Data-Driven Predictive Control With Switched Subspace Matrices for an SCR System

Selective catalytic reduction (SCR) systems are distributed systems with strong time-varying parameter characteristics such that an accurate model for it is difficult to establish. Its control task simultaneously achieving high NOx conversion efficiency and low NH3 slip is a typical multi-objective and multi-constraint problem, which is suitable to be solved in the framework of the model predictive control (MPC). However, how to find a data-driven identification method based on the dynamic characteristics of an SCR system and a corresponding MPC method for satisfying its emission requirements remain a formidable challenge. The sufficient identification for the traditional identification method with fixed subspace model requires an excessively high order subspace matrix, such that a degradation in real-time performance is caused and the generality of the method under non-identification conditions is limited. In this paper, utilizing the transient data of the SCR system under the WHTC cycle, a novel identification method for some lower order subspace matrices excited by the segmented data referring to the dynamic of the ammonia coverage ratio is established. A corresponding predictive controller with the switched subspace matrices according to working conditions is designed in order to further improve its real-time performance, generality and robustness. The simulation results show that under the identification condition the proposed predictive controller compared to the traditional method can improve the emissions of NOx and NH3, that under the non-identification condition the proposed predictive controller can also improve the emissions and its optimization effects have better robustness to uncertainties of the transient cycle, and that the proposed predictive controller saves an significant computation time.

Automated summary

SCR systems are complex distributed systems with strong time-varying characteristics, making it challenging to establish an accurate model. The control task of achieving high NOx conversion efficiency and low NH3 slip is a multi-objective and multi-constraint problem suitable for model predictive control (MPC). This study proposes a novel data-driven identification method and a corresponding MPC method for SCR systems, improving emissions under both identification and non-identification conditions and enhancing real-time performance, generality, and robustness. Simulation results demonstrate the effectiveness of the proposed predictive controller in improving emissions while saving computation time.