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X-ray microcomputed tomography (µCT) as a potential tool in Geometallurgy
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.ORCID iD: 0000-0002-8693-1054
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 [en]
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: urn:nbn:se:ltu:diva-76576ISBN: 978-91-7790-492-2 (print)ISBN: 978-91-7790-493-9 (electronic)OAI: oai:DiVA.org:ltu-76576DiVA, id: diva2:1366731
Presentation
2019-12-13, F531, Luleå University of Technology, Luleå, 10:00 (English)
Opponent
Supervisors
Funder
EU, Horizon 2020, 722677Available from: 2019-10-30 Created: 2019-10-30 Last updated: 2019-11-27Bibliographically approved
List of papers
1. Use of X-ray Micro-computed Tomography (µCT) for 3-D Ore Characterization: A Turning Point in Process Mineralogy
Open this publication in new window or tab >>Use of X-ray Micro-computed Tomography (µCT) for 3-D Ore Characterization: A Turning Point in Process Mineralogy
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, automated mineralogy has become an essential enabling technology in the field of process mineralogy, allowing better understanding between mineralogy and the beneficiation process. Recent developments in X-ray micro-computed tomography (μCT) as a non-destructive technique have indicated great potential to become the next automated mineralogy technique. μCT’s main advantage lies in its ability to allow 3-D monitoring of internal structure of the ore at resolutions down to a few hundred nanometers, thereby eliminating the stereological error encountered in conventional 2-D analysis. Driven by the technological and computational progress, the technique is continuously developing as an analysis tool in ore characterization and subsequently it foreseen thatμCT will become an indispensable technique in the field of process mineralogy. Although several software tools have been developed for processing μCT dataset, but the main challenge in μCT data analysis remains in the mineralogical analysis, where μCT data often lacks contrast between mineral phases, making segmentation difficult. In this paper, an overview of some current applications of μCT in ore characterization is reviewed, alongside with it potential implications to process mineralogy. It also describes the current limitations of its application and concludes with outlook on the future development of 3-D ore characterization.

Keywords
X-ray micro-tomography (µCT), process mineralogy, ore characterization
National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-73716 (URN)
Conference
26th International Mining Congress and Exhibition (IMCET 2019), Antalya, April 16-19, 2019
Funder
EU, Horizon 2020
Available from: 2019-04-23 Created: 2019-04-23 Last updated: 2019-10-30Bibliographically approved
2. X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods
Open this publication in new window or tab >>X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods
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
Keywords
X-ray microcomputed tomography, data analysis, mineral characterization, texture, mineralogy
National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
urn:nbn:se:ltu:diva-73224 (URN)10.3390/min9030183 (DOI)000464421700002 ()2-s2.0-85064225739 (Scopus ID)
Note

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

Available from: 2019-03-18 Created: 2019-03-18 Last updated: 2019-10-30Bibliographically approved
3. Application of machine learning techniques in mineral phase segmentation for X-ray microcomputed tomography (µCT) data
Open this publication in new window or tab >>Application of machine learning techniques in mineral phase segmentation for X-ray microcomputed tomography (µCT) data
Show others...
2019 (English)In: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 142, article id 105882Article in journal (Refereed) Published
Abstract [en]

X-ray microcomputed tomography (µCT) offers a non-destructive three-dimensional analysis of ores but its application in mineralogical analysis and mineral segmentation is relatively limited. In this study, the application of machine learning techniques for segmenting mineral phases in a µCT dataset is presented. Various techniques were implemented, including unsupervised classification as well as grayscale-based and feature-based supervised classification. A feature matching method was used to register the back-scattered electron (BSE) mineral map to its corresponding µCT slice, allowing automatic annotation of minerals in the µCT slice to create training data for the classifiers. Unsupervised classification produced satisfactory results in terms of segmenting between amphibole, plagioclase, and sulfide phases. However, the technique was not able to differentiate between sulfide phases in the case of chalcopyrite and pyrite. Using supervised classification, around 50–60% of the chalcopyrite and 97–99% of pyrite were correctly identified. Feature based classification was found to have a poorer sensitivity to chalcopyrite, but produced a better result in segmenting between the mineral grains, as it operates based on voxel regions instead of individual voxels. The mineralogical results from the 3D µCT data showed considerable difference compared to the BSE mineral map, indicating stereological error exhibited in the latter analysis. The main limitation of this approach lies in the dataset itself, in which there was a significant overlap in grayscale values between chalcopyrite and pyrite, therefore highly limiting the classifier accuracy.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
X-ray micro-tomography (µCT), Machine learning, Mineral segmentation, Feature-based classification, Feature matching
National Category
Metallurgy and Metallic Materials Geology
Research subject
Mineral Processing; Ore Geology
Identifiers
urn:nbn:se:ltu:diva-75703 (URN)10.1016/j.mineng.2019.105882 (DOI)000488141400014 ()2-s2.0-85070948239 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-08-27 (svasva)

Available from: 2019-08-27 Created: 2019-08-27 Last updated: 2019-10-30Bibliographically approved

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