Application of Zone Method based Physics-Informed Neural Networks in Reheating Furnaces
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866917600356728832 |
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| author | Dutta, Ujjal Kr Lipani, Aldo Wang, Chuan Hu, Yukun |
| author_facet | Dutta, Ujjal Kr Lipani, Aldo Wang, Chuan Hu, Yukun |
| contents | Foundation Industries (FIs) constitute glass, metals, cement, ceramics, bulk chemicals, paper, steel, etc. and provide crucial, foundational materials for a diverse set of economically relevant industries: automobiles, machinery, construction, household appliances, chemicals, etc. Reheating furnaces within the manufacturing chain of FIs are energy-intensive. Accurate and real-time prediction of underlying temperatures in reheating furnaces has the potential to reduce the overall heating time, thereby controlling the energy consumption for achieving the Net-Zero goals in FIs. In this paper, we cast this prediction as a regression task and explore neural networks due to their inherent capability of being effective and efficient, given adequate data. However, due to the infeasibility of achieving good-quality real data in scenarios like reheating furnaces, classical Hottel's zone method based computational model has been used to generate data for model training. To further enhance the Out-Of-Distribution generalization capability of the trained model, we propose a Physics-Informed Neural Network (PINN) by incorporating prior physical knowledge using a set of novel Energy-Balance regularizers. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2308_16089 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Application of Zone Method based Physics-Informed Neural Networks in Reheating Furnaces Dutta, Ujjal Kr Lipani, Aldo Wang, Chuan Hu, Yukun Machine Learning Artificial Intelligence Neural and Evolutionary Computing Systems and Control Foundation Industries (FIs) constitute glass, metals, cement, ceramics, bulk chemicals, paper, steel, etc. and provide crucial, foundational materials for a diverse set of economically relevant industries: automobiles, machinery, construction, household appliances, chemicals, etc. Reheating furnaces within the manufacturing chain of FIs are energy-intensive. Accurate and real-time prediction of underlying temperatures in reheating furnaces has the potential to reduce the overall heating time, thereby controlling the energy consumption for achieving the Net-Zero goals in FIs. In this paper, we cast this prediction as a regression task and explore neural networks due to their inherent capability of being effective and efficient, given adequate data. However, due to the infeasibility of achieving good-quality real data in scenarios like reheating furnaces, classical Hottel's zone method based computational model has been used to generate data for model training. To further enhance the Out-Of-Distribution generalization capability of the trained model, we propose a Physics-Informed Neural Network (PINN) by incorporating prior physical knowledge using a set of novel Energy-Balance regularizers. |
| title | Application of Zone Method based Physics-Informed Neural Networks in Reheating Furnaces |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing Systems and Control |
| url | https://arxiv.org/abs/2308.16089 |