Murathathunyaluk, Siripan

Preferred name

Alternative Name

Murathathunyaluk, S.

Main Affiliation

School of Engineering

siripan.mu@kmitl.ac.th

Now showing 1 - 5 of 5

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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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Integration of Genetic Algorithm with Machine Learning for Properties Prediction
(2025-01-01)
Chawuthai, Rathachai
;
Murathathunyaluk, Siripan
;
Amornratthamrong, Nalin
;
Arunchaipong, Run
;
Anantpinijwatna, Amata
Numerous studies have demonstrated that machine learning (ML) provides more accurate estimations of properties for oxygenated organic derivatives compared to the conventional Quantitative Structure-Property Relationship (QSPR) method. Consequently, ML’s predictive capabilities have been extended to encompass a broader range of properties, including Partition Coefficient, Boiling Point, and Solubility, among others, for oxygenated hydrocarbon derivatives. Algorithms such as Linear Regression, Support Vector Machine, Random Forest, and Gaussian Process are selected through trial-and-error to identify the most suitable approach. The models are trained and validated using experimental data from published literature. Despite the accuracy of these property predictions, they have limited practical utility in industry, where specific property ranges are essential for processes. To address this, Genetic Algorithms (GA) are employed to design chemical compounds that meet industrial requirements. Integrating GA with ML could yield alternative chemical compounds, enhancing overall production processes by increasing economic potential, sustainability, and reducing environmental impact.
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Theoretical study on a novel temperature breakpoint cyclic operation to enhance desiccant packed bed performance
(2021-08-01)
Murathathunyaluk, Siripan
;
Kitchaiya, Prakob
In a conventional desiccant packed bed dehumidification, the adsorption and desorption operations switched at a constant cycle. However, this Conventional Steady Cyclic (CSC) operation was not performed well under disturbances. Therefore, a Temperature Breakpoint Cyclic (TBC) operation is proposed. A numerical model of the desiccant packed bed dehumidification system has been constructed and validated. The model was then used to assess the desiccant packed bed dehumidification performances in term of moisture removal capacity (MRC) and dehumidification coefficient of performance (DCOP) under various cycle times and temperature factors. The calculation results showed that under CSC, larger amounts of energy were required in the desorption operation, while the TBC exhibited higher performances in term of both MRC and DCOP. Notably, at a high regeneration temperature, the MRC of the TBC was 10% higher than the CSC's; moreover, the DCOP of the TBC was twice higher than the CSC's. In other words, at high recovery temperature, both operations exhibited comparable capacities while the energy cost was halved under TBC operation.
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Development of machine learning enhanced low-cost spectrophotometer for pesticide prediction
(2025-05-15)
Murathathunyaluk, S.
;
Jinorose, M.
;
Janpetch, K.
;
Chanthapanya, N.
;
Sombatsri, W.
Conventional analytical methods for measuring pesticide concentrations, such as chromatography, offer high accuracy but require expensive instrumentation, prompting the investigation of cost-effective alternatives like smartphone-based spectrophotometers. Despite their potential, these methods face challenges related to assembly and precision, often requiring human intervention to select appropriate images for analysis. This study presents a novel, affordable spectrophotometer designed for integration with machine learning algorithms. The device captures images of two spectral bands and employs a six-step image processing methodology to prepare images for analysis. A machine learning model trained on four algorithms with feature selection and cross-validation demonstrates high accuracy in predicting chemical concentrations of coloured solutions. The approach achieves 98.5 % accuracy for KMnO<inf>4</inf> and 96.7 % for Carbosulfan solutions, comparable to high-end spectrophotometry devices. The design eliminates the need for human intervention, reducing biased selection and result manipulation. However, concentration estimation of non-coloured compounds remains inaccurate, indicating areas for further refinement.
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Sustainable drying of galangal through combination of low relative humidity, temperature and air velocity
(2020-02-01)
Namkanisorn, Apinan
;
Murathathunyaluk, Siripan
Galangal (Alpinia galanga) is a herb commonly grown in Southeast Asia. The galangal rhizomes contain compounds that have antimicrobial and antioxidative properties. Longer storage of galangal can be achieved by drying. In the household-based industry, farmers use tray dryers because they are relatively cheap and easy to use. However, the quality of the products is low due to inappropriate temperature and drying time. The drying process is also energy intensive. This study investigated the effects of drying conditions such as air velocity, drying temperature and relative humidity on the drying characteristics and product quality. Drying of galangal was carried out at the following conditions: air velocities 0.25 and 0.5 m/s, drying temperatures 45 and 75 <sup>∘</sup>C and relative humidity at 15 and 70 %RH, respectively. The mathematical modeling of thin layer drying kinetics of galangal was performed using Page model. The active ingredients in dried galangal were best preserved using a drying condition at air velocity of 0.25 m/s, 45 °C and 15%RH, in which the highest values of inhibition of free radicals by DPPH, total phenolic content (TPC) and 1’-acetoxychavicol acetate (ACA) content were 79.2 ± 1.3%, 91.9 ± 3.9 mg<inf>GAE</inf>/100 g and 384.1 ± 17.6 mg/100 g, respectively. Switching the relative humidity from low to high while keeping both air velocity and temperature low yielded a low drying rate. Hence, a combination of low relative humidity and low drying temperature at the optimum air velocity is promising for the design of more energy efficient dryer that gives desirable drying characteristics and product quality.

Murathathunyaluk, Siripan

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