Mathematical model of self-cycling fermentation

Area of Science:

• Biotechnology
• Biochemical Engineering
• Microbial Fermentation

Background:

• Self-cycling fermentation (SCF) is a microbial process requiring precise control.
• Understanding the dynamics of biomass, substrate, and oxygen is crucial for stable SCF operation.
• Existing models may not fully capture the intrinsic periodicity of stable SCF.

Purpose of the Study:

• To develop a mathematical model for predicting key parameters in stable self-cycling fermentation.
• To validate the model using experimental data from Acinetobacter calcoaceticus RAG-1 fermentation.
• To demonstrate the model's ability to simulate and predict the characteristic unimposed periodicity of SCF.

Main Methods:

• Development of a mathematical model incorporating biomass, limiting substrate (ethanol), and dissolved oxygen.
• Laboratory-scale fermentation experiments using Acinetobacter calcoaceticus RAG-1.
• Computer simulation based on the developed model for validation against experimental data.

Main Results:

• The mathematical model accurately represented biomass, substrate, and dissolved oxygen trends during SCF.
• Experimental data validated the model's predictions for intracycle dynamics.
• The simulation successfully replicated the stable, unimposed periodicity observed in experimental SCF.

Conclusions:

• The presented mathematical model provides a robust framework for understanding and predicting stable self-cycling fermentation.
• The model's validation confirms its utility in optimizing SCF processes.
• This work contributes to the control and design of microbial fermentation systems.