Improved particle separation, characterisation and analysis for ore beneficiation studies using 3D X-ray micro-computed tomography

In mineral processing, ore materials exist in the form of particles of various sizes. Being able to determine particle properties and population statistics is central to the efficient beneficiation of ore materials. X-ray µCT imaging is a powerful tool for characterizing particles in 3D and it has the capacity to image thousands of ore particles in a packing in a single scan. However, to exploit the full potential of this approach, there is a need for efficient and robust analysis methods that can separate and identify each ore fragments within a packing. This study introduces a new image analysis method for separating particles with complex shapes densely packed in a container. The method allows one to identify thousands of particles with irregular shapes and pores/fractures, which sets the foundation for accurate particle characterizations. A full workflow for 3D image-based particle characterization is also presented, including X-ray µCT imaging, image analysis and data visualization. The new method is demonstrated using more than 200 K particles generated by crushing a porphyry copper ore sample. Key particle properties are measured in 3D, including particle size, shape, grade and liberation. Critical investigations on particle sieve size, shape and size-grade-liberation relationship are undertaken. The results show great potentials of the new method in understanding the behavior of copper ore particles in mineral processing, which is desired for efficient ore treatment and recovery of valuable minerals.