Bardeeniz, Santi and Panjapornpon, Chanin and Fongsamut, Chalermpan and Ngaotrakanwiwat, Pailin and Hussain, Mohamed Azlan (2024) Digital twin-aided transfer learning for energy efficiency optimization of thermal spray dryers: Leveraging shared drying characteristics across chemicals with limited data. Applied Thermal Engineering, 242. p. 122431. ISSN 1359-4311, DOI https://doi.org/10.1016/j.applthermaleng.2024.122431.
Full text not available from this repository.Abstract
Efficient energy management is crucial for spray -drying units as it can substantially improve product yield, reduce operating costs, and enhance energy utilization. However, due to limited data problems, the monitoring performance of the energy efficiency of a model is inefficient and unreliable, making it difficult to adjust operating conditions and hindering effective utility management. Therefore, this study proposes a long shortterm memory -based transfer learning model with shared source -target characteristics for enhancing energy efficiency trackability under limited efficiency labels. Utilizing a long short-term memory structure improves the capability of capturing the process dynamic behavior. Synchronously, the digital twin -aided transfer learning concept supports the model by leveraging the parameters learned from the simulated source domain to assist the performance of the model in a limited data domain with different chemicals. The reliability and accuracy of the model are verified by a real industrial case study involving the detergent powder drying process. Results show that the model testing achieved an r -squared value of 0.938, outperforming conventional techniques by boosting the performance of the network up to 14.53 % and reducing surplus energy on demand and supply by 50.05 % and 81.27 %, respectively. The proposed model reveals the interconnection between source and target accuracy and provides a reliable learning process of the target domain observed based on the distribution of the testing performance. Notably, the model deployment indicates a considerable decrease of 16.63 % in natural gas consumption, leading to an enhancement of 11.92 % in evaporation efficiency and the prevention of 483 tonnes of carbon emissions annually.
| Item Type: | Article |
|---|---|
| Funders: | Faculty of Engineering, Kasetsart University, Bangkok, Thailand (66/04/CHEM/D.Eng), Center for Advanced Studies in Industrial Technology, Center of Excellence on Petrochemical and Materials Technology, Faculty of Engineering, Burapha University, Hub of Talent: Sustainable Materials for Circular Economy, National Research Council of Thailand (NRCT) |
| Uncontrolled Keywords: | Energy efficiency; Transfer learning; Detergent powder industry; Limited data; Digital twin |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TP Chemical technology |
| Divisions: | Faculty of Engineering > Department of Chemical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 12 Nov 2024 01:29 |
| Last Modified: | 12 Nov 2024 01:29 |
| URI: | http://eprints.um.edu.my/id/eprint/45767 |
Actions (login required)
 |
View Item |
