Modeling large-scale bioreactors with diffusion equations. Part II: Characterizing substrate, oxygen, temperature,...

Area of Science:

• Biochemical Engineering
• Industrial Biotechnology
• Process Modeling

Background:

• Large-scale fermentation involves complex interactions between mixing, reaction, and mass transfer.
• Current methods rely on empirical correlations or case-specific simulations for bioreactor performance prediction.
• Predictive models are crucial for optimizing industrial bioprocesses.

Purpose of the Study:

• To develop and validate a general, predictive model for large-scale fed-batch bioreactors.
• To characterize substrate, pH, oxygen, carbon dioxide, and temperature profiles in fed-batch operations.
• To assess the utility of one-dimensional axial diffusion equations for modeling bioreactor performance.

Main Methods:

• Applied one-dimensional axial diffusion equations with steady-state and kinetic models (first- and zeroth-order).
• Derived analytical solutions for substrate, dissolved oxygen, temperature, and pH profiles.
• Compared model predictions with experimental data from Escherichia coli and Saccharomyces cerevisiae fermentations and literature simulations.

Main Results:

• Achieved good agreement between predicted and experimental substrate profiles.
• Analytical profiles for dissolved oxygen, temperature, and pH were consistent with available data.
• Defined substrate distribution functions and derived efficiency factors for biomass growth and oxygen uptake.

Conclusions:

• One-dimensional axial diffusion equations provide a robust and predictive model for large-scale fed-batch fermentations.
• The model effectively captures the impact of mixing and reaction kinetics on key bioreactor variables.
• This approach offers a generalizable tool for understanding and optimizing industrial bioprocesses.