Jongyingcharoen, Jiraporn
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Jongyingcharoen, Jiraporn
Alternative Name
Jongyingcharoen, Jiraporn Sripinyowanich
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jiraporn.jo@kmitl.ac.th
19 results
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Item type:Publication, Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data(2024-01-01); ; Posom, JetsadaClassification of the crosslink density level of para rubber medical gloves by using near-infrared spectral data combined with machine learning is the first time reported in this paper. The spectra of medical glove samples with different crosslink densities acquired by an ultra-compact portable MicroNIR spectrometer were correlated with their crosslink density levels, which were referencely evaluated by the toluene swell index (TSI). The machine learning protocols used to classify the 3 groups of TSI were specified as less than 80% TSI, 80–88% TSI, and more than 88% TSI. The 80–88% TSI group was the group in which the compounded latex was suitable for medical glove production, which made the glove specification comply with the requirements of customers as indicated by the tensile test. The results show that when comparing the algorithms used for modeling, the linear discriminant analysis (LDA) developed by 2nd derivative spectra with 15 k-best selected wavelengths fairly accurately predicted the class but was most reliable among other algorithms, i.e., artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (kNN), due to higher prediction accuracy, precision, recall, and F1-score of the same value of 0.76 and no overfitting or underfitting prediction. This developed model can be implemented in the glove factory for screening purposes in the production line. However, deep learning modeling should be explored with a larger sample number required for better model performance.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Explainable machine learning for predicting thermogravimetric analysis of oxidatively torrefied spent coffee grounds combustionUnderstanding 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 R<sup>2</sup> 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 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Thermal and Non-Thermal Processing of Functional Foods(2014-05-27); Ahmad, ImranThermal processing is the most common and traditional technique used for food processing. An adverse effect on organoleptic properties of foods caused by thermal processing has been reported. Therefore, milder processing techniques including novel thermal, minimal thermal and non-thermal processing has become the new trend. These techniques are claimed to provide food of high quality and with improved functionality. This chapter aims to provide a critical review of the effects of these milder processing techniques on the functional components of processed food. Recent developments in food processing operations involve technologies that avoid deleterious effects of heat on organoleptic properties and nutrients of foods. Alternative or novel minimal-thermal and non-thermal processing methods include: ultrasound, high hydrostatic pressure, radiation and pulsed electric field processing are being examined to produce safe and better quality food products. They may replace conventional thermal processes in certain applications and are particularly beneficial for thermally sensitive products.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Synergistic torrefaction and co-combustion of rice husk and spent coffee grounds: Thermo-kinetics, ash morphology, and waste-to-energy implicationsCombining 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<sup>–6</sup>%<sup>2</sup> min<sup>–2</sup> °C<sup>–3</sup> and flammability indices exceeding 0.72 × 10<sup>–4</sup>% min<sup>–1</sup> °C<sup>–2</sup>. 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 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A thermogravimetric assessment of eco-friendly biochar from oxidative torrefaction of spent coffee grounds: Combustion behavior, kinetic parameters, and potential emissionsThis 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<sup>−1</sup>. 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<sup>−1</sup> to 38.46 kJ mol<sup>−1</sup>. Concurrently, the E<inf>a</inf> values pertaining to the oxidative pyrolysis stage decreased from 64.26 kJ mol<sup>−1</sup> to 52.65 kJ mol<sup>−1</sup>. 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<sup>−1</sup>) than lignite coal (76.55 g MJ<sup>−1</sup>). Additionally, biochar exhibited up to 27 times lower dust emissions than bituminous coal, emphasizing its eco-friendly fuel potential.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Effect of hot air assisted infrared drying on drying characteristics and quality of rice bran pellets(2018-08-14)1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design and development of continuous pineapple-peeling machineImportance of the work: A continuous pineapple peeler was designed having a mechanism to align the pineapple during peeling for use in small to medium-sized enterprises. Objectives: To design, fabricate and test the performance of a continuous pineapple peeler based on the physical and mechanical properties of pineapples. Materials & Methods: The physical and mechanical properties of pineapples were investigated, specifically their dimensions, the forces required to cut the ends and for peeling, and the bioyield force. A continuous pineapple-peeling machine was designed and developed. The offset of the fruit core centerline and the centerline of the cut fruit were investigated to evaluate machine performance. Results: The prototype machine consisted of four main units (cutting, gripping, peeling and pneumatic control). The most important part of the design was the V-shaped grippers angled at 90° that were driven by a rack and pinion mechanism. The machine could hold pineapples of various diameters with the axis of the pineapple aligned with the movement of a tubular knife. During operation, the pineapple ends were first removed by a cutting unit. Then, the grippers held the pineapple horizontally while it was peeled by the unit’s tubular knife. From performance testing, the prototype machine had a peeling capacity of approximately 530 fruits/h, with an effectiveness of 98.2%. Main finding: With the mechanism to align the pineapple during peeling developed in this study, the continuous pineapple-peeling machine had high peeling capacity and high effectiveness, making it acceptable by fresh pineapple processors.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Effect of torrefaction temperature on energy properties of spent coffee ground(2020-09-08); 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Drying kinetics and quality of cissus quadrangularis linn. Dried by convective hot air(2020-09-01); This research aimed to determine a suitable drying model of convective hot air drying of Cissus quadrangularis Linn. (CQ), determine the effective moisture diffusivity of the drying process and the activation energy of CQ, and investigate the effects of drying temperatures on energy consumption and quality of dried CQ. Experiments were performed at 40°C, 60°C, 80°C, and 100°C drying temperature and 1 m s<sup>-1</sup> air velocity to dry CQ from 10 g water/g dry matter to 0.1 g water/g dry matter. The generalized linear-plus-exponential-type model has been used to fit the drying kinetics of CQ and demonstrates, among others, the moisture data of hot air drying of CQ at the validation temperature of 50°C satisfactorily with an R<sup>2</sup> of 0.9977. The effective moisture diffusivity was in the range of 0.7302-9.1281 ×10<sup>-9</sup> m<sup>2</sup> s<sup>-1</sup>, a positive relationship was observed between this parameter and drying temperature. The activation energy of CQ was 39.78 kJ mol<sup>-1</sup>. The lowest energy consumption of 3.40 kWh was required when the highest drying temperature of 100°C was applied. The quality was well preserved when CQ was dried at a lower drying temperature. Drying at 60°C produced dried CQ with the lowest total color difference (16.76), shrinkage percentage (88.47%), and bulk density (0.1817 g cm<sup>-3</sup>) as well as the highest total phenolic (1062 mg GAE/100 g dry matter) and quercetin contents (0.955 mg/100 g dry matter).1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development of UV-treated cooked germinated brown rice and effect of UV-C treatment on its storability, GABA content, and qualityA process for preparing UV-treated cooked germinated brown rice (CGBR) was developed. The UV-C exposure time was varied to study its effect on the shelf life of CGBR at room temperature (RT; 25 ± 2 °C). The germinated brown rice with the highest GABA content was cooked and radiated with UV-C for 30 and 60 min, after which the aerobic plate count was reduced by 0.40 and 0.58 log CFU/g, respectively. Their shelf lives were 1 and 3 days, respectively. The GABA contents of control CGBR, 60-UV-CGBR, and canned CGBR were 7.49, 5.11, and 1.81 mg/100 g dry matter, respectively. The whiteness and hardness of 60-UV-CGBR were not significantly different from those of control CGBR (p ≥ 0.05). These quality attributes were poorer after canning. After storing 60-UV-CGBR at RT for 3 days, the GABA content and whiteness decreased, and the hardness increased significantly. Its quality was however better than that of canned CGBR.2