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In this paper, a model predictive control (MPC) case study is presented on the selective catalytic reduction (SCR) in a coal-fired power plant. The MPC is used for optimizing the volume of ammonia injection in order to meet the emission standard of nitrogen oxides (NO x ) as well as to prevent ammonia slip. A gain scheduling model is applied to deal with the nonlinearity of the SCR unit. The model gain is developed by kernel density estimation and the dynamic model is estimated by the asymptotic method of identification. The MPC control result shows that the standard deviation of outlet NO x concentration can be reduced by two-thirds, which leads to about 25% reduction of reagent consumption and the prevention of ammonia slip. [ABSTRACT FROM AUTHOR]

This work is concerned with the minimum variance (MV) benchmark estimation without a prior knowledge of time-delay. The idea is to use a small high-frequency test signal in order to obtain the accurate time-delay estimation (TDE) result, then the MV benchmark can be calculated from the closed-loop data. Based on the analysis, a well designed test signal for control performance monitoring (CPM) is not only possible but also necessary, as it increases the identifiability of the closed-loop data without having a large effect on routine operation. A new TDE algorithm by using high-order auto regressive exogenous (ARX) model is proposed as well as the multivariable issue is investigated. Simulation examples of performance monitoring with low signal-to-noise ratio (SNR) are employed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: This work introduces a method of multivariable model error detection in model prediction control (MPC). The idea is to use non-disturbing small sinusoidal test signals to obtain accurate estimates of process frequency responses at several frequency points. Then, the differences between estimated frequency responses and the frequency responses of current MPC model are used to form the model error index matrix which is used to access the model error of the MPC controller. An upper error bound is developed for quantifying the error of frequency response estimation. The method works in closed-loop operation with the MPC controller online. Simulation studies are used to demonstrate the use of the method. [Copyright &y& Elsevier]