Digital twin-aided transfer learning for energy efficiency optimization of thermal spray dryers: Leveraging shared drying characteristics across chemicals with limited data
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, m...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Elsevier
2024
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/45767/ https://doi.org/10.1016/j.applthermaleng.2024.122431 |
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| Summary: | 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. |
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