Mineral matter–organic matter association characterisation

by QEMSCAN and applications in coal utilisation

Yinghui Liua, Raj Guptaa,*, Atul Sharmaa, Terry Walla, Alan Butcherb,d,Gavin Millerb, Paul Gottliebb,d, David Frenchc

aCooperative Research Centre for Coal in Sustainable Development, Department of Chemical Engineering, University of Newcastle,

EB Building, Callaghan, NSW 2308, AustraliabCSIRO Minerals, Queensland Centre for Advanced Technologies, Brisbane, Qld, Australia

cCSIRO Energy Technology, Lucas Heights, NSW, AustraliadIntellection Pty Ltd, Milton, Brisbane, Qld, Australia

Received 13 November 2003; received in revised form 14 June 2004; accepted 14 July 2004

Available online 18 November 2004

Abstract

The association of mineral matter with organic matter is extremely important for coal utilization process such as pf coal combustion. With

the development of advanced analytical instruments such as QEMSCAN, it is now possible to measure directly the mineral matter–organic

matter association on a particle-by-particle basis. The mineral matter and mineral–organic associations of a suite of fourteen CCSD coal bank

coals (as pf) have been determined by QEMSCAN. An interface program was developed to make QEMSCAN data compatible with the

CCSEM-based ash formation model developed previously in CCSD. Size and chemistry of flyash was predicted by a partial coalescence sub-

model for included mineral grains, and a fragmentation sub-model for excluded mineral grains, respectively. The size and chemistry of

predicted flyash was estimated on a particle-by-particle basis, and was used to rank the ash effect on heat transfer reduction for all the CCSD

coals using the CCSEM-based model, in which coal property, furnace geometry and operational conditions have been taken into account.

Other applications and further developments of the technique are also outlined.

q 2004 Elsevier Ltd. All rights reserved.

Keywords: Coal; Mineral matter; QEMSCAN; Mineral–organic association

1. Introduction

Coal is a complex heterogeneous mixture of organic

matter (OM) and inorganic matter (IM) containing intimately

mixed solid, liquid, and gaseous phases with different

abundances and origins [1]. The organic components are

fundamental to define the nature of coal (e.g. rank and type),

and to its value in different utilization processes. All of the

benefits from coal are derived essentially from the maceral

constituents [2]. A number of problems relating to boiler

operations (slagging, fouling, corrosion, erosion) and

environmental issues (particulate matter emissions and

SOx, NOx emissions) are caused by the mineral matter

0016-2361/$ - see front matter q 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.fuel.2004.07.015

* Corresponding author. Tel.: C61 2492 16195; fax: C61 2492 16920.

E-mail address: rajender.gupta@newcastle.edu.au (R. Gupta).

and inorganic constituents in coal, rather than directly from

the maceral components [3]. During crushing, grinding and

milling processes, IM may be liberated from the organic

matrix. Thus, mineral matter in pulverised coal (pf) particles

presents different mineral–organic associations and has been

classified into organically associated mineral matter and

discrete mineral grains, namely included mineral and

excluded mineral. The information on mineral–organic

association is extremely important for coal utilization

processes. For example, during pf coal combustion, different

mineral matter may experience different temperature-time

histories resulting in different physical–chemical transform-

ations, thus generating ash particles of different sizes and

chemistry, which influence flyash-related utilisation and

related environmental issues.

Several researchers used a size fractionation method to

study mineral–organic associations in different size

Fuel 84 (2005) 1259–1267

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Y. Liu et al. / Fuel 84 (2005) 1259–12671260

fractions. In the work of Rayner and Marskell [4], Littlejohn

[5], and Cloke [6], it was noted that, in pf coal as the particle

size decreased from over 200 mm to less than 10 mm there

was an increase in the ash content of the coal. Raask [7]

demonstrated mineral distribution in size fractions: clay

minerals were found to be concentrated in the fractions

below 20 mm, more quartz and carbonates were found in the

size fractions between 20 and 75 mm and pyrite was

concentrated in the fractions between 10 and 45 mm. Spears

[8] studied trace elements associations in size fractionated

pf coal using an air classifier. The disadvantage of the size

fractionation method is that density fractionation can occur

in the air classifier because coal particles containing

minerals will be in hydrodynamic equilibrium with larger,

purer particles. Consequently, less dense coal particles and

the shape of particles has a great effect on the size of

fractions when the sieving method is used to separate coal

particles.

Density fractionation methods using centrifugal float-

sink separation techniques have been adopted to study

mineral–organic association in density fractions of pf coal

by several researchers, such as Zeki [9], Spears [10], Nigel

[11] and Pusz [12] due to the great difference in specific

gravity between mineral matter and organic matter in coal.

The excluded mineral tends to enrich in higher density

fraction but fine liberated mineral grains also occur in float

fraction. Pusz described mineral–organic association for

several minerals: quartz accumulates in the highest density

fraction; clay minerals are probably independent from coal

organic matter. Pyrite is significantly associated with the

organic matter of lower rank coals, as shown by its

accumulation in the lightest fraction despite the high density

of this mineral.

Apart from these physical separation methods, chemical

separation methods have been proposed to study mineral–

organic associations, especially for trace elements analysis.

Koichi [13] used chemical leaching methods to study

organically associated calcium in various coals. The effect

of operating variables on leaching such as solution type,

temperature, leaching time, pH, concentration of leaching

agent, solution/coal ratio has been studied. The conclusion

drawn was that it is difficult to selectively remove ion-

exchangeable calcium by leaching with aqueous solutions,

and chemical leaching methods are not reliable for the

quantitative analysis of organically associated calcium.

All the physical and chemical separation methods used to

study mineral–organic association only provide association

information on one specific aspect (varying with size,

density, etc.) and this kind of information is strongly

affected by operating variables for each method as

mentioned above. Furthermore, those methods only provide

average features for each fraction. With the development of

computer-controlled scanning electron microscopy

(CCSEM), it is possible to study mineral–organic associ-

ations on the cross-section of the sample on a particle-by-

particle basis without any previous separation. CCSEM

technology has been used by Straszheim and Markuszewski

[14], Wigley and Williamson [15,16], and Gupta [17], to

study mineral–organic associations. Mineral grains are

identified from the embedding matrix and coal by their

higher backscatter intensity. Further image analysis is then

applied to classify mineral grains into included/excluded

minerals. The information on mineral–organic associations

has been used to predict ash formation behaviour by Yan

[18,19] and coal burnout behaviour [15]. CCSEM-based ash

formation modelling work has also been carried out in the

US by Brigham Young University (BYU) [20], the Energy

and Environmental Research Center (EERC) of the

University of North Dakota [21,22], PSI Technologies,

Sandia National Laboratory, MIT [23], and Reaction

Engineering International, as summarized by Bryer [24].

A different analytical technique to characterize the

mineral matter in coal particles, called QEM*SEM, has

been developed by CSIRO, Australia [25,26]. QEMSCAN

is the latest model in the QEM*SEM family which is an

automated image analysis system that uses Backscattered

Electron (BSE) and Energy Dispersive X-ray (EDX) signals

from a Scanning Electron Microscope (SEM) to create

digital images in which each pixel corresponds to mineral

species in a small region under the electron beam. Once an

individual particle section has been located from the BSE

image, it is scanned using a grid of points, and the

backscattered electrons and the Energy Dispersive X-ray

(EDX) photons emanating from a given point are used to

identify the elements present, and thus classify the mineral

species present. The image of the particle sections is built up

pixel-by-pixel in this manner. Each pixel in each particle

section image is assigned a number, which is a unique

identifier of the species at that point in the section using a

Species Identification Program (SIP). With the development

of QEMSCAN, it is now feasible to study mineral–organic

associations on a pixel-by-pixel basis without any further

manual image processing procedure. In this paper, mineral–

organic association for a suite of CCSD coal bank coals has

been characterized by QEMSCAN and its application in ash

formation and ash effect evaluation is demonstrated.

2. Coal samples and sample preparation

A suite of CCSD coal bank coals were examined during

this study. The proximate analyses and ultimate analyses for

these coals are shown in Table 1.

Pf coal particles to be measured by QEMSCAN are

first mixed with a carnauba wax and cast in 30 mm

diameter moulds to form a sample block. The sample

block surface is lapped and polished, and then coated with

a thin layer of carbon to ensure electrical conductivity.

The coal particles are thus separated from each other and

appear in random orientations within the sections exposed

by the polishing.

Table 1

Proximate analysis and ultimate analysis for CCSD coals (data from CCSD)

CRC_code Proximate analysis (a.d.) Ultimate analysis (d.a.f.)

Moisture Ash content Volatile matter Fixed carbon C H N S O

CRC 240 2.1 13.2 29.9 54.8 84.0 5.74 2.12 0.9 7.2

CRC 252 6.7 12.1 40.2 41.0 78.6 6.1 1.07 0.48 13.7

CRC 263 3.0 9.2 31.2 56.6 84.8 5.24 1.91 0.46 7.6

CRC 272 2.8 13.5 34.3 49.4 83.3 5.78 1.79 1.13 8.0

CRC 274 7.9 8.1 27.0 57.0 83.5 4.84 1.84 0.35 9.5

CRC 281 1.7 9.8 8.7 79.8 91.4 3.77 1.88 0.76 2.2

CRC283 2.2 14.8 29.7 53.3 83.7 5.45 1.81 0.47 8.6

CRC 284 1.6 18.6 25.8 54.0 86.3 5.5 2.04 0.78 5.3

CRC 296 2.2 14.8 29.7 53.3 84.8 5.23 1.87 0.67 7.5

CRC 297 1.4 14.1 43.2 41.3 81.6 6.09 1.25 6.29 4.8

CRC 298 2.8 12.4 29.1 55.7 83.5 5.04 1.81 0.34 9.3

CRC 299 8.0 21.4 24.5 46.1 78.1 4.3 1.12 0.3 16.2

CRC 306 1.3 19.9 19.1 59.7 88.1 4.57 1.7 0.9 4.7

CRC 310 2.7 11.7 24.5 61.1 85.8 5.03 1.99 0.81 6.4

Y. Liu et al. / Fuel 84 (2005) 1259–1267 1261

3. Measurement parameters

All QEMSCAN measurement parameters (such as

magnification, pixel spacing, number of particles, etc.),

can be changed at any time and are application specific. The

parameters chosen for the present study, which are shown in

Table 2, were specifically selected following a program of

experimental analysis.

Table 2

Measurement parameters and number of particles analysed by QEMSCAN

Particle section

size range (mm)

C10/K212 mm !10 mm

Magnification 250 350

Frame size (mm) 949!949 1329!1329

Pixel size (mm) 1.95 0.93

CRC code No. of

frames

No. of

particles

No. of

frames

No. of

particles

CRC272 6 4501 2 5081

CRC274 6 4419 3 4744

CRC297 6 4021 3 4227

CRC299 5 4474 2 4496

CRC306 4 4036 2 8389

CRC240 5 4900

CRC252 6 4408

CRC263 5 4532

CRC281 4 4240

CRC283 5 4618

CRC284 4 4370

CRC296 4 4460

CRC298 4 4882

CRC310 4 4457

3.1. Magnification and stepping interval

In order to obtain sufficient spatial resolution on both

large and small particles, measurements are made at two

magnification settings. The first run rejected particle

sections larger than 10 mm and smaller than 1 mm; the

second run rejected particle sections smaller than 10 mm and

larger than 212 mm in size. This is to ensure that a

representative number of both large and small particles

were analyzed under different magnifications.

The pixel stepping interval for each magnification is

chosen by the analyst according to the magnification used

and the desired resolution for the sample under measure-

ment. Fig. 1 shows the QEMSCAN analysis of a coal

particle at varying pixel spacing compared to BSE image

(the length of scale bar is 100 mm). It can be seen that as the

pixel spacing is reduced, the QEMSCAN image becomes

more representative of what is seen in the BSE image.

However, the increase in detail comes at the cost of analysis

time. The 1 mm pixel stepping interval analysis takes four

times as long as the 2 mm pixel analysis and 100 times as

long as the 10 mm pixel analysis. The choice of pixels size

therefore becomes a trade off between analysis time and

resolution.

The data from these two size ranges are then combined,

with the appropriate weighting based on the total area

analyzed. The combined results (PSD and chemistry) are

close to the results obtained from coarser particles because

coarser particles contribute more weight than fine particles.

Thus, for the remaining nine coals, only single magnifi-

cation and stepping interval (1.95 mm) was used.

3.2. Number of particles

To determine the number of particle measurements

required to obtain a representative analysis, a running

average composition was calculated. The running average

was determined by progressively calculating the average

composition as additional particles are measured. The point

at which the running average gave a reasonably consistent

value gives the number of particles required to give a

representative analysis. From former measurement experi-

ence, 4000 particle measurements are required before the

composition becomes consistent.

Fig. 1. QEMSCAN particle maps illustrating the effect of changing the stepping interval on the resulting false colored pixel mineral map, together with a BSE

photomicrograph of the same particle measured by QEMSCAN.

Y. Liu et al. / Fuel 84 (2005) 1259–12671262

3.3. Species identification program

As mentioned by Wigley [15], CCSEM only analyzes

mineral grains and encountered problems sometimes when

identifying coal organic material in cross-section from BSE

images because the average atomic number, and therefore

the BSE intensity of organic matter, was close to that of the

mounting medium used. Also for some coals, coal particle

cross-sections could not be satisfactorily identified.

To eliminate these kinds of problems, in our current

study, we apply a new method to combine BSE intensity and

X-ray spectrum to discriminate mineral grains from coal and

background. Fig. 2 shows a particle BSE image with an

accompanying X-ray spectrum from point A within the

particle, together with a spectrum from the carnauba wax

mounting medium. It clearly shows that brightness of point

A and background are different to each other and can be

distinguished by backscattered electron brightness. The

spectra indicate they can also be distinguished based on

elemental composition. By this way it is easy to discrimi-

nate coal particles, minerals and the mounting medium.

Spectra from the output of up to four EDS X-ray

detectors from each measurement point is interpreted on-

line through a Species Identification Program (SIP). Each

pixel is identified during the scan and the pixel value stored

for that point in the image is a number representing the

mineral classification observed in the specimen. The

mineral classification is based on the elements observed in

the X-ray spectrum and the backscattered electron bright-

ness. The spectrum collection and analysis are overlapped to

save measurement time. The beam is moved to the next

measurement point and the X-ray acquisition started while

the previous point is being analysed. Thus, particle-by-

particle measurements are integrated to give a volumetric

assessment of the relative proportions of the different

minerals, or element-associations, present in the coal

sample. Other image analysis functions, such as determi-

nation of size and shape distributions, can also be applied to

the mineral particles in the coal using QEMSCAN. The

output from QEMSCAN is an image in which each mineral

or phase type is labeled with a different color to represent

different species.

4. Results and discussion

4.1. Mineral–organic associations

Mineral–organic associations in pulverized coal particles

are very important for describing the transformation of

mineral matter in coal into ash particles and its related ash

deposit effects due to different behavior of different types of

particles. From QEMSCAN image processing analysis, the

total area and organic matter area can be calculated for each

individual particle. Following this, the fraction of organic

matter in each particle can then be derived. This fraction

Fig. 2. A single pf particle with BSE image (scale bar 10 mm), QEMSCAN mineral map, and example EDS X-ray spectra.

Y. Liu et al. / Fuel 84 (2005) 1259–1267 1263

represents how much mineral matter is associated with

organic matter. For example, if the ratio equals to zero, this

particle is a pure excluded mineral grain, in another words

there is no coal associated with mineral matter in this

particle. If the ratio equals to 100%, this particle is a

complete coal particle showing no association between coal

and mineral matter. In the other situations, the particle is

defined as a coal particle containing included mineral matter

and only in these kinds of particles is coal associated with

mineral matter. According to the mineral–organic associ-

ations, pulverized coal particles are grouped into a variety of

coal mineral association patterns including:

†

mineral matter-free organic particles;

†

excluded mineral grains;

†

Fig. 3. Mineral–organic association profiles for two CRC coals.

composite particles in which coal particle contains

included mineral matter in a range of proportions.

From statistics by number counting, pf coal particle cross

sections without mineral grain was found to be most

common in each coal studied, and only around 10%

particles contain mineral grains. Fig. 3 shows the variation

in mineral content as a cumulative distribution for two

extreme cases. Coal CRC 283 has least mineral–organic

association while coal CRC 297 has highest mineral–

organic association. The mineral–organic associations for

other CCSD coals lie between these two.

To utilize information on mineral–organic association,

particles are usually classified into groups for simplicity.

Based on a series of mineral-association distributions with

organic matter for all the 14 coals, we adopt assumptions

below to classify particles:

1.

coal particles, in which contains more than 95% carbon;

2.

coal particles containing included mineral matter, in

which carbon content ranges from 95 to 40%;excluded

Fig. 4. Mineral distributions, in terms of included/excluded mineral, following classification.

Y. Liu et al. / Fuel 84 (2005) 1259–12671264

mineral grains, containing less than 40 area% organic

constituents.

In the current study, all particles with sizes less than 2

pixels are assumed to be coal particles.

After classification, mineral distributions for 14 CCSD

coals in terms of their included mineral and excluded

mineral matter, are shown in Fig. 4; typical particle size

distributions and mineralogy distribution are shown in Fig. 5

and Table 3.

4.2. Interface to convert QEMSCAN data into CCSEM

compatible format

QEMSCAN measures mineral-containing and mineral-

free coal particles. CCSEM only measures mineral grains.

Fig. 6 illustrates the difference between a QEMSCAN-like

image (a) and CCSEM-like image (b). To make QEMSCAN

data compatible with the Ash Effect Predictor (AEP)

developed formerly by the CCSD, an interface was

developed to convert QEMSCAN data into CCSEM-like

format. All the pixels presenting organic matter in

QEMSCAN image are removed so that only mineral grains

remain. Size and chemistry for each mineral grain are

obtained and output tabulated in a form compatible with the

AEP.

Fig. 5. PSD for a CCSD coal (CRC240).

4.3. Ash formation and flyash behaviour

The ash formation models currently used worldwide

have been described in detail by Yan [19]. Here only a brief

introduction is made. The CCSD ash formation model

consists of three sub-models, namely the mineral distri-

bution sub-model, included mineral transformation sub-

model, and excluded mineral transformation submodel. The

Monte Carlo method is used to randomly disperse included

minerals among simulated coal particles. Included mineral

grains are distributed, one by one, into randomly selected

coal particles. An inventory is kept to record mineral

inclusions accumulated in each coal particle. Excluded

minerals are kept as is. During coal combustion, char

morphology exhibits a wide range of structural distribution

in terms of char macroporosity or cenospherical shell

thickness after the devolatilization as summarized by

Benfell [27]. Char structure and coalescence behaviour of

included minerals has been considered in included mineral

transformation submodel. The fragmentation behaviour of

excluded minerals was described by excluded mineral

transformation submodel in which Poisson distribution of

offspring fly ash particle was adopted. Size and chemistry

predicted by ash formation model was used to evaluate fly

Table 3

Modal abundance of major, minor and trace phases in CRC272

Wt% Total

minerals

Included minerals Excluded minerals

63.43% 36.56%

Ankerite 2.15 100

Apatite 0.61 100

Calcite 10.54 93.25 6.75

Dolomite 1.11 100

Siderite 2.27 100

Chamosite 0.37 98.11 1.89

Gypsum 0.04 100

Illite 5.09 53.72 46.28

Kaolinite 57.04 53.71 46.29

Unclassified 4.89 89.12 10.88

Pyrite 1.54 93.39 6.61

Quartz 10.7 72.4 27.6

Rutile. 3.66 74.6 25.4

Fig. 6. Conversion from QEMSCAN data into CCSEM-like format data.

Y. Liu et al. / Fuel 84 (2005) 1259–1267 1265

ash by CCSEM-based models. Fig. 7 shows the procedure

for data processing and slagging prediction.

The size and chemistry of fly ash obtained from the ash

formation model is used as input to other submodels to

evaluate ash behaviour such as slagging, fouling, erosion,

heat transfer, etc. Fig. 8 shows the ranking of ash effect

on heat transfer for all the fourteen CCSD coals obtained

by the heat transfer reduction submodel. This model is based

on coal properties from CCSEM-like data, considering the

geometry on a simple furnace and boiler operating

conditions, such as flue gas velocity. The model calculates

the deposition rate by taking into account the ash content,

particle stickiness and instantaneous stickiness of the heat

transfer surface. A higher deposition rate is not desired for

efficient operation due to low thermal conductivity or the

difficulty in removing the deposit formed. The index shows

the time elapsed at which the heat transfer drops by 25%

when compared to heat transfer through a clean surface. It

may be noted that the index is applicable to heat transfer at

water walls and does not consider the effect of soot blowing

operations.

Fig. 7. Flowchart of data processing and AEP.

4.4. Further development and applications for pf

It is necessary to note that from QEMSCAN measure-

ments, mineral distributions in coal are not random. There

are only a small proportion of particles that contain mineral

grains from a mineral–organic association point of view.

Another important feature is mineral–mineral association,

which describes whether minerals preferentially occur in

association with each other. Non-random mineral distri-

butions were also observed by Yu [28] and Richards [29].

The ash formation model currently used is based on the

random mineral distribution model similar to the URN

model by Barta [30] and Charon [31], in which all the

mineral grains are distributed into randomly selected coal

particles, thus all the mineral grains have been distributed

into coal particles uniformly. The random distribution

model gives included mineral grains less chance to coalesce

as all the included mineral grains have been distributed

throughout all the particles.

It could be expected that if all the mineral grains were

only randomly distributed into a small portion of particles,

size and chemistry of ash particles would change signifi-

cantly for coalescence of included mineral grains would be

enhanced greatly. The particle size distribution of ash

particles would be shifted towards the coarser side while

chemistry would present more intimately mixed basic

oxides with Al and Si oxides resulting in a large mount of

lower viscosity particles and more severe slagging.

From a mineral–mineral association point of view,

whether basic oxides tend to be associated with Al–Si

oxides is something that QEMSCAN can quantify on

particle-by-particle basis, which is also critical for predic-

tion of ash particles size and chemistry. Thus, viscosity is

determined by direct measurement from QEMSCAN rather

than from a random distribution assumption.

Fig. 8. Ranking of CCSD coals by heat transfer reduction.

Y. Liu et al. / Fuel 84 (2005) 1259–12671266

To summarize, it is necessary to develop a non-random

distribution model in future to consider mineral–organic

association and mineral–mineral association to utilize all

unique features provided by QEMSCAN.

5. Conclusions

†

QEMSCAN has light element detectors that can measure

elements with atomic numbers down to 6 (carbon) and

therefore it is possible to measure organic matter and

mineral matter at the same time. Identification of each

pixel for organic matter and mineral matter is determined

by a combination of BSE image intensity and X-ray

spectral information. The output of QEMSCAN is a

particle image in which each pixel is assigned a mineral

or phase type. From such particles, mineral–organic

associations, and mineral–mineral association can be

obtained. This capability is unique to QEMSCAN,

making it a more powerful technique than the conven-

tional CCSEM.

†

Mineral matter with differing grain size, chemistry,

mineral–organic associations, in a suite of the 14 CCSD

coals, has been characterized using QEMSCAN on a

particle-by-particle basis. QEMSCAN analysis shows

that minerals are not randomly distributed in coal.

†

An interface has been developed to convert QEMSCAN

data into CCSEM-like format compatible with CCSEM-

based ash formation model to predict size and chemistry

of fly ash. This could be used to evaluate fly ash effects on

operational issues such as deposition, erosion, heat

transfer reduction, etc.

†

To utilize information on mineral–organic association

and mineral–mineral association, the output from

QEMSCAN needs to be converted to a form suitable

for inclusion into more advanced ash formation models.

Acknowledgements

The authors wish to acknowledge the financial support

provided by the Cooperative Research Centre for Coal in

Sustainable Development, which is funded in part by the

Cooperative Research Centres Program of the Common-

wealth Government of Australia. A1 Copp is thanked for

help in data collation.

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