Identification of different woody biomass for energy purpose by means of Soft Independent Modeling of Class Analogy applied to thermogravimetric analysis

Toscano, Giuseppe; Duca, Daniele; Rossini, Giorgio; Mengarelli, C.; Pizzi, A.

doi:10.1016/j.energy.2015.02.032

Cited by 26 publications

(15 citation statements)

References 31 publications

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“…The thermograms are an average (n = 10) for each of the four biomass types. In Fig 1A , the TGs follow the characteristic thermal behavior of lignocellulosic biomass in the presence of nitrogen [ 8 , 13 , 17 , 20 ]. At about 150°C, the devolatilization process starts, causing mass loss.…”

Section: Resultsmentioning

confidence: 99%

“…This amount was enough to provide a good contact between the sample and the crucible, and also reduce the limitations associated with mass and heat transfer. In addition, approximately the same sample mass was used for each test run to ensure reproducibility and reduce the run to run variation [ 17 , 20 , 29 ]. A test sample was heated from 30°C to 105°C at a rate 20°C/min in an atmosphere of nitrogen.…”

Section: Methodsmentioning

confidence: 99%

“…Systems integrating TGA with other analytical tools such as Fourier transform infrared spectroscopy (FTIR), gas chromatography (GC) and mass spectroscopy (MS) have also been used to enable the identification and quantification of the composition and evolution rates of gaseous and liquid products during the pyrolysis and gasification of biomass feedstocks [ 14 , 15 , 16 ]. In recent times, a couple of studies have explored the application of TGA in the quantitative [ 17 , 18 ] and qualitative [ 19 , 20 ] characterization of lignocellulosic biomass. Several researchers have also reported the successful use of this tool to investigate the physicochemical changes that occur with the pretreatment of lignocellulosic biomass.…”

Section: Introductionmentioning

confidence: 99%

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Chemometric modeling of thermogravimetric data for the compositional analysis of forest biomass

et al. 2017

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The objective of this study was to investigated the use of chemometric modeling of thermogravimetric (TG) data as an alternative approach to estimate the chemical and proximate (i.e. volatile matter, fixed carbon and ash contents) composition of lignocellulosic biomass. Since these properties affect the conversion pathway, processing costs, yield and / or quality of products, a capability to rapidly determine these for biomass feedstock entering the process stream will be useful in the success and efficiency of bioconversion technologies. The 38-minute long methodology developed in this study enabled the simultaneous prediction of both the chemical and proximate properties of forest-derived biomass from the same TG data. Conventionally, two separate experiments had to be conducted to obtain such information. In addition, the chemometric models constructed with normalized TG data outperformed models developed via the traditional deconvolution of TG data. PLS and PCR models were especially robust in predicting the volatile matter (R2–0.92; RPD– 3.58) and lignin (R2–0.82; RPD– 2.40) contents of the biomass. The application of chemometrics to TG data also made it possible to predict some monomeric sugars in this study. Elucidation of PC loadings obtained from chemometric models also provided some insights into the thermal decomposition behavior of the chemical constituents of lignocellulosic biomass. For instance, similar loadings were noted for volatile matter and cellulose, and for fixed carbon and lignin. The findings indicate that common latent variables are shared between these chemical and thermal reactivity properties. Results from this study buttresses literature that have reported that the less thermally stable polysaccharides are responsible for the yield of volatiles whereas the more recalcitrant lignin with its higher percentage of elementary carbon contributes to the yield of fixed carbon.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Chemometric modeling of thermogravimetric data for the compositional analysis of forest biomass

et al. 2017

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“…Thermogravimetric analysis (TGA) has been widely used to evaluate the thermal decomposition process of solid state materials such as lignocellulosic materials (Tenorio et al 2013, Pétrissans et al 2014, Toscano et al 2015. The thermal decomposition of wood and other natural fibers involved many competitive and consecutive reactions because of the chemical complexity of lignocellulosic materials.…”

Section: Thermogravimetric Analysismentioning

confidence: 99%

Effect of extractive content on the thermal stability of two wood species from Brazil

Poletto

2016

Maderas, Cienc. tecnol.

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The influence of extractive content on the thermal stability and kinetic degradation of two wood species has been investigated using chemical analysis and thermogravimetry. Two wood species were studied: Pinus taeda and Eucalyptus grandis. Thermogravimetric results showed that higher extractive contents in the wood accelerate the degradation process and promote an increase in the conversion values at lower temperatures reducing the wood thermal stability. After removing the extractives from wood the thermal stability for both wood species increased. The results also demonstrated that prior information about the wood composition can be helpful to increase the range of industrial applications of wood.

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“…In more detail softwood lignin is mainly (<98%) composed of guaiacyl-units (Feldman, 1985), while hardwood one is composed by guaiacyl/syringil units in variable rates. Schwanninger et al, 2004;Deka et al, 2008) (Evans, 1991;Popescu et al, 2009) 1234 cm −1 C-C stretching, (b) OH in plane (Reyes et al, 2013) 1233 cm −1 (Emandi et al, 2011) 1226-1234 cm −1 6 1328 C1-O syringyl derivates plus guaiacyl L, C (Emandi et al, 2011) 1320-1328 cm −1 (characteristic of hardwoods) (Pandey and Pitman, 2004) 1328 cm −1 and C-H vibration in cellulose (Faix, 1991) (Nuopponen et al, 2003;Rautkari et al, 2010) 1697 cm −1 10 1734 (s) Unconjugated C=O in ketones, H (Chen et al, 2010) 1739 cm −1 carbonyls and ester groups in xylans, (Pandey and Pitman, 2003) 1738/1734 cm −1 well defined in hardwood respect to softwood (Emandi et al, 2011) 60-70% glucomannans and 15-30% arabinogalactan are the main constituents in the softwood (Toscano et al, 2015).…”

Section: Articlementioning

confidence: 99%

Fast measurement by infrared spectroscopy as support to woody biofuels quality determination

Duca

Pizzi

Mancini

et al. 2016

J Agricult Engineer

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The increase in the demand for energy supply during the past few decades has brought and will bring to a growth in the utilisation of renewable resources, in particular of solid biomasses. Considering the variability in the properties of biomass and the globalisation of the timber market, a chemical and physical characterisation is essential to determine the biomass quality.The specific international standards on solid biofuels (ISO 17225 series) describe proper specification and classification of wood chip and pellet, to ensure appropriate quality. Moreover, standard requires information about origin and source of the biomass, normally only to be declared by the producers. In order to fulfill the requirements for the biomass quality, the origin and the source should be assessed, even if currently is hard to determine, in particular on milled or densified biomass. Infrared spectroscopy can provide information on the biomass at the chemical level, directly linked also to its origin and source. This technique is fast and not destructive thus suitable also for online monitoring along the biofuel production chain. In this study, 60 samples belonging to 8 different species were collected and related spectra were acquired using a Fourier transform infrared (IR) spectrometer equipped with a module for solid analysis and analysed by principal component analysis. The results obtained show that the method is very efficient in the identification between coniferous and deciduous wood (99% confidence level) and good results were obtained in the recognition of coniferous/deciduous mixtures, too. Nevertheless, some clear differences have been also noted among intra-class grouping, but additional tests should be carried out.This technique can provide useful information to solid biofuel stakeholders about wood quality and origin, important especially for sustainability issues. Further work will be oriented to the development of IR methodologies for the fast measurement of other important biomass parameters (e.g., ash content, high calorific value, nitrogen content, etc.).

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Identification of different woody biomass for energy purpose by means of Soft Independent Modeling of Class Analogy applied to thermogravimetric analysis

Cited by 26 publications

References 31 publications

Chemometric modeling of thermogravimetric data for the compositional analysis of forest biomass

Chemometric modeling of thermogravimetric data for the compositional analysis of forest biomass

Effect of extractive content on the thermal stability of two wood species from Brazil

Fast measurement by infrared spectroscopy as support to woody biofuels quality determination

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