Repetitive model predictive control applied to a simulated moving bed chromatography system

Abstract

In this paper, we investigate the application of the repetitive model predictive control (RMPC) technique on a simulated moving bed (SMB) process that performs continuous chromatographic separation of a phenylalanine—tryptophan mixture. RMPC is a model-based control technique developed by incorporating the basic concept from repetitive control into the model predictive control technique; it is specifically suited for continuous processes with periodic operation patterns or behavior. Balanced model reduction is used to reduce a finite difference approximation of a PDE model drawn from a material balance of the SMB system. The reduced order state space model is used for the control calculation. Start-up control of the SMB process is simulated and the results are presented.