Biotechnology and Bioprocess Engineering

, Volume 23, Issue 6, pp 710–725 | Cite as

Development of a Computational Fluid Dynamics Model for Scaling-up Ambr Bioreactors

  • Xianhua Li
  • Kara Scott
  • William J. KellyEmail author
  • Zuyi HuangEmail author
Research Paper


It is known that process scaling-up has always been a challenge in biopharmaceutical and food industry. In recent years, newly emerging microscale bioreactors like Ambr15 and Ambr250 have attracted significant attention for that they can provide high throughput and accelerate upstream process development. In this work, we developed the first multiphase Computational Fluid Dynamics (CFD) model for an in-depth characterization of Ambr bioreactor systems. A number of advanced computational methods, including Reynolds stress turbulence model, population balance model, multiple reference frame (MRF), sliding mesh (SM) and user defined functions (UDFs), were integrated for the first time to systematically study the gas-liquid mixing in Ambr250 bioreactor. We provided detailed comparison between MRF and SM method, demonstrated the limitation of MRF for predicting bubble distribution in asymmetric reactors. Characteristics of hydrodynamics, mass transfer, turbulent dissipation and bubble size distribution were predicted from our CFD models and validated by existing experimental data for a variety of operating conditions for both the Ambr15 and Ambr250 bioreactors. The predicted kLa value ranges are 0.18–7.90 h-1 and 2.15–11.52 h-1 for Ambr250 and Ambr15, respectively. This work thus provides a superior framework for the computational modeling of microscale stirred bioreactors.


scale up Ambr bioreactor CFD kLa 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Muller, M. M. (2016) Scale–up and scale–down topics facing the industry. Proceedings of the Engineering Conferences International. May.Google Scholar
  2. 2.
    Xu, P., C. Clark, T. Ryder, C. Sparks, J. Zhou, M. Wang, R. Russell, and C. Scott (2017) Characterization of TAP Ambr 250 disposable bioreactors, as a reliable scale–down model for biologics process development. Biotechnol. Prog. 33: 478–489.CrossRefGoogle Scholar
  3. 3.
    Rafiq, Q. A., A. W. Nienow, and C. J. Hewitt (2017) Process development of human mesenchymal stem cell microcarrier culture using an automated high–throughput microbioreactor.Google Scholar
  4. 4.
    Yoshida, T., S. Y. Lee, J. Nielsen, and G. Stephanopoulos (2017) Applied Bioengineering: Innovations and Future Directions. John Wiley & Sons.Google Scholar
  5. 5.
    Pandey, A., C. Larroche, and C. R. Soccol (2017) Current Developments in Biotechnology and Bioengineering: Current Advances in Solid–State Fermentation. Elsevier.Google Scholar
  6. 6.
    Sherman, M., V. Lam, M. Carpio, N. Hutchinson, and C. Fenge (2016) Continuous cell culture operation at 2,000–L scale. BioProcess Int. 14.Google Scholar
  7. 7.
    Hsu, W. T., R. P. Aulakh, D. L. Traul, and I. H. Yuk (2012) Advanced microscale bioreactor system: a representative scaledown model for bench–top bioreactors. Cytotechnology 64: 667–678.CrossRefGoogle Scholar
  8. 8.
    Ngibuini, M. (2012) Reducing biomanufacturing bottlenecks: Scale–down reactor automates parameter control and facilitates development. Gen. Eng. Biotechnol. News 33.Google Scholar
  9. 9.
    Rameez, S., S. S. Mostafa, C. Miller, and A. A. Shukla (2014) High–throughput miniaturized bioreactors for cell culture process development: reproducibility, scalability, and control. Biotechnol. Prog. 30: 718–727.CrossRefGoogle Scholar
  10. 10.
    Kim, B. J., J. Diao, and M. L. Shuler (2012) Mini–scale bioprocessing systems for highly parallel animal cell cultures. Biotechnol. Progress 28: 595–607.CrossRefGoogle Scholar
  11. 11.
    Xia, B. and D.–W. Sun (2002) Applications of computational fluid dynamics (CFD) in the food industry: a review. Comput. Electron. Agric. 34: 5–24.CrossRefGoogle Scholar
  12. 12.
    Kremer, D. M. and B. C. Hancock (2006) Process simulation in the pharmaceutical industry: a review of some basic physical models. J. Pharm. Sci. 95: 517–529.CrossRefGoogle Scholar
  13. 13.
    Sarkar, J., L. K. Shekhawat, V. Loomba, and A. S. Rathore (2016) CFD of mixing of multi–phase flow in a bioreactor using population balance model. Biotechnol. Progress 32: 613–628.CrossRefGoogle Scholar
  14. 14.
    Zhang, H., W. Williams–Dalson, E. Keshavarz–Moore, and P. A. Shamlou (2005) Computational–fluid–dynamics (CFD) analysis of mixing and gas–liquid mass transfer in shake flasks. Biotechnol. Appl. Biochem. 41: 1–8.CrossRefGoogle Scholar
  15. 15.
    Utomo, T., Z. Jin, M. Rahman, H. Jeong, and H. Chung (2008) Investigation on hydrodynamics and mass transfer characteristics of a gas–liquid ejector using three–dimensional CFD modeling. J. Mech. Sci. Technol. 22: 1821–1829.CrossRefGoogle Scholar
  16. 16.
    Gimbun, J., C. D. Rielly, and Z. K. Nagy (2009) Modelling of mass transfer in gas–liquid stirred tanks agitated by Rushton turbine and CD–6 impeller: a scale–up study. Chem. Eng. Res. Design 87: 437–451.CrossRefGoogle Scholar
  17. 17.
    Gelves, R., A. Dietrich, and R. Takors (2014) Modeling of gasliquid mass transfer in a stirred tank bioreactor agitated by a Rushton turbine or a new pitched blade impeller. Bioprocess Biosyst. Eng. 37: 365–375.CrossRefGoogle Scholar
  18. 18.
    Higbie, R. (1935) The rate of absorption of a pure gas into still liquid during short periods of exposure. Trans. AIChE 31: 365–389.Google Scholar
  19. 19.
    Kapic, A. and T. J. Heindel (2006) Correlating gas–liquid mass transfer in a stirred–tank reactor. Chem. Eng. Res. Design 84: 239–245.CrossRefGoogle Scholar
  20. 20.
    Dhanasekharan, K. M., J. Sanyal, A. Jain, and A. Haidari (2005) A generalized approach to model oxygen transfer in bioreactors using population balances and computational fluid dynamics. Chem. Eng. Sci. 60: 213–218.CrossRefGoogle Scholar
  21. 21.
    Khopkar, A. R., G. R. Kasat, A. B. Pandit, and V. V. Ranade (2006) CFD simulation of mixing in tall gas–liquid stirred vessel: Role of local flow patterns. Chem. Eng. Sci. 61: 2921–2929.CrossRefGoogle Scholar
  22. 22.
    Xia, J.–Y., Y.–H. Wang, S.–L. Zhang, N. Chen, P. Yin, Y.–P. Zhuang, and J. Chu (2009) Fluid dynamics investigation of variant impeller combinations by simulation and fermentation experiment. Biochem. Eng. J. 43: 252–260.CrossRefGoogle Scholar
  23. 23.
    Scargiali, F., A. D’Orazio, F. Grisafi, and A. Brucato (2007) Modelling and simulation of gas–liquid hydrodynamics in mechanically stirred tanks. Chem. Eng. Res. Design 85: 637–646.CrossRefGoogle Scholar
  24. 24.
    Cheung, S. C. P., G. H. Yeoh, and J. Y. Tu (2007) On the numerical study of isothermal vertical bubbly flow using two population balance approaches. Chem. Eng. Sci. 62: 4659–4674.CrossRefGoogle Scholar
  25. 25.
    Ahmed, S. U., P. Ranganathan, A. Pandey, and S. Sivaraman (2010) Computational fluid dynamics modeling of gas dispersion in multi impeller bioreactor. J. Biosci. Bioeng. 109: 588–597.CrossRefGoogle Scholar
  26. 26.
    Sajjadi, B., A. A. A. Raman, S. Ibrahim, and R. S. S. R. E. Shah (2012) Review on gas–liquid mixing analysis in multiscale stirred vessel using CFD. Rev. Chem. Eng. 28: 171–189.CrossRefGoogle Scholar
  27. 27.
    Nienow, A. W., C. D. Rielly, K. Brosnan, N. Bargh, K. Lee, K. Coopman, and C. J. Hewitt (2013) The physical characterisation of a microscale parallel bioreactor platform with an industrial CHO cell line expressing an IgG4. Biochem. Eng. J. 76: 25–36.CrossRefGoogle Scholar
  28. 28.
    Rutherford, K., K. C. Lee, S. M. S. Mahmoudi, and M. Yianneskis (1996) Hydrodynamic characteristics of dual Rushton impeller stirred vessels. AIChE J. 42: 332–346.CrossRefGoogle Scholar
  29. 29.
    Bareither, R., N. Bargh, R. Oakeshott, K. Watts, and D. Pollard (2013) Automated disposable small scale reactor for high throughput bioprocess development: A proof of concept study. Biotechnol. Bioeng. 110: 3126–3138.CrossRefGoogle Scholar
  30. 30.
    De Wilde, D., T. Dreher, C. Zahnow, U. Husemann, G. Greller, T. Adams, and C. Fenge (2014) Superior scalability of single–use bioreactors. BioProcess Int. 12: 14–19.Google Scholar
  31. 31.
    Azargoshasb, H., S. M. Mousavi, O. Jamialahmadi, S. A. Shojaosadati, and S. B. Mousavi (2016) Experiments and a threephase computational fluid dynamics (CFD) simulation coupled with population balance equations of a stirred tank bioreactor for high cell density cultivation. Can. J. Chem. Eng. 94: 20–32.CrossRefGoogle Scholar
  32. 32.
    Elqotbi, M., S. D. Vlaev, L. Montastruc, and I. Nikov (2013) CFD modelling of two–phase stirred bioreaction systems by segregated solution of the Euler–Euler model. Comput. Chem. Eng. 48: 113–120.CrossRefGoogle Scholar
  33. 33.
    Kerdouss, F., A. Bannari, and P. Proulx (2006) CFD modeling of gas dispersion and bubble size in a double turbine stirred tank. Chem. Eng. Sci. 61: 3313–3322.CrossRefGoogle Scholar
  34. 34.
    Jaworski, Z., K. N. Dyster, V. P. Mishra, A. W. Nienow, and M. L. Wyszynski (1998) A study of an up–and a down–pumping wide–blade hydrofoil impeller: Part II. CFD analysis. Can. J. Chem. Eng. 76: 866–876.CrossRefGoogle Scholar
  35. 35.
    ANSYS, I., ANSYS Fluent Theory Guide Release 17.0.Google Scholar
  36. 36.
    Schiller, V. L. (1933) A drag coefficient correlation. Z. Vereines Ingenieure 77: 318–320.Google Scholar
  37. 37.
    Murthy, B. N. and J. B. Joshi (2008) Assessment of standard k–e, RSM and LES turbulence models in a baffled stirred vessel agitated by various impeller designs. Chem. Eng. Sci. 63: 5468–5495.CrossRefGoogle Scholar
  38. 38.
    Delafosse, A., A. Line, J. Morchain, and P. Guiraud (2008) LES and URANS simulations of hydrodynamics in mixing tank: comparison to PIV experiments. Chem. Eng. Res. Design 86: 1322–1330.CrossRefGoogle Scholar
  39. 39.
    Paul, E. L., V. A. Atiemo–Obeng, and S. M. Kresta (2004) Handbook of Industrial Mixing: Science and Practice. John Wiley & Sons.Google Scholar
  40. 40.
    ANSYS, I., ANSYS Fluent Population Balance Module Manual Release 17.0.Google Scholar
  41. 41.
    Luo, H. and H. F. Svendsen (1996) Theoretical model for drop and bubble breakup in turbulent dispersions. AIChE J. 42: 1225–1233.CrossRefGoogle Scholar
  42. 42.
    Ranganathan, P. and S. Sivaraman (2011) Investigations on hydrodynamics and mass transfer in gas–liquid stirred reactor using computational fluid dynamics. Chem. Eng. Sci. 66: 3108–3124.CrossRefGoogle Scholar
  43. 43.
    Miyahara, T., Y. Matsuba, and T. Takahashi (1983) The size of bubbles generated from perforated plates. Int. Chem. Eng. 23: 517–523.Google Scholar
  44. 44.
    Kane, J. (2012) Measuring kLa for better bioreactor performance. BioProcess Int. 10.Google Scholar
  45. 45.
    Kelly, W. J. (2008) Using computational fluid dynamics to characterize and improve bioreactor performance. Biotechnol. Appl. Biochem. 49: 225–238.CrossRefGoogle Scholar
  46. 46.
    Van’t Riet, K. (1979) Review of measuring methods and results in nonviscous gas–liquid mass transfer in stirred vessels. Ind. Eng. Chem. Process Des. Dev. 18: 357–364.CrossRefGoogle Scholar
  47. 47.
    Nienow, A. W. and M. D. Lilly (1979) Power drawn by multiple impellers in sparged agitated vessels. Biotechnol. Bioeng. 21: 2341–2345.CrossRefGoogle Scholar
  48. 48.
    Nienow, A. W. (1998) Hydrodynamics of stirred bioreactors. Appl. Mech. Rev. 51: 3–32.CrossRefGoogle Scholar
  49. 49.
    Hall, S. (2017) Rules of Thumb for Chemical Engineers. 6th ed. Butterworth–Heinemann.Google Scholar
  50. 50.
    Khopkar, A. R. and P. A. Tanguy (2008) CFD simulation of gas–liquid flows in stirred vessel equipped with dual rushton turbines: influence of parallel, merging and diverging flow configurations. Chem. Eng. Sci. 63: 3810–3820.CrossRefGoogle Scholar
  51. 51.
    Zhou, G. and S. M. Kresta (1996) Impact of tank geometry on the maximum turbulence energy dissipation rate for impellers. AIChE J. 42: 2476–2490.CrossRefGoogle Scholar
  52. 52.
    Hortsch, R. and D. Weuster–Botz (2010) Power consumption and maximum energy dissipation in a milliliter–scale bioreactor. Biotechnol. Prog. 26: 595–599.Google Scholar
  53. 53.
    Wernersson, E. S. and C. Trägårdh (1999) Scale–up of Rushton turbine–agitated tanks. Chem. Eng. Sci. 54: 4245–4256.CrossRefGoogle Scholar
  54. 54.
    Wang, T., J. Wang, and Y. Jin (2006) A CFD–PBM coupled model for gas–liquid flows. AIChE J. 52: 125–140.CrossRefGoogle Scholar
  55. 55.
    Calderbank, P. H. (1958) Physical rate processes in industrial fermentation, Part I: The interfacial area in gas–liquid contacting with mechanical agitation. Trans. Instn. Chem. Engrs. 36: 443–463.Google Scholar
  56. 56.
    Paceka, A. W., C. C. Mana, and A. W. Nienow (1998) On the Sauter mean diameter and size distributions in turbulent liquid/ liquid dispersions in a stirred vessel. Chem. Eng. Sci. 53: 2005–2011.CrossRefGoogle Scholar
  57. 57.
    Olmos, E., C. Gentric, C. Vial, G. Wild, and N. Midoux (2001) Numerical simulation of multiphase flow in bubble column reactors. Influence of bubble coalescence and break–up. Chem. Eng. Sci. 56: 6359–6365.Google Scholar
  58. 58.
    Camarasa, E., C. Vial, S. Poncin, G. Wild, N. Midoux, and J. Bouillard (1999) Influence of coalescence behaviour of the liquid and of gas sparging on hydrodynamics and bubble characteristics in a bubble column. Chem. Eng. Processing: Process Intensification 38: 329–344.CrossRefGoogle Scholar
  59. 59.
    Laakkonen, M., P. Moilanen, V. Alopaeus, and J. Aittamaa (2007) Modelling local gas–liquid mass transfer in agitated vessels. Chem. Eng. Res. Design 85: 665–675.CrossRefGoogle Scholar
  60. 60.
    Chapple, D., S. M. Kresta, A. Wall, and A. Afacan (2002) The effect of impeller and tank geometry on power number for a pitched blade turbine. Chem. Eng. Res. Design 80: 364–372.CrossRefGoogle Scholar
  61. 61.
    Rutherford, K., S. M. Mahmoudi, K. C. Lee, and M. Yianneskis (1996) The influence of Rushton impeller blade and disk thickness on the mixing characteristics of stirred vessels. Chem. Eng. Res. Design 74: 369–378.Google Scholar
  62. 62.
    Rushton, J. H. (1950) Power characteristics of mixing impellers Part 1. Chem. Eng. Prog. 46: 395–404.Google Scholar
  63. 63.
    Bates, R. L., P. L. Fondy, and R. R. Corpstein (1963) Examination of some geometric parameters of impeller power. Ind. Eng. Chem. Process Design Dev. 2: 310–314.CrossRefGoogle Scholar
  64. 64.
    Gill, N., M. Appleton, F. Baganz, and G. Lye (2008) Quantification of power consumption and oxygen transfer characteristics of a stirred miniature bioreactor for predictive fermentation scale–up. Biotechnol. Bioeng. 100: 1144–1155.CrossRefGoogle Scholar
  65. 65.
    Betts, J. I., and F. Baganz (2006) Miniature bioreactors: current practices and future opportunities. Microb. Cell Fact. 5: 21.CrossRefGoogle Scholar
  66. 66.
    Garcia–Ochoa, F. and E. Gomez (2009) Bioreactor scale–up and oxygen transfer rate in microbial processes: an overview. Biotechnol. Adv. 27: 153–176.CrossRefGoogle Scholar
  67. 67.
    Kaiser, S. C., C. Löffelholz, S. r. Werner, and D. Eibl (2011) CFD for characterizing standard and single–use stirred cell culture bioreactors. pp. 97–122 In: P. I. Minin (ed.). Computational Fluid Dynamics Technologies and Applications. InTech, City.Google Scholar
  68. 68.
    Nienow, A. W. (2006) Reactor engineering in large scale animal cell culture. Cytotechnology 50: 9–33.CrossRefGoogle Scholar
  69. 69.
    MixIT, MRF versus Sliding Mesh.–reference–frame–mrf–versus–sliding–mesh.Google Scholar
  70. 70.
    Montante, G., K. C. Lee, A. Brucato, and M. Yianneskis (2001) Numerical simulations of the dependency of flow pattern on impeller clearance in stirred vessels. Chem. Eng. Sci. 56: 3751–3770.CrossRefGoogle Scholar
  71. 71.
    Jakirlic, S. and R. Maduta (2015) Extending the bounds of ‘steady’ RANS closures: Toward an instability–sensitive Reynolds stress model. Int. J. Heat Fluid Flow 51: 175–194.CrossRefGoogle Scholar
  72. 72.
    Vikhansky, A. and A. Splawski (2015) Adaptive multiply size group method for CFD–population balance modelling of polydisperse flows. Can. J. Chem. Eng. 93: 1327–1334.CrossRefGoogle Scholar

Copyright information

© The Korean Society for Biotechnology and Bioengineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Chemical EngineeringVillanova UniversityPhiladelphiaUSA

Personalised recommendations