Saechua, Wanphut

Preferred name

Saechua, Wanphut

Alternative Name

Saechua, W.

Main Affiliation

School of Engineering

wanphut.sa@kmitl.ac.th

Now showing 1 - 10 of 24

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Prediction of elemental components of ground bamboo using micro-NIR spectrometer
(2019-09-09)
Posom, J.
;
Saechua, W.
The combustion performance depended on heating value of as-received bamboo which involved elemental components (C, H, N, O, S). The experiment using Micro-NIR spectrometer to investigate of 80 ground bamboo samples and partial least squares regression (PLSR) with cross validation technique was used to develop the model. The reference method was performed on the elemental analyser. It was showed that only C (carbon) was efficiently possible to determine with R2 approximately of 0.6, RMSECV 0.612%, SECV 0.616% and Bias 0.001%. Important peaks in regression coefficient plot at wavelengths 1174 nm and 1927 nm corresponded to C-H overtone and C=O str. 2nd overtone for lignin. At wavelength 1395 nm and 1728 nm were related to 2C-H str.+C-H def. and C-H str. overtone for hemi-cellulose. At wavelength 1580 nm, 1632 nm, 1779 nm, 1824 nm, and 2023 nm conformed respectively to O-H str.1st overtone, C-H str.1st overtone, C-H str. overtone, O-H str.+2C-O str. and 2O-H def.+C-O def. for cellulose.
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Precision test for spectral characteristic of NIR spectroscopy and bomb calorimeter for measuring higher heating value of Leucaena Leucocephala
(2018-08-14)
Posom, Jetsada
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Sirisomboon, Panmanas
;
Saechua, Wanphut
The higher heating value (HHV) plays a significant role in determining of the energy potential of biomass. The method to receive this value using a bomb calorimeter takes a long time approximately of 15 minutes. The implementation of near infrared spectroscopy (NIR) technique by generating a prediction model can be replaced the former method in order to reduce time into 2 minutes. Therefore, the precision of both techniques have to be concerned in order to get the reliable values. The repeatability and reproducibility processes were carried out to evaluate the precision of both methods. It was found that the percentage difference of standard deviation (SD) from the mean of repeatability for the NIR was less than 5 % and for reproducibility was slightly over 5 % (but not over than 10%). The SD of repeatability and reproducibility of the bomb calorimeter were 54.6 J/g (0.3 % difference of average HHV value) and 81.3 J/g (0.5 % difference of average HHV value) respectively. The average HHV from the bomb calorimeter was 18,166.77±236.29 J/g. Both techniques showed very low percentage difference of the SD from the mean that revealed very high reliability and precision of measurements.
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GEOGRAPHICAL IDENTIFICATION OF DURIAN (CV. MONTHONG) FROM PRACHUAP KIRI KHAN AND CHANTHABURI PROVINCE BY USING NEAR INFRARED SPECTROSCOPY
(2021-01-01)
Chanachot, Kingdow
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Saechua, Wanphut
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Sirisomboon, Panmanas
Geographic origin of durian (Durio zibethinus Murray) can influence consumers purchasing decisions. A guaranteed geographical origin is extremely useful to maintain the credibility and popularity of this fruit. The objective of this work is to confirm geographical identification (GI) of durian Monthong from Prachuap Kiri Khan Province from south of Thailand which achieved the GI lable from the department of intellectual property by near infrared spectroscopy (NIRs). Total of 60 durian Monthong samples were acquired in the wavenumber range of 12,500-4,000 cm-1by FT-NIR spectrometer. The sample presentation for scanning were 3 forms including intact peel (with thorns), cut-thorns peel and stem of the fruit. Soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and support vector machine classification (SVMC) were used to establish identification model using the full spectra. Overall classification accuracy of SVMC was the highest in training and validation set of stems which was 100% and 100%, respectively. This study indicated that NIRs might be a method to guaranteed geographic origin of durian Monthong from Prachuap Kiri Khan. However, more robust model should be developed by using more samples.
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Precision test for the spectral characteristic of FT-NIR for the measurement of water content of wheat straw
(2019-09-09)
Fonseca, F. G.
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Funke, A.
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Saechua, W.
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Sirisomboon, P.
Near Infrared (NIR) Spectroscopy is widely employed as a rapid technique for the evaluation of properties of biomass materials. Precision and accuracy of the instruments is an important aspect in order to minimize error in the determination of results. The objective of this publication is to determine scanning repeatability and reproducibility of the NIR spectrometer for wheat straw (Triticum aestivum L.), using either a fixed scan or a rotating scan. The former presented marginally better repeatability but worse reproducibility. Samples in equilibrium with the local atmosphere versus samples of controlled and different moisture contents were also compared, and the latter performed better on the precision test but both fixed and rotating scans. As the ultimate objective of this test is the use of this method to determine variations between different moisture content, and as the rotating scan presents better reproducibility, this method was selected as the reference method for further NIR analyses focused on the variation of moisture content.
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Evaluation of pyrolysis characteristics and kinetic parameters from several prospected biomass residues by thermogravimetric analysis
(2024-01-01)
Pambudi, S.
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Jongyingcharoen, J. S.
;
Saechua, W.
With fossil fuel supplies dwindling and persistent environmental concerns surrounding their consumption, biomass has emerged as a highly promising renewable energy source. Understanding the characteristics of biomass pyrolysis is crucial as it provides valuable insights and guidance for designing and optimizing the pyrolysis process. In this regard, a thermogravimetric assessment was conducted to evaluate the pyrolysis characteristics and kinetic parameters of four prospective biomass sources: Yang Na wood (Dipterocarpus alatus), palmyra palm shell, cotton stalk, and spent coffee grounds with a condition temperature range of 33 °C to 700 °C and a heating rate of 10 °C·min-1 in a nitrogen atmosphere. The kinetic parameters were evaluated using the Coats and Redfern methods, employing various reaction order models. The activation energy and pre-exponential factor were determined for the active pyrolysis stage. The results revealed that the high heating values for all samples ranged from 18.20 to 23.00 MJ·kg-1. Additionally, the onset temperature fell within the range of 243 to 254 °C, while the offset temperature ranged from 365 to 452 °C for all samples. The conversion rate at the offset temperature was 0.67 for Yang Na wood, 0.62 for palmyra palm shell, 0.65 for cotton stalk, and 0.74 for spent coffee grounds. Moreover, the activation energies were measured as 46.47 kJ·mol-1 for Yang Na wood, 52.46 kJ·mol-1 for palmyra palm shell, 64.20 kJ·mol-1 for cotton stalk, and 69.01 kJ·mol-1 for spent coffee grounds. The higher activation energy corresponded to a higher pre-exponential factor. In conclusion, the pyrolysis characteristics and kinetic parameters of the four types of biomasses have been found to be favourable, indicating their potential for promotion and application as a raw material for the pyrolysis process.
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Explainable machine learning for predicting thermogravimetric analysis of oxidatively torrefied spent coffee grounds combustion
(2025-04-01)
Pambudi, Suluh
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Jongyingcharoen, Jiraporn Sripinyowanich
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Saechua, Wanphut
Understanding the combustion behavior of oxidatively torrefied spent coffee grounds (SCG) is crucial for advancing sustainable fuel technologies. This study introduces a novel, explainable machine learning (ML) framework as a cost-effective alternative to traditional thermogravimetric analysis (TGA) that is designed to accelerate the evaluation of oxidatively torrefied SCG combustion properties. Four ML models: artificial neural network (ANN), k-nearest neighbor (k-NN), random forest (RF), and decision tree (DT), were compared to predict TGA data using proximate analysis and combustion temperature (CT). Among the evaluated models, k-NN exhibited the highest performance, achieving near-perfect R2 values that exceeded 0.9904 and RMSE values below 0.9552 on the validation set for both TG (mass loss) and DTG (derivative mass loss). It also accurately predicted key combustion properties, including ignition, peak, and burnout temperature when tested on unknown data. LIME (Local Interpretable Model-agnostic Explanations) analysis revealed that CT was the most influential predictor for TG and DTG, enhancing model interpretability. The results highlight the effectiveness of the k-NN-LIME approach in analyzing the combustion of oxidatively torrefied SCG, offering a robust and explainable model with significant implications for bioenergy research and sustainable fuel development.
1
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Synergistic torrefaction and co-combustion of rice husk and spent coffee grounds: Thermo-kinetics, ash morphology, and waste-to-energy implications
(2026-09-01)
Pambudi, Suluh
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Jongyingcharoen, Jiraporn Sripinyowanich
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Saechua, Wanphut
Combining silica-rich agricultural residues such as rice husk (RH) with energy-dense food-processing waste like torrefied spent coffee grounds (TSCG) offers a circular strategy to exploit the complementary advantages of each biomass while improving fuel performance and combustion reliability. Therefore, the objective of this study is to systematically investigate the co-combustion behavior, kinetic performance, and ash morphology of RH and TSCG blends using thermogravimetric analysis across multiple blend ratios and heating rates. Optimal co-combustion characteristics were observed for blends containing 40–60% RH, demonstrated by comprehensive combustion index values up to 6.02 × 10–6%2 min–2 °C–3 and flammability indices exceeding 0.72 × 10–4% min–1 °C–2. Furthermore, these blends exhibited low activation energies during dehydration (150–161 kJ mol⁻¹), devolatilization (143–175 kJ mol⁻¹), and char oxidation (89–134 kJ mol⁻¹). Notably, these blends exhibit high carbon conversion efficiency, leading to a relatively low residual ash content (10.25–14.36%). Synergistic effects, characterized by experimental mass loss exceeding theoretical predictions by up to 2%, were confirmed above 300 °C. This synergistic behavior is attributed to the interaction between oxygenated volatiles released from RH and the catalytic effects of alkali and alkaline earth metals present in TSCG, along with the stabilizing role of silica in mitigating ash-related issues. Additionally, increasing the proportion of TSCG promoted earlier ignition (T<inf>i</inf> reduced to 213 °C) but prolonged the char combustion stage, resulting in higher burnout temperatures of up to 678 °C. Morphological and elemental ash analysis revealed that moderate Si content in 40–60% RH blends contributed to thermal stability while suppressing alkali-induced slag formation. Collectively, these results demonstrate that co-combustion of RH with TSCG significantly enhances combustion reactivity, promotes kinetic synergy, and improves thermal stability. These findings highlight the potential of RH–TSCG blends as efficient and environmentally sustainable fuels for bioenergy applications by utilizing readily available local biomass residues.
1
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A Geographical Origin Classification of Durian (cv. Monthong) Using Near-Infrared Diffuse Reflectance Spectroscopy
(2023-10-01)
Chanachot, Kingdow
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Saechua, Wanphut
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Posom, Jetsada
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Sirisomboon, Panmanas
The objective of this research was to classify the geographical origin of durians (cv. Monthong) based on geographical identification (GI) and regions (R) using near infrared (NIR). The samples were scanned with an FT-NIR spectrometer (12,500 to 4000 cm−1). The NIR absorbance differences among samples that were collected from different parts of the fruit, including intact peel with thorns (I-form), cut-thorn peel (C-form), stem (S-form), and the applied synthetic minority over-sampling technique (SMOTE), were also investigated. Models were developed across several classification algorithms by the classification learner app in MATLAB. The models were optimized using a featured wavenumber selected by a genetic algorithm (GA). An effective model based on GI was developed using SMOTE-I-spectra with a neural network; accuracy was provided as 95.60% and 95.00% in cross-validation and training sets. The test model was provided with a testing set value of %accuracy, and 94.70% by the testing set was obtained. Likewise, the model based on the regions was developed from SMOTE-ICS-form spectra, with the ensemble classifier showing the best result. The best result, 88.00FF% accuracy by cross validation, 86.50% by training set, and 64.90% by testing set, indicates the classification model of East (E-region), Northeast (NE-region), and South (S-region) regions could be applied for rough screening. In summary, NIR spectroscopy could be used as a rapid and nondestructive method for the accurate GI classification of durians.
1
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Evaluation of pyrolysis characteristics of milled bamboo using near-infrared spectroscopy
(2017-01-01)
Posom, Jetsada
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Saechua, Wanphut
;
Sirisomboon, Panmanas
This paper reports the development of a rapid and low-cost method based on near-infrared spectroscopy as an alternative for thermogravimetric determination of the pyrolysis characteristics, including T<inf>onset</inf>, T<inf>sh</inf>, T<inf>peak</inf>, T<inf>offset</inf>and DTG<inf>peak</inf>, of milled bamboo. T<inf>onset</inf>is the extrapolated onset temperature that is calculated from the partial peak resulting from the decomposition of the hemicellulose component, T<inf>sh</inf>is the temperature corresponding to the overall maximum of the hemicellulose decomposition rate, DTG<inf>peak</inf>is the overall maximum of the cellulose decomposition rate, T<inf>peak</inf>is the temperature corresponding to the overall maximum of the cellulose decomposition rate and T<inf>offset</inf>is the extrapolated offset temperature of the DTG<inf>peak</inf>curves determined using thermogravimetric analysis (TGA). The models may be used to control the pyrolysis processes of bamboo to achieve the most economical and environmental conditions. 80 samples of bamboo with various circumferences of culms in the ranges of approximately 16–18, 18–20, 20–22, 22–24, 24–26, 26–28, 28–30, 30–32, 32–34, 34–36, 36–38 and 38–40 cm were randomly collected for optimization of the models. The models were optimized by partial least squares regression (PLSR) with 80% of samples for the calibration set and 20% for the validation set. For T<inf>onset</inf>, T<inf>sh</inf>, T<inf>peak</inf>, T<inf>offset</inf>and DTG<inf>peak</inf>the models showed coefficients of determination (R2) of 0.566, 0.845, 0.917, 0.973, and 0.671; root mean square errors of prediction (RMSEP) of 9.7 °C, 4.36 °C, 3.77 °C, 2.66 °C, and 0.428 wt loss %/min; ratios of prediction to deviation (RPD) of 1.52, 2.58, 3.48, 3.55, and 1.75; and biases of −0.344 °C, −0.765 °C, 0.349 °C, −5.41 °C, and 0.045 wt loss %/min, respectively. In addition, the results showed that pyrolysis characteristics did not depend on the circumference. The vibrational bands of water and CH<inf>3</inf>, O[sbnd]H stretch, first overtones of Ar–OH, CH<inf>2</inf>and HC[dbnd]CH in the cellulose and lignin structures, O[sbnd]H hydrogen bonds of polyvinyl alcohol and C[sbnd]H stretch corresponding to the first overtone of CH<inf>2</inf>had the highest influence on the values of T<inf>onset</inf>, T<inf>sh</inf>, T<inf>peak</inf>, and T<inf>offset</inf>, respectively. The vibrational band of the C[sbnd]O[sbnd]C asymmetrical stretches of cellulose and hemicellulose, and the combination of O[sbnd]H stretch and HOH bend of polysaccharides influenced the DTG<inf>peak</inf>value. These results are beneficial for studying the thermal behaviour of milled bamboo as a potential resource for producing biofuels, especially in the pyrolysis process.
2
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A thermogravimetric assessment of eco-friendly biochar from oxidative torrefaction of spent coffee grounds: Combustion behavior, kinetic parameters, and potential emissions
(2024-02-01)
Pambudi, Suluh
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Saechua, Wanphut
;
Jongyingcharoen, Jiraporn Sripinyowanich
This study investigated how temperature and residence time variations impact the combustion behavior, kinetic, and potential emissions of biochar produced through oxidative torrefaction of spent coffee grounds (SCG). The study examined the characteristics of combustion and kinetics by conducting thermogravimetry analysis under heating rates of 10 °C·min−1. The Coats-Redfern model was utilized to calculate kinetic parameters. While the emission indices were approximated using the data obtained from elemental analysis. The results indicated that biochar's comprehensive combustion index (C<inf>ci</inf>) from oxidative torrefaction was lower than that of raw SCG, suggesting stable combustion behavior. Moreover, with the escalation of torrefaction intensity, the activation energy (E<inf>a</inf>) values exhibited an upward trend for the char combustion stage, ranging from 22.08 kJ mol−1 to 38.46 kJ mol−1. Concurrently, the E<inf>a</inf> values pertaining to the oxidative pyrolysis stage decreased from 64.26 kJ mol−1 to 52.65 kJ mol−1. Besides, this study emphasized that the ash content of the biochar was lower than that of coal and remained consistent with the ash content of raw SCG (p > 0.05). Moreover, the study revealed that biochar from oxidative torrefaction emitted less CO<inf>2</inf> (67.35 g MJ−1) than lignite coal (76.55 g MJ−1). Additionally, biochar exhibited up to 27 times lower dust emissions than bituminous coal, emphasizing its eco-friendly fuel potential.
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Saechua, Wanphut

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