Adaptive disturbance rejection model predictive control and its application in a selective catalytic reduction denitrification system

Abstract

This study aims to increase the control performance of a selective catalytic reduction (SCR) denitrification system through modeling and disturbance rejection. The concept of converted ammonia flowrate is introduced to transform the nonlinear problem into a quasi-linear problem, so that transfer-function models can be used directly. The augmented process model is used to estimate the disturbance using the Kalman filter. Because of its superiority in time-series prediction, the online least-squares support vector machine is implemented to develop an adaptive disturbance model. State-space model predictive control with an adaptive disturbance model is presented. The simulation results show that the proposed control scheme can improve the control performance of the SCR denitrification system markedly. Based on simulations, this control structure has been used successfully in a real SCR denitrification process, which shows the effectiveness of the proposed control scheme further.