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X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.ORCID iD: 0000-0002-8693-1054
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.ORCID iD: 0000-0002-5228-3888
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.ORCID iD: 0000-0003-4800-9533
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
2019 (English)In: Minerals, ISSN 2075-163X, E-ISSN 2075-163X, Vol. 9, no 3, article id 183Article in journal (Refereed) Published
Abstract [en]

The main advantage of X-ray microcomputed tomography (µCT) as a non-destructive imaging tool lies in its ability to analyze the three-dimensional (3D) interior of a sample, therefore eliminating the stereological error exhibited in conventional two-dimensional (2D) image analysis. Coupled with the correct data analysis methods, µCT allows extraction of textural and mineralogical information from ore samples. This study provides a comprehensive overview on the available and potentially useful data analysis methods for processing 3D datasets acquired with laboratory µCT systems. Our study indicates that there is a rapid development of new techniques and algorithms capable of processing µCT datasets, but application of such techniques is often sample-specific. Several methods that have been successfully implemented for other similar materials (soils, aggregates, rocks) were also found to have the potential to be applied in mineral characterization. The main challenge in establishing a µCT system as a mineral characterization tool lies in the computational expenses of processing the large 3D dataset. Additionally, since most of the µCT dataset is based on the attenuation of the minerals, the presence of minerals with similar attenuations limits the capability of µCT in mineral segmentation. Further development on the data processing workflow is needed to accelerate the breakthrough of µCT as an analytical tool in mineral characterization.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2019. Vol. 9, no 3, article id 183
Keywords [en]
X-ray microcomputed tomography, data analysis, mineral characterization, texture, mineralogy
National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
URN: urn:nbn:se:ltu:diva-73224DOI: 10.3390/min9030183ISI: 000464421700002Scopus ID: 2-s2.0-85064225739OAI: oai:DiVA.org:ltu-73224DiVA, id: diva2:1296775
Note

Validerad;2019;Nivå 2;2019-03-18 (svasva)

Available from: 2019-03-18 Created: 2019-03-18 Last updated: 2019-10-30Bibliographically approved
In thesis
1. X-ray microcomputed tomography (µCT) as a potential tool in Geometallurgy
Open this publication in new window or tab >>X-ray microcomputed tomography (µCT) as a potential tool in Geometallurgy
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

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).

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2019. p. 74
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords
X-ray microcomputed tomography, geometallurgy, automated mineralogy, ore characterization
National Category
Mineral and Mine Engineering Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-76576 (URN)978-91-7790-492-2 (ISBN)978-91-7790-493-9 (ISBN)
Presentation
2019-12-13, F531, Luleå University of Technology, Luleå, 10:00 (English)
Opponent
Supervisors
Funder
EU, Horizon 2020, 722677
Available from: 2019-10-30 Created: 2019-10-30 Last updated: 2019-11-27Bibliographically approved

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