The electric arc furnace (EAF) is a highly energy intensive process used to convert scrap metal into molten steel. The aim of this research is to develop a dynamic model of an industrial EAF process, and investigate its application for optimal EAF operation. This work has three main contributions; the first contribution is developing a model largely based on MacRosty and Swartz (2005) to meet the operation of a new industrial partner (ArcelorMittal Contrecoeur Ouest, Quebec, Canada). The second contribution is carrying out sensitivity analyses to investigate the effect of the scrap components on the EAF process. Finally, the third contribution includes the development of a constrained multi-rate extended Kalman filter (EKF) to infer the states of the system from the measurements provided by the plant. A multi-zone model is developed and discussed in detail. Heat and mass transfer relationships are considered. Chemical equilibrium is assumed in two of the zones and calculated through the minimization of the Gibbs free energy. The most sensitive parameters are identified and estimated using plant measurements. The model is then validated against plant data and has shown a reasonable level of accuracy. Local differential sensitivity analysis is performed to investigate the effect of scrap components on the EAF operation. Iron was found to have the greatest effect amongst the components present. Then, the optimal operation of the furnace is determined through economic optimization. In this case, the trade-off between electrical and chemical energy is determined in order to maximize the profit. Different scenarios are considered that include price variation in electricity, methane and oxygen. A constrained multi-rate EKF is implemented in order to estimate the states of the system using plant measurements. The EKF showed high performance in tracking the true states of the process, even in the presence of a parametric plant-model mismatch.

Master of Applied Science (MASc)