Downscaling Industrial-Scale Syngas Fermentation to Simulate Frequent and Irregular Dissolved Gas Concentration...

Area of Science:

• Biochemical Engineering
• Computational Fluid Dynamics (CFD)
• Microbial Fermentation

Background:

• Large-scale syngas fermentation reactors, like external-loop gas-lift reactors (EL-GLR), are prone to significant gradients in dissolved gases (carbon monoxide [CO] and hydrogen [H2]).
• These gradients arise from variations in mass transfer and convection rates, potentially impacting microbial performance and process efficiency.

Purpose of the Study:

• To analyze dissolved gas concentration gradients in an industrial-scale EL-GLR using Euler-Lagrangian CFD simulations.
• To investigate the impact of varying biomass concentrations and CO inhibition on gas uptake by microorganisms.
• To develop a bench-scale simulator that replicates the dynamic environmental fluctuations observed at industrial scale.

Main Methods:

• Euler-Lagrangian CFD simulations were employed to model gas-liquid mass transfer and concentration profiles within the EL-GLR.
• Lifeline analysis was used to quantify the frequency and magnitude of dissolved gas concentration oscillations experienced by microorganisms.
• A conceptual scale-down simulator, a stirred-tank reactor with adjustable stirrer speed, was designed to mimic industrial-scale fluctuations.

Main Results:

• Lifeline analyses revealed frequent (5-30 seconds) oscillations in dissolved gas concentrations, spanning one order of magnitude.
• High biomass concentrations were found to reduce inhibitory effects and enhance operational flexibility and product yield.
• The study suggests that peaks in dissolved gas concentration may boost syngas-to-ethanol yield due to rapid uptake mechanisms in *C. autoethanogenum*.

Conclusions:

• Industrial operation at high biomass concentrations is preferable for mitigating inhibitory effects and improving process outcomes in syngas fermentation.
• The developed scale-down simulator provides a valuable tool for validating hypotheses regarding gas concentration peaks and for parameterizing kinetic metabolic models.
• Understanding and replicating dynamic environmental fluctuations are crucial for optimizing microbial fermentation processes and improving yield.