Chawuthai, Rathachai

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Chawuthai, Rathachai
Alternative Name
Chawuthai, R.
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Email
rathachai.ch@kmitl.ac.th

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    Item type:Publication,
    A machine learning approach for predicting osmotic coefficients and deriving activity coefficients in alkyl ammonium salts
    (2026-12-01)
    Chawuthai, R.
    ;
    Murathathunyaluk, S.
    ;
    Saengsuradech, S.
    ;
    Nukaew, A.
    ;
    Simasatitkul, L.
    Quaternary Ammonium Salts (Quats) have diverse applications across various domains. They are extensively used as phase-transfer catalysts (PTCs) in chemical reactions, facilitating the transfer of reactants between aqueous and organic phases. Their unique structure enables the formation of ion pairs, enhancing reaction rates at phase boundaries. This research develops a novel method for predicting Quats’ osmotic coefficients using Simplified Molecular Input Line Entry System (SMILES) notation and supervised machine learning. A comprehensive dataset of 1,654 data points from 52 distinct Quats was compiled. The structural characteristics were encoded using SMILES notation. The data was evaluated using random splitting and Leave-One-Group-Out (LOGO) validation to train seven machine learning algorithms. Gaussian Process (GP) emerged as the optimal algorithm. The GP model achieved a mean absolute percentage error (MAPE) of 5.29% and root mean square error (RMSE) of 0.034. Comparisons with Electrolyte-NRTL and Extended UNIQUAC models demonstrate that this data-driven approach offers competitive accuracy while enabling generalization to structurally similar compounds. This work marks a significant starting point for the machine learning-enhanced prediction of activity coefficients, with considerable potential for future refinement and application.
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    Novel method for predicting the cracks of oxide scales during high temperature oxidation of metals and alloys by using machine learning
    (2025-12-01)
    Chawuthai, Rathachai
    ;
    Promchan, Teeratat
    ;
    Rojsanga, Jularak
    ;
    Chandra-ambhorn, Somrerk
    ;
    Nilsonthi, Thanasak
    Material degradation is one of the main problems in various high-temperature processes, directly resulting in the failure of the material. Crack and protective oxide film spallation caused either by mechanical stress development in the oxidation process or thermal stress due to a mismatch of the thermal expansions of the formed oxide and alloy are common forms of failure in high-temperature processes. Typically, the Pilling-Bedworth ratio (PBR) is employed to predict crack and spallation of the oxide by determining the volume changes of oxide and alloy because of its simplicity. However, this approach provides poor crack and spallation predictions. Hence, machine learning was adopted in the present work to predict oxide formation and spallation in the temperature range of 600-1,200 °C. The inputs for the present developed model were alloy compositions, oxide formed during oxidation, and oxidation conditions and periods. Furthermore, the predicted results of the present developed machine learning model were compared to those obtained by the PBR method. The present results revealed that the accuracy of the oxide spallation prediction of the present model was better than that of the PBR method. The random forest with 15 estimators was the best machine learning model. Finally, it can be concluded that the machine learning model is essential for accurate material failure prediction.
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    Novel method for properties prediction of pure organic compounds using machine learning
    (2021-01-01)
    Chorbngam, Nattasinee
    ;
    Chawuthai, Rathachai
    ;
    Anantpinijwatna, Amata
    In classical thermodynamic, the estimation method of pure compounds properties was based on Newtonian physics, which required experimental data. It is proven to be inadequate for the growing demand of the novel chemical synthesis. There were several studies on the prediction of the pure compound properties based on QSPR methods. However, the conventional group-contribution based methods predictive capability was limited by the available measured data. Therefore, this study aims to approach the property prediction with a novel statistical-based method. The proposed method is derived using supervised machine learning algorithms. The experimental data used to train and validate the models were collected from the published literature. These data set are composed of the alkanes, alkenes, and alkynes derivatives containing 1-12 carbon atoms. The results show the improved accuracy of the model prediction compare to the conventional method in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE).
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    A Recommender System for Insurance Packages Based on Item-Attribute-Value Prediction
    (2021-01-01)
    Chawuthai, Rathachai
    ;
    Choosak, Chananya
    ;
    Weerayuthwattana, Chutika
    Finding a proper insurance package becomes a challenging issue for new customers due to the variety of insurance packages and many factors from both insurance packages’ policies and users’ profiles for considering. This paper introduces a recommender model named INSUREX that attempts to analyze historical data of application forms and contact documents. Then, machine learning techniques based on item-attribute-value prediction are adopted to find out the pattern between attributes of insurance packages. Next, our recommender model suggests several relevant packages to users. The measurement of the model results in high performance in terms of HR@K and F1-score. In addition, a web-based proof-of-concept application has been developed by utilizing the INSUREX model in order to recommend insurance packages and riders based on a profile from the user input. The evaluation against users demonstrates that the recommender model helps users get start in choosing right insurance plans.