International Journal of Engineering Trends and Technology (IJETT) – Volume 30 Number 1 - December 2015
Petrographic Analysis and its Relationship with Calorific
Values of Non- Coking Coals, Talcher Coalfield, Orissa
N.Rao Cheepurupalli1, G.V.S.Sharma 2, C.S. Singh3, and E.N. Dhanamaya Rao1
1
Department of Geology, Andhra University, India
Department of Chemical Engineering, Andhra University, India
3
Department of Mining Engineering, IIT (BHU), Varanasi, India
2
Abstract: Coal petrography can be described
broadly as the microscopic determination of the
organic and inorganic constituents of coal and the
degree of metamorphosis, or rank, which the coal
has obtained. Petrographic constituents can be
directly correlated to important properties affecting
the combustion behaviour of coal. In the present
study, the petrographic composition of high ash
Indian non-coking coal samples from Talcher has
been determined and its reactivity has also been
predicted. The relationship between macerals and
GCV of coal samples has been predicted by multivariable regression analysis. It has been found that
coal is having high ash. Petrographic study reveals
that coal is of good quality with low to medium
maceral matter association. It is dominated by
terrestrially derived organic debris (vitrinite and
Inertinite) with low amounts of Liptinite. The
relationship between vitrinite contents and GCV
show strong linear correlation. The inertinite shows
linear correlation with GCV. The liptinite, mineral
matter and ash on the other hand show the reverse
correlation with GCV.
Keywords: Coal Petrology, Non-Coking Coal,
Macerals, Gross Calorific Value, Regression.
I. INTRODUCTION
Energy plays an important role in ensuring industrial
progress which depends on the creation of wealth
and establishment of high standard of living for the
people. Among the fossil fuels, coal plays a vital
role. Coal is a mixture of organic and inorganic
compounds and the proportion of these compounds
varies in different types of coals. Coal originates
from plant remains. The ultimate constituents of
pure coals are the same as those found in plants viz.,
carbon, hydrogen, oxygen, nitrogen and minor
amounts of sulfur and other elements. In order to
explain the various characteristics and properties of
coal which are prerequisite for its utilization in
industries,
ultimate,
proximate,
chemical,
petrographic as well as gross calorific values (GCV)
analyses are to be carried out. The popular method
of proximate analysis of coal have been carried to
determine the moisture content, volatile matter, fixed
carbon, etc., is the first simple step to get an idea of
the constituent compounds. The ultimate or
ISSN: 2231-5381
elemental analysis gives the amounts of carbon,
hydrogen, nitrogen, sulfur, and oxygen in the coal.
Investigations on coal petrology also have a bearing
on the utilization of coal. Organic petrology and
inorganic petrology are independent aspects that
show coal quality. Coal is currently a major energy
source worldwide, especially among many
developing countries, and will continue to be so for
many years by Miller, B.G. 2005. Heating value is
the basis for purchase of coal which indicates the
useful energy content of coal, and thereby its value
as a fuel. Heating value is defined as the amount of
heat evolved when a unit weight of the fuel is burnt
completely and the combustion products cooled to a
standard temperature (Patel, S.U et al. 2007) and
called it gross calorific value (GCV). GCV as a rank
parameter depends on the maceral and mineral
composition of coal (Hower, J.C., & Eble, C.F. 1996;
Chehreh Chelgani, S. et al. 2011). Many equations
have been developed for the estimation of gross
calorific value (GCV) based on proximate analysis
and/or ultimate analysis (Mason, D.M., & Gandhi,
K.N. 1983; Mesroghli, Sh. et al. 2009; Given, P.H.
et al. 1986; Parikh, J., et al. 2005. Majumder, A.K.,
et al. 2008; Chehreh Chelgani, S. et al. 2010). The
chemical composition of coal ash varies widely
depending upon the inorganic and organic
constituents of coal and their association with each
other. Various authors (Thiessen 1945; Francis 1961;
Dixon et al. 1964; Machowsky 1968; Shibaoka 1972;
Stach et al. 1975; Jenkins and Walker 1978;
Yudovich 1978; Berkowitz 1979; Adolphi and Storr
1985; Van der Flier-Keller and Fyfe 1988; Vassilev
et al. 1997) have grouped the ash and inorganic
constituents of coals into different classes as
inherent adventitious (extraneous) and free dirt
syngenetic and epigenetic primary, secondary, and
tertiary, detrital and authigenic or biogenic (plant),
sorption, concretion (diagenetic), chemigenetic,
clastic (detrital) and infiltration (epigenetic). The
source rock types, nature of the coal forming
environment, conditions of burial and degree of
coalification can be interpreted to infer the genesis
of coal (Van der Flier-Keller and Fyfe 1988).
Coal petrography can be described broadly as the
microscopic determination of the organic and
inorganic constituents of coal and the degree of
metamorphosis, or rank, which the coal has obtained.
Petrographic constituents can be directly correlated
http://www.ijettjournal.org
Page 13
International Journal of Engineering Trends and Technology (IJETT) – Volume 30 Number 1 - December 2015
to important properties affecting the combustion
behavior of coal. The term 'maceral' is used to
represent the different plant tissue from which the
coal has originally been formed. Vitrinite, liptinite
and inertinite are the main maceral categories.
Vitrinite, for instance, is derived from cell walls, cell
contents of precipitated gels preserved under water
and is relatively oxygen rich. Algae, spores and
waxy leaves etc. formed liptinite, the hydrogen-rich
maceral type. Inertinite originated from plant tissue
that has been oxidized, altered, degraded or burnt in
the peat stage of coal formation and is carbon rich.
The term microlithotype describes the association of
the macerals and mineral matter. The degree of
"mixing" of the macerals and the nature of the
minerals bound within the coal will influence its
mechanical and technological properties. A coal
with higher inertinite content will burn with a longer
flame at higher temperatures while a vitrinite rich
coal will burn more quickly and produce a shorter
flame (Sanyal, A. 1983, Yakorskii et al. 1968) They
reported that combustibles in ash were largely the
result of incompletely burnt inertinite particles. A
large number of research work have been done on
the
different
chemical,
petrographic
and
mineralogical parameters in the world. Detailed
and/or summarised data on bulk composition, as
well as some comparative similarities or differences
in common characteristics for various coals have
been reported (Francis, 1961; Berkowitz, 1979;
Bouska, 1981; Finkelman, 1981; Stach et al., 1982;
Kler et al., 1987; Korobetskii and Shpirt, 1988;
Swaine, 1990; Diessel, 1992; Taylor et al., 1998;
Querol et al., 2001a; Goodarzi, 2002; Shao et al.,
2003; Lopez and Ward, 2008; Belkin et al., 2009).
Gondwana coals have a moderate vitrinite content
(< 60 %), low liptinite contents and high inertinite
content (> 40 %). On the other hand North east
Indian Tertiary coals usually have a high vitrinite
content (80 % average), with the non-vitrinite
fraction being predominantly inertinite. The liptinite
content is usually < 20 %. Vitrinite and liptinite are
reactive. They enhance the rate of combustion.
Inertinite has a low reactivity, which retards
combustion. Gondwana coals are inherently less
reactive and also frequently have higher ash contents
than other Indian coals. This paper deals with a
characterization of coal samples from Talcher
coalfields, which include proximate, ultimate,
petrographic analysis and GCV. The relationship
between maceral contents and Gross Calorific Value
(GCV) of coal samples have been investigated by
multi-variable regression analysis.
II. SAMPLING
The samples have been collected from Talcher
coalfields, Mahanadi Coalfield Ltd., a subsidiary of
Coal India Ltd. It has been collected from working
ISSN: 2231-5381
benches with the help of a Haul pack and Payloader,
by scraping the entire cross section of the bench at
various places and thoroughly mixed. After proper
and thorough mixing, samples have been brought to
the laboratory for studies.
A. Chemical analysis
The bulk samples from the field are air dried and
reduced to 0.5 kg by coning and quartering
method. The coal samples are analysed
(proximate, and ultimate). The samples for
proximate, ultimate and Petrographic analyses
are pulverized to less than -200 mesh size and
dried for 12 hour in a dessicator. Proximate
analysis of the coal sample has been made by
standard methods (IS: 1350, Part I-1984). Perkin
Elmer elemental analyzer (model 2400) has been
used to analyze carbon, hydrogen and nitrogen.
The standard method is explained in (IS: 1350,
Part II-2000). The Calorific Values of coal
samples have been determined by using
Automatic Bomb Calorimeter (LECO AC-350).
Total sulphur has been determined by using
sulphur determinator (Leco, SC132) and oxygen
percentage calculated by difference. The results
of the analysis are shown in table 1.
B. Maceral Analysis
Petrographic analysis and the identification of
maceral types were done according to standard
procedures (Stach et. al., 1975). For this purpose
coal samples were crushed to obtain ±18 mesh
(<1 mm) size fraction to prepare polished mounts
or pallets. A mixture of hardener and Canada
wax or coal mounting resin in the ratio 1:5 is
used for embedding the coal samples for pellet
preparation, followed by their grinding and
polishing. Microscopic observation has been
made on Leica DMLP microscope under both
white incident light as well as fluorescence mode.
The microscope is provided with 10x ocular and
10x dry lens objective. Recommendations of
Stach et al. (1982) and ICCP (1963, 1971, 1975,
1998) have been followed for data collection and
interpretation and the volume percent of various
macerals are calculated using a swift point
counter. The maceral analysis of coal samples
are given in Table 2.
C. Statistical Analysis
The elements were subjected to univariate
(minimum, maximum, mean, standard deviation,
and skewness) statistical analysis with the aid of
the SPSS 16.0 for Windows (SPSS Inc., Chicago,
IL, USA, 2007). The regression analysis also
done for macerals relationship with gross
calorific value and measures how well the
http://www.ijettjournal.org
Page 14
International Journal of Engineering Trends and Technology (IJETT) – Volume 30 Number 1 - December 2015
variance of each constituent can be explained by
relationship with each other.
III. RESULTS AND DISCUSSION
The proximate analysis of coals is presented in
Table 1. It has been observed from the table 2
that there are major variations in the moisture,
ash, volatile matter and fixed carbon contents in
the coals of study area. The moisture content in
the coals of the study area ranges from 2.9% to
7.6% (mean 5.04%), volatile matter ranges from
19% to 24% (mean 21.57%), fixed carbon
content varies from 16.6% to 27.5% (mean
21.3%) and ash content from 42.7% to 61.5%
(mean 51.86%). High ash contents in these coals
indicate relatively slow burial and transportation
of long distance of vegetative matter. The
variation in the volatile matter contents may be
due to the compounds released from organic and
mineral matter in coals.
The results of ultimate analysis and ash fusion
test have also been described in table 2. The coal
contains less C (23.9% to 38.44%, average
30.49%), relatively low H (1.98% to 3.60%,
average. 2.81%), low nitrogen (0.70% to 1.14%,
average 0.927%), sulfur (0.31 to 0.54%, average
0.438%), oxygen (11% to 15.20%, average
13.469%) and O+N combined comprise 14.39%.
The atomic H/C and O/C ratios have also been
determined which is indicative of humic nature
of coal. Thermal value is amount of heat
produced by burning one kilogram of coal. It has
been measured by calorimeter. The minimum
Calorific value is 2110 Kcal/Kg and maximum
calorific value is 3600 Kcal/Kg (average 2823
Kcal/Kg).
Petrographic studies on the samples from each
source were carried out as this might play an
important role in the combustion of coals and the
data as shown in Table 2. The results shows
relatively a medium vitrinite (18.6 -32 %, av.=
23.987 %), medium inertinite (22.28-27.97%,
av.= 25.22 %), and low liptinite (6.8-11.7%, av.=
9.41%) contents.
The univariate statistics, of the petrographic
analysis in terms of their mean, minimum,
maximum, skewness and standard deviation has
been computed and presented in the table 3. It
has been observed that the mean value of
Vitrinite, Liptinite, Inertinite, mineral matter,
Mean Ro and ash is 23.987%, 9.41%, 25.228%,
41.375%, 0.445% and 51.86% respectively. The
skewness value is negative except vitrinite.
Regression Analysis:
Regression analysis is a statistical tool that is
used to investigate the relationships between
ISSN: 2231-5381
variables. The investigator assembles data on the
underlying variables of interest and employs
regression analysis to estimate the quantitative
effect of the causal variables upon the variable
that they influence. The investigator also
typically assesses the statistical significance of
the estimated relationships, that is, the degree of
confidence that the true relationship is close to
the estimated relationship by Sykes, A. O. (1993).
The organic constituents (macerals) singly, and
particularly in combination, have a fundamental
influence on coal properties. Vitrinite and
liptinite are both reactive; they enhance the rate
of combustion. Inertinite has a low reactivity,
which retards combustion. The GCV of the coal
samples is found to be 2110-3600 Kcal/kg. The
coal seams of Talcher coal field are usually of
high moisture, medium to high volatiles, high
ash non-coking type and they are dull in
appearance.
Relationships between Macerals and GCV:
The Talcher coals of different samples of Orissa
region are characterized by relatively a medium
vitrinite (18.6 -32 %, av.= 23.987 %), medium
inertinite (22.28-27.97%, av.= 25.22 %), and low
liptinite (6.8-11.7%, av.= 9.41%) contents.
The inter-relationships of vitrinite, inertinite,
liptinite, mineral matter and ash with GCV of
coals have been plotted and shown (figs. 1 to 5).
It has been observed that the relationship
between vitrinite contents and GCV appears to
provide a good measure of coal rank or maturity.
The vitrinite content of Talcher coals shows a
linear trend as indicated by its linear correlation
with the Calorific value (R2=0.894) indicating its
increasing nature with the rise of vitrinite content.
The relationship of liptinite with GCV is
significant as it is having low H with less H/C
ratio and has lowest individual calorific value
followed by vitrinite and inertinite. Thus, in this
investigation vitrinite show strong linear
correlation with GCV (R2=0.894,). The inertinite
shows linear correlation with GCV (R2 = 0.617)
and goodness of fit is <80%. The liptinite,
mineral matter and ash on the other hand shows
the reverse correlation with GCV (R2=0.306,
R2=0.966 and R2=0.989 respectively).
IV. CONCLUSION
It has been observed that coal is having high ash
indicating relatively slow burial and long
transportation of vegetative matter. The atomic
H/C and O/C ratios are indicative of humic
nature of coal. Petrographic study reveals that
coal is of good quality with low to medium
maceral matter association. It is dominated by
http://www.ijettjournal.org
Page 15
International Journal of Engineering Trends and Technology (IJETT) – Volume 30 Number 1 - December 2015
terrestrially derived organic debris (vitrinite and
Inertinite) with low amounts of Liptinite. The
inter-relationship between vitrinite contents and
GCV appears to provide a good measure of coal
rank or maturity. The vitrinite show strong linear
correlation with GCV (R2=0.894,). The inertinite
shows linear correlation with GCV (R2 = 0.617)
and goodness of fit is <80%. The liptinite,
mineral matter and ash on the other hand shows
the reverse correlation with GCV (R2=0.306,
R2=0.966 and R2=0.989 respectively). The
results were shown that coal petrographic
analysis (macerals) can be used as predictors of
GCV successfully.
V. ACKNOWLEDGEMENT
Authors are thankful to the management of
Talcher coalfield, Mahanadi Coalfields Limited
(MCL) for providing necessary arrangements in
conducting the study. They also thankfully
acknowledge to the Director, Central Institute of
Mining & Fuel Research, Dhanbad and Head,
Department of Mining Engineering, IIT (BHU),
Varanasi for giving permission to conduct
laboratory studies.
Table 1: Proximate, Ultimate and GCV analysis of coals
Proximate Analysis
Ultimate Analysis
Fixed
Samples M% Ash % VM % Carbon % C %
H% N%
Coal-1
4.4
57.5
19.3
18.8
25.45
1.98 0.7
Coal-2
4.5
55.3
20.5
19.7
28.79
2.3
0.84
Coal-3
4
52.2
20.3
23.5
29.9
2.57 0.86
Coal-4
2.9
61.5
19
16.6
23.9
2.34 0.78
Coal-5
3.7
51.2
21.2
23.9
31.13
2.82 1.09
Coal-6
5.7
42.7
24.1
27.5
38.44
3.5
0.94
Coal-7
5.3
55.8
21.1
17.8
27.63
2.25 0.89
Coal-8
7.2
44.2
24
24.6
35.48
3.6
1.14
Coal-9
5.1
47.7
24.3
22.9
34.33
3.26 1.04
Coal-10 7.6
50.5
21.9
20
29.91
3.48 0.99
Average 5.04 51.86
21.57
21.53
30.496 2.81 0.927
Table 2: Petrographic analysis of coal
Vitrinite Inertinite Liptinite
Samples %
%
%
19.4
7.4
27.97
Coal-1
20.97
8.6
26.02
Coal-2
22.9
10.5
24.06
Coal-3
18.6
6.8
27.6
Coal-4
26.6
9.1
22.28
Coal-5
32
10.6
22.71
Coal-6
22.7
7.7
25.16
Coal-7
28.6
10.3
26
Coal-8
24.3
11.7
26.38
Coal-9
11.4
24.1
Coal-10 23.8
Average 23.987
9.41
25.228
Mineral
Matter %
45.23
44.41
42.54
47
42.02
34.69
44.44
35.1
37.62
40.7
41.375
S%
0.46
0.43
0.54
0.52
0.47
0.46
0.41
0.4
0.31
0.38
0.438
O%
13.9
12.3
13.9
11
13.29
14
13
15.2
13.4
14.7
13.469
O/C
0.55
0.43
0.46
0.46
0.43
0.36
0.47
0.43
0.39
0.49
0.44
H/C
0.08
0.08
0.09
0.10
0.09
0.09
0.08
0.10
0.09
0.12
0.09
Mean
Ro %
0.48
0.48
0.46
0.49
0.42
0.41
0.46
0.41
0.42
0.42
0.445
(Mineral matter free basis, %)
ISSN: 2231-5381
http://www.ijettjournal.org
Page 16
GCV
(Kcal/Kg)
2370
2540
2710
2100
2930
3600
2520
3390
3190
2880
2823
International Journal of Engineering Trends and Technology (IJETT) – Volume 30 Number 1 - December 2015
Table 3: Statistical analysis of petrographic data and ash.
Vitrinite %
Liptinite %
Inertinite %
Mineral
Matter %
Mean Ro %
Ash %
Mean.
23.987
9.41
25.228
41.375
0.445
51.86
Skew.
0.678
-0.209
-0.159
St.Dev.
4.143
1.736
1.925
Min.
18.6
6.8
22.28
Max.
32
11.7
27.97
-0.538
0.215
-0.078
4.288
0.032
5.910
34.69
0.41
42.7
47
0.49
61.5
Fig 1: Relationship of Vitrinite with GCV
Fig 2: Relationship of Inertinite with GCV
ISSN: 2231-5381
http://www.ijettjournal.org
Page 17
International Journal of Engineering Trends and Technology (IJETT) – Volume 30 Number 1 - December 2015
Fig 3: Relationship of Liptinite with GCV
Fig 4: Relationship of Mineral Matter with GCV
ISSN: 2231-5381
http://www.ijettjournal.org
Page 18
International Journal of Engineering Trends and Technology (IJETT) – Volume 30 Number 1 - December 2015
Fig 5: Relationship of Ash with GCV
REFERENCES
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
Adolphi, P and Storr, M. 1985. Glow discharge excited low
temperature ashing; Fuel 64 151–155.
Belkin, H.E., Tewalt, S.J., Hower, J.C., Stucker, J.D.,
O’Keefe, J.M.K. 2009. Geochemistry and petrology of
selected coal samples from Sumatra, Kalimantan, Sulawesi,
and Papua, Indonesia. International Journal of Coal Geology
77, 260–268.
Berkowitz, N. 1979. An Introduction to Coal Technology.
Academic Press Inc., London. 345 pp.
Bouska, V. 1981. Geochemistry of Coal. Academia, Prague,
284 pp.
Chehreh Chelgani, S., Hart, B., Grady, W.C., & Hower, J.C.
2011. Study relationship between inorganic and organic coal
analysis with Gross Calorific Value by multiple regression
and ANFIS. International Journal of Coal Preparation and
Utilization, 31, 9-19.
Chehreh Chelgani, S., Mesroghli, Sh., & Hower, J.C.
(2010). Simultaneous prediction of coal rank parameters
based on ultimate analysis using regression and artificial
neural network. International Journal of Coal Geology,
83(1), 31- 34.
Dixon, K., Skipsey, E and Watt, J, T. 1964. The distribution
and composition of inorganic matter in British coals. Part
1:Initial study of seams from the east midlands division of
the national coal board; J. Inst. Fuel 37 485–493.
Diessel, C.F.K. 1992. Coal Bearing Depositional Systems,
Springer, Berlin, 721 pp.
Finkelman, R. 1981. Modes of Occurrence of Trace
Elementsin Coal. U.S. Geological Survey, Open-File Report
81–99, 322 pp.
Francis, W. 1961. Coal. Its Formation and Composition.
Edward Arnold Ltd., London, 806 pp
Goodarzi, F. 2002. Mineralogy, elemental composition and
modes of occurrence of elements in Canadian feed-coals.
Fuel 81, 1199–1213.
Given, P.H., Weldon, D., & Zoeller, J.H. 1986. Calculation
of calorific values of coals from ultimate analyses:
theoretical basis and geochemical implications. Fuel, 65,
849–854.
Hower, J.C., & Eble, C.F. 1996. Coal quality and coal
utilization. Energy Miner. Div. Hourglass, 30 (7), 1–8.
ICCP, 1998, The new vitrinite classification (ICCP System
1994): Fuel v. 77, p. 349-358.
ICCP, 2001, The new inertinite classification (ICCP System
1994): Fuel v. 80, p. 459-471.
ISSN: 2231-5381
16. I.S. (Indian Standard): 1350 (Part-I) (1984). Methods of
Test for Coal and Coke: Proximate Analysis, Bureau of
Indian Standards, New Delhi. pp. 3-28.
17. I.S. (Indian Standard): 1350 (Part II) (2000). Methods of
Test for Coal and Coke: Determination of Calorific Value,
Bureau of Indian Standards, New Delhi. pp. 3-24.
18. Jenkins ,R, G and Walker P, L .1978. In analytical methods
for coal and coal products; vol-II (ed.) Karr CJ Jr, Academic
Press, New York, 265p.
19. Kler, V., Volkova, G., Gurvich, E., Dvornikov, A., Jarov,
Y., Kler, D., Nenahova, V., Saprikin, F., Shpirt, M. 1987.
Metallogeny and Geochemistry of Coal and Shale Bearing
Stratum in USSR: Geochemistry of Elements. Nauka,
Moscow, 240 pp. (in Russian).
20. Korobetskii, I & Shpirt, M. 1988. Genesis and Properties of
the Coal Mineral Components. Nauka, Novosibirsk, 227 pp.
(in Russian).
21. Lopez, I.C., Ward, C.R. 2008. Composition and mode of
occurrence of mineral matter in some Colombian coals.
International Journal of Coal Geology 73, 3–18.
22. Miller, B.G. 2005. Coal Energy Systems, Elsevier Academic
Press, ISBN: 0- 12-497451-1, USA.
23. Mason, D.M., & Gandhi, K.N. 1983. Formulas for
calculating the calorific value of coal and chars. Fuel
Process. Technol, 7, 11–22.
24. Mackowsky, M. 1968. Mineral matter in coal. In:
Murchison, D., Westall, T. (Eds.), Coal and Coal-bearing
Strata. American Elsevier, New York, 309–321.
25. Mesroghli, Sh., Jorjani, E., & Chehreh Chelgani, S. 2009.
Estimation of gross calorific value based on coal analysis
using regression and artificial neural networks. International
Journal of Coal Geology, 79, 49–54.
26. Majumder, A.K., Jain, R., Banerjee, J.P., & Barnwal, J.P.
2008. Development of a new proximate analysis based
correlation to predict calorific value of coal. Fuel, 87, 3077–
3081.
27. Patel, S.U., Kumar, B.J., Badhe, Y.P., Sharma, B.K., Saha,
S., Biswas, S., Chaudhury, A., Tambe, S.S., & Kulkarni,
B.D. 2007. Estimation of gross calorific value of coals using
artificial neural. Fuel, 86 (3), 334-344.
28. Parikh, J., Channiwala, S.A., & Ghosal, G.K. 2005. A
correlation for calculating HHV from proximate analysis of
solid fuels. Fuel, 84, 487–494.
29. Querol, X., Alastuey, A., Zhuang, X., Hower, J.C., LopezSoler, A. 2001a. Petrology, mineralogy and geochemistry of
the Permian and Triassic coals in the Leping area,Jiangxi
Province, southeast China. International Journal of Coal
Geology 48, 23–45.
http://www.ijettjournal.org
Page 19
International Journal of Engineering Trends and Technology (IJETT) – Volume 30 Number 1 - December 2015
30. Shibaoka, M. 1972. Silica/alumina ratios of the ashes from
some Australian coals. Fuel 51, 278–283.
31. Stach, E., Mackowsky, M., Teichmuller, M., Taylor, G.,
Chandra, D., Teichmuller, R. 1982. Stach’s Textbook of
Coal Petrology. Gebruder Borntraeger, Berlin, 535 pp.
32. Swaine, D. 1990. Trace Elements in Coal. Butherworths,
London, 296 pp.
33. Sykes, A. O. 1993. An Introduction to regression analysis.
34. Sanyal, A. 1983. The role of coal macerals in combustion.
Journal of the Institute of Energy, p 9
35. Shao, L., Jones, T., Gayer, R., Dai, S., Li, S., Jiang, Y.,
Zhang, P. 2003. Petrology and geochemistry of the highsulphur coals from the Upper Permian carbonate coal
measures in the Heshan Coalfield, southern China.
International Journal of Coal Geology 55, 1–26.
36. Stach E, Mackowsky M-Th, Teichmiiller M, Taylor G H,
Chandra G and Teichmiiller R 1975 Stach’s Textbook of
Coal Petrology; 2nd edn, Berlin: Gebriiden Borntraeger,
428p.
37. Thiessen, G. 1945. Composition and origin of mineral
matter in coal; In: Chemistry of Coal Utilization (ed.)
ISSN: 2231-5381
38. Lowry H H (New York: John Willey & Sons, Inc.) 1 485–
495.
39. Taylor, G.H., Teichmuller, M., Davis, A., Diessel, C.F.K.,
Littke, R., Robert, P. 1998. Organic Petrology, Gebruder
Borntraeger, Berlin – Stuttgart, 704 pp.
40. Van der Flier-Keller, E and Fyfe, W, S. 1988. Mineralogy of
Lower Cretaceous coals from the Moose River Basin,
Ontario, and Monkman, British Columbia; Can. Mineral 26
343–353.
41. Vassilev, S., Vassileva, C. 1997. Geochemistry of coals,
coal ashes and combustion wastes from coal-fired power
stations. Fuel Processing Technology 51, 19–45.
42. Yarkorskii. Et. al. 1968. Influence of the petrographic
composition of coal on the efficiency of fired furnaces
Teploenergetika.
43. Yudovich, Ya, E. 1978. Geochemistry of Fossil Coals;
Science Publ. House, Moscow: Nauka, Leningrad; 262p.
http://www.ijettjournal.org
Page 20