Dynamic modeling of syngas fermentation in a continuous stirred-tank reactor: Multi-response parameter estimation and...

Area of Science:

• Biotechnology
• Chemical Engineering
• Sustainable Energy

Background:

• Syngas fermentation offers a sustainable route for converting waste carbon into valuable chemicals and fuels.
• Systems engineering principles are crucial for optimizing syngas fermentation processes.

Purpose of the Study:

• To develop and validate a dynamic multi-response model for syngas fermentation using acetogenic bacteria.
• To optimize process conditions for maximizing ethanol productivity and carbon monoxide conversion.

Main Methods:

• A dynamic multi-response model was created, incorporating gas-liquid mass transfer, substrate uptake, biomass dynamics, and product selectivity.
• Model parameters were estimated using maximum likelihood and metaheuristic optimization, with statistical validation.
• A multiobjective genetic algorithm was employed to optimize conflicting objectives of ethanol productivity and CO conversion.

Main Results:

• The model successfully simulated syngas fermentation, accounting for key biological and physical processes.
• Conflicting objectives of ethanol productivity and CO conversion were observed, particularly with CO-rich syngas.
• Increasing hydrogen content in syngas favored higher ethanol productivity while maintaining complete CO conversion.

Conclusions:

• The developed model provides a valuable tool for understanding and optimizing syngas fermentation.
• Optimal conditions for maximum ethanol productivity (2 g·L⁻¹·hr⁻¹) were predicted with 54% CO, 46% H₂, a dilution rate of 0.06 hr⁻¹, and 90% cell recycle.
• Syngas fermentation holds significant potential for future biobased economies and waste valorization.