Abstract
This paper demonstrates a new method to classify mineral phases in 3D images of particulate materials obtained by X-ray computed micro-tomography (CT), here named mounted single particle characterization for 3D mineralogical analysis (MSPaCMAn). The method allows minimizing the impact of imaging artefacts that make the classification of voxels inaccurate and thus hinder the use of CT to characterize natural particulate materials. MSPaCMAn consists of (1) sample preparation as particle dispersions; (2) image processing optimized towards the labelling of individual particles in the sample; (3) phase identification performed at the particle level using an interpretation of the grey-values of all voxels in a particle rather than of all voxels in the sample. Additionally, the particle’s geometry and microstructure can be used as classification criteria besides the grey-values. The result is an improved accuracy of phase classification, a higher number of detected phases, a smaller grain size that can be detected, and individual particle statistics can be measured instead of just bulk statistics. Consequently, the method broadens the applicability of 3D imaging techniques for particle analysis at low particle size to voxel size ratio, which is typically limited due to unreliable phase classification and quantification. MSPaCMAn could be the foundation of 3D semi-automated mineralogy similar to the commonly used 2D image-based semi-automated mineralogy methods.
Highlights
The demand for mineral resources is expected to increase in order to carry on the green energy transition [1]
Despite the advantages of measuring 3D microstructures, classifying the phases composing those microstructures based on grey-values remains challenging due to imaging artefacts [21,22] that cause a broadening of the grey-scale interval that can be attributed to a phase [23,24]
Vertical cross-sections of the three samples show that particles are randomly dispersed throughout the volume of the samples (Figure 2)
Summary
The demand for mineral resources is expected to increase in order to carry on the green energy transition [1]. Due to the steady decrease in ore grades, the demand for raw materials must be sustained by more efficient minerals processing and recycling technologies. It is our hypothesis that evolving from the currently used material characterization techniques towards more comprehensive 3D particle-based characterization could be the starting point of more efficient particle processing methods. Volume, grain sizes, surface area, spatial distribution and associations of individual particles or phases can be quantified [14,15,16,17,18,19,20]. The ability to distinguish between phases is reduced and less accurate, especially for complex multiphase materials containing small grains characteristic of ore particles [25]
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