Fast method for slag characterization during ladle furnace steelmaking process based on spectral reflectance
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2017
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Abstract
Hyperspectral imaging reflectance analysis has proven to be successful in online characterization
applications such as material recycling [1], soil composition analysis [2], quality control [3] among
others. The measurement of a narrow spectral reflectance of specific materials allows the use of
feature extraction and regression machine learning techniques to classify the material into a specific
group or estimate some chemical parameters under controlled conditions. A method for Fast slag
composition estimation on the ladle furnace process, together with the steel composition information
from in-process steel spectrometers, would allow implementing thermo-dynamical equilibrium models
to optimize the use of steel additives to obtain a target steel grade at the optimal additive cost.
In this work, we present a fast method for slag characterization which is based on the indirect analysis
of the spectral reflectance of the slag. This method is based on a normalization procedure to remove
the specular component of the spectra, a calibration method to correct lighting conditions and a
spectral feature extraction algorithm combined with a SVr (Support vector regression) based
regression method.
A system consisting of a hyperspectral imaging system and a calibration method has been
constructed. The system has been trained with more than 600 real slag samples taken from ladle
furnace at different ArcelorMittal steel plants. In order to cover the whole slag oxidation process, three
slag samples were taken at each heat. Each sample was analysed by XRF spectroscopy and the
regression system was trained to map the values for CaO, SiO2, .S, FeO, MnO Al2O3, MgO, P2O5
obtaining composition errors below 10% on the calibrated ladle furnace oxidation process. The
estimated slag composition was used to feed a thermo-dynamical equilibrium model that, together with
the steel composition from the in-process spectrometer estimates the required additives for the
specific steel grade. This showed lower additive costs than manual additive estimation with equivalent
final steel quality.
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Vicente, Asier & Macaya, Iñaki & Picon, Artzai & Arteche, Jose Antonio. (2017). Fast method for slag characterization during ladle furnace steelmaking process based on spectral reflectance. EEC 2016 - 11TH Electric Steelmaking conference & Expo.