Pornchaloempong, Pimpen
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Preferred name
Pornchaloempong, Pimpen
Alternative Name
Pornchaloempong, P.
Main Affiliation
Email
pimpen.po@kmitl.ac.th
7 results
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Item type:Publication, Integrating Vis-SWNIR spectrometer in a conveyor system for in-line measurement of dry matter content and soluble solids content of durian pulp(2021-11-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Predicting biomass global warming potential with FT-NIR spectroscopy(2025-12-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, NIR Spectroscopy as an Alternative to Thermogravimetric Analyzer for Biomass Proximate Analysis: Comparison of Chip and Ground Biomass Models(2024-02-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Effect of Environment Temperature and Relative Humidity on Thermal Emissivity: Study Case of Mango Fruit(2024-01-01); ; Dachoupakan Sirisomboon, CheewanunThis research was to study the effect of the environment condition during image captured including temperature and relative humidity in the packaging house of the mango exporting factory and in the orchard on the emissivity of mango fruit. The result showed that in the controlled environment of the packaging house in factory, the emissivity was increased (the mango emitted more energy) when the surface temperature was lower and the emissivity of mango is 0.71-0.84 and in the uncontrolled environment of open-air packaging yard in the mango orchard, the different transpiration rates of mango effected mainly by ambient temperature and relative humidity make the morning condition emissivity of 0.44-0.66 and the afternoon condition of 0.72-0.98. There was the effect of the different measured positions on the fruits where the physiology was different and the fluctuated environment in the latter condition made the wide range of emissivity of mangoes. This indicated the shortcoming of thermal imaging of horticultural product if the correct emissivity varied and difficult to set in the thermal camera setting, hence the inaccurate thermogram to be obtained. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Author Correction: Predicting biomass global warming potential with FT-NIR spectroscopy (Scientific Reports, (2025), 15, 1, (33725), 10.1038/s41598-025-10584-z)(2026-12-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Near-Infrared Spectroscopy Modeling of Combustion Characteristics in Chip and Ground Biomass from Fast-Growing Trees and Agricultural Residue(2024-03-01); ; Shrestha, Bim PrasadThis 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Identification and Removal of Negative Biomass Samples via Scatter Plot Analysis to Improve GWP Predictive Modeling(2025-11-13); Accurate prediction of Global Warming Potential (GWP) from biomass constituents is essential for evaluating the sustainability of bioenergy sources. However, the inclusion of biomass samples with weak or negative correlation to key elemental components such as Carbon (C). Hydrogen (H). Nitrogen (N). and Oxygen (O)'can reduce model accuracy and lead to misleading conclusions. This study utilizes scatter plot regression analysis to evaluate and remove "negative biomass samples."defined as those with consistently low R<sup>2</sup> values across constituent-GWP relationships using HHV = 0.2949C + 0.82 50H developed for wood biomass by Yin. Regression models were generated for each biomass species using elemental concentrations as predictors of GWP. Notably, several non-wood species (e.g.. Zea Mays-Shell. Bagasse. Bamboo) exhibited very low R- values (often <0.05) for model between elemental composition and GWP. where all elemental correlations indicated weak predictive relationships. In contrast, wood-based species such as Alnus demonstrated significantly higher R<sup>2</sup> values, especially with Carbon (R<sup>2</sup> = 0.69). Hydrogen (R<sup>2</sup> = 0.57). and Oxygen (R<sup>2</sup> = 0.68), suggesting a stronger linear influence on GWP. Removing these low-contributing samples improved the clarity and reliability of the predictive model related to HHV and each type of element (C.H.N and O) as evidenced by a sharper regression slope of a graph plotted between predicted GWP and measured GWP of positive species and better fit (increased R<sup>2</sup>) for the remaining samples. These results highlight the value of preliminary scatter plot analysis in identifying biomass species that obscure rather than support predictive modeling. This filtering step ultimately enhances the robustness and inteipretability of constituent-based GWP prediction frameworks, particularly when applying FT-XIR spectroscopy and chemometric modelling.