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

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 (R²_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 R²_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. [ABSTRACT FROM AUTHOR]