analysis of coal by diffuse reflectance near-infrared spectroscopy

Analytica Chimica Acta 535 (2005) 123–132

Analysis of coal by diffuse reflectance near-infrared spectroscopy

J.M. Andres∗, M.T. BonaInstituto de Carboqu´ımica, CSIC, Procesos Quimicos, Miguel Luesma Cast´an, no. 4, 50018 Zaragoza, Spain

Received 28 July 2004; received in revised form 2 December 2004; accepted 2 December 2004Available online 1 January 2005

Abstract

Diffuse reflectance infrared fourier transform spectroscopy (DRIFTS) in the near-infrared (NIR) range is a technique able to determinemoisture, ash, volatile matter, fixed carbon, heating value and percentage of carbon, hydrogen, nitrogen and sulfur in coal samples. In thispaper, spectra from 142 coal samples of different origins were acquired in absorbance, reflectance and Kubelka–Munk units. Physical effectsdue to particle size were minimized after applying different pre-treatments to each spectra. The resultant spectra were correlated to the abovementioned coal properties using partial least squares regression (PLS). Moreover, a principal component analysis (PCA) of the full set ofsamples suggested the use of more homogeneous sample sets. The results obtained for a homogeneous set improved the prediction ability oft©

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eywords:Near-infrared spectroscopy (NIR); Coal analysis; Partial least squares (PLS); Pre-treatments effect

. Introduction

There is a major interest in finding instrumental techniqueso characterize coals in a fast and reliable procedure to be im-lemented prior to final injection in combustion power plants.he commercially available solutions to on-line analysis ofoal determine, with adequate accuracy and precision, mois-ure using microwave attenuation and ash content by neutronctivation analysis or X-ray fluorescence (XRF)[1]. These

echniques determine the inorganic content of coal and calcu-ate the properties related to the organic part by local calibra-ion for each kind of coal analyzed. In search of an alternativeechnique able to give a complete and fast characterization ofoal (elemental composition, heating value and moisture, ashontent and fixed carbon), diffuse reflectance infrared fourierransform spectroscopy (DRIFTS) in the near-infrared (NIR)ange, covering the region between 1100 and 2500 nm is pre-ented in this work.

DRIFTS sampling technique gained popularity for theeasurement of coals since Fuller and Griffiths[2] described

it as a proper technique for rapid identification of powdExtensively used for the characterization of scattering sples, this technique presents as main advantages, noa reduced sample time preparation but also high signanoise ratio (SNR) spectra collected in minimal time. Refltion, absorption and scattering light are combined, resuin a spectrum very similar to a transmission one, excepthe relative intensities of the bands.

The optimization of sample grinding time[3], the reduction of the bandwidths[4] and the control of the linearity[5]are considered parameters with high influence in the impment of the resolution and reproducibility of the spectru

It is worth emphasizing how DRIFT took part in the anysis of blends of coals. Whereas the determination of biblend coals was carried out by the method of absorption r[3], blends containing more than two coals were analyzeFredericks et al.[6] taking larger regions of the FT-IR spetrum and relating the composition of the sample to its infraspectra with a processing package based on factor anaNowadays, blending coals from different origins is a nmal practice in commercial boilers to fulfill environmen

∗ Corresponding author. Tel.: +34 976 73 39 77; fax: +34 976 73 33 18.E-mail address:[email protected] (J.M. Andres).

regulations, to achieve the design power output, or due toeconomical criteria.

003-2670/$ – see front matter © 2004 Elsevier B.V. All rights reserved.oi:10.1016/j.aca.2004.12.007

124 J.M. Andres, M.T. Bona / Analytica Chimica Acta 535 (2005) 123–132

Provided that coal is a complex, diverse and heteroge-neous substance[7], the assignation and quantification ofsingle functional groups to specific bands do not give anaccurate description of its composition, characteristics andperformance in combustion. For that, the complete spectrumshould be considered as a whole, which involves handlingthousands of variables. The combination of complex mate-rials and the need for rapid, reliable, accurate and precisedeterminations has motivated researchers to develop and usemultivariate calibration methods. Thus, multiple linear re-gression (MLR), principal-components regression (PCR) orpartial least squares (PLS) have been the object of several re-views[8–12]. Good correlations were found for several coalproperties using this technique.

There are works in the literature where coal is character-ized by DRIFT–near infrared[13,14], and lately the modifiedDRIFT–near-IR spectra have been correlated with severalcoal properties applying multivariate calibration[15]. Thenear-infrared spectroscopy is widely accepted as an on-lineprocess control technique due to the simplicity of measure-ments and the amount of information of each spectrum[16].The load of information given in the spectrum is on the otherhand a drawback: there are physical effects (instrument noise,irregularity of sample surface, heterogeneous solid structure)in the spectra that produce inaccurate or biased results.

re-fl anda nor-m ondd et ofs

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(7) and Teruel (5) from power stations. All of them were re-ceived following the conventional specifications for pulver-ized coal burners, typically: 98%, <300�m; 95%, <150�m;75%, <75�m and used without further treatment. They wereanalyzed for moisture, ash, volatile matter (VM), fixed carbon(FC), heating value (HV), carbon, hydrogen, nitrogen and sul-fur by the usual ISO/ASTM standard methods.Table 1showssome statistical results of the analysis. The samples were clas-sified according to ASTM D388 resulting 1 anthracite, 17medium volatile bituminous, 12 high volatile A bituminous,3 high volatile B bituminous, 14 high volatile C bituminous,2 subbituminous A, 58 subbituminous B, 27 subbituminousC, 3 lignite A, 5 lignite B.

2.2. Apparatus and software

NIR spectra were recorded on an ATI Mattson InfinitySeries FTIR spectrometer equipped with a tungsten–halogensource, quartz beamsplitter and an InGaAs detector. Twopurge connectors provide separately the interferometer andsample compartment with dry air.

The instrument was controlled and data acquired usingthe WinFIRST 3.5 software package by Mattson Instruments.Multivariate calibration and pre-treatment spectra were per-formed with the Unscrambler 7.5, from CAMO.

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In this report, spectral data have been acquired inectance, log(1/reflectance) and Kubelka–Munk units,study of different pre-treatments (baseline correction,alization, multiplicative signal correction, first and secerivatives) has been performed in depth for a wide samples.

. Experimental

.1. Samples

One hundred and forty two samples of raw coal oferent suppliers (Germany, Poland, Czech Republic,ia, China, North-America, Australia and Spain) were uome of them are blocks of samples from Garzweilernd Hambach (41) mines and other, Almerıa (12), As Ponte

able 1tatistical results of full set sample analysis (142 samples), Almerıa coal a

Full set

Minimum Maximum Mean S.D

Moisture (%) 0.48 37.2 11.2Ash (%) 2.09 73.65 13.46 1Volatile matter (%) 6.27 47.81 35.07Fixed carbon (%) 10.23 78.92 40.27 1Heating value (kcal/kg) 1425 7992 5153 1C (%) 16.18 82.24 54.95 1H (%) 0.63 5.76 4.28N (%) 0.2 2.04 0.9S (%) 0.11 5.13 0.91

.3. Recording of spectra

A diffuse reflectance sample cup (13 mm diameter)verfilled with each sample and the surface flattenedspatula. The sample cup was placed into the diffus

ectance attachment and a spectrum was acquired. Potaromide was used as reference material.

Spectra obtained were the result of co-adding 32 sver the range 1100–2500 nm performed at 1 cm−1 of digitalesolution. All the spectra were acquired in absorbance miz. log(1/reflectance).

.4. Data processing

All the regression models were based on the whole wength range (1100–2500 nm). The spectral data were

(12 samples) and the reference method applied in each case

Almerıa power plant Reference meth

Minimum Maximum Mean S.D.

5.26 11.94 8.76 2.1 ISO-589-19819.46 13.03 11.53 1.1 ISO-1171-1976

23.45 28.36 25.74 1.41 ISO-562-197450.25 56.95 53.97 2.14 ASTM D 3172-896041 6493 6242 151 ISO-1928-9

63.58 68.12 66.01 1.56 Elemental analys3.35 3.58 3.46 0.07 Elemental analys1.39 1.7 1.56 0.1 Elemental analys0.33 0.51 0.44 0.06 ASTM D 3177-89

J.M. Andres, M.T. Bona / Analytica Chimica Acta 535 (2005) 123–132 125

Table 2Correlation between coal properties

Moisture (%) Ash (%) Volatilematter (%)

Fixedcarbon (%)

Heating value(kcal/kg)

C (%) H (%) N (%) S (%)

Moisture 1Ash −0.02 1Volatile matter 0.16 −0.60 1Fixed carbon −0.65 −0.54 −0.19 1Heating value −0.63 −0.65 0.06 0.93 1C −0.62 −0.68 0.09 0.93 0.99 1H −0.18 −0.74 0.86 0.23 0.48 0.49 1N −0.53 −0.41 −0.15 0.79 0.83 0.80 0.25 1S 0.04 0.27 −0.24 −0.12 −0.08 −0.14 −0.31 −0.05 1

sidered in reflectance (R), log(1/R) and Kubelka–Munk units.Mathematical pre-treatments were used for reducing extrane-ous effects such as differences in surface roughness or particlesize[17].

All spectral data were centered prior to the application ofbaseline offset correction, baseline followed by normaliza-tion, range normalization, multiplicative scatter correction(MSC) and first and second derivatives for each unit.

3. Results and discussion

3.1. Properties

The correlation coefficients for the properties consideredare displayed inTable 2; as expected due to its common ori-gin, % fixed carbon, heating value and % carbon have a sig-nificant positive correlation between them; volatile matter,mainly composed by organic compounds, and % hydrogenhave a noticeable positive correlation just like heating valueor % carbon with % nitrogen. Percentage of sulfur and mois-ture do not correlate with other properties.

3.2. Spectral profiles

in thiss ala ue int eouss rought sT plesa It isw gnites water[ par-t em-i aks.T s andc ups,s n-s ssaryt

Fig. 1. NIR diffuse reflectance spectra for coals. From top to bottom: (1)subbituminous C, (2) subbituminous A, (3) subbituminous B, (4) lignite B,(5) lignite A, (6) high volatile C bituminous, (7) high volatile A bitumi-nous, (8) high volatile A bituminous, (9) medium volatile bituminous, (10)anthracite.

All spectra to be used for quantitative measurements needto be examined in a format where the ordinate axis is linearwith sample concentration. While simple techniques requirethe ordinate to be linear with concentration (MLR), moreadvanced routines, such as principal-component regressionand partial least squares, can cope with data with small non-linearities arising from chemical interactions in the sample[20]. When dealing with reflectance data, the Kubelka–Munktransformation is sometimes applied, even though the re-quirements are not completely fulfilled in DRIFT[13,21].That is the reason why the three types of data, reflectance,log(1/reflectance) and Kubelka–Munk transformation, havebeen studied.

3.3. Reproducibility

A basic requirement for a quantitative analysis is that themethod must yield reproducible results. A convenient wayto determine reproducibility is to define the precision of themeasurements. In this work, different sources of spectra vari-ations have been studied: instrument devices, the position ofthe sample cup and sample preparation. For that, a group of13 samples chosen by their different origins were acquired inseveral situations.

The influence of different parts in the instrument (de-t ring

The reflectance spectra of some coal samples usedtudy are shown inFig. 1. Although NIR spectra of core featureless, the use of reflectance diffuse techniq

his interval range allows the measurement of heterogenamples such as coals, provided that it can penetrate thhe samples deeper than other spectroscopic technique[15].he absorption bands appear in the low rank coal samnd intensity is generally reduced with increasing rank.orth emphasizing the features at 1400 and 1900 nm in liamples associated with free, bonded and/or adsorbed18,19]. Several physical (instrument geometry, sampleicle size, form and distributions, refractive index) and chcal effects result in overlapping and often in broad pehe NIR spectrum is a result of overtones absorbanceombinations of absorbances of several functional grouch as CH, N H and O H. This is the reason why exteive use of multivariate data analytical methods are neceo reveal specific and useful information.
ector, movement mirror, etc.) was studied by acqui


six times the same sample once prepared in the diffusereflectance sample cup. The calculation of the mean andthe standard deviation for every sample set revealed thatthe interference of the apparatus was negligible for all thecases.

A major influence was presented in the acquisition pro-cedure of the spectra, where the position of the sample cupinto the diffuse reflectance device was studied. As mentionedabove, six replicates were done, slightly rotating the samplecup each time. In this case, considering the roughness of thesurface, a change in the position of the particle sample couldmodify the optical pathlength, resulting in different light scat-tering effect for each replicate. The standard deviations areplotted inFig. 2(a).

In the study of the sample preparation, six aliquots fromthe same coal sample were acquired. The standard deviations,plotted in Fig. 2(b), showed that the influence of varyingthe aliquot was comparable with those obtained rotating thesample cup. This similarity suggests that neither instrumentdevices nor sample handling for its preparation seem to affectthe replication negatively.

Therefore, these results indicate that the main influence inthe replication procedure is the sample itself. Moreover, thedeviations are not constant for every replication set, beingworse for low rank coals where the influence of the moisturec

plew n beo e fora

F nflu-e .

3.4. Pre-treatment of spectra

Provided that scaling would amplify the relative impor-tance of noise in non-absorbing regions, no scaling has beenapplied to the spectra. In this way spectral data is con-sidered homogeneous for each sample. Spectral slope orlight scattering effects due to particle size could not be re-duced by milling the samples because of the final applica-tion requirements in the industrial control system. So, sev-eral mathematical pre-treatments are applied to spectral datain order to minimize the physical features of the sample.Among all possible pre-treatments, the most commonly usedones to correct the spectral variations are applied in thiswork:

- Baseline offset: As it has been demonstrated in the re-producibility section, the particle size of the sample andthe cup-filling procedure cause variations in the amountof light scattered/reflected or in the focal height. Forthis reason, the application of this pre-treatment wasdeveloped, providing an offset of the baseline, whichcan partly correct the deviations produced by theseeffects.

- Range normalization: All spectra are scaled to a com-mon range considering the maximum value of each spec-

axis.corr-

-ious

andarlyd in-

-um ispec-refer-hem-or asi-

- c-ctralsameddi-condeings thatter-tives. Bothlica-

owst-

ontent is noticeable.For the rest of the work, only one spectrum per sam

as included in the data set. Better calibration results cabtained introducing replicates in the set but is of no usn on-line system.

ig. 2. Standard deviations obtained in the reproducibility study. (a) Ince of the acquisition procedure, (b) influence of sample preparation

tra as 1. No signal corresponds to zero in ordinateSo, small differences in weights between samples areected.Baseline offset and range normalization: This pre-treatment is the result of a combination of the two prevones. In this way, every spectrum is scaled between 01 units. The application of this pre-treatment is particulnoticeable in low rank coals where more accurate antense bands are shown.Full Multiplicative Scatter Correction(MSC): MSC isdone by adjusting baselines and slopes. Each spectrcompared with a reference one, usually the mean strum, and the same offset and average slope as theence spectrum are assigned. Thereby, the specific cical information remains almost unchanged except fscaling[22]. Multiplicative and additive effects are condered.First and second derivatives: The aim of derivative spetroscopy is isolating absorption bands, flattening spebaseline and correcting scatter offset effects at thetime. Thus, the first derivative spectra remove an ative baseline, correcting baseline shifts, whereas sederivative is used to handle scatter effects. Despite ban easy and fast technique, the major disadvantage iderivative spectra are not as intuitively obvious to inpret as the original data are. Moreover, taking derivaincreases spectrum noise and reduces the resolutionfirst and second derivatives are obtained by the apption of a Savitzky–Golay differentiation, taking a windof nine points and fourth order polynomial for the adjument.


Fig. 3. Cross correlation between the wavelengths for the spectral data in NIR range.

3.5. Calibration models

In order to find any kind of linear relation among the vari-ables, the cross correlation between the wavelengths was de-veloped for the spectral data. As it is shown inFig. 3, a highdegree of collinearity is presented in NIR coal spectra. There-fore, the use of simple multivariate methods, such as multiplelinear regression, are not appropriate for getting a non-uniquesolution. So, PCR or PLS calibration methods could be stud-ied, preferring the latter one as it takes into account the de-pendent variable in the covariance matrix.

To develop the calibration method, the samples studied arefrequently divided in two sets. Nevertheless, in our case, theexistence of several unique samples could heavily affect theresults obtained if one of them is classified in either of the twosets. To avoid this problem, a full cross-validation procedurehas been chosen instead. The root mean squared error of cal-ibration (RMSEC) and prediction (RMSEP) values obtainedby this method have been considered optimistic but adequatefor model comparisons. These errors are defined by:

RMSEC=√∑N

i=1(yi − yi)2

(N − A − 1)

wherey are obtained by testing the calibration equation di-r -

nents;

RMSEP=√∑N

i=1(yCV,i − yi)2

N

where yCV,i is the estimate foryi based on the calibrationequation with samplei deleted.

Along this work, for comparison purposes all the errorvalues will be expressed in percentage error around the mean:RMSE (%) = RMSE× 100/average property.

The detection of outliers during calibration is a very im-portant step to increase the prediction ability of the esti-mated calibration coefficients. In order to ensure the selec-tion of the outliers, we have considered the score plot ofprincipal component analysis, samples with high leveragesand studentized residual values over±3. In any case, eachoutlier must be considered subjectively. The outlier detec-tion system tends to select unique samples among the wholeset.

All the errors obtained after applying PLS regres-sion for the three spectral units (reflectance, log(1/R) andKubelka–Munk), are arranged in bar charts presented inFig. 4. The first bar represents the error obtained for theoriginal spectral data, where no pre-treatment is applied.The successive bars are the errors for the following pre-treatments: baseline offset correction, range normalization,b first
ectly on the calibration data andA is the number of compo aseline/normalization, multiplicative scatter correction,


Fig. 4. RMSEC (%) and RMSEP (%) obtained for each property after applying different pretreatments: the first bar corresponds to original spectra followedby baseline correction, baseline/normalization, normalization, MSC, first derivative and second derivative. Reflectance (R), log(1/R) and Kubelka–Munk unitsare considered in each case.

and second derivatives. This is established for each propertystudied.

In the light of the results, there is no important differencesbetween reflectance or absorption units, although it is truethat MSC and derivatives present a certain improvement ifreflectance units are considered. The major difference is pre-sented when pre-treatments are carried out in Kubelka–Munkspectra units. Homogeneous samples and low concentrationsin a suitable diluent are some of the requirements to applyK–M transformation, nevertheless coal need concentrations

over 5% to absorb energy in NIR spectroscopy. Clearly K–Mconditions are not fulfilled in our experimental setup.

It is noteworthy that the application of baseline offset,full MSC, first and second derivatives to correct shifts anddrifts in baseline, not only do not improve the error obtainedwithout any pre-treatment but it gets worse, specially whenabsorbance units are used. Among them, the application offirst and second derivatives always lead to larger errors thanthe raw spectra so they were considered useless as a pre-treatment, whereas full MSC is suitable as a pre-treatment

Table 3Best results for calibration RMSEC (%) and prediction RMSEP (%) errors obtained after every data pre-treatment, indicating the number of componentsusedin each case

Property Data Pre-treatment RMSEC (%) RMSEP (%) PC

Moisture log(1/R) Normalization 15.21 36.89 9Moisture R – 26.43 28.75 5Moisture R Baseline 26.52 28.75 5

Ash R Normalization 33.88 38.86 8

Volatile matter R Normalization 8.18 10.56 8

Fixed carbon R Baseline/normalization 8.02 12.74 9

Heating value R Baseline/normalization 7.09 12.7 9Heating value R Normalization 7.19 10.81 6

C R Normalization 7 10.98 9

ation

ation

H log(1/R) Baseline/normalizH R Normalization

N log(1/R) Baseline/normalizN log(1/R) –N log(1/R) Baseline

S R –S R First derivativeS log(1/R) –

7.24 11.68 107.31 9.93 8

14.44 23.33 916.67 22.22 1016.67 22.22 6

42.86 92.31 942.86 90.11 5

52.75 83.52 8


only if it is applied to reflectance data. In the case of normal-ization and baseline/normalization pre-treatments, the errorsobserved are lower than those obtained without any spec-tra modification. Therefore, the normalization of the spectraprovides the best results because it makes them independentof the sample weight improving the correlation with relativeproperties.

The best results for each property considering either theminimum RMSEC (%) or RMSEP (%) for every studiedproperty are shown inTable 3.

The obtained results for properties such as ash and sul-fur content revealed that they are not well predicted usingthese calibration models. Ash content refers to the inorganicresidue that remains after coal combustion[7], and as inor-ganic compounds do not absorb in the NIR region, the pre-diction ability is extremely low. On the other hand, sulfur ispresent in coal samples in inorganic and organic compounds.As explained above, inorganic compounds such as pyrite andsulfate salts, do not present features in the NIR region. Onthe contrary, the presence of functional groups with organicsulfur, such as SH, that absorb in NIR spectroscopy canbe detected, but are not representative of the whole sulfurcontent.

The best results are obtained for volatile matter, fixed car-bon, heating value, % carbon and % hydrogen, with Pearson’sc lueso Al-t ilityt lica-b tivea om-p

Fig. 5. Loadings of PC1–PC4 corresponding toR-normalization.

In spite of being DRIFT–near IR a proper technique todetermine moisture content with high accuracy and preci-sion, no good results are obtained when moisture of coal ismodeled. Moisture is present in coal in different ways suchas chemisorbed, physisorbed, crystralization and free water.While the spectroscopic technique acquires all type of water,ISO/ASTM procedures only measure the one that evaporatesat 105◦C in nitrogen atmosphere. This could be the cause ofthe bad results obtained in this an other work[15].

More information about the calibration models is obtainedif we focus in loading plots, where the correlation betweenthe studied property and the spectral data can be analyzed.As an example, the loading plots obtained for the modelingof % hydrogen, using reflectance units and a normalizationpre-treatment, are shown inFig. 5. Eight principal compo-nents are considered to account for 86.90% of the calibrationsample set variability but the last four PC only correct noisespectra. Thus, the first four principal components are pre-sented, where the common features for PC1, PC2 and PC4

re plots

oefficients (r) between the calculated and measured vaf above 0.93 for calibration and 0.88 for prediction set.

hough their errors are much higher than the reproducibolerances of the ISO/ASTM reference methods, the appility of the method could be focused on a semi-quantitapproximation as it shows considerably better results caring to those obtained with commercial instruments[1].

Fig. 6. PCA sco
(PC1xPC2) forR.


indicate a tendency to correct continuously the same non-linearities between the property and the spectral data. OnlyPC3 explains the scatter presented in the spectra.

3.6. Principal component analysis (PCA)

Principal component analysis is one of the most commonmultivariate techniques. The purpose of this method is todecompose the data matrix into the first few PCs. After com-puting the first three principal components on the originalreflectance spectra, the model accounts for 99.91% of thevariance in the data. This explained variance is consideredgood enough to study the system. The scatter plot of PC1against PC2 is shown inFig. 6. It can be observed how thedispersion of the samples tends to come together in three clus-ters. So, since the NIR technique seems to be not suitable forsuch broad range of coals, it could be useful for smaller andmore homogeneous sets of samples. In this way, we havechosen a group of samples supplied by the same power sta-tion, known to be homogeneous in nature. Twelve samples –medium volatile and high volatile bituminous coals – werecorrelated to moisture, ash, volatile matter, fixed carbon, heat-ing value, carbon, hydrogen, nitrogen and sulfur using partialleast squares regression. The statistical results for their anal-ysis are shown inTable 1. It is noteworthy the narrow intervalr

un-s ationa nings as de-s tancea for-

Fig. 7. RMSEC (%) and RMSEP (%) obtained for Almerıa coals after ap-plying different pretreatments: the first bar corresponds to original spectrafollowed by baseline correction, baseline/normalization, normalization andMSC. Reflectance (R) and log(1/R) units are considered for each case.

mations were not studied considering previous results.Fig. 7shows the RMSEC (%) and RMSEP (%) obtained for eachcase. All properties present better calibration and predictionerrors approaching the levels established by the referencemethod. Once more, properties such as moisture, ash, nitro-gen and sulfur content are difficult to correlate leading to highdetermination errors.

In the present case, the effect of the pre-treatments arenegligible due to the similarities between the samples. It isconfirmed inTable 4where the best results are shown.

TB errors obtained after every data pre-treatment on spectra Almerıa coal, indicating then

RMSEC (%) RMSEP (%) PC

2.63 28.42 34.22 21.57 3

0.68 7.37 34.16 6.24 2

2.56 7.34 14.78 2.49 1

0.56 3.71 33.26 3.65 1

0.27 3.04 30.49 2.34 3

ation

ange considered for each property.The models were developed by taking centered and

caled data. In this case, samples were split into a calibrnd prediction set consisting of eight samples for traiet and the rest for test set. The same pre-treatmentscribed above were applied to spectral data using reflecnd log(1/reflectance) units. K–M and derivatives trans

able 4est results for calibration RMSEC (%) and prediction RMSEP (%)umber of components used in each case

Property Data Pre-treatment

Moisture R BaselineMoisture log(1/R) Normalization

Ash R BaselineAsh log(1/R) Normalization

Volatile matter log(1/R) NormalizationVolatile matter R MSC

Fixed carbon R BaselineFixed carbon R Normalization

Heating value R BaselineHeating value log(1/R) Normalization

C log(1/R) –C log(1/R) Normalization

H R BaselineH log(1/R) Baseline/normaliz

N log(1/R) BaselineN R Normalization

S R BaselineS log(1/R) Normalization

0.09 2.58 40.44 2.01 3

0.75 1.59 21.45 1.53 1

4.79 5.11 15.11 3.77 1

5.91 16.14 16.59 14.09 1


Fig. 8. Residual values for calibration samples set (�) and prediction sam-ples set ( ) determined for different properties compared to reproducibility(grey line) and repeatibility (pointed line) ASTM/ISO allowed levels.

In any case, most of the calibration set residuals lye insidethe precision interval as defined by the norm as is shown inFig. 8 . The reproducibility and repeatability levels allowedby ASTM/ISO reference method are considered in the plots.As expected, hydrogen content presents the best precisionlevel, with all the samples in the precision range defined bythe norm. It is clear that the number of samples is too reduced,specially in the prediction set, leading to high RMSEC andRMSEP values. An increase in the number of samples consid-ered could yield even better figures of merit for the technique,

Fig. 9. Loadings of PC1–PC3 corresponding to log(1/R)-baseline/normalization (% hydrogen).

even though it is not expected that properties like ash contentcan be determined by this technique.

Fewer principal components than in the former case arenecessary to correlate the properties to the spectral data, butthe loading plots presented inFig. 9 suggest that PC1 andPC2 correct the same effect whereas PC3 tends to correctnoise spectra.

In the light of the results a new study using cluster classi-fication has been undertaken. The usability of this techniqueis tested with more homogeneous coal samples where it issupposed that fewer principal components and lower errorscould be obtained than the studied broad coal range ones.

4. Conclusions

The advantage of no mobile part devices brings out NIRspectroscopy as a technique to apply on on-line analysis ofcoal. The existing collinearity between the wavelength NIRspectrum determines the use of multivariate regression mod-els. When samples supplied by different power stations ormines are studied, acceptable correlations were found for% fixed carbon, heating value % carbon and % hydrogenbut the error values are too high to consider a quantificationmodel. Properties such as nitrogen and sulfur, with very nar-r lt toc them

enta f sam-p cali-b le fort et ofs

A

SteelC orko WEA

R

Res.

uel

603.ture,

344

ow ranges and concentrations below 1% are very difficualibrate unless intense signals were distinguished fromatrix spectrum.The results and interpretation of a principal compon

nalysis suggest the use of more homogeneous sets oles where a major improvement is obtained in the errorration and prediction. So, the technique appears suitab

he determination of several coal properties if a large samples from the same rank are used.

cknowledgements

The authors are grateful to the European Coal andommunity for funding this research within the framewf Project 7220-PR/118. We also thank to ENDESA, Rg, EVN Ag and PPC for providing us coal samples.

eferences

[1] G.R. Couch, Coal preparation-automation and control, IEA Coal22 (1996).

[2] M.P. Fuller, P.R. Griffiths, Anal. Chem. 50 (13) (1978) 1906.[3] M.P. Fuller, I.M. Hamadeh, P.R. Griffiths, D.E. Lowenhaupt, F

61 (1982) 529.[4] S.-H. Wang, P.R. Griffiths, Fuel 64 (1985) 229.[5] P. Iwanski, P.R. Griffiths, Energy Fuels 4 (1990) 589.[6] P.M. Fredericks, R. Kobayashi, P.R. Osborn, Fuel 66 (1987) 1[7] H.H. Schobert, Coal, The Energy Source of the Past and Fu

American Chemical Society, 1987.[8] S. Tesch, K.-H. Rentrop, M. Otto, Fresenius’ J. Anal. Chem.

(1992) 206.


[9] C.E. Alciaturi, M.E. Escobar, R. Vallejo, Fuel 75 (4) (1996) 491.[10] C.E. Alciaturi, T. Montero, C. De La Cruz, M.E. Escobar, Anal.

Chim. Acta 340 (1997) 233.[11] C.E. Alciaturi, M.E. Escobar, C. De La Cruz, R. Vallejo, Anal. Chim.

Acta 436 (2001) 265.[12] E.A. Cloutis, Fuel 82 (2003) 2239.[13] O. Ito, H. Seki, M. Iino, Fuel 67 (1988) 573.[14] O. Ito, Energy Fuels 6 (1992) 662.[15] M. Kaihara, T. Takahashi, T. Akazawa, T. Sato, S. Takahashi, Spec-

trosc. Lett. 35 (3) (2002) 369.[16] F. McClure, Anal. Chem. 66 (1) (1994) 43A.

[17] T. Naes, T. Isaksson, T. Fearn, T. Davies, A User-Friendly Guide toMultivariate Calibration and Classification, NIR Publications, 2002,105.

[18] N.A. McAskill, Appl. Spectrosc. 41 (2) (1987) 313.[19] S.A. Fysh, D.A.J. Swinkels, P.M. Fredericks, Appl. Spctrosc. 39 (2)

(1985) 354.[20] P.B. Coleman, Practical Sampling Techniques for Infrared Analysis,

CRC Press, 1993, p. 246.[21] L. Tremblay, J.-P. Gagne, Anal. Chem. 79 (13) (2002) 2985.[22] P. Geladi, D. MacDougall, H. Martens, Appl. Spectrosc. 39 (3)

(1985) 491.

analysis of coal by diffuse reflectance near-infrared spectroscopy

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