Mass Transfer Modeling For CO2 Removal from Environment with the Aim of Green Biomethanation and Methanogens Growth Optimization

Subject Areas : Air Pollution

Seyed Ali Jafari ¹ (PhD Sudent, Department of Chemical Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran.)
Shahriar Osfouri ² (Associated professor Department of Chemical Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran. *(Corresponding Author))
Reza Azin ³ (Associated professor Department of Chemical Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran.)

Received: 2019-03-06 Accepted : 2019-11-13 Published : 2021-02-19

Keywords: CO2 removal, Mathematical Modeling, Biomethane, Mass Transfer, Hydrogen,

Abstract :

Background and Objective: CO2 concentration, as the main greenhouse gas, is growing in atmosphere and many alternatives have been investigated to deal with it. However, harnessing with the aim of biomethanation seems to be more economic. Method: In this study a mass transfer modeling was conducted for a biomethanation process under a batch strategy aiming at maximizing liquid active volume. The accuracy of modeling results was assessed via comparing with experimental data and kinetic results under zero-dimension study. Then one-dimensional study was conducted in order to investigate biomass and hydrogen concentration profiles within liquid phase of the bioreactor and active volume calculation. Response surface method (RSM) was also served to investigate effect of temperature, pressure and as three main factors on active volume followed by response optimization. Findings: Model accuracy was confirmed by zero-dimension study. One-dimensional study was also revealed that biomass growth dispersion within liquid phase depends on hydrogen profile concentration on condition that both hydrogen and biomass diffusion coefficients were assumed to be equal. Their degree of magnification was 10-9  in standard conditions. RSM showed that the three studied factors significantly affected on bioreactor active volume. Meanwhile, pressure and temperature influenced the most, respectively. Discussion and Conclusion: A batch bioreactor with  and high pressure and temperature met optimal conditions for biomethanation; however, process economy defines operational limitations.  

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