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From experiments, data, hybrid models, and digital twins. Several up- and downstream success stories highlighting the benefits of using advanced process modeling

Presented at: Bioprocessing 2021
C.E. Credits: P.A.C.E. CE Florida CE
Speaker
  • CEO Novasign GmbH, Project Lead BOKU - Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna
    Biography
      With more than 10 years of experience, Mark is a passionate expert in the field of biopharmaceutical process development. He worked together with several Top25 Pharma companies in different projects targeting their needs in process development and understanding. Together with his team, he is developing a hybrid modeling software for both up and downstream processes. This software utilizes AI applied to process data and first principle models (hybrid models) to quickly predict critical quality attributes (CQAs) from critical process parameters (CPPs). Hereby, bioprocess development and characterization are accelerated significantly while simultaneously a better process understanding is gathered. From this research project, the company Novasign was founded in 2019 offering this unique software solution.

    Abstract

    Within the last decade, bioprocess modeling received increased attention. But what are actually the crucial things to consider to create value through process modeling? During this talk, the modeling circle (from data to digital twin) is highlighted for both upstream and downstream showcases. A hybrid modeling methodology is presented and it will be highlighted how  the modeling circle can be closed with an intuitive toolbox which you as a user can directly use to solve your own bioprocess related modeling tasks. The Toolbox can be used to apply model-based and intensified Design of Experiments (m&iDoE) to significantly accelerated bioprocess development. The models can further be used in a digital twin environment to  explore the process or exported to use the models online in-real time as soft-sensor and for process control.

    Learning Objectives:

    1. Understand the modeling circle (from data to digital twin)

    2. Learn how hybrid modeling outperforms purely data-driven modeling

    3. Understand how intensified and model-based DoE help to accelerate bioprocess development


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