2 research outputs found
Fast method for slag characterization during ladle furnace steelmaking process based on spectral reflectance
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.Partial financial support of this work by the Basque Government (Etorgai NUPROSS
ER-2010/00001 and DAVOS ER-2014/0004 Projects) is gratefully acknowledged
Ladle furnace slag characterization through hyperspectral reflectance regression model for secondary metallurgy process optimization
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.Partial financial support of this work by the Basque Government (Etorgai NUPROSS ER-2010/00001 and DAVOS ER-2014/0004 Projects) is gratefully acknowledged