In recent years, simulated moving bed (SMB) technology has become increasingly attractive for complex separation tasks and emerged as the leading chromatographic separation technique in the fields of pharmaceuticals, fine chemicals, and biotechnology. Extensive usage of SMB technology has created a need for robust automatic control techniques to exploit its full economic potential. Automatic control of SMB units is a challenging task not only because of its nonsteady-state, nonlinear, mixed discrete, and continuous nature, but also because of long delays involved in exhibiting the effect of disturbances. This work proposes a new optimization-based control strategy, in which a linearized time-varying reduced-order model that accounts for the periodic nature of the SMB process is used for on-line optimization and control of SMBs applied to systems characterized by linear adsorption isotherms. Four internal flow rates, which can be adjusted via external flow rates, are used as the manipulated variables. On-line concentration measurements at the product outlets together with a periodic Kalman filter are used to reduce the effect of model errors. Optimal input adjustments that allow for the achievement of process specifications and optimal performance are calculated on the basis of the predicted evolution of the plant. The realization and implementation of the concept on a virtual eight-column SMB unit is described, and the capability of the controller to run the SMB plant at its economic optimum regardless of possible disturbances and model uncertainties is assessed.