Towards enhancement of gas–liquid mass transfer in bioelectrochemical systems: Validation of a robust CFD model
Journal article, 2021

Mass transfer has been identified as a major bottleneck in gas fermentation and microbial conversion of carbon dioxide to chemicals. We present a pragmatic and validated Computational Fluid Dynamics (CFD) model for mass transfer in bioelectrochemical systems. Experiments were conducted to measure mixing times and mass transfer in a Duran bottle and an H-cell. An Eulerian–Eulerian framework with a simplified model for the bubble size distribution (BSD) was developed that utilized only one additional equation for the bubble number density while including the breakup and coalescence. Validations of the CFD model for mixing times showed that the predictions were within the confidence intervals of the measurements, verifying the model's capability in simulating the hydrodynamics. Further validations were performed using constant and varying bubble diameters for the mass transfer. The results showed the benefits of a simplified BSD model, as it yielded improvements of seven and four times in accuracy when assessed against the experimental data for the Duran bottle and H-cell, respectively. Modeling of the H-cell predicted that a lower stirring rate improves mass transfer compared with higher stirring rates, which is of great importance when designing microbial cultivation processes. The model offers a feasible framework for advanced modeling of gas fermentation and microbial electrosynthesis.

microbial cultivations

H-cells

computational fluid dynamics

bubble number density

mass transfer

Author

Mohsen Karimi

Chalmers, Mechanics and Maritime Sciences, Fluid Dynamics

Tove Widén

Chalmers, Biology and Biological Engineering, Industrial Biotechnology

Yvonne Nygård

Chalmers, Biology and Biological Engineering, Industrial Biotechnology

Lisbeth Olsson

Chalmers, Biology and Biological Engineering, Industrial Biotechnology

Henrik Ström

Chalmers, Mechanics and Maritime Sciences, Fluid Dynamics

Biotechnology and Bioengineering

0006-3592 (ISSN) 1097-0290 (eISSN)

Vol. 118 10 3953-3961

Subject Categories

Energy Engineering

Bioinformatics (Computational Biology)

DOI

10.1002/bit.27871

PubMed

34173986

More information

Latest update

9/16/2021