Model-assisted DoE applied to microalgae processes

Current Research in Biotechnology

Fabian Kuhfuß¹  Sahar Deppe¹  Kim B. Kuchemüller¹  Johannes Möller¹  André Moser²  George Ifrim³  Björn Frahm⁴  Ralf Pörtner⁵  Veronika Gassenmeier⁵  Volker C. Hass⁵  Tanja Hernández Rodríguez⁵ 

[1] Bioprocess Engineering, Lemgo, Germany;Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Industrial Automation branch INA, Lemgo, Germany;Rentschler Biopharma SE, Production and Manufacturing, Laupheim, Germany;Furtwangen University of Applied Sciences, Institute of Applied Biology, Faculty of Medical and Life Sciences, Villingen-Schwenningen, Germany;;Ostwestfalen-Lippe University of Applied Sciences and Arts, Biotechnology &

关键词: DoE;    Model-assisted;    mDoE;    Algae;    Mathematical process model;    Light intensity;   

DOI  :  

来源: DOAJ

【 摘 要 】

This study assesses the performance of the model-assisted Design of Experiment (mDoE) software toolbox for the design of two microalgae bioprocesses. The mDoE-toolbox was applied to maximize biomass growth for Desmodesmus pseudocommunis in a photobioreactor by varying the light intensity and pH and for Chlorella vulgaris in shake flasks, by varying the light intensity and duration. For both case studies, a mathematical mechanistic model was applied. In the first study only one experiment was necessary to adapt the mathematical model and identify a combination of light intensity and pH that improved biomass yield, as confirmed experimentally. In the second study, no well-established model was available for the specific experimental arrangement. On the basis of the literature, a mathematical model was constructed and a first cycle of mDoE was performed, thus identifying the desired factor combinations. Experiments confirmed the high biomass yield but revealed shortcomings of the model. The model was improved and a second cycle of mDoE was performed. The recommended factor combinations from both cycles were comparable. The mDoE was found to be a time-saving, cost-effective and useful method enabling the identification of factor combinations leading to high biomass production for the design of two different microalgae bioprocesses with low experimental effort.

【 授权许可】

Unknown