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.

中文翻译：

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铬铁矿颗粒的定量 3D 表征


目前用于表征原材料的主要技术是块料或 2D。这是由于目前缺乏标准化和自动化的 3D 颗粒材料表征方法的结果。在这里，我们应用了一个工作流程，使用 X 射线计算机断层扫描来表征 9 个粒度等级小于 1 mm 的碎铬铁矿矿石。所有样品的所有数据处理都遵循相同的步骤顺序，这意味着分析可以在有限的用户输入下实现自动化，而传统的 3D 图像处理方法则需要用户输入特定于每个粒度分数。获得单个颗粒的铬铁矿组成、粒度分布和铬铁矿释放的结果，并与 X 射线衍射和基于 2D 的自动矿物学的结果进行比较。结果显示，低至 75 μm 的所有尺寸等级的精度均保持一致。对于较大的颗粒尺寸 （>600 μm），铬铁矿游离曲线比从 2D 自动矿物学中获得的曲线更一致，这可能是由于 2D 切片的立体偏差。粒度分布是 2D 偏差导致所有粒度与 3D 结果出现较大差异的属性。总之，与其他 3D 图像分析方法相比，该工作流程更可自动化（因此更快、更便宜）且偏差更小（因此更准确和标准化）。此外，它还可以作为基于颗粒的表征的成熟技术的补充，特别是当采样受限时，可以测量基于 2D 的方法可能无法代表性测量的颗粒特性。 有必要在越来越复杂的材料中进一步测试工作流程，但已证明其改变矿物颗粒材料表征方式的潜力。