Ladle Eye Area Measurement using Multivariate Image Analysis

Despite the importance of ladle metallurgy to the overall steel making process, very little has been achieved in the way of advanced ladle control. Limited sensors are available to monitor progress during refining and current control methods involve manual procedures. This paper details a vision-based sensor for analyzing ladle eye dynamics in real time using a multivariate image analysis (MIA) technique based on principal component analysis (PCA). Predictive capabilities of the developed model are demonstrated using previously published cold model data over a wide range of operating variables. Further, preliminary work has confirmed the ability of the sensor for potential use in an industrial setting.