Digital twin-aided transfer learning for energy efficiency optimization of thermal spray dryers: Leveraging shared drying characteristics across chemicals with limited data
Document Type
Article
Publication Date
4-1-2024
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.
Keywords
Energy efficiency, Transfer learning, Detergent powder industry, Limited data, Digital twin
Divisions
sch_che
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)
Publication Title
Applied Thermal Engineering
Volume
242
Publisher
Elsevier
Publisher Location
THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND