Characterization of reactions and growth in automated continuous flow and bioreactor platforms—From linear DoE to model-based approaches

Tilman Barz, Julian Kager, Christoph Herwig, Peter Neubauer, Mariano Nicolas Cruz Bournazou, Federico Galvanin

Publikation: Beitrag in Buch oder TagungsbandBuchkapitelBegutachtung

Abstract

Laboratory automation that drives integrated miniaturized and parallel reactor systems incorporating analytical devices for online reaction monitoring has reached a remarkable level of sophistication. The result is a significant increase in experimental throughput allowing the generation of large amounts of data under complex experimental settings and dynamic conditions, making the design of experiments a very challenging task. Model-based optimal experimental design method is a systematic approach for the most effective exploration of the experimental design space toward a consistent characterization of nonlinear dynamic processes, reactions, catalysts, hosts, model candidates, etc. This contribution presents a critical examination of recent experimental applications performed in automated platforms in the field of classical DoE, model-free, and model-based approaches for the identification and optimization of biochemical reactions. The comparison of applications in continuous flow and bioreactor platforms reveals significant differences in the level of maturity of developed solutions toward an autonomous operation for the generation and analysis of the most informative data.
OriginalspracheEnglisch
TitelSimulation and Optimization in Process Engineering - The Benefit of Mathematical Methods in Applications of the Chemical Industry
Redakteure/-innenMichael Bortz, Norbert Asprion
Herausgeber (Verlag)Elsevier
Seiten273-319
Seitenumfang47
ISBN (Print)978-0-323-85043-8
PublikationsstatusVeröffentlicht - 2022

Research Field

  • Efficiency in Industrial Processes and Systems

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