Impact of the project:
This project aims to develop predictive models for nitrogen content in molten iron and steel throughout the steelmaking process. Leveraging a strong foundation of thermodynamic and kinetic research in liquid metal systems, this 24-month project will expand upon existing knowledge by focusing on the critical stages of steel production. The goal is to create precise models capable of predicting nitrogen levels in:
- Pig iron after desulfurization
- Crude steel before tapping from the main furnace
- Liquid steel at the start of secondary metallurgy
- Final steel at the end of secondary metallurgy.
Predictive modeling has become indispensable in the steel industry for comprehending intricate processes and refining production operations. Analytical modeling techniques have shown significant promise in forecasting key metallurgical variables. This project concentrates on developing and employing an analytical model to accurately predict nitrogen levels in both molten pig iron and steel.
This project aims to develop and implement a novel analytical model for predicting nitrogen content in molten pig iron and molten metal. Unlike existing industrial applications, which are either non-existent or limited in scope, this project will create a comprehensive model capable of handling the complexities of the steelmaking process. While ambitious, the project's goals are attainable within the proposed timeframe.
By leveraging advanced modeling to predict nitrogen levels, steel producers can make data-driven decisions to optimize production processes. This includes reducing melting and oxygen injection times, ultimately resulting in higher quality steel products with lower nitrogen content. This approach will address current industry challenges and meet evolving market demands.
The escalating climate crisis demands immediate and decisive action to meet the EU's ambitious goals of a 2030 emissions reduction and climate neutrality by 2050. Central to achieving these targets is the decarbonization of energy-intensive industries like steelmaking. By optimizing raw material and energy usage, the steel sector can significantly reduce its carbon footprint. Predictive models, providing real-time insights into nitrogen content, empower steel producers to make informed decisions, minimizing waste and resource consumption. The broader impact of these advanced statistical methods extends beyond the industry, contributing to scientific progress, economic growth, and societal well-being while safeguarding our planet.