Modelling, optimization and control of an electric arc furnace.

摘要

The main objective of this research was to develop methods to optimally operate an industrial electric arc furnace (EAF). EAFs are widely used in the steel industry to recycle scrap steel. Simply stated, steel recycling involves melting down the scrap metal using both chemical and electrical energy sources and making adjustments to the chemistry. EAFs are complex processes involving limited automation and are typically operated based on what has worked well in most situations; however, this is not necessarily optimal.; The contributions from this work can be divided into three main sections, with each successive section building on the developments from the previous sections. The first component of the project involved the development of a comprehensive model, which makes it possible to explore the complex multivariate interactions and subtle relationships of the process. The next step incorporated the model within an optimization framework, where the optimal operating input profiles for a range of different conditions and objective functions were explored. The final step was to implement a nonlinear feedback controller that updated the input profiles online.; The first section of work describes the development of a dynamic model for an industrial steelmaking EAF. The model is sufficiently detailed so as to describe the melting process and chemical reactions, and account for reagent and energy additions. The lack of knowledge of reaction mechanisms due to the complex nature of the reacting system is overcome by modelling the process as a system of equilibrium zones with mass transport limitations. An important objective in developing this model is to ensure that it can be used within an optimization framework, thus particular consideration is given to the continuity of model equations and the robustness of the model over a wide range of conditions. Parameter estimation is carried out using industrial data and the model performance illustrated through simulation studies. (Abstract shortened by UMI.)