KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 3 of 3

Some of the metrics are blocked by your
consent settings
NIR Spectroscopy for Non-Destructive Prediction of Greenhouse Gas Emissions and Global Warming Potential by Biomass Combustion
(2026-05-01)
Sirisomboon, Panmanas
;
Gyawali, Prakash
;
Posom, Jetsada
;
Lapcharoensuk, Ravipat
;
Shrestha, Bim Prasad
Greenhouse gas (GHG) emissions from biomass combustion include carbon dioxide (CO<inf>2</inf>), methane (CH<inf>4</inf>) and nitrous oxide (N<inf>2</inf>O), which cause climate change and global warming. By measuring GHG emissions by biomass combustion, a potent protocol for the calculation of global warming potential (GWP), which is how much the global temperature has risen due to combustion processes, can be achieved, contributing to determining the mean reduction in global temperature rise and fostering a transition towards more sustainable energy systems. Additionally, warning can be given of the GHG and GWP risks associated with different species of biomass. This review includes the GHG emissions and GWP of biomass combustion and their measurement and estimation directly through biomass sample combustion, using unmanned aerial vehicles (UAVs) and satellite measurements of radiation interacting with atmospheric gases, or satellite-derived data and calculations according to IPCC guidelines. In addition, the relationship of lignocellulosic compounds and elements in biomass to HHV and GHG emissions is described. The key mechanism of molecular vibration of hydrogen bonds in biomass caused by NIR radiation related to GHG emissions is revealed and recorded regarding the possibility of using NIR spectroscopy for the prediction of GHG emissions and GWP. Calculation examples for sugarcane bagasse and other biomass species are shown. The comparative advantages and limitations of NIR spectroscopy with respect to other methods are included. These factors lead to elucidation of the possibility of using NIR spectroscopy for non-destructive prediction of GHG emissions. In this review, the feasibility of using NIR spectroscopy to evaluate GHG emissions, GWP and emission factors (EFs) as an alternative to IPCC estimation methods related to climate change by biomass combustion is confirmed. NIR spectroscopy is a novel methodology for predicting GHG emissions and GWP directly from intact chip or powder biomass spectral data without explicit gas measurement. This article records the essential spectroscopic knowledge of biomass polymer valorization that is of value in polymer science.
Some of the metrics are blocked by your
consent settings
NIR Spectroscopy as an Alternative to Thermogravimetric Analyzer for Biomass Proximate Analysis: Comparison of Chip and Ground Biomass Models
(2024-02-01)
Shrestha, Bijendra
;
Posom, Jetsada
;
Sirisomboon, Panmanas
;
Shrestha, Bim Prasad
;
Pornchaloempong, Pimpen
This study investigates the non-destructive analysis of proximate parameters (moisture content, MC; volatile matter, VM; fixed carbon, FC; ash content) in various chipped and ground biomass using a combination of destructive thermogravimetric analysis (TGA) and non-destructive near-infrared spectroscopy (NIRS) with partial least squares regression (PLSR). The thermogravimetric method determines proximate analysis data through TG and DTG curves, tracking biomass mass loss over time or temperature. NIRS scans chipped biomass in diffuse reflectance, and ground biomass in transflectance mode, covering the wavenumber range from 3595 to 12,489 cm−1. PLSR-based models (Full-PLSR, GA-PLSR, SPA-PLSR, MP PLSR 5-range method, and MP PLSR 3-range method) are developed and evaluated based on R2P, RMSEP, and RPD. MC and FC models for chip biomass exhibit satisfactory performance, making them cautiously applicable in various applications, including research. Optimal models for MC and FC in chip biomass, constructed using GA-PLSR with the second derivative and Full-PLSR with a constant offset, yield high R2P values (0.8654 and 0.8773), low RMSEP values (0.85% and 2.12%), and high RPD values (2.9 and 3.0), indicating applicative capabilities. Other parameters such as MC and FC in ground biomass, as well as VM and ash content in both chip and ground biomass, are found suitable for rough screening. Model sensitivity, assessed by calculating LOQ, indicates high sensitivity for VM in both chip and ground biomass and FC in chip biomass, as the calculated LOQ value is lower than the minimum reference values used during model development. However, for the remaining parameters, LOQ values surpass the established minimum reference value, suggesting limitations in predicting samples below the calibration range. Continuous model enhancement incorporating an ample number of representative biomass samples and consistent validation with unknown samples are imperative for ensuring accurate predictions.
Some of the metrics are blocked by your
consent settings
Effect of Combined Non-Wood and Wood Spectra of Biomass Chips on Rapid Prediction of Ultimate Analysis Parameters Using near Infrared Spectroscopy
(2024-01-01)
Shrestha, Bijendra
;
Posom, Jetsada
;
Sirisomboon, Panmanas
;
Shrestha, Bim Prasad
;
Funke, Axel
The ultimate analysis parameters, including carbon (C), hydrogen (H), nitrogen (N), and oxygen (O) content in biomass, were rarely found to be predicted by non-destructive tests to date. In this research, we developed partial least squares regression (PLSR) models to predict the ultimate analysis parameters of chip biomass using near-infrared (NIR) raw spectra of non-wood and wood samples from fast-growing tree and agricultural residue and nine different traditional spectral preprocessing techniques. These techniques include first derivative (sd1), second derivative (sd2), constant offset, standard normal variate (SNV), multiplicative scatter correction (MSC), vector normalization, min-max normalization, mean centering, sd1 + vector normalization, and sd1 + MSC. Additionally, we employed a genetic algorithm (GA), successive projection algorithm (SPA), multi-preprocessing (MP) 5-range, and MP 3-range to develop a PLSR model for rapid prediction. A dataset consisting of 120 chip biomass samples was utilized for model development in which the samples were non-wood samples of 65–67% and wood samples of 33–35%, and the model performance was evaluated and compared. The selection of the optimum performing model was mainly based on criteria such as the coefficient of determination in the prediction set (R2<inf>P</inf>), root mean square error of the prediction set (RMSEP), and the ratio of prediction to deviation (RPD). The optimal model for weight percentage (wt.%) of C was obtained using GA–PLSR, yielding R2<inf>P</inf>, RMSEP, and RPD values of 0.6954, 1.1252 wt.%, and 1.8, respectively. Similarly, for wt.% of O, the most effective model was obtained using the multi-preprocessing PLSR–5 range method with R2<inf>P</inf> of 0.7150, RMSEP of 1.3088 wt.%, and RPD of 1.9. For wt.% of N, the optimal model was obtained using the MP PLSR-3 range method, resulting in R2<inf>P</inf>, RMSEP, and RPD values of 0.6073, 0.1008 wt.%, and 1.6, respectively. However, wt.% of the H model provided R2<inf>P</inf>, RMSEP, and RPD values of 0.5162, 0.2322 wt.%, and 1.5, respectively. Notably, the limit of quantification (LOQ) values for C, H, and O were lower than the minimum reference values used during model development, indicating a high level of sensitivity. However, the LOQ for N exceeded the minimum reference value, implying the samples to be predicted by the model must be in the range of reference range in the calibration set. By scatter plot analysis, the effect of combined non-wood and wood spectra of biomass chips on rapid prediction of ultimate analysis parameters using NIR spectroscopy was investigated. To include different species in a model, the species have to be not only in the different values of the constituents to make a wider range for a robust model, but also must provide their trend line characteristics in the scatter plot, i.e., correlation coefficient (R), slope, and intercept (same slope and slope approached to 1, and intercept is same (no gap) and approached zero, high R approached to 1). The effect of the R, slope, and intercept to obtain the better-optimized model was studied. The results show that the different species affected the model performance of each parameter prediction in a different manner, and by scatter plot analysis, which of these species were affecting the model negatively and how the model could be improved was indicated. This is the first time the effect has been studied by the principle of a scatter plot.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results