Model Fitting for Electric Arc Furnace Refining

Abstract

The dissertation forms part of an ongoing project for the modelling and eventual control of an electric arc furnace (EAF) process. The main motivation behind such a project is the potential benefits that can result from automation of a process that has largely been operator controlled, often with results that leave sufficient room for improvement. Previous work in the project has resulted in the development of a generic model of the process. A later study concentrated on the control of the EAF where economic factors were taken into account. Simulation results from both studies clearly demonstrate the benefits that can accrue from successful implementation of process control. A major drawback to the practical implementation of the results is the lack of a model that is proven to be an accurate depiction of the specific plant where control is to be applied. Furthermore, the accuracy of any process model can only be verified against actual process data. There lies the raison d'etre for this dissertation: to take the existing model from the simulation environment to the real process. The main objective is to obtain a model that is able to mimic a selected set of process outputs. This is commonly a problem of system identification (SID): to select an appropriate model then fit the model to plant input/output data until the model response is similar to the plant under the same inputs (and initial conditions). The model fitting is carried out on an existing EAF model primarily by estimation of the model parameters for the EAF refining stage. Therefore the contribution of this dissertation is a model that is able to depict the EAF refining stage with reasonable accuracy. An important aspect of model fitting is experiment design. This deals with the selection of inputs and outputs that must be measured in order to estimate the desired parameters. This constitutes the problem of identifiability: what possibilities exist for estimating parameters using available I/O data or, what additional data is necessary to estimate desired parameters. In the dissertation an analysis is carried out to determine which parameters are estimable from available data. For parameters that are not estimable recommendations are made about additional measurements required to remedy the situation. Additional modelling is carried out to adapt the model to the particular process. This includes modelling to incorporate the oxyfuel subsystem, the bath oxygen content, water cooling and the effect of foaming on the arc efficiency.