le

C. van Alphen *

ging prediction and combustion performance models. Coal and ash analysis is now entering a new and exciting era.

tions, whereas ash consist of minerals, char and amorphousaluminosilicate glasses.

the minerals deportment in the source coal and the operat-ing conditions and conguration of the boiler or furnace.With the high proportion of amorphous phases in coaland ash, accurately characterising elemental distributions,elemental proportions and mineralogical characteristics isdicult for coal and ash, irrespective of the technique used.

* Tel.: +27 117046657.E-mail address: cncc@mweb.co.za

Minerals Engineering 20 (2 2007 Elsevier Ltd. All rights reserved.

Keywords: Automation; Coal; Fly ash; CCSEM; QEMSCAN; MLA

1. Introduction

Simplistically, coal consists of organic amorphous mac-erals and inorganic crystalline minerals in varying propor-

The element composition (carbon, hydrogen, oxygen,nitrogen and organically bound inorganic elements) of themacerals is inuenced by the rank of the coal, whereasthe elemental composition of the glasses is controlled byVan Alphen Consultancy, P.O. Box 1648, Jukskei Park 2153, Johannesburg, South Africa

Received 25 September 2006; accepted 13 December 2006Available online 15 February 2007

Abstract

To ensure accurate and reliable automated mineralogical analysis of a sample, it is a common practise to use chemically derived ele-mental proportions to validate the mineralogical proportions. Automated mineralogical analysis of coal and corresponding ash is a chal-lenge as a signicant proportion of the phases/minerals are amorphous with variable elemental proportions. In coal, the organic maceralsare the amorphous phases and depending on the rank of the coal have varying carbon, hydrogen, nitrogen, oxygen and inorganic elementcompositions. In ash, aluminosilicate glasses and partially transformed minerals are the major amorphous phases. The elemental com-position of these glasses is controlled by the mineral deportment in the source coal, operating conditions and conguration of the boiler,gasier or furnace.

To compound the problem, the traditional chemical analysis of coal (proximate, ultimate and XRF ash elemental analysis) is based onheating the coal to dierent temperatures and recording mass losses or analysing the ash derived from the coal. These analyses each haveinherent limitations, sampling and analytical errors, which when combined can provide misleading interpretations.

If accurate and precise mineralogical data are to be provided to coal practitioners for optimising processing plant, predicting com-bustion/gasication performance and for resolving coal quality problems, it is necessary to develop a cost eective and reliable auto-mated coal and ash analytical system.

With the advancement in X-ray detector and scanning electron microscope technology it is potentially feasible to provide a cost eec-tive and quick mineralogical (minerals and macerals) and elemental analysis of raw coal and ash. The new automated systems have thepotential of combining the traditional proximate, ultimate, XRF ash elemental, XRD and petrographic analyses into one analysis, with-out heating or modifying the existing coal sample.

Detailed coal and ash particle characterisation is now feasible, enabling the development of coal processing, y ash formation, slag-Automated mineralogicaproducts Challeng0892-6875/$ - see front matter 2007 Elsevier Ltd. All rights reserved.doi:10.1016/j.mineng.2006.12.013analysis of coal and ashs and requirements

This article is also available online at:

www.elsevier.com/locate/mineng

007) 496505

Proximate and ultimate and to lesser extent X-ray uo-rescence (XRF) ash elemental are some of the traditionaland routine analyses undertaken by coal suppliers, usersand buyers to characterise coal (Fig. 1).

Proximate analysis is the inherent moisture, volatilematter, ash, xed carbon (by dierence) and ultimate anal-ysis is the carbon, hydrogen, nitrogen, total sulphur andoxygen (by dierence) content of the coal. Ash elementalis the elemental composition of the ash derived from thecoal. For each analysis, the coal is heated and the mass%loss, mass% of the reaction products and mass% of the res-idue (ash) are measured.

Inherent moisture is the mass loss after heating the coalto 100110 C (SABS Method 925), volatile matter is massloss after heating the coal to 900 C (SABS Method 927,ISO562M and ASTMD3175M) and ash is non-combusti-ble residue after slowly heating the coal to 815 C (SABSMethod 926) or 900 C (ASTM D3174). The volatile mat-ter is derived from organic fraction and from certain min-erals, whereas the ash% can include volatile organicsulphur, which has reacted with CaO and uncombustedcarbon. Ash% is not a direct measure of the total mineralmatter content of the coal. The sum of inherent moisture,

C. van Alphen / Minerals Enginvolatile matter and ash% subtracted from 100% is termedxed carbon. Fixed carbon should represent the proportionof non-volatile carbon and is the accumulation of any ana-lytical errors.

The ultimate analysis reports the carbon, hydrogen,nitrogen, total sulphur and oxygen content. Carbonincludes organic carbon and inorganic carbon from car-bonates, hydrogen includes organic hydrogen and hydro-gen in the water derived from minerals and nally total

Proximate Analysis

Inherent moistureAsh-%

Volatile matter

Fixed carbon (by difference)

Ultimate AnalysisCarbonHydrogenNitrogenOxygen (by difference)

XRF Ash elementalSiO2, Al2O3, Fe2O3,

TiO2, CaO, MgO,K2O, Na2O, P2O5,

MnO, SO3LOI

OtherAFTCO2 (carbonates) Total sulphurHGICalorific value (CV)XRD Petrography CCSEM

COAL

MINERALSFig. 1. The variety of typical coal and ash analyses.sulphur is organically bound sulphur and sulphur derivedfrom pyrite. Oxygen is a calculated value.

Determining the mineral matter in coal is either calcu-lated using X-ray uorescence (XRF) ash elemental pro-portions (SEDNORM, Cohen and Ward, 1991) ordetermined directly using X-ray diraction (XRD) and byscanning electron microscope (SEM) based automatedmineralogical systems such as CCSEM, QEMSCAN andMLA.

Normative programs such as SEDNORM assumes thatspecic elements can be assigned to one mineral. Unfortu-nately, this is not always possible as certain elements occurin more than one mineral. An example is Fe, which canoccur in pyrite, siderite Fe-chlorite and Fe-oxides, K inmica/illite and feldspar and nally Si in quartz, clays,mica/illite and feldspars. In lower rank coal, a proportionof the elements are organically bound to carbon and notassociated with minerals. Extensive knowledge of the coalprior to normative analysis is benecial.

An X-ray diraction pattern of raw coal and ash is char-acterised by high background continuum originating fromthe amorphous macerals in coal and amorphous alumino-silicate glass in the ash. This broad background can maskpeaks of crystalline phases. Ashing the coal at low temper-ature (120150 C) in electronically excited oxygen plasmacan isolate minerals from the organic fraction (LTA).Unfortunately, LTA can take 34 days and in lowerrank-coals secondary calcium and iron sulphates areformed during the ashing process (Ward, 2002). A tech-nique was developed to determine the proportion and ele-mental composition of glass in ash (Ward and French,2003). The calculated elemental proportion of the glassrelies on the accuracy of XRF ash elemental analysis andX-ray diraction derived mineral proportions in the ash.X-ray diraction and normative calculation predicts themineral composition of the coal, but does not determinethe morphological features (size, association) of the miner-als and particle characteristics.

Petrographic analysis describes the proportion of macer-als in coal and to a limited extent the proportion of miner-als in coal. Maceral discrimination is visual and based ontexture, morphology and light reectance observed undera reected light microscope tted with an oil immersionobjective. Since maceral identication is visual and requireshuman intervention, it can be subjective, especially for neparticles. Micrinite, a maceral described by petrographerswas shown by Faraj and Mackinnon to be ne-grainedkaolinite (Faraj and Mackinnon, 1993) and not a carbonbearing phase. The mineral proportions are reported asthe three main groups quartz/clays, pyrite and carbonates.It is not possible to accurately distinguish between the dif-ferent minerals, especially if they are ne grained andincluded in coal matrix.

The US Steel Research Laboratories initiated the scan-ning electron microscope (SEM) based automated mineral-

eering 20 (2007) 496505 497ogical analysis of coal in the early 1970s with thedevelopment of the particle recognition and characterisation

mounting the coal sample in either carnauba wax (Strasz-heim et al., 1988) or iodoform doped epoxy resin (Gomez

nginet al., 1984) and by the introduction of light element detec-tors. Since CCSEM mineralogical identication is basedon the elemental proportions derived from X-rays, it canaccurately determine themineral type and elemental propor-tions of the minerals in coal (Van Alphen and Falcon, 2000)and amorphous glass phases (Van Alphen, 2005). In its cur-rent form, CCSEM is unable to accurately distinguishbetween the dierent macerals and all the macerals aregrouped as coal. This is a limitation.

Each of the chemical and mineralogical analytical tech-niques described above have limitations and do not providethe full particle and mineralogical characterisation requiredfor coal-processing optimisation, coal combustion perfor-mance assessment and the impact on boiler/furnace/gasierperformance. For ne (

Further research is required and new techniques need tobe developed to improve quantitative elemental analysis,maceral discrimination and mineral and particle chara-cterisation.

Fortunately, coal and ash have certain mineralogicaland physical characteristics that can be exploited to achievethe objectives listed above. These characteristics are out-lined in the following sections.

3.1. Quantitative elemental proportions

In coal, elements can be distributed between organic(macerals) and the inorganic components (minerals). Lowrank lignites and sub-bituminous coals commonly have asignicant proportion of organically bound inorganic ele-ments (Fig. 4). The organically bound elements increasethe average backscatter electron intensity of organic coalparticles (Fig. 3).

The mineral free organic phase in a lignite sample had

ngineering 20 (2007) 496505 499QEMSCAN X-ray acquisition speed is 1015 ms, ASCANis 100 ms and remaining systems in excess of 400 ms25 s.Either the electron beam is positioned at the centre of aphase of similar backscatter electron intensity (centroidalapproach) or a closely spaced raster of points are posi-tioned across the particle (Fig. 2). MLA and the traditionalCCSEM systems use the centroidal approach, whereasASCAN, QEMSCAN and EERC CCSEM use the rasterapproach. QEMSCAN and the traditional CCSEM sys-tems use a sequential mineral identication program,ASCAN uses fuzzy logic and MLA compares standardX-ray spectrum of the unknown to identify the minerals.

The two commercially available automated mineralo-gical systems, QEMSCAN and MLA have included thesignicant advances in X-ray detector technology, back-scatter electron detectors and the modern digital scanningelectron microscopes. The introduction of the liquid nitro-gen free silicon drifted detectors (SDD) with counts rates inexcess of 1,000 000 cps are 10 times faster than the tradi-tional Si(Li) detectors.

Increase counting times will ensure better statistics andrapid analytical times. The introduction of these new tech-nologies will advance the analytical capabilities of thesetwo systems, especially for analysing coal and ashproducts.

3. Automated mineralogical system requirements

In the authors opinion, to provide the necessary miner-

Fig. 2. Electron beam positioning. Centroidal or raster of closely spacedpoints.

C. van Alphen / Minerals Ealogical information required by coal practitioners,researches and users, the new generation automated miner-alogical systems must:

1. accurately quantify the elemental proportions and ele-mental distribution between the organic (macerals) andinorganic (minerals) fractions;

2. discriminate and quantify the proportion of maceralsand determine the maceralmineral association charac-teristics on a particle-by-particle basis;

3. accurately determine the mineral/coal proportions andmineral/coal morphological features;

4. be cost-eective with a minimum turnaround time oftwo days for a batch of six sections. The sample mustnot be heated or chemically treated prior to the analysis.signicant proportions of Ca, Mg, Fe and S (Fig. 4). It isperceived that these inorganic elements are bound to thecarboxyl group (COOH). As the rank increases, the pro-portion of organically bound inorganic elements decrease(Fig. 4).

The macerals of a South African high volatile bitumi-nous (RoV% random = 0.64) coal, have distinct organi-cally bound elements (Fig. 5). Generally, sulphur andtitanium were elevated in vitrinite and pseudovitrinite, alu-minium, silicon, sulphur, and to a lesser extent calcium andmagnesium are elevated in reactive and inert semifusinite,calcium and sulphur are elevated in sclerotinite and alu-minium, silicon, sulphur in liptinite.

Electron microprobe analysis of telecollinite in Austra-lian DM Bando coal with a Rmax varying from 0.68 to2.20 identied organically bound Al, Si and Fe (Gurba

Fig. 3. Backscatter electron intensity image of lignite mounted in normal

epoxy resin. The organic component (light grey) has a high BSE intensityrelative to epoxy resin (grey) and the included minerals white. Scale bar is100 lm.

ngin0 1 2 3 4 5 6 7 8 9 10Peak Positions

0

0.05

Coun

ts

0.25

0.3

0.35

0.4

0.45

0.5

C

O

Na MgS Ca Fe

Fig. 4. X-ray spectrum obtained from the organic phase in a lignite.

0.005

0.006

s/s) Reactive Semifusinite

Inert SemifusiniteAl

Si

500 C. van Alphen / Minerals Eand Ward, 2000). Organically bound sulphur is widelyreported (Timmer and van der Burgh, 1984; Straszheimet al., 1983) in bituminous coals.

Improvement in elemental quantication is important asthe automated mineralogical system must be able to quan-tify the proportion of organically bound inorganic ele-ments, discriminate between organic and inorganic C, Hand N and the elements associated with minerals.

Elemental quantication using the rapidly acquired 1000count X-ray spectrum was achieved on the ASCAN system(Van Alphen, 2005). Two to three second count energy dis-persive (EDS) X-ray spectra were acquired from mineralstandards. Using a random number generator, the stan-dard X-ray spectrum was broken down into a 1000 countspectrum a 100 times and the number of counts for eachelement was computed. Good correlations were obtainedbetween the average elemental count and the known ele-mental proportion (Fig. 6) for the major elements. The cor-relation coecient varied from 0.98 to 0.99. The linearequation was used to determine the oxide proportions ofcoal and ash from the ASCAN 1000 count X-ray spectrum.Compared to the standard XRF ash elemental analysis, the

0.8 1.3 1.8 2.3 2.8 3.3 3.8 4.3 4.8 5.3 5.8 6.3 6.8KeV

0

0.001

0.002

0.003

0.004

Nor

mal

ised

cou

nts

(ct LiptiniteVitrinite

S

Ca

Ti

Mg

Fig. 5. Organically bound elements associated with dierent macerals inhigh volatile bituminous coal.ASCAN elemental proportion was within 15% of the stan-dard XRF ash elemental analysis for the major elements.This provided sucient information for trending and toconrm the elemental variation between samples.

Increasing the number of counts from 1000 to 10,000will obviously improve the accuracy, but will unfortunatelywill increase the time and cost to analyse a sample using theexisting Si(Li) EDS detectors. However, with the introduc-tion of new liquid nitrogen free SDD detectors thisbecomes a feasible option, without signicantly compro-mising the time required to analyse a sample.

Organic hydrogen will be the only element, which can-not be directly quantied using the existing EDS detectortechnology. This however, could be calculated by dier-ence, analogous to determining the proportion of oxygenin an ultimate analysis and xed carbon in the proximateanalysis. Since the macerals and glasses are amorphous,the elemental proportions are not consistent. If the elemen-tal composition of macerals and glasses are to be accuratelyquantied the automated mineralogical system should bebased on raster of analytical points across particles andnot on the centroidal positioning method (Fig. 2).

3.2. Maceral discrimination

Accurately discriminating and quantifying macerals incoal is important as the proportion of reactive maceralsis used to predict combustion behaviour (Su et al., 2001).Su maceral index is a function of vitrinite, liptinite andinertinite. Magasiner used the reactive maceral propor-tions to predict the ratings of chain grate stockers combust-ing coal and biomass (Magasiner et al., 2001). This indexwas based on the proportion of vitrinite, liptinite, reactiveinertodetrinite and reactive inertinite. Following thisresearch, the automated mineralogical system, should atbest be able to discriminate between reactive macerals (vitr-inite and liptinite) and inert macerals (inertinite).

Under the light reectance optical microscope maceralcharacterisation is based on texture, association and lightreectance intensity. It is reliant on the interaction of lightwith the sample and the ability of the eye and human brainto discriminate textures. Mineral discrimination in a scan-ning electron microscopes (SEM) is based on the interac-tion of a high-energy electron beam and the sample.Typically, backscatter electron intensity (BSI), secondaryelectrons intensity (SEI) and X-rays are produced and usedto acquire images and the elemental composition of themineral or phase. Since SEM discrimination is electronbased and the optical microscope is light based, the SEMis unable to identify all the macerals.

JKTech at University of Queensland has initiated mac-eral discrimination by integrating the Mineral LiberationAnalyser (MLA) and the MACE 300 system. The MLAusing the centroidal approach will dene the mineral con-tent and the MACE 300 system will dene the macerals.

eering 20 (2007) 496505A composite image of each eld of view will be developedby combining the MACE 300 maceral image and the

5ngin0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.4Al X-Ray Counts Fraction (Total Spectra)

0

5

10

15

20

25

Elem

ent %

Al

Alwt%=0.128 + 56.78ctsn=39

r=0.99

303540

Ca

Ca%=-0.43 + 79.18ctsn=23

r=0.98

C. van Alphen / Minerals EMLA mineral image, from which the maceral and min-eral proportions will be determined (Fandrich et al.,2006). If liptinite proportions are required the images areedited manually to separate liptinite from the dark miner-als (OBrien et al., 2006). The combined MACE 300and MLA system will quantify maceral and mineral pro-portions and will characterise each particle into speciedgroups depending on the maceral and mineral associations.As the electron beam is not positioned on the macerals, itwill not be able to automatically determine the elementalproportions of the macerals. The expected turnaround timeof a single sample is one day (OBrien et al., 2006, personalcommunication).

It is proposed that the macerals can be discriminatedusing a combination of backscattered electron intensityand elemental proportions. This could be the focus of thenew automated mineralogical systems.

0

0.05 0.1

0.15 0.2

0.25 0.3

0.35 0.4

0.45 0.5

Ca X-Ray Count Fraction (Total Spectra)

05

10152025

Elem

ent %

0

0.01

0.02

0.03

Mg X-Ray Count Fra

00.5

11.5

22.5

33.5

44.5

5

Elem

ent%

Mg

Mg%=0.11+ 73.87cn=19

r=0.98

Fig. 6. Correlation between element counts and known elemental prop0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Si X-Ray Count Fraction (Total Spectra)

05

101520253035404550

Elem

ent %

Si Si%=0.17 + 60.46ctsn=36

r=0.98

607080

e

Fe%=2.78 + 94.93cts n=36

r=0.98

eering 20 (2007) 496505 501The density of vitrinite varies from 1.27 g/ml in high vol-atile bituminous coals to 1.8 g/ml in anthracites (Falconand Snyman, 1986). Liptinite ranges in density from 1.18to 1.25 g/ml with increasing rank. The density of the indi-vidual inertinite macerals vary from 1.35 to 1.7 g/ml, butdo not change signicantly with an increase in rank. Sincebackscattered electron images are atomic weight contrastimages, the density variation of macerals will be reectedby a variation in grey levels in backscattered electronimages (Fig. 7). Any low-density phase (vitrinite/liptinite)will be black and high-density phase will be grey(inertinite).

For a given rank, liptinite has the highest hydrogen con-tent, vitrinite the highest oxygen content and inertinite thehighest carbon content (Tang et al., 2005; Ward andGurba, 1999). Published data on mainly density-separatedfractions in general support the trend observed by Tang

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Fe X-Ray Count Fraction (Total Spectra)

01020304050

%El

emen

talF

0.04

0.05

0.06

0.07

ction (Total Spectra)

ts

ortions. Data acquired on the ASCAN system (Van Alphen, 2005).

ngin502 C. van Alphen / Minerals Eand Ward (Fig. 8). Any variation is attributed to the den-sity fraction not necessary containing 100% of the maceralof interest.

It is conceivable that a new SEM based maceral clas-sication scheme could be developed based on elementalproportions and backscattered electron intensity. This

Fig. 7. Individual high volatile bituminous coal particles highlighting the textudensity fraction). Low-density coal is black, higher-density coal is light grey a

0.44 0.5 0.56 0.63 0.82 0.91Rank (Romax)

747678808284868890

Mas

s-%

car

bon

ExiniteVitriniteInertinite

0.44 0.5 0.56 0.63 0.82 0.91Rank (Romax)

33.5

44.5

55.5

66.5

77.5

8

Mas

s-%

hyd

roge

n

ExiniteVitriniteInertinite

Fig. 8. Carbon, hydrogen, nitrogen and oxygen variation with rank (Burragaeering 20 (2007) 496505new classication scheme is not subjective and once setup will not require an experience operator.

If backscatter electron intensity is the main discrimina-tor, then the automated mineralogical system has to beextremely stable as the BSE variations are a few atomicmass units. It is imperative to monitor the beam current

ral relationship and variation in backscatter electron intensity (

nginduring the analysis and if necessary automatically adjust thebeam. The mineral identication software used by the auto-mated mineral identication software must include BSEintensity as a discriminating factor. Using the raster of ana-lytical points instead of centroidal method is preferable asthe slight changes in BSE intensity and elemental composi-tions can be recorded and used to dene the maceral. Thisne detail will produce an image texture, which couldbe used to classify macerals (Fig. 6). Maceral discriminationin high rank coals, using BSE based automated mineralog-ical system will probably not be as successful as in lowerrank coals. From a practical point of view, this is not amajor issue as a signicant proportion of the coal utilisedis high to low volatile bituminous coal. Obviously, if therank of the coal is known prior to analysis, the maceral dis-crimination system can modied to suit this rank of coal.

In ash, the prominent carbon phase is char. Char iden-tication is based on morphological features (Alvarezet al., 1997). The automated mineralogical system mustrst identify carbon-rich phases in the ash samples and sec-ondly have image analysis capabilities that measure wallthickness and porosity of the char particles. It is importantto have the necessary o-line processing software, whichcan classify the particles into dierent classes based onthe wall thickness and porosity.

3.3. Mineral quantication and particle characterisation

Quantifying and qualifying minerals in coal is routinefor any automated mineralogical system, but not necessaryas routine for ash. In coal, ne clay is nely disseminated incoal. It is this reason why kaolinite is often over or underreported (Galbreath et al., 1996). The automated mineral-ogical system must have the mechanism to quantify themineralogical variations of these nely disseminated grains.A system using a raster of closely spaced analytical pointswill provide the best opportunity to record the elementaland subsequently mineral variations.

Simplistically, ash (y ash, slag and clinkers) consists ofcrystalline phases (e.g. anorthite, mullite and spinels),amorphous Ca- and Fe-bearing aluminosilicate glasses ofvarying elemental compositions, amorphous Ca-oxide richash particles, amorphous Fe-oxide rich particles and char.

Quantifying and identifying the crystalline phasesshould not propose a problem as by denition these phaseshave a dened elemental composition and structure. Theproblem is classifying the amorphous phases. Groupingthe dierent amorphous phases based on the elementalcomposition is the only feasible route.

In South African y ashes and slag deposits at least sixgroups have being identied (Van Alphen, 2005). Theseinclude

1. Aluminosilicate, with a Al/Si ratio similar to the originalkaolinite source and no uxing elements (Ca, Fe, Mg, K

C. van Alphen / Minerals Eand Na). This phase represents the intermediate to hightemperature transformation products of kaolinite.2. CaFe-bearing aluminosilicate glass, typically occur asspherical particles and is derived by the coalescence ofpyrite, carbonates and kaolinite. Invariably, thesephases initiate the development of slag deposits.

3. K-bearing aluminosilicate glass is derived from K-bear-ing illite/mica and feldspar.

4. Quartz and Si-rich glasses with minor proportions of Al,Ca, Fe and Mg. Quartz is the nal transformation prod-uct of quartz and the Si-rich glasses represent the hightemperature coalescence of quartz with kaolinite, cal-cite/dolomite and pyrite.

5. Ca(Mg)-rich oxides (lime) with minor concentrations ofAl and Si. This y ash phase is transformation productof extraneous calcite and dolomite.

6. Fe-rich oxides with minor concentrations of Al and Si.This y ash phase is the transformation product ofextraneous pyrite.

Operating conditions and ash formation processeswithin the boiler/furnace can be inferred from the mineral-ogy and elemental distribution of the ash. Comparing theelemental distribution of the y ash phases to the elementaldistribution in the source coal provides valuable informa-tion, which can be used to trace mineral transformationand reactions within the combusting coal particles and inthe combustion zone. In eect, the elements are used astracers.

Measuring the sulphur content in Fe-oxide rich y ashparticles, C-content in Ca-rich y ash particles and theAl/Si ratio in aluminosilicate y ash particles are indicativeof the extent of pyrite, carbonate and kaolinite transforma-tion. If the data from an automated mineralogical systemare to be used to model mineral transformation, y ash for-mation and slag development in any commercial boiler,gasier or furnace, it is important to accurately character-ise the y ash phases and to fully understand mineral andelemental deportment in the source coal (Van Alphen,2005). Without this detail, the model will not produce accu-rate and meaningful results.

Identifying these ash phases and measuring the variationin elemental proportion is only possible if a raster of pointsis used. Since there is a tenuous link between the backscat-tered electron intensity and elemental composition ofglasses, the centroidal approach is not feasible.

4. Conclusion

To satisfy the mineralogical requirements for coal util-isation, coal-processing optimisation, coal resource assess-ment and coal combustion performance it is important thatthe automated mineralogical system should be able toquantify and qualify elemental proportions (organic andinorganic), major maceral groups, mineral deportmentand proportions in coal. This same system must identifythe numerous amorphous phases in glass and accurately

eering 20 (2007) 496505 503quantify the elemental proportion of these amorphousglasses.

nginCoal process engineers require the particle size distribu-tion, density distribution and mineral(ash) distribution ofcoal particles for optimising the circuit and improving thequality of the product. The maceral composition and sur-face characteristics of ne particles are important forotation.

Reducing the number of ame-outs, unplanned outagesand the proportion of char in the y ash are important forcombustion engineers. Flame-outs are partially a functionof the maceral composition and unplanned outages arecommonly attributed to extensive slagging and fouling.To resolve these problems, the automated mineralogicalsystem must accurately determine mineral proportion anddeportment, proportion and deportment of reactive macer-als, particle characteristics, size and angularity of abrasiveminerals, distribution of elements and nally the distribu-tion of carbon, hydrogen and oxygen.

The introduction of liquid nitrogen free SDD detectors,stable digital scanning electron microscopes and high-reso-lution backscatter electron detectors coupled with uniquemineralogical features will enable the automatic mineralog-ical analysis of coal and ash samples. In the authors opin-ion, the raster of closely spaced points is preferable to usingthe centroidal method of position the electron beam. Fur-ther research is required to substantiate the trends andobservations mentioned in this paper. Coal and ash analy-sis is now entering a new and exciting era.

Acknowledgements

The original mineral matter transformation, y ash for-mation and slag development research and setting up theASCAN system was funded by Eskom Resources andStrategy Division. This funding enabled the author todevelop the techniques and to comprehend the complexityof analysing coal and ash. Eskoms support and funding isgratefully acknowledged.

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C. van Alphen / Minerals Engineering 20 (2007) 496505 505

Automated mineralogical analysis of coal and ash products - Challenges and requirementsIntroductionCoal and ash automated mineralogical systemsAutomated mineralogical system requirementsQuantitative elemental proportionsMaceral discriminationMineral quantification and particle characterisation

ConclusionAcknowledgementsReferences