1. business and industrial
2. energy
3. coal

Journal of Science and Technology
1 (2), 2007, 167-183
©BEYKENT UNIVERSITY
DETERMINING THE SIZE OF LINEAR
STOCKPILE FOR THE AFŞİN - ELBİSTAN (B)
POWER STATION (TURKEY)
Suphi URAL
Çukurova University, Department of Mining Engineering, Adana, Türkiye,
[email protected]
ABSTRACT
The aim of this study is to show how to estimate optimum size of a linear coal
stockpile for the Afşin - Elbistan (B) power station using geostatistical
modeling techniques. After a description of the geology of the area a
geostatistical study based on 2968 core samples of 193 drill holes is presented.
The coal samples present a symmetric frequency distribution. Experimental
variograms representing three main directions - strike plunge, cross-dip and
downdip directions - are calculated and a simple spherical-type model is fitted
to the variograms. The model parameters are validated. Storages capacities for
buffering and homogenisation are estimated taking into account of longer
operation standstill of the Çöllolar mine and daily fluctuations of quality
parameters. In order to estimate the storage capacity of stockpile and number
of stacking layers, block variance of varies size blocks are calculated utilising
F chart. Finally, numbers and dimensions of prismatic type linear stockpile
bunkers are determined.
Key Words: Afşin-Elbistan, geostatistics, coal, stock pile, block variance
ÖZET
Bu çalışmanın amacı, jeoistatistiksel modelleme teknikleri kullanılarak bir
kömür stok sahasının optimum boyutlarının nasıl kestirilebileceğini
göstermektir. Çalışmada, Afşin-Elbistan (B) termik santralinde, lineer bir
kömür stok sahasının stoklama kapasitesi ve boyutlandırılmasına ilişkin olarak
yapılan çalışmalar anlatılmıştır. Bölgenin jeolojik yapısı tanıtıldıktan sonra,
193 adet sondaj kuyusundan sağlanan 2968 adet karot numunesine ilişkin
veriler esas alınarak sahanın jeoistatistik modeli oluşturulmuştur. Üç ana
yönde de deneysel variogramlar ve bu variogramlara uygun modeller
seçilmiştir. Çöllolar maden işletmesinde ortaya çıkabilecek uzun süreli
duruşlar, kömür harmanlama gerekleri ve kalite kontrolüne ilişkin kısıtlarda
167
Determining The Size Of Linear Stockpile For The Afşin - Elbistan (B) Power Station
(Turkey)
dikkate alınarak, stok sahasının kapasitesi kestirilmiştir. Bu hesaplamalarda,
farklı büyüklüklere sahip blokların varyansları F kartları kullanılarak
hesaplanmıştır. Son olarak stok sahasındaki bunker sayıları hesaplanmış ve
bunlar boyutlandırılmıştır.
Anahtar kelimeler: Afşin-Elbistan,
varyansı
jeoistatistik,
kömür ,stok sahası , blok
1. INTRODUCTION
The targets of the mining-technical planning are to guarantee that the power
station operations will have to be stopped at no time due to lack of fuel,
fluctuations with regard to volume and quality of the product should be
balanced and the total calorific value of mining field should be used as fully as
possible by blending the coal. Stockpile with its capacity for long-term low
frequency quality variation filtering has been widely accepted as a key method
of raw coal homogenization. It has dual role within the raw coal preparation
system, being a buffer storage unit and capability for homogenization of raw
coal if properly designed and operated. The purpose of the present paper is to
estimate the size of linear stockpile for the Afşin - Elbistan (B) power station.
The Afşin-Elbistan lignite deposit with its 3.4 billion metric tons of reserve is
the biggest lignite basin and one of the most important resources for electrical
energy production in Turkey [10]. The Elbistan lignite is best suited to use in
thermal power generation and are capable of supplying 16 x 340 MW power
stations. The lignite deposit is located north of Elbistan, Kahramanmaras
Province, in southeast Turkey (Figure 1). The Çöllolar mining field is the
second open cast mine to feed Afsin-Elbistan (B) power station with the
installed capacity of 1376 MW. Afsin-Elbistan (A) power station has been
under operation since 1984 [11].
Figure 1. Geographic location of study area
168
Suphi Ural
The Elbistan basin encompasses an area of 900km , and its centre lies about
1150m above mean sea level. The sedimentation took place mainly in the
upper Pliocene period. The Collolar sub-basin of the Afsin-Elbistan basin is a
multi-layer lignite deposit with associated clays and gyttja partings. The
thickness of the coal bed varies from 60m to 80m respectively. The basal part
of the coal beds consists of clay, marl and sandy clay. These formations are
overlain by gyttja, which may also occur in the form of calcareous ooze,
enveloping coal beds. The thickness of the gyttja is up to 80m. Alluvium,
occurring very widespread along Hurman Stream, consists of gravels, sands
and silt [5]. Lithological profile of the Collolar field is presented in Figure 2.
Figure 2. Lithological profile of the Collolar coal -field
2. PRESENTATION OF THE DATA SET
The information available comes from 193 holes drilled vertically on about a
200m to 250m grid (Figure 3). The coordinates and the number of the hole,
and the results of the analyses for raw coal are supplied. The raw coal had been
analysed for moisture content, ash content and calorific value. Core recoveries
in the lignite are between 92% and 96% with an average of 94%.
169
Determining The Size Of Linear Stockpile For The Afşin - Elbistan (B) Power Station
(Turkey)
47000^
46000
N
45000^
t
44000^
43000^
42000^
41000^
21000
22000
23000
24000
25000
26000
27000
28000
29000
Figure 3. Drill hole location map of Çöllolar coal-field
The deposit is made up of a great many layers of coal and intercalation. In this
case the question of defining what is to be considered as "coal" and what is the
"waste" is very real. This choice is governed by the technical constraints
imposed by the mining method used. To be mineable, a coal seam must be at
least 50cm thick. Similarly any intercalation less than 25cm thick is mined
with the coal. These two constraints are applied to each drill hole in turn to
distinguish mineable coal from waste. Figure 4 illustrates this procedure for a
typical drill hole. In the first step the thicknesses of all contiguous coal seams
are summed. Any coal seams that are too thin to be mined are then redefined
as waste. Lastly any narrow bands of waste sandwiched in between exploitable
coal seams are included with the coal.
Since the coal is destined for power generation the variables of interest include
calorific value and ash content. The core samples are composited into a
constant length of 5m from sample lengths that varied from 0.5m to 5m. The
basic statistics of the variables to be studied is calculated in order to check
outliers (extremely large or small values) or nonhomogeneous population. The
histograms of the variables show that they present nearly normal distribution.
2968 composite samples have a mean of 1167 kcal/kg calorific value and
37.2% ash content in dry coal (Figure 5).
170
Suphi Ural
ORIGINAL
DRILL HOLE
MINING
DRILL HOLE
Waste
Coal
Waste< 0 5
m
Coal
1
1
1
1
Coal
Waste
Waste
Coal< 0.5 m
Waste
Figure 4. Procedure for calculating the quantities of coal and waste in a drill hole
Total number of samples: 2968
Mean: 1167; Variance: 99225
Total number of samples: 2968
Mean: 37.2; Variance: 42.25
14
12 n
12
10 8 6 4
2
0
İlli JUL
»il
CN
CN
„ojlil
E
CO CO
Ash content in dry basis (%)
Calorific value in raw coal (kcal/kg)
(a)
(b)
Figure 5. Histograms of variables: (a) ash content; (b) calorific value
171
Determining The Size Of Linear Stockpile For The Afşin - Elbistan (B) Power Station
(Turkey)
3. VARIOGRAM ANALYSIS AND MODELING
Geostatistical theory is based on the observation that the variability of such
quantities as grade and thickness has a particular spatial structure. The grades
z(x) and z(x+h) at point x and x+h are correlated; this spatial correlation
depends on the vector h, which separates the two points and decreases as the
distance I h I increases. Beyond a particular distance the two quantities are no
longer correlated. The spatial variability can also differ from one direction to
another [7]. A characteristic behaviour or structure of the spatial variability can
be discerned behind a locally erratic aspect. Geostatistics consider these
variables as regionalized variables and both the random and structured aspect
of the regionalized variables are expressed in the probabilistic language of
random functions, every point of the deposit defines a random variable and the
value at that point is interpreted as a realization of that random variable. A
random function is seen as a set of random variables defined at each point in
the deposit and a regionalized variable is a realization of the random function.
In geostatistics the spatial variability of a regionalized variable is characterized
by the variogram function, 2y(h) [6].
2y(h) = Var[Z(x) - Z(x+h)];
where h is the separation vector, y(h) is the semi-variogram and Z(x) is a
random variable defined at point x. The variogram defines a few parameters
like the zone of influence in all directions (which is named range, a), sampling
error (which is named nugget effect, C0) and the variance of the samples
(which is named sill, C) and is used in estimating an unknown value of the
regionalized variable under study. Experimental variograms and model
variograms of the variables of calorific value and ash content are seen in
Figure 6 and Figure 7. The first experimental variogram of calorific value is
computed for the downdip direction (Figure 6 (a)); this variogram shows a
range of around 40m with a nugget effect value 2400 kcal/kg. The
experimental variograms cross dip (Figure 6 (b)) and in strike-plunge (Figure 6
(c)) indicate the existence of geometric anisotropy, and the ranges in these
directions are 750m and 500m, respectively. The following three-dimensional,
simple, spherical type of variogram model is adopted
y(h) = 22080
y(h) = 0
h > a())
h = a(9)
172
Suphi Ural
The experimental variogram of ash content for the downdip direction (Fig.
7(a)); shows a range of around 40m. The experimental variograms cross dip
(Fig. 7(b)) and in strike-plunge (Fig. 7(c)) indicate the existence of geometric
anisotropy, and the ranges in these directions are 682m and 455m,
respectively.
(a)
Experimental variogram
Model
E
™ 30000
<0 d) ^ 20000
S
~
10000
0
0
500
1000
Distance (metres)
(b)
•Experimental variogram
Model
Jw
C
m
a re 40000
r c
o JC 20000
e
0
> rs
0
>
r
a
200 400 600 800 1000 1200
Distance (metres)
(c)
Figure 6. Experimental variograms and models for calorific value: (a) downdip;
(b) cross-dip; (c) strike plunge
173
Determining The Size Of Linear Stockpile For The Afşin - Elbistan (B) Power Station
(Turkey)
-Experimental variogram
( 15
ğ
10-
İ
•g
0
2
10
20
30
40
50
Distance (metres)
(a)
•Experimental variogram
3
e 15
10
>
m
a 5
0
>
0
200
400
600
800
Distance (metres)
(b)
"Experimental variogram
e 15
>
Ü
10
5
0
0
500
1000
1500
Distance (metres)
(c)
Figure 7. Experimental variograms and models for ash content: (a) downdip; (b) crossdip; (c) strike plunge
The following three-dimensional, simple, spherical type of variogram model is
adopted:
y(h) = 10 1.5-
h
a(9)
•-0.5
(
h
I a(G)
Y(h) = 12
^3
+2
h <a(G)
h > a(G)
174
Suphi Ural
y(h) = 0
h = a(d)
The parameters of the variogram model are validated by use of the backestimation technique, in which each sample is removed from the data set in
turn and then estimated from the remaining data and the variogram model.
For conditionally unbiased estimates, the linear regression of actual values on
estimated values should be close to a 45° line-that is, an intercept of zero and a
slope of 1 [4].
The linear regressions of actual values on estimated values are presented in
Figure 8 and Figure 9, respectively. Figures 8 and 9 can be taken as a
verification of the model chosen.
~
t
1600
!
1400 -
t
100
1
Act. Value = 0.889Est. Value +
85.88
R
^
=
W
Ü 1000 -
=
o
<
•,
800 J — ksC?*
'
800
1000
Î
P
^
'
*
1200
,
,
1400
1600
Estimated Value (kcal/kg)
Figure 8. Linear regression of actual values on estimated values for the calorific value
of coal
Act. Value = 0.898Est. Value +
20
30
40
50
Estimated Value (%)
Figure 9. Linear regression of actual values on estimated values for ash content of coal
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Determining The Size Of Linear Stockpile For The Afşin - Elbistan (B) Power Station
(Turkey)
4. STORAGES CAPACITY FOR BUFFERING
The following points favour the use of stockpile as a buffer storages unit: (1)
activities both at the mine and during production need not interact
dynamically; (2) a steady supply of homogeneous raw coal to the power
station is ensured; and (3) reduces demand for complex automation of
subsequent processing stages.
The minimum buffering storages capacity of a power station coal stockpile can
be determined
Qb = T P (SE /LCV) /1000
Vsb = Qb / p
where Qb is stocking capacity of stockpile, tonne; T is duration of
maximum stoppage time for coal production, hour; P is installed capacity
of power station, kW; SE is specific energy consumption of power station,
kcal/kWh; LCV is lower calorific value of raw lignite, kcal/kg; Vsb is
stocking volume of stockpile, m3; and p is average bulk density of the coal,
t/m3.
It is reported that, duration of the longer operating standstill is 264 hours
[9]; the installed capacity of Çöllolar power station is 1376000 kW,
specific energy consumption of power station is 2300 kcal/kWh, average
bulk density of the Elbistan coal is 0.85 t/m 3 and average calorific value is
1167 kcal/kg. The stocking volume of stockpile can be calculated as
Qb = (264 x 1376000 x (2300 /1167))
/1000
Qb = 7160001
Vsb = 716000 / 0.85
Vsb =842353 m3
5. STORAGES CAPACITY FOR HOMOGENISATION
Raw coal arriving from the mine has certain unwanted characteristics
fluctuations in their quality composition that must be filtered out if a consistent
good quality coal feed is to be achieved. The following considerations favour
the use of stockpile as a raw coal homogenisation unit: (1) sufficient overall
input/output variability reduction ratio can be achieved, (2) raw coal from
poorer deposits with large scale heterogeneity can be utilised, (3) mixing of
various component raw coals can be achieved and (4) power station demands
for improved product quality can be met.
176
Suphi Ural
Several attempts have been made to determine the size of stockpile utilising
the theory of regionalised variables [1, 3] and also using unrealistic black-box
macroscopic design techniques based on standard Gaussian ideal statistical
formulae for assumed approximately normal distributed, statistically
independent coal quality variation [8].
Geostatistical approach has been applied to determining the size of stockpile.
In many cases a regionalized variable is defined as the average over a certain
volume or a surface rather than a point. The basic volume on which a
regionalized variable is defined is called its support. If we change the support
we obtain a new regionalized variable, which is related to the preceding one
but which has different structural characteristics. In case a stockpile is being
considered to reduce stockpile input quality standard deviations, one can
compute which size it should be so that stockpile output quality standard
deviations are reduced to a certain level. Geostatistics can be used to calculate
the variability of the average value of blocks of certain sizes, in other words
the coal is homogenised over the size of the stockpile [1]. For that reason, it is
essential to know the variance of blocks of a given size in the deposit. For the
sake of generality, the supports will be referred to as volume v (i.e. daily
production) and volume V (i.e. deposit). After Kriging's relationship [2] it is
known that the variance of a block within a deposit is equal to the variance of a
point within a deposit minus the variance of a point in the block. Thus all that
is needed is the within block variance for a point as the first one is
experimentally available. The variance of a block v within a large block V will
be obtained by (David, 1977)
a2 (v/V)
= a2(0/V)-a2(0/v)
= F(V)-F(v)
;
where v is volume of daily production, V is volume of stockpile, a2(0/V) is
variance of a point within stockpile and a2(0/v) is variance of a point within
daily production.
To determine the size of the stockpile, one has to find the size of block, which
has the value of a2(0/V) (David, 1988)
a2(0/V)
= a2 (v/V)
+
a2(0/v)
Assume that the effect of stockpiling and subsequent reclaiming is perfect,
in other words the coal is homogenized over the size of stockpile. Regarding
the quality parameters as calorific value and ash content, the necessary size of
stockpile for perfect homogenization can be calculated as follows:
Calorific value: The variogram of calorific value is spherical with the
horizontal ranges across-dip and a stnke-plmge are 750m and 500m, the vertical range
177
Determining The Size Of Linear Stockpile For The Afşin - Elbistan (B) Power Station
(Turkey)
is 40m with the nugget effect value of 2400 kcal/kg and the sill of 19680
kcal/kg. Each day 75m x 50m x 15m block, oriented as the main variation
axes, is mined out. Using the chart of Fig. 10 and taking the anisotropies into
account to calculate a2(0/v)
F [(75 / 750), (50 / 500), (15 / 40)] = 0.21
a2(0/v) = F(v) C = 19680 x 0.21 = 4133
It is desired to keep daily fluctuation of calorific value to 30 kcal/kg. To
determine the size of the stockpile, one has to find the size of block, which has
a variance (a2(v/V)) of 900 kcal/kg, as stockpiling and reclaiming to send the
power station will show the variability of these larger blocks. The block must
be 105m and 15m in two of its dimensions. To obtain a variance of 900
kcal/kg, we need a block within which the variance of a point will be
a2(0/V) = 900 + 4133= 5033
F [(h/750),(105/500),(15/40)]= 5033 /19680
F[(h/750), (0.210), (0.375)] = 0.26
Figure 10. Variance aa2(0/V) of a point within a parallelepipedic block (h, h, l) function
(1/C) F(h/a, h/a, l/a) [2]
178
Suphi Ural
Using the chart of Figure 10, we can determine the third dimension of the
block that should be 0.21 x 750m = 157.5m long. So, volume of the block is
V =157.5m x 105m x 15m = 248063 (bank) m3
and the volume of the stockpile regarding the calorific value is
Vc = V x
Vc = 248063 (bank) m3 x 1.18
Vc = 292714 (loose) m3,
where Vc is volume of the stockpile regarding the calorific value, m3; and y is
average swell factor of Elbistan coal.
Ash content: The variogram of ash content is spherical with the horizontal
ranges across_dip and astrike_plunge are 682m and 455m, the vertical range is 40m
with the nugget effect value of 2% and the sill of 10%. Using the chart of Fig.
10 and taking the anisotropies into account to calculate a2(0/v)
F[(75 / 682), (50 / 455), (15 / 40)] = 0.22
a2(0/v) = F(v) C = 10 x 0.22 = 2.2
It is desired to keep daily fluctuation of ash content to 1.0%. To determine the
size of the stockpile, one has to find the size of block, which has a variance
(a2(v/V)) of 1.0%. The block must be 105m and 15m in two of its dimensions.
To obtain a variance of 1.0%, we need a block within which the variance of a
point will be
a2(0/V) = 1.0% + 2.2% = 3.2%
F [(h/682), (105/455), (15/40)] = 3.2 /10
F[(h/682),
(0.230), (0.375)] = 0.32
Using the chart of Fig. 10, we can determine the third dimension of the block
that should be 0.23 x 682m = 157m long. So, volume of the block is
157m x 105m x 15m = 247275(bank) m3
and the volume of the stockpile regarding the ash content is:
Va = V y
Va = 247275 (bank) m3 x 1.18
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Determining The Size Of Linear Stockpile For The Afşin - Elbistan (B) Power Station
(Turkey)
Va = 291785 (loose) m3
If it is desired to keep daily standard deviation of calorific value and ash
content to 30 kcal/kg and 1% respectively, the total volume of stockpile is to
be at least 292714m 3 .
6. STACKING AND RECLAIMING
Most of stockpile equipment manufacturers utilize design methods that are
essentially based on standard statistical macroscopic relationships [3]. These
are used primarily to specify the number of layers of coal (Figure 11) that
would be theoretically required in the stockpile to achieve a certain variability
reduction ratio within the pile. The variability reduction ratio is also shows the
homogenisation effect.
n = c /c
where n is homogenisation effect, c o is stockpile output sample-to-sample
quality standard deviation and c is stockpile input sample-to-sample quality
standard deviation.
Figure 11. Representing of stacking in layered linear stockpile
The assumptions made for the specification of the number of layers of coal are:
(1) that the variability of the raw coal to be homogenised are of a total random
character with no underlying trends and present a near normal or Gaussian
probability density function, (2) that adjacent quality sample values for any
sample quantity/size are statistically independent. The sampling distribution of
means is related to the original distribution for the data in having the same
mean and a variance proportional to the original variance divided by the
number of elements in each sample [8]
a = k a2 j / n
where n is number of elements that can be interpreted as being equal to the
number of layers (N) and k is the proportionality constant the value of which is
x
180
Suphi Ural
dependent on the amount of sample to sample correlation. If it can be assumed
that the variation within the individual reclaimed slices is so insignificant to be
of no interest and sample-to-sample correlation is zero then the a x can be
expressed as:
a2x = a2o
a o = a2, / N
N = a2, / a2o
Relating the Kriging's relationship [2], number of layers for perfect blending is
equal the variance of a point within the bunker divide to the variance of a point
within a layer.
N
_ a2
(0/Vb)
2
(0/Vl)
a
2
where a (0/Vb) is variance of a point within the bunker, Vb is volume of a
bunker, m 3 ; a2(0/Vl) is variance of a point within a layer and Vl is volume of a
layer, m3.
If it is desired to keep stockpile daily output standard deviation of calorific
value to 30 kcal/kg and a2(0/Vb) equal to a2(0/V) and a2(0/Vl) equal to a2(0/v);
number of layers for the variable of calorific value can be calculated
ir
N _
5033
900
N =6
7. DIMENSIONS OF STOCKPILE
Total volume of the stockpile and number of bunkers can be given by
Vsb > Vc and Vc = Va
Vb = Vc
Vb = 292714 m3
Nb = Vsb / Va
Nb = 842353 m3 /292714 m3
Nb =3
where Nb is number of bunkers. Total volume of the stockpile is equal to
buffering storages capacity and stockpile can consist of three identical bunkers.
The maximum storages volume of a prismatic stockpile bunker is given by
n
H3
Vb = — x
— +
3 tan2 a
181
H2L
tana
Determining The Size Of Linear Stockpile For The Afşin - Elbistan (B) Power Station
(Turkey)
where L is the length of a bunker, m; H is total height of pile, m; a is the angle
of repose of the coal; and D is the base with of the pile, m. Base with of the
pile can be expressed as
D = J H .
tana
Length of bunker can be expressed as:
2(Vb
D2H
6
L =
)
DH
It is reported that [9], total height of pile is 14m and the angle of repose of the
coal is 32°. The base width of the pile is
D =
2xI4
D = 46.6m
tan SI
Length of bunker is
2x(2927I4
L=
n
6
46.6 xI4
L = SSSm
x(46.6)2xI4)
Proposed dimensions of the stockpile bunkers are presented in Figure 12.
Figure 12. Modeling of prismatic type linear stockpile
182
Suphi Ural
8. CONCLUSIONS
The main findings of this study are the following:
The deposit is made up of a great many layers of coal and intercalation. To be
mineable, a coal seam must be at least 50 cm thick. Similarly any intercalation
less than 25 cm thick is mined with the coal. These two constraints are applied
to each drill hole in turn to distinguish mineable coal from waste.
Geostatistical modeling techniques are good tool for the coal quality
controlling purposes. The study presented here indicates that three-dimensional
variograms of the quality parameters as calorific value and ash content of the
Çollolar coal deposit can be modeled by spherical models. The models are then
used to estimate fluctuation of daily production and storage capacity of the
stockpile for perfect blending. The results of this study show that; the
necessary storage volume for blending is 292714 m 3 regarding to the coal
quality parameters. As a result, total volume of the stockpile is to be at least
842353m 3 and consists of 3 identical bunkers. The length and base width of
each bunker is to be 888.0m and 46.6m respectively.
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