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  4. Process Simulation of Power-to-X Systems-Modeling and Simulation of Biological Methanation
 
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2023
Journal Article
Title

Process Simulation of Power-to-X Systems-Modeling and Simulation of Biological Methanation

Abstract
Through utilization of state-of-the-art power-to-x technology, biological methanation is a novel method to capture the intermittent electricity generated by renewable energy sources. In this process, biomass grows in a liquid solution by consuming H2 and CO2 and produces CH4. This study aims to improve the accuracy and comprehensibility of an initial bio-methanation model by reviewing and comparing existing technologies and methods, correcting miswritten equations, adding complementary equations, and introducing a new initialization approach. In addition, a mean value approach was used for calculating the axial mixing coefficients. Gas–liquid mass transfer in the reactor, along with other aspects, is considered the most challenging aspect of the biological methanation process due to hydrogen’s low solubility. This highlights the need for a modeling approach to improve understanding and optimize the design of the process. The improved MATLAB code was used to test different variations of parameters in the reactor and observe their effects on the system’s performance. The model was validated using experimental cases, and the results indicate that it is more accurate than Inkeri’s for certain parameter variations. Moreover, it demonstrates better accuracy in depicting the pressure effect. The sensitivity analysis revealed that liquid recycle constant λ had little effect on methane concentration, while impeller diameter dim and reactor diameter dre had significant impacts. Axial mixing constants b1 and b2 and biological kinetics constants kD, µmax, and mX had relatively small effects. Overall, the study presents a more comprehensive bio-methanation model that could be used to improve the performance of industrial reactors.
Author(s)
Ashkavand, Mostafa
Fraunhofer-Institut für Fabrikbetrieb und -automatisierung IFF  
Heineken, Wolfram  
Fraunhofer-Institut für Fabrikbetrieb und -automatisierung IFF  
Birth-Reichert, Torsten  
Fraunhofer-Institut für Fabrikbetrieb und -automatisierung IFF  
Journal
Processes  
Open Access
DOI
10.3390/pr11051510
Additional link
Full text
Language
English
Fraunhofer-Institut für Fabrikbetrieb und -automatisierung IFF  
Keyword(s)
  • biological methanation

  • gas–liquid mass transfer

  • numerical modeling

  • power-to-methane

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