Optimization of the Electric Arc Furnace Process

This paper presents an electric arc furnace (EAF) optimization framework intended to define optimal control profiles for the EAF, in order to increase its efficiency and thus reduce the energy consumption. The framework aims to minimize controllable losses and to maximize energy transfer to the bath and, consequently, minimize the operational costs. This is achieved through improved actuation of the EAF inputs, i.e., transformer power, oxygen lancing, and carbon addition. To achieve maximal energy transfer to the bath and to reduce the heat losses from the arcs, proper properties of the slag, such as foaminess and basicity, are a subject of considerable attention. The framework is designed as a model-based optimization, intended to be executed online in parallel to the actual EAF process. In order to achieve sufficiently low computational complexity and to allow process optimization by arbitrary time intervals, the framework uses path constraints instead of end-point constraints. A combination of several optimization algorithms is used to solve the optimization problem. The validation of the framework was performed by comparing the predicted and the measured operational variables. Simulation results show that optimized operation profiles lead to a significant decrease in operational costs and production times.