bio reactor scale-up sso

4/7/2011

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Bioreactor Scale‐up and Scale‐down

Sadettin S. Ozturk, Ph.D.

Scale‐up and Scale‐down

• Select geometry, configuration, dimensions, and operating conditions so that the performance ofoperating conditions so that the performance of bioreactor at different scale is comparable

• Focus on:

– Mixing and agitation

– Mass Transfer

– Heat Transfer

• We will focus on scale‐up, same principles are applied to scale‐down as well

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Geometric Similarity

• Aspect ratio

• Impeller to Diameter ratio

• Impeller type and location H

V

NH /DT

D /DT

• Sparger type D

DT

Characteristic Times

Some Time Constants

Transport Processes Equationp q

Flow L/v or V/Q

Diffusion L2/D

Oxygen Txfr 1/kLa

Heat Txfr VCp/UAMixing tm=4V/(1.5ND

3)

Growth 1/µ

Heat Production Cp∆kT/r∆H

See N.W.F.Kossen in T. K. Ghose, ed. Biotechnology and Bioprocess Engineering, United India Press Link House, 1985, pp. 365-380.

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Scale‐up Considerations

• Keep the same power input per volume

• Keep the same impeller tip speed

• Keep the same mixing time

Po P V

v ND

• Keep the volumetric mass transfer coefficient

f (1/N)

kla

Scale‐up of agitation and mixing parameters

ND 2

Reynolds Number (dimensionless)

NRe ND 2

Np Po

N 3D5H

V

NPower Number (dimensionless)

D

DT

velocity ND

pumping number QND 3

Blending time N

Dimensionless Parameters

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Scale‐up based on Constant Power Number

Np Po

N 3D5

Under turbulent conditions (high Re), power number is constant

NRe ND 2


Constant power number

N K P /V

H

V

NUse constant Power number to scale‐up agitation and mixing parameters

Also keep dimensionless parameters constant

NP K P /V

Po K (N 3D 5 )

K Tip speed

D

DT

ND K1

Q

ND 3 K2

N K 3

Tip speed

Pumping rate

Mixing time

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Tip Velocity

3 5

Po /V cons tan t N 3 D 5

D 3 N 3 D 2

N 3 D 2 Cons tan t C

v K1 N D K1 (C D )1 3

Tip velocity increases by a power of 1/3


Pumping

N 3 D 2 C

Pumping increases by volume. However, pumping per volume decreases

N 3 D 2 C

Q K2 N D 3 K2 (N3 D 9 )1/ 3 K2 (N

3 D 2 D7 )1/ 3

Q K2 (N 3 D 2 )1/ 3 D7 / 3 K2 C1/ 3 D7 / 3

Q K2

' D7 / 3

Q /V ~ Q /D 3 K2

' D7 / 3 /D 3 K2

' D 2 / 3

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Mixing Time

N K N K3

K3 / N

N (C /D 2 )1/ 3 C1/ 3 D 2 / 3

K3 / N (K3 /C1/ 3 ) D 2 / 3 K3

' D 2 / 3

Mixing time increases with scale

Different results are obtained for different scale‐up criteria

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Mixing and Shear

Other Considerations for Scale‐up

• Shear and damage to the cells should also be id d d i lconsidered during scale‐up

• Aeration (mass transfer) characteristics should be maintained as well

• Heat transfer should also be considered

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Mixing and Shear

(N N 3 Di5 )/V

Rate of Energy Dissipation Kolmogorov Theory of Turbulence

Size of the eddies

Velocity of the eddies

( 3 /)1/ 4

u ( )1/ 4

(Np N Di )/V

Cells in the bioreactor can be damaged under Intense mixing conditions

size of the eddies becomes comparable to the size of the cells

high shear velocity

Scale‐up of Aeration Systems

k (C* C ) X OCR OTR

Mass transfer capacity can limit the number of cells achievable in the bioreactor

‐> Keep same volumetric mass transfer coefficient during scale‐up to maintain the same cell density

kla (C CL ) qO2 Xv OCR OTR

Volumetric mass transfer coefficient is affected by:Sparger design (geometry, location, dimensions, orifice size)Mi i t (i ll d i )Mixing system (impeller design)Operating condition (sparger flow rate, agitation rate)Medium properties (viscosity, surface tension, density)

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Sparger designMacrospargers

O iOpen pipe spargerVenturi spargerRing sparger

MicrospargersSintered metalPorous polymers


Macrosparger Scale‐up

Gas Out

Keep the type of the sparger and the orifice size the sameMultiply the number of orifices to maintain the same gas flow rate per orificeLocate the sparger below the impellerScale‐up gas flow rate and agitation

Vp g g

rate based on scaling parameters

QG

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Superficial Gas Flow Rate (Linear rate)

u QG /A

k a (P /V ) (u)

Au

Cross Sectional Area

Superficial Gas Flow Rate, cm/min Mass Transfer Coefficient is dependent on

Power per volume (related to agitation rate) and superficial gas flow rate

vvm QG /V

kla (P /V ) (u)

QGGas Flow Rate (L/min)

Scaled Gas Flow Rate is important as well


kla (P /V ) (u)

Specific to sparger impeller bioreactor

Increasing power input (agitation rate) disperses and breaks the bubbles thus

Specific to sparger, impeller, bioreactor configuration, and media composition

Varies between 0.2‐0.8 for macrospargers

Varies between 0.3‐0.8 for macrospargers

Increasing power input (agitation rate) disperses and breaks the bubbles, thuscreates smaller bubbles and higher gas exchange (interfacial) areaIncreasing gas flow rate generates more bubbles in the system and increases turbulence

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Scale‐up of Aeration Systemskla (P /V ) (u)

Specific to sparger impeller bioreactor

Increasing power input (agitation rate)

Specific to sparger, impeller, bioreactor configuration, and media composition

Varies between 0‐0.2 for microspargers

Varies between 0.3‐0.8 for microspargers

disperses and breaks the bubbles, thuscreates smaller bubbles and higher gas exchange (interfacial) areaIncreasing gas flow rate generates more bubbles in the system and increases turbulence

Scale‐up of Heat TransferHeat transfer is important to maintain and control the temperature

Heating:Important for both microbial and cell culture applications

Cooling: Microbial fermentation: Is needed to remove the heat generated

by the cellsCell Culture: temperature shift for cell culture production

cell harvesting at the end of the culture

Heat Transfer MethodsSurface

Water Jacket (steam or chilled water)Electric blanket (for smaller bioreactors)

Coils in the bioreactorUsed for microbial fermentation

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Scale‐up of Heat TransferHeat Transfer Rate

q h A (T T)q h A (Tm T)

A

h

Tm

Surface area for heat transfer

Temperature of heating (cooling) fluid

Heat transfer coefficient

Maintain a similar heat transfer coefficient, calculate surface area neededSurface area decreases as volume increases for surface heat transfer

Scale‐up Predictions based on Computational Fluid Dynamics (CFD)

• Flow simulation, or Computational Fluid Dynamics (CFD), is the science of predicting fluid flow, heat transfer, mass transfer, chemical reactions, and related phenomena b sol in the mathematical eq ations hich o ernrelated phenomena by solving the mathematical equations which govern these processes using a numerical process (that is, on a computer).

• The result is detailed information about all flow variables in the system• Species concentration

• Flow pattern

• Turbulence levels

• Local rate of mixing

Th i i t l i t i t f t ft th t• The mixing tool is a custom user interface to a software program that performs flow simulation of stirred tank reactors

– To understand how it works, let’s look in more detail at the CFD process

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Flow Simulation: What is CFD

• Flow simulation, or Computational Fluid Dynamics (CFD), is the science of predicting fluid flow, heat transfer, mass transfer, chemical reactions, and related phenomena b sol in the mathematical eq ations hich o ernrelated phenomena by solving the mathematical equations which govern these processes using a numerical process (that is, on a computer).

• The result is detailed information about all flow variables in the system• Species concentration

• Flow pattern

• Turbulence levels

• Local rate of mixing

Th i i t l i t i t f t ft th t• The mixing tool is a custom user interface to a software program that performs flow simulation of stirred tank reactors

– To understand how it works, let’s look in more detail at the CFD process

How CFD Works• Flow in the stirred tank is calculated using the finite volume method

The calculation domain is a 2000L, baffled vessel with 2 A315method

– The calculation domain is discretized into a

finite set of control volumes or cells.

– General conservation (transport) equations for mass, momentum, energy, etc.,

di ti d i t l b i ti

Fluid region of tank discretized into finite set of control volumes (mesh).

with 2 A315 Impellers

VAAV

dVSdddVt AAV

unsteady convection diffusion generationare discretized into algebraic equations.

– All equations are solved to render flow field.

Eqn.continuity 1

x-mom. uy-mom. venergy h

A Single Control Volume

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CFD Modeling for Aeration: Velocity Vectors and Pathlines

CFD Modeling for Aeration: Qualitative prediction of gas distribution

• Bubble trajectories are calculated in the continuous phase flow field sin DPM particle trackinusing DPM particle tracking

• Bubbles cannot break up or coalesce

– Inherent in model’s underlying assumption of no interaction between particles

• More rigorous methods accounting for this are available in FLUENT

– Significant computational overhead is associated with these methods

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CFD Modeling: The Effects of Sparger Geometry

• The discrete holes of the sparger are not modeled.

• A strip along the top and/or bottomA strip along the top and/or bottom of the sparger geometry is used to introduce the gas flow. – The strip, which covers 15% of

the sparger surface area is larger than the sum of the areas of the discrete holes.

– Impact of reduced sparge velocity is insignificant relative to local impeller‐induced flow.

• Bubbles come to equilibrium• Bubbles come to equilibrium with surrounding liquid flow field in a very short distance

Detail of representative sparger surface

CFD Modeling: Sparging and Mass Transfer

• Relevant to kLa predictions, the multiphase flow model provides field data for:– Turbulent dissipation rate– Gas volume fraction– Bubble diameter (specified or predicted)

• This data can be used to evaluate the mass transfer coefficient kL and the specific interface surface area, a.

• A number of correlations for kL are available. One suggested correlation is based on Higbie’s penetration theory:

• The specific interfacial surface area, a, can be determined using the relation:

2/14/1314.0 SckL

6

where air is the volume fraction of air.– Assumes spherical bubbles– Reflects total surface area available for mass transfer per unit volume

b

air

da

6

bio reactor scale-up sso

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