Integrated Ladle Metallurgy Control

Abstract

The ladle metallurgical furnace (LMF) is a very flexible and common unit operation found in most steelmaking melt shops, and enables the adjustment and fine-tuning of molten steel's composition and temperature prior to casting. 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 heat progress during refining and current control methods involve manual procedures. This thesis represents part of an ongoing study on the modelling of a full-scale LMF in real-time with the forward goal of improved control and optimization. The first part of this thesis details a vision-based sensor for analyzing ladle eye dynamics online 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. The second part of this study concerns the development of metallurgical models for assessing the state of a ladle metallurgical furnace. Specifically, a multi-component kinetic model in combination with developed slag and steel thermodynamic solution models were used to quantitatively describe the kinetics of slag-metal reactions within 41 industrially sampled heats at ArcelorMittal Dofasco's LMF#2. Metal phase mass transfer coefficients for all elements in steel were assumed to follow the empirical relation derived from measured sulphur contents, while slag phase mass transfer coefficients were calculated by fitting the ratio of k^Mm/ k^MxOysl to the experimental results. On the basis of the fitted results, slag phase mass transfer coefficient correlations were evaluated using linear regression. Computed results from the model using these slag phase mass transfer coefficient correlations were found to be consistent with the experimental data. In regard to the developed thermodynamic solution models, original contributions to the modified interaction parameter formalism and cell model are presented. As process model predictions are invariably uncertain, the final part of this work involves the use of a stochastic model (extended Kalman filter) to account for process disturbances, model-mismatch and other sources of uncertainty that may result in significant error propagation causing poor process control and plant economics. Several case studies were performed to illustrate the effectiveness of the extended Kalman filter and its application to optimal sensor selection was introduced.