Despite the high economic relevance of Foundation Industries, certain
components like Reheating furnaces within their manufacturing chain are
energy-intensive. Notable energy consumption reduction could be obtained by
reducing the overall heating time in furnaces. Computer-integrated Machine
Learning (ML) and Artificial Intelligence (AI) powered control systems in
furnaces could be enablers in achieving the Net-Zero goals in Foundation
Industries for sustainable manufacturing.
In this work, due to the infeasibility of achieving good quality data in
scenarios like reheating furnaces, classical Hottel's zone method based
computational model has been used to generate data for ML and Deep Learning
(DL) based model training via regression. It should be noted that the zone
method provides an elegant way to model the physical phenomenon of Radiative
Heat Transfer (RHT), the dominating heat transfer mechanism in high-temperature
processes inside heating furnaces. Using this data, an extensive comparison
among a wide range of state-of-the-art, representative ML and DL methods has
been made against their temperature prediction performances in varying furnace
environments. Owing to their holistic balance among inference times and model
performance, DL stands out among its counterparts. To further enhance the
Out-Of-Distribution (OOD) generalization capability of the trained DL models,
we propose a Physics-Informed Neural Network (PINN) by incorporating prior
physical knowledge using a set of novel Energy-Balance regularizers. Our setup
is a generic framework, is geometry-agnostic of the 3D structure of the
underlying furnace, and as such could accommodate any standard ML regression
model, to serve as a Digital Twin of the underlying physical processes, for
transitioning Foundation Industries towards Industry 4.0.

通过使用计算模型生成数据进行机器学习和深度学习模型训练，本文比较了大范围的机器学习和深度学习方法在不同炉环境下的温度预测性能，DL 方法在推理时间和模型性能方面具有综合平衡，在增强 DL 模型的外域概括能力方面，提出了一种基于物理知识的神经网络（PINN），该设置适用于任何标准的 ML 回归模型，以实现基础工业向工业 4.0 的过渡。