Chungcharoen, Thatchapol
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Preferred name
Chungcharoen, Thatchapol
Alternative Name
Chungcharoen, T.
Main Affiliation
Email
thatchapol.ch@kmitl.ac.th
5 results
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Item type:Publication, Enhanced biodiesel purification using coffee husk bioadsorbents: The role of pyrolysis temperature, KOH activation, and adsorption efficiency(2025-05-01); ; ; ; Ruttanadech, NuttapongThis study evaluates the performance of bioadsorbents derived from coffee husk pyrolyzed at temperatures of 600, 700, and 800 °C (CH600, CH700, and CH800), along with activated CH700 (ACH700), in biodiesel purification. The results indicate that CH700 significantly enhances biodiesel purity, with optimal purification conditions achieved at a dosage of 2 wt% CH700, a stirring rate of 400 rpm, and a contact time of 45 min. CH700 demonstrated modest performance, achieving approximately 20 % removal of methanol and water. However, after activation with potassium hydroxide (KOH), ACH700 demonstrated improved efficiency, achieving 96.92 % methanol removal and 39.46 % water removal. ACH700 also refined biodiesel quality to meet EN14214 standards and maintained a higher biodiesel yield compared to other adsorbents. The bioadsorption process is influenced by the chemical interactions between the surface functional groups of the bioadsorbent and the contaminants, which is further enhanced by the optimized pore structure of ACH700. The use of ACH700 represents a novel and highly effective approach to biodiesel purification, combining both technical efficiency and economic feasibility. Furthermore, the valorization of agricultural waste adds significant environmental benefits, reinforcing the potential of ACH700 for large-scale biodiesel production. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Low-Cost Near-Infrared Spectroscopy for Rapid Prediction of Biodiesel Properties: Acid Value, Density, Viscosity, and Water Content(2026-03-31); ; Partial least squares (PLS) regression, combined with various spectral pre-processing techniques, was employed to compare the performance of two diode array near-infrared (NIR) spectrometers in predicting key biodiesel quality parameters, including acidity, viscosity, density, and water content. An AvaSpec-Mini4096CL NIR spectrometer, operating within the 350–1100 nm wavelength range, was used as the representative shortwave near-infrared (SW-NIR) spectrometer, while a NIRQuest512 spectrometer, covering the 900–1700 nm range, was employed as the longwave near-infrared (LW-NIR) spectrometer. Both spectrometers were equipped with a transflection probe for spectral collection from oil palm-based biodiesel samples. The SW-NIR spectrometer outperformed the LW-NIR spectrometer. The optimal PLS models achieved root mean square errors of prediction (RMSEP) of 0.0037 mg KOH/g for acidity, 0.062 cSt (mm<sup>2</sup>/s) for viscosity, 2.67 kg/m<sup>3</sup> for density, and 59.14 mg/kg for water content, highlighting the potential of compact SW-NIR spectrometers as effective, low-cost tools for rapid biodiesel quality monitoring. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Investigation of physiological disorder classification in mangosteen fruit using visible and shortwave near-infrared spectroscopy combined with machine learning(2025-12-01); ; Thongphut, ChitwadeeAccurate classification of physiological disorders in mangosteen fruit is crucial for ensuring production quality, safety, sustainability, and economic viability. This study investigates the application of visible and shortwave near-infrared (Vis/SWNIR) reflectance spectroscopy, combined with machine learning algorithms, to classify three primary disorders: normal fruit (NF), translucent flesh disorder (TFD), and TFD with yellow gummy latex (TFD & YGL). The study specifically examines the effects of light intensity, spectral pretreatments, and machine learning models on classification performance. Spectral data were collected using two light intensities (50 % and 100 % of a 150 W light source) and processed with three pretreatments: standard normal variate (SNV), second derivative Savitzky-Golay (SGD2), and a combination of SNV and SGD2. Random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms were used for classification. The SGD2 method improved differentiation, especially for the TFD & YGL class, in the 700–725 nm wavelength range, which is associated with xanthone content in the fruit's pericarp. Higher light intensity (100 %) significantly improved classification accuracy, achieving an overall accuracy of 0.71 and an average F1 score of 0.61 with the RF model. Despite these improvements, the model struggled to distinguish the TFD class from NF due to their similar spectral profiles. Overall, the Vis/SWNIR spectroscopy and machine learning combination shows strong potential for the non-destructive classification of mangosteen fruit disorders. Both light intensity and spectral pretreatments play critical roles in enhancing performance. Future studies should focus on improving spectral sensitivity to better capture internal fruit characteristics. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhancing biodiesel yield and purification with a recently developed centrifuge machine: A response surface methodology approach(2024-04-15); ; ; ; Limmun, WanidaBiodiesel production processes, such as gravity settling, have limitations in terms of biodiesel yield, purification efficiency, operating time in the separation process, and more extensive equipment. Therefore, this study has focused on using a recently developed centrifuge machine for biodiesel separation to address these challenges due to its compact design, high efficiency, and simplicity. Additionally, this study aimed to optimize the separation efficiency of glycerol from biodiesel using a centrifuge machine, employing response surface methodology (RSM) with central composite design (CCD). The optimum conditions for separating glycerol from biodiesel via centrifuge machine are a rotation speed of 1800 rpm, a mixture flow rate of 192.25 ml/min, and a temperature of 55 °C, respectively. In optimum conditions, 94.52% separation efficiency was achieved. Biodiesel production can be improved, leading to higher yields and greater purity. The utilization of RSM proved valuable in determining the optimum conditions for separation. Furthermore, the machine successfully separated the biodiesel to meet ASTM D6751 and EN 14,214 standards. The results highlight the potential of the centrifuge machine for efficient and reliable biodiesel production, contributing to the advancement of the biodiesel industry. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The improvement of germination method for producing the germinated brown rice using a water spraying system with a revolved sieve(2024-11-01); ; Fonghiransiri, SurasakWater soaking is an important method in germinated brown rice (GBR) production that causes fermentation, leading to an unpleasant smell of GBR. In this research, a water spraying system with a revolved sieve is applied to produce the GBR. The increased speed and time of spray break led to higher moisture content and water absorption. The spray break of 30 min and revolved speed of 15 rpm provided the shortest time to obtain the paddy with a moisture content of 30% (w.b.). The incubation pattern with a revolved sieve and water spray provided the shortest incubation time for 90% germination. When producing the GBR with a water spraying system with a revolved sieve (GBR-WSSRS), it had a lower number of microorganisms compared to the GBR with a water soaking (GBR-WS), leading to higher scores of overall acceptability. However, the GBR-WSSRS had a lower GABA content than the GBR-WS.