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

Bijendra Shrestha, Jetsada Posom, Pimpen Pornchaloempong, Panmanas Sirisomboon (), Bim Prasad Shrestha () and Hidayah Ariffin
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Bijendra Shrestha: Department of Agricultural Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
Jetsada Posom: Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
Pimpen Pornchaloempong: Department of Food Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
Panmanas Sirisomboon: Department of Agricultural Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
Bim Prasad Shrestha: Department of Mechanical Engineering, School of Engineering, Kathmandu University, Dhulikhel P.O. Box 6250, Nepal
Hidayah Ariffin: Department of Bioprocess Technology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia

Energies, 2024, vol. 17, issue 6, 1-27

Abstract: This 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 i ) and burnout index (D f ), the comprehensive combustion performance (S i ) and flammability index (C i ) 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 −1 . The optimal model was selected by evaluating the coefficients of determination in the prediction set (R 2 P ), root mean square error of prediction (RMSEP), and RPD values. The results suggest that the proposed model for D f , obtained through GA-PLSR using the first derivative (D1), and S i , achieved through full-PLSR with MSC, both in ground biomass, is usable for most applications, including research. The model yielded, respectively, an R 2 P , RMSEP, and RPD, which are 0.8426, 0.4968 wt.% min⁻ 4 , and 2.5; and 0.8808, 0.1566 wt.% 2 min⁻ 2 °C⁻ 3 , and 3.1. The remaining models (D i in chip and ground, D f , and S i in chip, and C i 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.

Keywords: biomass; combustion; thermogravimetric analyzer; near-infrared spectroscopy; partial least squares regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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