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Minerals Engineering 20 (2007) 428–434

Semi-automated petrographic assessment of coal by coal grain analysis

Graham O’Brien a,*, Barry Jenkins b, Philip Ofori c, Kirsty Ferguson a

a CSIRO Exploration and Mining, 1 Technology Court, Pullenvale 4069, Brisbane, QLD, Australiab Jenkins-Kwan Technology Pty Ltd., PO Box 1805, Milton BC 4064, Brisbane, QLD, Australia

c CSIRO Energy Technology, 1 Technology Court, Pullenvale 4069, Brisbane, QLD, Australia

Received 21 August 2006; accepted 7 November 2006Available online 16 January 2007

Abstract

A new classification method, coal grain analysis, which uses optical imaging techniques for the microscopic characterisation of theindividual grains present in coal samples is discussed. This differs from other coal petrography imaging methods in that a mask is usedto remove the pixels of mounting resin to obtain compositional information of the maceral (vitrinite, inertinite and liptinite) and mineralabundances on each individual grain within each image. Experiments were conducted to establish the density of individual constituents inorder to enable the density of each grain to be determined and the results reported on a mass basis. The grains were sorted into eightgrain classes of liberated (single component) and composite grains. By analysing all streams (feed, concentrate and tailings) of the flo-tation circuit at a coal washing plant, the flotation response of the individual grain classes was tracked. This has implications for flotationprocess diagnostics and optimisation.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Coal; Froth flotation; Liberation analysis; Process optimisation

1. Introduction

Coal petrography is a standard method for characte-rising the organic (maceral) and inorganic (mineral) con-stituents of coal. Analyses are conducted on polishedgrain-mounts using an oil immersion lens fitted to areflected-light microscope. The petrographic constituentsor macerals in the sample are characterised primarily onthe basis of their reflectance, texture and association withother constituents, including mineral matter. All maceralsincrease in reflectance with increasing rank (vitrinite reflec-tance gives a measure of rank). The three maceral groupsare vitrinite, inertinite and liptinite and are present in vary-ing proportions in all coals, along with minerals. Two typesof data are derived from petrographic analysis: rank(defined by vitrinite reflectance) and composition (maceralproportions).

0892-6875/$ - see front matter � 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.mineng.2006.11.006

* Corresponding author. Tel.: +61 7 33274457; fax: +61 7 33274455.E-mail address: Graham.O’Brien@csiro.au (G. O’Brien).

Mostly, coal petrography is a manual characterisationtest although imaging techniques have been developed thatprovide maceral composition and vitrinite reflectanceinformation. These techniques generally rely on usingthresholding to discriminate between the background resinand the coal particles so that reflectance measurements areonly recorded on the coal material. This means that mostof the dark minerals contained within coal particles, orpresent as liberated minerals, and liptinites are generallyexcluded along with the resin. The summary informationobtained from a number of images is used to produce thereflectance distribution of the samples from which the mac-eral composition and vitrinite reflectance information isextracted.

An alternative imaging method called coal grain analysishas been developed for performing routine coal petrogra-phy assessment. This differs from other coal petrographyimaging methods in that a mask is used to remove the pix-els of mounting resin and consequently compositionalinformation is obtained on each individual grain withineach image. It is important to note that the word ‘grain’

G. O’Brien et al. / Minerals Engineering 20 (2007) 428–434 429

is used in the same sense as ‘particle’ in this discussion. Theapplication of this method to the analysis of samplesobtained from a coal flotation circuit is discussed in thispaper.

Fig. 1. Imaging system used at QCAT.

2. Imaging

Microscopic analyses were conducted on polished grain-mounts using an oil immersion lens fitted to a reflected-lightmicroscope. Microscope images were captured using theMACE�300 system1 for coal petrography (Jenkins et al.,2003) setup at CSIRO laboratories at the Queensland Cen-tre for Advanced Technologies (QCAT) in Brisbane(Fig. 1). This system produced reflectance-calibrated imagesof each grain, together with bulk petrographic informa-tion, including maceral abundance and vitrinite reflec-tance data (determined from reflectograms). All imageswere collected at a fixed magnification and sub-sampledto produce working images with an array size of 512 ·512 pixels. Each pixel represented a specimen area of0.51 · 0.51 lm.

The images were first processed with the reflectogramprogram to determine the coal rank (mean vitrinite reflec-tance) and composition (maceral abundance) for the bulksample (Fig. 2). The images were then reprocessed toobtain compositional information on each grain in eachimage. The abundances of vitrinite, inertinite, liptinitesand dark minerals and bright minerals, as well as macropores (>5 lm), in each grain were determined. The areaof each grain and its maximum and minimum dimensionswere also determined. Generally 248 images were used toproduce information on between 2000 and 5000 grains.When processing finer size fractions, which contained alot of grains in each image, the number of images processedwas reduced. A full explanation of the process is containedin O’Brien et al. (2003).

3. Manual editing of image sets

This system was developed to automatically characteriseproduct coals that generally contain minor amounts ofmineral grains. In order to provide accurate grain informa-tion on flotation feed and flotation tailings samples whichcontain significant amounts of liberated dark and translu-cent minerals it was necessary to perform manual imageediting. Example mineral grains that are difficult to analysefor all optical systems that use manual petrographic meth-ods or imaging are shown in Figs. 3 and 4.

The manual image editing step also allows improved dis-crimination of liptinite from dark minerals for lower rankcoals so they can be added in.

1 (MACE� is a trade mark of Jenkins-Kwan Technology Pty. Ltd.registered in Australia and New Zealand.)

4. Determination of maceral density and the ash% of the

individual grains

To accurately determine the density of each grain it isnecessary to know not only the abundance of each constit-uent in each grain but also each constituent’s density. Min-erals are currently grouped as being either dark (lowreflecting) or bright (high reflecting). Dark minerals aremainly comprised of clays, carbonates and quartz andbright minerals consist of pyrite, iron oxides and calcite.Maceral density is rank dependent and hence it was neces-sary to determine maceral densities at a number of differentranks. Float sink testing was done for coals of three differ-ent ranks (mean vitrinite reflectances of 0.6%, 1.35%, and1.8%) and the maceral composition of each of the densityfractions for each coal was determined by coal grain anal-ysis. This data enabled (using the Solver function in Micro-soft Excel) the relationships between the densities of eachof the maceral constituents (vitrinite, inertinite, liptinite)and coal rank to be determined (Fig. 5). This informationwas also used to determine the density of the dark andbright mineral classes.

Once maceral and mineral densities had been determinedthe density of each grain was estimated from the abundancesof the constituents in the grains (Fig. 6). Fig. 6 also high-lights that the area of each grain is measured and that somegrains are comprised of only a single maceral or mineralentity whilst other grains are composites. The area of eachgrain was used to calculate its volume, and when combinedwith the mean grain density information enabled the massof each grain to be calculated. Fig. 6 also shows the ash%of each grain.

A relationship between the mineral abundances andash% for the audit samples and washability testing(Fig. 7) enabled the ash% of each grain to be estimatedand hence the data to be presented as yield-ash% curves(Fig. 8).

Fig. 2. Whole coal reflectogram information produced by processing reflectance-calibrated images.

Fig. 3. Photomicrograph (250 · 250 lm) of a polished grain-mount of araw coal feed which contains a liberated translucent mineral grain.

Fig. 4. Photomicrograph (250 · 250 lm) of a polished grain-mount of atailings sample containing dark and translucent mineral grains.

Fig. 5. Maceral densities at different coal ranks.

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5. Grain classification

The number of vitrinite, inertinite, liptinite and mineralentities is measured for each grain. For now this informa-tion is used to determine the area abundance of each con-stituent in each grain, but it is possible to discriminatebetween texturally different grains with the same bulk graincomposition. The grains were classified into eight classesconsisting of four liberated (single component) and fournon-liberated (composite) grains. Single component grainswere comprised of >95%, by volume, of a single phase andother grains were classified as composites. If required theadditional textural parameters that are recorded duringthe analysis can be used for grain classification. The incor-poration of these additional parameters will increase thenumber of grain classes. For the applications to which

Fig. 7. Experimentally determined relationship between ash% andmineral%.

Fig. 8. Estimated yield-ash% curves for 0.25 · 0.125 mm samples offlotation feed (21.1% ash), product (8.1% ash) and tailings (50.6% ash).

Table 1Grain classification system (grain composition was determined on a vol.%basis)

Grain class

Single component grains

Vitrite (>95% vitrinite)Inertite (>95% inertinite)Liptite (>95% liptinite)Minerals (>95% minerals)

Composite grains

Vitrinite rich (V > I, L, mins)Inertinite rich (I > V, L, mins)Liptinite rich (L > V, I, mins)MinRich (Mins > V, I, L)

Fig. 6. Grain information obtained shows that some grains are purecomponents and others are composite particles.

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the grain information has been applied to, this has not yetproved necessary.

Microlithotype nomenclature was used for the singlecomponent grains with grains comprised of >95% vitrinite,inertinite, liptinite and minerals classified respectively asvitrite, inertite, liptite and minerite. The composite grainswere subdivided based on their maceral/mineral associa-tions as being either vitrinite-rich (VitRich), inertinite-rich(InertRich), liptinite-rich (LipRich) or mineral-rich com-posites (MinRich) (Table 1).

6. Analysis of size fractions instead of total unsized samples

Initially it was envisaged that it would be possible toconduct a single analysis of the unsized samples of feed,concentrate and tailings and from the size informationavailable for the individual grains in each image determinesize distribution, size by density information, and the den-sity distribution of the different grain types.

An investigation was undertaken using samples of�0.125 mm fraction of the flotation feed material. Eachanalysis typically measured in excess of 4000 grains. Whenthese imaging results were broken into appropriate sizefractions (0.125 · 0.063, 0.063 · 0.038 and �0.038 mm)the bulk of the grains were in the �0.038 mm fractionand the 0.125 · 0.063 mm size fraction generally containedfewer than 100 grains. Whilst these results were able to pro-file the washability characteristics of the size fractions theygenerally contained insufficient grains to provide reliablegrain compositional information, and washability informa-tion on the individual grain classes. This problem wouldobviously be exacerbated if unsized flotation samplesthat could contain grains of up to 1 mm in size wereanalysed.

Hence it was necessary to analyse size fractions ratherthan unsized samples. This approach also reduced stereol-ogical issues associated with attempting to determine grainsize from a 2D section of the grain. Depending on the sizefraction between 60 and 248 images were processed so thatinformation was generally obtained on between 2000 and5000 grains in that size fraction. The finer fractions con-tained many grains and required fewer images to providesufficient grains to generate the data. When multiple sizefractions of a sample are analysed information is obtained

Table 2Audit results showing size and ash analysis information of the flotation feed, tails and concentrate samples for the flotation circuit of a Bowen Basincoking coal mine in central Queensland, Australia

Size fractions (mm) Feed Tails Conc Yield%

Mass% Ash% Mass% Ash% Mass% Ash%

+0.5 28.1 26.9 43 38.1 16.3 3.4 32.40.50 · 0.25 14.9 13.3 7.2 42.5 21.1 5.3 78.60.25 · 0.125 15.87 18.1 8.1 57 21.9 6.6 77.20.125 · 0.063 7.5 25.6 5.4 64.6 9.2 7.3 68.10.063 · 0.038 3.3 31.1 3 66.1 3.6 8.6 60.8�0.038 30.3 46.7 33.3 83.6 27.9 11.8 51.4

Total 100 29.5 100 57.4 100 7.4 55.8

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on approximately 2000–5000 grains (or particles) for eachsize fraction.

7. Analysis details

Depending on the type of samples being analysed, oneor two samples a day can currently be analysed and pro-cessed with either of our imaging systems. The collectionof calibrated images and the production of the whole coalreflectogram information shown in Fig. 2 takes approxi-mately 30 min. The amount of editing required to producea clean data set for grain processing is dependent on thesize distribution and type of sample being analysed. Coarse(>0.5 mm) samples of low ash% material such as flotationconcentrate samples require minimal editing whereas finefractions (�0.038 mm) of high ash% tailings samples cur-rently require about 4 or 5 h of manual editing to producea clean image set for processing. Final grain processing of aclean image set takes about 1 h. Imaging developments toinitially reduce the amount of editing required and ulti-mately to eliminate the need for editing, and to provide bet-ter mineral discrimination is ongoing.

8. The application of coal grain analysis for assessing coal

flotation performance

Particle separation by froth flotation occurs because ofthe differences in the surface chemical characteristics ofthe components of the feed coal. Coal is a heterogeneousmaterial composed of a variety of organic and inorganic

Table 3Coal grain analysis results for the 0.250 · 0.125 mm feed sample

Grain class Vitrinite pixels Inertinite pixels Dark mi

Single component grains

Vitrite (>95% vitrinite) 8,867,826 42,074 54,496Inertite (>95% inertinite) 3616 300,132 3600Minerals (>95% mins) 6174 4480 1,454,853

Composite grains

Vit rich (V > I, L, mins) 4,447,184 703,649 466,703Inert rich (I > V, L, mins) 300,737 864,818 112,897Min rich (M > V, I, L) 78,956 68,358 413,285

Total 13,704,493 1,983,511 2,505,834

constituents whose surface properties and therefore flota-tion response vary widely. As grains are not necessarilycomposed of a single maceral or mineral the overallresponse of a particle in an encounter with an air bubblemay be further complicated by maceral and mineral associ-ations within grains.

An audit was conducted on the flotation circuit of aBowen Basin coking coal mine in central Queensland, Aus-tralia. This mine produces approximately 10 million tonnesof hard coking coal (mean vitrinite reflectance 1.3%) for theexport market and processes approximately 25% of plantfeed using column flotation. Slurry samples of flotationfeed, concentrate and tailings were wet screened at 0.5,0.25, 0.125, 0.063 and 0.038 mm. The mass percentage,ash percentage, and grain composition were determinedfor each size fraction of each of the streams. The reconciledsize and ash analysis data is given in Table 2. A moredetailed account of this audit is contained in Ofori et al.(2006).

9. Coal grain analysis results

Coal grain analysis information was obtained on each ofthe six size fractions of the flotation feed, tails and concen-trate. Due to its rank this coal sample studied did not con-tain liptinites. The result obtained for the 0.25 · 0.125 mmfeed sample (Table 3) show that in excess of 18 millionpixel measurements were made on 2025 individual grains.

This information was used to generate the summarygrain information for this analysis that is presented in

neral pixels Bright mineral pixels Total Number of grains

82 8,964,478 1150168 307,516 14983,513 1,549,020 102

11,144 5,628,680 4191295 1,279,747 14454,828 615,427 61

151,030 18,344,868 2025

Table 4Summary grain compositional for the 0.250 · 0.125 mm feed sample

Grain class Vitrinite Inertinite Dark mineral Bright mineral Grain volume abundance Grain density Mass abundance

Single component grains

Vitrite (>95% vitrinite) 48.3 0.2 0.3 0.0 48.9 1.24 41.3Inertite (>95% inertinite) 0.0 1.6 0.0 0.0 1.7 1.31 1.5Minerals (>95% mins) 0.0 0.0 7.9 0.5 8.4 2.82 16.2

Composite grains

Vit rich (V > I, L, mins) 24.2 3.8 2.5 0.1 30.7 1.37 28.6Inert rich (I > V, L, mins) 1.6 4.7 0.6 0.0 7.0 1.40 6.7Min rich (M > V, I, L) 0.4 0.4 2.3 0.3 3.4 2.47 5.6

Total 74.7 10.8 13.7 0.8 100.0 1.46 100.0

Fig. 9. Grain washability data for 0.250 · 0.125 mm flotation feed sample.

G. O’Brien et al. / Minerals Engineering 20 (2007) 428–434 433

Table 4. The overall maceral analysis results are shown inthe vertical columns (vol.%). This sample contained74.7% vitrinite, 10.8% inertinite, 13.7% dark minerals and0.8% bright minerals. The columns also show the distri-butions of the maceral groups in the grain classes. Forexample, the bulk of the vitrinite was contained in vitriteand vitrinite-rich grains, with lesser amounts in theother grain classes. The grain compositional informationis shown in the horizontal rows, as volume and massabundances. The grain density (analogous to relative den-sity) for each grain class and for the whole sample is alsoshown.

The density and mass information that is obtained foreach grain enables the sample’s overall washability andthe washability of each grain class to be determined(Fig. 9). This provides a simple visual method for showingthe types of grains that would be recovered at any desireddensity. For example, the all grains washability plot inFig. 9 shows that about 68% of the sample would be recov-ered at a relative density of separation of 1.45. At this den-sity 100% of the vitrite and inertite grains, 68% of thevitrinite rich grains, 61% of the inertinite rich grains and

negligible amounts of the minerite or mineral-rich grainswould be recovered. Hence this analysis method makes itpossible to determine the expected maceral compositionof the product and reject material at any density.

The analysis of size fractions of the three streamsenabled the component recovery data to be presented asa function of particle size (Fig. 10) to show the flotationbehaviour of the components over the entire size range.Fig. 10 shows vitrite recovery remains high and relativelyconstant over almost the entire size range except at the veryfine size fraction where the recovery drops slightly. Allother recoverable components exhibit lower recoveries atthe coarse and fine size fractions with maximum recoveriesat the intermediate size fractions. It is observed that vitriteand vitrinite-rich composites show higher recoveries thaninertite and inertinite-rich composites over the entire sizespectrum. The inertinite-rich composites show slightlyhigher recoveries than the inertite grains due to their asso-ciation with vitrinite. The size range at which maximumrecovery of inertite and inertinite-rich composites isattained is finer than for vitrite and vitrinite-richcomposites.

Fig. 10. Component recovery as a function of particle size.

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10. Conclusions

Grain-by-grain processing provides compositionalinformation on coals that cannot be obtained by any othermeans with the same degree of precision. Coal grain anal-ysis has provided grain data that has enabled the flotationpartitioning of the coal grains to product or tailings to bestudied in greater detail than ever before possible. Size,composition and density information on individual grains

of flotation feed, product and tailings was used to maptheir partitioning in the coal flotation system.

The behaviour of each of the macerals, minerals andtheir associations as composite grains can be used to pin-point the root cause of problems and identify potentialsolutions. This study showed that pure grains of coarseinertite and composite particles containing inertinite hadlower flotation response than pure grains of vitrite and vitr-inite-rich grains. In the flotation circuit processing materialwith a nominal top size of 0.5 mm and a significant propor-tion of coarser particles, considerable loss of coal, mainlycoarse inertite and composite particles was observed.

Acknowledgements

The financial support provided by the Australian CoalAssociation Research Program (ACARP) and CSIROand permission to publish the information is gratefullyacknowledged.

References

Jenkins, B.M., Kwan, H.A., 2003. Calibration of the MACE300TM System.In: 55th Meeting of the International Committee for Coal and OrganicPetrology, Utrecht, The Netherlands, 2003 B.

O’Brien, G., Jenkins, B.M., Beath, H. 2003. Coal Grain Analysis, ACARPProject C10053, Final Report.

Ofori, P.K., O’Brien, G., Firth, B.A., Jenkins, B., 2006. Flotation processdiagnostics and modelling by coal grain analysis. Minerals Engineering10, 102–113.