A solid knowledge of the mineralogical properties (e.g., flake size, flake size distribution, purity, shape) of graphite ores is necessary because different graphite classes have different product uses. To date, these properties are commonly examined using well-established optical microscopy (OM), scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS) and SEM-based automated image analysis. However, these 2D methods may be subject to sampling errors and stereological effects that can adversely affect the quality of the analysis. X-ray microcomputed tomography (CT) is a nondestructive imaging technique allowing for examination of the interior and exterior of solid materials such as rocks and ores in 3D. This study aimed to explore whether CT can provide additional mineralogical information for the characterisation of graphite ores. CT was used in combination with traditional techniques (XRD, SEM-EDS, OM) to examine a flake graphite ore in 3D. A scanning protocol for the examined graphite ore was established to acquire high-quality CT data. Quantitative mineralogical information on key properties of graphite was obtained by developing a deep learning-based image processing strategy. The results demonstrate that CT allows for the 3D visualisation of graphite ores and provides valid and reliable quantitative information on the quality-determining properties that currently cannot be obtained by other analytical tools. CT allows improved assessment of graphite deposits and their beneficiation.
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