Motivation and Goals of this position
The ability to accurately and automatically control biotechnological processes at their optimal state is of considerable interest to many fermentation industries, since it can enable them to reduce their production costs, while, at the same time, maintaining the quality of the metabolic products. However, the highly dynamic and non-linear nature of bioprocesses poses great difficulties to standard control approaches. For accurate control, the system dynamics need to be identified to derive needed control actions. We use models (Digital Twins) for this task.
An exponentially increasing feedrate gives constant growth within fed batch processes but what feedrate is needed to get constant bioproduct formation rates?
The thesis aims to:
- find optimal production levels in a Fab-fragment producing coli fed-batch process
- implement a readily developed model-based feedback controller into a lab-scale fermentation system
- run verification experiments at different production levels
- Willingness to intensify numerical programming skills such as Matlab® and Python®
- Curiosity to test developments within real experiments
- Some experience in standard lab work and basics in aseptic work with biomaterial
- Work in a multidisciplinary Team
- Connect with industry and learn their language and thinking
- Intensification of skills in process system engineering and data science
- This work is scheduled for 6 months and compensated with 300 €/m.
- Eventually, work may be done part-time for 9 or 12 months.
- Scheduled start (Autumn/Winter 2020).