(297c) CFD Modeling of Oxygen Dissolution in Bioreactors: Mass Transfer and Population Balance Study in Stirred Tanks

Aerobic microorganisms need oxygen for sustenance, growth and product formation. Furthermore, a continuous supply of oxygen is critical in aerobic bioreactors due to its low solubility in aqueous solutions. While maintaining continuous oxygen transfer from air to the broth is necessary for the optimum design and scaling of these reactors, it is a rather complex task. The intricacies commonly encountered in the design of these systems are highly influenced by various parameters including the geometry, operating conditions, turbulence, gas distribution among others. A typical configuration for bio-reactors encountered in the bio-pharmaceutical and fine-chemical manufacturing industry, is gas-liquid dispersion in stirred tanks. Most studies encountered in the literature focus on the separate analysis of gas holdup, bubble size distribution, and mass transfer coefficients. However only a few thus far have considered using a complete set of robust CFD models at operating conditions (Nallamothu et al., 2015).

The current work extends the research presented by Nallamothu (Nallamothu et al., 2015), by including more detailed physics such as a swarm factor drag correction for the Ishii-Zuber drag coefficient (Ishii et al., 1979) which takes into account the presence of clusters of bubbles. A parametric study has also been conducted to account for the effect of the particle size distribution using the Inhomogeneous Discrete population balance model in the CFD software ANSYS/FLUENT. In contrast to assuming the same velocity field for all particle sizes within a phase, this approach models the particle size distribution across multiple phases which can realistically predict the segregation of large and small bubbles.

The current study considers different rotational speeds and gas flow rates. The mass transfer coefficient is obtained by experimental measurement of dissolved oxygen concentration by absorption in pure water. Finally, the simulation results are validated against experimental data published by Laakkonen et al. (Laakkonen et. al., 2007) in terms of temporal variation of normalized concentration as well as variation of gas holdup with gas flow rate.

References:

1) Nallamothu, S.K.; Ozarkar, S.; Joshi, S. “Advances in Gas Sparging Simulation for Bioreactor Modeling to Create Comprehensive Simulation Design Space”. AIChE Annual Meeting (2015).

2) Ishii, M.; Zuber N. “Drag Coefficient and Relative Velocity in Bubbly, Droplet or Particle Flows”. AIChE J. 25: 843-855 (1979).

3) Laakkonen, M.; Moilanen, P.; Alopaeus, V.; Aittamaa, J. “Modelling local bubble size distribution in agitated vessels”. Chem. Eng. Sci. 62: 721-740 (2007).