Iterative set-point optimization of batch chromatography

An iterative optimization strategy is proposed and applied to the set-point optimization of batch chromatography in presence of a plant-model mismatch. As process-dependent constraints have to be met, the model-based constraint functions are modified using measured plant information in order to satisfy the unknown real constraints. The gradients of the plant mapping which are required by the iterative optimization strategy are computed by a technique, which considers the influence of measurement errors and the number of additional set-point perturbations. Simulation studies illustrate the potential of the strategy in the set-point optimization of batch chromatography.