Pornchaloempong, Pimpen

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Pornchaloempong, Pimpen
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Pornchaloempong, P.
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pimpen.po@kmitl.ac.th

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    Predicting biomass global warming potential with FT-NIR spectroscopy
    (2025-12-01)
    Gyawali, Prakash
    ;
    Shrestha, Bijendra
    ;
    Phanomsophon, Thitima
    ;
    Posom, Jetsada
    ;
    Pornchaloempong, Pimpen
    This research is to predict the global warming potential (GWP) of biomass by using Fourier transform near-infrared (FT-NIR) spectroscopy. A partial least squares regression model of 197 biomass chip samples was developed for predicting GWP of fast-growing trees and agricultural residues. The reference value of GWP of biomass sample was calculated by the method provided by Intergovernmental Panel on Climate Change (IPCC). After applying different spectral pretreatments and variable selection methods, the best model for predicting GWP was found using the 1st derivative spectrum pretreatment and covariance method (COVM) based variable selection. The results indicate GWP model exhibit good predictive capabilities, where the model can be usable with caution for any purpose including research, by achieving a coefficient of determination for prediction set (R<sup>2</sup><inf>P</inf>) of 0.86, and ratio of prediction to deviation (RPD) of 2.6. Additionally, the RMSEP of 0.00063 suggests a low prediction error. This pioneering approach presents a swift and efficient means to determine GWP, the complex functionality parameter, which reveals an optimal relationship model, showcasing its efficacy in a significant advancement in the assessment of biomass functionality related to climate change issue. Additionally, the further research is recommended to integrate FT-NIR data with thermogravimetric analyser to simulate of different thermal conversion of biomass type where different emission gases are generated and with gas chromatography–mass spectrometry for evaluation of concentration of the generated gases for further refine GWP predictions which providing more comprehensive insights and exact content of emission gases affect global warming to support the IPCC.
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    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<sup>−1</sup>. 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 R<sup>2</sup>P, 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 R<sup>2</sup>P 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.
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    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)
    Gyawali, Prakash
    ;
    Shrestha, Bijendra
    ;
    Phanomsophon, Thitima
    ;
    Posom, Jetsada
    ;
    Pornchaloempong, Pimpen
    Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-025-10584-z, published online 30 September 2025 The original version of this Article contained errors. In the original version of this article, the climate-carbon effect value was not included as introduced in AR6. As a result, in the Materials and methods section, under the subheading ‘Estimation of global warming potential and emission of greenhouse gas (GHG)’, where: “Step 1: (CO<inf>2</inf>, CH<inf>4</inf>, N<inf>2</inf>O) emissions (kg) = Mass of Fuel (kg) × Carbon emission factor (kg TJ<sup>-1</sup>) × HHV (kg TJ<sup>-1</sup>) Step 2: Total GWP = (GWP of CO<inf>2</inf>×CO<inf>2</inf> emissions) +(GWP of CH<inf>4</inf>×CH<inf>4</inf> emissions) +(GWP of N<inf>2</inf>O×N<inf>2</inf>O emissions)” now reads: “Step 1: GHG (CO<inf>2</inf>, CH<inf>4</inf>, or N<inf>2</inf>O) emissions (kg) = Mass of Fuel (kg) × Specific GHG emission factor (kg TJ<sup>-1</sup>) × HHV (TJ kg<sup>-1</sup>) Step 2: Total GWP (kg CO<inf>2</inf>e) = (GWP of CO<inf>2</inf>×CO<inf>2</inf> emissions) + (GWP of CH<inf>4</inf>×CH<inf>4</inf> emissions) + (GWP of N<inf>2</inf>O×N<inf>2</inf>O emissions)” In addition, under the subheading ‘Model development and validation’, where: “The model was optimized by selecting wavenumbers through various variable selection methods, including the Correlation Method (CM), Variance Method (VM), Co-Variance Method (COVM), and Variable Importance Projection (VIP). The spectral data were pretreated using raw spectra, standard normal variate (SNV), as well as first derivative and second derivative transformations. The following spectra pretreatment methods: Standard Normal Variate (SNV) is for corrects scatter effects and baseline variations<sup>35</sup>,” now reads: “The model was optimized by selecting wavenumbers through various variable selection methods, including the Correlation Method (CM), Variance Method (VM), Co-Variance Method (COVM), and Variable Importance Projection (VIP). The following spectra pretreatment methods: Standard Normal Variate (SNV) is for correcting scatter effects and baseline variations<sup>35</sup>,” In addition, Equations 3, 4 and 6 contained typesetting errors. As a result, Equation 3: (Formula presented.) now reads, (Formula presented.) Equation 4: (Formula presented.) now reads: (Formula presented.) And Equation 6 (Formula presented.) now reads: (Formula presented.) Furthermore, under the Results section, subheading ‘Predicting performance of biomass GWP using PLSR’, where: “The model developed with CM (reduction of 1150 wavenumber of full range to 325 wavenumber) of 1st derivative spectra, gave best performance with R<sup>2</sup><inf>P</inf> was 0.87 (Table 4).” now reads: “The model developed with COVM (reduction of 1150 wavenumber of full range to 325 wavenumber) of 1st derivative spectra, gave the best performance with R<sup>2</sup><inf>P</inf> was 0.85 (Table 4).” Under the subheading ‘Prediction result of HHV using PLSR’, where: “This approach reduced the number of variables from 1150 to 365 wavenumbers, significantly enhancing the model’s performance (R<sup>2</sup><inf>C</inf> of 0.98 and R<sup>2</sup><inf>P</inf> of 0.87)” now reads: “This approach reduced the number of variables from 1150 to 365 wavenumbers, significantly enhancing the model’s performance (R<sup>2</sup><inf>C</inf> of 0.98 and R<sup>2</sup><inf>P</inf> of 0.86)” Under the subheading ‘Regression coefficient and x-loading of GWP model’, where: “Prominent peaks were identified and the bond vibration interpretation is shown in Table 7 and the vibration indicated by Workman and Weyer<sup>38</sup> at the wavenumbers in bold were not found or not related to biomass.” now reads: “Prominent peaks were identified, and the bond vibration interpretation is shown in Table 7, which was indicated by Workman and Weyer<sup>38</sup>.” Equation 8: GWP for CO<inf>2</inf>, or CH<inf>4</inf>, or N<inf>2</inf>O Emissions = (1×CO<inf>2</inf> Emission) or (29.8×CH<inf>4</inf> Emission) or (273×N<inf>2</inf>O Emission) = (1×112 HHV) for CO<inf>2</inf> Emission or (29.8×30 HHV) for CH<inf>4</inf> Emission or (273×4 HHV) for N<inf>2</inf>O Emission = 112.0×HHV for CO<inf>2</inf> Emission or 894.0×HHV for CH<inf>4</inf> Emission or 1092.0×HHV for N<inf>2</inf>O Emission and GWP total = 2098.0 (HHV, TJ kg<sup>-1</sup>) = 0.000002098 kJ kg<sup>-1</sup> = 0.000002098 Jg<sup>-1</sup> now reads: GWP for CO<inf>2</inf>, or CH<inf>4</inf>, or N<inf>2</inf>O Emissions = (1×CO<inf>2</inf> Emission) or (27.2×CH<inf>4</inf> Emission) or (273×N<inf>2</inf>O Emission) = (1×112 HHV) for CO<inf>2</inf> Emission or (27.2×30 HHV) for CH<inf>4</inf> Emission or (273×4 HHV) for N<inf>2</inf>O Emission = 112.0×HHV for CO<inf>2</inf> Emission or 816.0×HHV for CH<inf>4</inf> Emission or 1092.0×HHV for N<inf>2</inf>O Emission and GWP total (kg CO<inf>2</inf>e) = 2020.0 (HHV, TJ kg<sup>-1</sup>) = 0.000002020 kJ kg<sup>-1</sup> = 0.000002020 J g<sup>-1</sup> Moreover, Tables 1 and 3 have been corrected. Incorrect Table 1: Remarks IPCC guideline Calculation of CO<inf>2</inf> emission Higher Heating Value = 17932000 J kg<sup>-1</sup> CO<inf>2</inf> emission factor = 112 kg TJ<sup>-1</sup>, we can follow these steps: Convert HHV to TJ kg<sup>-1</sup>: Since 1 TJ = 10<sup>12</sup> J, we need to convert the HHV from J kg<sup>-1</sup> to TJ kg<sup>-1</sup>: HHV in TJ kg<sup>-1</sup> = 17932000 J kg<sup>-1</sup> / 10<sup>12</sup> = 1.7932×10<sup>-3</sup> TJ kg<sup>-1</sup> CO<inf>2</inf> emission (kg) = Mass of fuel (kg) × CO<inf>2</inf> emission factor (kg TJ<sup>-1</sup>) × HHV (TJ kg<sup>-1</sup>) CO<inf>2</inf> emission (kg) = 1 kg × 112 kg TJ<sup>-1</sup> × 0.017932 TJ kg<sup>-1</sup> Therefore, the CO<inf>2</inf> emission from stationary fuel combustion with an HHV of 17932000 J kg<sup>-1</sup> and using the default CO<inf>2</inf> emission factor of 112 kg TJ<sup>-1</sup> would be approximately 2.0083×10<sup>-3</sup> kg of CO<inf>2</inf> per kg of fuel. CO<inf>2</inf> emission factor: 112 kg dry matter TJ<sup>-1</sup> (typical for wood combustion) Calculation of CH<inf>4</inf> emission Hight Heating Value = 17932000 J kg<sup>-1</sup> CH<inf>4</inf> emission factor = 30 kg TJ<sup>-1</sup> Convert HHV unit to TJ kg<sup>-1</sup> 1 TJ= 10<sup>12</sup> J HHV from J kg<sup>-1</sup> to TJ kg<sup>-1</sup>: HHV in TJ kg<sup>-1</sup> = 17932000 J kg<sup>-1</sup> / 10<sup>12</sup> = 1,7932×10<sup>-5</sup> TJ kg<sup>-1</sup> CH<inf>4</inf> emission (kg) = Mass of Fuel (kg) × CH<inf>4</inf> emission factor (kg TJ<sup>-1</sup>) × HHV (TJ kg<sup>-1</sup>) CH<inf>4</inf> emission (kg) = 1 kg × 30 kg TJ<sup>-1</sup> × 1,7932×10<sup>-5</sup> TJ kg<sup>-1</sup> CH<inf>4</inf> emission (kg) = 5.3796×10<sup>-4</sup> kg of CH<inf>4</inf> per kg of fuel CH<inf>4</inf> emission factor: 30 kg dry matter TJ<sup>-1</sup> Calculation of N<inf>2</inf>O emission Hight Heating Value = 17932000 J kg<sup>-1</sup> N<inf>2</inf>O emission factor = 4 kg TJ<sup>-1</sup> Convert HHV unit to TJ kg<sup>-1</sup> 1 TJ = 10<sup>12</sup> J HHV from J kg<sup>-1</sup> to TJ kg<sup>-1</sup>: HHV in TJ kg<sup>-1</sup> = 17932000 J kg<sup>-1</sup> / 10<sup>12</sup> = 1.7932 ×10<sup>-5</sup> TJ kg<sup>-1</sup> N<inf>2</inf>O emission (kg) = Mass of Fuel (kg) × N<inf>2</inf>O emission factor (kg TJ<sup>-1</sup>) × HHV (TJ kg<sup>-1</sup>) N<inf>2</inf>O emission (kg) = 1 kg × 4 kg TJ<sup>-1</sup> × 1.7932×10<sup>-5</sup> TJ kg<sup>-1</sup> N<inf>2</inf>O emission (kg) = 7.1728×10<sup>-5</sup> kg of N<inf>2</inf>O per kg of fuel N<inf>2</inf>O emission factor 4 kg dry matter /TJ The concept of global warming potential (GWP) was introduced in IPCC –AR1 (Shine et al. 1990) to compare the greenhouse effects of different greenhouse gases relative to a reference gas, normally taken as carbon dioxide, under this definition, CO<inf>2</inf> would have a GWP value of 1. Total GWP = (GWP of CO<inf>2</inf>×CO<inf>2</inf> emissions)+(GWP of CH<inf>4</inf>×CH<inf>4</inf> emissions)+(GWP of N<inf>2</inf>O×N<inf>2</inf>O emissions) Total GWP =1×2.0083×10<sup>-3</sup>+29.8× 5.379 ×10<sup>-4</sup>+273×7.1728×10<sup>-5</sup> Total GWP=0.0376 kg CO<inf>2</inf>e This calculation is by 100 years based GWP of emission gases followed AR6 <sup>7</sup> Correct Table 1: Remarks IPCC guideline Calculation of CO<inf>2</inf> emission Higher Heating Value = 17932000 J kg<sup>-1</sup> CO<inf>2</inf> emission factor = 112 kg TJ<sup>-1</sup>, we can follow these steps: Convert HHV to TJ kg<sup>-1</sup>: Since 1 TJ = 10<sup>12</sup> J, we need to convert the HHV from J kg<sup>-1</sup> to TJ kg<sup>-1</sup>: HHV in TJ kg<sup>-1</sup> = 17932000 J kg<sup>-1</sup> / 10<sup>12</sup> = 1.7932×10<sup>-5</sup> TJ kg<sup>-1</sup> CO<inf>2</inf> emission (kg) = Mass of fuel (kg) × CO<inf>2</inf> emission factor (kg TJ<sup>-1</sup>) × HHV (TJ kg<sup>-1</sup>) CO<inf>2</inf> emission (kg) = 1 kg × 112 kg TJ<sup>-1</sup> × 1.7932 × 10<sup>-5</sup> TJ kg<sup>-1</sup> Therefore, the CO<inf>2</inf> emission from stationary fuel combustion with an HHV of 17932000 J kg<sup>-1</sup> and using the default CO<inf>2</inf> emission factor of 112 kg TJ<sup>-1</sup> would be approximately 2.0083 × 10<sup>-3</sup> kg of CO<inf>2</inf> per kg of fuel. CO<inf>2</inf> emission factor: 112 kg dry matter TJ<sup>-1</sup> (typical for wood combustion) Calculation of CH<inf>4</inf> emission Higher Heating Value = 17932000 J kg<sup>-1</sup> CH<inf>4</inf> emission factor = 30 kg TJ<sup>-1</sup> Convert HHV unit to TJ kg<sup>-1</sup> 1 TJ= 10<sup>12</sup> J HHV from J kg<sup>-1</sup> to TJ kg<sup>-1</sup>: HHV in TJ kg<sup>-1</sup> = 17932000 J kg<sup>-1</sup> / 10<sup>12</sup> = 1,7932×10<sup>-5</sup> TJ kg<sup>-1</sup> CH<inf>4</inf> emission (kg) = Mass of Fuel (kg) × CH<inf>4</inf> emission factor (kg TJ<sup>-1</sup>) × HHV (TJ kg<sup>-1</sup>) CH<inf>4</inf> emission (kg) = 1 kg × 30 kg TJ<sup>-1</sup> × 1,7932×10<sup>-5</sup> TJ kg<sup>-1</sup> CH<inf>4</inf> emission (kg) = 5.3796×10<sup>-4</sup> kg of CH<inf>4</inf> per kg of fuel CH<inf>4</inf> emission factor: 30 kg dry matter TJ<sup>-1</sup> Calculation of N<inf>2</inf>O emission Higher Heating Value = 17932000 J kg<sup>-1</sup> N<inf>2</inf>O emission factor = 4 kg TJ<sup>-1</sup> Convert HHV unit to TJ kg<sup>-1</sup> 1 TJ = 10<sup>12</sup> J HHV from J kg<sup>-1</sup> to TJ kg<sup>-1</sup>: HHV in TJ kg<sup>-1</sup> = 17932000 J kg<sup>-1</sup> / 10<sup>12</sup> = 1.7932 × 10<sup>-5</sup> TJ kg<sup>-1</sup> N<inf>2</inf>O emission (kg) = Mass of Fuel (kg) × N<inf>2</inf>O emission factor (kg TJ<sup>-1</sup>) × HHV (TJ kg<sup>-1</sup>) N<inf>2</inf>O emission (kg) = 1 kg × 4 kg TJ<sup>-1</sup> × 1.7932×10<sup>-5</sup> TJ kg<sup>-1</sup> N<inf>2</inf>O emission (kg) = 7.1728×10<sup>-5</sup> kg of N<inf>2</inf>O per kg of fuel N<inf>2</inf>O emission factor 4 kg dry matter /TJ<sup>-1</sup> The concept of global warming potential (GWP) was introduced in IPCC –AR1 (Shine et al. 1990) to compare the greenhouse effects of different greenhouse gases relative to a reference gas, normally taken as carbon dioxide, under this definition, CO<inf>2</inf> would have a GWP value of 1. Total GWP = (GWP of CO<inf>2</inf>×CO<inf>2</inf> emissions) + (GWP of CH<inf>4</inf>×CH<inf>4</inf> emissions) + (GWP of N<inf>2</inf>O×N<inf>2</inf>O emissions) Total GWP = 1×2.0083×10<sup>-3</sup>+ 27.2× 5.3796 × 10<sup>-4</sup>+ 273×7.1728×10<sup>-5</sup> Total GWP= 0.03622 kg CO<inf>2</inf>e This calculation is by 100 years based GWP of emission gases followed AR6 <sup>7</sup> Incorrect Table 3: Calibration set Prediction set Parameter Method NT NC Max Min Mean SD NP Max Min Mean SD GWP IPCC Guidelines 197 147 0.03905 0.03080 0.03564 0.00180 50 0.038943 0.033002 0.03577 0.00165 HHV (J g<sup>-1</sup>) Bomb Calorimeter 197 147 18616 16405 16976 910 50 17950 15268 17051 787 Correct Table 3: Parameter Method NT Calibration Set Prediction Set NC Max Min Mean SD NP Max Min Mean SD GWP (kg CO<inf>2</inf>e) IPCC Guidelines 197 147 0.03905 0.03080 0.03564 0.00180 50 0.038943 0.033002 0.03577 0.00165 HHV (J g<sup>-1</sup>) Bomb Calorimeter 197 147 18616 16405 15268 910 50 17950 16976 17051 787 Finally, the legends of Tables 4 and 7 have been updated: “Table 4: Prediction of GWP of biomass of fast-growing tree and agriculture residue by PLSR. N: Number of samples in calibration set, R<sup>2</sup><inf>c</inf>: coefficient of determination of calibration set, n: number of samples in prediction set, R<sup>2</sup><inf>p</inf>: coefficient of determination of prediction set, RPD: ratio of prediction to deviation, CM: correlation method, VM: variance method, COVM: co-variance method, VIP: variable. Significant values are in [bold].” now reads: “Table 4. Prediction of GWP of biomass of fast-growing tree and agriculture residue by PLSR. N: Number of samples in calibration set, R<sup>2</sup><inf>c</inf>: coefficient of determination of calibration set, n: number of samples in prediction set, R<sup>2</sup><inf>p</inf>: coefficient of determination of prediction set, RPD: ratio of prediction to deviation, CM: correlation method, VM: variance method, COVM: co-variance method, VIP: variable important projection, FstDev: 1<sup>st</sup> derivative, SecDev: 2<sup>nd</sup> derivative. Significant values are in [bold].” “Table 7. The function groups corresponding to the wavenumber shown in regression coefficient plot and x-loading plot of models for GWP and HHV. *1ν, fundamental stretching vibration; 2ν, 1st overtone of fundamental stretching vibration; 3ν, 2nd overtone of fundamental stretching vibration; 5ν, 4th overtone of fundamental stretching vibration; 1δ, fundamental bending (deformation) vibration; 3δ, 2nd overtone of fundamental bending (deformation) vibration; 1, symmetric stretching vibration; 2, bending vibration; 3, asymmetric stretching vibration; and + is combination. now reads: “Table 7. The function groups corresponding to the wavenumber shown in regression coefficient plot and x-loading plot of models for GWP and HHV. 1ν, fundamental stretching vibration; 2ν, 1st overtone of fundamental stretching vibration; 3ν, 2nd overtone of fundamental stretching vibration; 5ν, 4<sup>th</sup> overtone of fundamental stretching vibration; 1δ, fundamental bending (deformation) vibration; 3δ, 2nd overtone of fundamental bending (deformation) vibration; and + is combination.” The original version of this Article has been corrected.
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    Identification and Removal of Negative Biomass Samples via Scatter Plot Analysis to Improve GWP Predictive Modeling
    (2025-11-13)
    Gyawali, Prakash
    ;
    Shrestha, Bijendra
    ;
    Posom, Jetsada
    ;
    Pornchaloempong, Pimpen
    ;
    Sirisomboon, Panmanas
    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.