Application of Zone Method based Physics-Informed Neural Networks in Reheating Furnaces

TL;DR

This paper introduces a physics-informed neural network model that leverages the zone method to accurately predict temperatures in reheating furnaces, aiming to reduce energy consumption in foundation industries.

Contribution

It proposes a novel PINN framework incorporating zone method-based regularizers to improve temperature prediction in reheating furnaces with limited real data.

Findings

Enhanced temperature prediction accuracy in reheating furnaces.

Improved generalization to out-of-distribution data.

Potential energy savings in industrial processes.

Abstract

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.