scholarly journals Dynamic modeling of syngas fermentation in a continuous stirred‐tank reactor: Multi‐response parameter estimation and process optimization

2019 ◽  
Vol 116 (10) ◽  
pp. 2473-2487 ◽  
Author(s):  
Elisa M. Medeiros ◽  
John A. Posada ◽  
Henk Noorman ◽  
Rubens Maciel Filho
Keyword(s):  
Parameter Estimation ◽  
Process Optimization ◽  
Dynamic Modeling ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Syngas Fermentation ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Response Parameter ◽  
Continuous Stirred Tank

scholarly journals Investigation and Modeling of Gas-Liquid Mass Transfer in a Sparged and Non-Sparged Continuous Stirred Tank Reactor with Potential Application in Syngas Fermentation

Fermentation ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 75 ◽  
Author(s):  
Kan Liu ◽  
John R. Phillips ◽  
Xiao Sun ◽  
Sayeed Mohammad ◽  
Raymond L. Huhnke ◽  
...  
Keyword(s):  
Mass Transfer ◽  
Operating Conditions ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Syngas Fermentation ◽  
Mass Transfer Limitation ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Working Volume ◽  
Continuous Stirred Tank

Syngas (mixture of CO, H2 and CO2) fermentation suffers from mass transfer limitation due to low solubility of CO and H2 in the liquid medium. Therefore, it is critical to characterize the mass transfer in syngas fermentation reactors to guide in delivery of syngas to the microorganisms. The objective of this study is to measure and predict the overall volumetric mass transfer coefficient, kLa for O2 at various operating conditions in a 7-L sparged and non-sparged continuous stirred-tank reactor (CSTR). Measurements indicated that the kLa for O2 increased with an increase in air flow rate and agitation speed. However, kLa for O2 decreased with the increase in the headspace pressure. The highest kLa for O2 with air sparged in the CSTR was 116 h−1 at 600 sccm, 900 rpm, 101 kPa, and 3 L working volume. Backmixing of the headspace N2 in the sparged CSTR reduced the observed kLa. The mass transfer model predicted the kLa for O2 within 10% of the experimental values. The model was extended to predict the kLa for syngas components CO, CO2 and H2, which will guide in selecting operating conditions that minimize power input to the bioreactor and maximize the syngas conversion efficiency.

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