phenomena. This review synthesizes recent advances in understanding dissolution mechanisms in composite sulphidic ores containing pentlandite, chalcopyrite, and associated gangue phases. It emphasizes the need for a geo-metallurgical characterization framework that integrates host-rock composition and microstructural features to predict leach behaviour. The galvanic interactions within mixed pentlandite–chalcopyrite assemblages ((Fe,Ni) 9 S 8 –CuFeS 2 ) are examined, highlighting their distinct behaviour compared to single-mineral systems. Findings reveal that single-mineral phases dissolve through transient intermediate phases, which retard recovery rates. The review also highlights the positive impact of chloride (Cl − ) as a potential additive during leaching or pretreatment. Finally, it promotes emerging computational tools such as machine learning and process automation that can enhance predictive accuracy and operational efficiency.

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Publication Details

Journal
Minerals Engineering
Published
2026-02-12
DOI
https://doi.org/10.1016/j.mineng.2026.110141
Primary Topic
Metal Extraction and Bioleaching
Type
article
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article

Optimizing heap leaching of complex sulphide ores ((Fe,Ni)9S8-CuFeS2): A review on dissolution kinetics and process improvement for nickel (Ni) and copper (Cu)

J. K. Nyembwe, Martina Petraniková, Christian Ekberg, Fei Wang et al.
Minerals Engineering
Metal Extraction and Bioleaching
article

Optimizing heap leaching of complex sulphide ores ((Fe,Ni)9S8-CuFeS2): A review on dissolution kinetics and process improvement for nickel (Ni) and copper (Cu)

J. K. Nyembwe, Martina Petraniková, Christian Ekberg, Fei Wang, Ilyes Mahti
article en

Abstract

• Ore complexity and leach media influences recovery and secondary phase formation. • pH controls leaching; ((Fe,Ni) 9 S 8 ) dissolve at wide range, CuFeS 2 requires pH < 2. • ((Fe,Ni) 9 S 8 ) and CuFeS 2 dissolution involves transient intermediate phase formation. • Chloride-based systems reduce dissolution time and significantly improve metal recovery. • Machine learning and automation enables better prediction and heap process optimization. Heap leaching of polymetallic sulphide ores has become a cost-effective and scalable method for recovering base metals such as copper (Cu) and nickel (Ni). However, optimizing metal recovery remains challenging due to mineralogical heterogeneity, electrochemical reactivity, and surface passivation phenomena. This review synthesizes recent advances in understanding dissolution mechanisms in composite sulphidic ores containing pentlandite, chalcopyrite, and associated gangue phases. It emphasizes the need for a geo-metallurgical characterization framework that integrates host-rock composition and microstructural features to predict leach behaviour. The galvanic interactions within mixed pentlandite–chalcopyrite assemblages ((Fe,Ni) 9 S 8 –CuFeS 2 ) are examined, highlighting their distinct behaviour compared to single-mineral systems. Findings reveal that single-mineral phases dissolve through transient intermediate phases, which retard recovery rates. The review also highlights the positive impact of chloride (Cl − ) as a potential additive during leaching or pretreatment. Finally, it promotes emerging computational tools such as machine learning and process automation that can enhance predictive accuracy and operational efficiency.

Minerals EngineeringVol. 240
Université Laval (CA), Chalmers University of Technology (SE)
Openalex Percentile: Top 58%
Metal Extraction and Bioleaching
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