Description
The economic and environmental advantages of bioprocess engineering have inclined many industrial sectors, namely food, pharma, and the production of compound chemicals and proteins, towards industrial biotechnology. Fermentation is a key stage in many bioprocesses and requires close control of several key parameters to ensure optimum conditions for microbial performance. Although maintaining an ideal environment is achievable in lab-scale fermentation, it can be rather daunting on a larger scale due to non-ideal mixing. Monitoring of important process parameters, such as substrate and oxygen concentration, is a particular challenge in large bioreactors as the mounting of monitoring devices and probes disrupts the sterile environment and because the number of probes is limited by space constraints. The lack of local data for important parameters means that concentration gradients and microbial heterogeneity remain unnoticed, which may result in poor process performance. Numerical techniques based on computational fluid dynamics can be a valuable approach to gain insight into the local distribution of these parameters. Additionally, a microorganism's journey over the period of its residence inside the bioreactor can be analyzed, which would help to optimize the bioreactor configuration (i.e. feed position, impeller type, impeller spacing) and operating conditions, as well as to develop scale-down simulators for mimicking the harsh industrial gradients in lab scale to assess and study the reaction of the organism to this environment [1-3].In this contribution, a 200L pilot scale fermenter was modelled using ANSYS Fluent for the scale-up process of Pseudomonas putida fermentation in fed-batch mode. Kinetics were coupled to the multiphase fluid dynamic model of a tank equipped with three Rushton turbine impellers [4]. The effect of oxygen and glucose gradients on the microorganism’s growth rate was investigated at different stages of mixing by applying the CFD-kinetic model to a mixing tank with different liquid levels representing the fed-batch system at various periods during the continuous feeding. Lagrangian particle tracking was used as the last step for generating microorganism trajectories.
| Period | 18 Oct 2022 |
|---|---|
| Event title | 13th Asian Computational Fluid Dynamics Conference |
| Event type | Conference |
| Conference number | 13 |
| Location | Jeju-do, Korea, Republic ofShow on map |
| Degree of Recognition | International |
Keywords
- Fed-batch fermentation
- Euler-Lagrange model
- multiphase
- kinetic modelling
- mass transfer
- scale-up
- scale-down