Robust Optimizing Control of Fermentation Processes based on a Set of Structurally Different Process Models

The performance of most bioprocesses can be improved significantly by the application of model-based methods fromadvanced process control (APC). However, due to the complexity of the processes and the limited knowledge about them, plant-model mismatch is unavoidable. A variety of different modeling strategies (each with individual advantages and deficiencies) can be applied, but still the confidence in a single process model is often low and therefore the application of classical APC is difficult. In order to operate under possible plant-model mismatch, a robust closed-loop optimizing control strategy was developed in which the mismatch is counteracted by an adaptive model correction and the parallel usage and evaluation of structurally different models. Robust multistage nonlinear model predictive control (NMPC) is used in the online optimization of the process trajectories in order to maximize the performance. The adapted, structurally different models are used herein as weighted scenarios for the predi...