This dissertation forms part of the ongoing process at UP to model and control the electric arc furniture process. Previous work focused on modelling the furnace process from empirical thermodynamic principles as well as fitting the model to actual plant data. Automation of the process mainly focused on subsystems of the process, for example the electric subsystem and the off-gas subsystem. The modelling effort, especially the model fitting resulted in parameter values that are described with confidence intervals, which gives rise to uncertainty in the model, because the parameters can potentially lie anywhere in the confidence interval space. Robust model predictive control is used in this dissertation, because it can explicityly take the model uncertainty into account as part of the synthesis process. Nominal model predictive control – not taking model uncertainty into account – is also applied in order to determine if robust model predictive control provides any advantages over the nominal model predictive control. This dissertation uses the process model from previous wok together with robust model predictive control to determine the feasibility of automating the process with regards to the primary process variables. Possible hurdles that prevent practical implementation are identified and studied. / Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2007. / Electrical, Electronic and Computer Engineering / MEng / unrestricted

This dissertation forms part of the ongoing process at UP to model and control the electric arc furniture process. Previous work focused on modelling the furnace process from empirical thermodynamic principles as well as fitting the model to actual plant data. Automation of the process mainly focused on subsystems of the process, for example the electric subsystem and the off-gas subsystem. The modelling effort, especially the model fitting resulted in parameter values that are described with confidence intervals, which gives rise to uncertainty in the model, because the parameters can potentially lie anywhere in the confidence interval space. Robust model predictive control is used in this dissertation, because it can explicityly take the model uncertainty into account as part of the synthesis process. Nominal model predictive control – not taking model uncertainty into account – is also applied in order to determine if robust model predictive control provides any advantages over the nominal model predictive control. This dissertation uses the process model from previous wok together with robust model predictive control to determine the feasibility of automating the process with regards to the primary process variables. Possible hurdles that prevent practical implementation are identified and studied. / Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2007. / Electrical, Electronic and Computer Engineering / MEng / unrestricted