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Author: search.filters.author.Jongyingcharoen, Jiraporn SripinyowanichSubject: search.filters.subject.Machine learning

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    Understanding drying processes of steam-blanched Wolffia globosa under microwave vacuum drying through data-driven and semi-theoretical modeling
    (2026-06-15)
    Pambudi, Suluh
    ;
    Churat, Chutikan
    ;
    Nitinarot, Manassanan
    ;
    Buntreekanok, Withitpong
    ;
    Saechua, Wanphut
    Steam blanching and microwave vacuum drying (MVD) are increasingly applied to preserve heat-sensitive, protein-rich biomaterials such as Wolffia globosa . However, accurately modeling moisture removal under MVD remains challenging due to the highly nonlinear and stage-dependent nature of microwave-induced drying. This study investigates the combined effects of steam blanching pretreatment and microwave power (540, 720, and 900 W at 5 kPa) on the drying kinetics of W. globosa and evaluates the capability of machine-learning models to predict moisture evolution in comparison with conventional semi-theoretical models. The results indicated that steam-blanched samples consistently exhibited higher maximum drying rates across all power levels, increasing from 1.088 to 1.298 g<inf>w</inf>/(g<inf>dm</inf>·min) at 540 W and from 1.974 to 2.126 g<inf>w</inf>/(g<inf>dm</inf>·min) at 900 W. The pretreatment also reduced total drying time from 19.5 to 18.0 min at 540 W, while drying time remained unchanged at higher microwave powers. Among the evaluated semi-theoretical models, the Midilli equation provided the best fit to condition-specific experimental data (R<sup>2</sup> = 0.9981–0.9994). However, the generalized Midilli model showed systematic underestimation at low moisture ratios across operating conditions (R<sup>2</sup> = 0.9865, RMSE = 0.9990). In contrast, the k-nearest neighbors (k-NN) model demonstrated strong generalized predictive capability across multiple drying conditions, achieving R<sup>2</sup> = 0.9798 and RMSE = 0.0481 on the testing dataset. Beyond improved predictive accuracy, the machine-learning framework effectively captured stage-dependent drying behavior patterns that conventional generalized semi-theoretical models failed to represent. These findings highlight the potential of data-driven approaches for modeling complex drying behavior under microwave vacuum conditions. The accurate prediction of moisture ratio may further support the optimization of MVD processes for W. globosa -based food ingredients.
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    Machine learning based prediction and iso-conversional assessment of oxidatively torrefied spent coffee grounds pyrolysis
    (2024-12-01)
    Pambudi, Suluh
    ;
    Jongyingcharoen, Jiraporn Sripinyowanich
    ;
    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 (R<sup>2</sup> > 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<sup>−1</sup>, 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.