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.

翻译：尽管基础工业具有很高的经济重要性，但其制造链中的某些组件（如加热炉）是能源密集型的。通过减少炉内的整体加热时间，可以显著降低能源消耗。计算机集成的机器学习（ML）和人工智能（AI）驱动的炉内控制系统，有望帮助基础工业实现可持续制造的净零排放目标。在本研究中，由于在加热炉等场景中难以获得高质量数据，我们采用基于经典Hottel区域法的计算模型来生成数据，用于通过回归训练基于机器学习（ML）和深度学习（DL）的模型。需要注意的是，区域法提供了一种优雅的方式来模拟辐射传热（RHT）这一物理现象，而辐射传热是加热炉高温过程中的主导传热机制。利用这些数据，我们对广泛的最先进代表性机器学习和深度学习方法在不同炉内环境下的温度预测性能进行了全面比较。由于在推理时间和模型性能之间取得了整体平衡，深度学习在同类方法中脱颖而出。为了进一步增强训练好的深度学习模型在分布外（OOD）泛化能力，我们提出了一种物理信息神经网络（PINN），通过使用一组新颖的能量平衡正则化器将先验物理知识融入其中。我们的框架是一个通用框架，与底层炉体的三维结构几何无关，因此可以容纳任何标准机器学习回归模型，作为底层物理过程的数字孪生，推动基础工业向工业4.0转型。