Hybrid models allow for the integration of knowledge from different domains, e.g. fundamental process knowledge and process data. This knowledge integration has the potential to reduce the number of experiments required for model development and in addition the extrapolation properties of the hybrid model are typically much better as e.g. those of strictly data driven models. These properties make hybrid modeling approaches particularly interesting for bioprocess development, optimization and scale-up and their improved interpretability has been recognized as an advantage in the QbD and PAT concepts. This course is directed towards PhD-students, Postdoctoral researchers and industry experts that have interest in process modeling and seek for methods to improve process modeling, such ultimately enhancing process operation and design in an efficient way.
The topics, covering both hybrid modeling fundamentals and their application, will be presented by leading experts. While the fundamental part covers questions on “How to develop a hybrid model?”, “How to enhance the model quality by design of experiments?” and “How to compare different hybrid models?”, it will also let the participants in on tips and tricks for the efficient development of high quality hybrid models. Hands on training will support the learning process and result in a profound understanding of the fundamentals. These courses will be held by leading experts. Hands on training will substantiate the presented topics, i.e. based on data from realized laboratory fermentations, models for E.coli bioprocesses will be developed. This will leave the participants with the necessary knowledge and skills to try hybrid modeling on their own. The industrial oriented part covers the application of hybrid modeling for typical bioprocess engineering and its utilization to support the Quality by Design concept, as promoted by the Process Analytic Technology initiative (PAT). Concepts and industrial applications for process development (e.g.: Shake Flasks to 15l Bioreactor), process characterization (e.g.: E.coli fermentation) and ways towards closed optimization (e.g.: model predictive control of fermentation processes) will be presented and discussed with the experts.