%0 Journal Article 
%A Picon, Artzai
%A Vicente Rojo, Asier
%A Rodriguez-Vaamonde, Sergio
%A Armentia, Jorge
%A Arteche, Jose Antonio
%A Macaya, Inaki
%T Ladle furnace slag characterization through hyperspectral reflectance regression model for secondary metallurgy process optimization
%D 2017
* IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
%X 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.
%@ 1551-3203
%K Hyper-spectral image processing
%K Slag characterization
%K Ladle furnace
%K Steel casting
%K Secondary metallurgy process optimization
doi 10.1109/tii.2017.2773068
%U http://hdl.handle.net/11556/465
%~ GOEDOC, SUB GOETTINGEN