Potential of Near-Infrared Spectroscopy for Distinguishing Charcoal

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POTENTIAL OF NEAR INFRARED SPECTROSCOPY FOR DISTINGUISHING CHARCOAL PRODUCED FROM PLANTED AND NATIVE WOOD FOR ENERGY PURPOSE Fernanda Maria Guedes Ramalho, Paulo Ricardo Gherardi Hein, Jéssica Moreira Andrade, and Alfredo Napoli Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b02446 • Publication Date (Web): 03 Jan 2017 Downloaded from http://pubs.acs.org on January 4, 2017

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POTENTIAL OF NEAR INFRARED SPECTROSCOPY FOR DISTINGUISHING

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CHARCOAL PRODUCED FROM PLANTED AND NATIVE WOOD FOR ENERGY

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PURPOSE

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*Fernanda Maria Guedes Ramalhoa, Paulo Ricardo Gherardi Heinb, Jéssica Moreira

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Andradeb, Alfredo Napolic

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a

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b

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c

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Department of Forest Sciences. Federal University of Lavras, Lavras 37200, Brazil. Department of Forest Sciences. Federal University of Lavras, Lavras 37200, Brazil.

Center for International Cooperation in Agronomic Research for Development, Montpellier,

France.

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*Corresponding Author

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E-mail: [email protected]

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Phone: +55 38 988397064

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ABSTRACT

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The objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy

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associated with multivariate statistics to distinguish charcoal produced from wood of planted

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and native forests in Brazil. Timber forest species from the Cerrado (Cedrela sp.,

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Aspidosperma sp., Jacaranda sp. and Apuleia sp.) and Eucalyptus clones from forestry

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companies (Vallourec steel producer and Cenibra pulp producer) were pyrolysed under well

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controlled laboratory scale conditions at the final temperatures of 300 (573,15), 500 (773,15)

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and 700°C (973,15°K) respectively. Fifteen charcoals of each species were produced for each

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temperature leading to heighten controlled pyrolysis treatments and finally 270 charcoal

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samples (3 treatments x 15 repetitions x 6 materials). Principal Component Analysis (PCA)

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and Partial Least Squares Regression (PLS-R) were carried out in the spectra recorded from

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charcoal specimens. NIR spectroscopy associated with PCA was not able to differentiate the

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charcoals produced from native and planted woods if the 270 samples were considered in the

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same analysis. However, the separation of native and planted charcoal was achieved when the

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samples were analyzed separately by final pyrolysis temperature. Thus, the prediction of

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native or planted classes by PLS-R presented better performance for samples pyrolysed at

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300°C, followed by those at 500°C, 700°C and all together.

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Keywords: NIR; charcoal identification; Cerrado wood; Multivariate analysis; PLS-R; PCA

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1. INTRODUCTION Charcoal, originating from charring or slow pyrolysis of wood, with absence or

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controlled presence of oxygen1 is an important source of energy. Among the major producers

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of charcoal, Brazil ranks first with a production of 7.24 million tons per year.2 The produced

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charcoal consumption is concentrated in the domestic market. The charcoal produced from

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planted forests is a major source of energy used in reducing iron ore in the steel industry

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mainly in Brazil.3 Brazil is the only country that produces pig iron using charcoal reduction.

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Of the 7.74 million hectares of trees planted in Brazil, 34% are allocated to pulp and paper

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and 15.2% for charcoal production for the steel industry.4

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Deforestation and illegal logging in natural forests are a global concern because of the

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threats they pose to the rich biodiversity of some regions and because of their contributions

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towards climate change. In many tropical countries, such as Brazil, despite improved

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enforcement against illegal logging, deforestation is still increasing. There is no official

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information concerning the proportion of use of legal and illegal wood for energy purposes.

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One of the great challenges is to identify illegal charcoal from legal. The distinction of

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charcoals from planted and native forests is difficult to conduct by environmental control


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agencies, making it difficult to identify fraud or false documentation. The identification of the

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material is possible through anatomical characterization which is time consuming and

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requires specialized professionals. In order to improve the monitoring of charcoal source, it

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would be useful to develop fast and efficient techniques for charcoal classification.

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Near infrared (NIR) spectroscopy is a suitable technique for characterization of various

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materials, including wood and its products. This technique can be applied to any biological

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material, the analysis is rapid, non-destructive and may require little or no preparation of the

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sample to be analyzed.5,6 Few studies have applied NIR spectroscopy for evaluating charcoal.

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In regard to quantitative approaches, Barcellos7 has used NIR spectroscopy to measure the

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calorific value, the fixed carbon content and the volatiles content of the charcoal produced

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from the genus Eucalyptus. Andrade et al.8 have estimated volatile material, fixed carbon and

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gravimetric yield of charcoal obtained from Eucalyptus wood by NIR spectroscopy.

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Concerning the qualitative analyses, Monteiro et al.9 have associated NIR spectroscopy with

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principal component analysis (PCA) for discriminating carbonization processes (experimental

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and industrial) used to produce Eucalyptus charcoal. Nisgoski et al.10 have applied NIR

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spectroscopy to differentiate wood and charcoal of Brosimum acutifolium and Ficus citrifolia

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(family Moraceae) and Hieronyma laxiflora and Sapium glandulosum (family

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Euphorbiaceae). They successfully distinguish those species and families by PCA from NIR

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spectra recorded from wood; However, using the NIR spectra taken from charcoal, Nisgoski

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et al.10 could distinguish families but not species. Davrieux et al.11 have verified the feasibility

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of using spectroscopic reflection in the mid-infrared (MIR) and NIR region for discriminating

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charcoal produced from Tabebuia serratifolia and Eucalyptus grandis. Their results allowed a

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successfully discriminant function for two species from their MIR and NIR spectra; where the

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differentiation was more pronounced using MIR data. Muniz et al.12 also have applied the

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NIR associated with PCA for discrimination of wood and charcoals of four forest species:


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three native species (Mimosa scabrella, Tabebuia capitat and Hymenaea aurea) and one

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species considered exotic (Eucalyptus alba) in Brazil. The authors have reported the need to

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test the efficiency of this technique in different forest species for developing robust statistical

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models able to identify tree species and, perhaps, the precursor wood used for producing the

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charcoal.

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In all of the studies mentioned above, there was no variation in the final wood

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carbonization temperatures. As final temperature is important in charcoal quality, this aspect

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deserves further investigation.13 Moreover, it would be useful to include charcoals produced

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from different wood species in the analyses in order to verify the efficiency of the proposed

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approach. Thus, the aim of this study was to evaluate the potential of NIR spectroscopy

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coupled with multivariate analyses to distinguish charcoal produced from planted and native

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wood and the influence of final carbonization temperature on classifications estimated by

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NIR-based models.

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2. MATERIALS AND METHODS

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2.1. Plant material. Wood species from the Cerrado biome and reforestation were used

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in this study. The Cerrado species were Cedrela sp. (Cedar, labelled as “C”), Aspidosperma

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sp. (Peroba labelled as “P”), Jacaranda sp. (Rosewood, labelled as “J”) and Apuleia sp.

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(Garapa labelled as “T”). In regard to the commercial plantation, two Eucalyptus clones

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coming from two forestry companies, Vallourec (clones of Eucalyptus grandis x E. urophylla

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hybrids, 6.5 years old) with a focus on production of charcoal for energy and Cenibra Nipo-

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Brasileira (clones of Eucalyptus grandis x E. urophylla hybrids, 6 years old) focused on the

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production of pulp and paper. The four native species occur very frequently in the Brazilian

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Cerrado while the two types of Eucalyptus materials were chosen in order to cover a large

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genetic variation existent among reforestation materials in Brazil.


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2.2. Specimen preparation. Central planks were removed from trees and 45 specimens

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(defect free) were obtained for each material. Specimens presenting the dimensions of 3.5 cm

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x 3.5 cm x 10 cm (R x T x L) and 2.5 cm x 2.5 cm x 10 cm (R x T x L) were produced from

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the native and reforested wood, respectively. All specimens were properly identified using a

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special pencil (labeling did not disappear after pyrolysis). Before pyrolysis, specimens were

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kept in an acclimatized room and until reaching 12% moisture.

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2.3. Pyrolysis process. Wood samples were charred in a Macro ATG oven (Fig. 1)

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developed by a researcher from the Center for International Cooperation in Agronomic

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Research for Development (CIRAD, France) in partnership with Federal University of Lavras

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(UFLA, Brazil). The Macro ATG is a prototype equipped with an electric oven where the

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temperature can reach up to 1,000 degrees Celsius. The system is provided with a pyrolysis

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reactor pressure controller, condenser pyroligneous liquid, load cell, and gas chromatograph

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flow meter. The experiments can be performed with the introduction of various gases such as

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N2, O2, CO2, CO and H2, simulating different conditions of partial combustion or complete

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pyrolysis in the presence of an inert atmosphere. A control panel and software were

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developed by CIRAD, specifically for use in trials in the Macro ATG oven14. Fifteen (15)

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specimens of each plant material (4 native and 2 Eucalyptus types) were carbonized at three

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different final temperatures (300°C/ 573,15°K, 500°C/ 773,15°K and 700°C/ 973,15°K)

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resulting in 270 charcoal samples (6 materials x 3 temperatures x 15 repetitions). The

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pyrolysis conditions were:

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• Initial temperature: 40°C.

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• Heating rate: 5°C min-1.

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• Final temperature: 300ºC, 500ºC and 700ºC.

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• Residence time at the final pyrolysis temperature: 1 hour.

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• Atmosphere: nitrogen gas - N2.


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• Gas flow (N2): 20 NL.min-1 (Normal Litre/min)

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• Cooling period by natural convection: 15 hours.

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Figure 1

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Three temperatures were used for representing the thermal variation in an industrial

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oven. For each carbonization, the specimens were placed in the crucible located within the

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pyrolysis reactor. Four thermocouples were placed around the specimens to verify the desired

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temperature within the system. The condensable gases produced during the thermal

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decomposition process were collected through a condenser connected to the oven. After

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cooling the furnace, the produced charcoals were removed from the crucible and taken to an

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air-conditioned room to stabilize the humidity.

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2.4. NIR spectra acquisition. A Bruker FT-NIR (model MPA, Bruker Optik GmbH,

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Ettlingen, Germany) was used in diffuse reflectance mode. This Fourier transform

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spectrometer is designed for reflectance analysis of solids with an integrating sphere that

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measures the diffuse reflected infrared energy from a 150 mm² spot. Spectral analysis was

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performed within the 12,500-3,600 cm-1 range at 8 cm-1 resolution (each spectrum consisted

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of 2,307 absorption values). Each NIR spectrum was obtained with 32 scans; means were

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calculated and compared to the sintered gold standard used as background to obtain the

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absorption spectrum of the sample. Two NIR spectra were recorded in the center of the each

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side of the transversal surfaces of wood and charcoal samples. The spectrometer was

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connected to a computer which stored the spectra data collected through the OPUS program,

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version 7.5. The NIR spectra were considered without pre-treatment and after mathematical

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treatment by first derivative for these multivariate analyses.

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2.5. Multivariate statistical analysis. Principal Component Analysis (PCA) and Partial

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Least Squares Regression (PLS-R) were calculated by means of the Unscrambler (CAMO

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AS, Norway, v. 9.7) software. First, PCA was used for compressing the main information in a


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set of variables into a lower number of new variables and for exploring the dependence of the

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studied materials by means of clusters. The PCA were calculated using full model size,

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maximum of 6 latent variables (LV’s) and a cross-validation model was adopted (6 segments

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of 30 NIR spectra per segment). Outliers were identified from the Residuals X-variances and

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leverage value plot analyses. The number of LV’s adopted for each model corresponded to the

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first minimal residual X-variance.

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In a second approach, a discriminant analysis was carried out using partial least squares

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regression (PLS-R) to estimate the category (native or planted) of the charcoal samples. For

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the PLS regressions, two variables were attributed to charcoal samples: 0 for native charcoals

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and 1 for Eucalyptus charcoals. PLS regressions were then carried out in four different data

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sets: NIR spectra taken from charcoals produced at 300, 500 and 700°C individually (90

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samples each, 6 x 15) and an another data set including all temperatures together (6 x 15 x 3;

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270 samples, 540 NIR spectra). In a first step, four PLS-R models were generated and cross-

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validated. Thus the category (0 or 1) was estimated. The estimated values (rational number)

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the PLS regression were rounded to 0 or 1 and used as categorical variables. In a second step,

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an independent validation of the PLS-R models was performed using 20% of total samples

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(36 samples selected at random) for validating the models of the charcoal produced at 300°C,

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500°C, 700°C and all together.

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As estimates of the classes also include numbers less than zero and greater than one, the

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estimates were rounded and samples presenting estimated values above 0.5 were considered

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in Category 1 (charcoal from planted woods) while samples whose values were estimated

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below 0.5 were considered as 0, belonging to the class of native woods. Each PLS-R model

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was evaluated by the coefficient of determination (R²), the mean square error (RMSE) and the

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percentage of correct classification.

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3. RESULTS


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3.1. NIR spectra of charcoals produced at 300, 500 and 700°C. The normal

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(untreated) NIR spectra measured on charcoal samples produced at 300, 500 and 700°C (Fig.

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2A) show three distinct spectral signature patterns. It is possible to see that there is noise in

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the signal at the third overtone region of the untreated spectrum while the first derivative of

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the NIR spectra (Fig. 2B) improved the signal-to-noise ratio. Delwiche and Reeves15 have

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demonstrated that the application of pre-treatments on the NIR spectra helps to optimize the

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NIR information and derive models from them.

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Figure 2

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3.2. Principal component analysis (PCA). Fig. 3 shows the scores of the PCA carried

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out from carbonized samples in the three heat treatments. The three groups formed in this

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analysis were organized according to the final carbonizing temperature, indicating that the

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NIR is capable of detecting parameters of the carbonization process of the charcoal

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specimens. The first principal component (PC) explained 92% of the spectral variability of

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charcoal and the second PC explained the remaining 6%, taking into account 98% of the total

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variation (Fig. 3). Monteiro et al.9 also investigated charcoal of seven Eucalyptus species and

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twenty native species from the cerrado of Minas Gerais, Brazil. They presented clusters from

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PCA of the NIR spectra showing that it is possible to distinguish the carbonization processes

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of the samples, but no cluster was formed according to the species.

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Figure 3

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It was possible to clearly distinguish two clusters in the PCA two-dimensional plots

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carried out from the wood (Fig. 4A) and charcoal (Fig. 4B, C and D) sample spectra by final

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pyrolyses temperature. In the four charts, the two groups are represented by the PCA scores of

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the samples coming from planted forests (EV and EC) and from native species (J, P, C and T).

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In regard to charcoal analyses, the lower the final carbonization temperature, the clearer the

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separation of the two groups (charcoal from planted and native wood). Few samples of


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Cedrela charcoal (C) fall within the Eucalyptus group, especially in analyses of charcoal from

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500 and 700 degrees.

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Figure 4

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The PCA performed from the first derivative of the NIR spectra was also able to

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differentiate the charcoal samples into two major groups (planted or native) when only

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charcoals produced at 300°C were considered (not shown). However, it was not possible to

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clearly separate the NIR scores of charcoal from 500 and 700 degrees into groups, because the

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points were randomly distributed along the graphics.

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3.3. Classification of charcoal origin by multivariate regression. Partial Least

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Squares Regression (PLS-R) was held from NIR spectral data to develop models for

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predicting the classification of charcoal samples in the native or planted group. As dependent

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variable, the value 0 (zero) was assigned to charcoal of native wood and the value one (1) to

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charcoal of planted wood. Four regressions were obtained to estimate the class of charcoals

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from 300, 500, 700 degrees and all temperatures. The statistics associated with models and

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cross and independent validations are presented in Table 1.

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Table 1

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PLS-R models developed from the charcoal sample set of 300°C yielded the lowest root

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mean square error and highest correlation values between real and estimated classifications

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for cross-validation, as well as for independent validation (Table 1). It can be seen that the

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R²cv and R²p decrease and RMSE of the models increases as the carbonization temperature

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increases.

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Charcoal classifications were performed from the estimates of charcoal classes

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calculated by cross- and test set-validation models (Table 1). The estimation of class was

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made on charcoals produced at 300, 500 and 700 degrees and all charcoal together. In this

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approach, the samples with estimates lower than 0.5 were classified as belonging to the


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category of native wood charcoal while samples whose estimates were equal to or greater than

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0.5 were classified as reforestation charcoal. Fig. 5 (upper chart) shows the classifications

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according to the estimates of the cross-validated model. This classification was performed

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using 15 charcoal samples for plant material produced at the three thermal treatments and for

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the 45 samples of each plant material taking into account all treatments. The lower chart of

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Fig. 5 shows the classification estimates of the 36 samples used in the independent validation

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of each model and the 108 samples of the global model.

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Higher percentage of correct classifications occurred for the samples carbonized at

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300°C both for models obtained by cross-validation (Fig. 5A, upper chart), as well as by

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independent validation (Fig. 5A, lower chart). Only one incorrect classification (red point)

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was done from the model generated with samples of 300 degrees. However, the estimates of

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classification obtained from the models of charcoal of 500°C and 700°C and the global model

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showed greater number of incorrect classifications.

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Figure 5

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The details of the number of correct classifications and the charcoal samples more

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difficult to correctly classify are shown in Table 2. The cross-validated model developed with

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charcoals produced at 300°C presented 99.2% of correct classifications for native wood

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charcoals and 100% for reforestation wood charcoal. The percentage of correct classifications

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decreases as the final carbonization temperature increases. The classifications made from the

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models validated by an independent validation also showed this trend, but the estimates

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present a higher percentage of correct classifications compared to class estimates of the cross-

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validated models.

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Table 2 4. DISCUSSION


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This study showed that it was possible to distinguish charcoal originating from native

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and planted wood by combining NIR spectroscopy and multivariate statistics, since the

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analysis is performed on samples carbonized under the same final temperature. NIR

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spectroscopy associated with PCA was not able to differentiate charcoals of native or

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reforested wood simultaneously using charcoal samples produced at different final

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temperatures. On the other hand, NIR spectra can successfully be used for detecting the

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temperature at which the charcoal was produced, indicating some information about its

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quality as an energy source.

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4.1. NIR spectra of charcoal varies during pyrolysis process. The NIR spectra

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obtained from charcoal from 300°C (Fig. 2) presented a spectral signature similar to that of

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wood. In comparison with the studies based on NIR spectra and wood6 the spectra of

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vegetable charcoal show little absorption in the NIR region. 7, 11, 12, 10

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As the final carbonization temperature increases, the NIR spectra of the charcoal

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samples changes and acquires a signature that does not resemble the spectrum of wood that

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originate the charcoal (not shown). This reflects the thermochemical conversion of wood

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constituents during pyrolysis.10 At 300°C, there are polymers, like cellulose and lignin, which

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have not undergone partial or complete conversion into charcoal.16 Due to this fact,

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interaction between radiation and the chemical constituents of charcoal from 300°C is more

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informative and it is easier to use its spectral information to distinguish the precursor raw

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materials. Therefore, it is difficult to differentiate charcoal samples in NIR spectroscopy when

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the charcoal is produced at high temperatures, losing or homogenizing the chemical

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information of the chemical carbon link in charcoals. This point may be explained by the fact

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that the conversion of the main components of wood (extractives, hemicellulose, cellulose and

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lignin) are converted to charcoal at different temperatures. Pyrolysis at 300°C is mainly

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responsible for the hemicellulose conversion and a large part of the extractives products. On


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the other hand, cellulose pyrolysis reactions start at 300°C with a fast kinetic conversion.

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Lignin conversion is reached at a wide range of temperature. 4.2. Analysing charcoal samples produced under different final temperatures at

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the same time. The groupings resulting from PCA of the original (untreated) NIR spectra

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recorded on charcoal showed better results than those obtained from first derivative NIR

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spectra (not shown). However, the PCA made from all samples together (carbonized at the

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three final temperature levels) was not adequate to distinguish the plant material, since the

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effect of the spectral change caused by the final carbonization temperature in the NIR spectra

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is greater than the effect of chemical composition variation in the precursor raw material (Fig.

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3).

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Taking into account the sensitivity of NIR spectroscopy in clearly separating charcoal

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samples by final pyrolysis temperature (Fig. 3), it is possible to use this model to

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preliminarily identify the final temperature at which the charcoal was produced. In a

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subsequent step, the inspection agent could compare the spectrum of the unknown sample

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with the database of the temperature that the sample (supposedly) belongs. Thus, depending

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on the source group in which the charcoal sample spectrum falls, it will be possible to classify

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it as originating from native or planted wood. The ability of NIR spectroscopy in predicting

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the final temperature at which the charcoal is produced can be useful as a tool for ranking

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charcoal in terms of quality. Many studies have shown that charcoal produced at high

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temperatures present higher fixed carbon content17 and stiffness18. These properties are

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important for charcoal to be used as bioreducing agents in blast furnaces for steel

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production19.

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4.3. Discrimination of wood and charcoal specimens by thermal treatment. The graph in Fig. 4 A indicates that it was possible to clearly distinguish samples of native wood


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from planted wood. This separation shows the sensitivity of NIR spectroscopy in separating

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wood types according to the plant material.

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The separating of charcoal samples produced from these woods into groups was also

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possible (Fig. 4B, C and D). However, as the final carbonization temperature increases, the

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variations in the charcoal chemical composition are reduced and therefore the technique's

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ability to separate the groups is reduced.

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This may be explained by the degradation of the polymers that make up the samples

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according to their thermal decomposition during pyrolysis. On the other hand, the polymers

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are preserved in wood and they interact with the electromagnetic radiation generating NIR

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spectra with information useful to distinguish between materials (Fig. 4A).

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The distribution of samples within each temperature (Fig. 4) presents differences in

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terms of homogeneity. The sample carbonized at 300°C are distributed more heterogeneously

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compared with samples from 500°C and 700°C, that present homogeneous sample

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dispersion. This can be explained by the degradation of polymers. The degradation is more

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advanced at higher temperatures and thus the material becomes more chemically

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homogeneous (greater concentration of carbon). Therefore, it is not possible to clearly

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separate the carbonized plant materials by temperature.

315

The PLS regressions generated for classifying the origin of charcoals of 300°C showed

316

better statistical performance (higher R² and lower RMSE, Table 1) and the percentage of

317

correct classifications (Table 2) when compared to other models. However, all predictive

318

models showed potential to be applied in the classification of charcoal samples by regulatory

319

agencies.

320

4.4. Limitations and need for further research. The specimens of Cedrela sp. had a

321

greater number of samples incorrectly predicted from all NIR based models. This behavior

322

shows that the charcoal of some native species can be confused with charcoal planted planted


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323

wood. The similarity between some native and reforested wood can be an obstacle in

324

implementing this approach in real situations, since the chemical composition of some woods

325

are similar. In the PCA, some spectra of Cedar charcoal were also mistakenly grouped in the

326

planted charcoal group.

327 328 329

The approach of this work is a preliminary study to develop a system to identify the source of charcoal that is produced, transported and sold illegally in many countries. Further studies using this approach may include more native and planted forest species

330

in the models. Moreover, models may be developed with charcoal samples produced at

331

different temperatures and from different carbonization kilns in order to generate a

332

comprehensive database that can be effectively used in identifying the source of charcoal.

333

4.5. Perspectives. Forestry companies are seeking genetic improvement of their clones,

334

so that these trees present greater uniformity in their properties and higher quality for the

335

production of charcoal. As can be seen in Fig. 4, the homogeneity of charcoal was more

336

pronounced depending on the final temperature, not only because of genetic variation of

337

materials. This result suggests that companies should pay more attention to the parameters of

338

the carbonization process, such as the final temperature, than just the quality of its raw

339

material, if they want to get a more homogeneous charcoal quality.

340

From this study, it is suggested that the charcoal source be evaluated using a sequential

341

analysis: initially, the final charcoal carbonization temperature is predicted, then the type of

342

wood used to produce the charcoal can be more easily estimated by means of a model

343

calibrated for such final temperature. This methodology may be useful in the actions of

344

regulatory agencies for identifying illegal commerce, contributing to the sustainability of

345

areas not (yet) deforested and the preservation of biodiversity.

346

5. CONCLUSIONS


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The association of NIR spectroscopy with multivariate statistics allowed the

347 348

identification of the temperature at which the charcoal was produced. NIR spectroscopy also

349

could successfully detect the type of wood (native and planted) used for producing the

350

charcoal, since charcoals produced under the same final temperature are considered.

351

The prediction of classes, native or planted, by PLS regression yielded a higher

352

percentage of correct classifications for charcoal produced at 300°C, followed by the

353

estimations calculated from NIR models of charcoals from 500°C and 700°C and from the

354

model done simultaneously with all charcoal samples.

355

The higher the final carbonization temperature of the charcoal, the lower the efficiency

356

of predictive models based on NIR spectroscopy for distinguishing charcoals in regard to the

357

wood which originated them.

358

ACKNOWLEDGEMENT

359

The author expresses your special thanks to the Department of Wood Science and Technology

360

of the Universidade Federal de Lavras (UFLA, Brazil) for supporting the experimental work

361

and to the Centre de Cooperation Internationale en Recherche Agronomique pour le

362

Development (UPR114 of CIRAD, Montpellier, France) for laboratory facilities. This study

363

was funded by CNPq (National Council for Scientific and Technological Development,

364

Brazil), CAPES (Higher Education Personnel Improvement Coordination, Brazil) and

365

FAPEMIG (Foundation for Research Support of the State of Minas Gerais, Brazil).

366 367

REFERENCES

368

(1) Wenzl, H. F. J. The chemical technology of wood, 1th ed.; Academic Press: New York;

369

1970.

370

(2) FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS.

371

Forestry: forestry production and trade. Rome, 2014.


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(3) Producer of brazilian association of forests planted. Statistical Yearbook of ABRAF 2013

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years based 2012. Brasília, 2013.

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(4) INDÚSTRIA BRASILEIRA DE ÁRVORES. Relatório IBÁ 2015. São Paulo, 2015.

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(5) Pasquini, C. Near infrared spectroscopy: fundamentals, practical aspects and analytical

376

applications. Journal of the Brazilian Chemical Society 2003, 14, 198-219.

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(6) Tsuchikawa, S.; Schwanninger, M. A review of recent near-infrared research for wood and

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paper. Applied Spectrosc Reviews 2013, 48, 560-587.

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(7) Barcellos, D. C. Characterization of the charcoal through the use of spectroscopy in the

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near infrared 2007. 129 p. Thesis (PhD in Forest Science) - Federal University of Viçosa,

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Viçosa, MG, 2007.

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(8) Andrade, C. R.; Trugilho, P. F.; Hein, P. R. G.; Lima, J. T. ; Napoli, A. Near infrared

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spectroscopy for estimating Eucalyptus charcoal properties. Journal of Near Infrared

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Spectroscopy 2012, 20, 657-666.

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(9) Monteiro, T. C.; Da Silva, R. V.; Lima, J. T.; Hein, P. R. G.; Napoli, A. Use of near

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infrared spectroscopy to distinguish carbonization processes and charcoal sources. Cerne

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2010, 16, 16-381.

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(10) Nisgoski, S. ; De Muniz, G. I. B. ; Marrone, S. R.; Schardosin, F. Z., França, R. F. NIR

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and anatomy of wood and charcoal from Moraceae and Euphorbiaceae species. Ciência da

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Madeira 2015, 6, 183-190.

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(11) Davrieux, F.; Rousset, P. L. A.; Pastore, T. C. M.; De Macedo, L. A.; Quirino, W. F.

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Discrimination of native wood charcoal by infrared spectroscopy. Química Nova 2010, 33,

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1093-1097.

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(12) De Muniz, G. I. B.; Carneiro, M. E.; Nisgoski, S.; Ramirez, M. G. L.; Magalhães, W. L.

395

E. SEM and NIR characterization of four forest species charcoal. Wood Science and

396

Technology 2013, 47, 815-823.


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(13) Kinney, T. J.; Masiello, C. A.; Dugan, B.; Hockaday, W. C.; Dean, M. R.; Zygourakis,

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K.; Barnes, R. T. Hydrologic properties of biochars produced at different temperatures.

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Biomass and Bioenergy 2012, 41, 34-43.

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(14) Jesus, M. S.; Napoli, A.; Andrade, F. W. C.; Trugilho, P. F.; Rocha, M. F. V; Gallet, P.;

401

Boutahar, N. Macro ATG Kiln: gaseous flow study in the pyrolysis process of Eucalyptus

402

Brazilian. Journal of Wood Science 2015, 6, 269-274.

403

(15) Delwiche, S. R.; Reeves, J. B. The effect of spectral pretreatments on the PLS modeling

404

of agricultural products. Near Infrared Spectroscopy Journal 2004, 12, 177-182.

405

(16) Ponder, G. P.; Richards, G. N. A review of some recent studies on mechanisms of

406

pyrolysis of polysaccharides. Biomass and Bioenergy 1994, 7, 1-24.

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(17) Couto, A. M.; Trugilho, P. F.; Napoli, A.; Lima, J. T.; Silva, J. R. M.; Protásio, T. P.

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Quality of charcoal from Corymbia and Eucalyptus produced at different final carbonization

409

temperatures. Scientia Forestalis 2015, 43, 817-831.

410

(18) Assis, M. R.; Brancheriau, L.; Napoli, A.; Trugilho, P. F. Factors affecting the mechanics

411

of carbonized wood: Literature review. Wood Science and Technology 2016, 50, 519-536.

412

(19) Griessacher, T.; Antrekowitsch, J.; Steinlechner, S.; Charcoal from agricultural residues

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as alternative reducing agent in metal recycling. Biomass and Bioenergy 2012, 39, 139-146.

414 415 416 417 418 419 420 421


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422 423 424

425 426

LIST OF FIGURES

Figure 1 - Macro ATG Oven.

427 428

429 430

Figure 2 - Original (A) and first derivative NIR spectra (B) recorded on charcoal samples

431

produced at 300°C, 500°C and 700°C from 6 types of wood (4 native and 2 Eucalyptus).


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Page 19 of 23

432 433 434 EC

EV

T

C

J

P

8 6

300°C 700°C

4 Principal Component 2 (6%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

2 0 -2 -4 -6 -8 500°C

-10 -25

-20

-15

-10 -5 0 5 Principal Component 1 (92%)

10

15

20

435 436

Figure 3 - Principal Component Analysis of the NIR spectra of charcoal samples produced at

437

300°C, 500°C and 700°C.


Energy & Fuels

3

3 Native

2 2 1 1 PC 2 (6%)

PC 2 (17%)

0 -1

0

Planted

-2

-1 -3

Planted

Native

-2

-4

A

Wood

B

300°C

-5

-3 -5

-3

-1

1 PC1 (81%)

3

5

7

-15

-10

-5

0

5 10 PC 1 (94%)

15

20

25

2 6

Native

4

1 Planted

PC 2 (6%)

2 PC 2 (22%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0

-2

0 Planted

-1

Native

-4

-2 -6

C

500°C -8

438

D

700°C -3

-11

-6

-1 PC 1 (77%)

4

9

-10

-5

0 5 PC 1 (93%)

10

15

439

Figure 4 – Bi-dimensional plot of the principal component (PC) analysis scores of NIR

440

spectra recorded on wood (A) and charcoal samples produced at 300°C (B), 500°C (C) and

441

700°C (D) from native (T, C, J and P) and planted (EC and EV) wood.

442 443


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444

Cross validated PLS Models 1,5 700°C

All

Planted

500°C

1,0

0,5

Native

Prediction of class by NIR

300°C

0,0

-0,5

-1,0

Test set validated PLS Models 1,5 500°C

700°C

All

Planted

300°C 1,0

0,5

Native

Prediction of class by NIR

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

0,0

-0,5

A

B

C

D

-1,0 Native

Planted

Native

Planted

Native

Planted

Native

Planted

445 446

Figure 5 – Classification of specimens as charcoal of native or planted wood through cross-

447

validated (above) and test set validated (below) PLS-models. Red circles represent specimens

448

incorrectly classified as planted, while yellow circles are the specimens mistakenly classified

449

as native. Classifications were estimated by models developed for charcoal produced at 300°C

450

(A), 500°C (B), 700°C (C) and from all specimens (D). Reference values: zero (0) for

451

charcoal of native wood and one (1) for charcoal of planted wood.

452 453


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454

List of Tables

455 456

Table 1 – Statistics associate to PLS-R models for estimating the classification of charcoal

457

samples according to its origin by thermal treatment and analyzing all treatments together. Model 300°C 500°C 700°C All

R²cv 0.817 0.729 0.561 0.479

RMSECV 0.204 0.246 0.314 0.342

R²p 0.796 0.744 0.562 0.475

RMSEP 0.227 0.244 0.306 0.342

LV 6 4 3 4

458

R²cv: Coefficient of determination for cross-validation. RMSECV: root mean square error for

459

cross-validation. R²p: Coefficient of determination for validation using a independent test set.

460

RMSEP: root mean square error for test set validation. LV: Latent variables.


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461

Energy & Fuels

Table 2 – Prediction of sample as being native or planted, by means of PLS-R models of each heat treatment and for all samples together.

Validationmethod

Sample set

Type

N

NIR prediction Native

300°C 500°C Cross-validation 700°C All 300°C 500°C Test set 700°C All

462

Correct classification

Incorrect classification by species

%

Planted

EC

Native

120

119

1

119

99.2

Planted

60

0

60

60

100

Native

120

114

6

114

95.0

Planted

60

4

56

56

93.3

Native

120

111

7

111

94.2

Planted

60

13

48

48

78.3

Native

360

339

21

339

94.2

Planted

180

61

119

119

66.1

Native

21

20

1

20

95.2

Planted

15

0

15

15

100

Native

23

21

2

21

91.3

Planted

13

1

12

12

92.3

Native

25

23

2

23

92.0

Planted

11

1

10

10

90.9

Native

72

62

10

62

86.0

Planted

36

8

28

28

77.7

EV

C

J

P

T

1 6 4 5 10

3 12

25

2 1

8

36 1 2

1 1

1

1 4 3

1

5

5

EC: Clones of Eucalyptus sp. from Cenibra. EV: Clones of Eucalyptus sp. from Vallourec. C: Cedrela sp. J: Jacaranda sp. P: Aspidosperma sp


Potential of Near-Infrared Spectroscopy for Distinguishing Charcoal

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