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.

这项研究旨在通过建模和抑制干扰来提高选择性催化还原（SCR）反硝化系统的控制性能。引入了氨转化率的概念，将非线性问题转化为准线性问题，从而可以直接使用传递函数模型。增强的过程模型用于使用卡尔曼滤波器估计干扰。由于其在时间序列预测方面的优势，在线最小二乘支持向量机可用于开发自适应干扰模型。提出了具有自适应扰动模型的状态空间模型预测控制。仿真结果表明，所提出的控制方案可以显着提高SCR脱硝系统的控制性能。根据模拟，