REDUCING COAL QUALITY VARIABILITY WITH PROPERLY DESIGNED BLENDING PILES 47

Introduction

Homogenization is the processing of one type of material sothat the inherent fluctuations in respect of quality and/orsize distribution are evened out. In blending coal, the aim isto achieve a final product with a well defined composition,from two or more coal types, so that the elements are verywell distributed and no large pockets of one type can beidentified. In mixing, traces of the individual componentscan still be identified. There are various reason whyblending is necessary. According to Wall et al.1, indigenouscoals can be too low in rank and/or high in ash; in thesecases, traditional feed coals have some quality deficiencies,which require compensation. This means that powerstations have design limitations.

Wall et al1. also explains that in a coal fired thermalpower station, many parameters interact but if we change anoperating condition of the plant to account for one aspect ofthe coal’s character, this may significantly impact on thearea of the plant not considered. Following their example,changing a particular type of coal in a blend to reduce costsmay increase the total moisture, resulting in a decrease inflame temperature, which will affect the heat transfer in thefurnace and may alter the deposition of ash. The increasedgas volume associated with increased moisture, willdecrease the operating efficiency of particulate removalsystems and any de SOx plant. Therefore, it is veryimportant to cite that the interactions within the plant arevery complex, and it would be unreasonable or difficult toensure an operator that changing one simple operatingparameter does not have a negative impact on other areas.

Coal blending is one of several options available forreducing sulphur emissions from coal fired power plants.However, decisions about coal blending must deal withuncertainty and variability in coal properties, and with the

effect of off design coal characteristics on power plantperformance and cost. According to Shih and Frey2, sulphurcontent, ash content and heating value are considered asnormally distributed random variables. The objectives of amodel to optimize operation of a plant must includeminimizing: (i) the expected (mean) costs of coal blending;and (ii) the variance of coal blending costs. The costobjective function includes coal purchasing cost, ashdisposal cost, sulphur removal cost, and fuel switchingcosts. Chance constraints include several risk measures,such as the probability of exceeding the sulphur emissionstandard.

Finally, homogenization piles are frequently used in orderto reduce variability and ensure ore stock. In this context,the most important factors, in respect to pile building can becited as the type of layer disposition, number of layers andadded mass. The last variable can significantly reduce pilevariability when a large material amount is homogenized,and variability among the lots decreases (David3 andParker4).

The Deposition sequence of the material also is able toaffect grade predictability of each pile, requiring a morecareful mining advance forecast. Often, this scheduling isrepresented by a vector of grades to ore blocks that will bemined along a given activity time, and geostatisticaltechniques are used to predict them.

Kriging technique is a very good estimator, but it is notadequate to predict the uncertainty of a process (Isaaks andSrivastava5). In these cases, it is strongly recommended toemploy conditional simulation techniques, which are capableto represent oscillations, and then generate a properuncertainty model. According to Costa et al.6, on contrary tokriging, geostatistical simulation methods aim to reproducein situ variability, and the spatial continuity of the input dataset. Models used in this way, aim to replicate the spatial

BERETTA, F.S., COSTA, J.F.C.L., KOPPE, J.C. and DE SOUZA, V.C. Reducing coal quality variability with properly designed blending piles. Fourth WorldConference on Sampling & Blending, The Southern African Institute of Mining and Metallurgy, 2009.

Reducing coal quality variability with size optimizationof blending piles

F.S. BERETTA, J.F.C.L. COSTA, J.C. KOPPE and V.C. DE SOUZAUFRGS, Brazil

Mineral processing efficiency depends on various factors and among them includes head gradefluctuations feeding the processing plant. Head grades can fluctuate beyond an admissible range,implying in recovery decrease for the mineral of interest. Grades feeding the processing plantdepend on the geological domain which is being mined and on the mining schedule. Therefore, itis necessary to keep grade variability controlled. Blending piles are an efficient alternative toreduce grade fluctuations. The main factors influencing blending efficiency are the pile size or itsmass and the number of layers used to pile up. Large piles are more efficient to reduce variability,however, there are economical and operational limits associated with them. This studyinvestigates a methodology to analyse the ideal pile size to reach an acceptable variability giventhe in situ grade fluctuations. Geostatistical simulations provide the means to assess gradevariability using an optimized mining schedule. These block grades are used to simulate any givenpile throughout the mine`s life. The methodology was illustrated using a case study to control coalash content at a mine supplying ROM coal to a power plant.

Keywords: homogenization, piles, geostatistical simulation.

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structure of a data set as a whole rather than provide reliablelocal estimates of an attribute at particular locations.

The simulated model is said to be conditionally simulatedif it honors values at the sampled points and it reproduces thesame dispersion characteristics of the original data set, (i.e.the mean, variance and covariance or variogram.) In aconditionally simulated model, it is possible to addressquestions referring to the dispersion of the grades duringmining or processing, since the dispersion characteristics ofthe original data are maintained. As the spatial continuity andvariability of the real deposit are properly determined, a gainin quality in the numerically simulated model is obtained.

In this study, we aimed at verifying how thehomogenized mass of the pile affects the expected ashcontent fluctuations. The results provide the means tooptimize the pile size for an acceptable range offluctuations in the coal quality.

MethodologySimulated block models were used to feed an emulator of achevron pile system and used to test piles with severalsizes. Simulations can be obtained using stochasticconditional simulation algorithms including sequentialGaussian (Isaaks7), sequential indicator (Alabert8) and theturning bands (Matheron9) among others. These algorithmsare available in most geostatistical softwares such asGSLIB (Geostatistical software’s library) (Deutsch10 andJournel11,12,13) or Isatis®.

After its construction, a block model is used for miningplanning and scheduling. The sequence of blocks minedform a 1D time series, which is a vector of block values andtheir respective ash content are organized in a time frame.Each generated pile is based on a scheduled sequence ofblocks forming the pile.

The proposed methodology can be summarized as:i. build equally probable models for the variable of

interest, in this case, ash content;ii. determine a specific mining sequence;

iii. use a simulated grade block model to check pile sizeinfluence on interpile variability;

iv. repeat the process in (iii) for a different simulatedblock model, and

v. compose the results (iv) to check in situ gradevariability and pile size influence on interpile gradefluctuations.

The deposit used to illustrate the methodology has sixmineable coal seams each with its own quality and ownaverage thickness. To compose an ash value for a miningblock, it is necessary to combine multiple ash values fromeach different coal seam. Ash values are required to beweighted by coal thickness, forming an accumulationauxiliary variable. This auxiliary variable was conditionallysimulated using the turning bands algorithm (Emery andLantuéjoul14). Ash block values were finally obtaineddividing the accumulation by the simulated thickness. Thissequence provides an unbiased ash simulation as itcombines samples at equal support.

Each simulated block weights approximately 3.7 kt ofROM and to fulfill the monthly production, 18.5 kt of ROMdaily or five blocks are required. To form a pile, it isnecessary to combine multiple sequenced blocks, each withits own conditionally simulated ash content, following themine schedule until the mass of the pile is reached.

Results and discussion The dataset comprises 57 diamond drill holes wherethickness and ash content were available for each seam.Figure 1 shows the area used to restrict the simulationwithin the deposit.

Twenty, equally probable, sequential simulations wereperformed; each one of them returned a value for theblocks. These simulations were validated checking theoriginal and simulated histograms and variograms. Figure 2shows the results for this comparison, selecting threerealizations out of the twenty for the upper seam. Theprocedure was repeated for all seams.

Figure 1. Polygon containing the selected samples from the dataset (samples are represented by ‘+’ signal)where the simulations are performed

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Figure 2. Histogram for the input data (a) and for three (b, c and d) randomly selected simulations. Note the similitude among the plots

Table IProportion (%) of the mass exceeding the limits for each pile mass

Simulations selected 18 kt 90 kt 180 kt 360 kt 630 kt 900 ktby average ash contentLowest 0.68 0.74 0.26 0.17 0.0 0.0Median 1.55 1.60 1.65 1.04 0.91 0.0Highest 0.67 0.56 0.43 0.17 0.00 0.00

Figure 3. Variogram validation to the accumulated variable (a) and length (b) of the upper seam in the N0 direction

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Figure 4. Stripping mining sequence, (b) detailed mining sequence at a typical pit 100 m wide. Numbers refer to the sequenceeach 25 x 25 m block is mined

Figure 5. Ash content values for each pile formed along the mining life using the grades for three randomly selected simulations. Three pilesize were selected to show the decrease on inter pile fluctuation as the pile size increases

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Figure 6. Decay on grade variability among piles as the mass of the pile increases. Three simulations were chosen among the set

The variograms validation (for the accumulated variableand length from upper seam) are shown in Figure 3, whereexperimental variograms to the twenty possibilities ofsimulated grid were combined with the model inserted onsimulation routines. The twenty simulations were rankedaccording to their average ash content. For instance, theranked simulations for the upper seam are the ones with thelowest (52.09 %), the median (52.28 %) and the highest(52.48 %) average ash. These realizations were selected toproceed. Final ash content for each block was obtained byweighting each seam ash content by each seam masspercentage in a block.

Next, using a pre defined mining sequence, it wassimulated the construction of a chevron type blending pilefor a selected number of layers used to pile up the entiremass. Ash content fluctuations for three pile sizes tested areshown in Figure 3. Note the reduction on grades exceedingthe bounding limits (lower limit 50 %, upper limit 56 %) asthe pile size increases.

Table I shows this reduction in grade fluctuationexpressed in percentage for various pile mass.

To highlight the benefit of the blending system and todemonstrate the efficiency of the methodology, the standarddeviation among piles for various pile sizes (inter pilevariability) was calculated. The average standard (Theaverage refer to the value obtained by using multiple blockmodels, each with different simulated ash content. For eachsequence of simulated grades there is a probable miningscenario and a different set of grade values for each pilewhich will lead to a different standard deviation amongpiles.) deviation among piles (ASDP) decreases as the massof the pile (MP) increases (Figure 4).

ConclusionsAccording to the model, it is possible to reduce thevariability of the grades exponentially as the mass of thepile increases. The system showed that a 15% reduction ongrades variability can be achieved with a 112 kt pile. Theuser should select the pile size which leads to an acceptablerange in the grade fluctuations.

A variance decay model was proposed combining in situgrade variability simulation and a chevron type pileemulator. This model provides the means to a balanced

decision, weighing the capital and operational costs for acertain blending system selected and the amount of gradefluctuation expected to be achieved.

References

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2. SHIH, J.S. and FREY, H.C. Coal blendingoptimization under uncertainty. PCC. Proceedings ofthe Tenth Annual International Pittsburgh CoalConference, University of Pittsburgh, Pittsburgh,Pennsylvania, September, 1993, pp. 1110–1115.

3. DAVID, M. Developments in Geomathematics 2:Geostatistical ore reserve estimation. First Edition.Amsterdam: Elsevier Scientific Publishing Company,1977. 2v.

4. PARKER, H. The Volume Variance Relationship: AUseful Tool for Mine Planning. Engineering andMining Journal, vol. 180, 1979, pp.106–123.

5. ISAAKS, E.H. and SRIVASTAVA, R.M. AppliedGeostatistics. New York: Oxford University Press,1989. 551pp.

6. COSTA, J. F.C. L., MARQUES, D., PILGER, G.G.,KOPPE, J. C. , RIBEIRO, D.T., BATISTON, E.L.and COSTA, M.S. A. Incorporating in situ gradevariability into blending piles design using geostisticalsimulation. WCSB3. Proceedings of the Third WorldConference on Sampling and Blending, Porto Alegre.Fundação Luiz Englert. vol. 1, 2007. pp. 378–390.

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Filipe Schmitz Beretta UFRGS, Brazil

Mine Engineer, Graduate Student From Universidade Federal do Rio Grande do Sul - UFRGS -Porto Alegre - Brazil.

7. ISAAKS, E.H., The Application of Monte CarloMethods to the Analysis of Spatially Correlated Data,Ph.D. Thesis, Stanford University, USA, 213p, 1990.

8. ALABERT, F. Stochastic Imaging of SpatialDistributions Using Hard And Soft Information,M.Sc. Thesis, Stanford University, Stanford, 197p,1987.

9. MATHERON, G. The intrinsic random functions andtheir applications. Advances in Applied Probability,vol. 5, 1973, pp. 437–468.

10. Deutsch, C.V. and Journel, A.G., GSLIB:Geostatistical Software Library and User’s Guide,2nd Edition, Oxford University Press. New York,1998. 345p.

11. Journel, A.G. Geostatistics for conditional simulationof ore bodies. Economic Geology, vol. 69, 1974, pp.673–687.

12. JOURNEL, A.G. and HUIJBREGTS, C.J. MiningGeostatistics. Academic Press. London, 1978. 600p.

13. JOURNEL, A.G. Fundamentals of Geostatistics infive lessons. American Geophysical Union.Washington, D.C., 1989.

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