Application of Zone Method based Physics-Informed Neural Networks in Reheating Furnaces
CoRR(2023)
摘要
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.
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关键词
zone method,neural networks,machine learning,physics-informed
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