C.R. Andrade et al., J. Near Infrared Spectrosc. 20, 657–666 (2012)Received: 16 July 2012 n Revised: 5 October 2012 n Accepted: 9 October 2012 n Publication: 12 December 2012

ISSN: 0967-0335 © IM Publications LLP 2012doi: 10.1255/jnirs.1028 All rights reserved

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JOURNALOFNEARINFRAREDSPECTROSCOPY

Steel can be produced from vegetable charcoal used as a thermo reducer in steel blast furnaces. Hence, the metallurgical industry requires new solutions for controlling the quality of their charcoal in order to maintain the same quality standard. The objective of this study was to assess the properties of charcoal by near infrared spectroscopy. Partial least square (PLS) regressions were established for estimating fixed-carbon, volatile matter content and gravimetric yield of charcoal. These models were validated by cross-validations and independent test sets and presented similar performance. PLS-R models obtained from untreated NIR spectra for FCC and VMC yielded promissory statistics [r²p of ~0.9; root mean squared error of prediction (RMSEP) of ~3.3%; and ratio of the standard error of prediction to standard deviation (RPD) >3] while the statistics of the calibrations for gravimetric yield had relatively lower magnitude (R²p of ~0.8, a RMSEP of ~2.2 and RPD<2). Since new solutions are required for controlling the quality of charcoal with speed and precision, these models can be useful for monitoring charcoal quality in steel industries.

IntroductionCharcoal is a secondary source of energy produced from the pyrolysis (or carbonisation) of wood at temperatures above 270°C in the absence or controlled presence of amounts of atmospheric oxygen.1–3 The charcoal produced from wood originating from planted forests is an important source of energy used in metallurgical operations as a reducing agent, especially by the Brazilian steel industries, in order to reduce iron ore. Currently, Brazil stands out in the world market as the largest producer of steel produced from charcoal as a thermo reducer in steel blast furnaces.4 In this context, steel compa-nies are increasingly seeking self-sufficiency of raw materials in quantity and quality, giving substantial importance to the development of new technologies for producing and improving the charcoal quality used in their blast furnaces.

Charcoal is a material originating from wood but with a completely different molecular arrangement and chemical

components. Wood is a complex three-dimensional bio polymer material composed of an interconnected network of cellulose, hemicelluloses and lignin, with minor amounts of extrac-tives and inorganics, while charcoal consists mainly of carbon. The quality of charcoal is directly related to the wood from which it originates, as well as its physical, mechanical and chemical traits.5 The physical properties of charcoal influence the output of the blast furnace, whereas chemical properties are more related to the amount of charcoal needed per tonne of iron and the composition of the finished iron or steel.6 The chemical composition can be determined through a proximate analysis7 which basically indicates the volatile matter, ash and fixed carbon content.

The volatile matter, other than water in charcoal, comprises all those liquid and tarry residues not fully driven-off in the carbonisation process.8 It is an important characteristic

Keywords: bioenergy, char, characterisation, prediction, quality control

Near infrared spectroscopy for estimating Eucalyptus charcoal propertiesCarlos Rogério Andrade,a Paulo Fernando Trugilho,b Paulo Ricardo Gherardi Hein,b José Tarcísio Limab and Alfredo Napolic

aDepartamento de Ciências Florestais, Universidade Federal de Goiás, Jataí, Goiás, BrazilbCiência e Tecnologia da Madeira, Departamento de Ciências Florestais, Universidade Federal de Lavras, Lavras, Minas Gerais, Brazil CEP 37200-000. E-mail: phein1980@gmail.comcCIRAD–PERSYST Department–Production and Processing of Tropical Woods, 73 rue Jean-François Breton TA B-40/16, 34398 Montpellier Cedex 5, France

658 Estimating Eucalyptus Charcoal Properties

because its output modifies the structure of the charcoal causing a change in its physical propertie, such as porosity, average pore diameter, density etc. Ash is the residue of mineral oxides existing in the wood, which is obtained by complete combustion of the charcoal. Ash content is gener-ally very low and does not represent a significant volume of Eucalyptus charcoal, but the values can range from 0.5% to 5% depending on the wood species.

Fixed carbon is the carbon from the wood which was retained in the carbonaceous mass. It is responsible for the formation of an amorphous mass of charcoal and represents the amount of carbon contained in the charcoal.9 According to Trugilho,10 the fixed carbon content (FCC) has a direct influence on the calorific value of charcoal. Thus, charcoal presenting high FCC may yield a higher calorific value in the blast furnaces. Bataus et al.11 stated that the FCC of charcoal is one of the most important characteristics for steel quality, since it is directly correlated with the calorific value of fuel. The evalu-ation of the yields obtained in the carbonisation process is extremely important when biomass is intended for charcoal production. The gravimetric yield (GY) expresses the amount of carbon produced per unit mass of wood used to produce it.5 The traditional method of charcoal production is char-acterised by the use of brick kilns with varied dimensions, shapes and management, which affects the yield and product quality. A gravimetric yield of about 20% or less is reached by traditional methods, while the modern industrial methods give GY ranging from 25% to 37%.12 High gravimetric yield in the carbonisation processes is desired for several aspects, for instance, for a better utilisation of the blast furnace in terms of volume per unit of wood used to produce charcoal.

The control analysis of charcoal used in blast furnaces is generally limited, due to the high number of samples, high sampling costs and slowness in obtaining results, making the characterisation of the industrial charcoal technically and economically impractical.13 In this context, techniques allowing reliable and fast evaluation of the quality of charcoal are of key importance for steel producers.

Near infrared (NIR) spectroscopy is a suitable technique for characterising many types of materials, including wood and its products.14,15 Few studies have applied NIR spectra for qualitatively evaluating charcoal. For instance, Labbé et al.16 have used mid-infrared spectroscopy for classifying vegetable charcoal of four species of woods used in industry during the filtration and maturity of the production of whiskey, while Davrieux et al.4 presented NIR-based models for identifying charcoal produced from different native species and Monteiro et al.17 for discriminating carbonisation processes used to produce commercial and industrial charcoal.

Limited studies are available for quantitative approaches. Reeves et al.18 evaluated the effect of charring on the NIR spectra of pine bark and pine wood charred under a range of experimental conditions (duration and temperature). They have established promising models for estimating measures of acidity (total acids, carboxylic acids, lactones and phenols as determined by titration) in 56 samples. Reeves19 has used NIR spectroscopy to examine biochars produced from wheat

straw charred at various temperatures, showing that using spectrometer settings which work well for other materials (soils or forages), especially with a Fourier Transform spec-trometer, are not satisfactory for working with biochars or similar materials such as coals.

To our knowledge, there is no report presenting NIR-based calibrations for estimating the properties of charcoal made from wood. Hence, the aim of this study was to assess the volatile matter, fixed carbon content and gravimetric yield of vegetable charcoal from Eucalyptus wood by near infrared spectroscopy.

Materials and methodsOrigin of charcoal samplesTwo clones of seven year-old Eucalyptus hybrids were used in this study. The plantation was located in Paraopeba, a state of Minas Gerais, in Brazil and managed for energy produc-tion. The hybrid of Eucalyptus urophylla with E. camaldulensis and the natural hybrid of Eucalyptus urophylla were planted at 3 m × 3 m apart.

Carbonisation processT w o - h u n d r e d a n d f i f t e e n w o o d s p e c i m e n s 2.5 cm × 2.5 cm × 10 cm were cut from various radial and longitudinal positions of two trees taking into account the whole spatial variation of these clones. The specimens were oven-dried at 103 ± 2°C and then carbonised through a muffle furnace (Quimis, model: Q318S24; Quimis aparelhos cientí-ficos LTDA, São Paulo, Brazil). The initial temperature pre-established within the furnace was 60°C and, thereafter, heating was increased by 0.5°C per minute until the final carbonisation temperature was reached. The carbonisation process was conducted under inert atmosphere (nitrogen gas flow at a rate of 300 mL min–1) at four maximum final tempera-tures: 350°C, 450°C, 550°C and 900°C. The charcoal samples were oven-dried at 103 ± 2°C then weighed. The charcoal GY as a proportion of wood for each wood specimen was calculated using the following equation:

GY = (Wc / Ww) × 100 (1)

where, Wc is the oven-dry weight of charcoal specimen and Ww is the oven-dry weight of wood specimen.

Charcoal characterisationChemical propertiesMoisture content, volatile matter and ash content were deter-mined using the proximate chemical analysis of wood charcoal according to the procedure D-1762-84 of ASTM7, while fixed carbon content was calculated following the equation of Anon.9

The moisture content of each oven-dried ground charcoal specimen was calculated as a proportion of its initial weight. The same specimen was used for volatile matter and ash content determination. Volatile matter was extracted by pre-heating the specimen in a tube furnace for 2 min at 300°C then heating for 3 min at 500°C and for 6 min at 950°C. Volatile

659C.R. Andrade et al., J. Near Infrared Spectrosc. 20, 657–666 (2012)

matter content was calculated as a proportion of the oven-dry weight of the charcoal specimen. Ash content was determined by heating the charcoal specimen in an uncovered crucible at 750°C for 6 h. Ash content was calculated as a proportion of the oven-dry weight of the residue to the oven-dry weight of charcoal specimen. Fixed carbon content in the charcoal specimen was calculated according to Anon,9 as the differ-ence between 100 and the sum of moisture content, volatile matter and ash content. According to FAO,8 the carbon content can be estimated as a difference: all the other constituents are deducted from 100 as percentages and the remainder is assumed to be the fixed carbon.

NIR spectra acquisitionA Bruker spectrophotometer (model Vector 22/N, Bruker Optik GmbH, Ettlingen, Germany) was used in the diffuse reflec-tance mode. This Fourier transform spectrometer is designed for reflectance analysis of solids with an integrating sphere which measures the diffuse reflected light on a 150 mm² spot. This integrating sphere collects light from all angles; thus, the effects of charcoal texture and other non-homogeneities are minimised. It is also practical that the sphere is “upward looking”, with a window on top of the sphere. A sintered gold standard was used as background. Spectral analysis was performed within the 12,500–4000 cm–1 range at 8 cm–1 resolution.

Each NIR spectrum was obtained with 32 scans; means were calculated and compared to the standard to obtain the absorption spectrum of the sample. NIR spectra were recorded in the centre of the two sides of the longitudinal radial surfaces of each charcoal sample and averaged. First derivatives (13-point filter and a second order polynomial) and second derivatives (25-point filter and a third order poly-nomial) were applied on the NIR spectra using the Savitzky–Golay20 algorithm to enhance the quality of the information and calibrations.

PLS-R model statisticsPartial least squares (PLS) regressions were developed to describe the relationship between the charcoal properties and the NIR spectra by means of the Unscrambler (Camo AS, Norway, v. 9.7) software.

First, the PLS-R calibrations based on a complete data set (215 specimens) were performed in cross-validation mode with a maximum of ten latent variables (LVs). Martens’ uncer-tainty test21 was used to select the wavenumbers with signifi-cant regression coefficients. Outliers were indentified from

the Student residuals and leverage value plot analyses. The number of LVs adopted for each model corresponded to the first minimal residual variance. These first cross-validated PLS-R models were used to pre-define the parameters (such as wavenumbers, number of latent variables and outliers) to be used in calibrations for charcoal properties. Thus, PLS-R models were performed based on the pre-defined parameter in the charcoal samples of a training set and validated by the charcoal samples of a test set. The selection of the samples of each subset was made manually, as follows: the complete sample set (215 specimens) was ranked in ascending order (by FCC values) and split into two uniformly distributed subsets (training set: 133 samples and test set: 82 samples). This procedure allowed high control of the trait variability within each subset.

Selection of the PLS-R modelsTo compare the PLS-R model validations, the following statis-tics were used: the coefficient of determination of calibration (R²c), cross-validation (r²cv) and test set-validation (r²p); the root mean standard error of calibration (RMSEC), cross- validation (RMSECV) and test set-validation (RMSEP); the number of LVs and the ratio of performance to deviation (RPD). Root mean standard errors should be as low as possible while the coefficient of determinations should be high. The RPD value is the ratio of the root mean standard error to the standard deviation (SD) of the reference data.22 This statistic provides a basis for standardising the RMSECV or RMSEV and enables a comparison between PLS-R calibrations and its validations for different wood traits.23

Results and discussionEucalyptus charcoal propertiesTable 1 shows the descriptive statistics of the charcoal proper-ties. The charcoal samples presented a high range of values for each property. The fixed-carbon content ranged from 55.1% to 98.3% while the gravimetric yield ranged from 23.5% to 53.4%. The volatile matter content was the charcoal property which presented the highest variability (CV = 62.4%). Although having different ranges of variation, the FCC and VMC are complementary properties (the correlation (r) between them was 0.99; the small difference comes from variations in mois-ture and ash content). The variation range of the charcoal (Table 1) is important for this study: the higher this property, the more chances there are to get better statistics for the

Table 1. Summary of the descriptive statistics for fixed carbon content (FCC, %), gravimetric yield (GY, %), and volatile matter content (VMC, %) from Eucalyptus charcoal for the complete sample set (215 specimens).

Trait Average SD Min Max CV (%)FCC (%) 82.46 10.68 55.10 98.33 12.9GY (%) 33.77 5.19 23.54 53.39 15.4VMC (%) 17.22 10.74 1.43 44.56 62.4

SD: standard deviation; Min: minimum and Max: maximum values; CV: coefficient of variation; and N: number of observations.

660 Estimating Eucalyptus Charcoal Properties

calibration model. The variability between charcoal samples in this study was mainly due to the carbonisation process which was carried out under different final temperatures (350°C, 450°C, 550°C and 900°C); however, the effects of the different carbonisation processes on the charcoal properties are not within the scope of the present paper.

NIR spectra from Eucalyptus charcoal produced at different final temperaturesCharcoal is a material originating from wood but with completely different molecular arrangement and chemical components. During pyrolysis, biomass is subjected to thermal decomposition, a thorough change in its components taking place with the consequent release of gases, water vapour, liquid organic waste and formation of liquid (tar) and solid (charcoal).24 Thus, NIR spectra recorded from charcoal are totally different to those normally observed in wood samples. Figure 1 illustrates the NIR spectra measured directly from charcoal samples carbonised at 350°C, 450°C, 550°C and 900°C. As the NIR spectra of charcoal contain information regarding its chemical composition and molecular structure, there were clear variations in the NIR spectra of the charcoal samples. These NIR spectra indicate that charcoal samples produced at different final temperatures present different characteristics in terms of chemical characteristics. The NIR spectra of the chars reflect the energy reflected (or captured) by chemical bonds from different components and interac-tions of this new and complex material.

Figure 2 shows the results of principal component analysis (PCA) of the NIR spectra recorded from the charcoal samples. The principal components (PC) 1 and 2 accounted for 99% of the spectra variability and no clusters could be clearly found in the scatter plot of scores for PC1 and PC2 (Figure 2). According

to the PCA result, there were no clear differences between NIR spectra of charcoal produced by different carbonisation conditions.

It is possible to note that the charcoal samples produced under higher final temperatures produces a more homoge-neous product. The cluster of charcoal samples produced at 900°C (Figure 2) is spread less than those produced at low temperatures.

NIR-based calibrationsPreliminary NIR-based calibrations were developed and cross-validated for estimating the charcoal properties using the complete data set (215 samples, Table 1). These PLS-R models were calculated from NIR spectra recorded from 12,500 cm–1 to 4000 cm–1 in order to detect outliers, to select wavenumbers and the number of LVs to retain in the calibra-tion for each charcoal property. Table 2 presents the model statistics of these preliminary NIR models. The PLS-R models for FCC and VMC provided strong coefficients of determination (R²cv from 0.86 to 0.91) and RPD values ranging from 2.7 to 3.3. The RPD of these models, as defined in AACC,22 indicates that the predictions would be adequate for classifying chars in industrial operations. According to Fujimoto et al.,25 the higher the RPD value, the more reliable the predictions.

In regard to the gravimetric yield, the model statistics were weaker (R²cv from 0.62 to 0.76) and RPD values varied from 1.6 to 2.0.

PLS-R models for charcoal traits were developed from a training set using the information obtained from cross- validations simulations and then validated by independent test sets. Table 3 presents the descriptive statistics for the selected training and test sets. The training and test sets were manually selected in order to guarantee that the test set had the varia-

Figure 1. Absorbance versus wavenumber plot for NIR spectra from 4000 cm–1 to 12,500 cm–1 range.

661C.R. Andrade et al., J. Near Infrared Spectrosc. 20, 657–666 (2012)

tion interval of the calibration set. Hence, the variation in the test set is included within the limits of the training set. As the investigated samples present high variability, it was supposed that the set of validation samples includes all possible sources of variation that can be encountered later in real applications.

The statistics associated with the PLS-R models validated by test sets (Table 4) were similar to those obtained from

cross-validations (Table 3). The application of mathematical treatments during the modelling is an important procedure to improve the statistics of the PLS regression models.26 In calibrations for predicting wood traits, first and second derivative NIR spectra have been demonstrated as useful treatments to enhance the signal quality. However, raw NIR spectra provided the best r2

p statistics for FCC, VMC and GY

Figure 2. Two-dimensional scatter plot of scores for PC1 and PC2 from principal component analysis (PCA) of the NIR spectra recorded from Eucalyptus charcoal produced at 350°C, 450°C, 550°C and 900°C.

Table 2. Cross-validated PLS-R models for fixed carbon content (FCC, %), gravimetric yield (GY, %), and volatile matter content (VMC, %) from Eucalyptus charcoal

Trait Spectra R²c RMSEC R²cv RMSECV Outlier LV RPDFCC Log1/R- 0.92 3.03 0.91 3.31 2 5 3.23

1d* 0.94 2.69 0.90 3.40 2 4 3.142d** 0.90 3.38 0.86 3.95 3 4 2.70

VMC Log1/R- 0.93 2.90 0.91 3.23 2 6 3.331d 0.89 3.58 0.87 3.88 2 2 2.772d 0.90 3.37 0.86 4.02 5 4 2.67

GY Log1/R- 0.82 2.16 0.76 2.51 4 6 2.071d 0.69 2.77 0.62 3.12 2 2 1.662d 0.70 2.80 0.63 3.11 3 2 1.67

*, ** First and second derivatice spectra of log1/Reflectance spectra R2

c, R2

p coefficients of determination for calibration and cross validation RMSEC and RMSECV root mean square error of calibration and cross-validation LV: latent variables used in the calibration model RDP: ratio of SD:RMSECV

662 Estimating Eucalyptus Charcoal Properties

compared with those obtained from mathematically treated NIR spectra.

The plot of laboratory-determined FCC versus NIR-predicted FCC is given in Figure 3. The correlation between volatile matter values obtained in the laboratory and by NIR spectro-scopy is shown in Figure 4. These figures were built from the values predicted by untreated NIR spectra.

As FCC and VMC are complementary properties, the NIR-predicted versus laboratory-measured values plot for fixed-carbon content (Figure 3) and volatile matter (Figure 4) showed opposite behaviour. It is possible to note the grouping of samples in three clusters, which are not related to the final temperature of carbonisation in which they were submitted. Possibly due to the spatial variation along the tree, the chars produced at different temperatures present properties varying in magnitude. For instance, the FCC of the chars produced at 450°C varied from 73% to 86%, while those produced at 550°C ranged from 80% to 96%.

Figure 5 presents a plot of NIR-predicted versus laboratory-determined values for gravimetric yield. Only five samples had a GY higher than 45%. Even disregarding them, the corre-lation between the estimate and the calculated value was high. Higher gravimetric yields are desired in metallurgical operations. This important parameter for charcoal production

seems to be more controlled by process than by the raw mate-rial from which it originates.

Regression coefficients for estimating charcoal properties from NIR spectraThe band assignments in NIR may be helpful to understand the variations in NIR spectra due to variations in chemical structure of the charcoal. Workman and Weyer15 show the correspondence between functional group, spectra struc-ture and material type as a function of the wavenumbers. Schwanninger et al.27 presented a review of band assignments in near infrared spectra of wood and wood components. To our knowledge, no information is available concerning the band assignments in NIR spectra of charcoal.

The analysis of the regression coefficients for estimating char-coal properties from NIR spectra is helpful in examining the underlying relationships that have made the estimation of char-coal properties possible by NIR spectroscopy. Figure 6 shows the general plot of weighted PLS regression coefficients to estimate fixed-carbon and volatile matter contents. The assignments of absorption bands indicated by numbered arrows can be used to identify which components were important for the NIR cali-brations. Again, the complementarity between FCC and VMC is confirmed by the opposite trend of regression coefficients.

Table 3. Statistical summary of the training and test sets for fixed carbon content (FCC, %), volatile matter content (VMC, %) and gravimetric yield (GY, %) from Eucalyptus charcoal.

Trait Set Average SD Min Max CV (%) NFCC (%) training 81.99 10.65 55.10 98.33 13.0 133

test 83.22 10.74 56.44 95.65 12.9 82VMC (%) training 17.51 10.89 1.43 44.56 62.2 133

test 16.74 10.56 3.18 41.75 63.1 82GY (%) training 33.61 5.26 23.54 53.39 15.6 133

test 34.02 5.09 23.65 49.21 15.0 82SD: standard deviation; Min: minimum and Max: maximum values; CV: coefficient of variation; and N: number of observations.

Table 4. Test set-validated PLS-R models for fixed carbon content (FCC, %), gravimetric yield (GY, %), and volatile matter content (VMC, %) from Eucalyptus charcoal

Trait Spectra R²c RMSEC r²p RMSEP Outlier LV RPDFCC Log1/R- 0.92 3.04 0.91 3.22 2 5 3.21

1d 0.93 2.99 0.91 2.97 2 3 3.482d 0.89 3.46 0.85 4.04 3 3 2.56

VMC Log1/R- 0.92 3.07 0.90 3.31 2 5 3.001d 0.88 3.69 0.90 3.36 2 2 2.942d 0.91 3.10 0.82 4.73 5 3 2.10

GY Log1/R- 0.82 2.22 0.80 2.22 4 6 1.991d 0.70 2.74 0.62 3.05 2 2 1.452d 0.80 2.30 0.65 3.04 3 3 1.45

*, ** First and second derivatice spectra R2

c, r2

p coefficients of determination for calibration and cross-validation RMSEC and RMSEP root mean square error of calibration and performance LV: latent variables used in the calibration model RDP ratio of SD:RMSEP

663C.R. Andrade et al., J. Near Infrared Spectrosc. 20, 657–666 (2012)

In this study, the focus was only on the assignment of the bands having high PLS-regression coefficients. The high

regression coefficients at 4061 cm–1, 4324 cm–1, 5095 cm–1 and 5280 cm–1 indicates that these bands played an impor-

Figure 3. NIR predicted versus measured values plot for fixed-carbon content for both training (open circles) and test sets (filled rhombs).

Figure 4. NIR predicted versus measured values plot for volatile matter content for both training (open circles) and test sets (filled rhombs).

664 Estimating Eucalyptus Charcoal Properties

tant role in PLS-R models for estimating FCC and VMC from NIR spectra. The band at 4061 cm–1 can be associ-ated with C–H methyl C–H, associated with linear aliphatic CH3 (CH2)N CH3, while the band at 4324 cm–1 represents the C–H methylene C–H, as in linear aliphatic R(CH2)NR.15 The high regression coefficients of these bands may be

related to variations in hydrocarbons and in their molec-ular structure.

The plot of weighted PLS regression coefficients for esti-mating gravimetric yield is presented in Figure 7. The NIR range from 4000 cm–1 to 5000 cm-1 seems to be important for calibrating GY. The regression coefficient of the calibration for

Figure 5. NIR predicted versus measured values plot for gravimetric yield for both training (open circles) and test sets (filled rhombs).

Figure 6. Regression coefficients for PLS model predicting fixed-carbon and volatile matter contents.

665C.R. Andrade et al., J. Near Infrared Spectrosc. 20, 657–666 (2012)

GY was particularly high at 4123 cm–1. According to Workman and Weyer,15 this band is associated with C–H aromatic C–H (aryl), benzene band assignment. The band at 4200 cm–1 showed a significant regression coefficient and represents the C–H stretching and C–C stretching combination. In short, the regression coefficients at these bands indicate, by means of the NIR spectra, the variations in the charcoal properties.

ConclusionsIn this study, the NIR-based PLS-R models for estimating fixed-carbon, volatile matter content and gravimetric yield of charcoal were established. These models were validated by cross-validations and independent test set. The cross-valida-tions and test set validations for charcoal properties presented similar performance. PLS-R models calibrated from untreated NIR spectra for fixed-carbon and volatile matter content yielded encouraging statistics (R²p of ~0.9; RMSEP of ~3.3%; and RPD>3) and the calibrations for gravimetric yield had rela-tively lower statistics (R²p of ~0.8, a RMSEP of ~2.2 and RPD<2). Since new solutions are required for controlling the quality of charcoal with speed and precision, these models can be useful for monitoring charcoal quality in steel industries.

AcknowledgementsThe authors express their special thanks to the Vallourec and Mannesmann for providing material and for their finan-cial support; to the Tropical Forest Products Research Unit of the Centre de Cooperation Internationale en Recherche Agronomique pour le Development (CIRAD, Montpellier, France) and to the Department of Wood Science and Technology of the Universidade Federal de Lavras (UFLA, Lavras, Brazil)

for supporting the experimental work. This study was partially funded by CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil) and FAPEMIG (Fundação de Amparo à Pesquisa do Estado De Minas Gerais, Brazil).

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