Optimizing model predictive control of multi-column chromatographic processes

The manufacturing and downstream processing of single enantiomer drugs and therapeutic biopharmaceuticals requires advanced separation and purification techniques in order to meet the stringent requirements imposed by the regulatory agencies such as the Food and Drug Administration (FDA) or the European Medicines Agency (EMEA). Multi-column chromatographic processes, like simulated moving bed (SMB) and multi-column solvent gradient purification (MCSGP) processes, have attracted interest in the fields of fine chemicals, pharmaceuticals and biotechnology for this task. Multi-column chromatographic processes can be rapidly and reliably scaled up from drug development to industrial production, a rather important feature in industries where time-to-market is crucial. However, optimal operation of multi-column chromatographic processes is still an open and challenging issue. This is because of the uncertainty involved in determining the physical data that is used in the process models. The models describing the dynamics of such processes are rather complex, due to their cyclic and hybrid nature, with inlet/outlet port switching, strong nonlinearities and delays. The full economic potential of the multi-column chromatographic processes can be realized by using a feedback control scheme that will guarantee the fulfilment of product constraints while respecting process limitations in the face of uncertainty. The current work deals with different aspects of the optimization and control of multi-column chromatographic processes. The main contributions made by this thesis can be put into three categories: (1) Systematic methods to build reduced order linear models. (2) Application of optimizing model predictive control to multi-column chromatographic processes. (3) Development and implementation of online monitoring techniques for chiral SMB separations and experimental work to validate the control concepts for the SMB process. (1) Systematic methods to build reduced order linear models: This thesis proposes a cycle to cycle optimizing control scheme based on linear model predictive control (MPC). A fundamental part of this approach is the linear dynamical model that describes the evolution of the process on a cycle to cycle basis.

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