X-ray microcomputed tomography (µCT) as a potential tool in Geometallurgy

Document identifier: oai:DiVA.org:ltu-76576
Keyword: Engineering and Technology, Environmental Engineering, Mineral and Mine Engineering, Teknik och teknologier, Naturresursteknik, Mineral- och gruvteknik, Materials Engineering, Metallurgy and Metallic Materials, Materialteknik, Metallurgi och metalliska material, X-ray microcomputed tomography, Geometallurgy, Automated mineralogy, Ore characterization, Mineral Processing, Mineralteknik
Publication year: 2019
Abstract:

In recent years, automated mineralogy has become an essential tool in geometallurgy. Automated mineralogical tools allow the acquisition of mineralogical and liberation data of ore particles in a sample. These particle data can then be used further for particle-based mineral processing simulation in the context of geometallurgy. However, most automated mineralogical tools currently in application are based on two-dimensional (2D) microscopy analysis, which are subject to stereological error when analyzing three-dimensional(3D) object such as ore particles. Recent advancements in X-ray microcomputed tomography (µCT) have indicated great potential of such system to be the next automated mineralogical tool. µCT's main advantage lies on its ability in monitoring 3D internal structure of the ore at resolutions down to few microns, eliminating stereological error obtained from 2D analysis. Aided with the continuous developments of computing capability of 3D data, it is only the question of time that µCT system becomes an interesting alternative in automated mineralogy system.

This study aims to evaluate the potential of implementing µCT as an automated mineralogical tool in the context of geometallurgy. First, a brief introduction about the role of automated mineralogy in geometallurgy is presented. Then, the development of µCT system to become an automated mineralogical tool in the context of geometallurgy andprocess mineralogy is discussed (Paper 1). The discussion also reviews the available data analysis methods in extracting ore properties (size, mineralogy, texture) from the 3D µCT image (Paper 2). Based on the review, it was found that the main challenge inperforming µCT analysis of ore samples is the difficulties associated to the segmentation of the mineral phases in the dataset. This challenge is adressed through the implementation of machine learning techniques using Scanning Electron Microscope (SEM) data as a reference to differentiate the mineral phases in the µCT dataset (Paper 3).

Authors

Pratama Istiadi Guntoro

Luleå tekniska universitet; Mineralteknik och metallurgi
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Yousef Ghorbani

Luleå tekniska universitet; Mineralteknik och metallurgi
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Jan Rosenkranz

Luleå tekniska universitet; Mineralteknik och metallurgi
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Matthew Pankhurst

Instituto Tecnològico y de Energìas Renovables (ITER), Tenerife, Canary Islands, Spain
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