Design of robust preparative chromatography

This work presents a methodology for analyzing and optimizing preparative chromatographic processes. It uses a model-based approach to find the critical process parameters, analyze the control space and suggest a design space that can be registered with the regulatory agencies. The methodology starts with a method for model calibration and validation. The model is then used to find an optimal operating point for an ideal scenario without process disturbances. Process disturbances are then added and the operating point is moved to a more robust one so that the process has a high overall performance. Sample-based uncertainty analysis can be used to estimate the probability of batch failure at a point or to determine robust UV cut points such that no batches will fail due to process disturbances. These same simulations can also be used to determine the critical process parameters. The methodology is then extended to define a design space. This is done by defining the design space by optimal operating points for different scenarios. This means that the process can be optimized for future changes in cost and process parameters. This gives a limited set of operating points to be validated and registered with the regulatory agencies. The method can also be used to determine the benefits of reducing the process disturbances at a particular operating point. This provides decision support when determining the quality of process equipment that is required, as higher quality equipment will require a larger investment