1. Introduction
Global energy demand continues to escalate, prompting the exploration of diverse energy sources to meet this growing need, while mitigating the negative impacts on both energy availability and the environment. The predominant reliance on non-renewable fossil fuels not only contributes to environmental degradation but also raises concerns about future energy security due to the finite reserves. According to the International Energy Agency, as of 2020, 80% of global primary energy consumption was attributed to fossil fuels, resulting in a substantial carbon footprint [
1]. The financial burdens associated with fossil fuels, exacerbated by fluctuating prices and geopolitical uncertainties, have triggered an urgent quest for alternative energy options.
A balanced and sustainable energy portfolio necessitates the promotion of renewable energy sources, primarily including hydro, wind, solar, biomass, and geothermal. Among these, biomass energy stands out as a promising solution, which accounts for 15% of the total energy consumption [
2], and is derived from continuously renewable organic materials, such as wood, agricultural residues, and organic waste. Biomass energy conversion occurs predominantly through direct combustion [
3], thermochemical processes (specifically pyrolysis and gasification) to produce solid (charcoal) and gaseous (syngas) fuels, as well as biological methods involving fermentation to produce ethanol and anaerobic digestion to yield methane-rich biogas. The utilization of biomass for energy purposes not only reduces reliance on non-renewable sources but also aids in waste management, contributing to rural development.
The fulfillment of global primary energy relies on the direct combustion of biomass and the co-combustion of two or more different fuels within the same combustion system, such as biomass and biochar [
4], textile dyeing sludge and waste rubber [
5], phytoremediation biomass and textile dyeing sludge [
6], calcium-rich oil shale with biomass [
7]. Despite biomass being deemed a carbon-neutral fuel [
8], it exhibits varied combustion behaviors [
9]. Therefore, careful management of the combustion process is vital to minimize emissions of additional pollutants, including particulate matter, sulfur oxides (SO
x), nitrogen oxides (NO
x), and volatile organic compounds [
10]. A thorough comprehension of the combustion properties across different types of biomass is imperative to appropriately choose suitable biomass and design efficient combustion systems. Hence, combustion characteristic parameters, such as biomass ignition time (t
i) and ignition temperature (T
i), burnout time (t
f) and burnout temperature (T
f), maximum and average combustion rate, etc., are essential for evaluating combustion performance indices such as the D
i, D
f, S
i, and C
i [
11]. Accurate assessment of these indices can enhance the overall efficiency of the biomass combustion system, reduce environmental impacts, and bring us closer to achieving a sustainable energy future driven by renewable sources.
TGA is typically employed to determine combustion characteristic parameters for evaluating different combustion performance indices [
12]. Biomass combustion in TGA mainly consists of three stages: (i) water evaporation, (ii) volatile release and its combustion, and (iii) char combustion [
13,
14,
15]. TGA logs the mass loss of biomass as a function of time and temperature. As a result, the thermogravimetric (TG) curve obtained through TGA provides information about the mass loss of the biomass sample as it undergoes thermal decomposition and combustion. The DTG curve is derived as the D1 from the TG curves, providing additional information about the rate of mass loss at various times and temperatures [
15]. Based on the TG and DTG curves, various combustion characteristic parameters can be identified. These parameters are used to evaluate the D
i, D
f, S
i, and C
i. TGA has been employed in a various range of studies, covering diverse aspects of combustion and thermal behavior. It has been used to assess the self-ignition potential of woody biomass and wheat straw [
2], investigate the thermal behavior of Malaysian oil palm biomass, low-rank coal, and their respective blends under oxidative atmosphere [
16], and identify thermo-chemical characteristics data for date palm biomass [
17]. TGA has also been instrumental in studying the ignition behavior of straw pellets [
18] and investigating ignition and burnout in bamboo and sugarcane bagasse [
19]. Furthermore, TGA has been utilized to analyze the combustion characteristics of various biomass pellet types, including rubberwood sawdust pellets, teak sawdust pellets, eucalyptus bark pellets, cassava rhizome pellets [
20], as well as agricultural solid waste torrefied pellets [
21] and briquettes [
15]. These studies collectively provide valuable insights into the reactivity, flammability, and thermal properties of these biomass materials, exhibiting their potential as fuels and their role in sustainable energy solutions.
The T
i is the lowest temperature at which solid fuel initiates ignition in air without requiring an external ignition source [
2]. Ignition of biomass is a pivotal stage that initiates combustion. A lower D
i indicates that the biomass can be easily ignited and combusted at lower temperatures, while a higher D
i indicates that the biomass requires higher temperature to ignite and combust [
22], making it more challenging to start the combustion process. Biomass with a higher volatile matter poses a lower T
i and lower D
i, exhibiting ease of combustion [
23]. It is important for biomass to ignite neither too quickly nor too slowly. Therefore, calculating the D
i is essential for understanding biomass ignition properties. The T
f indicates the temperature at which the combustion process of the biomass is completed [
19]. A high D
f signifies complete combustion, leaving minimal unburned fuel or ash content [
19,
22]. A higher D
i and D
f indicate greater reactivity of the biomass, making it more suitable and flammable as a fuel [
21]. The peak temperature is the point on the TGA curve at which the rate of weight loss of biomass due to combustion is at its maximum. This value typically varies around 280–300 °C [
8]. For a thorough assessment of combustion behavior, it is essential to consider the S
i, which integrates three main properties of biomass combustion: ignition, burnout, and combustion characteristics [
12]. A higher value of the S
i indicates efficient combustion, characterized by early ignition and thorough burnout [
12,
24,
25]. Similarly, C
i is a crucial factor in assessing the fire risk and combustion behavior of biomass fuels. A higher C
i will have better combustion stability. It indicates that biomass can ignite easily at lower temperatures, releasing excess heat during combustion and supporting strong flames [
26]. All of these indices provide valuable insights into the combustion characteristics of various biomass samples, enabling informed decisions when selecting suitable biomass and optimizing combustion system designs for efficient energy production and the effective use of the biomass as a fuel source, all while carefully considering safety aspects.
NIRS is one of the non-destructive, rapid, and low operation cost methods that do not require the employment of chemicals and chemical expertise. A mathematical correlation is established between the spectral and reference data of samples, containing either full wavelength ranges or a few significant wavelengths. This correlation is used to create the calibration equation for the prediction and evaluation of properties of biomass [
27], such as elemental compositions (C, H, N, and S), determined by ultimate analysis [
28,
29], as well as moisture, volatile matter, fixed carbon, and ash content, assessed by proximate analysis [
29,
30]. The approach demonstrates acceptable performance and serves as an alternative to reference analysis, i.e., ultimate analysis and proximate analysis, which are characterized by their destructive nature, complexity, time-consuming process, and high operational costs, requiring chemicals and chemical expertise. The proximate constituents affect combustion performance [
31], as well as the elemental composition, e.g., ignition temperature, which is determined by the H/C ratio and some other parameters [
32], indicating the possibility of using NIRS to determine the combustion performance of biomass or fuel.
To the best of our knowledge, no study has been conducted to non-destructively evaluate combustion performance indices, such as the Di, Df, Ci, and Si in chipped and ground biomass using FT-NIRS. Therefore, this research is structured into two main sections. The first section involves determining the combustion parameters, including ti and Ti, tf and Tf, the maximum combustion rate , and the average combustion rate , using TGA to calculate the Di, Df, Si, and Ci of biomass from fast-growing trees and agricultural residues. The second section focuses on developing calibration models using Full-PLSR, GA-PLSR, SPA-PLSR, the MP 5 range-PLSR, and the MP 3 range-PLSR for the non-destructive assessment of the Di, Df, Si, and Ci in both chipped and ground biomass. Then, the best-performing PLSR-based model for each index is selected, establishing it as a rapid, reliable, non-destructive alternative method for assessing combustion performance indexes in both chipped and ground biomass.
The research outcomes will assist industries in selecting the most suitable biomass for cost-effective energy production and resource optimization. Additionally, the developed non-destructive evaluation methods will serve as an alternative method to other destructive thermal analysis methods. Furthermore, they will provide a foundation for designing safe, economical, and environmentally balanced biomass combustion systems.
4. Conclusions
The combustion characteristics parameters and combustion performance indices of fast-growing trees and agricultural residues were analyzed through a combined study of TG and DTG curves obtained via TGA. Ti and Tf for fast-growing trees were observed to be higher than those of agricultural residues. This suggests that fast-growing trees were harder to ignite; however, they burnt for a longer duration and produced ash more slowly compared to agricultural residues. While the calculated Di and Df were high for fast-growing trees, the Si and Ci were higher for agricultural residues. This indicates that, even though agricultural residues were easier to ignite and burned more quickly and intensely (exhibiting higher thermal and combustion reactivity), their combustion processes were more controlled and less likely to experience unexpected fluctuations (better combustion stability) during thermal energy generation.
Similarly, five distinct PLSR-based models were developed and compared using NIRS to assess the Di, Df, Si, and Ci under direct combustion conditions in both chip and ground biomass samples. The models with optimal performance were selected based on higher R2C, R2P, and RPD values and lower RMSEC, RMSEP, and bias values. The results conclude that the models for Df and Si in ground biomass were found to be usable with caution for most applications, including research. All other combustion performance indices, both in chip and ground biomass, were suitable solely for the rough screening purpose. Therefore, a more suitable machine learning algorithm needs to be explored to improve the model performance.
The quality of reference data and spectral data, the inclusion of both agricultural residue samples and fast-growing tree samples to broaden the reference data range, proper identification of outliers, careful selection of the calibration set, and the development and evaluation of models, including spectral pre-treatment and regression methods, all play a pivotal role in establishing a reliable NIR application. Regularly updating calibration and validation procedures, including more representative samples and validating with unknown samples is crucial. Minimizing analytical errors is equally imperative for optimizing the model performance.
This research significantly contributes to the sustainable energy sector and advances our broader understanding of biomass combustion, bridging the gap between research and practical application. With its environmentally friendly behavior, the non-destructive evaluation method by NIR spectroscopy proposed in this study offers an essential and valuable alternative to traditional thermal destructive techniques, potentially revolutionizing biomass analysis. As NIR models are inherently dynamic, continual improvements and refinements in both experimental methodologies and modeling approach are essential, leading the way for future advancements to be implemented in biomass industries for both production and usage purposes.