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• W4387242309 abstract "The main techniques used to characterize raw materials are currently bulk or 2D. This is a consequence of the current lack of standardized and automated methods to characterize particulate materials in 3D. Here, we apply a workflow to characterize a crushed chromite ore with nine particle size classes below 1 mm using X-ray computed tomography. All data processing of all samples follows the same sequence of steps, which means that the analysis can be automated with limited user input as opposed to traditional 3D image processing methods that require user input specific to each particle size fraction. Results of chromite composition, particle size distribution and chromite liberation are obtained for individual particles and compared with the results from x-ray diffraction and 2D-based automated mineralogy. The results shows a consistent accuracy across all size classes down to 75 μm. For the larger particle sizes (>600 μm) the chromite liberation curves are more consistent than those obtained from 2D-based automated mineralogy, possibly due to the stereological bias of 2D sections. The particle size distributions is the property for which the 2D bias causes a larger divergence from 3D results across all particle sizes. In conclusion, the workflow is more automatable (thus, faster and cheaper) and less bias (thus, more accurate and standardisable) than other 3D image analysis methods. Additionally, it stands as complementary to established techniques for particle-based characterization, especially to measure particle properties that 2D-based methods may not measure representatively for larger particle sizes and when sampling is limited. Further testing of the workflow in progressively more complex materials is necessary, but its potential to transform the way mineral particulate materials are characterized is demonstrated." @default.
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• W4387242309 date "2023-12-01" @default.
• W4387242309 modified "2023-10-15" @default.
• W4387242309 title "Quantitative 3D characterization of chromite ore particles" @default.
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• W4387242309 doi "https://doi.org/10.1016/j.mineng.2023.108403" @default.
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