Simulating gas-liquid mass transfer in a spin filter bioreactor
DOI:
https://doi.org/10.17533/udea.redin.n75a16Keywords:
scale up, multiple reference frame (MRF), population balance model (PBM), spin filter, bioreactorAbstract
Computational fluid dynamics (CFD) and population balance model (PBM) model have been used to simulate hydrodynamics and mass transfer in a 0.014 m3 Spin Filter Bioreactor. The operating conditions chosen were defined by typical settings used for culturing plant cells. Turbulence, rotating flow, bubbles breakage and coalescence were simulated by using the k-e, MRF (Multiple Reference Frame) and PBM approaches, respectively. The numerical results from different operational conditions are compared with experimental data obtained from measurements and good fitting data is achieved. Interested by these simulated and experimental results CFD simulations are qualified as a very promising tool not only for predicting gas-liquid hydrodynamics but also for finding design requirements that must be implemented to optimize an aerobic bioprocessing useful for plant cell culture applications which are characterized by the constrain of achieving relatively high mass transfer conditions and avoiding cellular damage due to hydrodynamic conditions.
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References
S. Sens, P. Roychoudhury. “Step-up/step-down perfusion approach for increased mAb 520C9 production by a hybridoma cell line”. Biotechnol Lett. Vol. 35. 2013. pp. 153-163.
M. Jenne, M. Reuss. “A critical assessment on the use of k–e turbulence models for simulation of the turbulent liquid flow induced by Rushton turbine in baffled stirred-tank reactors”. Chem Eng Sci. Vol. 54. 1999. pp. 3921-3941.
J. Luo, R. Issa, A. Gosman. “Prediction of impeller induced flows in mixing vessels using multiple frames of reference”. Inst. Chem. Eng. Symposium Series. Vol. 136. 1994. pp. 549-556.
G. Micale, F. Grisafi. “Prediction of flow fields in a dual-impeller stirred tank”. AICHE J. Vol. 45. 1999. pp. 445-464.
K. Rutherford, C. Lee, S. Mahmoudi, M. Yianneskis. “Hydrodynamic characteristics of dual Rushton impeller stirred vessels”. AICHE J. Vol. 42. 1996. pp. 332-346.
A. Tabor, G. Gosman, R. Issa. “Numerical simulation of the flow in a mixing vessel stirred by a Rushton Turbine”. Inst. Chem. Eng. Symposium Series. Vol. 140. 1996. pp. 25-34.
V. Bakker, V. Akker. “A computational model for the gas–liquid flow in stirred reactors”. Transactions of the Institution of Chemical Engineers. Vol. 72. 1994. pp. 594-606.
F. Kerdouss, A. Bannari, P. Proulx, R. Bannari, M. Skrga, Y. Labrecque. “Two-phase mass transfer coefficient prediction in stirred vessel with a CFD model”. Comput and Chem Eng. Vol. 32. 2008. pp. 1943-1955.
F. Kerdouss, L. Kiss, P. Proulx, J. Bilodeau, C. Dupuis. “Mixing characteristics of an axial flow rotor: Experimental and numerical study”. Int J of Chem React Eng. Vol. 3. 2005. pp. 35.
F. Kerdouss, A. Bannari, P. Proulx, R. Bannari, M. Skrga, Y. Labrecque. “Two-phase mass transfer coefficient prediction in stirred vessel with a CFD model”. Comput and Chem Eng. Vol. 3. 2007. pp. 1-13.
G. Lane, M. Schwarz, G. Evans. “Numerical modeling of gas–liquid flow in stirred tank”. Chem Eng Sci. Vol. 60. 2005. pp. 2203-2214.
C. Venneker, V. Derksen. “Population balance modeling of aerated stirred vessels based on CFD”. AICHE J. Vol. 48. 2002. pp. 673-684.
R. Gelves, A. Dietrich, R. Takors. “Modeling of gasliquid mass transfer in a stirred tank bioreactor agitated by a rushton turbine or a new pitched blade impeller”. Bioprocess and Biosystem Engineering. Vol. 37. 2014. pp. 365-375.
Y. Deo, M. Mahadevan, R. Fuchs. “Practical Considerations in Operation and Scale-up of SpinFilter Based Bioreactors for Monoclonal Antibody Production”. Biotechnol Prog. Vol. 12. 1996. pp. 57- 64.
P. Himmelfarb, P. Thayer, H. Martin. “Spin-filter culture: The propagation of mammalian cells in suspension”. Science. Vol. 164. 1969. pp. 555-557.
L. Castilho, R. Medronho. “Cell retention devices for suspended-cell perfusion cultures”. Adv Biochem Eng Biotechnol. Vol. 74. 2002. pp. 129-169.
L. Chu, D. Robinson. “Industrial choices for protein production by large-scale cell culture”. Curr Opin Biotechnol. Vol. 12. 2001. pp. 180-187.
A. Figueredo, J. Navarrete, P. Vitón, E. Martínez, A. Castro, E. Chico. “Effect of different variables on the long-term spinfilter clogging during pilot-scale animal cell perfusion runs”. F. Gòdia, M. Fussenegger (editors). Animal cell technology meets genomics. Vol. 2. Ed. Springer. Dordrecht, Netherlands. 2005. pp. 683-685.
D. Lide. CRC Handbook of Chemistry and Physics. 83rd ed. Ed. CRC Press. Boca Raton, USA. 2002. pp. 8-232.
G. Kasat, A. Pandit, V. Ranade. “CFD Simulation of Gas-Liquid Flows in a Reactor Stirred by Dual Rushton Turbines”. Int J of Chem React Eng. Vol. 6. 2008. pp. 1-28.
L. Wei. Multiple Impeller Gas-Liquid Contactors. Technical report, National Taiwan University. Taipei, Taiwan. 2004. pp. 13-47.
V. Ranade, J. Dommeti. “Computational Snapshot of flow generated by axial impellers in baffled stirred vessels”. J. Chem. Vol. 74. 1990. pp. 476-484.
V. Ranade. Computational Flow Modeling for Chemical Reactor Engineering, Process Systems Engineering Series. 1st ed. Ed. Academic Press. New York, USA. 2002. pp. 35-54.
M. Jahoda, L. Tomášková, M. Moštěk. “CFD prediction of liquid homogenization in a gas-liquid stirred tank”. Chem Eng Res and Des. Vol. 87. 2009. pp. 460-467.
E. Marshall, A. Bakker. Computational fluid mixing. Technical report. Ed. Fluent-Inc. Lebanon, USA. 2002. pp. 1-94.
R. Gelves, A. Benavides, J. Quintero. “Predicción del comportamiento hidrodinámico en el escalado de un reactor de tanque agitado para procesos aerobios, mediante CFD”. Ingeniare. Rev. chil. ing. Vol. 21. 2013. pp. 347-361.
M. Ishii, N. Zuber. “Drag coefficient and relative velocity in bubbly, droplet or particulate flows”. AIChE J. Vol. 25. 1979. pp. 843-855.
S. Elgobashi, M. Rizk. “A two-equation turbulence model for dispersed dilute confined two-phase flows”. Int J of Multiph Flow. Vol. 15. 1989. pp. 119-133.
M. Hounslow, R. Ryall, V. Marschall. “A Discretized Population Balance for Nucleation, Growth and Aggregation”. AIChE J. Vol. 34. 1988. pp. 1821-1832.
J. Litster, D. Smit, M. Hounslow. “Adjustable Discretization Population Balance for Growth and Aggregation”. AIChE J. Vol. 41. 1995. pp. 591-603.
D. Ramkrishna. Population Balances: Theory and Applications to Particulate Systems in Engineering. 1st ed. Ed. Academic Press. New York, USA. 2000. pp. 47-116.
P. Chen, M. Dudukovic, J. Sanyal. “Numerical simulation of bubble columns flows: effect of different breakup and coalescence closures”. Chem Eng Sci. Vol. 60. 2005. 1085-1101.
L. Hagesaether, H. Jakobsen, A. Hjarbo, H. Svendsen. “A coalescence and breakup module for implementation in CFD-codes”. European Symposium on Computer Aided Process Engineering. Vol. 8. 2000. pp. 367-372.
J. Sanyal, D. Marchisio, R. Fox, K. Dhanasekharan. “On the comparison between population balance models for CFD simulation of bubble columns”. Ind Eng and Chem Res. Vol. 44. 2005. pp. 5063-5072.
B. Pocurull. Mechanistic modeling of increased oxygen transport using functionalized magnetic fluids in bioreactors. PhD Thesis, Massachusetts Institute of Technology. Cambridge, USA. 2005. pp. 1-180.
S. Oh, A. Nienow, M. Al-Rubeai, A. Emery. “The effects of agitation intensity with and without continuous sparging on the growth and antibody production of hybridoma cells”. J of Biotechnol. Vol. 12. 1989. pp. 45-61.
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