Resumen
In steelmaking process, close control of slag evolution is as important as control of steel composition. However, there are no industrially consolidated techniques that allow in-situ analysis of the slag chemical composition, as in the case of steel with OES-spectrometers. In this work, a method to analyze spectral reflectance of ladle furnace slag samples to estimate their composition is proposed. This method does not require sample preprocessing and is based on a regression algorithm that mathematically maps the spectral reflectance of the slag with its actual composition with errors lower than 10%. Specifically designed normalization and calibration steps have been proposed to allow a global model training with data from different locations. This allows real-time monitoring of the thermodynamical state of the steel process by feeding a thermodynamic equilibrium optimization model. The system has been validated on several ArcelorMittal locations achieving process savings of 0.71 Euro per liquid steel tons.
Idioma original | Inglés |
---|---|
Número de artículo | 8106802 |
Páginas (desde-hasta) | 3506-3512 |
Número de páginas | 7 |
Publicación | IEEE Transactions on Industrial Informatics |
Volumen | 14 |
N.º | 8 |
DOI | |
Estado | Publicada - 13 nov 2017 |
Palabras clave
- Hyper-spectral image processing
- Slag characterization
- Ladle furnace
- Steel casting
- Secondary metallurgy process optimization
Project and Funding Information
- Funding Info
- Partial financial support of this work by the Basque Government (Etorgai NUPROSS ER-2010/00001 and DAVOS ER-2014/0004 Projects) is gratefully acknowledged.