Application Of Model-Based Online Monitoring And Robust Optimizing Control To Fed-Batch Bioprocesses

The aim of the quality by design initiative is to assure a continuous and high-quality production of pharmaceuticals despite the presence of process variations and disturbances. This need for optimal process operation necessitates the use of accurate prediction and fault detection methods in combination with advanced control strategies. However, the critical component for the success of such an approach is a mathematical model providing an adequate representation of the bioprocess under study. This work presents a framework for bioprocess online optimization that utilizes rigorous modelling and control methods tailored for fed-batch and perfusion cultures. The basis of the methodology is a hybrid process modelling approach which enables both monitoring and optimization of cell culture processes. To account for inherent process variability of biological organisms, an adaptive state estimation approach is utilized which employs multiple models in parallel thus providing improved robustness to a possible occurrence of model-plant mismatch. Furthermore, optimal process trajectories for online optimization are calculated using a robust multistage nonlinear model predictive control approach which considers different scenarios based on the employed process models. Recent promising results from experimental fed-batch CHO fermentations are presented which show significant productivity increases. Copyright (C) 2020 The Authors.