Dutta, Ujjal KR;
Lipani, Aldo;
Wang, Chuan;
Hu, Yukun;
(2025)
Hottel Zone Physics-Constrained Networks for Industrial Furnaces.
IEEE Access
, 13
pp. 75130-75152.
10.1109/ACCESS.2025.3563413.
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Abstract
This paper investigates a novel approach to improve the temperature profile prediction of furnaces in foundation industries, crucial for sustainable manufacturing. While existing methods like the Hottel Zone model are accurate, they lack real-time inference capabilities. Deep learning methods excel in speed and prediction but require careful generalization for real-world applications. We propose a regularization technique that leverages the Hottel Zone method to make deep neural networks physics-aware, improving prediction accuracy for furnace temperature profiles. Our approach demonstrates effectiveness on various neural network architectures, including Multi-Layer Perceptrons (MLP), Long Short-Term Memory (LSTM), Extended LSTM (xLSTM) and Kolmogorov-Arnold Networks (KANs). We also discussion the data generation involved.
| Type: | Article |
|---|---|
| Title: | Hottel Zone Physics-Constrained Networks for Industrial Furnaces |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ACCESS.2025.3563413 |
| Publisher version: | https://doi.org/10.1109/access.2025.3563413 |
| Language: | English |
| Additional information: | © 2025 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Keywords: | Extended LSTM (xLSTM), furnace, Hottel zone method, Kolmogorov-arnold networks (KANs), long short-term memory (LSTM), multi-layer perceptrons (MLP), physics-informed neural networks (PINNs), sustainable manufacturing, temperature profile |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10215324 |
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