Geostatistical coal quality control in longwall mining

International Journal of Coal Geology 81 (2010) 139–150

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International Journal of Coal Geology

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Geostatistical coal quality control in longwall mining

Mehmet Ali Hindistan ⁎,1, Abdullah Erhan Tercan 2,3, Bahtiyar Ünver 4,5

Hacettepe University, Dept. of Mining Engineering, Beytepe, 06800 Ankara, Turkey

⁎ Corresponding author. Tel.: +90 312 297 76 66; faxE-mail addresses: [email protected] (M.A. Hin

[email protected] (A.E. Tercan), unver@hacettepe1 449.sok. No.50/22 Ümitköy–Yenimahalle, 06530 An2 Temsa Sitesi No.10/2 Çayyolu–Yenimahalle, 06530 A3 Tel.: +90 312 297 76 77; fax: +90 312 299 21 55.4 13.Cadde Öztamer Sitesi No.9/3 3.Etap Eryaman, 065 Tel.: +90 312 297 76 96; fax: +90 312 299 21 55.

0166-5162/$ – see front matter © 2010 Elsevier B.V. Aldoi:10.1016/j.coal.2009.12.014

a b s t r a c t
a r t i c l e i n f o
Article history:Received 28 October 2009Received in revised form 23 December 2009Accepted 24 December 2009Available online 6 January 2010

Keywords:Short term planning3D seam modelBlock modelKrigingCompositing

The coal quality is an important aspect of coal mine planning. This paper presents a case study in which anunderground coal mine is faced with severe penalty cost because it does not consider in situ coal qualitycontrol at all. To help short term planning of the coal production the mean calorific values of the blocks insidethe production panels are estimated by kriging. The estimated calorific values are compared with thoseobtained from actual production. The ratio of the calorific values of actual production to estimated values isfound to be 0.73 in average due to adverse effect of dilution on the quality of run-of-mine coal. This studyreveals the importance of geostatistical block modelling in short term mine planning.

: +90 312 299 21 55.distan),.edu.tr (B. Ünver).kara, Turkey.nkara, Turkey.

374 Ankara, Turkey.

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© 2010 Elsevier B.V. All rights reserved.

1. Introduction

Underground coal mine planning must incorporate coal qualityaspects as well as traditional safety and design considerations. This isespecially true when coal with certain specifications has to besupplied to consumer. If the contract specifications are not met, theconsumer applies severe penalty cost. This requires controlling thequality of coal produced from the mine. There are basically threelevels of controlling: long term controls over life of mine, short termcontrols built upon the mining panels and on-line controls of coalstream. This study is concerned with short term in situ control.

One way around this problem is to estimate in situ coal quality ofmining blocks from the surrounding information and to make shortterm plans based on these estimated values. This paper presents ageostatistical case study on estimation of coal quality of the blocks tohelp short term planning of longwall mining. There are many studiesin the literature that are relevant to geostatistical modelling of coaldeposits. Tercan and Karayigit (2001), Watson et al. (2004), Faliveneet al. (2007), Heriawan and Koike (2008) are some examples.However, studies about use of geostatistics in short term planning

of coal mines are limited. (See for example, Kim and Barua (1984),Vieira et al. (1993), Kawalec (2004)).

Buyukduz coal mine is an undergroundmine operated by a privatecompany. Coal produced from the mine is bought by thermal powerplant as run-of-mine (ROM). The mine is faced with severe penaltiesbecause ROM coal does not meet the contract specifications regardingcalorific value. To help short term planning, the mean calorific valuesof coal blocks for the two panels currently in production has beenestimated by using geostatistical method (Tercan et al., 2008). For thispurpose, 3D solid models are constructed to represent the seam andthe solid is divided into the blocks with size of 12.5 m×12.5 m×1 m.Then the experimental variograms for composited calorific values ofcore samples obtained from 57 drill holes and data collected from allof existing underground openings are computed and modelled. Usingthe model variogram and the surrounding data, the mean calorificvalues of the blocks inside the panels are estimated by ordinarykriging. Thus an original model is obtained for in-situ data. At thisstage of study, the original model is generated for only coal zone anddoesn't incorporate dilution effect at all. The values obtained from thismodel are compared with the actual production data to see the effectof dilution on the quality of ROM coal. An average ratio between actualand estimated values is determined and block values of the originalmodel are revised to generate a reduced block model whichincorporates effect of dilution.

2. Definition of problem

Consider a panel P which is divided into the blocks of equal sizeand shape, and then it is sampled at intervals which close enough toallow reliable estimation (Fig. 1).

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Fig. 1. Schematic representation of blocks inside a panel.

Fig. 2. a) Generalized stratigraphy of area and

140 M.A. Hindistan et al. / International Journal of Coal Geology 81 (2010) 139–150

The problem is to estimate the mean value of each block Z(Bj),j=1,...,m from the surrounding information Z(xi), i=1,...,n for therelevant attribute by ordinary krigingwith the estimator expressed as:

Z*ðBÞ = ∑n

i=1λiZðxiÞ

where the λi are the kriging weights and Z(xi) are the known values atlocations xi. The kriging weights are obtained by solving the followinglinear equations of system:

∑n

j=1λjγðxi−xjÞ + μ = γ−ðB; xiÞ

∑n

i=1λi = 1; i = 1; :::;n

where γ(xi−xj)is the variogram value between the locations xi and xj,γ ̅(B,xi) is the mean variogram value between the location xi and theblock B, and μ is Lagrange multiplier.

b) longwall mining with top coal caving.

141M.A. Hindistan et al. / International Journal of Coal Geology 81 (2010) 139–150

3. Description of study area

Buyukduz coal mine is located in Tuncbilek, Turkey. Miningmethod is retreat longwall with top coal caving (Unver and Yasitli,2006). Because the field is subject to severe tectonic movements,classical longwall method has been used rather than mechanizedmethod for production.

Fig. 2a presents the generalized stratigraphy of the area where coalzone contains three seams (namely upper, middle and lower seams)with four clay partings. Total thickness of coal zone is, in average,10.3 m including 0.9 m partings. According to production method,1.8 m of lower seam is excavated from the face which is advancing onfloor of the seam and the rest of the coal zone (top coal) is caved andrecovered from the face back as shown in Fig. 2b. Although thelongwall method with top coal caving is widely used in mining ofthick coal seams the main drawback of the method is that someamount of waste rock from roof is mixed in coal produced. It is definedas dilution and has an adverse effect on the quality of ROM coal.

The production in the field is currently made from two panels: thepanel A with four faces (A01, A02, A03 and A04) and the panel B withtwo faces (B01 and B02) (Fig. 3). Daily production is 2500 tonnes inaverage. The quality of ROM coal is not controlled at any way. As a

Fig. 3. Coal production in p

result of this it is highly variable. Fig. 4 shows daily variations inaverage lower calorific value for the days between 2nd April, 2007 and24th May, 2008. This period includes 370 days for production and 49off days (i.e., Sundays and holidays). The coal sales contract madebetween the producer and the receiver states that if the calorific valueof delivered ROM coal is less than 2000 kCal/kg, the receiver paysnothing. If it is between 2000 and 2350 kCal/kg, the receiver appliespenalty cost based on the following equation:

Penalty cost = Amount of production

× Sale price ½1−ðActual calorific value = 2350Þ�

No payment was made for 7 days in the total production period of370 days due to the calorific value of the ROM coal having a calorificvalue of less than 2000 kCal/kg. Besides, penalty costs were appliedfor 208 days within the period of 370 days when the calorific value ofthe ROM was between 2000 and 2350 kCal/kg.

4. In situ quality modelling

Data obtained from both drill holes and all underground openingsare used for 3D geometrical and coal quality modelling. There are a

anels A and B by years.

Fig. 4. Daily average lower calorific value of production versus days between 2nd April 2007 and 24th May 2008.


total number of 105 drill holes in the study area. However, only 57 ofthem contain quality analysis of coal (calorific value, ash andmoisturecontents) and the remaining 48 holes only have coal seamintersection levels without quality data. Fig. 5 shows drill holelocations together with project and panel boundaries.

Data obtained from drill holes only were not sufficient for a proper3D modelling of the coal seam. Therefore, a comprehensive surveyingstudy has been carried out to determine details of observed conditionsin underground openings. Results of in situ structural surveying studywere used as an additional data set in modelling. An example ofstructural data obtained from the sections of main gate andproduction face of Face A03 in the Panel A is presented in Fig. 6. Useof structural data reflecting the actual conditions together with drillhole data has allowed to model the study area properly.

Fig. 5. Drill hole loc

Modelling is achieved in two stages: 3D solid modelling of seamassociated with the panels A and B, and estimation of calorific value ofthe blocks inside the solid. In the first stage, the roof surface and thefloor surface are interpolated by ordinary kriging. Interpolated gridnodes are combined with a set of non-overlapping adjacent trianglesin two dimensions to construct a digital terrain model (DTM) for eachsurface. The roof and floor DTMs are then combined with a set of non-overlapping adjacent triangles in three dimensions to define the 3Dsolid for the seam. In the second stage, the solid is divided into equalsize and shaped blocks and the mean calorific values of the blocks areestimated by ordinary kriging. Quality modelling data come from 57core drillings fromwhich 594 core samples are collected and these areanalyzed for calorific value, ash and moisture contents. In this studyonly calorific value is considered.

ations in area.

Fig.

6.Viewsof

sections

alon

gmainga

tean

dprod

uction

face

ofFa

ce-A

3.


Fig. 7. Experimental variogram map of a) roof and b) floor elevations.


4.1. Interpolation of seam roof and floor

Drill hole and underground surveying data are used in theinterpolation of seam roof and floor. Experimental variograms for

Fig. 8. Experimental and mode

Fig. 9. Experimental and mode

the roof and the floor are calculated in 36 directions for anisotropysearch and then these are shown in variogram maps in Fig. 7. Thevariogram maps indicate that both surfaces exhibit anisotropicbehavior and the longest axis of continuity is N66°E for the roof and

l variograms of seam roof.

l variograms of seam floor.

Table 1Variogram model parameters belong to roof and floor surfaces of seam.

Parameter Roof Floor

C0 5 5C1 300 280C2 300 300a1 (N24°E); a1 (N66°W) 375; 375 –

a2 (N24°E); a2 (N66°W) 560; 1820 –

a1 (N30°E); a1 (N60°W) – 325; 440a2 (N30°E); a2 (N60°W) – 720; 1500

Table 2Variogram model parameters for calorific value.

Parameter Value

C0 725,000C1 1,550,000a Down-dip range=4 m, across-dip range=80 m


N60°E for the floor. Note also the presence of the nested structures(blue zone at smaller scale and green zones around it). Experimentalvariograms in the longest continuity direction and also in thedirection perpendicular to it are retained and then modeled. Modelsconsist of pure nugget plus two spherical schemes:

γðhÞ = 0; h = 0

γðhÞ = C0 + C132

ha1ðθÞ

−12

h3

a31ðθÞ

" #+ C2

32

ha2ðθÞ

−12

h3

a32ðθÞ

" #; 0 b h b a1ðθÞ

γðhÞ = C0 + C1 + C232

ha2ðθÞ

−12

h3

a32ðθÞ

" #; a1ðθÞ b h b a2ðθÞ

γðhÞ = C0 + C1 + C2; h N a2ðθÞ

where C0 is nugget effect, C1 and C2 are sill values of the first and secondstructure, a1(θ ) and a2(θ) are the ranges depending on the direction θ.Experimental andmodel variograms are shown in Fig. 8 for the roof andFig. 9 for the floor. The model parameters are given in Table 1.

The first structure for the seam roof exhibits isotropic behaviorwhile the second structure shows a zonal anisotropy. The reason forzoning in the direction N66°W is the distance less than the range inthis direction inwhich the area reaches to the project boundary beforethe variogram reaches to the sample variance. This is also true for thefloor. In the second structure, the area reaches to the project boundarybefore the variogram in the direction N60°W reaches to the samplevariance.

Fig. 10. Estimation map of

The seam roof and floor are interpolated at 25 m×25 m regularintervals in 2D by using kriging. Minimum 3 and maximum 30 data areused in interpolation and themaximum search neighborhood is chosento be 1000 m. The estimation maps for the roof and the floor of the coalseam are shown in Figs. 10 and 11 respectively.

4.2. Estimation of mean calorific values of blocks

Estimation data come from core drilling in coal zone only, whichcontain 594 assay values for calorific value. As a result of miningmethod, partings in coal zone are produced together with coal. Forthis reason it is assumed that calorific value for these partings is1 kCal/kg and they are included in database. Core samples are ofvarious lengths ranging from 0.02 m to 10.50 m. The mean length is0.6 m. These are composited into 1 m length. The histogram ofthe composited data is shown in Fig. 12 together with summarystatistics.

The ratio of the number of composites less than 2000 kCal/kg tothe total number of data is 0.23. Similarly the ratio for the compositesless than 2350 kCal/kg is 0.27. This suggests that 1/4 of thecomposited data falls below the critical region.

Experimental variograms for the composited calorific value arecalculated in down-dip and across dip directions. The model fitted is apure nugget effect plus one spherical scheme (Fig. 13). The modelparameters are given in Table 2.

The solidmodel of the seam in the panels A and B is divided into theblocks with size of 12.5 m×12.5 m×1 m. 1 m corresponds to thevertical height of the block and 12.5 m is the size in horizontaldirections which is determined by considering geostatistical criteriaand distance between drill-holes. The most important one of these

seam roof elevation.

Fig. 11. Estimation map of seam floor elevation.


criteria is not to lead to over smoothed values while the mean blockvalues are estimated. It is well known that the estimates of smaller sizeblocks are much smoother than the real values (Armstrong, 1998).

Themean calorific values of each block are estimated by kriging. Inestimation, minimum 4 and maximum 30 data are used. The searchneighborhood is 100 m in across-dip and 10 m in down-dip direction.The histogram of the estimated values for Panel A and Panel B areshown in Fig. 14 together with summary statistics.

5. Results and discussion

Figs. 15 and 16 show the estimated mean calorific values of theblocks for the panel A and the panel B respectively. The figures show

Fig. 12. Histogram of composited calor

the values of the most upper block (top view) and some section viewsare also included to show changes in vertical direction. These figuressuggest that a very small part of the panel A contains coal withcalorific values less than the critical level and almost the entire panel Bis above the critical level.

On the other hand this is not the case for reality. Indeed Table 3compares actual tonnages and mean calorific values with thecorresponding values estimated from the model over the periodbetween June 2007 and May 2008. As seen from Table 3, the actualproduction amounts are higher than the estimated model tonnagesand the actual mean calorific values are less than the estimated meanvalues. This can be explained by waste rock coming from the gob areaand coal from the development galleries. As it is expected, calorific

ific value with summary statistics.

Fig. 13. Experimental and model variograms of lower calorific value.


values of produced coal decrease while its tonnages increase due todilution effect. The average ratios between actual and estimatedvalues are found to be 1.35 for tonnages and 0.73 for calorific values.

Model coal quality is estimated by considering coal and partingsbetween the seam roof and floor. On the other hand, in practice, partof the coal is left in the gob area and the waste material from hangingwall (roof) is mixed in the coal produced. Hence the previous model isreconstructed by considering the mining method and this model isnamed as the reduced model. For this purpose the mean calorific

Fig. 14. Histograms of estimated lower calorific value of a) Panel A and b) Panel B.

Table 3Actual and estimated results for production and calorific values.

Period Production (tonnes) Calorific value (kCal/kg)

Actual Estimated Ratioa Actual Estimated Ratioa

June–July'07 133,377 75,249 1.77 2313 3228 0.72August–September'07 135,909 111,125 1.22 2343 3170 0.74October–November'07 131,003 98,125 1.34 2213 3162 0.70Dec.07–Jan.08 133,286 112,250 1.19 2289 3129 0.73February–March'08 138,000 97,625 1.41 2274 3073 0.74April–May'08 144,000 123,375 1.77 2355 3099 0.76Total 815,575 617,749Average 1.35 2298 3144 0.73

a Ratio=actual value/estimated value.

values of the blocks are reduced in the rate of 27%. The reduction ratiois obtained from the average value as noted in the last row of Table 3.This result is consistent with the results of studies carried out bySenkal et al. (1988), Ozfirat and Simsir (2007) and Simsir and Ozfirat(2008) in Tuncbilek region. Senkal et al. estimated the ratio as 26.4%while the other authors determined the ratio as 25% in their studies.The estimation maps of the mean calorific values for the reducedmodel for the panel A and the panel B are shown in Figs. 17 and 18,respectively.

The mean block values for the original model and the reducedmodel are classified into three intervals: less than 2000 kCal/kg,2000–2350 kCal/kg and greater than 2350 kCal/kg. Tonnage, intervalratio, cumulative ratio and average calorific value of the blocks fallinginside each interval are calculated and the results are given in Table 4for the panels A and B. Interval ratio is the ratio of interval tonnage tototal tonnage.

Table 4 shows that cumulative ratio below critical level(2350 kCal/kg) is about 2–2.5% for the original model while itincreases to 56–76% for the reduced model. This increment clearlydemonstrates the effect of dilution on quality of produced ROM coal.

6. Conclusions

Quality of ROM coal depends not only in-situ quality of coal butalso the mining method applied, i.e., dilution of top coal during cavingis of major importance. Quality of ROM coal can be estimated by usinggeostatistical methods prior to actual mining operations. This will

Fig. 15. Estimation map of original calorific values of Panel A.

Fig. 16. Estimation map of original calorific values of Panel B.


Fig. 17. Estimation map of mean calorific values of reduced model for panel A.

Fig. 18. Estimation map of mean calorific values of reduced model for panel B.


Table 4Tonnages and average calorific values for corresponding LCV intervals.

Panel A Panel B

LCV interval(kCal/kg)

Tonnage(tonnes)

Interval ratio(%)

Cumulative ratio(%)

Av. LCV(kCal/kg)

Tonnage(tonnes)

Interval ratio(%)

Cumulative ratio(%)

Av. LCV(kCal/kg)

Original b2000 1875 0.2 0.2 1885 6376 1.1 1.1 16172000–2350 14,374 1.7 1.9 2265 7875 1.4 2.5 2230N2350 817,628 98.1 100.0 3050 565,125 97.5 100.0 3190Total 833,877 100.0 3035 579,376 100.0 3158

Reduced b2000 158,963 14.1 14.1 1866 90,958 11.6 11.6 17892000–2350 699,976 62.2 76.3 2187 344,926 44.1 55.7 2205N2350 266,795 23.7 100.0 2498 346,274 44.3 100.0 2542Total 1,125,734 100.0 2215 782,158 100.0 2306


certainly facilitate realistic production planning. Hence depending onquality requirement of ROM coal mine production planning can bedesigned properly.

The reduced model indicates that major part of reserve (76% inpanel A, 56% in panel B) in the area is below the critical level ofcalorific value. This suggests that coal production from differentfaces with different qualities be blended to meet contract specifica-tions for ROM coal. In this manner use of reduced block model isimportant to see the quality of produced block and thus controlblending process.

In conclusion this case study presents the importance of use ofgeostatistical techniques in quality modelling and short term mineplanning.

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