About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Thermodynamics and Kinetics
|
| Presentation Title |
Predictive Billet Heating in Industrial Reheating Furnaces Using a CFD Model |
| Author(s) |
Anna Eshenbaugh, Nicholas J. Walla, Armin K. Silaen, Chenn Q. Zhou |
| On-Site Speaker (Planned) |
Chenn Q. Zhou |
| Abstract Scope |
Industrial reheating furnaces are major sources of energy cost in steel production, making the evaluation of combustion modifications such as hydrogen and oxygen lancing an important pathway towards improving energy efficiency. However, accurately modeling combustion and heat transfer in large–scale industrial furnaces using computational fluid dynamics (CFD) is computationally expensive due to the complexity of governing flow physics. This work develops a two-dimensional CFD model of an industrial reheating furnace to provide a computationally efficient approach for predicting billet heating under varying fuel blends, oxygen lancing conditions, and burner power distributions. Preliminary validation against a three-dimensional model demonstrates furnace temperature predictions within 10% while reducing computational time by more than two orders of magnitude. Overall, the proposed modeling approach provides a framework for rapidly evaluating furnace operating strategies while maintaining sufficient accuracy for predicting billet heating in industrial reheating applications. |
| Proceedings Inclusion? |
Planned: |