| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
REWAS 2025: Automation and Digitalization in Recycling Processes
|
| Presentation Title |
Controlling Minor Element Phosphorus in Green Electric Steelmaking Using Neural Networks |
| Author(s) |
Elmira Moosavi, Riadh Azzaz, Valentin Hurel, Mohammad Jahazi, Samira Ebrahimi Kahou |
| On-Site Speaker (Planned) |
Elmira Moosavi |
| Abstract Scope |
The scrap-based electric arc furnace (EAF) is pivotal in sustainable steelmaking by recycling steel and minimizing raw material extraction. The precise control of phosphorus, a critical impurity affecting steel quality, remains a significant challenge in the industry. This work details the development of an advanced artificial neural network (ANN) model designed to predict the final phosphorus content of steel based on the operational parameters within an EAF. This model leverages systematic data integration and rigorous model validation, demonstrating superior predictive accuracy compared to existing models. Inherent model limitations will also be addressed and future research directions aimed at further enhancing predictive capabilities and expanding the applicability of the proposed approach in steelmaking context will be presented. Industrial implementation of the model will be discussed, highlighting opportunities to optimize EAF operations for improved green steel quality. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Iron and Steel, Machine Learning, Recycling and Secondary Recovery |