Simulated Moving Bed (SMB) processes are efficient continuous chromatographic methods for separating components through counter-current flow of liquid and solid phases. This process is particularly valuable for temperature-sensitive components or substances with similar thermodynamic properties. The counter-current flow is achieved by periodically switching inlet and outlet ports in the direction of the liquid flow. However, SMB processes present challenges for optimization and control due to their mixed discrete and continuous dynamics, steep spatially distributed state variables, and nonlinear responses of concentration profiles to changes in operating parameters. To maximize the economic potential of these processes, advanced optimization and control strategies based on rigorous nonlinear models and efficient simulation algorithms are essential. This work focuses on optimizing SMB processes, including ModiCon SMB and reactive Hashimoto SMB variants, through direct mathematical optimization, demonstrating their superior performance in reducing eluent consumption and increasing feed throughput. Additionally, an advanced control scheme is developed to manage product purities amid plant disturbances, incorporating moving horizon state and parameter estimation (MHE) and nonlinear model predictive control (NMPC). The NMPC scheme directly optimizes economic performance, referred to as optimizing control.
