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Cycle to cycle control of simulated moving bed processes


C. Grossmann, M. Amanullah, M. Mazzotti, M. Morbidelli, M. Morari

International Congress on Industrial and Applied Mathematics, pp. IC / PP 1960/ 113, (ICIAM 6). Zurich, Switzerland. Poster.

SMB is a continuous chromatographic separation technique that offers high productivity and low solvent consumption. SMB has gained importance in the pharmaceutical industry to perform difficult separation tasks, like purification of chiral molecules for single enantiomer drug development. However, an automatic control algorithm for SMB units that guarantees an optimal, robust operation is still an open and challenging problem because of the complex dynamics involved in this process, e.g. its cyclic and hybrid nature of inlet/outlet port switching, with nonlinearities and delays. We have developed a control scheme that integrates both, the optimization and control of the SMB unit. The control problem is formulated as a constrained dynamic optimization problem within the repetitive model predictive control framework. The controller predicts and optimizes the future inputs over a given horizon of time by making use of a linear time-invariant cycle to cycle SMB model derived from the system of PDEs describing the process. This scheme is implemented according to a receding horizon strategy, i.e. a new optimization problem based on the latest estimate of the plant state is solved as new measurements are available. The state estimate is computed using a linear Kalman filter. This approach provides the flexibility to apply the controller to a wide range of separation tasks, regardless of the type of isotherm characterizing the mixture or the operation mode of the SMB unit, i.e. synchronized or asynchronized switching of the ports. This work presents the mathematical development of the cycle to cycle controller. Its performance is illustrated through simulations and experiments that show how the controller fulfills the product and process specifications while operating the SMB unit optimally, regardless of the uncertainties in the parameters of the system or the disturbances.


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M. Morari

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