Non-linear Model Predictive Control of the Hashimoto Simulated Moving Bed Process

Abstract

In recent years, continuous Chromatographic processes have been established as an efficient separation technology in industry, especially when temperature sensitive components or species with similar thermodynamic properties are involved. In SMB processes, a counter-current movement of the liquid and the solid phases is achieved by periodically switching the inlet and the outlet ports in a closed loop of Chromatographic columns. The integration of reaction and separation in one single plant is a promising approach to overcome chemical or thermodynamic equilibria and to increase process efficiency. Reactive Chromatographie SMB processes in which the columns are packed with catalyst and adsorbent have been proposed and demonstrated successfully. However, a full integration often is not efficient because in the columns in the separating zones, the catalyst is not used or even counterproductive. By placing reactors between the separation columns at specific positions around the feed port, a more efficient process, the Hashimoto SMB process, is established. In this contribution, a non-linear predictive control concept for the Hashimoto SMB process is presented. The controller computes optimal control variables (flow rates and the switching time) to optimize an economic objective over a moving horizon. The purity requirements of the product streams are implemented as constraints and not as controlled variables. The optimization-based controller is combined with a scheme to estimate selected model parameters in order to reduce the influence of the inevitable model errors. Simulative results are presented for the example of the racemization of Troger’s base.