Near-Infrared Spectroscopy Modeling of Combustion Characteristics in Chip and Ground Biomass from Fast-Growing Trees and Agricultural Residue

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dc.contributor.authorShrestha, Bijendra
dc.contributor.authorPosom, Jetsada
dc.contributor.authorPornchaloempong, Pimpen
dc.contributor.authorSirisomboon, Panmanas
dc.contributor.authorShrestha, Bim Prasad
dc.contributor.authorAriffin, Hidayah
dc.date.accessioned2026-08-06T10:45:41Z
dc.date.available2026-08-06T10:45:41Z
dc.date.issued2024-03-01
dc.description.abstractThis study focuses on the investigation and comparison of combustion characteristic parameters and combustion performance indices between fast-growing trees and agricultural residues as biomass sources. The investigation is conducted through direct combustion in an air environment using a thermogravimetric analyzer (TGA). Additionally, partial least squares regression (PLSR)-based models were developed to assess combustion performance indices via near-infrared spectroscopy (NIRS), serving as a non-destructive alternative method. The results obtained through the TGA reveal that, specifically, fast-growing trees display higher average ignition temperature (227 °C) and burnout temperature (521 °C) in comparison to agricultural residues, which exhibit the values of 218 °C and 515 °C, respectively. Therefore, fast-growing trees are comparatively difficult to ignite, but sustain combustion over extended periods, yielding higher temperatures. However, despite fast-growing trees having a high ignition index (D<inf>i</inf>) and burnout index (D<inf>f</inf>), the comprehensive combustion performance (S<inf>i</inf>) and flammability index (C<inf>i</inf>) of agricultural residue are higher, indicating the latter possess enhanced thermal and combustion reactivity, coupled with improved combustion stability. Five distinct PLSR-based models were developed using 115 biomass samples for both chip and ground forms, spanning the wavenumber range of 3595–12,489 cm<sup>−1</sup>. The optimal model was selected by evaluating the coefficients of determination in the prediction set (R<sup>2</sup><inf>P</inf>), root mean square error of prediction (RMSEP), and RPD values. The results suggest that the proposed model for D<inf>f</inf>, obtained through GA-PLSR using the first derivative (D1), and S<inf>i</inf>, achieved through full-PLSR with MSC, both in ground biomass, is usable for most applications, including research. The model yielded, respectively, an R<sup>2</sup><inf>P</inf>, RMSEP, and RPD, which are 0.8426, 0.4968 wt.% min⁻<sup>4</sup>, and 2.5; and 0.8808, 0.1566 wt.%<sup>2</sup> min⁻<sup>2</sup> °C⁻<sup>3</sup>, and 3.1. The remaining models (D<inf>i</inf> in chip and ground, D<inf>f</inf>, and S<inf>i</inf> in chip, and C<inf>i</inf> in chip and ground biomass) are primarily applicable only for rough screening purposes. However, including more representative samples and exploring a more suitable machine learning algorithm are essential for updating the model to achieve a better nondestructive assessment of biomass combustion behavior.
dc.identifier.citationEnergies, 17(6), 2024
dc.identifier.doi10.3390/en17061338
dc.identifier.issn19961073
dc.identifier.other2-s2.0-85188704704
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15524
dc.sourceEnergies
dc.subjectbiomass
dc.subjectcombustion
dc.subjectnear-infrared spectroscopy
dc.subjectpartial least squares regression
dc.subjectthermogravimetric analyzer
dc.titleNear-Infrared Spectroscopy Modeling of Combustion Characteristics in Chip and Ground Biomass from Fast-Growing Trees and Agricultural Residue
dc.typeArticle

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