Published April 4, 2026 | Version v1

Preg-Robbing in Refractory Gold Ores: A Critical Review of Mineralogical Controls, Pretreatment Strategies and Flowsheet Selection

Authors/Creators

  • 1. PhD in Chemical Engineering Federal University of Minas Gerais – UFMG, Department of Chemical Engineering Belo Horizonte – MG – Brazil ORCID: https://orcid.org/0000-0001-8115-4279

Description

Abstract :

Preg-robbing remains a major constraint in the processing of refractory gold ores, particularly those containing carbonaceous matter, fine clays, and iron-bearing phases capable of adsorbing dissolved gold during leaching. This review critically evaluates advances reported between 2020 and 2025, focusing on the mineralogical controls of preg-robbing, its interaction with gold dissolution chemistry, and the effectiveness of current mitigation strategies. Gold adsorption is governed by carbon structure, surface functional groups, sulfide associations, and textural features that control accessibility, determining whether adsorption is reversible or irreversible and, consequently, whether conventional recovery routes remain viable. Pretreatment options, including roasting, pressure oxidation, bio-oxidation, chemical passivation, and selective removal of reactive phases, are assessed in terms of metallurgical performance, operational complexity, energy demand, and environmental impact. The review also examines alternative lixiviants such as thiosulfate, glycine, and ammonia-based systems, highlighting their potential to reduce sensitivity to carbonaceous matter while emphasizing their dependence on mineralogy and process control. Rather than proposing a universal solution, the analysis defines decision-oriented criteria for flowsheet selection based on mineralogical and operational constraints, identifying the conditions under which specific approaches are technically robust and economically justified. Key research gaps are highlighted, particularly in the quantitative characterization of adsorption capacity and kinetics, and in the integration of mineralogical data into predictive process design frameworks.

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