Characterizing bubble column bioreactor performance using computational fluid dynamics. (22nd April 2016)

• Record Type: Journal Article
• Authors: McClure, Dale D.; Kavanagh, John M.; Fletcher, David F.; Barton, Geoffrey W.
• Is Part Of: Chemical engineering science. Volume 144(2016)
• Page Start: 58
• Page End: 74
• Publication Date: 2016-04-22
• Subjects: Bubble column -- CFD -- Bioreactors -- Bioprocess -- Scale-up; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2016.01.016
• Languages: English
• ISSNs: 0009-2509

Abstract

Abstract: In this work we have coupled microbial kinetics with a Computational Fluid Dynamics (CFD) model of the hydrodynamics within a bubble column. Saccharomyces cerevisiae was used as a model organism due to its well characterized kinetics. A range of methodologies was used to quantify the impact of reactor design on performance. These methodologies included those based on the average substrate concentration, those based on the instantaneous substrate concentration, as well as those based on Lagrangian particle tracking. By using the particle tracking approach it was possible to quantify the duration of any oscillations in substrate concentration that the cells experience, a question of key physiological relevance. It was found that the relative yield as calculated using both approaches depended on the sugar addition location. Values of the relative yield between 75% and 93% were calculated based on the average concentration approach, 73–81% based on the instantaneous concentration approach; while values of 93–97% were calculated using particle tracking. Overall, the results from this work clearly demonstrate the potential of using CFD to characterize the complex and highly dynamic behaviour occurring in bubble column bioreactors. Graphical abstract: Highlights: Kinetics of microbial growth implemented in CFD model. Lagrangian particle tracking used to quantify impact of reactor design. A range of metrics were used to quantify impact of bioreactor design on performance. Impact of substrate addition point on yield examined. Approach developed can be used for design and optimisation of bioreactors.