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 18

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The energy potential evaluation of biomass fuel from weed pellets
(2023-03-01)
Saechua, W.
;
Posom, J.
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Hongwiangjan, J.
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Nakawajana, N.
The giant sensitive plant had the most appropriated qualifications for pellet production, as it contained a low content of moisture (3.07%) and ash (2.68%), and a high percentage of fixed carbon (14.13%) and volatile matter (80.12%) with the highest lower heating value of 18,334.08 J/g. It was found that a higher mixing ratio of water for pelletization led to increasing moisture and ash content but resulted in lower volatile matter and fixed carbon. The best mixing ratio for was 8% of water by weight, at which the evaluated properties of the giant sensitive plant pellets: LHV, FC, VM and ash content, were 18,747.46 J/g, 10.92%, 78.77% and 2.24%, respectively. The evaluation of the physical properties of the pellets revealed that a higher amount of water resulted in a larger diameter, greater length and higher fines content. Comparison of pellets from this work with the standard classes of biomass pellets found that the giant sensitive plant pellets could be classified in class I3. It was clearly seen that the weed can be utilized as a useful biomass fuel and formed in a pellet shape for combustion in industrial sectors.
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Total soluble solids, dry matter content prediction and maturity stage classification of durian fruit using long-wavelength NIR reflectance
(2023-12-01)
Saenphon, Chirawan
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Ditcharoen, Sirirak
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Malai, Chayuttapong
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Saengprachatanarug, Khwantri
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Wongpichet, Seree
The DM and TSS of durian pulp moving on a conveyor belt were measured for their rapid and non-destructive qualities based on quantitative and qualitative measurements. The calibration set and prediction set equaled 209 and 69 pulps, respectively. The quantitative test compared the performance of PLS regression for DM and TSS prediction developed from full wavelength (860–1754 nm) and a few significant variables using SPA, GA, and VIP methods. The qualitative test identified the possibility of maturity stage classification by comparing three supervised machine learning classifiers, namely SVM, random forest (RF), and LDA. Effective models for DM and TSS prediction were developed from second derivatives spectra combined with the GA method, exhibiting r2, SEP, and RPD of 0.85, 4.50%, and 2.64, respectively for DM, and 0.66, 5.15%, and 1.60, respectively, for TSS. The model classifying samples into two distinct groups, namely “reject” and “pass,” utilizing the LDA algorithm, exhibited an impressive accuracy rate of 94.20%, making it a suitable choice for quality assurance purposes. This result indicates that the few effective variables were more efficient than full wavelength and improved model accuracy with greater model stability. Enhancing the classification model could involve data sample balancing in each group, leading to further improvements.
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Thermochemical treatment of spent coffee grounds via torrefaction: A statistical evidence of biochar properties similarity between inert and oxidative conditions
(2024-03-01)
Pambudi, Suluh
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Jongyingcharoen, Jiraporn Sripinyowanich
;
Saechua, Wanphut
This study used several statistical analyses to explore the impact of both inert and oxidative conditions on the characteristics of biochar derived from the torrefaction of spent coffee grounds (SCG). The study also considered variations in torrefaction temperature and residence time. Various fuel analyses were conducted, including high heating value (HHV), torrefaction index, proximate characteristics, thermogravimetric analysis (TGA), hygroscopicity, and Fourier-transform infrared spectroscopy (FTIR). The analysis of variance (ANOVA) revealed that the influence of both inert and oxidative conditions on HHV and mass yield was insignificant (p ≥ 0.05). Moreover, considering the same temperature and residence time, principal component analysis (PCA) and hierarchical cluster analysis (HCA) identified oxidative and inert conditions belonging to the same group or cluster. This finding indicated that neither atmospheric condition significantly affected the characteristics of the biochar measured in this research. Therefore, oxidative torrefaction offers significant advantages as it can produce biochar of comparable quality under inert conditions.
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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)
Saechua, Wanphut
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Sharma, Sneha
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Nakawajana, Natrapee
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Leepaitoon, Kritsanaphon
;
Chunsri, Rashphon
The prediction of dry matter content (DMC) and soluble solids content (SSC) in durian pulp were performed using a small laboratory scale in-line visible and short wave near infrared (Vis-SWNIR) spectroscopic system. The fiber optic diode array spectrometer with a charged coupled device (CCD) detector in a wavelength range of 450−1000 nm was used for spectral data acquisition. The spectra of the sample were acquired on the moving conveyor belt in two different orientations, including scanning in the upright position of pulps collected in 2018 and the stable position by scanning on the side of the pulps collected in 2019. Partial least squares regression (PLSR) was used to establish the relationship between the spectra and observed DMC and SSC values using the different wavelength ranges, including 450−1000, 700−1000, and 800−1000 nm for the comparison. The results showed that the durian pulp should be scanned in the upright position at the center of the pulp. Moving average smoothing preprocessing combined with the standard normal variate (SNV) for DMC and multiple scatter correction (MSC) for SSC gave the best result. The suitable wavelength range for model development to predict the DMC and SSC was 700−1000 nm and 800−1000 nm, respectively. After comparing the results, the optimum model showed the coefficient of determination of calibration (R<inf>C</inf>2), and prediction (R<inf>P</inf>2), root mean square error of prediction (RMSEP), bias, and the ratio of performance to interquartile distance (RPIQ) of 0.88, 0.83, 4.32 %, 1.25 %, and 3.52 for DMC and 0.70, 0.70, 4.0 %, 0.4 %, and 2.2 for SSC prediction.
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Combined ultrasound–hydrothermal pretreatment enhances moisture diffusivity, energy efficiency, and quality retention during hot air drying of holy basil (Ocimum sanctum L.)
(2026-06-01)
Pambudi, Suluh
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Khamon, Duanghathai
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Ngo, Tai Van
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Saechua, Wanphut
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Jongyingcharoen, Jiraporn Sripinyowanich
Drying of holy basil (Ocimum sanctum L.) is limited by slow internal moisture diffusion and severe degradation of heat-sensitive pigments and bioactive compounds during prolonged thermal exposure. This study investigates the combined effects of ultrasonic and hydrothermal pretreatments on moisture transport, energy efficiency, and quality retention during hot air drying at 60 °C. Ultrasonication (30 and 60 min), hot-water blanching, steam blanching, and their combinations were systematically evaluated through drying kinetics, effective moisture diffusivity (D<inf>eff</inf>), microstructural analysis, POD enzyme activity, specific energy consumption (SEC), and comprehensive quality assessment. Drying occurred entirely in the falling-rate regime, confirming diffusion-controlled moisture transport. The combined ultrasound–blanching treatment (US30B) exhibited the greatest changes in drying performance, enlarging stomatal openings and enhancing moisture transport during drying, which increased D<inf>eff</inf> nearly threefold (from 1.70 × 10−11 to 5.02 × 10−11 m2/s) and reduced drying time from 100 to 30 min. This transport intensification decreased total SEC by 11% while simultaneously minimizing shrinkage and enhancing rehydration capacity. Importantly, accelerated moisture removal may have reduced thermal and oxidative degradation, resulting in superior retention of chlorophyll, phenolics, flavonoids, and antioxidant activity (p ≤ 0.05), alongside improved green color stability. These findings suggest that pretreatment strategies that enhance internal moisture transport may improve both energy efficiency and functional quality in heat-sensitive leafy herbs. The combined ultrasound–hydrothermal pretreatment shows potential as a practical approach for improving drying performance and quality retention in herbal dehydration.
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Combustion study of rice husk under different heating rates by integrating thermogravimetric analysis and decision tree regression
(2025-01-15)
Pambudi, Suluh
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Jongyingcharoen, Jiraporn Sripinyowanich
;
Saechua, Wanphut
This study investigates the combustion behavior of rice husk using thermogravimetric analysis coupled with decision tree regression. Results indicated that increasing heating rates caused elevated burnout (Tb) and peak temperatures (Tp) while extending the active combustion stage. The optimized decision tree model effectively predicts mass loss, demonstrated by a perfect coefficient of determination (R2) of 1 with a low root mean square error (RMSE) of 0.1993 on the validation set. The model's robustness suggested its potential for accurate mass loss prediction in rice husk combustion.
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Machine learning based prediction and iso-conversional assessment of oxidatively torrefied spent coffee grounds pyrolysis
(2024-12-01)
Pambudi, Suluh
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Jongyingcharoen, Jiraporn Sripinyowanich
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Saechua, Wanphut
This research focused on developing a predictive model for mass loss during the pyrolysis of oxidatively torrefied spent coffee grounds (SCG) using machine learning techniques. Four algorithms were employed: artificial neural networks (ANN), k-nearest neighbors (k-NN), random forest (RF), and decision tree (DT), with the RF model demonstrating superior performance (R2 > 0.9981, RMSE <1.346) for both training and testing sets. The pyrolysis behavior, kinetics, and thermodynamics of SCG were also investigated using thermogravimetric analysis (TGA) under an inert atmosphere at different heating rates. Higher heating rates in TGA cause T<inf>peak</inf> values to shift to higher temperatures with increased DTG<inf>peak</inf> values, while also resulting in lower T<inf>onset</inf> and higher T<inf>offset</inf>. Kinetic analysis, using the Flynn-Wall-Ozawa (FWO) method, was identified as the most suitable approach for determining activation energy (E<inf>a</inf>), with values ranging from 192.66 to 288.13 kJ mol−1, indicating differences in energy requirements for pyrolysis across samples. Thermodynamic analysis further revealed that both raw SCG and oxidatively torrefied SCG pyrolysis were endothermic reactions. These findings contribute valuable insights into the optimization of biomass conversion technologies, highlighting the potential of machine learning in improving predictive accuracy and efficiency in thermal behavior modeling. This research advances sustainable bioenergy production by promoting the use of SCG, an abundant waste material, as a renewable feedstock in pyrolysis-based processes.
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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.
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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
;
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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Effect of torrefaction temperature on energy properties of spent coffee ground
(2020-09-08)
Bangkha, Natthanant
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Saechua, Wanphut
;
Nuamyakul, Tharathip
;
Jongyingcharoen, Jiraporn Sripinyowanich
The aim of this research was to investigate the effect of torrefaction temperature on 4 energy properties as high heating value (HHV), enhancement factor, solid yield, and energy yield of spent coffee ground (SCG). Four different torrefaction temperatures (200, 250, 300, and 350°C) were selected. Torrefaction process was conducted at the heating rate of 10°C/min. HHV and enhancement factor were the highest when SCG was torrefied at the highest temperature of 350°C. However, at this temperature, solid and energy yields were the lowest. Torrefaction temperature highly affected these four energy properties with R of higher than 0.9. Regression models representing the relationship between torrefaction temperature and HHV and energy yield were HHV = 0.0519T+15.917, R2 = 0.9483 and energy yield =-0.2743T+155.1, R2 = 0.9976. These models are helpful for prediction of the energy properties of SCG undergoing torrefaction process in the studied temperature range.

Saechua, Wanphut

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