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Modern SEM-based mineral liberation analysis
Int. J. Miner. Process. 84 (2007) 310 – 320 www.elsevier.com/locate/ijminpro
Modern SEM-based mineral liberation analysis Rolf Fandrich ⁎, Ying Gu, Debra Burrows, Kurt Moeller JKTech Pty Ltd, Isles Rd, Indooroopilly QLD 4068, Australia Received 31 March 2006; received in revised form 24 July 2006; accepted 31 July 2006 Available online 5 October 2006
Abstract Modern digital mine planning, plant design and mineral processing operations demand detailed characterisation of the ore and plant feed. Textural parameters, such as mineral liberation size and mineral association, combine with modal mineralogy data to strongly influence mineral processing conditions and recovery. Traditionally, the measurement of these ore characteristics employed the tools of an optical microscope and/or a semi-automated SEM. These methods are time consuming, costly and frequently produce semi-quantitative results from data sets that are too small to be statistically valid. Thus, the results cannot be used reliably and effectively in digital mine planning and design. In the last 10 years, modern SEM-based quantitative mineralogy tools have advanced rapidly with increasing computer power, improved SEM hardware and the development of sophisticated image analysis methods. Texture resolutions can now be submicron and SEM measurement times have reduced to less than an hour for simple analyses, where previously they required many hours. Through image analysis, particle sections are recognised and separated, and the mineral grains within are delineated for discrete Xray analysis to determine mineralogy. The modern tools not only increase the speed and accuracy of liberation analysis, but also enhance measurement automation. Automated standard collection assists with the setup of new ore types for routine analysis and automated elemental quantification of target minerals enables the tracking of variations in the composition of the minerals of interest. The key to success for any modern SEM-based mineral liberation analysis system is the close integration of BSE image and EDS X-ray analyses. Integration of the SEM-based quantitative mineral liberation analysis with optical microscope, dual beam systems and X-ray tomography will further enrich the analysis results and the derived user experience. © 2006 Elsevier B.V. All rights reserved. Keywords: Scanning electron microscope; Mineral liberation analysis; Image analysis
1. Introduction The significance and value of mineral liberation analysis to the subject of applied mineralogy and metallurgical processing has been well documented (Jones, 1987; Petruk, 2000; King, 1993). Mineral liberation data are ⁎ Corresponding author. Tel.: +1 61 7 3365 5922. E-mail address: [email protected] (R. Fandrich). 0301-7516/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.minpro.2006.07.018
fundamental parameters used for process plant design and optimization. In recognition of this, several measurement systems based on the application of scanning electron microscope (SEM) technology to polished particles sections have been developed (Hall, 1977; PignoletBrendom and Reid, 1988; Jones, 1987; King, 1993; Lastra et al., 1998; Petruk, 2000; Gu, 2003). The latest advancement of such systems and the quality of the mineralogical data being produced by them have
R. Fandrich et al. / Int. J. Miner. Process. 84 (2007) 310–320
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ray measurements in a process to be known as particle mapping. 2. Mineral liberation analyser
Fig. 1. Witwatersrand linear intercept measurement system.
extended their relevance upstream into the field of ore characterisation for geological mapping purposes (Williams and Richardson, 2004). One of the earlier attempts to automate the gathering of mineral liberation data from polished sections was undertaken by Peter King at Witwatersrand University (King, 1977). This system measured linear intercept lengths of the pyrite phase in a Witwatersrand quartzite using an optical microscope. A mini computer controlled both a constant velocity motorized stage and a photometer circuit to register when the bright pyrite phase moved under the lens (Fig. 1). As is still the case today, the desired automation was motivated by the need to find alternatives to the labourintensive manual viewing of sufficient particles to obtain a statistically significant set of data. The principle of applying thresholding to a signal generated by the reflectance of a mineral (here of photons) to achieve phase discrimination was also being applied to achieve likewise with electrons in SEM-based systems such as CESEMI (Troutmann et al., 1974). The Mineral and Metallurgical Image Analysis (MMIA) software developed at the University of Utah (King and Schneider, 1993) applied thresholds to the grey scale histograms from BSE images of polished particle sections for phase discrimination (see Fig. 2). MMIA allowed the automated batch processing of large sets of high contrast and high-resolution images to produce statistically reliable sets of both linear and areal liberation data. One of the first automated systems to intensively use X-ray spectra was QEM ⁎ SEM (quantitative evaluation of materials by scanning electron microscopy) (Miller et al., 1982). Digital images of particle sections where built up in a pixel by pixel fashion by thousands of X-
The JKMRC Mineral Liberation Analyser (MLA) was first presented as a new development in the field of SEM-based automated mineral measurement tools in 1997 (Gu and Napier-Munn, 1997). At the time it represented a unique method of combining BSE image analysis and X-ray mineral identification to provide automated quantitative mineral liberation characterisation. The advanced analysis techniques implemented by the MLA today are presented, as are the many methods of combining them to provide targeted and optimised, automated mineralogical information. 2.1. BSE image analysis Imaging and image analysis are fundamental to mineral liberation analysis. A low noise, high-resolution image is a prerequisite for mineral identification and quantification. The very stable BSE signals from a modern SEM can generate quality high-resolution (0.1– 0.2 μm) images of particle sections. These images allow the MLA, through its advanced image analysis techniques, to accurately discriminate the mineral phases within a particle. The principal image analysis functions used by the MLA are known as particle de-agglomeration and phase segmentation. 2.1.1. Particle de-agglomeration A liberation analysis using the MLA involves the setting of particles into a mould (typically 30 mm diameter) with epoxy resin to form a hardened block. Typical particle sizes range from 10 μm to 1 mm and are
Fig. 2. MMIA software.
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preferably of a defined narrow size fraction. The block is then ground down to expose a representative crosssection of particles which is subsequently polished and then coated with carbon before being presented to the SEM. Despite precautions to prevent it, inevitably some particles in the prepared sample block will touch each other. If not recognised by the system and treated appropriately the agglomeration of particles can lead to biased liberation results. The MLA system has an automated de-agglomeration function that detects agglomerates and separates them according to a set of predetermined parameters. Fig. 3 shows this process. The de-agglomeration function can be used both during the on-line measurement or performed off-line. Particle shape parameters determine if particles are agglomerated. The de-agglomeration procedure has three methods or criteria at its disposal to find the best separation option: 1) shadow or boundary identification, 2) linear feature recognition and 3) an erosion/ dilation procedure. The operator can control the
Fig. 4. Grey level distribution of a typical lead–zinc ores. The x-axis is the BSE grey level and the y-axis is the frequency.
weighting applied to each of the separating criteria through a set of parameters. The adjustment of parameters may be needed to account for the influence
Fig. 3. MLA de-agglomeration process: (a) Original BSE image. (b) After background removal, several particles are agglomerated. (c) One of the agglomerates is highlighted. (d) After de-agglomeration, the agglomerate is broken apart and one particle is highlighted.
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Fig. 5. BSE and segmented particle image.
on separation by varying particle characteristics such as shape and size. 2.1.2. Phase segmentation Once individual particles have been identified, the next step of the liberation analysis identifies all distinct mineral phases (or grains) and defines their boundaries accurately. This process is called phase segmentation and is performed on each individual particle. The MLA phase segmentation function outlines the regions of homogeneous grey levels within a particle BSE image. The average BSE grey value of every defined region corresponds with a mineral of unique average atomic number (AAN). The AAN determines the number of backscatter electrons emitted by the mineral and hence is directly proportional to the grey level registered in the BSE image. An example of a grey scale histogram with its peaks corresponding to the minerals in a lead–zinc ore is shown in Fig. 4. Phase segmentation also involves the recognition and elimination of features of a BSE particle image that do not represent an independent phase. These
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artefacts can be cracks, shading, tiny voids or the dark perimeter or halo that appears around many particles. The phase segmentation results for an enlarged BSE image of a composite particle are shown in Fig. 5. Six regions of homogeneous grey levels or mineral grains have been identified. The ‘segmented image’ is created with each mineral grain in a particle being assigned a unique colour. In an ideal system and set of measurement conditions each mineral within a sample will have a defined BSE grey value. Should this grey level coincide with that of another mineral, in the same particle, due to the same or very similar average atomic numbers, as is the case for pentlandite and chalcopyrite (AAN = 23.5), the MLA resorts to another rendering method to be explained later. As particle-based segmentation uses the grey scale histograms of each individual particle, the influence of any grey level effects due to changes in measurement conditions, such as beam drift, are eliminated. 2.2. Mineral identification with X-ray analysis The MLA uses three X-ray analysis techniques to identify mineral species: point X-ray, area X-ray and Xray mapping. 2.2.1. Point X-ray analysis In a typical sample measurement the MLA performs one X-ray analysis (typically N 2000 counts) for each grey level region identified within a segmented particle. The spectrum is collected at the centre of a phase to
Fig. 6. MLA area X-ray analysis of a composite particle.
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avoid contamination from bordering phases and hence acquire the “cleanest” spectrum possible. This spectrum is linked to its corresponding particle and grain in the segmented image to generate what is known as an X-ray image. The stored spectra are then compared with a predefined list or library of standard mineral spectra to complete the identification procedure and produce a classified image. In the classified image in Fig. 6 four of the five delineated phases were identified as chalcopyrite and the other as bornite. The classified image, and specifically the pixel phase data contained within, is the basis for all further quantitative analysis. In addition to particle location data, the pixel data and related mineral characteristics are stored in a database for subsequent presentation using the MLA software: DataView. 2.2.2. Area X-ray analysis During area X-ray analysis the spectra are collected by rastering the beam over the phase area. The perimeter of the phase is not scanned to avoid contamination by adjoining phases. Area X-ray analysis can be implemented where there is the possibility of two or more associated minerals having the same AAN (i.e. associated pentlandite and chalcopyrite grains). Poor BSE grey level contrast can result in two phases not being segmented and hence no delineation of phase boundaries (see Fig. 7, top left). With point X-ray analysis, depending on which mineral happens to be at the point of X-ray collection, the resulting combined phase will be identified as either pentlandite or chalcopyrite. Area X-ray analysis detects that two or
more phases are present through the resulting mixed spectra. X-ray mapping can then be employed to identify the mineral phases and to resolve the phase boundaries. 2.2.3. X-ray mapping X-ray mapping imposes a grid over an entire particle image, or specific grains thereof, and collects X-ray data at each grid point to determine the mineral identity. Fig. 7 illustrates the procedure with the example of pentlandite and chalcopyrite. Mineral identification using mapping requires significantly more time (greater than one order of magnitude) than point and area X-ray analysis as many more spectra are collected to generate a comprehensive mineral map. The three fundamental X-ray analysis methods of point and area X-ray analysis and X-ray mapping are the building blocks of all MLA measurement modes involving X-ray identification. 2.2.4. Mineral standards library Mineral identification through X-ray analysis requires a library of mineral standards. This library is usually constructed before an automated run and involves the collection of a high quality X-ray spectra spectrum for each mineral in the sample. The building of a standards library directly from the sample ensures that measurement conditions are reflected in the standards, such as beam energy (i.e. keV), and it also provides for an elemental deportment that better reflects the chemistry of the sample.
Fig. 7. Illustration of X-ray mapping for a particle containing pentlandite and chalcopyrite.
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The many measurement modes offered by the MLA implement the fundamental BSE image, and the various X-ray analyses to produce a suite of measurements designed to accommodate many different mineralogical information requirements. The measurement types and their applications are described.
2.3.2. Extended BSE liberation analysis (XBSE) XBSE implements area X-ray analysis to efficiently and effectively analyse ore samples containing phases with sufficient BSE contrast to ensure effective segmentation. The high resolution of BSE imaging for grain boundary definition and the speed of single X-ray mineral identification make this method ideal for a great majority of mineralogical samples. An extension of this mode is XBSE with automated standards collection (XBSE_STD). If a spectrum from a particular phase cannot be identified using the existing library of standards, a standard for the ‘unknown’ phase will be collected in the usual manner and added to the library for later classification.
2.3.1. Standard BSE liberation analysis (BSE) This is the most basic liberation analysis method in which a series of BSE images are collected on-line and then processed off-line to produce liberation data. Mineral discrimination is based solely on BSE grey level contrast and the liberation data is generated exclusively through image analysis. BSE is employed for applications where good grey level contrast exists and a correlation between grey level peak and mineral species is established. This is the case for certain lead/ zinc and copper ores and some slags. BSE is also useful for textures or features that are finer than the resolution of X-ray analyses (i.e. 2–5 μm).
2.3.3. Ford analysis or grain-based X-ray mapping (GXMAP) GXMAP employs X-ray mapping to the phases that cannot be segmented by BSE grey levels alone and the employs the faster area X-ray analysis for phases that are readily segmented. The operator selects the grains for mapping through a BSE trigger or a specific X-ray standard trigger. Fig. 8 illustrates the advantages of the selective mapping of GXMAP over traditional X-ray mapping for the example of a particle containing pentlandite, chalcopyrite and quartz. A BSE trigger set at a grey level below that of pentlandite and chalcopyrite ensures that all grains of interest are mapped, saving
The spectra identification process involves an errorbased search for the measured spectrum in the standards library to find the most probable fit. Statistics on the spectral pattern matching can be examined to assess the confidence of classification for specific minerals. 2.3. MLA measurement modes
Fig. 8. Illustration of a GXMAP analysis.
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valuable measurement time. Alternatively the X-ray standards for pentlandite and chalcopyrite can be implemented to trigger the mapping. A series of mixed spectra, interpolated at 10% intervals between the nominated pure pair of spectra, is generated and compared with the mixed spectra measured by the area X-ray analysis to force the trigger. GXMAP is a flexible mapping technique an advancement on the older MLA mapping methods of particle X-ray mapping (PXMAP) and selected particle X-ray mapping (SXMAP) (Gu, 2003). It has been successfully applied to certain types of sphalerite and chalcopyrite as well as textually complex ores, such as nickel ores containing pentlandite as fine flames in pyrrhotite. This mode is named after its co-developer Dr. Fred Ford (INCO). 2.3.4. Sparse phase liberation analysis (SPL) This measurement mode searches BSE images for particles containing phases of interest using a BSE grey scale range and then performs an XBSE analysis on them. The off-line processing is identical to the XBSE method and generates the same mineral liberation and association data. It does not provide bulk mineralogy information, as only selected particles in the sample are analysed. The selectivity of the SPL measurement is designed to efficiently measure tailings and low grade feed ores, such as platinum group mineral (PGM) ores where the mineral associations of the phase of interest is of importance. Two specialised versions of SPL analysis are also available. SPL-Lite (SPL-LT) only measures the sparse
phase of interest and its external mineral associations. This is appropriate for rock mass applications, such as drill cores, where liberation data is not relevant. SPLMAP maps the internal associations of the phase of interest once found or can be described as a GXMAP applied to a SPL. 2.3.5. X-ray modal analysis (XMOD) XMOD is the classical point counting method in which mineral identification is determined by one X-ray analysis at each counting point. This mode uses BSE imaging to discriminate particle matter from background and then collects one X-ray spectrum from each grid point across the particle. The X-ray spectra are saved for off-line classification. This method only produces modal mineralogy information, i.e. percentages of the mineral components of the sample. XMOD can also be implemented to produce a line scan measurement mode that produces traditional linear intercept data. X-ray spectra are taken at a step size of one pixel in the x direction and a given y displacement determines the line spacing. 2.3.6. Rare phase search (RPS) The RPS mode searches the BSE images for phases of interest using a trigger and collects a corresponding characteristic X-ray spectrum. For each grain found, the system saves the image of the particle containing the grain, the stage location and its X-ray spectrum. The operator can subsequently move to the SEM stage
Fig. 9. Gold found by the RPS mode.
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location where the grain was located and manually investigate it and its surroundings further. RPS is designed to efficiently locate very fine (sub-micron) components in large particle populations, such as gold in tailings and deliver data such as grain size and associated minerals (see Fig. 9). The ability to classify off-line allows the operator to automatically eliminate other bright phases, such as galena, from the phases of interest. 2.3.7. Latti analysis (SXBSE) The Latti measurement mode adds an elemental quantification capacity to the XBSE analysis and is also known as a Super XBSE analysis. An X-ray standards trigger is employed, in a similar fashion to the GXMAP mode, to initiate a so-called “long count” X-ray analysis. This X-ray collection can last 20 s or more and contain up to over 1,000,000 counts and is comparable with a high quality quantitative EDS measurement of the mineral concerned. These long count spectra are stored separately for subsequent analysis to obtain accurate elemental quantification for the minerals of interest, such as those with variable stoichiometry e.g. sphalerite.
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This mode is named after its co-developer Dewetia Latti (Rio Tinto). 2.3.8. Schouwstra analysis (SPL-dual zoom) Here, a standard SPL analysis is performed and when a phase of interest is detected in an image, the image capture is repeated at a higher resolution. The SPL analysis is then performed for the higher resolution particle images. This measurement mode combines the advantages of a rapid search for the phase of interest at low resolution (typically 1024 × 800) and that of accurate imaging at a high resolution (up to 4096 × 3200). Schouwstra analysis is widely used for PGM minerals and is named after its co-developer Dr. Robert Schouwstra (Head of Mineralogical Research, Anglo Platinum). 2.4. MLA data presentation DataView is the data presentation software that enables the operator to examine, process, present and store the quantitative mineralogical data generated by the MLA measurement software. Pixel data is combined
Fig. 10. MLA data presentation software: DataView.
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Fig. 11. Mineral grouping and corresponding grade recovery curves.
with the elemental composition and densities of the identified minerals to produce a variety of mineralogical data: modal mineralogy, calculated assay, elemental distributions, elemental and mineral grade recoveries, particle and mineral grain size distributions, particle density distributions, mineral associations and locking, phase specific surface area (PSSA) and mineral liberation by particle composition and free surface. Both tabular and standard graphical representations of the data are available (see Fig. 10). The combination and comparison of data sets are possible, as well as the ability to select particle populations through filtering and to manage mineral lists via grouping. 2.4.1. Filtering Filtering enables the creation of subsets of a particle population stored within a particle database according to given criteria. The criteria currently available are filtering by particle size, density, and shape, as well as mineral and elemental weight composition. For example
a filter of density N 3.0 would create a subset of all particles with a calculated density of greater than 3.0. All the analyses offered by DataView can then be performed on this subset. 2.4.2. Grouping As the list of minerals identified by an MLA analysis can be extensive and can contain many minor minerals of little consequence to an analysis, a grouping function allows mineral groups to be defined and be further treated as an individual mineral phases. Fig. 11 illustrates how the copper sulphides in a mineral list have been grouped and used to display the theoretical grade recoveries of three measured size fractions in a feed sample. 3. Future developments The goal of ongoing MLA development activities is to provide improved and diverse mineralogical information through the provision of advanced analysis tools
Fig. 12. Fusion of images from light microscopy and SEM.
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and high levels of measurement automation. The generation of mineralogical information from measurement data is enhanced through linkages with simulation tools. Levels of measurement automation are improved by integration with other measurement systems and technologies. 3.1. Links with simulation Commercial software packages designed to simulate mineral processing operations provide valuable optimisation and design tools for plant metallurgists and require reliable, quantitative mineralogical data to deliver meaningful results. Establishing efficient links between simulation and measurement tools increases the value of the information derived. For example, flotation models can utilise particle grade and mineral liberation distributions to predict and optimize grade recovery curves for flotation circuits. Linkages of this nature contribute to the goal of upgrading mineralogical data to metallurgical information for decision making. 3.2. Integration with optical microscopy systems Materials with predominately organic components and hence with very low atomic numbers, such as coal, are not conducive to liberation analysis with electron microscopy systems. Light optical microscopy systems such as MACE®300 (Jenkins et al., 2004) offer solutions to characterising coal particles based on the light reflectance and texture of the organic phases or macerals. Combining the identification and phase discrimination capabilities of both measurement principles through image fusion techniques can offer comprehensive liberation analysis solutions for coal, incorporating both maceral and mineral matter (Fig. 12). Previously unattainable detailed maceral–mineral association data can provide new information for process optimisation, the prediction of coal utilisation performance and the definition of new coal quality levels. 3.3. 3D mineral analysis The topic of stereological correction in mineral liberation analyses has been examined extensively and various models for extrapolating 3D liberation results from 2D particle section measurements have been proposed and reviewed (Latti and Adair, 2001). An option to apply such methods to measured liberation distributions will be offered by future versions of DataView. Pioneering 3D liberation measurement studies on materials such as a sphalerite/dolomite ore and coal
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using X-ray computed tomographic (CT) techniques at the University of Utah have been reported by Miller and Lin (2004). The combination of MLA analysis with the ion beam milling and deposition capability of a dual beam SEM system (Quanta 3D system offered by FEI) will similarly enable the direct measurement of 3D mineral liberation data. Through serial sectioning with subsequent repeated MLA analysis, a tool for undertaking automated 3D quantitative mineral liberation analyses will be available. 4. Conclusion Prudent use of both BSE and X-ray signals from an SEM to exploit their advantages, in conjunction with advanced image and pattern recognition analysis, are the keys to success for a modern automated SEM-based mineral liberation analysis system. The speed of acquisition and the high resolution nature of BSE imaging are combined with the accurate mineral identification capabilities of X-ray analysis in such a way to optimise an MLA analysis to the requirements of specific samples and applications. The eight basic MLA measurement modes vary from a purely BSE-based technique (BSE method) through to an almost exclusively X-ray analysis point counting technique (XMOD). In addition to a high level of automation, the modern MLA system has achieved speed, resolution, versatility and accuracy. The current levels of automation extend from de-agglomeration functions to the ability to probe unidentifiable phases (Latti method) and to recognise mixed spectra (standards trigger). Future linkage and integration developments will provide for a new definition for a modern SEM-based mineral liberation analysis. Acknowledgements The authors acknowledge the work of the entire MLA group at JKTech. The contributions of programmers to software development and those of analysts to measurement methodology development have been invaluable. References Gu, Y., 2003. Automated scanning electron microscope based mineral liberation analysis. Journal of Minerals and Materials Characterization and Engineering 2, 33–41. Gu, Y., Napier-Munn, T., 1997. JK/Philips mineral liberation analyzer – an introduction. Minerals Processing '97 Conf. Cape Town, SA, p. 2.
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Hall, J.S., 1977. Composite mineral particles – analysis by automated scanning electron microscopy, PhD thesis, JKMRC, The University of Queensland, Brisbane. Jenkins, B.M., O'Brien, G., Beath, H., 2004. Coal grain texture analysis using the MACE™300 system, ICCP News. International Committee for Coal and Organic Petrology, vol. 32. Jones, M.P., 1987. Applied mineralogy: a quantitative approach. Graham & Trotman, London, Norwell, MA. King, R.P., 1977. A Model for the Quantitative Estimation of Mineral Liberation by Grinding, Progress Report No. 1. Department of Metallurgy, University of Witwatersrand. King, R.P., 1993. Basic image analysis for mineralogy. ICAM'93 Demonstration Workshop Manual, pp. 119–139. King, R.P., Schneider, C.L., 1993. An effective SEM-based image analysis system for quantitative mineralogy. KONA Journal 11. Lastra, R., Petruk, W., Wilson, J., 1998. Image analysis techniques and applications to mineral processing. In: Cabri, L.J., Vaughan, D.J. (Eds.), Modern Approaches to Ore and Environmental Mineralogy. Mineral. Assoc. Can., Short Course, vol. 27, pp. 327–366. Latti, D., Adair, B.J.I., 2001. An assessment of stereological correction procedures. Minerals Engineering 14 (12), 1579–1587.
Petruk, W., 2000. Applied Mineralogy in the Mining Industry. Elsevier Science BV, Amsterdam. Pignolet-Brendom, S., Reid, K.J., 1988. Mineralogical characterization by QEM ⁎ SEM. In: Carson, D.J.T., Vassoilou, A.H. (Eds.), Process Mineralogy, vol. VIII. TMS, Warrendale, PA, pp. 337–346. Miller, J.D., Lin, C.L., 2004. Three-dimensional analysis of particulates in mineral processing systems by cone-beam X-ray microtomography. Minerals and Metallurgical Processing 3 (21), 113–124. Miller, P.R., Reid, A.F., Zuiderwyk, M.A., 1982. QEM ⁎ SEM® image analysis in the determination of modal assays, mineral associations and mineral liberation. CIM-XIV International Processing Congress, Toronto Canada. Troutmann, S., Johnson, G.G.M., White, E.W., Lebiedzik, J., 1974. Automated quantitative SEM characterization of complex particular samples. American Laboratory 6, 31–38. Williams, S.R., Richardson, J.M., 2004. Geometallurgical mapping: a new approach that reduces technical risk. Proceedings 36th Annual Meeting of the Canadian Mineral Processors, pp. 241–268.
Modern SEM-based mineral liberation analysis Rolf Fandrich ⁎, Ying Gu, Debra Burrows, Kurt Moeller JKTech Pty Ltd, Isles Rd, Indooroopilly QLD 4068, Australia Received 31 March 2006; received in revised form 24 July 2006; accepted 31 July 2006 Available online 5 October 2006
Abstract Modern digital mine planning, plant design and mineral processing operations demand detailed characterisation of the ore and plant feed. Textural parameters, such as mineral liberation size and mineral association, combine with modal mineralogy data to strongly influence mineral processing conditions and recovery. Traditionally, the measurement of these ore characteristics employed the tools of an optical microscope and/or a semi-automated SEM. These methods are time consuming, costly and frequently produce semi-quantitative results from data sets that are too small to be statistically valid. Thus, the results cannot be used reliably and effectively in digital mine planning and design. In the last 10 years, modern SEM-based quantitative mineralogy tools have advanced rapidly with increasing computer power, improved SEM hardware and the development of sophisticated image analysis methods. Texture resolutions can now be submicron and SEM measurement times have reduced to less than an hour for simple analyses, where previously they required many hours. Through image analysis, particle sections are recognised and separated, and the mineral grains within are delineated for discrete Xray analysis to determine mineralogy. The modern tools not only increase the speed and accuracy of liberation analysis, but also enhance measurement automation. Automated standard collection assists with the setup of new ore types for routine analysis and automated elemental quantification of target minerals enables the tracking of variations in the composition of the minerals of interest. The key to success for any modern SEM-based mineral liberation analysis system is the close integration of BSE image and EDS X-ray analyses. Integration of the SEM-based quantitative mineral liberation analysis with optical microscope, dual beam systems and X-ray tomography will further enrich the analysis results and the derived user experience. © 2006 Elsevier B.V. All rights reserved. Keywords: Scanning electron microscope; Mineral liberation analysis; Image analysis
1. Introduction The significance and value of mineral liberation analysis to the subject of applied mineralogy and metallurgical processing has been well documented (Jones, 1987; Petruk, 2000; King, 1993). Mineral liberation data are ⁎ Corresponding author. Tel.: +1 61 7 3365 5922. E-mail address: [email protected] (R. Fandrich). 0301-7516/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.minpro.2006.07.018
fundamental parameters used for process plant design and optimization. In recognition of this, several measurement systems based on the application of scanning electron microscope (SEM) technology to polished particles sections have been developed (Hall, 1977; PignoletBrendom and Reid, 1988; Jones, 1987; King, 1993; Lastra et al., 1998; Petruk, 2000; Gu, 2003). The latest advancement of such systems and the quality of the mineralogical data being produced by them have
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311
ray measurements in a process to be known as particle mapping. 2. Mineral liberation analyser
Fig. 1. Witwatersrand linear intercept measurement system.
extended their relevance upstream into the field of ore characterisation for geological mapping purposes (Williams and Richardson, 2004). One of the earlier attempts to automate the gathering of mineral liberation data from polished sections was undertaken by Peter King at Witwatersrand University (King, 1977). This system measured linear intercept lengths of the pyrite phase in a Witwatersrand quartzite using an optical microscope. A mini computer controlled both a constant velocity motorized stage and a photometer circuit to register when the bright pyrite phase moved under the lens (Fig. 1). As is still the case today, the desired automation was motivated by the need to find alternatives to the labourintensive manual viewing of sufficient particles to obtain a statistically significant set of data. The principle of applying thresholding to a signal generated by the reflectance of a mineral (here of photons) to achieve phase discrimination was also being applied to achieve likewise with electrons in SEM-based systems such as CESEMI (Troutmann et al., 1974). The Mineral and Metallurgical Image Analysis (MMIA) software developed at the University of Utah (King and Schneider, 1993) applied thresholds to the grey scale histograms from BSE images of polished particle sections for phase discrimination (see Fig. 2). MMIA allowed the automated batch processing of large sets of high contrast and high-resolution images to produce statistically reliable sets of both linear and areal liberation data. One of the first automated systems to intensively use X-ray spectra was QEM ⁎ SEM (quantitative evaluation of materials by scanning electron microscopy) (Miller et al., 1982). Digital images of particle sections where built up in a pixel by pixel fashion by thousands of X-
The JKMRC Mineral Liberation Analyser (MLA) was first presented as a new development in the field of SEM-based automated mineral measurement tools in 1997 (Gu and Napier-Munn, 1997). At the time it represented a unique method of combining BSE image analysis and X-ray mineral identification to provide automated quantitative mineral liberation characterisation. The advanced analysis techniques implemented by the MLA today are presented, as are the many methods of combining them to provide targeted and optimised, automated mineralogical information. 2.1. BSE image analysis Imaging and image analysis are fundamental to mineral liberation analysis. A low noise, high-resolution image is a prerequisite for mineral identification and quantification. The very stable BSE signals from a modern SEM can generate quality high-resolution (0.1– 0.2 μm) images of particle sections. These images allow the MLA, through its advanced image analysis techniques, to accurately discriminate the mineral phases within a particle. The principal image analysis functions used by the MLA are known as particle de-agglomeration and phase segmentation. 2.1.1. Particle de-agglomeration A liberation analysis using the MLA involves the setting of particles into a mould (typically 30 mm diameter) with epoxy resin to form a hardened block. Typical particle sizes range from 10 μm to 1 mm and are
Fig. 2. MMIA software.
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preferably of a defined narrow size fraction. The block is then ground down to expose a representative crosssection of particles which is subsequently polished and then coated with carbon before being presented to the SEM. Despite precautions to prevent it, inevitably some particles in the prepared sample block will touch each other. If not recognised by the system and treated appropriately the agglomeration of particles can lead to biased liberation results. The MLA system has an automated de-agglomeration function that detects agglomerates and separates them according to a set of predetermined parameters. Fig. 3 shows this process. The de-agglomeration function can be used both during the on-line measurement or performed off-line. Particle shape parameters determine if particles are agglomerated. The de-agglomeration procedure has three methods or criteria at its disposal to find the best separation option: 1) shadow or boundary identification, 2) linear feature recognition and 3) an erosion/ dilation procedure. The operator can control the
Fig. 4. Grey level distribution of a typical lead–zinc ores. The x-axis is the BSE grey level and the y-axis is the frequency.
weighting applied to each of the separating criteria through a set of parameters. The adjustment of parameters may be needed to account for the influence
Fig. 3. MLA de-agglomeration process: (a) Original BSE image. (b) After background removal, several particles are agglomerated. (c) One of the agglomerates is highlighted. (d) After de-agglomeration, the agglomerate is broken apart and one particle is highlighted.
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Fig. 5. BSE and segmented particle image.
on separation by varying particle characteristics such as shape and size. 2.1.2. Phase segmentation Once individual particles have been identified, the next step of the liberation analysis identifies all distinct mineral phases (or grains) and defines their boundaries accurately. This process is called phase segmentation and is performed on each individual particle. The MLA phase segmentation function outlines the regions of homogeneous grey levels within a particle BSE image. The average BSE grey value of every defined region corresponds with a mineral of unique average atomic number (AAN). The AAN determines the number of backscatter electrons emitted by the mineral and hence is directly proportional to the grey level registered in the BSE image. An example of a grey scale histogram with its peaks corresponding to the minerals in a lead–zinc ore is shown in Fig. 4. Phase segmentation also involves the recognition and elimination of features of a BSE particle image that do not represent an independent phase. These
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artefacts can be cracks, shading, tiny voids or the dark perimeter or halo that appears around many particles. The phase segmentation results for an enlarged BSE image of a composite particle are shown in Fig. 5. Six regions of homogeneous grey levels or mineral grains have been identified. The ‘segmented image’ is created with each mineral grain in a particle being assigned a unique colour. In an ideal system and set of measurement conditions each mineral within a sample will have a defined BSE grey value. Should this grey level coincide with that of another mineral, in the same particle, due to the same or very similar average atomic numbers, as is the case for pentlandite and chalcopyrite (AAN = 23.5), the MLA resorts to another rendering method to be explained later. As particle-based segmentation uses the grey scale histograms of each individual particle, the influence of any grey level effects due to changes in measurement conditions, such as beam drift, are eliminated. 2.2. Mineral identification with X-ray analysis The MLA uses three X-ray analysis techniques to identify mineral species: point X-ray, area X-ray and Xray mapping. 2.2.1. Point X-ray analysis In a typical sample measurement the MLA performs one X-ray analysis (typically N 2000 counts) for each grey level region identified within a segmented particle. The spectrum is collected at the centre of a phase to
Fig. 6. MLA area X-ray analysis of a composite particle.
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avoid contamination from bordering phases and hence acquire the “cleanest” spectrum possible. This spectrum is linked to its corresponding particle and grain in the segmented image to generate what is known as an X-ray image. The stored spectra are then compared with a predefined list or library of standard mineral spectra to complete the identification procedure and produce a classified image. In the classified image in Fig. 6 four of the five delineated phases were identified as chalcopyrite and the other as bornite. The classified image, and specifically the pixel phase data contained within, is the basis for all further quantitative analysis. In addition to particle location data, the pixel data and related mineral characteristics are stored in a database for subsequent presentation using the MLA software: DataView. 2.2.2. Area X-ray analysis During area X-ray analysis the spectra are collected by rastering the beam over the phase area. The perimeter of the phase is not scanned to avoid contamination by adjoining phases. Area X-ray analysis can be implemented where there is the possibility of two or more associated minerals having the same AAN (i.e. associated pentlandite and chalcopyrite grains). Poor BSE grey level contrast can result in two phases not being segmented and hence no delineation of phase boundaries (see Fig. 7, top left). With point X-ray analysis, depending on which mineral happens to be at the point of X-ray collection, the resulting combined phase will be identified as either pentlandite or chalcopyrite. Area X-ray analysis detects that two or
more phases are present through the resulting mixed spectra. X-ray mapping can then be employed to identify the mineral phases and to resolve the phase boundaries. 2.2.3. X-ray mapping X-ray mapping imposes a grid over an entire particle image, or specific grains thereof, and collects X-ray data at each grid point to determine the mineral identity. Fig. 7 illustrates the procedure with the example of pentlandite and chalcopyrite. Mineral identification using mapping requires significantly more time (greater than one order of magnitude) than point and area X-ray analysis as many more spectra are collected to generate a comprehensive mineral map. The three fundamental X-ray analysis methods of point and area X-ray analysis and X-ray mapping are the building blocks of all MLA measurement modes involving X-ray identification. 2.2.4. Mineral standards library Mineral identification through X-ray analysis requires a library of mineral standards. This library is usually constructed before an automated run and involves the collection of a high quality X-ray spectra spectrum for each mineral in the sample. The building of a standards library directly from the sample ensures that measurement conditions are reflected in the standards, such as beam energy (i.e. keV), and it also provides for an elemental deportment that better reflects the chemistry of the sample.
Fig. 7. Illustration of X-ray mapping for a particle containing pentlandite and chalcopyrite.
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The many measurement modes offered by the MLA implement the fundamental BSE image, and the various X-ray analyses to produce a suite of measurements designed to accommodate many different mineralogical information requirements. The measurement types and their applications are described.
2.3.2. Extended BSE liberation analysis (XBSE) XBSE implements area X-ray analysis to efficiently and effectively analyse ore samples containing phases with sufficient BSE contrast to ensure effective segmentation. The high resolution of BSE imaging for grain boundary definition and the speed of single X-ray mineral identification make this method ideal for a great majority of mineralogical samples. An extension of this mode is XBSE with automated standards collection (XBSE_STD). If a spectrum from a particular phase cannot be identified using the existing library of standards, a standard for the ‘unknown’ phase will be collected in the usual manner and added to the library for later classification.
2.3.1. Standard BSE liberation analysis (BSE) This is the most basic liberation analysis method in which a series of BSE images are collected on-line and then processed off-line to produce liberation data. Mineral discrimination is based solely on BSE grey level contrast and the liberation data is generated exclusively through image analysis. BSE is employed for applications where good grey level contrast exists and a correlation between grey level peak and mineral species is established. This is the case for certain lead/ zinc and copper ores and some slags. BSE is also useful for textures or features that are finer than the resolution of X-ray analyses (i.e. 2–5 μm).
2.3.3. Ford analysis or grain-based X-ray mapping (GXMAP) GXMAP employs X-ray mapping to the phases that cannot be segmented by BSE grey levels alone and the employs the faster area X-ray analysis for phases that are readily segmented. The operator selects the grains for mapping through a BSE trigger or a specific X-ray standard trigger. Fig. 8 illustrates the advantages of the selective mapping of GXMAP over traditional X-ray mapping for the example of a particle containing pentlandite, chalcopyrite and quartz. A BSE trigger set at a grey level below that of pentlandite and chalcopyrite ensures that all grains of interest are mapped, saving
The spectra identification process involves an errorbased search for the measured spectrum in the standards library to find the most probable fit. Statistics on the spectral pattern matching can be examined to assess the confidence of classification for specific minerals. 2.3. MLA measurement modes
Fig. 8. Illustration of a GXMAP analysis.
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valuable measurement time. Alternatively the X-ray standards for pentlandite and chalcopyrite can be implemented to trigger the mapping. A series of mixed spectra, interpolated at 10% intervals between the nominated pure pair of spectra, is generated and compared with the mixed spectra measured by the area X-ray analysis to force the trigger. GXMAP is a flexible mapping technique an advancement on the older MLA mapping methods of particle X-ray mapping (PXMAP) and selected particle X-ray mapping (SXMAP) (Gu, 2003). It has been successfully applied to certain types of sphalerite and chalcopyrite as well as textually complex ores, such as nickel ores containing pentlandite as fine flames in pyrrhotite. This mode is named after its co-developer Dr. Fred Ford (INCO). 2.3.4. Sparse phase liberation analysis (SPL) This measurement mode searches BSE images for particles containing phases of interest using a BSE grey scale range and then performs an XBSE analysis on them. The off-line processing is identical to the XBSE method and generates the same mineral liberation and association data. It does not provide bulk mineralogy information, as only selected particles in the sample are analysed. The selectivity of the SPL measurement is designed to efficiently measure tailings and low grade feed ores, such as platinum group mineral (PGM) ores where the mineral associations of the phase of interest is of importance. Two specialised versions of SPL analysis are also available. SPL-Lite (SPL-LT) only measures the sparse
phase of interest and its external mineral associations. This is appropriate for rock mass applications, such as drill cores, where liberation data is not relevant. SPLMAP maps the internal associations of the phase of interest once found or can be described as a GXMAP applied to a SPL. 2.3.5. X-ray modal analysis (XMOD) XMOD is the classical point counting method in which mineral identification is determined by one X-ray analysis at each counting point. This mode uses BSE imaging to discriminate particle matter from background and then collects one X-ray spectrum from each grid point across the particle. The X-ray spectra are saved for off-line classification. This method only produces modal mineralogy information, i.e. percentages of the mineral components of the sample. XMOD can also be implemented to produce a line scan measurement mode that produces traditional linear intercept data. X-ray spectra are taken at a step size of one pixel in the x direction and a given y displacement determines the line spacing. 2.3.6. Rare phase search (RPS) The RPS mode searches the BSE images for phases of interest using a trigger and collects a corresponding characteristic X-ray spectrum. For each grain found, the system saves the image of the particle containing the grain, the stage location and its X-ray spectrum. The operator can subsequently move to the SEM stage
Fig. 9. Gold found by the RPS mode.
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location where the grain was located and manually investigate it and its surroundings further. RPS is designed to efficiently locate very fine (sub-micron) components in large particle populations, such as gold in tailings and deliver data such as grain size and associated minerals (see Fig. 9). The ability to classify off-line allows the operator to automatically eliminate other bright phases, such as galena, from the phases of interest. 2.3.7. Latti analysis (SXBSE) The Latti measurement mode adds an elemental quantification capacity to the XBSE analysis and is also known as a Super XBSE analysis. An X-ray standards trigger is employed, in a similar fashion to the GXMAP mode, to initiate a so-called “long count” X-ray analysis. This X-ray collection can last 20 s or more and contain up to over 1,000,000 counts and is comparable with a high quality quantitative EDS measurement of the mineral concerned. These long count spectra are stored separately for subsequent analysis to obtain accurate elemental quantification for the minerals of interest, such as those with variable stoichiometry e.g. sphalerite.
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This mode is named after its co-developer Dewetia Latti (Rio Tinto). 2.3.8. Schouwstra analysis (SPL-dual zoom) Here, a standard SPL analysis is performed and when a phase of interest is detected in an image, the image capture is repeated at a higher resolution. The SPL analysis is then performed for the higher resolution particle images. This measurement mode combines the advantages of a rapid search for the phase of interest at low resolution (typically 1024 × 800) and that of accurate imaging at a high resolution (up to 4096 × 3200). Schouwstra analysis is widely used for PGM minerals and is named after its co-developer Dr. Robert Schouwstra (Head of Mineralogical Research, Anglo Platinum). 2.4. MLA data presentation DataView is the data presentation software that enables the operator to examine, process, present and store the quantitative mineralogical data generated by the MLA measurement software. Pixel data is combined
Fig. 10. MLA data presentation software: DataView.
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Fig. 11. Mineral grouping and corresponding grade recovery curves.
with the elemental composition and densities of the identified minerals to produce a variety of mineralogical data: modal mineralogy, calculated assay, elemental distributions, elemental and mineral grade recoveries, particle and mineral grain size distributions, particle density distributions, mineral associations and locking, phase specific surface area (PSSA) and mineral liberation by particle composition and free surface. Both tabular and standard graphical representations of the data are available (see Fig. 10). The combination and comparison of data sets are possible, as well as the ability to select particle populations through filtering and to manage mineral lists via grouping. 2.4.1. Filtering Filtering enables the creation of subsets of a particle population stored within a particle database according to given criteria. The criteria currently available are filtering by particle size, density, and shape, as well as mineral and elemental weight composition. For example
a filter of density N 3.0 would create a subset of all particles with a calculated density of greater than 3.0. All the analyses offered by DataView can then be performed on this subset. 2.4.2. Grouping As the list of minerals identified by an MLA analysis can be extensive and can contain many minor minerals of little consequence to an analysis, a grouping function allows mineral groups to be defined and be further treated as an individual mineral phases. Fig. 11 illustrates how the copper sulphides in a mineral list have been grouped and used to display the theoretical grade recoveries of three measured size fractions in a feed sample. 3. Future developments The goal of ongoing MLA development activities is to provide improved and diverse mineralogical information through the provision of advanced analysis tools
Fig. 12. Fusion of images from light microscopy and SEM.
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and high levels of measurement automation. The generation of mineralogical information from measurement data is enhanced through linkages with simulation tools. Levels of measurement automation are improved by integration with other measurement systems and technologies. 3.1. Links with simulation Commercial software packages designed to simulate mineral processing operations provide valuable optimisation and design tools for plant metallurgists and require reliable, quantitative mineralogical data to deliver meaningful results. Establishing efficient links between simulation and measurement tools increases the value of the information derived. For example, flotation models can utilise particle grade and mineral liberation distributions to predict and optimize grade recovery curves for flotation circuits. Linkages of this nature contribute to the goal of upgrading mineralogical data to metallurgical information for decision making. 3.2. Integration with optical microscopy systems Materials with predominately organic components and hence with very low atomic numbers, such as coal, are not conducive to liberation analysis with electron microscopy systems. Light optical microscopy systems such as MACE®300 (Jenkins et al., 2004) offer solutions to characterising coal particles based on the light reflectance and texture of the organic phases or macerals. Combining the identification and phase discrimination capabilities of both measurement principles through image fusion techniques can offer comprehensive liberation analysis solutions for coal, incorporating both maceral and mineral matter (Fig. 12). Previously unattainable detailed maceral–mineral association data can provide new information for process optimisation, the prediction of coal utilisation performance and the definition of new coal quality levels. 3.3. 3D mineral analysis The topic of stereological correction in mineral liberation analyses has been examined extensively and various models for extrapolating 3D liberation results from 2D particle section measurements have been proposed and reviewed (Latti and Adair, 2001). An option to apply such methods to measured liberation distributions will be offered by future versions of DataView. Pioneering 3D liberation measurement studies on materials such as a sphalerite/dolomite ore and coal
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using X-ray computed tomographic (CT) techniques at the University of Utah have been reported by Miller and Lin (2004). The combination of MLA analysis with the ion beam milling and deposition capability of a dual beam SEM system (Quanta 3D system offered by FEI) will similarly enable the direct measurement of 3D mineral liberation data. Through serial sectioning with subsequent repeated MLA analysis, a tool for undertaking automated 3D quantitative mineral liberation analyses will be available. 4. Conclusion Prudent use of both BSE and X-ray signals from an SEM to exploit their advantages, in conjunction with advanced image and pattern recognition analysis, are the keys to success for a modern automated SEM-based mineral liberation analysis system. The speed of acquisition and the high resolution nature of BSE imaging are combined with the accurate mineral identification capabilities of X-ray analysis in such a way to optimise an MLA analysis to the requirements of specific samples and applications. The eight basic MLA measurement modes vary from a purely BSE-based technique (BSE method) through to an almost exclusively X-ray analysis point counting technique (XMOD). In addition to a high level of automation, the modern MLA system has achieved speed, resolution, versatility and accuracy. The current levels of automation extend from de-agglomeration functions to the ability to probe unidentifiable phases (Latti method) and to recognise mixed spectra (standards trigger). Future linkage and integration developments will provide for a new definition for a modern SEM-based mineral liberation analysis. Acknowledgements The authors acknowledge the work of the entire MLA group at JKTech. The contributions of programmers to software development and those of analysts to measurement methodology development have been invaluable. References Gu, Y., 2003. Automated scanning electron microscope based mineral liberation analysis. Journal of Minerals and Materials Characterization and Engineering 2, 33–41. Gu, Y., Napier-Munn, T., 1997. JK/Philips mineral liberation analyzer – an introduction. Minerals Processing '97 Conf. Cape Town, SA, p. 2.
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