A Simulation-Based Comparative Analysis of Physics and Data-Driven Models for Temperature Prediction in Steel Coil Annealing

Annealing of steel coils in bell-type furnaces is a critical process in steel production, requiring precise temperature control to ensure desired mechanical properties and microstructure. However, direct measurement of inner coil temperatures is impractical in industrial conditions, necessitating model-based estimation. This study presents a comparative analysis of physics-based and machine learning (ML) approaches for predicting internal temperatures during annealing. A finite difference method (FDM) was developed as a physics-based model and validated against experimental data from both laboratory and industrial annealing cycles. Furthermore, several ML models, including support vector regression (SVR), neural networks (NN), multivariate adaptive regression splines (MARS), k-nearest neighbors (k-NN), and random forests (RFs), were trained on surface temperature measurements to predict inner temperatures. The results demonstrate that the MARS, k-NN, and RF models achieved high prediction accuracy with performance index (PI) values below 1.0 on unseen data, demonstrating excellent generalization capabilities. In contrast, SVR with polynomial kernels and NN exhibited poor performance in specific configurations, highlighting their sensitivity to overfitting and data variability. The findings suggest that combining physics-based models with data-driven techniques offers a robust framework for soft-sensing in annealing operations, enabling improved process monitoring and control.