A bioreactor is a system that simulates adequate physiological environments to allow cell growth and metabolic activity (Gódia & López, 2005). Bioreactors' primary functions are to provide an adequate supply of nutrients to the cells, generate gaseous exchange, mechanically stimulate the culture, and remove waste. The bioreactors are commonly cylindrical, with sizes that range from few liters to some number of cubic meters, depending on their final application. They can be used for different purposes, for example, for biomass production (e.g., single-cell protein, Baker's yeast, animal cells, microalgae); for metabolite formation (e.g., organic acids, ethanol, antibiotic, aromatic compounds, pigments); to transform substrates (e.g., steroids) or even for production of an active cell molecule (e.g., enzymes). Bioreactor operation mode is classified into batch processes, bed-batch, and continuous processes.

The most common types of bioreactors that are used in bioprocess technology are:

  • Continuous Stirred Tank bioreactors

  • Bubble column bioreactors

  • Airlift bioreactors

  • Fluidized bed bioreactors

  • Packed bed bioreactors

  • Photobioreactor

In the past, bioreactors seemed black boxes within which cells were cultured mainly by trial and error. The science and technology involved in the design, functionality, and application of such bioreactors indicate otherwise (Singh, 2009). Generally, some of the aspects to consider in the bioreactor design processes are the temperature, the need for aeration, the agitation level required, and the flow dynamics behavior. Due to the lack of adequate sensors, it is impossible to perform a complete characterization of the flow inside the bioreactor. Also, the cost associated with experimentation in bioreactors, particularly for big ones, makes it unviable to develop trial and error processes. For this reason, Computational Fluid Dynamics (CFD) has been used in the last years for accelerating and improving the bioreactor design (Amani et al., 2018; Pessoa et al., 2019).

To better understand the role of the hydrodynamic flow behavior inside the bioreactor and the factors that govern it, computational fluid dynamics modeling is an invaluable tool that has only recently been applied to the area of bioprocessing technology. CFD has been traditionally used to the chemical and mechanical engineering fields; however, nowadays is also helping bioprocessing scientist and engineers to understand the implications of fluid flow and transport cell function on the bioreactor’s behavior, which allows them to remove much of the trial and error involved in the traditional methods.

Using CFD is possible to completely characterize the flow behavior in both simple and complex geometries. It is also possible to determine the influence that the flow behavior has over the bioreactor´s performance. For example, it is possible to predict the effect of shear stresses and oxygen transfer levels over the cell’s growth. According to this, the most frequent properties and characteristics extracted from CFD simulations for bioreactors are:

  • Velocity field

  • Culture uniformity

  • Mixing time

  • Species/phase distribution

  • Air distribution (In cases with aeration)

  • Shear stress field

  • Recognition of recirculation zones

  • Streamlines

  • KLa field

  • Power consumption (In a case where mechanical agitation is presented)

The velocity field allows determining death regions or zones with high velocities that do not allow an adequate bioreactor's operations. The culture uniformity indicates how well the bioreactor distributes the culture inside the bioreactor. In cases with mechanical agitation or aeration, the culture uniformity suggests the efficiency of these processes. The mixing time gives information about how fast the substance injected are mixed inside the bioreactor; this time must agree with the performed application's needs. In cases with air or other species injections, it is essential to evaluate how they are distributed inside the bioreactor; observing this will determine any improvement to the bioreactor's design. The shear strain rate has a significant effect on the bioreactor's performance; for example, in perfusion bioreactors, large values of shear stresses on the scaffold can, in turn, wash away the attached cells, adversely influence the cellular metabolism, and even damage the cells (Yan et al., 2012). Recognizing recirculation zones is crucial because it allows determining the bioreactor zones that do not contribute to their primary goal. Obtaining KLa values is very important for a bioreactor's design; however, calculating this value by experimentation is not an easy task because it is influenced by a high number of parameters (physical properties of gas and liquid, operational conditions, geometrical parameters of the bioreactor) and also by the consumption of oxygen by the cells (Garcia-Ochoa & Gomez, 2008). Regarding the difficulty of determining this value experimentally, some empirical approaches have been developed, which successfully predict the KLa value for bioreactors of different sizes and operational conditions. These empirical correlations can be used in CFD to calculate the KLa value quickly.

Finally, it is essential to note that CFD must be used in conjunction with experimentation. This coupling forms a synergistic relationship that can potentially yield more significant and yet, more cohesive data for future bioreactor studies (Singh, 2009).

Do you want to know how to perform a perfusion bioreactor simulation in ANSYS Fluent?

If you want to know more about how to built a model for a perfusion bioreactor using ANSYS Fluent, click on the following link and watch the video https://youtu.be/ksDv-aWS-JE


Amani, A., Jalilnejad, E., & Mousavi, S. M. (2018). Simulation of phenol biodegradation by Ralstonia eutropha in a packed-bed bioreactor with batch recycle mode using CFD technique. Journal of Industrial and Engineering Chemistry, 59, 310–319. https://doi.org/10.1016/j.jiec.2017.10.037

Garcia-Ochoa, F., & Gomez, E. (2008). Bioreactor scale-up and oxygen transfer rate in microbial processes: An overview. Biotechnology Advances, 27, 153–176. https://doi.org/10.1016/j.biotechadv.2008.10.006

Gódia, F., & López, J. (2005). Ingeniería Bioquímica. Ciencias Química: Tecnología bioquímica y de los alimentos. Barcelona, España: SINTESIS.

Pessoa, D. R., Finkler, A. T. J., Machado, A. V. L., Mitchell, D. A., & de Lima Luz, L. F. (2019). CFD simulation of a packed-bed solid-state fermentation bioreactor. Applied Mathematical Modelling, 70, 439–458. https://doi.org/10.1016/j.apm.2019.01.032

Singh, H. &. (2009). Bioreactor studies and computational fluid dynamics. En Bioreactor studies and computational fluid dynamics (págs. 231-249). Berlin, Heidelberg: Springer.

Yan, X., Chen, X. B., & Bergstrom, D. J. (2012). Modeling of the Flow within Scaffolds in Perfusion Bioreactors. American Journal of Biomedical Engineering, 1(2), 72–77. https://doi.org/10.5923/j.ajbe.20110102.13


Post by Jesus Ramirez
January 8, 2021

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