Model-based temperature control of a selective catalytic reduction system

Selective catalytic reduction (SCR) systems are commonly used for exhaust gas aftertreatment in many applications. For optimal NO_x reduction using the SCR technique a certain temperature must be reached. This study deals with modelling and control of the temperature inside the SCR system for optimal catalyst operation. A first principle-based model is described for the propagation of the temperature inside the catalyst. The model is described in linear parameter varying (LPV) state-space form and used for control of the temperature using a linear-quadratic-Gaussian (LQG) controller. Necessary conditions for obtaining an optimal controller without complete state information are defined. This leads to a discrete-time LQG controller for LPV systems. The results obtained for the controller are based on several assumptions to ensure the stability of the controller. The states of the proposed model are not measurable. For this purpose, a Kalman filter-based observer is designed for estimation of the states that are used for state feedback in the controller. The observer is designed for discrete-time LPV systems and necessary assumptions for the observer are derived in the work. The resulting model of the temperature gives a model fit of up to 77% for validation data and the controller requirements are met using the proposed controller applied in a simulator environment.