Fuel 94 (2012) 368–373

Contents lists available at SciVerse ScienceDirect

Fuel

journal homepage: www.elsevier .com/locate / fuel

Improving coke strength prediction using automated coal petrography

Sushil Gupta a,⇑, Fenglei Shen a, Woon-Jae Lee b, Graham O’Brien c

a Center for Sustainable Materials Research & Technology, School of Materials Science and Engineering, The University of New South Wales, Sydney, NSW 2052, Australiab POSCO Technical Research Laboratory, Pohang City, Gyeongbuk, Republic of Koreac CSIRO Earth Science and Resource Engineering, QCAT, Brisbane, Australia

a r t i c l e i n f o

Article history:Received 29 April 2011Received in revised form 20 September2011Accepted 21 September 2011Available online 5 October 2011

Keywords:CoalReflectogramMicroscopyFull maceral reflectogramCSR and DI

0016-2361/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.fuel.2011.09.045

⇑ Corresponding author. Tel.: +61 2 93854433.E-mail address: sushil@unsw.edu.au (S. Gupta).

a b s t r a c t

A range of Australian and overseas coals with a mean maximum vitrinite reflectance (Ro, max) range of0.68–1.71% were carbonized in a test coke oven. Coal properties were characterized using XRF, XRD andautomated imaging of polished sections of discrete coal grains. The Full Maceral Reflectance (FMR) param-eter was calculated from the reflectance data and was correlated with coal as well as coke properties. TheRo, max values and the vitrinite estimates from the semi-automated microscopic technique indicated agood correlation with similar data based on manual point count analysis. The FMR parameter is shownto increase with increasing carbon content, structural ordering of carbon and decrease with increasing vol-

atile matter. The FMR parameter of coal was related to cold coke strength DI15015

� �and coke strength after

reaction (CSR). The FMR parameter was modified by diluting the contribution of high reflectance coal grainsas well as incorporating the effect of ash contribution to propose a combined coal index (CCI). The new coalindex is shown to improve the accuracy of coke strength prediction. The combined coal index provides apromising objective measurement based alternative for predicting coke strength.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Coal composition is popularly characterized in terms of maceralswhich are the microscopically recognizable individual constituentsof coal. On the basis of their quantitative participation and associa-tion, macerals determine the chemical and physical properties of agiven coal. Coal macerals are divided in three groups: vitrinite, lipt-inite (or exinite) and inertinite and is distinguished by the propor-tion of light reflected from the surface of a polished specimensurface. Due to overlap of reflectance between the macerals, mor-phological features are also used for the identification [1]. Generally,vitrinite is the most abundant group in high rank coals and its reflec-tance is usually less than that of inertinites but greater than liptinites[2]. Inertinite displays highest reflectance and varies significantlyamong different coals, and characterized by highest carbon contentmostly in aromatic form, least volatiles components includinghydrogen [3]. Generally, inertinites remain inert during carboniza-tion with the exception of some Australian coals inertinite [4].

Coke strength is critical for ironmaking via blast furnace routeand is strongly influenced by coal properties. Majority of the existingcoke quality correlations are based on conventional coal petrogra-phy including coal rank, relative proportion of macerals and their

ll rights reserved.

characteristics. One of the popular models is the Schapiro model,which is based on the Russian approach [5]. This model considersall vitrinite, all liptinite and one-third of semifusinite as the reactivecoal matter while the remaining inertinites as well as the mineralmatter are treated as the non-reactive component for predictingcoke stability. Reactive macerals are further subdivided into vitri-noid type or V-type such that an optimum ratio of reactive to inertcomponents in each vitrinoid is desired to achieve maximum cokestrength [6,7]. This approach predicts ASTM stability factor by com-bining composition balance index (CBI) and strength index (SI),where CBI was defined as the ratio of the inert components in coalto the optimum ratio of reactive to inert components of a coal for agiven rank, while the SI is evaluated to determine the relative cokestrength depending on coal rank and types [6,7]. The same approachwas modified by Nippon Steel to predict cold coke strength [7].These relationships have been summarized in a past study [8]. Gen-erally, these correlations are suitable for predicting coke strength ofNorthern hemisphere coals but less reliable for Australian coalswhich often contains higher proportion of reactive inertinites[9,10]. Particularly, the consideration of one-third of total semifusi-nite as the reactive proportion of relatively lower rank coals (Ro, max<1.35) is not suitable for many Australian coals.

A coal reflectogram provides the reflectance distribution of coalsample in the form of a frequency histogram. Random or maximumvitrinite reflectance of the coal is measured following standardtechniques and the results are usually reported at a fixed interval

S. Gupta et al. / Fuel 94 (2012) 368–373 369

of 0.05% reflectance (half V-step or V-type). Random vitrinitereflectance is measured under non-polarized light, the reflectionsfrom all directions on the vitrinite surface are integrated to givea mean vitrinite random reflectance (Rr) percent [11]. Maximumvitrinite reflectance is measured in polarized light whilst themicroscope stage is rotated through 360�. Automatic microscopyprovides mean random vitrinite reflectance data which can be con-veniently related to consistently higher values of mean maximumvitrinite reflectance (Rv, max or Ro, max). Current practice of themeasurement of vitrinite reflectance is subjective, time-consumingand invariably requires skilled petrographer, and hence often leadsto large variance in the measurements [12,13]. Therefore, auto-matic microscopy of coals is increasingly preferred over manualpoint count approach [11–18]. A limited number of studies haveused automated microscopic approach to characterize coke mi-cro-textures [18–21] and relate to coal rank [18] or blast furnacecoke properties including coke strength [21].

With increasing advances of coal imaging techniques, it seemsfeasible to address some of the concerns towards developing anobjective and universal approach for characterizing coal composi-tion [4,11–18]. On the basis of semi-automated coal reflectancedata, the rank (vitrinite reflectance) and type (maceral groupabundance) of coal was combined to provide a single parameteri.e. Full Maceral Reflectance (FMR), which could be related to coaland coke properties [14]. The FMR has been a comprehensiveparameter as it considers the reflectance of all grains includingthat of inertinites whose reactivity is known to be rank dependent.The main aim of this study is to investigate the suitability, limita-tion and scope for further improvement of that automated micro-scopic approach in predicting coke strength. Therefore, the FMRparameter was obtained for a wide range of coals from aroundthe globe and related to coke strength, reactivity and micro-tex-ture data. The FMR parameter was modified to develop a new coalquality index which considered the reflectance contribution of allmaceral groups and the contribution of inorganic matter of coal.The combined coal index is shown to improve the coke strengthprediction.

Table 1Proximate, ultimate and oxide analysis of coals.

Coal samples

AC1 AC2 AC3 AC4 AC5

Proximate analysis (db%)Moisture % 0.57 0.63 0.90 0.57 0.81Ash yield % 10.26 9.55 9.19 11.90 6.74Volatile % 16.70 20.17 22.82 25.31 31.58Fixed carbon 72.46 69.64 67.08 62.22 60.87

Ultimate analysis (db%)Carbon % 91.09 91.77 90.38 89.00 87.39Hydrogen % 4.90 5.04 5.37 5.09 5.69Nitrogen % 2.01 2.05 2.25 2.00 2.26Sulfur % 0.65 0.60 0.54 0.53 0.64Oxygen % 1.36 0.54 1.46 3.38 4.02

Oxide analysis (wt.%)SiO2 57.5 69.9 54.4 53.4 52.6Al2O3 30.3 23.3 32.1 25.6 37.6TiO2 1.6 1.3 1.5 1.3 1.9Fe2O3 5.7 2.2 4.6 8.5 3.4CaO 1.6 0.9 2.1 4.4 1.5MgO 0.6 0.4 0.7 1.0 0.4K2O 1.0 0.9 1.3 1.4 0.9Na2O 0.3 0.4 1.3 0.5 0.5SO3 0.6 0.3 0.3 2.6 0.2P2O5 0.8 0.4 1.6 1.3 0.9SiO2/Al2O3 1.90 3.00 1.70 2.09 1.40MBIa 1.29 0.62 1.38 3.18 0.73

a Mineral Basicity Index (MBI) = 100 * ash content (%) * (Fe2O3 + CaO + MgO + K2O + N

2. Experimental

2.1. Sample selection

Seven Australian, two Canadian, one Chinese and one Russiancoals were selected on the basis of rank variation. Proximate, ulti-mate analysis and oxide analysis of the coals is provided in Table 1.

2.2. Semi-automated reflectance measurements

Semi-automatic full maceral reflectogram (FMR) of each coalwas measured at CSIRO, Brisbane. Coals crushed with in a sizerange from �212 lm or �90 lm were used to produce grainmount samples with a packing density of about 30% coal and70% mounting resin for microscopic analysis. In order to avoid pos-sible artifacts associated with subsurface particles, a soft polyesterresin containing a special red dye was used to prepare the grainmount samples. After curing, the blocks were cut perpendicularto the settling plane. After polishing, the cut surface was used asspecimen for acquiring reflectance data. Full phase reflectogramswere automatically produced for each coal using an 8-bit CCD cam-era attached to a Carl Zeiss Axiophot microscope. The imaging sys-tem was setup to provide monochromatic illumination at 546 nm(green light) and calibrated for intensity using reflectance stan-dards. Each digital image represented an area of approximately268 � 256 lm2 and had a resolution of 512 � 512 pixels and 256gray levels. Each pixel represented approximately 0.25 lm2 ofthe original coal surface and each image covered up to65,536 lm2. The field of view covered by an image was much lar-ger compared to the spot area (up to 25 lm2) often used in case ofmanual point count analysis as detailed elsewhere [14,15]. Fig. 1illustrates typical reflectance curve of a coal sample. An experi-mentally determined correlation between mineral abundanceand ash value can be used to determine the dark mineral compo-nent of the reflectogram, which gives the FMR information forthe maceral constituents. However, present paper aims to use

AC6 AC7 CA1 CA2 CC1 RC1

0.95 0.75 0.64 0.93 0.72 0.529.87 9.68 8.34 9.45 10.97 9.10

31.70 35.56 17.58 23.15 25.79 18.0157.47 54.01 73.44 66.47 62.52 72.37

84.46 85.55 93.40 91.31 89.00 93.495.52 6.01 4.73 5.12 5.31 4.821.90 2.19 0.98 1.26 1.55 0.750.52 0.53 0.50 0.46 1.21 0.227.60 5.72 0.39 1.85 2.94 0.72

72.0 50.2 60.4 59.1 45.8 45.819.2 28.7 26.5 30.0 39.2 33.1

1.0 1.5 1.3 1.6 1.8 1.44.1 5.7 2.5 2.7 4.3 6.00.9 5.6 3.6 2.7 3.6 5.70.6 1.8 0.4 0.4 0.9 1.81.0 1.1 0.8 0.8 0.4 0.60.4 1.1 0.7 0.1 0.2 0.40.7 3.9 2.0 0.7 3.1 4.70.1 0.4 1.7 1.9 0.8 0.53.75 1.75 2.28 1.97 1.17 1.381.11 2.90 0.94 0.93 1.63 2.04

a2O)/((100 � VM (%)) * (SiO2 + Al2O3)).

Fig. 1. Typical semi-automated curve plotting cumulative frequency againstreflectance.

370 S. Gupta et al. / Fuel 94 (2012) 368–373

reflectance values directly therefore dark minerals and liptinitewere not distinguished. Coal maceral and the FMR values basedon the reflectance measurements are provided in Table 2.

2.3. Coke sample preparation

Coke samples were prepared under similar conditions in a 30 kgmoveable wall test coke oven facility at Pohang at a fixed heatingrate. The test coke oven length, height and width are 400 mm,600 mm, 220 mm respectively. In order to achieve better simulationof industrial operation, the oven is equipped with two doors and onecharge hole on the top such that charging density is about 730 kg/m3. The wooden boxes were used to fix charging amount of coal. Fi-nal coking temperature was set to 1373 K and the oven was electri-cally heated at the rate of 3� per minute for 7 h before pushing cokes.Cokes were quenched in a box with nitrogen. Coke strength dataincluding DI150

15 (referred as DI in this paper) and the CRI/CSR werealso measured at POSCO laboratories. Coke strength data is givenin Table 3.

2.4. Coke micro-texture

Table 4 provides coke texture data. Isotropic and non-isotropictextures were examined using point count analysis at ALS Labora-tories, Brisbane following Australian convention [19]. Table 4 alsoincludes Coke Mosaic Size Index (CMSI) based on different mosaictypes using:

Table 2Coal macerals and FMR values based on semi-automated reflectance measurements.

Coal samples

AC1 AC2 AC3 AC4 AC5

Vitrinite % 72.6 68.1 77.4 45.1 68.3Inertinite % 17.5 25.4 16.9 45.9 23.1Liptinite % 9.5a 6.2a 0.5 3.8 3.4Dark mineral % 4.9 5.0 4.9Bright mineral % 0.4 0.2 0.3 0.2 0.3Rr 1.57 1.30 1.15 0.91 0.82Ro, max 1.67 1.38 1.22 0.96 0.87FMR 165 144 124 124 97

Rr and Ro, max indicate mean random and mean maximum vitrinite reflectance percena Sum of Liptinite and dark minerals.

Table 3Coke strength data.

Coke samples

AC1 AC2 AC3 AC4 AC5

DI or DI15015

� �79.3 80.9 81.8 72.6 79.1

CSR 71.7 73.2 68.5 30.9 55.2

Data based on measurement at POSCO laboratories [22].

CMSI ¼ ðaþ 2bþ 3cþ 4dþ 5eÞ=ðaþ bþ cþ dþ eÞ ð1Þ

In this equation a, b, c, d and e represent percentage of very fine,fine, medium, coarse and elongate domains respectively.

3. Results and discussion

Vitrinite maceral abundance was obtained from the semi-auto-mated reflectance data as detailed elsewhere [14]. Fig. 2 shows thatvitrinite percentage of coals based on the semi-automated micro-scopic is generally lower than the percentage of vitrinite based onmanual point count data. The differences of vitrinite estimates seemto be higher for lower rank coals particularly for those with Ro valueless than 1%. Manual vitrinite estimates of coals CA2 and AC7 is sig-nificantly higher compared to the automated estimates. Comparisonof point count data from three laboratories showed up to 5% stan-dard deviation of vitrinite estimates [22]. This difference can beattributed to the sample preparation, maceral identification stan-dards and the subjectivity of operators. In this paper, all further anal-ysis is based on semi-automated reflectance data.

3.1. Full Phase Maceral Reflectance Parameter (FMR)

A coal parameter based on semi-automated reflectance can becalculated from the full phase reflectance data as detailedelsewhere [14]. Accordingly, each of the 256 reflectance valuewas multiplied by the frequency of the associated coal grains andthen combined to provide a single number to incorporate the con-tribution of individual coal grains [14]. The ‘‘FMR’’ parameter,which includes the rank (vitrinite reflectance) as well as the type(maceral group abundance), is the characteristics of whole coalsample. This is an objective composition parameter and incorpo-rates the reflectance contribution from each coal grain examined.This eliminates the subjective identification of maceral group.The calculated FMR value of all coals is given in Table 2.

Fig. 3 shows that the mean maximum vitrinite reflectance (Ro,max) of coal samples based on automated microscopic data in-creases with increasing FMR parameter. The Ro, max values ofthe coals also indicated a good correlation with the same basedon the manual point count data from the POSCO laboratories notshown in this paper. This shows that automated reflectance datacan reliably assess the coal rank values.

AC6 AC7 CA1 CA2 CC1 RC1

65.5 68.7 55.6 47.5 59.3 80.117.5 12.8 35.2 44.5 32.7 10.211.8 13.3 9.0a 2.8 2.8 9.6a

5.0 5.0 5.1 4.70.2 0.2 0.2 0.1 0.5 0.20.67 0.64 1.47 1.04 1.07 1.600.71 0.68 1.56 1.11 1.13 1.7178 69 167 127 136 161

tage.

AC6 AC7 CA1 CA2 CC1 RC1

74.1 69.7 67.8 80.8 81.4 81.8

21.5 17.8 63.4 69.7 57.9 38.3

Fig. 2. Comparison of coal vitrinite estimates based on semi-automated microscopic and manual point count data. The calculated Ro, max values based on semi-automateddata are also indicated.

Fig. 3. Correlation between Ro, max and FMR and (b) correlation between calculated Ro, max values based on the CSIRO semi-automated measurements and POSCO manualpetrographic data.

Table 4Micro-texture data of cokes.

Coke samples

AC1 AC2 AC3 AC4 AC5 AC6 AC7 CA1 CA2 CC1 RC1

Organic inclusionIsotropic porous % 10.9 12.0 8.9 16.8 6.2 53.6 11.9 9.7 18.2 10.8 8.4Iso. non-porous % 0.4 1.8 1.2 6.6 2.0 1.0 0.2 1.0 1.0 1.8 1.2

Fused carbon domainVery Fine – – – 6.2 71.3 29.8 72.9 1.4 5.6 6.8 0.8Fine – – 1.8 60.2 17.0 9.3 8.1 1.0 25.3 48.2 0.8Medium – 7.8 77.8 5.0 0.2 1.2 1.0 6.3 47.7 19.7 4.2Coarse 7.9 74.5 7.7 – – – – 19.6 0.6 9.8 56.5Elongate 76.8 1.2 – – – – – 56.9 – 0.4 25.9CMSI 4.91 3.92 3.07 1.98 1.20 1.29 1.12 4.52 2.55 2.40 4.20Mineral matter 4.0 2.8 2.6 5.2 3.2 5.2 5.9 4.0 1.6 2.6 2.2

Note: Data is based on measurements at ALS laboratories. AC5 contained 0.2% pyrolitic carbon also.

S. Gupta et al. / Fuel 94 (2012) 368–373 371

The FMR parameter directly correlates with total carbon con-tent (Fig. 4a) and inversely with volatile matter of coals (Fig. 4b).High FMR value is generally indicative of high ordering of carbonstructure (Fig. 4c). Total vitrinite content of coals did not showany correlation with FMR values.

The FMR values of the tested coals were high for low fluidity coalsbut did not show any clear correlation with maximum Gieselerfluidity values (Fig. 5). The FMR parameter did not indicate anysatisfactory correlation with carbonization parameters such as totaldilation, free swelling index and Gieseler maximum fluidity data.

3.2. Association of FMR and coke properties

The FMR parameter did not provide any correlation with fusiblecarbon content of cokes. However, coal FMR parameter indicated a

good correlation with coke mosaic size index (Fig. 6) such that highFMR coal resulted in large CMSI of the tested samples.

Fig. 7a shows the correlation between coal FMR and cold cokestrength (DI150

15 or DI). The DI values of cokes increase with increasingcoal FMR values with the exception of AC4 and CA1 samples. Thesame figure further illustrates that consideration of only Australiancoals (shown as solid circles) improves the correlation with theexception of AC4 sample. This study suggests that medium FMRrange (100–150) of coals leads to high cold strength (DI > 80). TheFMR parameter indicated a similar trend with CSR values of cokes(Fig. 7b). High FMR coals were generally associated with high CSRvalues of cokes indicating a better correlation for Australian coals.

Coal AC4 is relatively lower rank while both CA1 and AC4 arerich in inertinite such that their inertinite to vitrinite ratio is great-er than 0.6. Both rank and inertinite could contribute to lower DI

Fig. 4. FMR parameter vs. carbon content (a), volatile matter (b), and Lc (c) values of coals.

Fig. 5. Maximum fluidity of coal vs. FMR parameter.

Fig. 6. Coal FMR parameter vs. Coke Mosaic Size Index (CMSI).

372 S. Gupta et al. / Fuel 94 (2012) 368–373

and CSR values. The FMR values of both coals are much highercompared to the FMR values of other coals of adjacent ranks (Table2). The exact reason for the higher FMR values of these coals is notclear as in addition to high amounts of inertinite, differences inrank and mineral types particularly pyrite could also influencethe reflectance data of coal grains and hence the FMR values.Therefore, further efforts will focus on clarifying the exact reasons

of high FMR values of certain coals and their association with mea-sured coke strength.

3.3. Combined coal index and coke strength

The FMR parameter was modified by tuning the possible contri-bution of inertinite and inorganic matter on FMR parameter. Coalreflectance data was further processed by using a number of itera-tions such that the reflectance contribution of coal grains in thegray-scale range of 12–62 was decreased to 0.95 and those in thegray-scale range of 91–118 was decreased to 0.6 times of that ori-ginal values. On the basis of known influence of coal mineralogy oncoke strength at high temperatures [23], a small correction forinorganic matter can also be introduced in the FMR parameter. Inpast, effect of ash chemistry on the CSR was related through Min-eral Basicity Index [13]. The impact of inorganic matter of coalswas incorporated into the above corrected FMR parameter bydividing with the square root of Mineral Basicity Index (MBI). Bycombining two types of modifications in the original FMR param-eter as discussed above, a new parameter is proposed which is re-ferred as combined coal index (CCI).

Fig. 8a and b correlate CCI with cold coke strength (DI) and CSRrespectively. These figures clearly illustrate that the new coal indeximproved the correlations with both cold and hot strength of cokeafter reaction except for coal CA1. These Figures shows that as theCCI values exceed 80, DI and CSR exceed more than 80 and 60respectively except coal CA1. It seems the DI value of Canadian coalCA1 unexpectedly low but exact reason is not clear. Fig. 8 furthershows that the correlation is better in case of Australian coals only.Therefore, under similar coking conditions, high CCI coals are ex-pected to provide cokes of high cold and hot strength after reac-tion. This new parameter completely eliminates the need ofsubjective identification of coal maceral or sub-maceral groups, itstill requires validation for a larger set by coals of varying inertiniteand mineralogy. The proposed parameter is expected to be applica-ble to coal blends but would require some additional processingand validation. Subsequent publications will consider the implica-tions of coal blending particularly relating to consequences ofdifferences in geological origins.

Fig. 7. (a) DI vs. FMR and (b) CSR vs. FMR. Australian samples are shown as solid circles.

Fig. 8. (a) Correlation between CCI and DI and (b) CCI and CSR. Australian cokes are shown as solid circles.

S. Gupta et al. / Fuel 94 (2012) 368–373 373

4. Conclusions

A wide range of coals were used to establish a correlation be-tween coal reflectance and coke strength. Coals were characterizedusing automated optical microscopy. Coal petrographic resultsbased on automated microscopy were generally found to be con-sistent with the manual point count data. The FMR parameterbased on reflectance of individual coal grains showed satisfactorycorrelations with coal rank, total carbon content and volatile mat-ter. The FMR parameter was modified to dilute the contribution ofhigh reflectance coal grains as well as oxide ash chemistry of coalsto propose a new combined coal index (CCI). The new index isshown to provide better correlation with both cold and hot cokestrength after reaction. The new coal index eliminates the relianceon subjective and manual identification of macerals and is applica-ble to a wider range of coals. The proposed combined coal indexprovides a promising objective measurement based alternativefor predicting coke strength. The proposed index can be further im-proved by considering the differences in the nature of inertiniteand mineralogy of similar rank coals from different geologicalorigins.

Acknowledgements

We acknowledge the financial support provided by POSCO forthis study and also the permission to publish this work. Authorsalso appreciate the help provided by Karryn Warren from CSIROfor coal analysis.

References

[1] Carpenter AM. Coal classification, technical review. No. CR 12. IEA CoalResearch London; 1988. p. 104.

[2] Ward CR, editor. Coal geology and coal technology, vol. 345. Melbourne:Blackwell Scientific Publications; 1984. p. 23.

[3] Stach E, Mackowsky M, et al. Stach’s textbook of coal petrography. Berlin:Gebruder Borntraeger; 1982. p. 535.

[4] Diessel CFK. Carbonisation reaction of inertinite macerals in Australian coals.Fuel 1983;62(8):883–92.

[5] Ammosov I, Eremin I, Sukhenko S. Calculation of coking charges on the basis ofpetrographic characteristics of coals. Kok Khim 1957;12:9–12.

[6] Schapiro N, Gray RJ. The use of coal petrography in coke making. J Inst Fuel1964;37:234–42.

[7] Kojima K. Prediction of the coke strength coal by coal microscopic analysis. JFuel Soc Jpn 1971;50(12):894–901.

[8] Diez MA, Alvarez R, Barriocanal C. Coal for metallurgical coke production:predictions of coke quality and future requirements for cokemaking. Int J CoalGeol 2002;50(1–4):389–412.

[9] Gray R, Bowling C. Petrographic prediction of coking properties for the curraghcoals of Australia. Int J Coal Geol 1995;27(2–4):279–98.

[10] Diessel CFK, Wolff-Fischer E. Coal and coke petrographic investigations intothe fusibility of Carboniferous and Permian coking coals. Int J Coal Geol1987;9(1):87–108.

[11] Davis A, Kuehn KW, Maylotte DH, St Peters RL. Mapping of polished coalsurfaces by automated reflectance microscopy. J Microsc 1983;132:297–302.

[12] Cloke M, Lester E, Allen M, Miles NJ. Repeatability of maceral analysis usingimage analysis systems. Fuel 1995;74:654–8.

[13] Gransden JF, Jorgensen JG, Manery N, Price JT, Ramey NJ. Applications ofmicroscopy to coke making. Int J Coal Geol 1991;91:77–107.

[14] O’Brien G, Jenkins B. Coal characterization by automated coal petrography.Fuel 2003:1067–73.

[15] O’Brien G, Jenkins B, Ofori P, Ferguson K. Semi-automated petrographicassessment of coal by coal grain analysis. Miner Eng 2007;20:428–34.

[16] Forrest R, Marsh H. Reflection interference colours in optical microscopy ofcarbon. Carbon 1977;15(5):348–9.

[17] Pusz S, Kwiecinska B, Koszorek A, Krzesinska M, Pilawa B. Relationshipsbetween the optical reflectance of coal blends and microscopic characteristicsof their cokes. Int J Coal Geol 2009;77(3–4):356–62.

[18] Sharma R, Dash PS, Banerjee PK, Kumar D. Effect of coke micro-textural andcoal petrographic properties on coke strength characteristics. ISIJ Int2005;45(12):1820–7.

[19] Coin C. Coke microtextural description: comparison of nomenclature,classification and methods. Fuel 1987;66(5):702–5.

[20] Marsh H. Coal carbonization-formation, properties and relevance ofmicrostructures in resultant cokes. In: Proc of 41st ironmaking conf,Pittsburgh, AIME, Iron and Steel Soc.; 1982. p. 2-1.

[21] Kojiro K, Yoshihisa S. A development of automatic coal petrographical analysesfor evaluating coking coals. Tetsu-to-Hagané 1978;64(12):1661–70.

[22] Gupta S, Shen F. Advanced Characterization of Coal Petrography forCokemaking, Final POSCO Project Report. Australia: UNSW Sydney; 2010.

[23] Gupta S, French D, Sakurovs R, Grigore M, Sun H, Cham T, et al. Coke mineralsand ironmaking reactions in blast furnaces. Prog Energy Combust Sci2008;34:155–97.