Conference PaperPDF Available

Use of X-ray Micro-computed Tomography (μCT) for 3-D Ore Characterization: A Turning Point in Process Mineralogy

Authors:

Abstract and Figures

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 that μCT 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.
Content may be subject to copyright.
USE OF X-RAY MICRO-COMPUTED TOMOGRAPHY (μCT) FOR 3-D ORE CHARACTERIZATION: A TURNING
POINT IN PROCESS MINERALOGY
P.I. Guntoro 1, Y. Ghorbani 1, *, J. Rosenkranz 1
1 Dept. of Civil, Environmental and Natural Resources Eng., Luleå University of Technology, Sweden
(*Corresponding author: yousef.ghorbani@ltu.se)
ABSTRACT
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 that
μCT 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 mineral characterization.
INTRODUCTION
Process Mineralogy
Process mineralogy is defined as the study of mineral characteristics and properties with
relation to their beneficiation process. The beneficiation process defined here can range from ore
beneficiation, metallurgical process, as well as environmental and waste management (Henley, 1983;
Lotter et al., 2018a). The key here is that by evaluating the characteristics of the minerals on a
representative sample of an ore, one could determine the optimum processing route of such ore based
on the characteristics of the minerals (both gangue and valuable minerals) in the ore. As the
characteristics of the ore is determined by the sample analyzed, sampling becomes ever increasingly
important in terms of process mineralogy (Lotter et al., 2018b).
In contrast to traditional separation between mineral processing and mineralogy, where
troubleshooting of processes are often focused more on process parameters; process mineralogy aims
to combine both field so both the characteristics of ore and process parameters can be taken into
account when designing and troubleshooting mineral processes. Process mineralogy requires
combination of knowledge from geology, mineralogy, metallurgy, and mineral processing. This can be
illustrated in Figure 1.
1044
IMCET 2019 / ANTALYA / TURKEY / April 16 – 19
PROCESS
MINERALOGY
Geology
Representative Sampling
Figure 1. Interdisciplinary of fields in process mineralogy (altered from Lotter et al. (2018, 2002)).
Several instruments and analytical techniques have been developed over the years to evaluate
mineralogical characteristics of an ore sample. The development of automated quantitative
mineralogical techniques such as Mineral Liberation Analyser (MLA) and Quantitative Evaluation of
Minerals by Scanning Electron Microscopy (QEMSCAN) was a significant breakthrough in process
mineralogy, as mineral characteristics of ore samples could now be analyzed in an automated, rapid,
and statistically reliable way (Fandrich et al., 2007; Gottlieb et al., 2000; Sutherland and Gottlieb, 1991).
With such system, information about mineral liberation (Fandrich et al., 2007), size and shape (Leroy et
al., 2011; Sutherland, 2007), and stationary textures (Pérez-Barnuevo et al., 2013, 2018; Tøgersen et al.,
2018) could be obtained and quantified. This information has been demonstrated to hold significant role
in evaluating ore beneficiation processes such as flotation (Alves dos Santos, 2018; Alves dos Santos and
Galery, 2018; dos Santos and Galery, 2018; Tungpalan et al., 2015) and comminution (Little et al., 2017,
2016; Tøgersen et al., 2018).
X-ray Tomography for Ore Characterization
While MLA and QEMSCAN offer a rapid data acquisition and processing, it possesses an obvious
weakness due to loss of dimensionality. Particles and ore samples are three-dimensional (3D) objects,
while automated mineralogical techniques produced a two-dimensional (2D) cross section analysis of
the ore samples. This phenomenon is known as stereological bias / error, in which the mineral liberation
may be overestimated, as the cross section of the sample might not represent the actual state of the
particles (Lätti and Adair, 2001) as shown in Figure 2. Over the years, several correction methods have
been developed to address this error in regards to mineral liberation and texture of the particles
(Fandrichi et al., 1998; Ueda et al., 2018a, 2018b).
1045
IMCET 2019 / ANTALYA / TURKEY / April 16 – 19
Figure 2. The effect of stereological bias on different type of particles with varying degree of liberation
by Spencer and Sutherland (2000). The possible cross-sections analyzed is indicated by the red lines
crossing the particles.
This inherent bias gives way to the development of instruments that are capable of acquiring 3D
data from an ore sample. Over the last decades, the development of X-ray microcomputed tomography
(μCT) in geosciences have received wide attentions. The main advantage of μCT lies on its ability to non-
destructively analyze the 3D interior of an object. Several reviews have been done in evaluating μCT
application in geosciences (Cnudde and Boone, 2013; Mees et al., 2003), particularly in relation to ore
characterization and mineral processing (Kyle and Ketcham, 2015; Miller et al., 1990).
Using μCT, 3D properties of an ore sample such as porosity (Lin and Miller, 2005; Peng et al.,
2011; Yang et al., 2017; Zandomeneghi et al., 2010), mineralogy (Ghorbani et al., 2011; Reyes et al.,
2017; Tiu, 2017), mineral liberation (Lin and Miller, 1996; Reyes et al., 2018), as well as size and shape
(Cepuritis et al., 2017; Lin and Miller, 2005; Masad et al., 2005) could be obtained. Additionally, as 3D
data offer additional information about depth, surface properties of an ore can also be evaluated, in
which such parameter is important for leaching, flotation, and to some extent grinding (Miller et al.,
2003; Tøgersen et al., 2018; Wang et al., 2017; Xia, 2017).
Recent development in μCT instruments also allows in-situ experiments to be carried while
scanning is performed, therefore obtaining the so-called four-dimensional (4D) data, which consist of
three dimensional of space plus one dimension of time. With such settings, the evolution of ore samples
during experiments can be obtained so that the relation of the mineralogical characteristics of the ore to
the process can be draw. Such settings have been implemented for example in evaluating ore breakage
(L. Wang et al., 2015; Wang et al., 2018) and leaching (Ghorbani et al., 2011). If the key in process
mineralogy lies in drawing the relations between mineralogy and mineral processing, then in-situ
experiments with μCT scanning could offer a valuable dataset for process mineralogy.
The main limitation of μCT scanning lies on the principle of the X-ray scanning, where minerals
are differentiated by their respective attenuation to the X-ray beam. This is reflected in the grayscale
intensity of the final image. The attenuation of each materials varies depending on the minerals density,
atomic number, as well as the energy of the X-ray beam (Omoumi et al., 2015). This phenomena creates
a trade-off situation, where one has to optimize the beam energy so that sufficient contrast between
minerals could be obtained. Using lower energy beam often means better contrast, as the attenuation is
more dependent on the atomic number of the minerals due to photoelectric effect, but it requires
1046
IMCET 2019 / ANTALYA / TURKEY / April 16 – 19
longer exposure time. Using higher energy would mean less exposure time, but the attenuation is now
more dependent on density due to Compton effect, therefore making mineral differentiation difficult as
many minerals have similar density.
CASE STUDIES
Ore Structural Characterization with μCT
At the early stages, μCT analysis of ore samples was more focused on structural analysis, which
includes pore, shape, as well as size analysis. Analysis with μCT could obtain several information of the
ore which includes porosity and crack (Deng et al., 2016; Lin and Miller, 2005; Peng et al., 2011; Yang et
al., 2017; Zandomeneghi et al., 2010), particle and grain size distribution (Tiu, 2017) as well as particle
shape descriptors such as solidity, elongation, flatness, and aspect ratio (Vecchio et al., 2012; Zhao et al.,
2015).
Pore and crack analysis is one of the most common application of μCT. While pore and crack
analysis is less emphasized in mineralogy, it holds a significant role in petroleum (Markussen et al.,
2019) and construction engineering (Yang et al., 2019). Nevertheless, pore and crack analysis is often
indispensable when dealing with processes such as leaching, especially in cases with packed particle bed
samples, where connectivity of the pores could help in understanding the permeability of the ore (Deng
et al., 2016; Wu et al., 2007).
Most of the automated mineralogy technique could produce analysis on particle size and shape,
but as said earlier, these parameters often not used in process mineralogy due to the stereological
error. Particle and grain size distribution analysis using μCT is quite well established, as several
researchers have optimized the image processing algorithm in acquiring such distribution analysis (Lux
et al., 2011; Pierret et al., 2002). One of the most commonly used algorithm is granulometry
morphological opening, illustrated in Figure 3.
Figure 3. Grain size distribution in 3D as obtained from μCT analysis with granulometry technique. The
technique uses a structuring element acting as a sieve, where grain smaller than the sieve is removed.
The sieve size is then increased gradually, so the cumulative undersize can be obtained.
Particle and grains are irregular objects; therefore, a descriptor of shape is often needed when
describing such parameters. With μCT system, such descriptors could be better acquired, as now the 3D
data is available. Most of the available shape descriptors in 3D follow the same logic as the one
commonly available in 2D. Particle and grain shapes in 3D can be described with convex hull (Pamukcu
et al., 2013; Zhao et al., 2015), bounding box (Vecchio et al., 2012) as well as relation to sphere shapes
(Pirard et al., 2009; Van Dalen et al., 2012). Example of bounding box and convex hull of a mineral grain
is shown in Figure 4.
1047
IMCET 2019 / ANTALYA / TURKEY / April 16 – 19
Figure 4. Bounding box and convex hull of an irregular grain. (a) Scatter plots representing the grain; (b)
convex hull of the particle; (c) minimum bounding box of the grain; (d) minimum bounding box of the
convex hull. Due to the high irregularity (non-convex) particle, convex objects such as polygons often are
not the best when describing such particle.
While it is obvious that particle size is of an importance when dealing with most of mineral
processes, the effect of shape is not so obvious. It is clear that the choice of comminution equipment
affects greatly the progeny particle shape, which then indicates that grain shape could be an important
indicator in modelling the breakage mechanism that occurs in the particle (Little et al., 2017, 2016).
With flotation, several researchers have analyzed the effect of particle shape (Ma et al., 2018; Pita and
Castilho, 2017; Xia et al., 2018), and it is clear that the effect of shape is intertwined with the particle
composition and size; in some cases the effect of shape is minimum while in others its effect is more
prevalent.
Ore Mineralogical Characterization with μCT
The use of μCT in mineralogical characterization is relatively limited, although it is outlined as
one of the future characterization technique in process mineralogy (Baum, 2014). Mineralogical
characterization with μCT is often limited to simple mineralogy, such as differentiating the gangue and
valuable mineral phases. In these cases, simple thresholding technique such as Otsu could work (Andrä
et al., 2013; Yang et al., 2017). Limitations do exist especially if the sample is heterogeneous (Yang et al.,
2017), or consist of fine particles with high density / high atomic number, as then the boundary between
particles and the background might not be segmented properly due to partial volume effect (Y. Wang et
al., 2015).
Several researchers have applied different techniques in dealing with multi-mineral ore samples,
especially those that contains minerals with similar attenuations. Such problems can be anticipated
earlier by optimizing the scanning conditions through reduction of sample size (Bam et al., 2019; Kyle
and Ketcham, 2015), using lower scanning energy (Reyes et al., 2017), or using dual energy scanning
(Ghorbani et al., 2011). In other cases, such problem could be addressed later at the data processing
stage, such as using machine-learning techniques (Chauhan et al., 2016; Tiu, 2017) as well as
combination with SEM-EDS of XRF (Laforce et al., 2017; Reyes et al., 2017; Suuronen and Sayab, 2018;
Tiu, 2017). Despite all the steps need to be performing mineralogical analysis with μCT, the
1048
IMCET 2019 / ANTALYA / TURKEY / April 16 – 19
mineralogical result does show considerable difference with traditional automated mineralogy
techniques (Reyes et al., 2017; Tiu, 2017). Example of the usage of machine-learning in μCT
mineralogical analysis is shown in Figure 5.
Figure 5. Comparison of different machine learning techniques in mineralogical analysis. (a) Original 3D
image of a drill core; (b) Unsupervised machine learning classification; (c) Supervised machine learning
classification. By supervised the learning user can better specify the minerals, as in this example pyrite is
lacking contrast.
Ore Texture Characterization with μCT
Textural measures such as grain size is well known to have effect to the downstream processes,
especially in terms of liberation size (Lotter et al., 2018a). Another equally important texture measure is
the spatial distribution (pattern) of different minerals in the ore, often referred as stationary textures
(Lobos et al., 2016). While grain size is quantifiable, stationary textures is often descriptive and
qualitative. Recent developments are leaning toward the quantification of stationary textures with the
help of microscopy based techniques (Donskoi et al., 2016; Koch, 2017; Lund et al., 2015), accounting
both grain size and spatial relationship of minerals. Stationary textures have been shown to affect the
ore behavior in mineral processes (Butcher, 2010; Dey et al., 2017; Tøgersen et al., 2018) and it has been
used as an important measure in geometallurgy (Lund et al., 2015; Pérez-Barnuevo et al., 2018).
μCT analysis opens up a new potential in analyzing textures, especially stationary textures, as
now the spatial relationship of minerals can be described in 3D, which in turns leads to better
understanding of its effect to the downstream processes (Becker et al., 2016). Additionally, information
about surface texture of the ore could be obtained as well, in which parameter such as grain surface
exposure affecting leaching processes (Miller et al., 2003; Wang et al., 2017); surface hardness affecting
grinding processes (Tøgersen et al., 2018); as well as surface roughness affecting flotation process (Xia,
2017).
In general, μCT ore texture analysis is very limited, as it requires a comprehensive mineralogical
analysis, in which μCT has a limitation. Several researchers have tried to use μCT to describe texture
better (Barnes et al., 2018, 2017), while others have used μCT data to quantify stationary textures
(Jardine et al., 2018). Example of texture quantification in 3D is shown in Figure 6.
1049
IMCET 2019 / ANTALYA / TURKEY / April 16 – 19
Figure 6. Textural analysis of 3D drill core image acquired from μCT. (a) Original 3D image of a drill core;
(b) and (c) Two μCT slices showing different textures; (d) and (e) shows the texture heat map of the
slices. The heat map reveals the association of each pixels in the image; more association between high
grayscale value pixels means more sulphide mineralization, as shown in (e).
CONCLUSION
The future is wide open for μCT in process mineralogy. Additional dimension in μCT analysis
allows better characterization of ore, leading to better understanding of ore behavior in the
downstream processes. Future work shall be emphasized to accelerate μCT application in ore
characterization through development of both instrumentation and data processing workflow.
Development of instrumentation could include sub-micron resolution, in-situ experiments, and
combination with μCT other instruments such as XRF, EDS, and XRD. Development of the data
processing includes better reconstruction techniques, optimized algorithm to handle large datasets, as
well as benchmarking data processing techniques applied in other field of material science.
REFERENCES
Alves dos Santos, N., 2018. Modelling flotation per size liberation class Part 3 – Modelling recoveries
using particle surface area. Miner. Eng. 129, 15–23.
https://doi.org/10.1016/J.MINENG.2018.08.036
Alves dos Santos, N., Galery, R., 2018. Modelling flotation per size liberation class – Part 2 – Evaluating
flotation per class. Miner. Eng. 129, 24–36. https://doi.org/10.1016/J.MINENG.2018.09.013
Andrä, H., Combaret, N., Dvorkin, J., Glatt, E., Han, J., Kabel, M., Keehm, Y., Krzikalla, F., Lee, M.,
Madonna, C., 2013. Digital rock physics benchmarks—Part I: Imaging and segmentation. Comput.
Geosci. 50, 25–32.
Bam, L.C., Miller, J.A., Becker, M., Basson, I.J., 2019. X-ray computed tomography: Practical evaluation
of beam hardening in iron ore samples. Miner. Eng. 131, 206–215.
https://doi.org/10.1016/J.MINENG.2018.11.010
Barnes, S.J., Le Vaillant, M., Lightfoot, P.C., 2017. Textural development in sulfide-matrix ore breccias in
the Voisey’s Bay Ni-Cu-Co deposit, Labrador, Canada. Ore Geol. Rev. 90, 414–438.
https://doi.org/10.1016/j.oregeorev.2017.03.019
1050
IMCET 2019 / ANTALYA / TURKEY / April 16 – 19
Barnes, S.J., Staude, S., Le Vaillant, M., Piña, R., Lightfoot, P.C., 2018. Sulfide-silicate textures in
magmatic Ni-Cu-PGE sulfide ore deposits: Massive, semi-massive and sulfide-matrix breccia ores.
Ore Geol. Rev. 101, 629–651. https://doi.org/10.1016/J.OREGEOREV.2018.08.011
Baum, W., 2014. Ore characterization, process mineralogy and lab automation a roadmap for future
mining. Miner. Eng. 60, 69–73. https://doi.org/10.1016/J.MINENG.2013.11.008
Becker, M., Jardine, M.A., Miller, J.A., Harris, M., 2016. X-ray Computed Tomography–a
Geometallurgical Tool for 3D Textural Analysis of Drill Core?, in: Proceedings of the 3rd AusIMM
International Geometallurgy Conference. pp. 15–16.
Butcher, A., 2010. A practical guide to some aspects of mineralogy that affect flotation. Flotat. Plant
Optim. 16, 83–93.
Cepuritis, R., Garboczi, E.J., Jacobsen, S., Snyder, K.A., 2017. Comparison of 2-D and 3-D shape analysis
of concrete aggregate fines from VSI crushing. Powder Technol. 309, 110–125.
https://doi.org/https://doi.org/10.1016/j.powtec.2016.12.037
Chauhan, S., Rühaak, W., Khan, F., Enzmann, F., Mielke, P., Kersten, M., Sass, I., 2016. Processing of
rock core microtomography images: Using seven different machine learning algorithms. Comput.
Geosci. 86, 120–128. https://doi.org/https://doi.org/10.1016/j.cageo.2015.10.013
Cnudde, V., Boone, M.N., 2013. High-resolution X-ray computed tomography in geosciences: A review
of the current technology and applications. Earth-Science Rev. 123, 1–17.
Deng, H., Fitts, J.P., Peters, C.A., 2016. Quantifying fracture geometry with X-ray tomography:
Technique of Iterative Local Thresholding (TILT) for 3D image segmentation. Comput. Geosci. 20,
231–244. https://doi.org/10.1007/s10596-016-9560-9
Dey, S., Mohanta, M.K., Singh, R., 2017. Mineralogy and textural impact on beneficiation of goethitic
ore. Int. J. Min. Sci. Technol. 27, 445–450. https://doi.org/10.1016/J.IJMST.2017.03.017
Donskoi, E., Poliakov, A., Holmes, R., Suthers, S., Ware, N., Manuel, J., Clout, J., 2016. Iron ore textural
information is the key for prediction of downstream process performance. Miner. Eng. 86, 10–23.
https://doi.org/10.1016/j.mineng.2015.11.009
dos Santos, N.A., Galery, R., 2018. Modelling flotation per size liberation class – Part 1 – Minimizing the
propagation of experimental errors in the estimate of flotation recovery. Miner. Eng. 128, 254
265. https://doi.org/10.1016/J.MINENG.2018.07.003
Fandrich, R., Gu, Y., Burrows, D., Moeller, K., 2007. Modern SEM-based mineral liberation analysis. Int.
J. Miner. Process. 84, 310–320. https://doi.org/https://doi.org/10.1016/j.minpro.2006.07.018
Fandrichi, R.G., Schneider, C.L., Gay, S.L., 1998. Two stereological correction methods: Allocation
method and kernel transformation method. Miner. Eng. 11, 707–715.
https://doi.org/10.1016/S0892-6875(98)00057-0
Ghorbani, Y., Becker, M., Petersen, J., Morar, S.H., Mainza, A., Franzidis, J.-P., 2011. Use of X-ray
computed tomography to investigate crack distribution and mineral dissemination in sphalerite ore
particles. Miner. Eng. 24, 1249–1257.
https://doi.org/https://doi.org/10.1016/j.mineng.2011.04.008
Gottlieb, P., Wilkie, G., Sutherland, D., Ho-Tun, E., Suthers, S., Perera, K., Jenkins, B., Spencer, S.,
Butcher, A., Rayner, J., 2000. Using quantitative electron microscopy for process mineralogy
applications. JOM 52, 24–25. https://doi.org/10.1007/s11837-000-0126-9
Henley, K.J., 1983. ORE-DRESSING MINERALOGY - A REVIEW OF TECHNIQUES, APPLICATIONS AND
RECENT DEVELOPMENTS., in: J.P.R., de V., P.A., C. (Eds.), . Geological Soc of South Africa, The
Australian Mineral Development, Lab, Geological Services Div,, Adelaide, Aust, The Australian
Mineral Development Lab, Geological Services Div, Adelaide, Aust, pp. 175–200.
Jardine, M.A., Miller, J.A., Becker, M., 2018. Coupled X-ray computed tomography and grey level co-
occurrence matrices as a method for quantification of mineralogy and texture in 3D. Comput.
Geosci. 111, 105–117. https://doi.org/10.1016/j.cageo.2017.11.005
Koch, P.-H., 2017. Particle generation for geometallurgical process modeling. Licent. thesis / Luleå
Univ. Technol. Luleå tekniska universitet, Minerals and Metallurgical Engineering, Department of
Civil, Environmental and Natural Resources Engineering, Luleå University of Technology.
Kyle, J.R., Ketcham, R.A., 2015. Application of high resolution X-ray computed tomography to mineral
1051
IMCET 2019 / ANTALYA / TURKEY / April 16 – 19
deposit origin, evaluation, and processing. Ore Geol. Rev. 65, 821–839.
https://doi.org/10.1016/j.oregeorev.2014.09.034
Laforce, B., Masschaele, B., Boone, M.N., Schaubroeck, D., Dierick, M., Vekemans, B., Walgraeve, C.,
Janssen, C., Cnudde, V., Van Hoorebeke, L., Vincze, L., 2017. Integrated Three-Dimensional
Microanalysis Combining X-Ray Microtomography and X-Ray Fluorescence Methodologies. Anal.
Chem. 89, 10617–10624. https://doi.org/10.1021/acs.analchem.7b03205
Lätti, D., Adair, B.J.I., 2001. An assessment of stereological adjustment procedures. Miner. Eng. 14,
1579–1587. https://doi.org/10.1016/S0892-6875(01)00176-5
Leroy, S., Dislaire, G., Bastin, D., Pirard, E., 2011. Optical analysis of particle size and chromite liberation
from pulp samples of a UG2 ore regrinding circuit. Miner. Eng. 24, 1340–1347.
Lin, C.L., Miller, J.D., 2005. 3D characterization and analysis of particle shape using X-ray
microtomography (XMT). Powder Technol.
https://doi.org///doi.org/10.1016/j.powtec.2005.04.031
Lin, C.L., Miller, J.D., 1996. Cone beam X-ray microtomography for three-dimensional liberation
analysis in the 21st century. Int. J. Miner. Process. 47, 61–73.
Little, L., Mainza, A.N., Becker, M., Wiese, J., 2017. Fine grinding: How mill type affects particle shape
characteristics and mineral liberation. Miner. Eng. 111, 148–157.
https://doi.org/10.1016/j.mineng.2017.05.007
Little, L., Mainza, A.N., Becker, M., Wiese, J.G., 2016. Using mineralogical and particle shape analysis to
investigate enhanced mineral liberation through phase boundary fracture. Powder Technol. 301,
794–804. https://doi.org/https://doi.org/10.1016/j.powtec.2016.06.052
Lobos, R., Silva, J.F., Ortiz, J.M., Díaz, G., Egaña, A., 2016. Analysis and Classification of Natural Rock
Textures based on New Transform-based Features. Math. Geosci. 48, 835–870.
Lotter, N.O., Baum, W., Reeves, S., Arrué, C., Bradshaw, D.J., 2018a. The business value of best practice
process mineralogy. Miner. Eng. 116, 226–238. https://doi.org/10.1016/J.MINENG.2017.05.008
Lotter, N.O., Evans, C.L., Engstrőm, K., 2018b. Sampling A key tool in modern process mineralogy.
Miner. Eng. 116, 196–202. https://doi.org/10.1016/J.MINENG.2017.07.013
Lotter, N.O., Whittaker, P.J., Kormos, L., Stickling, J.S., Wilkie, G.J., 2002. The development of process
mineralogy at Falconbridge Limited and application to the Raglan Mill. CIM Bull. 95, 85–92.
Lund, C., Lamberg, P., Lindberg, T., 2015. Development of a geometallurgical framework to quantify
mineral textures for process prediction. Miner. Eng. 82, 61–77.
https://doi.org/https://doi.org/10.1016/j.mineng.2015.04.004
Lux, J., Delisée, C., Thibault, X., 2011. 3D characterization of wood based fibrous materials: an
application. Image Anal. Stereol. 25, 25–35.
Ma, G., Xia, W., Xie, G., 2018. Effect of particle shape on the flotation kinetics of fine coking coal. J.
Clean. Prod. 195, 470–475. https://doi.org/10.1016/J.JCLEPRO.2018.05.230
Markussen, Ø., Dypvik, H., Hammer, E., Long, H., Hammer, Ø., 2019. 3D characterization of porosity
and authigenic cementation in Triassic conglomerates/arenites in the Edvard Grieg field using 3D
micro-CT imaging. Mar. Pet. Geol. 99, 265–281. https://doi.org/10.1016/j.marpetgeo.2018.10.015
Masad, E., Saadeh, S., Al-Rousan, T., Garboczi, E., Little, D., 2005. Computations of particle surface
characteristics using optical and X-ray CT images. Comput. Mater. Sci. 34, 406–424.
https://doi.org/https://doi.org/10.1016/j.commatsci.2005.01.010
Mees, F., Swennen, R., Van Geet, M., Jacobs, P., 2003. Applications of X-ray computed tomography in
the geosciences. Geol. Soc. London, Spec. Publ. 215, 1–6.
Miller, J.D., Lin, C.L., Cortes, A.B., 1990. A review of X-ray computed tomography and its applications in
mineral processing. Miner. Procesing Extr. Metall. Rev. 7, 1–18.
Miller, J.D., Lin, C.L., Garcia, C., Arias, H., 2003. Ultimate recovery in heap leaching operations as
established from mineral exposure analysis by X-ray microtomography. Int. J. Miner. Process. 72,
331–340.
Omoumi, P., Becce, F., Racine, D., Ott, J., Andreisek, G., Verdun, F., 2015. Dual-Energy CT: Basic
Principles, Technical Approaches, and Applications in Musculoskeletal Imaging (Part 1), Seminars in
musculoskeletal radiology. https://doi.org/10.1055/s-0035-1569253
1052
IMCET 2019 / ANTALYA / TURKEY / April 16 – 19
Pamukcu, A.S., Gualda, G.A.R., Rivers, M.L., 2013. Quantitative 3D petrography using X-ray tomography
4: Assessing glass inclusion textures with propagation phase-contrast tomography. Geosphere 9,
1704–1713.
Peng, R., Yang, Y., Ju, Y., Mao, L., Yang, Y., 2011. Computation of fractal dimension of rock pores based
on gray CT images. Chinese Sci. Bull. 56, 3346. https://doi.org/10.1007/s11434-011-4683-9
Pérez-Barnuevo, L., Lévesque, S., Bazin, C., 2018. Automated recognition of drill core textures: A
geometallurgical tool for mineral processing prediction. Miner. Eng. 118, 87–96.
https://doi.org/https://doi.org/10.1016/j.mineng.2017.12.015
Pérez-Barnuevo, L., Pirard, E., Castroviejo, R., 2013. Automated characterisation of intergrowth
textures in mineral particles. A case study. Miner. Eng. 52, 136–142.
https://doi.org/10.1016/J.MINENG.2013.05.001
Pierret, A., Capowiez, Y., Belzunces, L., Moran, C.J., 2002. 3D reconstruction and quantification of
macropores using X-ray computed tomography and image analysis. Geoderma 106, 247–271.
Pirard, E., Califice, A., Léonard, A., Gregoire, M., 2009. Multiscale shape analysis of particles in 3D using
the calypter.
Pita, F., Castilho, A., 2017. Separation of plastics by froth flotation. The role of size, shape and density
of the particles. Waste Manag. 60, 91–99. https://doi.org/10.1016/J.WASMAN.2016.07.041
Reyes, F., Lin, Q., Cilliers, J.J., Neethling, S.J., 2018. Quantifying mineral liberation by particle grade and
surface exposure using X-ray microCT. Miner. Eng. 125, 75–82.
https://doi.org/10.1016/J.MINENG.2018.05.028
Reyes, F., Lin, Q., Udoudo, O., Dodds, C., Lee, P.D., Neethling, S.J., 2017. Calibrated X-ray micro-
tomography for mineral ore quantification. Miner. Eng. 110, 122–130.
https://doi.org/10.1016/j.mineng.2017.04.015
Spencer, S., Sutherland, D., 2000. Stereological correction of mineral liberation grade distributions
estimated by single sectioning of particles. Image Anal. Stereol. 19, 175–182.
Sutherland, D., 2007. Estimation of mineral grain size using automated mineralogy. Miner. Eng. 20,
452–460. https://doi.org/10.1016/J.MINENG.2006.12.011
Sutherland, D.N., Gottlieb, P., 1991. Application of automated quantitative mineralogy in mineral
processing. Miner. Eng. 4, 753–762. https://doi.org/10.1016/0892-6875(91)90063-2
Suuronen, J.-P., Sayab, M., 2018. 3D nanopetrography and chemical imaging of datable zircons by
synchrotron multimodal X-ray tomography. Sci. Rep. 8, 4747. https://doi.org/10.1038/s41598-018-
22891-9
Tiu, G., 2017. Classification of Drill Core Textures for Process Simulation in Geometallurgy : Aitik Mine,
Sweden. EMerald Program.
Tøgersen, M.K., Kleiv, R.A., Ellefmo, S., Aasly, K., 2018. Mineralogy and texture of the Storforshei iron
formation, and their effect on grindability. Miner. Eng. 125, 176–189.
https://doi.org/10.1016/j.mineng.2018.06.009
Tungpalan, K., Wightman, E., Manlapig, E., 2015. Relating mineralogical and textural characteristics to
flotation behaviour. Miner. Eng. 82, 136–140. https://doi.org/10.1016/J.MINENG.2015.02.005
Ueda, T., Oki, T., Koyanaka, S., 2018a. A general quantification method for addressing stereological bias
in mineral liberation assessment in terms of volume fraction and size of mineral phase. Miner. Eng.
119, 156–165. https://doi.org/10.1016/J.MINENG.2018.01.034
Ueda, T., Oki, T., Koyanaka, S., 2018b. Numerical analysis of the general characteristics of stereological
bias in surface liberation assessment of ore particles. Adv. Powder Technol. 29, 3327–3335.
https://doi.org/10.1016/J.APT.2018.09.010
Van Dalen, G., Koster, M.W., Dalen, G. Van, Koster, M.W., 2012. 2D & 3D particle size analysis of micro-
CT images. Unilever Res. Dev. Netherlands. https://doi.org/10.1007/s10509-008-9775-x
Vecchio, I., Schladitz, K., Godehardt, M., Heneka, M.J., 2012. 3D GEOMETRIC CHARACTERIZATION OF
PARTICLES APPLIED TO TECHNICAL CLEANLINESS. Image Anal. & Stereol. Vol 31, No 3
(2012)DO - 10.5566/ias.v31.p163-174 .
Wang, L., Ooi, J.Y., Butler, I., 2015. Interpretation of Particle Breakage under Compression Using X-ray
Computed Tomography and Digital Image Correlation. Procedia Eng. 102, 240–248.
1053
IMCET 2019 / ANTALYA / TURKEY / April 16 – 19
https://doi.org/10.1016/J.PROENG.2015.01.138
Wang, Y., Li, C., Hou, Z., Yi, X., Wei, X., 2018. In Vivo X-ray Computed Tomography Investigations of
Crack Damage Evolution of Cemented Waste Rock Backfills (CWRB) under Uniaxial Deformation.
Minerals 8, 539.
Wang, Y., Lin, C.L., Miller, J.D., 2017. Quantitative analysis of exposed grain surface area for multiphase
particles using X-ray microtomography. Powder Technol. 308, 368–377.
https://doi.org/10.1016/j.powtec.2016.11.047
Wang, Y., Lin, C.L., Miller, J.D., 2015. Improved 3D image segmentation for X-ray tomographic analysis
of packed particle beds. Miner. Eng. 83, 185–191. https://doi.org/10.1016/j.mineng.2015.09.007
Wu, A., Yang, B., Xi, Y., Jiang, H., 2007. Pore structure of ore granular media by computerized
tomography image processing. J. Cent. South Univ. Technol. 14, 220–224.
Xia, W., 2017. Role of surface roughness in the attachment time between air bubble and flat ultra-low-
ash coal surface. Int. J. Miner. Process. 168, 19–24. https://doi.org/10.1016/J.MINPRO.2017.09.006
Xia, W., Ma, G., Bu, X., Peng, Y., 2018. Effect of particle shape on bubble-particle attachment angle and
flotation behavior of glass beads and fragments. Powder Technol. 338, 168–172.
https://doi.org/10.1016/J.POWTEC.2018.07.024
Yang, B., Wu, A., Narsilio, G.A., Miao, X., Wu, S., 2017. Use of high-resolution X-ray computed
tomography and 3D image analysis to quantify mineral dissemination and pore space in oxide
copper ore particles. Int. J. Miner. Metall. Mater. 24, 965–973. https://doi.org/10.1007/s12613-
017-1484-4
Yang, X., Kuru, E., Gingras, M., Iremonger, S., 2019. CT-CFD integrated investigation into porosity and
permeability of neat early-age well cement at downhole condition. Constr. Build. Mater. 205, 73–
86. https://doi.org/10.1016/J.CONBUILDMAT.2019.02.004
Zandomeneghi, D., Voltolini, M., Mancini, L., Brun, F., Dreossi, D., Polacci, M., 2010. Quantitative
analysis of X-ray microtomography images of geomaterials: Application to volcanic rocks.
Geosphere 6, 793–804.
Zhao, B., Wang, J., Coop, M.R., Viggiani, G., Jiang, M., 2015. An investigation of single sand particle
fracture using X-ray micro-tomography. Géotechnique 65, 625–641.
1054
IMCET 2019 / ANTALYA / TURKEY / April 16 – 19
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Cemented waste rock backfill (CWRB), which is a mixture of tailings, waste rock, cement, and water, is subjected to combination actions in underground mining operations and has been widely used in deep resource mining. While the strength requirement and macroscopic deformation behaviors of CWRB have been well studied, the mesoscopic damage evolution mechanisms are still not well understood. In this work, a CWRB sample with a waste rock proportion of 30% was studied with a uniaxial compression test under tomographic monitoring, using a 450 kV industrial X-ray computed tomography (CT). Clear CT images, CT value analysis, crack identification, and extraction reveal that CWRB damage evolution is extremely inhomogeneous and affected by the waste rock size, shape, and distribution. Furthermore, the crack initiation, propagation, and coalescence behaviors are limited to the existing waste rocks. When deformation grows to a certain extent, the cracks demonstrate an interlocking phenomenon and their propagation paths are affected by the waste rocks, which may improve the ability to resist compressive deformation. Volumetric dilatancy caused by the damage and cracking behavior has closed a link with the meso-structural changes, which are controlled by the interactions between the waste rocks and the cemented tailing paste.
Article
Full-text available
In mineral processing, ground ore particles containing valuable minerals are commonly separated from the gangue by flotation. For efficient flotation, surface liberation, in which the particle surface is composed of one phase, is important. Surface liberation is commonly measured by two-dimensional (2D) measurement of particle sections of resin-mounted samples. Such 2D measurement is considered to result in a form of error called stereological bias; however, the stereological bias associated with surface liberation assessment has not been fully studied. A series of numerical simulations was here conducted, to investigate the influence of a particle's texture (the 3D internal particle structure) and shape on such stereological bias. First, a total of 110 patterns of texture (10 patterns of grain size times 11 levels of grain content) were modeled, to determine the general characteristics of the stereological bias. Then, the influence on the stereological bias, of particle shape as represented by the aspect ratio (an index of global shape) and corrected sphericity (an index of surface roughness), was investigated. The results revealed that texture had the largest, aspect ratio the second largest, and corrected sphericity the smallest influence on the stereological bias in surface liberation measurement. Based on the results, it is suggested that a rough estimate of the stereological bias in the measurement of irregularly shaped real ore particles can be made from much simpler numerical models of spherical particles.
Article
Full-text available
Much of the value of magmatic Ni-Cu-PGE sulfide orebodies is contained within massive or semi-massive ores that show a wide variety of textural relationships to included or adjacent silicate rocks. We identify five mutually gradational textural types: (1) pure inclusion-free massive sulfide ores; (2) sulfide matrix ore breccias, of sharp-wall, soft-wall or mixed character; (3) emulsion textured ores formed by frozen mixtures of molten silicate and sulfide, most commonly developed as melt films at thermal erosion contacts; (4) vein-hosted sulfides formed at late magmatic or high temperature post-emplacement deformation stage close to the brittle-ductile transition in the country rocks or host igneous bodies; and (5) tectonic “durchbewegung” breccias, formed by mechanical inter-shearing of less-ductile silicate inclusions and more-ductile solid sulfides. Some deposits, the Moran Shoot at Kambalda being the type example, record the invasion of country rock footwall by downward- or sideways-percolating superheated molten sulfide liquid generating vertical sequences of pure massive sulfide, emulsion textured ores and finely-spaced invasive sulfide veins; these are referred to as sulfide melting-infiltration fronts and may provide a clue to the mechanism of formation of sulfide-rich magmatic ores as whole. Sulfide matrix ore breccias are particularly well developed in the Voisey’s Bay and Aguablanca deposits, where they developed by flooding of percolating sulfide melt through the silicate matrix of magmatic intrusion breccias, displacing silicate melt. The lithology of the silicate or carbonate rock inclusions determines the nature of the inclusion-matrix relationships. Non-refractory inclusions typically disaggregate along original grain boundaries to leave coherent inclusions surrounded by clouds of inclusion-derived or matrix-derived crystals, with the low-melting silicate component preferentially displaced by sulfide liquid, whereas refractory inclusions retain sharp boundaries. Zonation of inclusions and overgrowths preserves reaction between inclusion and silicate matrix that pre-dates invasion of the intrusion breccia by sulfide liquid. The process of percolation of dense, low-viscosity sulfide liquid into pore space and fractures within partially molten (or melting) silicate rock is a unifying theme that links sulfide matrix ore breccias and emulsion textured ores with distinctive textures in less sulfide rich rocks such as net-texture (matrix ore texture), leopard texture (poikilitic net texture) and interspinifex ore. Vein-hosted massive sulfides may be emplaced under magmatic conditions where the excess pressure of the sulfide liquid column drives or enhances fracturing of the country rock and injection of sulfide into the cracks. Such veins are commonly referred to as “remobilised”, a term which may obscure process understanding and should be reserved for cases where tectonic solid-state mobilisation of sulfide can be demonstrated on textural and structural grounds. The tendency of sulfide liquids to invade country rocks and potentially to drive the propagation of their own magmatic containers may be a critical feedback loop in the development of magmatic sulfide mineral systems.
Article
Full-text available
Liberation is a key driver in all mineral separation processes as it limits the maximum possible grade for a given recovery. In flotation, this is further complicated by the fact that it is surface exposure of the floatable minerals that determines the ultimate performance. Liberation, grade and surface exposure are commonly quantified using Scanning Electron Microscopy coupled to Energy Dispersive X-ray spectroscopy (SEM/EDX) analysis of polished sections. The intrinsically 2D nature of this technique can result in significant sampling errors and stereological effects that can affect the quantification of the ore's textural characteristics. X-ray microCT (XMT) is an imaging method that can non-invasively and non-destructively delineate ore fragments in 3D, thus providing an alternative method that eliminates the need for stereological corrections and readily provides surface exposure. A methodology and automated algorithm were developed for extracting this information from images of closely packed particles. By dividing these particles into classes based on both their surface exposure and grade, the extent to which there is preferential breakage of the particles can be assessed—an important consideration if sufficient surface liberation for good flotation performance is to be achieved at coarser particle sizes. Using low energy scanning simple 3D mineral maps can be obtained via XMT, allowing for the assessment of liberation and surface exposure for each mineral species. The methodology was tested on low grade porphyry copper ore as this is representative of the most commonly treated ore types for copper production.
Article
Mineral liberation has a strong effect on flotation performance. However, mainly due to stereological bias, the typical 2D scanning microscope mineralogical analysis is subject to limited representativeness and to high error propagation. In addition, grades calculated from the mineralogical distributions must be compatible with reconciled grades, estimated with mass balance calculations. Despite the existence of several stereological correction methods, only few approaches have been developed to solve this integration issue. Beta reconciliation method is able to minimise errors in liberation data while reconciling balanced grades with the grades calculated via the distribution of liberation classes. This is the first of a series of papers that aims at investigating and modelling flotation per size liberation classes. In this work, the beta adjustment method is detailed and optimized. Results from tests performed on a continuous chalcopyrite flotation circuit shows that the method provides the basis for a consistent balance, allowing to evaluate the flotation performance considering the size liberation classes.
Article
X-ray computed tomography (CT) is well known especially in medical applications, but more recently also in geosciences. In this study we present a model for characterizing the distribution and texture of pores and pore-throats in siliciclastic rocks using multi-scale 3D micro-CT imaging and apply this to reservoir rocks of the Edvard Grieg field comprising conglomerates and arenites. The conglomerates are highly heterogeneous and rich in clays which are carrying the majority of the porosity/microporosity. The sandstones display better sorting, permeabilities and porosities, in particular the aeolian samples, compared to the conglomerate matrix. The sandstones also have less clay and carbonate cement. In order to resolve porosity distribution below image resolution, the dry/wet method was applied. This method not only resolves porosity distribution below image resolution, but also the pore throats and connectivities. In this study we also investigate the effect carbonate cementation has had on the porosity and pore-throat characteristics. Low reservoir quality conglomerates (very tight conglomerates) are highly carbonate cemented which has reduced the pore-throat connectivity significantly. In the sandstone samples the pore filling carbonate cement is less abundant and has had a much smaller effect on the porosity reduction and pore throat geometries and, hence permeabilities.
Article
Investigating how ore mineralogy and texture affect the recovery from the processing plant is important for any mining operation. The results will assist in production planning and optimising the utilisation of a deposit. Easily available validated tests are desirable and useful. The Storforshei iron formation (IF) consists of several iron oxide deposits with mineralogical and textural differences. Although the Fe grades of the ores are similar, mineralogical and textural characteristics of the deposits affect the individual recoveries from the magnetic separation. For this paper three of the ore deposits were sampled, and important mineralogical and textural properties were investigated and tested. The investigations included geological mapping and optical microscopy, and the test work involved surface hardness measurements by Schmidt hammer and Equotip, and autogenous milling tests (i.e., grindability). The aim of the study was to investigate whether ore mineralogy and textures can be correlated to surface hardness measurements, and whether these three parameters can be used to evaluate grindability. The ores were classified into six ore types based on mineralogy and textures. The results show that the ore mineralogy and texture influence the surface hardness. Fine-grained ore types with irregular-to-no visible grain boundaries have higher surface hardness than coarser-grained ore types with straight grain boundaries. Furthermore, surface hardness measurements and grindability evaluations (using throughput (kg/h) and specific energy consumption (kWh/tonne)) of samples from three of the iron oxide deposits indicate that grindability decreases with increasing surface hardness. The relationship found between the parameters ore mineralogy, texture, surface hardness, and grindability suggests that geological mapping and surface hardness measurements can be used to evaluate grindability, and thus assess ore processing performance.
Article
Particle shape has a significant effect on the flotation process of fine mineral and fine coal particles. In this paper, the effects of particle shape on the flotation kinetics and behavior of fine coking coal of different size fractions were investigated. The coal particles with different shape properties were gained from the grinding and crushing products of the rod mill and jaw crusher, respectively. The shape parameter of the coal particle was measured by Image J software by analyzing the photos of coal particles taken by the microscope. The flotation tests of fine coking coal were done in a 0.5 L XFD flotation cell without any usage of flotation collector in order to study the natural floatability of coal particle. Six flotation models were used to assess how the particle shape affects the flotation behavior of fine coking coal of different size fractions. The results showed that the coal particle with a larger elongation ratio had a higher flotation recovery. The first-order model with rectangular distribution of floatability was found to be well consistent to the experimental data, and the modified flotation rate constant of the flotation process increased with the increase of elongation ratio.