Volume quantification in interphase voxels of ore minerals using 3D imaging

Godinho, José R. A.; Kern, Marius; Renno, Axel D.; Gutzmer, Jens

doi:10.1016/j.mineng.2019.106016

Cited by 15 publications

(18 citation statements)

References 34 publications

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“…The Pb‐spectrum shows the lowest signal with the K‐edge energy position comparable with the theory (NIST X‐Ray Transition Energies Database; [ 57 ] ). The small particle size results in an averaging with the surrounding pixels, which can explain the low signal of the Pb particle (partial volume effect; [ 18 ] ). The spectra of gold and tungsten show higher intensities and well visible K‐edges.…”

Section: Resultsmentioning

confidence: 99%

“…The most common method for 3D investigations is X‐ray computed micro tomography (CT) that has been used for many years in medical science, security, industrial inspection or geology [ 12–19 ] . In CT, X‐ray absorption images (radiographs) from different angles of the sample are recorded during the scan, which are then computationally reconstructed as a voxelized 3D image.…”

Section: Introductionmentioning

confidence: 99%

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Spectral X‐ray computed micro tomography: 3‐dimensional chemical imaging

et al. 2020

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We present a new approach to 3‐dimensional chemical imaging based on X‐ray computed micro tomography (CT), which enables the analysis of the internal elemental chemistry. The method uses a conventional laboratory‐based CT scanner equipped with a semiconductor detector (CdTe). Based on the X‐ray absorption spectra, elements in a sample can be distinguished by their specific K‐edge energy. The capabilities and performance of this new approach are illustrated with different experiments, i.e. single pure element particle measurements, element differentiation in mixtures, and mineral differentiation in a natural rock sample. The results show that the method can distinguish elements with K‐edges in the range of 20 to 160 keV, this corresponds to an element range from Ag to U. Furthermore, the spectral information allows a distinction between materials, which show little variation in contrast in the reconstructed CT image.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spectral X‐ray computed micro tomography: 3‐dimensional chemical imaging

et al. 2020

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“…Another problem is the partial volume effect (PVE) that is observed at voxels composed of more than one phase, e.g., at interphases. The grey-value of those voxels represents the attenuation coefficient of all phases inside the voxel, proportionally to their volume fraction [23]. Consequently, depending on shape, size and the number of phases in a particle, the PVE may affect a significant portion of the particle's volume and adds to the uncertainty of phase classification based only on grey-scale.…”

Section: Background Of the Methodsmentioning

confidence: 99%

“…For example, volume, grain sizes, surface area, spatial distribution and associations of individual particles or phases can be quantified [14][15][16][17][18][19][20]. Despite the advantages of measuring 3D microstructures, classifying the phases composing those microstructures based on grey-values remains challenging due to imaging artefacts [21,22] that cause a broadening of the grey-scale interval that can be attributed to a phase [23,24]. Consequently, the ability to distinguish between phases is reduced and less accurate, especially for complex multiphase materials containing small grains characteristic of ore particles [25].…”

Section: Introductionmentioning

confidence: 99%

Mounted Single Particle Characterization for 3D Mineralogical Analysis—MSPaCMAn

et al. 2021

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This paper demonstrates a new method to classify mineral phases in 3D images of particulate materials obtained by x-ray computed micro-tomography (CT), here named mounted single particle characterization for 3D mineralogical analysis (MSPaCMAn). The method allows minimizing the impact of imaging artefacts that make the classification of voxels inaccurate and thus hinder the use of CT to characterize natural particulate materials. MSPaCMAn consists of (1) sample preparation as particle dispersions; (2) image processing optimized towards the labelling of individual particles in the sample; (3) phase identification performed at the particle level using an interpretation of the grey-values of all voxels in a particle rather than of all voxels in the sample. Additionally, the particle’s geometry and microstructure can be used as classification criteria besides the grey-values. The result is an improved accuracy of phase classification, a higher number of detected phases, a smaller grain size that can be detected, and individual particle statistics can be measured instead of just bulk statistics. Consequently, the method broadens the applicability of 3D imaging techniques for particle analysis at low particle size to voxel size ratio, which is typically limited due to unreliable phase classification and quantification. MSPaCMAn could be the foundation of 3D semi-automated mineralogy similar to the commonly used 2D image-based semi-automated mineralogy methods.

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“…This creates a gradient boundary instead of a sharp boundary, making the mineral quantification uncertain. Several corrections for this bias have been developed [40,41]. Furthermore, there are also challenges with the limited spatial resolution, as typical µCT systems have spatial resolutions ranging from 10-50 µm [42], which makes it challenging to analyze small grains in that particle size range.…”

Section: D Mineral Liberation and Process Simulationmentioning

confidence: 99%

3D Ore Characterization as a Paradigm Shift for Process Design and Simulation in Mineral Processing

Guntoro

Ghorbani

Rosenkranz

2021

Berg Huettenmaenn Monatsh

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Current advances and developments in automated mineralogy have made it a crucial key technology in the field of process mineralogy, allowing better understanding and connection between mineralogy and the beneficiation process. The latest developments in X‑ray micro-computed tomography (µCT) have shown a great potential to let it become the next-generation automated mineralogy technique. µCT’s main benefit lies in its capability to allow 3D monitoring of the internal structure of the ore sample at resolutions down to a few hundred nanometers, thus excluding the common stereological error in conventional 2D analysis. Driven by the technological and computational progress, µCT is constantly developing as an analysis tool and successively it will become an essential technique in the field of process mineralogy. This study aims to assess the potential application of µCT systems, for 3D ore characterization through relevant case studies. The opportunities and platforms that µCT 3D ore characterization provides for process design and simulation in mineral processing are presented.

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Volume quantification in interphase voxels of ore minerals using 3D imaging

Cited by 15 publications

References 34 publications

Spectral X‐ray computed micro tomography: 3‐dimensional chemical imaging

Spectral X‐ray computed micro tomography: 3‐dimensional chemical imaging

Mounted Single Particle Characterization for 3D Mineralogical Analysis—MSPaCMAn

3D Ore Characterization as a Paradigm Shift for Process Design and Simulation in Mineral Processing

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