The performance of
most bioprocesses can be improved significantly
by the application of model-based methods from advanced process control
(APC). However, due to the complexity of the processes and the limited
knowledge of 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; 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 is used for 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 prediction of the process, which
account for structural uncertainties. It is shown in simulation studies
of a CHO cultivation process that the usage of multiple, adapted models
as scenarios improves (1) the accuracy of the state estimation and
(2) the overall process performance.