GRAVITY SEPARATOR PERFORMANCE EVALUATION 91

Introduction

Measurement of gravity separator performance in theheavy mineral industryHeavy mineral gravity separators’ performance is usuallymeasured using the grade and recovery of valuable heavyminerals. This information can be used for both circuitdesign and evaluation. The standard practice of gatheringvaluable heavy mineral grade and recovery information isby sampling the gravity separator product within ameasured time frame. The product is divided up intodifferent fractions using devices such as mouth organsplitters. Each of the sample fractions is analysed for itstotal heavy mineral content (THM) by performing a sink-float analysis in a heavy liquid medium (tetrabromoethanemedium at 2.9 g/cm3). The chemical or mineral content ofthe sink fractions is subsequently determined to derive thevaluable mineral content (VHM)—which is usually definedas the sum of the zircon, rutile and ilmenite percentageswithin the THM. A standard concentration curve can bedrawn up from the THM and VHM mass distribution acrossthe gravity separator profile—see Figure 1 for a spiralconcentrator.

Shortcomings in the measurement of gravity separatorperformanceThere are various shortcomings with this standard practice.The combination of particle density, size and shapedetermines where the particle reports to and not only theparticle mineral content. A pure mineral particle will havethe theoretical density of the mineral phase and it is for thisreason that mineral content has been used up to date to

characterize a gravity separator performance since itindirectly characterizes particle density (see Figure 1). Inthe case of large populations of mixed mineral phases insingle particles, overall mineral content would not be ableto describe density separation performance. The mineralcontent only would also not be sufficient to explainfundamental gravity separator behaviour. For example, therecovery of zircon would be negatively affected if theparticle size distribution of the zircon in the feed materialbecame finer, but since only mineral content is measuredand not in combination with particle size, it will not beunderstood. The shortcomings of the current measurementtechnique lead to shortcomings in understandingperformance, which is applicable to both the operation anddesign of gravity separators. The question that arises iswhat other, more effective, measurement techniques areavailable to quantify size and density simultaneously andwhat are their shortcomings?

Shortcomings in the simultaneous measurement ofparticle size and density

A particle size distribution can easily be determined byscreening particle packages, but the main challenge isdetermining the density distribution within these sizedparticle packages. A standard industrial technique that canquickly and easily divide the sized particle package into itsdifferent density classes is not available. Single particlepycnometry would be ideal but since the particles are sosmall (smaller than 200 μm) no accurate determination ofthe particle mass is possible. Sink-float fractionation athigher densities (3.5 to 4.0 g/cm3) is a costly and timeintensive exercise and it does not fractionate the more dense

GROBLER, J.D. and BOSMAN, J.B. Gravity separator performance evaluation using Qemscan® particle mineral analysis. The 7th International HeavyMinerals Conference ‘What next’, The Southern African Institute of Mining and Metallurgy, 2009.

Gravity separator performance evaluation using Qemscan®

particle mineral analysis

J.D. GROBLER* and J.B. BOSMAN†

*Exxaro Resources†PESCO

In a gravity separation device, particle shape, size and density all play a role. The combination ofthese determines where each individual particle reports to in a viscous fluid where particlecrowding (solids to water ratio) also plays a role. To understand the performance of gravityseparation devices in the heavy mineral industry, these particle characteristics need to bemeasured. There are various challenges in analysing particle density and particle sizesimultaneously for the purpose of quantifying gravity separator performance, not to mentionparticle shape. These analytical challenges include the high cost of high-density sink-floatfractionation, toxicity of high density sink-float mediums, inability of sink-float mediums tofractionate at densities greater than 4.0 g/cm3 and time intensive nature of these fractionations.The use of the detailed particle-by-particle output from Qemscan® particle mineral analyses(PMA) as a fast and cost-effective alternative is evaluated. The size and density outputs from theQemscan® were employed to characterize the performance of a heavy mineral spiral concentratoras an example. Critical analytical requirements are to be addressed before the Qemscan® outputdata can be utilized.

Keywords: gravity separator, performance, heavy minerals, Qemscan® particle mineral analysis.

minerals (for example ilmenite, zircon and monazite).Water-based separation techniques (small shaking table orelutriator) have separation inefficiencies whichcompromises the accuracy. Measuring the total density ofparticle packages by gas or liquid displacement gives acombined density for all the particles in the population in—unless it is similar in nature to the rest of the particlepopulation. Effective density fractionation remains achallenge unless a technique can be utilized that does notdivide the mineral particles into physical fractions. Thefollowing section explains an alternative size-densitymeasurement technique.

Experimental

Qemscan® particle mineral analysis outputOver 10 000 heavy mineral grains are set in a resin mixtureand polished to expose the individual particles.Approximately 3 500 grains are analysed by the Qemscan®

particle mineral analyses (PMA) technique on a 5 μm by 5 μm grid. The Qemscan® PMA produces a particle map,depicted in Figure 2, containing textural data such asparticle size and shape but also mineralogical data fromwhich particle density is derived.

A particle size distribution, shape distribution and densitydistribution can be drawn up from the data produced by theQemscan® PMA, as shown in Figure 3. Particle shape isdifficult to define as a single number and only the sphericityof the particle was evaluated for the purpose of thisinvestigation.

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Figure 1. A standard concentration curve for a spiralconcentrator

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Figure 2. Particle map from Qemscan® particle mineral analysis.Each colour represents a different mineral phase

Figure 3. Data produced by Qemscan® PMA performed on anunfractionated THM sample (a) particle size distribution (b)particle density distribution (c) particle shape distribution

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The particle masses in the population are mathematicallygrouped in the different size-density classes as depicted inFigure 4. The THM sample showed a high concentration ofparticles in the density range of 4.4 g/cm3 and particlediameter of 180 μm. Lower concentrations of low and highdensity particles are visible.

Prior to using the data from the Qemscan® PMA, thefollowing important analytical requirements need to beaddressed.

Analytical requirements in using Qemscan® PMAparticle-by-particle data

Sample preparation and representivity Since only a small fraction of the sample, i.e. less than 10milligram, will be exposed at the surface of the polishedsection and available for analysis, it is paramount to ensurethat representative splitting and sample preparationprocedures are applied. The splitting process throughmultiple splitting stages, the block preparation procedureand block orientation within the Qemscan® analysischamber were all evaluated to validate the output dataconsistency. The sample should also be scalped (850 μm) toensure that misplaced large particles do not disproportionatethe sample analysis.

Development of an accurate sip-file The sip-file is the term used in Qemscan® terminology todescribe the relationship between mineral data and analyseddata. This relationship is used to convert the electrondetectors’ raw output to mineral output. It is thereforeimportant that this relationship needs to be correct to ensurethe correct mineral data output and correct interpretation.The sip-file is developed and validated with other analyticaltechniques such as X-ray diffraction analysis, X-rayfluorescence analysis, induced coupled plasma (ICP)analysis and iron titration—all these analytical techniquesare performed on the same sample. Without a tried andproven sip-file, the outputs from the Qemscan® PMA aresubject to error.

Assigned mineral densitiesThe Qemscan® software requires the user to define thedensities of the primary mineral phases as developed in thesip-file. These densities are used to convert analysedparticle surface to particle mass. It is therefore importantthat the correct densities are used as supported by varioussources in literature1,2 to ensure that the mass conversion is

done correctly and that the particle density calculation isaccurate. However, phases showing extensive solid solutionsubstitution or alteration are also assigned with the samedensity as the pure mineral phase.

Declustering of particles The textural data from Qemscan® PMA needs to becorrected to characterize particle size. The discrepancyarises during the sample preparation process. During thesetting of thousands of particles in resin there are manyinstances of particles that are touching each other.Qemscan® analyser will not recognize that these particlesare not individual particles and will see it as a single largeparticle with an abnormally large particle diameter andirregular shape with a weighted mineral density of all theparticles present in the particle cluster, shown in Figure 5.

To overcome this problem the particle information shouldfirstly be processed and saved in the particle manager(Qemscan® software) after ripping the clusters as far aspossible ‘digitally’ apart so that the analysis representssingle particles as present in the separation vessel and not as

Figure 4. A size-density output from Qemscan® PMA performedon an unfractionated THM sample. The mass percentage is

depicted by the different gray scale

Figure 5. Visual presentation of particle declustering. Particle cluster caused by setting particles in resin (a) and particles declustered afterprocessing with Qemscan® touching particles preprocessor (b)

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clusters which is a result from the sample preparationtechnique. The declustering process can increase thenumber of analysed particles from 2 000 up to 3 500particles, thus increasing the particle populationsignificantly. Excessive declustering will alter the naturalsize distribution and create small artifacts. The particleshape trends of the population before and after the de-clustering process will assist the operator in selecting thecorrect settings.

After the correction process, that is declustering and rawparticle-by-particle data export, the PMA ‘picture’ output isconverted to a mathematical output—a list ofapproximately 3 500 particles, each particle with its uniquemass, diameter, density (a function of the minerals presentin the particle) and shape factor (a ratio of area to perimeterdistance indicating its closeness to a sphere).

Number of particles to be analysed and particle sizerestrictionsThe accuracy of the analysis is directly correlated with thenumber of particles being analysed, especially in thepresence of large composition variations. It is thereforesuggested that a simple magnetic or size fractionation couldaid to group similar particles together to be analysedseparately. A large number of particles imply longeranalysis acquisition times and a balance between thenumber of particles to be analysed and the level of accuracyrequired must be determined prior to data capturing. Duringthis investigation, more than 2 500 particles were analysed.Particles larger than 500 μm could be problematic sinceonly a small number of particles are available on the surfaceof the polished block to be analysed, which could beinsufficient from a representivity point of view. Particlessmaller than 1 μm are problematic since the interferencewith the background becomes too significant. This sizerange (-500 μm + 1 μm) restricts the application of thisparticle-by-particle technique to mostly heavy minerals(natural occurring particles) since other sample types (ironore and coal) need to be milled finer which destroys thenatural particle entity.

Generation of artificial ultra fine particlesIt is suspected that during sample block polishing somefragments are physically ripped off from larger particles but

remain behind in the resin of the polished block and arealso analysed. Since all the samples were wet screened at45 μm and subjected to sink-float analysis with TBE it ishighly unlikely that there would be particles smaller than 45 μm. These ultra fine particles would remain behind inthe heavy liquid medium that was used to do the sink-floatanalysis. This artefact will increase the ultra fine particlepopulation and it should be taken into account whencomparing conventional screen data to Qemscan® PMAdata.

Qemscan® PMA stereological correction

The Qemscan® PMA produces a two-dimensional image(from reflected light and backscattered electrons), which isconverted to a mathematical list of three-dimensionalparticles, which is used for calculations and interpretations.The Qemscan® software does consider the stereologicaleffect in the assignment of a diameter to the particle3. Thereare numerous articles discussing the theory behindstereological corrections4, 5 and it is not the purpose of thisinvestigation to validate the Qemscan® PMA two-dimensional to three-dimensional conversion, neither todemonstrate the statistical basis of the process. Theinvestigation rather aims to demonstrate the available size-density outputs, comparing it to other available techniquesand applying the technique to a density separator toevaluate the potential to improve the understanding of itsperformance.

Results and discussion

Some analytical requirements in using Qemscan® PMAparticle-by-particle dataThe fractional mass data from the particle sizes were usedto evaluate the consistency of the sample preparationprocess. The sample preparation procedure, which includesthe sample splitting with small and micro rotary splittersand block preparation, demonstrated a consistent particlesize data output, shown in Figure 6(a). The blockorientation evaluation in which different particlepopulations are analysed on the same polished block alsoshows a consistent size data output, shown in Figure 6(b).

Figure 6. The analytical requirement of sample preparation demonstrated by fractional particle size distribution data. (a) three representative samples prepared and analysed separately (b) same block but different particle population on block analysed

by changing block orientation

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The minimum amount of particles to be analysed wereinvestigated and it was demonstrated that 2 500 particlesproduce similar particle size data compared to a largerpopulation of 20 000 particles. (See Figure 7(a) The 1200particle analysis showed a significant variation compared tothe larger populations, indicated in Figure 7(b). Thisdemonstrated the sensitivity of the Qemscan® PMA withregards to the particle population size.

Particle size distribution comparison with conventionalscreeningConventional dry screening techniques were compared withthe Qemscan® PMA particle size distribution. Over 60samples were analysed using both techniques. The sampleswere grouped with the aim to develop trends that could beused in future comparisons and calculations. Figure 8 showsthree sample types or groups.

There is a clear distinction between the heavy mineralsample group and the quartz sample group. The Qemscan®

PMA tend to be slightly coarser than the screening withregards to the heavy mineral samples analysed, lying more

to the upper part of the straight line. The quartz samplestend to be lying more to the lower part indicating that theQemscan® PMA will predict a slightly finer particle sizedistribution. The ferrosilicon sample, which is amanufactured spherical particle population, lies between theother two sample sets.

It is still unclear why there is such a significant differencebetween the heavy mineral sample population and quartzsample population and it is suspected to be closely relatedto particle shape. Figure 9 shows the difference between theQemscan® shape factor distributions of each of the particlepopulation. Shape factor in this case is defined by thesimple equation (see Equation [1]).

[1]

For a perfect sphere the shape factor will be 12.6 whilefor a needle-shaped particle it is 400, a square shapedparticle is around 16 and a triangular particle is around 23.This shape factor only measure as single shape parameterand is not sufficient to explain the differences in Figure 8.

Figure 7. The analytical requirement of particle population demonstrated by fractional particle size distribution data. (a) comparisonbetween 20 000 particles and 2500 particles (b) comparison between 20 000 particles and 1200 particles

Figure 8. Particle size distribution comparison between Qemscan® PMA and conventional dry screening

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The heavy mineral samples were far more rounded andsignificant portions of particle population are elongatedwhile the quartz particles are far more angular with sharpedges. The ferrosilicon sample which contained 95%spherical particles by mass showed the closest correlationwith conventional screening since particle shape wasregular, as shown in Figure 8.

The grouping of the sample points at the lowercumulative mass percentages is most likely due to theartificial increase in fine particles as a result of the polishedblock preparation process. This effect is visible in all threeparticle populations. The -45 μm particles were removedfrom the ferrosilicon Qemscan® data since there was nomass in this fraction for the screen data on the same sample—a re-plot of the ferrosilicon sample is shown in Figure 10.The result shows a close correlation between screeningparticle size distribution and Qemscan® PMA particle sizedistribution.

Particle density distributionThere are no known techniques available to use as a directcomparison for the high resolution particle densitydistribution (PDD) output from the Qemscan® PMA. Theapproach followed was to measure final product sampleswith ‘known densities’. A wide range of mineral types wereconsidered in the PDD comparison, i.e. zircon product,rutile product, ilmenite product, and mixed mineral sample(shown in the Figure 11).

The PDD lines demonstrate the characteristic densities ofthe different mineral samples clearly. The zircon productshows a sharp and narrow PDD around 4.6 g/cm3 asexpected. The rutile product also has a sharp distribution at4.1 g/cm3 but 5% mass was slightly lower density whichindicates leucoxene intergrowths or leucoxene particles

Figure 11. Particle density distribution—‘known’ product samples

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Figure 9. Qemscan® shape factor distribution comparisonbetween heavy mineral, quartz and ferrosilicon populations

Figure 10. Particle size distribution comparison between thescreening and Qemscan® PMA of a manufactured spherical

ferrosilicon sample

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(confirmed by mineralogy). The ilmenite product showed adensity variation between 4.2 and 4.5 g/cm3 which isexpected. The non-mags contained zircon (60% by mass),rutile (around 15% by mass), lower density trash minerals(10% by mass between density 3 and 4) and with quartzbelow 3.0 g/cm3 (5% by mass). The altered ilmeniteshowed a wide PDD indicating that this mineral will bemost likely misplaced in the spiral beneficiation process.

Application to a density separator

Applying the particle size distribution characterization andthe particle density distribution characterization to theproducts of a gravity separation process, can lead to a betterunderstanding of the behaviour of the separation device.Figure 12 shows the three-dimensional character of thefeed, concentrate, middlings and tails of a spiralconcentrator as a result of the combination of size anddensity data from the Qemscan® PMA.

The feed distribution, Figure 12(a), shows a highconcentration of quartz and low concentration of heavy

minerals. The concentrate produced by the spiral (10% bymass) showed high concentration of heavy minerals, Figure 12(b), with a specific size and density distribution.Some fine quartz particles are present in the concentrate.The middling distribution, Figure 12(c), which accountedfor 5% of the mass, showed coarser heavy mineral particlesand finer quartz particles compared to the feed distribution.The tails distribution, Figure 12(d), showed a similar quartzdistribution compared to feed but no heavy mineralparticles are visible.

Although the size and density outputs from theQemscan® PMA were not specifically validated in theseresults it indicated potential for future confirmation of thewider application of this method. The high resolution size-density data can be effectively used for gravity separatormodelling. The time and cost involved characterizingdifferent feed materials and separator products in terms ofsize, density and shape by means of Qemscan® PMA formodel development and model validation, is a fraction ofthe cost of physical fractionation of size and densitypopulations.

Figure 12. The size-density distribution data as produced by Qemscan® PMA applied on a spiral concentrator (a) the feed at 100% mass (b)the concentrate at 10% mass (c) middlings 5% mass (d) tails at 85% mass (the mass percentage is depicted by the colour scale)

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ConclusionsRepresentivity of the sample to be analysed is paramountsince only small amounts of sample are analysed. Theanalytical requirements with regards to the correction of thedata are crucial if the Qemscan® PMA results are to besuccessfully applied to a gravity separation process and thestereological correction of the data and statisticalfoundation still need to be better understood. Based uponthe above, the Qemscan® PMA has demonstrated potentialto provide a time and cost-effective alternative producinghigher resolution results compared to the current size anddensity fractionation techniques available. The Qemscan®

PMA analysis of gravity separator products havedemonstrated the potential that could lead to improvedunderstanding of the performance of the device.

References

1. http://webmineral.com

2. http://www.mindat.org

3. http://www.intellection.com.au - iQ-0710-04 GrainSize External.doc

4. GAY, S.L. and KEITH, J.M. Geometric probabilitybased stereological corrections, ANZIAM J. 45 (E),2004. pp. C618–C631.

5. GAY, S.L. and KEITH, J.M. Stereological correctionof the surface area of particle, Applied mineralogy’01. Brisbane, March 26–28

J. de Clercq GroblerPrincipal Process Engineer Heavy Minerals, Exxaro Resources

Graduated from University of Pretoria in 2001 with distinction and appointed as metallurgical

engineer at Exxaro Research and Development. Completed honours degree in metallurgy and was

appointed as chief scientist in 2005. Received the award of performer of the year for Exxaro

Technology in 2006. Completed the SAIM programme in Business and Strategic management at

UNISA in 2008. Appointed as principal process engineer heavy minerals in 2008.