Abstract

A novel Bilevel Optimizing Control method has been proposed for simulated moving bed processes. Product purity regulation is conducted in the lower level using the repetitive model predictive control (RMPC) technique while off-line cyclic steady state optimization is performed in the upper level to determine the optimum feed / desorbent flow rates and switching length. The proposed technique was applied to SMB with nonlinear and linear isotherm separately to verify the applicability irrespective of the adsorption characteristics. A first principle SMB model with each isotherm case, which is continuously tuned on-line on the basis of the purity measurements, is used for the construction of the controller as well as the optimizer. For RMPC, the SMB model is linearized successively along the operating trajectories seen in the previous switching period. It is assumed that the flow rates can be varied within a switching period and the average product purities over each switching period can be measured albeit with a significant analysis delay. Numerical studies using linear and nonlinear isotherms showed that the proposed strategy is successful at driving the process to the intended optimum and maintaining it there while robustly regulating the product specification despite various uncertainties. Subsequently, experimental studies were conducted to implement the technique to the lab-scaled SMB process and it showed that the proposed strategy was performed satisfactorily in different scenarios.