Panjapornpon, Chanin and Chinchalongporn, Patcharapol and Bardeeniz, Santi and Jitapunkul, Kulpavee and Hussain, Mohamed Azlan and Satjeenphong, Thanatip (2024) Development of physics-guided neural network framework for acid-base treatment prediction using carbon dioxide-based tubular reactor. Engineering Applications of Artificial Intelligence, 138 (B). p. 109500. ISSN 0952-1976, DOI https://doi.org/10.1016/j.engappai.2024.109500.
Full text not available from this repository.Abstract
Accurate acid-base treatment prediction is necessary to achieve the required yield, given the inherent complexity, high nonlinearity, and restricted availability of data samples; to address this challenge, a data-driven approach was developed. However, the technique is constrained by the need for sufficient data to construct an accurate model and lacks both process insight and physical consistency. Therefore, this study introduces a physics-guided neural network model for acid-base treatment prediction in a dynamic tubular reactor using the fundamental physical intermediate variables obtained through the derivation process of the reaction schematic. By integrating batch experimental data, which provides key intermediate variables such as residence time and hydroxide ion concentration, the model addresses the challenge of high nonlinearity and limited data availability. The result shows that the physics-guided potential of a hydrogen predictor had outstanding performance in terms of prediction accuracy (greatest coefficient of determination value of 0.9381). The proposed model demonstrated an average improvement of 24.92% in pH prediction accuracy compared to traditional models without physical guidance, with a maximum improvement of up to 64.95% under limited data conditions. Moreover, downsampling tests revealed that the proposed model maintained robust performance with minimal accuracy reduction even when data was limited without overfitting implication.
| Item Type: | Article |
|---|---|
| Funders: | Faculty of Engineering, Kasetsart University, Bangkok, Thailand (66/04/CHEM/D.Eng), Center for Advanced Studies in Industrial Technology |
| Uncontrolled Keywords: | Physics-guided neural network; Acid-base treatment prediction; Dynamic tubular reactor; Batch experimental data; Downsampling analysis |
| Subjects: | T Technology > TP Chemical technology |
| Divisions: | Faculty of Engineering > Department of Chemical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 17 Feb 2025 02:43 |
| Last Modified: | 17 Feb 2025 02:43 |
| URI: | http://eprints.um.edu.my/id/eprint/47416 |
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