Towards the Virtual Processing of Heavy Mineral Sands on a Mineralogic Mining Automated Mineralogy System Igor Tonžetić Carl Zeiss Microscopy Limited, 363 Oak Avenue, Randburg, South Africa
Corresponding author contact details: Email:
[email protected] Mobile: +27 (82) 640 2442
ABSTRACT The ability to input density values into mineral categories recognized by automated mineralogy systems has been a functional capability since the dawn of such systems to calculate mineral mass percentages of mineral abundance analyses and in some instances to predict or model theoretical recoveries from mineral processing through gravity concentration. Circa 2008, initial attempts at modelling electrostatic and magnetic separation were made by Intellection Pty Ltd but through using elemental proxies (Si & Ti for electrostatic affinity, Fe for magnetic susceptibility) for virtual concentration or process modelling. The ability to input actual values related directly to a mineral’s electrostatic properties or magnetic properties was shortly thereafter implemented in the processing software of these systems, though no studies to date have been made public with regards either to what the ideal parameters are (to use in this type of modelling) or whether any such modelling has even been implemented. This study experiments with magnetic susceptibility values inputted into Zeiss’s Mineralogic Mining Explorer software in an attempt to model various concentration models which in turn could be used to monitor the quality of concentration processes.
Keywords: Magnetic Susceptibility, Ore Mineralogy, Modeling, Automated Mineralogy.
1 INTRODUCTION 1.1 Background “Heavy Mineral Sands” as a commodity type typically utilizes three separate concentration mechanisms, which capitalize on the different physical properties of minerals usually present in a heavy mineral sands suite, to create different mineral products. The systematic approach usually entails firstly gravity separation utilizing a particle’s density resulting in a heavy mineral concentrate (HMC), and a gangue reject. A HMC is then treated by a “Dry (Low Intensity) Magnetic Separation” to create an Ilmenite-Rutile-Zircon-Leucoxene (Fe-Ti Mineral + Zircon) Concentrate and Magnetite reject which acts on the magnetic susceptibility of particles. The above-mentioned concentrate can then, in its turn, be further separated into an Ilmenite Concentrate and a Rutile-Zircon Concentrate through “Wet (High Intensity) Magnetic Separation” (per particle) by leveraging on magnetic susceptibility once again (though of a lower intensity). Finally, the Rutile-Zircon Concentrate can be processed through electrostatic separation into a Zircon (and Monazite) Concentrate and a Rutile Concentrate, by making using of their electrostatic properties (specifically their electrical resistivities/conductivities). Thus in essence, heavy minerals utilize gravity separation, magnetic separation and electrostatic separation, which capitalize on particle densities, particle magnetic susceptibilities and particle electrical conductivities/resistivities respectively, to concentrate various products. Mineral abundances of feed, concentrate and tailing products are more useful to the minerals processing engineer or metallurgist when presented in terms of mass percent since it is convenient to monitor these products in terms of weight. It is for this reason, that historically the area abundances of samples measured in traditional technologies have converted these into mass percent abundances. These were achieved by first assigning densities to minerals measured. If particles within a sample block are:
1.
Representative.
2.
Randomly distributed.
3.
Randomly orientated.
4.
Non-touching.
Then, through the principle of stereology (see Sutherland, 1993; Sutherland & Fandrich, 1996; Sutherland et al., 1988 for more information) we can assume a more-or-less one-to-one relationship between the area measured of minerals in a sample and their inherent volume. By simply applying Equation 1, we can then convert the volume percent occurrence of minerals into a mass percent:
Equation 1: Conversion of volume to mass percent operation. 𝑔
𝑉 (𝑐𝑚3) × 𝐷 (𝑐𝑚3) = 𝑀 (𝑔) The importance of particle representivity is self-evident. Without a representative sample, no comments can be made about the viability of a certain ore to be mined. Representivity is also the absence of contamination. Random particle distribution ensures we are not biasing a certain measurement towards valuable ore minerals or invaluable minerals (gangue). This biasing can occur for instance when there is preferential settling of heavy minerals in a prepared sample block. Random particle distribution ensures the integrity of our mineral abundance calculations. Random particle orientation ensures we are not biasing a certain measurement towards particles of a certain shape. This biasing could occur for instance if a sample is rich in phyllosilicates like phlogopite (Benedictus et al., 2007). Random particle orientation ensures the integrity of our grain size calculations. Together, random particle distribution and random particle orientation, allow for the use of the principles of stereology to convert the area sections of particles measured into volumes, so that those in turn may be converted into mass. Non-touching particles allow for assumptions to be made about the mineral associations occurring in the measured particles. It ensures the integrity of our liberation, association and locking data. It also has implications in the calculation of certain particle properties applicable to mineral processing.
The need to convert area percent abundances into mass percent abundances meant, since mineral densities where already present on such systems, that particle specific densities could then be calculated. With the advent of more sophisticated query languages, virtual density separation could then be attempted by automated mineralogical systems. By late 2007, basic experiments were being conducted where “raw”, unprocessed heavy mineral sands samples derived from
exploration, were measured with automated mineralogical systems and treated as an experimental feeds (Tonžetić, 2007). This “feed” could then be “virtually processed” into a heavy mineral concentrate (by using virtual density separation), magnetic concentrates (using Fe content as a proxy for magnetic susceptibility), and an electrostatic concentrate (using Si and Ti as proxies for electric resistivity/conductivity). This could theoretically facilitate process/product control (QA/QC procedures), since processing inefficiencies could be modelled more exactly, thereby offering a platform for being able to monitor processor or technician key performance indicators thereby improving on operator accountability. The scarcity of published data after almost a decade of capability is thus peculiar and may be ascribed to a number of reasons. Either not much experimentation has been attempted on virtually processing feeds which might have to do with the scarcity of available data (open source or otherwise) concerning mineral magnetic susceptibilities, electrical resistivities, floatation constants etc. or lack of confidence in mineral identification (it is very difficult to justify assigning a magnetic susceptibility value of x to a phase of unknown or spurious identity). These types of experiments may have indeed been attempted but for intellectual property reasons have not made their way to open market. An exception to this was the paper by Ford et al. (2009). This paper attempts to begin to address the scarcity of such data by attempting to deal, whether directly or indirectly, with some of the issues hypothesized above, as being the main impedance towards progress in this field. 2.2 Impedance – Availability of Data Any routine web search of the mainstays of mineralogical information, such as MinDat, WebMineral, Athena, MineralienAtlas, RRUFF (www.mindat.org, www.webmineral.com, www.athena.unige.ch, www.mineralienatlas.de, www.rruff.info or www.rruff.info/doclib/hom/) for physical mineral attributes reveals that without exception only physical attributes that deal with density, hardness, crystallography, colour, diaphaneity, habit, cleavage/fracture/tenacity, optics (light, electron, luminescence, pleochroism etc.) and diffraction are ever mentioned or listed. Occasionally radioactivity and solubility are mentioned. This is probably a carry-over of
older mineralogical books and atlases (Klein & Hurlbut, 1999; Deer et al., 1996) with these also being relatively limited in their publication of physical mineral traits not already mentioned. When papers deal directly with attributes such as magnetic susceptibility or electrical resistivity, they either deal specifically with a select few minerals (Manouchehri et al., 2001); are of the university course type for theoretical study (Hunt et al., 1995) or deal with chemical compounds rather than minerals (Kurtus, 2003). Occassionally, the lists presented may be comprehensive with regards to qualitative characterization (Outotec, ?) but not numerical and without numbers, modelling of concentration mechanisms is virtually impossible. By far the most comprehensive listing of the magnetic susceptibilities of minerals is given by the USGS (Rosenblum & Brownfield, 1999) though these are presented as the amperages needed of a Frantz Isodynamic Magnetic Separator to concentrate certain minerals. The applicability of these values in other magnetic concentrators is considered relative (proportional) and heuristic. Peculiarly, an important and comprehensive source of physical data of minerals (that is with mineral lists of attributes) is not found through mineralogical sources but through geophysical ones (Telford, 1984). 2.3 Impedance – Complexity of Physical Parameters The complexity of the physical parameters we are attempting to model may be described by: 1. Complex physics – The physical properties of minerals, such as magnetism can only be appreciated and modelled through an understanding of quantum numbers such as electron spin and concepts such as dipole moments, magnetic domains etc. This results in the multitudinous
types
of
magnetism
(ferromagnetic,
ferrimagnetic,
diamagnetic,
antiferromagnetic, paramagnetic along with various subdivisions - see Table 1) and is also the reason why magnetic properties are not immediately predictable or intuitive and also why one cannot simply use Fe/Ni/Co % as proxies for magnetic susceptibilities. 2. Optionality of Multiple Variables – The number of variables one can use to model physical concentration processes can sometimes be greater than a single one since these properties may form two sides of the same equation, such as the case between electrical conductivity/electrical resistivity and magnetic susceptibility/relative permeability (Equations 2 and 3). This in itself is not problematic if authors have different and distinct preferences for reporting properties since values can be easily converted and manipulated
according to the equations. It does however mean more work for the author if values are to be converted into consistent definitions for modelling. In the case of electrical theory, introducing terms such as “dielectric constant” which is ambiguous in the physics and engineering communities, and which relates more specifically to electric permittivity, can introduce further confusion since it relates analogously to the relationship of electrical conductivity to electrical resistivity (low conductivity materials have high dielectric constants). However, dielectric constant and electrical resistivity are most definitely not synonymous. 3. Interference of Other Physical Processes – In principle some concentration processes may be relatively simple but may be affected by other processes operating in tandem. For instance in the case of flotation, hydrophobic concentration mechanisms may be affected by small particles being entrapped (“entrainment”). Also, particle size is known to have a pronounced effect in certain concentration processes because of momentum/inertia dynamics. 4. Large Value Ranges – Most mineral physical attributes discussed in this paper have large ranges of values recorded in literature. The question then becomes, “How do we model a mineral that can have a magnetic susceptibility of 2 gauss to 2000 gauss?” Approaches to this problem may entail ignoring these large values for modelling by instead using percentage recoveries obtained from experimental data as a proxy for these mineral physical attributes. For instance zircon recovery from an anode can be subtracted from the cathode recovery in a triboelectrostatic experiment to obtain a triboelectrostatic differential (Ferguson, 2009). 5. Property Liberation Dependance – It is not always intuitive whether the properties and numbers we are trying to model should be applied to volumes (as is accepted for magnetic susceptibility) or free surface area (as is accepted for electrical resistivity). A further question becomes, “To what extent are these measured accurately on 2D automated mineralogical systems?” 6. Effects of Lattice Defects and Imperfections on Concentration – Lattice defects and imperfections will have an effect on the concentration of various minerals. Yet these defects are for the most part difficult to measure, at least routinely, for ore characterization. The point defects of atomic vacancies, interstitial atomic addition and
solid substitutions will have a pronounced effect on magnetic susceptibilities for instance, since magnetic susceptibilities rely so heavily on free spin electrons and magnetic charge compensation. Mg/Fe solid solution may for instance go a long way to explaining large magnetic susceptibility values briefly mentioned in point 4 above. The line defects of dislocation and twinning will probably have an effect on magnetic domains as will the area defects of grain boundaries. 7. Multiplicity of Terms – Occasionally, the use of different terms for the same minerals can lead to confusion when one of these has an accepted physical attribute which is not carried over to the other term. Compare for instance, the common use of the unrecognized term “leucoxene” for particles which are actually mixtures of rutile, ilmenite, hematite and clays; or “high Ti ilmenite” for minerals such as ferropseudobrookite.
Table 1: Types of magnetism. χm Attributes
Diamagnetic Negative
Non-Magnetic Magnetic Paramagnetic Antiferromagnetic Ferrimagnetic Ferromagnetic Anything from slight positive Slight Negative to Slight Positive Positive Very Positive
χm Values (Conceptual) -0.00001 to -0.0001 Electron Attributes Paired Spin Attributes (Ordering) ↓↑↓↑
0.00001 to 0.01 Unpaired ↓↑←→↖↗↘↙↑
-0.01 to 0.01 Unpaired Various
1 to 10 Unpaired ↑ ↓ ↑ ↓↑
1000000 Unpaired ↑↑↑↑↑
Δχ with increasing T0 None Field Dependance No Relative Premeability (μr) 1
Increase Yes ~1
Decrease Yes >1
Decrease Yes >>1
Equation 2: The inversely proportional conceptual relationship of electrical conductivity (σ) to electrical resistivity (ε). 𝜎∞
1 𝜀
Equation 3: The relationship of magnetic susceptibilty (χm) to relative magnetic permeability (μr). 𝜒𝑚 = 𝜇𝑟 − 1
2 METHODOLOGY 2.1 Instrumentation A Carpco MLH(13) 111-5 Magnetic Separator was used on a feed sample (Mag-0) to render six (6) concentrate fractions (Mag-1, Mag-2, Mag-3 etc.) in order of increasing magnetic field applied to create the concentrates (i.e. a very high magnetic field was required for Mag-6 to produce a concentrate from essentially non-magnetic minerals). Splitter settings, feed chute vibration rate, roll speed, current settings etc. were incorporated into “Magnetic Index” numbers seen in Table 2.
Table 2: “Magnetic Index” values of various measured magnetic concentrates. Sample Mag-0 Mag_1 Mag_2 Mag_3 Mag_4 Mag_5 Mag_6
Magnetic Index Feed Sample 5.7 6.9 7.4 8.8 11.3 13.7
2.2 Sample Preparation The feed sample along with the concentrate samples were then prepared in 30mm resinimpregnated polished sample blocks, carbon-coated and measured with the Mineralogic Mining automated mineralogical analysis system which consists essentially of a platform Carl Zeiss EVO LS15 Scanning Electron Microscope run with a Tungsten filament and Bruker Xflash 6I30 SDD x-ray detectors (30mm2 detecting crystals).
2.2 Measurement & Calculation The entire particle suite of the sample blocks was measured to maximise representivity. The method of analysis used was of an x-ray centroid type requesting 5 milliseconds ensuring x-ray counts of ~25 000 per analysis point for ~4000 particles. Due to the liberated nature of heavy
mineral sands, x-ray centroid measurements were considered to be adequate as a proof of concept method.
The mineral abundances per magnetic concentrate which corresponds to abundances per magnetic susceptibility category were individually plotted on histograms. Where normal distributions were evidenced on these histograms and adequate mineral abundances pointed to adequate representivity, the peak height occurrence was assigned to the mineral entry as a magnetic susceptibility value (to be used in future studies – Figure 1).
30
Area %
25 20 15 10 5 0 5,7
6,9
7,4
8,8
11,3
13,7
Magnetic Suceptibility Category
Figure 1: Example of how to assign a new magnetic susceptibility value based on peak height of measured histogram. Arrow represents new assigned magnetic susceptibility.
3 RESULTS Table 3 presents a selection of preliminary results for a mineral abundance analysis of the various concentrates produced from the Mag-0 feed through magnetic separation. Highest occurences are highlighted in green. (Ss) represents mineral entries consisting of solid solutions. All other brackets, for instance (Mg), represent the elemental end members of mineral groups. Figure 2 represents the mineral abundances plotted as histograms which allows for the inference of magnetic susceptibilities in cases where mineral occurences are sufficient enough to warrant statistical reliability and occurrences represent essentially normal distributions.
Table 3: Selection of mineral abundance encountered in various concentrates (Area %).
Mineral
5,7
6,9
7,4
8,8
Allanite (Ss) Almandine Analcime Andradite Anhydrite Annite Anorthite Anthophyllite Antigorite Apophyllite Augite Axinite (Mg) Babingtonite Barrandite (Ss) Bastnaesite (Ss) Beidellite Beryl Biotite (Ss) Bredigite Britholite (Ss) Chabazite Chamosite Chesterite Chloritoid Etc.
0.06 0.10 0.20 0.00 0.00 0.00 0.03 0.10
0.03 0.07 0.01 0.02 0.00
0.10 0.03 0.00 0.15 0.04
0.19 0.03 0.00 0.07 0.07
0.18 0.15 0.03 0.00 0.79 0.26 0.26
0.45 0.03 0.00 0.00 0.03 0.22 0.26 0.00
0.28 0.61 0.01 0.01 0.00 0.02 0.29 0.63 0.01 0.84 0.49 0.06 0.24 0.14 0.00 2.95 0.00
0.43 0.12 0.03 1.52 0.25 0.02 0.11 0.10 0.47 0.04 0.00 0.00 0.00 0.00 0.00 0.01 0.02 1.83 7.02 2.06 4.24 6.83 1.18 0.15 0.13 0.02 4.81 22.89 7.97 3.48 4.39 0.46 0.29 0.08 0.03 0.73 0.69 0.10 0.29 0.73 0.81 0.00 0.00 0.00 7.78 2.50 0.47 0.06 0.11 0.30 0.00 0.00 0.02 0.00 0.01 0.10 0.07 0.08 12.11 7.08 0.78 4.07 0.83 0.05 0.05 0.25 0.15 0.14 0.39 0.13 … … …
0.05 0.07
0.00 … …
0.00 0.19 2.21 1.26 0.01 0.01 …
11,3
13,7
25 Allanite (Ss) Almandine Analcime Andradite
20
Anhydrite
Annite Anorthite Anthophyllite Antigorite
Area Percent
15
Apophyllite Augite Axinite (Mg) Babingtonite 10
Barrandite (Ss) Bastnaesite (Ss) Beidellite Beryl Biotite (Ss)
5
Bredigite Britholite (Ss) Chabazite Chamosite Chesterite
0 5,7
6,9
7,4
8,8
11,3
13,7
Chloritoid
Magnetic Susceptibility Categories
Figure 2: Mineral abundance values plotted against magnetic concentrates (essentially representing magnetic susceptibility categories) for select minerals.
4 DISCUSSION Proceeding with the above methodologies allowed for the ascertainment of 107 magnetic susceptibilities which were subsequently input into the Mineralogic Minning plug-in software suite as mineral attributes in the “EDS Recipe” tab. This will allow for the future “virtual processing” of heavy mineral sands through a “virtual” magnetic concentration process on measured feeds to establish the quality of real world magnetic concentration procedures. What must be born in mind for the present study is that the initial mineral abundance of “Mag-0” (and the limited abundance of certain minerals in other concentrates) forms a natural bias to the results achieved through the analysis of the subsequent concentrates, so that qualitative uncertainty values should be assigned to interpreted magnetic susceptibility values.
5 CONCLUSION This study forms the first part of the initial stages in attempting to correct the current lack of readily obtainable physical attributes of minerals pertinent to the minerals processing industry. Future studies will need to verify the quality of the mineral analysis attempted and also improve on the values obtained for the mineral physical properties measured thus far, for instance by creating more concentrate divisions (refining magnetic susceptibility values) and by the appropriate grouping of mineral varieties (improve on experimental statistics). As such, the knowledge creation is very much in the line of the “Constructivist” approach where the gaining of knowledge is only linear to the point where the practitioner has to go back on what he/she knows to refine and further consolidate that knowledge (Figure 3). In a word, further studies will improve on mineral recognition and delimit the physical properties of minerals better to be included in programmes, such as Mineralogic Mining to aid in mineral processing quality control procedures.
Refine & Consolidate
Figure 3: Visual construct of the “Constructionist” approach to knowledge creation (as contrasted to behaviourist, cognitivist, connectivist etc. approaches).
6 ACKNOWLEDGEMENTS The author wishes to gratefully acknowledge Clive Hardwick and Denham Ferguson for their valuable input into discussions on the topic and for sample access and preparation. The author also wishes to acknowledge Carl Zeiss for access to its Mineralogic Mining technology and for the freedom to approach the subject from an ambitious objective.
7 REFERENCES Benedictus, A.; Berendsen, P. and Kjaer, E. (2007). Phlogopite Quantification from Hydrosizer and Flotation Processing Utilizing QEMSCAN Mineral Analyzer and Struers Sample Preparation Techniques. SME’07, Denver, Colorado 25–28 February Conference Proceedings. Deer, W.A.; Howie, R.A. and Zussman, J. (1996). An Introduction to the Rock-Forming Minerals (2nd Edition) Paperback. Ferguson, D.N. (2009). A basic triboelectric series for heavy minerals from inductive electrostatic separation behaviour. The 7th International Heavy Minerals Conference: ‘What next’, The Southern African Institute of Mining and Metallurgy. Ford, F., Lee, A., Davis, C., Xu, M. and Lawson, V. (2009). Predicting Clarabelle Mill recoveries using mineral liberation analyzer (MLA) grade-recovery curves. Proceedings of the 48th Conference of Metallurgists, Sudbury, Ontario, Canada. Hunt, C.P.; Moskowitz, B.M. and Banerjee, S.K. (1995). Magnetic Properties of Rocks and Minerals. Klein, C. and Hurlbut, C.S. (1999). Manual of Mineralogy. Twenty First Edition. John Wiley & Sons Inc., New York. Kurtus, R. (2003). The Triboelectric Series of Materials Causing Static Electricity.
Manouchehri, H.R.; Hanumantha-Rao, K. and Forssberg, K.S.E. (2001). Triboelectric charge, electrophysical properties and electrical beneficiation potential of chemically treated feldspar, quartz and wollastonite. Magnetic and Electrical Separation, Vol. 11, No. 1-2, pp. 9-32. Outotec (?). Physical characteristics of select minerals & materials. Product Brochure. Rosenblum, S. and Brownfield, I.K. (1999). Magnetic susceptibilities of minerals. USGS Publication. Sutherland, D. and Fandrich, R. (1996). Selective Fracture and Liberation of Minerals. Chemeca 96, Sydney, Ed G Weiss (IChemE Aus) Vol. 3, pp. 83 – 88. Sutherland, D.N. (1993). Image Analysis for Off-Line Characterisation of Mineral Particles and Prediction of Processing Properties. Part. Syst. Charact. Vol. 10, pp. 271 – 274. Sutherland, D.; Leigh, G.; Gottlieb, P. and Wilkie, G. (1993). The Measurement of Liberation in Section. XVIIIth International Mineral Processing Congress. Sydney, II pp. 471 – 474. Sutherland, D.N.; Gottlieb, P.; Jackson, B.R.; Wilkie, G.W. and Stewart, P.S.B. (1988). Measurement in Section of Particles of Known Composition. Minerals Engineering. Vol. 1 pp. 317. Telford, ?. (1984). Electrical Properties of Rocks and Minerals. Chp 5. Tonžetić, I.Ž. (2007). Analysis of Heavy Mineral Concentrate. Intellection Pty Ltd. Report #1, Project #07-062.
