Stochastic parcel tracking in an Euler-Lagrange compartment model for fast simulation of fermentation processes.

Area of Science:

• Biochemical Engineering
• Computational Modeling
• Bioreactor Design

Background:

• Compartment models (CM) offer affordable hydrodynamic modeling but lack detailed intracellular dynamics.
• Existing models use black-box kinetics, failing to capture cell population responses in heterogeneous bioreactor environments.

Purpose of the Study:

• To implement a Lagrangian reaction model for tracking microbial responses within bioreactors.
• To couple intracellular dynamics with extracellular variations for a more accurate simulation of cell behavior.

Main Methods:

• A Lagrangian approach tracks biomass parcels with intracellular composition and structured reaction models.
• Stochastic parcel tracking is used, contrasting with resolved trajectories in CFD.
• A penicillin production process serves as a case study for model validation.

Main Results:

• The model accurately predicts extracellular gradients and intracellular pool responses compared to CFD simulations.
• Mixing time is used as a criterion for matching model performance with CFD.
• Computational time is significantly reduced, with the CM solving 80 hours of flow in minutes versus weeks for CFD.

Conclusions:

• The developed Lagrangian reaction model provides a computationally efficient and accurate method for simulating bioreactor processes.
• This approach overcomes limitations of black-box kinetics and accelerates analysis and optimization of industrial fermentation.
• The model enables detailed study of intracellular changes and cell population dynamics in response to environmental heterogeneity.