Mineral characterization of low-grade gold ore to support geometallurgy

Abstract

SEM-based automated image analysis is one of the most comprehensive tools in mineralogical characterization and plays an important role in the mining sector, mainly due to its statistical robustness, reliability of results and rapid analysis compared to analogue methods. Mineralogical ore characterization, such as gold distribution, grain size and mode of occurrence, together with density separation, cyanidation and diagnostic leaching tests, are the key for the appropriate process design of the geometallurgy concept. The present research focuses on the development of a mineralogical characterization of low-grade gold ore (4000 ppm) due to a unique set of compositional properties, such as grain size, sulphide mineralogy and accessibility, which directly affect the metallurgical performance. The results demonstrated that there is a direct correlation between the arsenic grades and the gold content, as well as an influence of arsenic grades on gold accessibility. Furthermore, high arsenic content in gold grains tends to provide greater accessibility. Although gold grains occur mainly as inclusions in pyrite and arsenopyrite, they are rarely associated with other sulphide (pyrrhotite and galena).

Keywords

• Geometallurgy
• SEM-based automated image analysis
• Low-grade gold ore
• Accessibility