Modeling large-scale bioreactors with diffusion equations. Part I: Predicting axial dispersion coefficient and mixing...

Area of Science:

• Chemical Engineering
• Bioprocess Engineering
• Fluid Dynamics

Background:

• Bioreactor scale-up is complex due to interactions between mixing, reaction, mass transfer, and biological processes.
• Current methods often rely on simplified correlations or case-specific simulations, limiting generalizability.
• Predictive modeling of mixing times is crucial for efficient bioreactor design and operation.

Purpose of the Study:

• To investigate the use of axial diffusion equations for calculating mixing times in large-scale stirred bioreactors.
• To develop a general and predictive model for bioreactor characterization without fitting the dispersion coefficient.
• To validate the model using extensive published experimental data.

Main Methods:

• Developed a resistances-in-series model analogous to heat transfer theory to estimate the dispersion coefficient.
• Utilized hydrodynamic numbers and literature data for dispersion coefficient calculations.
• Predicted over 800 experimentally determined mixing times using the transient axial diffusion equation.

Main Results:

• The model demonstrated excellent performance for typical multi-impeller configurations, avoiding flooding.
• Accurate predictions were achieved across various reactor sizes (up to 160 m³), impeller types, and flow regimes.
• Predictions were less accurate for single-impeller and some nonstandard bioreactors.

Conclusions:

• The transient axial diffusion equation combined with the developed transfer resistance analogy provides a convenient and predictive model for mixing in typical large-scale bioreactors.
• This approach offers a more generalizable alternative to current scale-up prediction methods.
• Further refinement may be needed for nonstandard or single-impeller configurations.