CFD Modeling of Mixing in mammalian Cell Bioreactor Thank to Computer sciences, we have some good platform like CFD (Computational fluid dynamics) to perform appropriate studies before design and fabricate bioreactor. These sorts of software’s give the opportunity to the Bioprocess Engineers to calculate physical, Geometrical as well as transport phenomena in reactors and give them overview how much their design is near to the real practice. Also, it is very economical to do Try and Error before investing huge amount of money to build a reactor and then check the efficiency of design and equipment.

1. CFD Modeling of Mixing in
Mammalian Cell Bioreactors
University of Tehran
By: Arash Zamani Renani
Under Supervision of: Dr. Mohammad Hossein Sarafzadeh
Adviser: Dr. Navid Mostoufi

2. Outlines
1
Introduction
Literature Review
2
General objectives
Specific objectives
3
Materials and Methods
Results and Discussion
4
Conclusion
Suggestions
2

3. Introduction
• Recombinant monoclonal antibodies and vaccines
3

4. Introduction
• Stirred tank bioreactor
4

5. Introduction
• Mixing
5

6. Introduction
• Bioreactor Design
6

7. Axial- & Radial flow impellers
7

8. 8

9. CFD
• Computational Fluid Dynamics
• Provides detailed modeling about hydrodynamics
and mixing
• Scale-up simulation of a bioprocess from lab-scale
to industrial scale
9

10. Literature Review
Author/s Year Relevance
Jaworski et al. 2000
Homogenization with dual Rushton
turbines/ k-ɛ E and RNG k-ɛ E models
Gelves et al. 2014
Modeling of mass transfer/
Rushton turbine & a new pitched blade
impeller/ k-ε model
Zhang et al. 2009 Oxygen transfer/ scale up to 1000L
Alok 2014
Effect of different impellers and baffles
on Aerobic Stirred Tank Fermenter/ κ-ε
model, κ-ω model, (SST), (SAS-SST)
SanDadi et al. 2009
Experimental; Cell viability; Rushton VS
Marine
10

11. General Objectives
CFD simulation of mixing in mammalian
cell bioreactors due to different
types impeller
11

12. Specific Objectives
• Investigating the effects of the marine impeller
(axial flow impeller) and the Rushton turbine (radial
flow impeller) on mixing
• CFD Simulation of mixing using ANSYS FLUENT
12

13. Materials and Methods
Geometry
Meshing
CFD
Simulating
Model
validation
13

14. Geometry Design
10
• ANSYS CLAIMSPACE
• Dual Rushton turbines & dual Marine impeller
• 20 and 60 Liter tanks
• Dimensions(mm) according to H:D (1.5:1)
Baffle to
tank
wall
Diameter
of stirring
blade
Diameter of
the blade
Blade
length
HeightDiameterReactor
41452936.544029020 Liter
5.5200405060040060 Liter

15. Meshing & Speeds
• ANSYS MESHING
• Mesh independency
• Rotation speeds
15
TIP Speed
m/s
rad/sRPMReactor
1.5109820 Liter
1.57.57160 Liter

16. Simulation CFD
• FLUENT PARALLEL
• Single phase flow (water liquid)
• Simulations of the turbulent flow
• Re >10^4
• Realizable k-𝜖
16

17. Model Validation
• Power number
P0
=
𝑝
𝜌𝑁3 𝐷5
𝑝 = 2𝜋𝑁𝑇𝑞
• Pressure, velocity and Strain rate profiles
• Mixing Time
• The Uniformity Index
17

18. Results
&
Discussion
18

19. Geometry
19
• 20 Liter
bioreactor
equipped
with dual
Rushton
turbine

20. 20
• 20 Liter
bioreactor
equipped
with dual
Marine
Impeller

21. Meshing
21

22. Mesh Independency
22
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
MeanVelocity
Reactor Length
20 Liter Reactor equipped with Rushton turbine
Mesh 1
Mesh 2
Mesh 3
Optimum mesh: 5 mm

23. 23
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
MeanVelocity
Reactor Length
20 Liter Reactor equipped with Marine impeller
Mesh 1
Mesh 2
Mesh 3
Optimum mesh: 4-6 mm

24. 24
0
0.01
0.02
0.03
0.04
0.05
0.06
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
MeanVelocity
Reactor Length
60 Liter Reactor equipped with Rushton turbine
Mesh 1
Mesh 2
Mesh 3
Optimum mesh: 6 mm

25. 25
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
0 0.1 0.2 0.3 0.4 0.5 0.6
MeanVelocity
Reactor Length
60 Liter Reactor equipped with Marine impeller
Mesh 1
Mesh 2
Mesh 3
Optimum mesh: 6 mm

26. Pressure Profiles
26

27. 27

28. 28

29. 29

30. 30

31. Shear rate Profiles
31

32. 32

33. 33

34. 34

35. 35

36. Velocity profiles
36

37. 37

38. 38

39. 39

40. Power Number
60 Liter/
Marine turbine
60 Liter/
Rushton Impeller
20 Liter/
Marine Impeller
20 Liter/
Rushton Turbine
Reactor
2.850.871.6Power Number
40

41. Mass fraction over time
41

42. Mixing Time
60 Liter/
Marine turbine
60 Liter/
Rushton Impeller
20 Liter/
Marine Impeller
20 Liter/
Rushton Turbine
Reactor
620500205160Mixing Time (s)
42

43. Uniformity Index
43
0
0.2
0.4
0.6
0.8
1
1.2
0 2000 4000 6000 8000 10000 12000
UniformityIndex
Time steps
Marin
Rushron

44. Conclusion
• Calculated maximum pressure: 2.5 Pa or N/M2
• Tolerable for Mammalian cells: <4.5 to 5 N/M2
• Calculated Maximum shear rate: 900 s-1
Tolerable for Mammalian cells: 0~3000 s-1
• Results confirms that Rushton turbine is more
effective than Marine in the term of mixing in
bioreactor
• Also if we regulate Tip Speed by 1.5 m/s there won’t
be hydrodynamic damage on the CHO cells
44

45. Suggestion
• CFD simulation for Mixed Rushton and Marine
• CFD simulation of Spurger-equipped reactors as in
the same condition
• CFD simulation of reactors equipped with three
rotating impellers as in the same condition
45

46. Reference
• Z. Jaworski,W. Bujalski, N. Otomo, A.W. Nienow, CFD Study of Homogenization with Dual Rushton
Turbines—Comparison with Experimental Results, Chemical Engineering Research and Design 78
(2000) 327–333.
• R. Gelves, A. Dietrich, R.Takors, Modeling of gas-liquid mass transfer in a stirred tank bioreactor agitated
by a Rushton turbine or a new pitched blade impeller, Bioprocess and Biosystems Engineering 37 (2014)
365–375.
• X. Zhang, C.-A. Bürki, M. Stettler, D. de Sanctis, M. Perrone, M. Discacciati, N. Parolini, M. DeJesus, D.L.
Hacker, A. Quarteroni, F.M.Wurm, Efficient oxygen transfer by surface aeration in shaken cylindrical
containers for mammalian cell cultivation at volumetric scales up to 1000L, Biochemical Engineering
Journal 45 (2009) 41–47.
• S. Alok, Effect of Different Impellers and Baffles on Aerobic Stirred Tank Fermenter using Computational
Fluid Dynamics, J Bioproces Biotech 04 (2014).
• 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, Computers & Chemical Engineering 32 (2008) 1943–1955.
46

47. • M. Cortada-Garcia, V. Dore, L. Mazzei, P. Angeli, Experimental and CFD studies of power consumption in the
agitation of highly viscous shear thinning fluids, Chemical Engineering Research and Design 119 (2017) 171–182.
• A. Delafosse, M.-L. Collignon, S. Calvo, F. Delvigne, M. Crine, P. Thonart, D. Toye, CFD-based compartment
model for description of mixing in bioreactors, Chemical Engineering Science 106 (2014) 76–85.
• K.M. Dhanasekharan, J. Sanyal, A. Jain, A. Haidari, A generalized approach to model oxygen transfer in
bioreactors using population balances and computational fluid dynamics, Chemical Engineering Science 60
(2005) 213–218.
• B.J. Kim, T. Zhao, L. Young, P. Zhou, M.L. Shuler, Batch, fed-batch, and microcarrier cultures with CHO cell lines in
a pressure-cycle driven miniaturized bioreactor, Biotechnology and Bioengineering 109 (2012) 137–145.
• J.B. Sieck, T. Cordes, W.E. Budach, M.H. Rhiel, Z. Suemeghy, C. Leist, T.K. Villiger, M. Morbidelli, M. Soos,
Development of a Scale-Down Model of hydrodynamic stress to study the performance of an industrial CHO cell
line under simulated production scale bioreactor conditions, Journal of Biotechnology 164 (2013) 41–49.
47

48. Thank You
For
Your Attention
48