Dynamic Optimization of a Simulated Moving Bed (SMB) Chromatographic Separation Process

The recent literature on simulated moving bed (SMB) chromatography suggests the modulation of either the feed concentration or the flow rates in order to improve the process performance with respect to the classical (constant feeding) approach. The "best" profiles are selected from a finite set of combinations of step heights and numbers of steps. Therefore, the results, although promising, can be far from truly optimal. In this contribution, the general dynamic optimization (open loop optimal control) of an SMB chromatographic separation process is considered, allowing the calculation of the optimal feed concentration and/or feed flow rate, over each switching period, with maximum flexibility. To numerically solve the problem, the combination of the control vector parametrization scheme with suitable state-of-the art nonlinear programming (NLP) problem solvers is considered. Further, we also show how the use of global optimization methods is required to surmount the convergence difficulties exhibited by local NLP solvers. The advantages of this approach are illustrated through the solution of three case studies, achieving significant improvements in the process productivity and the raffinate purity when compared to traditional feeding profiles.