2010_J Chem Tech Biotech_Bioprocess Scale-up

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Review

Received: 30 September 2009 Revised: 12 February 2010 Accepted: 12 February 2010 Published online in Wiley Interscience: 7 April 2010

(www.interscience.wiley.com) DOI 10.1002/jctb.2387

Bioprocess scale-up: quest for the parameters

to be used as criterion to movefrom microreactors to lab-scaleMarco P. C. Marques,Joaquim M. S. Cabral and Pedro Fernandes

Abstract

Advancesin high-throughputprocessdevelopmentand optimizationinvolvethe rationaluse of miniaturizedstirredbioreactors,instrumented shaken flasks and microtiter plates. As expected, each one provides different levels of control and monitoring,requiring a compromise between data quantity and quality. Despite recent advances, traditional shaken flasks with nominalvolumes below 250 mL and microtiter plates are still widely used to assemble wide arrays of biotransformation/bioconversiondata, because of their simplicity and low cost. These tools are key assets for faster process development and optimization,

provided data are representative. Nonetheless, the design, development and implementation of bioprocesses can presentvariations depending on intrinsic characteristics of the overall process. For each particular process, an adequate andcomprehensive approach has to be established, which includes pinpointing key issues required to ensure proper scale-up. Recently, focus has been given to engineering characterization of systems in terms of mass transfer and hydrodynamics(through gaining insight into parameters such as kLa and P/V at shaken and microreactor scale), due to the widespread useof small-scale reactors in the early developmental stages of bioconversion/biotransfomation processes. Within this review,engineering parameters used as criteria for scaling-up fermentation/bioconversion processes are discussed. Particular focus ison thefeasibilityof the application of such parameters to small-scale devices and concomitant usefor scale-up. Illustrativecasestudies are presented.c 2010 Society of Chemical Industry

Keywords: scale-up; small-scale reactors; kLa; volumetric power consumption; fermentation; bioconversion

NOTATIONa Specific interfacial area (m1)

ai Initial specific surface area (m1)

af Final specific surface area (m1)

Bo Bond number, D2gWt1 ()

d Maximum inside shaking flask diameter (m)

D Well or vessel diameter (m)

di Diameter of drops in size class i(m)

Di Diffusivity (m2 s1)

dmax Maximum drop diameter (m)

dn Nozzle diameter (m)

do Shaking diameter (m)

d32 Sauter mean diameter (m)

Fr Froude number, do(2N)

2

(2g)

1

()g Gravitational constant (m s2)

h Liquid height (m)

k Number of size classes ()

kL Mass transfer coefficient (m s1)

kLa Volumetric oxygen mass transfer coefficient (s1)

N Shaking frequency, stirring speed (s1)

ni Number of drops ()

Nf Pumping number ()

NP Power number ()

P Gassed power input (W)

Po Ungassed power input (W)

P/V Volumetric power consumption (W m3)

Q Volumetric gas flow rate (m3 s1)

Re Reynolds number, Ndo21, NT21 ()Sc Schmidt number, (Di)

1 ()

T Stirrer diameter (m)

uo Nozzle velocity (m s1)

V Filling volume (m3)

vg Superficial gas velocity (m s1)

Vo Flask volume (m3)

vtip Impeller tip speed (m s1)

W Width of turbine blades (m)

We Weber number, N2T31 ()

Wt Wetting tension (N m1)

Subscripts

c continuous

T Tank

Greek symbols

Viscosity (Pa s)

Correspondence to: Marco P. C. Marques, IBB-Institute for Biotechnology andBioengineering, Centre for Biological and Chemical Engineering, Instituto

Superior T ecnico, Av. Rovisco Pais,1049-001 Lisboa, Portugal.E-mail: [email protected]

IBB-Institute for Biotechnology and Bioengineering, Centre for Biological and

Chemical Engineering, Instituto Superior T ecnico, Av. Rovisco Pais, 1049-001

Lisboa, Portugal

J Chem Technol Biotechnol2010; 85: 1184 1198 www.soci.org c 2010 Society of Chemical Industry


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Bioprocess scale-up: parameters to be used www.soci.org

Density (kg m3)

Local energy dissipation rate (W kg1)

Volume fraction of the dispersed phase ()

Interfacial tension (N m1)

V Viscous dissipation term (m2 s2)

INTRODUCTIONDespite sharing a common pattern, the design, development andimplementation of microbial processes present subtle variations,

depending on the product, microbial strain, growth conditions,

bioconversion/biotransformation conditions, among others. Thus,

for a given product, process or facility, an adequate and

comprehensive approach has to be established that encompasses

the detailed characterization of the process and the timely

identification of key process parameters likely to affect product

yield, quality and consistency. These are to be kept as constant

as possible throughout the scale-up process, in order to ensure

success of the later task.1,2

The development of microbial processes is strongly anchored

in small-scale reactors, typically Erlenmeyertype flasks and bench-

scale reactors, which are used for strain screening, media designand optimization and strategies of operation, ultimately aiming

for the highest attainable productivity.3 In recent years, the

range of small-scale vessels has increased to include multi-well

plates (MWPs) with different levels of complexity and built-in

devices, and miniature reactors that clearly emulate the larger

vessels, but with a volume in the milliliter (or lower) range4,5

Along with the technological developments that allowed for such

hardware, efforts have been made to ensure the data gathered

from experiments performed at these scales is reproducible

throughout scales. Such efforts have relied on gaining further

insight into mass (and heat) transfer and fluid dynamics at

microliter scale, identification and validation of key parameters

for scale-up, preferably from MWPs to bench-scale bioreactor,

and predictive modelling.6,7,8 Knowledge gathered allows fulladvantage to be taken of the high level of parallelization

provided by most miniaturized devices (in particular MWPs),

and to speed up bioprocess development in a cost-effective

manner.9,10,11 The task at hand is quite complex since there

are several parameters influencing transport phenomena and

chemical dynamics within a bioreactor. These parameters relate to

mass transfer, mixing, partitioning, power input, shear induced by

agitation, dilution rates, substrate and products concentration,

nutrients, micronutrients and stabilizing agents, temperature

and pH. Parameters of (bio)chemical nature are screened and

optimized for bioconversion/growth kinetics. Physical parameters

are, on the other hand, conditioned by process design and

operational conditions. The parameters for scale-up are selectedwithin the range of physical parameters. Naturally favored

candidates to fulfill such role are the process parameters

or coefficients that are known to have some effect on the

biological agent (enzyme or cell), particularly in their physiology.

These include those affecting oxygen supply, heat transfer and

mixing, namely aeration, agitation, mixing time, power input

and oxygen mass transfer coefficient.2,12 In many cases, these

physical parameters have to be combined with each other,

or with other variables, in dimensionless numbers that are

kept constant throughout scaling, therefore establishing scale-

up criteria. In any case, the environmental conditions have to

remain constant.1,2 Again, and despite the relevance of the

scale-up issue in biotechnology, there is no straightforward

and uniform strategy to tackle this matter. A suitable scale-up

approach has therefore to be established again on a casuistic

basis for a given product, process or facility.1,2,12 Most of these

strategies, when bioconversion, fermentation or cell culture

processes are involved, rely on the use of kLa or volumetric

power consumption as criteria for scale-up, although constant Re,

constant impeller tip speed and equal mixing and recirculation

time are occasionally used.2,13 Given the relevance of the

two former, they will be addressed in detail in this present

work.

SCALE-UP BASED ONKLAOxygen mass transfer coefficient

Oxygen is a key substrate in most microbial processes of industrial

relevance, where it can be required for growth, maintenance

or production of metabolites.14,15 These processes are typically

performed in an aqueous environment, but oxygen is sparingly

soluble in water, roughly 0.272 mmol L1, at 25

C and 101 kPa

air pressure, and thus often becomes the limiting substrate.16 A

suitable supply of oxygen to the liquid media, typically from air,

is mandatory, but the process of mass transfer is influenced byseveral variables,such as physical properties of the fluids involved,

operational conditions and geometry of the reactor. The oxygen

transfer rate (OTR) can be increased by altering stirring speed and

gas flow, which concomitantly alters the power input. Increasing

the OTR is required to cope with the microbial oxygen demand,

the oxygen uptake rate (OUR). It is possible to assess the rate

limiting step of a microbial process, i.e. mass transfer or reaction

limited, through the use of a modified Damkohler number (Da),

which is calculated as the ratio between the maximum oxygen

uptake and transfer rates.17

Da =OURmax

OTRmax(1)

A large qO2 or low diffusivity leads to Da > 1, hence the

process is mass transfer limited; oppositely, small qO2 or high

diffusivity results in Da 1, thus the process is limited by the

biochemical reaction rate.18 Oxygen transfer rate is thus a critical

feature for the characterization of a given process and likewise

an engineering parameter suitable for the design, selection and

scale-up of bioreactors is to address OTR. One such parameter

is the volumetric mass transfer coefficient kLa, which relates the

oxygen mass transfer rate to the oxygen concentration gradient,

according to (2). In a bioprocess the oxygen mass transfer rate can

be described by12,16

OTRC CL

= kLa (2)where OTR is expressed as the molar flux of oxygen through the

gasliquid interface; C is the dissolved oxygen concentration

which would be in equilibrium with the gas phase, CL is the

dissolved oxygen concentration in the bulk liquid, kL is the

local mass transfer coefficient in the liquid phase, and a is the

specific interfacial area. Although oxygen is transferred from the

bulk gas phase to the bulk liquid phase, it is assumed that

the gas phase resistance to mass transfer is negligible. Oxygen

consumption through the process due to biochemical reactions

can be considered using a biological enhancement factor, E,13,18

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www.soci.org MPC Marques, JMS Cabral, P Fernandes

leading to an overall volumetric mass transfer coefficient KLa given

by

KLa = E.kLa (3)

E incorporates the transport enhancement due to the oxygen

uptake by microorganisms, alongside with opposing mass

transfer resistances caused by layers of materials placed between

the gas bubble and the bulk liquid phase, namely adsorbed

surfactant and cells, and stagnant liquid film.18,19

E has beenshown experimentally to change with the concentration of

biomass, typically increasing with increased cell concentration,

but with varying patterns, according to different strains and

incubation media. In these dedicated experiments, E was within

0.81.3, although values as high as 5 were also reported.13,18,19

Nonetheless, and for most cases, the biochemical rate is not

significantly larger than the mass transfer rate, hence it is usually

assumed that E= 1. Details on this matter can be found in the

recent review by Garcia-Ochoa and Gomez.13

The volumetric mass transfer coefficient in bioreactors can be

obtained experimentally or predicted using empirical correlations

for kLa, or for kL and a, which are thoroughly described

elsewhere,13 coupled to a model for the estimation of E. The

introduction of the latter parameter in predictive determinationshas nevertheless been seldom reported, which may account for

shifts between predicted and experimental data at high biomass

concentrations. The use of constant kLa as scaling criterion is

widely disseminated in conventional scales, from laboratory to

production scales, encompassing volumes ranging from 1 L to

1000 m3, and ultimately has also been found to be suitable down

to the milli/micro-liter scale.68,2025

Experimental determination of the volumetric mass transfercoefficient (kLa)

Several methods have been developed to determine kLa

experimentally.13,26,27 Within the scope of bioreactor design,

several items are considered: stoichiometry, thermodynamics,microbial kinetics, transport phenomena (heat and mass transfer)

and economics. While the first three items are scale-independent,

transport phenomena and economics are extremely dependent

on scale. The relevance of the transport phenomena in the design

and scale-up of the bioreactor is particularly noticeable, since the

overall rateof aerobic bioprocesses is generally controlled by mass

transfer rates. The mass balance for the dissolved oxygen in the

well-mixed liquid phase can be established as2,12,13,16

dC

dt= OTR OUR (4)

where dC/dt is the oxygen accumulation rate in the liquid phase.

The determination of OTR can be performed either when oxygenis being depleted by growing biomass (direct methods) or when

no oxygen uptaketakesplace (indirectmethods).In thelatter case,

equation (4) reduces to

dC

dt= OTR = kLa

C C

(5)

The direct methods rely on oxygen probes, which allow for

the determination of OTR through gas phase analysis or through

the dynamic method. Until recently, only relatively bulky probes

were available, which prevented oxygen monitoring in miniature

vessels (MWPs, miniature/micro bioreactors), limiting its use to

bench scale and larger bioreactors. Recently Erlenmeyer-type

shaken flasks were adapted in order to be equipped with oxygen

gas sensors, allowing for on-line determination of OTR in several

parallel experiments in shaken vessels.28,29,30 Developments in

fluorescence methods, (micro)fabrication techniques and optic

fiber, have allowed for the implementation of sensitive dyes that

can either be inserted into a patch and adhered inside a vessel,

including individual wells from multiwall plates, yielding the so-

called sensor spots, or incorporated onto the tip of fiber optic

probes.3,4,11,25,31 37

The indirect approach for the determination of OTR relies on

chemical or physical methods. The most commonly used among

the former is the cobalt-catalyzed oxidation of sulfite, which was

optimized for application in miniature bioreactors (MWPs and

shaken flasks),38 and is routinely used in such formats.33,39 A

methodbasedonCO2 absorption is morerarely used.The physical

methods, again relying on probes, allow for the dynamic method

of OTR determination.13 Chemical methods, and in particular

the sulfite method, may be biased due to modifications in fluid

dynamics, fluid properties and surface tension, as a result of the

addition of chemicals. They may therefore lead to misleading

information and data gathered.13,27,40,41The use of fast enzymatic

methods has also recently been introduced within the scope ofthe indirect approach. These are clearly designed for application

in miniature systems, such as MWPs.42,43 These methods are

based on the use of glucose oxidase and, preferably, of catechol

2,3-dioxygenase. The latter method relies on a single-step well

defined stoichiometric reaction, whereas the former requires

calibration with the sulfite method (or a similar one) but on

the other hand, all reagents are easily available. Among physical

methods, the dynamic method is the most commonly used to

evaluate kLa, because of its simplicity and relative accuracy. Both

the absorption and desorption measurements give equal values

of kLa under identical hydrodynamics conditions.44 When the

characteristic time for the oxygen electrode and the characteristic

time for the oxygen transfer process (1/kLa) are of the same

magnitude, the dynamic response of the electrode has to beconsidered in the determination of kLa. A detailed description

on the nature and limitations of the methods used for the

experimental determination ofkLa is given by Garcia-Ochoa and

Gomez.13

Empirical correlations for the determination ofkLa in stirredtank reactors

Both dimensional and dimensionless equations for the volumetric

mass transfer coefficient as functions of different variables have

been proposed.45 There are, however, considerable problems

concerning the accuracy ofkLa estimation. Discrepancies between

experimental data and those estimated from these equations are

often found. The discrepancies are mainly found when kLa forreal broths are estimated from equations proposed for aqueous

solutions. This can be due to the strong influence of the type and

sizeof thebioreactor,the differentrangeof operational conditions,

the system considered (solutions or real broths), or even the

measuring method used.46,47 The addition of ions, hydrocarbons

or temperature increase kLa, whereas the addition of surfactants

or antifoams or increases in media viscosity decrease kLa, when

compared with data obtained from water.26,4852

Vant Riet26 proposed an overall correlation of kLa with

volumetric power consumption and superficial gas velocity:

kLa = C1 P

VC2

vgC3 (6)

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where P is the gassed power input, V is the liquid volume, vgthe gas superficial velocity and C1, C2 and C3 are constants that

may vary considerably. Weuster-Botz etal.53 used correlation (6)

to predict kLa in a magnetically driven stirred column, designed for

parallel operation in an incubator chamber, operated as shaking

flasks. Constants varied from 0.11 to 0.14, 0.06 to 0.37 and 0.73 to

0.22, for C1, C2 and C3, respectively, according to differentturbines.

Mass transfer coefficients of up to 0.34 0.05 s1 were obtained

in the reactors, using defined medium for growing E. coli, using

the dynamic gassing-out method.

Montes etal.54 determined values of kLa in yeast broths

(Trigonopsis variabilis) over wide ranges of both impeller speeds

and superficial gas velocities, in three different mechanically-

stirred, baffled reactors (2, 5 and 15 L). Experimental data were

fitted using Equation (6) and the values for the parameters C2, C3and C1 were 0.35, 0.41 and 3.2 10

3, respectively. Since most

of the yeast broths behave as non-coalescent fluid, according to

the authors, the correlation improved the prediction ofkLa values

with respect to other generic correlations usually developed for

strong coalescent and non-coalescent fluids. Additionally, Shin

etal.55 verified that in high cell density cultures of fast-growing

aerobes, such as recombinant E. coli, where the biomass mayincrease to more than 70 gL1, the oxygen availability can be the

rate-limiting step of the fermentation process. Accordingly, the

following correlation for kLa incorporating the effect of cell density

(X) in oxygen transfer has been proposed:

kLa = 0.0192

P

V

0.55 vg0.64

1+ 2.12X+ 0.2X20.25

(7)

Extensive details on this matter can be found elsewhere.13

Another approach for the estimation of kLa relies on the use

of empirical correlations incorporating dimensionless groups.13,45

This approach has certain advantages because the correlations

obtained for a known system can be used to estimate kLa in other

systems with different dimensions.5658

Although several correlationshave beendevelopedfor different

systems, most of them are not specific to fermentation broths or,

when developed with such a purpose, they do not take into

consideration all the variations of parameters (surface tension,

viscosity) throughout the time course of cultivation, which may

hamper its effectiveness.

Two-phase partitioning bioreactors have demonstrated signif-

icant potential for enhancing the productivity of many biopro-

cesses by overcoming issues of poor substrate solubility and

toxicity. The oxygen mass transfer coefficient can also be evalu-

ated in these systems. In order to take into account the effect of

the organic phase and the organic phase volume fraction on kLa

in aerated liquidliquid dispersions, empirical correlations havebeen proposed, assuming that the two liquid phases behave as a

single homogeneous phase:59

kLa =

P

V

vg

(1 ) (8)

where is the volume fraction of the dispersed liquid phase, and

, , and are numerical constants.

Gomes etal.60 applied the correlation to the biotransformation

of methyl ricinoleate into -decalactone by the yeast Yarrowia

lipolytica. They showed that kLa had an influence on the

aroma production; however, for the low hydrophobic substrate

concentration used (1.08% v/v) and cellular density of 2 .0 107

cells mL1, a minimal kLa value of 70 h1 was necessary to attain

the maximum aroma production, 141 21 mg L1 (obtained at

agitation andaeration rates of 400 rpmand 0.6 vvm, respectively).

The numerical constants used were 650, 0.3, 0.7 and 1.7, for , ,

and , respectively.

Hydrodynamic studies in two-phase partitioning bioreactors

have focused on gaining further insight into understanding the

mechanismsrelatedwith formation of the interfacial areaavailable

formass transfer, so that substrate supply (normally from the non-aqueous phase) does not become the rate limiting step of the

process. The interfacial area has previously been correlated with

the dispersed phase hold-up fraction and the Weber number.61

The interfacial area available for mass transfer (a) is given by

a =6

d32(9)

where d32 is the Sauter mean drop diameter. Accurate knowledge

of the effectof bioreactor operating conditions on d32 is therefore

very important. Knowledge ofd32 can also give an early indication

of the stability of the liquidliquid dispersion created. The

physicochemical properties of the media can influence both

mean drop size and drop size distribution, as observed by Torres-Martinez, in the characterization of a multiphase system involving

ionic liquids.62

kLa determination in shaken devices

One of the key challenges for shaken fermentation technology

is to provide sufficient oxygen for the optimal growth of aerobic

microorganisms.63 Adequate oxygen supply is crucial not only

for industrial production, but also for meaningful screening and

processdevelopment.64 Underoxygen-limitingconditionsaerobic

microbes grow slowly, production of the intended metabolites is

scarce, if any, and furthermore, unwanted synthesis of metabolites

typical of anoxic conditions is prone to occur. Results obtained

under such conditions are likely to be misleading, particularly forscale-up purposes.63 To study the effect of organism properties,

medium composition or cultivation strategy on growth and

production, incubation in a non-limiting oxygen environment

is absolutely necessary. Otherwise wrong information about the

variables under study might be obtained.65,66

Several operational parameters affect the OTR in shaking

devices, namely the shape and size of the vessel; the shaking

frequency; the shaking amplitude; the shaking angle; the type

of agitation (orbital or linear); and the filling volume. OTR is

also influenced by the surface properties of the flask material,

which may be either hydrophobic or hydrophilic; and by the

physical chemical properties of the liquid (viscosity, oxygen

solubility, diffusivity and surface tension, the latter being more

noticeable in MWP).3,27,67

Some particular setbacks are likely to occur when operating

shaken vessels, specifically:

1. The reproducibility of microbial growth might be poor.68

2. When baffled vessels are used, small differences in depth

and positioning of the baffles lead to significant differences

in oxygen supply, growth and product formation of parallel

cultivations.

3. Out-of-phase phenomena might occur.67

4. Shaking frequency in these flasks has to be reduced to avoid

splashing of the liquid. Were droplets to reach the plug of

the flasks, gas transfer limitations or contaminations might

occur.63



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Engineering features and correlations forkLa in shaken flasksand MWP

Since bioprocess development (strain selection, strain enhance-

ment,process optimization) is widely carried out in shakenvessels,

and efforts towards engineering characterization of the oxygen

transfer mechanisms in such devices have been undertaken.

Maier and Buchs determined the maximum gasliquid mass

transfer capacity in 250 mL shaking flasks on orbital shaking

machines, using the sulfite oxidation method, by variation of

the shaking frequency and diameter, filling volume and viscosity

of the medium. The distribution of the liquid within the flask

was modeled, taking as reference the intersection between the

rotational hyperboloid of the liquid and the inner wall of the

shaking flask.69

The mass transfer within the shake flask is conditioned by

two resistances: one due to the sterile closure of the vessel, the

other due to the gasliquid interface. Owing to the resistance of

the closure, the oxygen partial pressure within the flask is lower

than the oxygen partial pressure of the surroundings. Mrotzek

etal.70 concludedthat, under normal conditions, the mass transfer

resistance of sterile closures is far smaller than the resistance of

the gasliquid interface.The gas exchange through the sterile closure was described

by the extended model of Henzler and Schedel,71 where besides

Fick diffusion flow, other parameters and issues are taken into

consideration, such as: (i) gas transfer by combined action of

diffusion and convection due to non-equimolar mass exchange

(Stefan flow); (ii) diffusion coefficients are notregardedas constant

but are instead calculated as a function of the respective local gas

concentration; and (iii) consideration of the water vapor flow.

Operating with baffled flasks, and using suitable operating

conditions, such as large shaken amplitudes, high OTRs can be

obtained. In 250 mLshaking flasks witha filling volume of50 mL, a

kLa of 400 h1 was obtained with a shaking amplitude of 2.5 cm. If

theamplitude wasincreased to 5 cm,similar valueswere obtained

at lower shaking frequencies. In these cases, the resistance of thesterile closure can become the limiting factor.

Experimental data from given sets of experiments were fitted

by least-squares after dimensional analysis,67 and the following

proportionality for the maximum oxygen transfer capacity

(OTRmax) was found

OTRmax N0.84V0.84d0

0.27d1.25 (10)

Consequently, an increase of the maximum oxygen transfer

capacity of a given system can be achieved by increasing the

shaking frequency, reducing the filling volume, increasing the

shaking diameter (d0) or reducing the maximum flask diameter (d)

(this is only valid when the V1/3

/dratio remains constant).Taking this into account, Maier etal.72 modeled the gas liquid

mass transfer in shake flasks at water-like liquid viscosity, in flask

sizes between 50 and 1000 mL. Relative filling volumes of 4 16%,

shaking diameters of 1.25, 2.5, 5, 7, 10 cm andshaking frequencies

of 50 500 rpm were tested. Furthermore, the previous model of

the gasliquid mass transferEquation (10)was extended to a two

sub-reactor model, to account for different mechanisms of mass

transfer in the liquid film on the flask wall, and the bulk of the

liquid rotating within the flask. The two-reactor system approach

consists of a stirred tank reactor (bulk liquid) and a film reactor

(film on flask wall and base). The mass transfer into the film on the

flask wall and base at in-phase operating conditions, is described

by Higbie penetration theory. Two differentmass transfer theories

were applied to successfully describe the mass transfer into the

bulk liquid: a model by Kawase and Moo-Young73 and a model by

Gnielinski.74 Extensive details can be found elsewhere.72

The agreement between the new modeling approach and the

experimental data was within 30%. The applicability of the

latter models to a biological system was shown using a Pichia

pastoris culture. The OTRmax was determined using the sodium

sulfite method which displayed a correlation factor of 2.8, when

compared with the OTR determined using an oxygen limited

P. pastoris culture, under variation of the operating conditions

(250 mL shake flask with filling volumes of 15, 25 and 40 mL,

under shaking frequencies of 50500 rpm with 5 cm of shaking

diameter).72 Moreover, the volumetric mass transfer coefficient

models allowed a significant agreement between experimental

dataandmodelpredictions,asreflectedbyacorrelationcoefficient

of 0.88. The maximum volumetric mass transfer coefficient of the

experimental investigation was found to be 0.157 s1 inthe 50 mL

flask, at a relative filling volume of 4%, a shaking frequency of

450 rpm and a shaking diameter of 7 cm.

Liu etal. correlated the experimental data for the determination

of OTR with a multivariable power correlation in carotenoid

(astaxanthin) production by the red yeast Phaffia rhodozyma.15

The constant parameters were derived by linear regression of the

data, resulting in the following equation:

kLa = 0.141N0.88

V

V0

0.80(11)

where Vo is the flask volume. Results showed a direct linear

correlation between carotenoid yield and OTR (OTR from 0 to

690 mg L1 h1), indicating that carotenoid production is limited

by oxygen transfer. Mantzouridou etal.75 also investigated the

effect of oxygen transfer rate on -carotene production by

Blakelsea trispora in shake flasks. The results indicated that the

concentration of-carotene (704.1 mg L1) was highest in culture

grown at maximum OTR of 20.5 mmol L1 h1. Moreover, OTRlevelshigher than20.5 mmol.L1h1 werefound to be detrimental

to cell growth and pigment formation.

Besides increasing the diameter or frequency of shaking or

decreasing the filling volume, one possibility to achieve high

maximum oxygen transfer capacities (OTRmax) is to modify the

usual round geometry of the shaken vessel. As shown by many

groups, changing the geometry of the vessel (i.e. from round-

bottomed base to square-bottomed base) and/or introducing

baffles into shake flasks resultin a significant increase in maximum

oxygen transfer capacity.76 The OTRmax can be increased up to

5 10-fold even at low shaking frequencies.

Alternatives for the well bottom design have been established,

among them the square shaped well. This type of well geometryhas been investigated by Duetz and Witholt42,77 and Duetz etal.78

The effects found for square wells are comparable with those in

baffledshake flasks.Eventhough squarewells allowfor an increase

in OTRmax of roughly 100% when compared with round wells, the

aforementioned problems described for shake flasks (splashing

and out-of phase phenomena) may limit the utilization of square

deep well plates as cultivation vessels.

More recently, Funke etal.79 presented well configurations with

a geometry thataimed at avoiding splashing while simultaneously

allowing for a high filling volume. Different plate formats were

studied, where variation were achieved by increasing the number

of edges and rounding the edges in square geometry plates,

rounding edges in pentagonal shape wells, star and flower



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shape wells, among others. Dissolved oxygen tension (DOT) was

measured in an experimental setup described by Samorski etal.80

The well geometry proved influential for OTRmax. By introducing

such baffles, an OTRmax in excess of 100 mmol L1 h1 (kLa over

600 h1) was obtained, roughly double that of round 48-well

MWPs. In conventional MWPs, values for OTRmax reported so far

have not generally exceeded 50 mmol L1 h1.27 Values of the

OTR in stirred tank reactors can reach 500 mmol L1 h1, but

overall, in standard batch fermentations, the OTR does notexceed

100 mmol L1 h1.26 The final well design, a six-petaled format,

provided the best compromise among different levels of baffling,

allowing simultaneously for a stable liquid height at the well

center during shaking (strong), high OTRmax independently of

filling volume and shaking frequency (moderate), and high filling

volume without splashing.79

Nonetheless, there are two common problems associated with

the use of microtiter plates for carrying out fermentations. These

are evaporation and cross-contamination due to spillover.In order

to avoid these drawbacks, MWPs can be sealed with adhesive

tape. The use of sealing tape leads to airtight closure of the wells

decreasing oxygen transferto the reactor welland consequentially

to the reaction medium.11

Doig etal.81 modeled the volumetric oxygen transfer coefficient

in MWPs using dimensionless groups. The basis for the modeling

was the experimental measurements of airliquid specific surface

area, determined both by the rate of evaporation and by high-

speed video photography.

Flowbehaviorwas modeled separatelyaccording to the specific

microplate configuration (24, 96 and 384-well), and the following

correlations, respectively, were obtained:

af

ai= 2.895 Fr0.86Bo0.03 (12)

af

ai= 1.092 Fr0.64Bo0.15 (13)

af

ai= 0.607 Fr0.51Bo0.18 (14)

The different models for predicting specific air liquid surface

area (af/ai) converged, with most data points lying within 25%

of the predicted values.

The kLa values ranged from about 0.005 to 0.055 s1 (shaking

amplitude of 3 mm and shaking frequencies ranging from 200

to 800 rpm) and are within the range reported by other authors

for these devices.5,35,78,82 Since the specific surface air liquid area

increased by a maximum factor of 4, and kLa increased by up to

10, it is clear that the liquid side oxygen transfer coefficient kL was

also affected by shaking conditions. Hermann etal.82 observed

an increase in kL from about 5.5 10

5

to 1.4 10

4

ms

1

asshaking frequency was increased from 200 to 800 rpm at 25 mm

amplitude. Values calculated by Doig etal.81 for kL varied from

2.8 105 ms1 to 8.3105 ms1 (shaking amplitudes of 3 mm

to 8 mm, in 24, 96 and 384 well microtiter plates).

Correlations for kL typically encompass Reynolds and Schmidt

numbers,and areusuallyin theformSh = a RebScc,where b ranges

from 0.7 to 0.8 and c is usually 0.33.81 The optimized correlation

for all three microplate geometries is

Sh = 0.19 Re0.68 Sc0.36 (15)

All the experimental data were modeled within 30% de-

viation, using correlation (15). Combining equation (15) with

Equations (12)(14), an overall correlation for predicting the vol-

umetric oxygen transfer coefficient kLa, in round bottom plates

becomes

kLa = 31.35DiaiRe0.68 Sc0.36 FrxBoy (16)

where Di is the diffusion coefficient, ai is the initial specific surface

area, and x and y are constants depending on the microplate

geometry (according to Equations (12)(14).

Islam etal.83

modified Equation (16) in order to predict kLa insquare well microtiter plates. The correlation obtained was:

kLa = 3.94 104

T

D

ai Re

1.91eaFrb

(17)

where the values for a and b were 1.66 and2.47 for48 rectangular,

flat wells; 0.70 and 1.51 for 24 square wells, round bases; and 0.88

and 1.24 for square wells, pyramidal bases. The predicted values

were in good agreement with the experimental data for kLa.

Case studies: scale-up from shaken devices and miniaturestirred reactors based inkLa similarity

In previous studies84 kLa was identified as the key engineering

parameter for characterization of an E. coli based process forheterologous protein expression, in microtiter plate format. A

scale-up to 7.5 and 75 L stirred tanks was performed, using as

criterion kLa fixed at either 0.069 s1 or at 0.015 s1. At 0.069 s1

boththe fermentation profile (biomass concentrationand glycerol

consumption) and product yield were identical in all scales,

enabling a 15 000-fold quantitative scale-up, representative of

fermentation performance. At 0.015 s1, due to poor gasliquid

distributions observed within the larger stirred tanks at matched

kLa, the overall fermentation profile was not reproduced.

Micheletti etal.6 studied the scale-up of aerobic fermentation

of E. coli JM107:pQR706 for overexpressing transketolase (TK),

from microtiter plates to stirred reactors. Using the correlation

displayed in Equation (17), a kLa of 0.079 s1 at 1000 rpm for

a 96 deep well microtiter plate was obtained. Maintaining the

volumetric oxygen mass transfer coefficient, it was possible to

scale-up the process directly from the microwell reactor to a lab-

scale reactor (1.4 L). Apart from some differences in duration of the

lag phase observed when the two scales were compared, similar

values ofmax and final biomass concentration were obtained.

Likewise, the rates of L-erythrulose formation when the cells

from the respective fermentations were used for the subsequent

HPA(-hydroxypyruvate) and GA (glycolaldehyde) bioconversion,

were also similar.

Zhang etal.8 used computational fluid dynamics (CFD) to

provide a detailed characterization of fluid mixing, energy

dissipation rate and mass transfer in single well bioreactors,

from deep square 24-well and 96-well microtiter plates. The CFDsimulations showedthat liquidmixing is more intensive in 96-well

than in 24-well bioreactors, due to the vertical movement of the

bulk fluid, in addition to the rotational movement. Liquid motion

was strongly dependent on the orbital shaking amplitude which

generally has a greater impact than the shaking frequency.

Predicted kLa values were compared with experimental results

obtained from dynamic gassing out experiments using a fibre-

optic dissolved-oxygen probe. The resulting kLa values measured

were 0.036 s1 and 0.023 s1, at 1000 rpm and 500 rpm, respec-

tively, in 96-well MWPs, at an orbital shaking amplitude of 3 mm.

The corresponding predictedvalues, 0.065 s1 and 0.056 s1 ,were

higher. The discrepancy was ascribed to some features of the dy-

namicmethod,namelytotheprobesensitivitytowardstheshaking



7/15


movement of the microtiter plates, as well as to the positioning of

the probe andto the mixingtime. CFDstudies could give more ac-

curate values if mediumlosses due to evaporation were taken into

account, and if the actual physical properties of the fermentation

media (viscosity, density) were used instead of water, as input for

performing the calculation. Batch cultures ofE. coliDH5 showed

similar maximum specific growth rates and final biomass yields

in shaken 24-well MTP and Erlenmeyer bioreactors, and in stirred

miniature and 20 L bioreactors at matched kLa values.

The biotransformation of benzaldehyde to l-phenyl acetyl

carbinol was scaled-up from a 100 mL shake flask to a 5 L reactor,

using resting cells of Saccharomyces cerevisiae.85 The following

correlations were used for the prediction of kLa in the growth

medium and in the biotransformation medium, respectively,

kLa = 0.024

P

V

0.725v0.892g (18)

kLa = 6.99 106

P

V

1.14v0.365g (19)

The volumetric oxygen mass transfer coefficient varied from0.010.07 s1 in the growth medium to 0.0050.02 s1 in the

biotransformation medium. Maintaining the kLa constant as

scale-up criteria, both for the cell growth process and for the

biotransformation, a 50-fold scale-up was achieved.

Gill etal.7,86 studied the influence of kLa on the fermentation

of E. coli TOP10 pQR239 in a 100 mL reactor. A correlation was

developed for the miniature reactor

kLa = 0.224

P

V

0.35v0.52g (20)

which was validated using the dynamic gassing out technique.

For scale-up to a 2 L bioreactor, different values of kLa were

used, varying from 0.06 to 0.11 s1. The results showed that therewas good agreement between cell growth and dissolved oxygen

tension profiles across the range ofkLa values studied, compared

with experiments at matched P/V values. The trend in oxygen

depletion during the growth phase and the time taken for both

systems to become oxygen limited were similar and reproducible

in both cases, thus resulting in a satisfactory 20-fold scale-up.

Using an E. coli JM107:pQR706 overexpressing transketolase

system, Micheletti etal.6 showed that kLa is the most suitable

scale-up parameter from a 24-well microtiter plate (1 mL filling

volume) to a 2L stirred tank reactor. On the other hand, the

prediction for the power consumption in the mechanically stirred

bioreactor (3.64 W m3) working under operational conditions

that allowed for growth patterns that matched those in shakenvessels was roughly 10-fold lower.

More recently, Marques etal.5 achieved an 8000-fold scale-up

for the side-chain cleavage of -sitosterol performed by whole

cells of Mycobacterium sp. NRRL B-3805 in 24-well microtiter

plates to a 5 L reactor. Scale-up was performed based on kLa

similarity at 0.058 s1 (at a shaking frequency of 250 rpm and

filling volume of 0.5 mL). Similar profiles (glycerol consumption

and 4-androstene-3,17-dione (AD) production) were achieved in

both scales validating multi-well plates as a small-scale reactor

for performing complex bioconversions. Nonetheless, there was

a consistent gap between values obtained in the multi-well

plate system and the bench-scale reactor for AD production and

biomass formation visible also in the levels of oxygen depletion.

The behavior was ascribed to diverse intrinsic hydrodynamic

conditions in the different reactor configurations, since mass

transfer plays an important role, both for oxygen and substrate

uptake.

Both MWP as well as miniature bioreactors proved effec-

tive as starting platforms for scaling up to bench/pilot scale

biotransformation/fermentation, but further studies in different

environments would be welcome that would hopefully further

validate this approach. Case studies are relatively scarce, where

miniature bioreactors are concerned. This can be partly ascribed

to the wider dissemination and lower cost of MWP platforms,

when compared with miniature bioreactors, whose availability in

the market is clearly lower.11 The studies referred to in this paper

related to the use of miniature bioreactors are based on in-house

developed prototypes. The use of the simpler MWPplatforms may

be of limited use when pH shifts take place during the process,

given the lack of mechanisms for pH control, only available in

more complex platforms.11 Processes involving significant shifts

in broth rheology (i.e. production of polymers) or viscous envi-

ronments may be more adequately dealt with using miniature

bioreactors, since these may provide more efficient mixing.

SCALE-UP BASED ON VOLUMETRIC POWERCONSUMPTIONPower consumption is a key parameter in (bio)chemical engineer-

ing,i.e. anengineering characterization parameter. Concomitantly,

it is a strong candidate for use as a criterion for (bio)reactor de-

sign and process scale-up. Often referred to as volumetric power

consumption (P/V), it is defined as the amount of energy required

to generate movement of a fluid within a vessel in a given pe-

riod of time. Along with the power actually drawn by the fluid,

relevant to the outcome of a given process, further power is re-

quired to account for energy losses, mostly due to friction, and

power consumption by motorsand gearboxes. This excess power,

although often relevant in terms of overall power consumption,

is not considered for design or scale-up of the process. The vol-

umetric power consumption is representative of the turbulence

degree and media circulation in vessels, and influences heat and

mass transfer, mixing and circulation times.87 Constant volumetric

power input wasapplied successfully as scale-up parameterfor the

early industrial penicillin fermentations (1 hp gallon1, equivalent

to1.8 kW m3), andin fermentations with lowenergy inputs,88 but

it is limited in fermentations requiring high energy inputs, such

as recombinant E. coli cultures,1 possibly due to high associated

costs and to high shear stress in the larger-scale stirred vessels.

AccordingtoRushton etal.,89,90 thepowerinputfortheagitation

of a non-aerated mixture, Po is characterized by the dimensionless

variable power number (NP):

P0 = Np N3T5 (21)

where N is the stirrer speed and T the stirrer diameter. The

power number depends on other dimensionless groups such as

Reynolds number and Froude number, as well as on the number

of agitator turbines. Thepowerconsumption in ungassed systems

is always higher than the power consumption in gassed systems,

since aeration significantly influences the power drawn from the

impeller by the fluid.7 The effect of aeration has been studied

extensively by Nienow etal.,91 Oosterhuis and Kossen,92 Yawalkar

etal.93 and Gogate etal.46 It has been shown that the gassed

power input is usually 3040% of the ungassed power input,



8/15


depending on the type of impeller and aeration rates used.79

Cavities (gas pockets) are formed behind the impeller blades in

gassed systems, resulting in different densities of the fluid under

gassed and ungassed conditions.94,95 Too high gas flow is to be

avoided since this may lead to cavities extending between blades,

which result in flooding and mechanical instability. Cavities may

be minimized,hence gas handling capacityimproved, withthe use

of adequate impellers, such as the Rushton turbine. Alternative

blade impeller configurations (i.e. concave blades) have also been

introduced.96,97

Hughmark98 suggests the following relationship for estimating

the volumetric power consumption:

P

Po= 0.1

N2T4

gWV23

1

6 Q

NV

14

(22)

where W is the width of turbine blades and Q is the volumetric

gas flow rate. Over the years several modification were proposed

in order to improve this model. Such modifications included

taking into account the number and type of impellers and reactor

dimensions, among others.99,100,101

Along with the use of predictive correlations, power con-

sumption may be determined experimentally through the use

of electrical or calorimetric measurements, or through the use of

dynamometers, torquemeters and strain gauges.87

In bench- and pilot-scale bioreactors typically used for the

fermentation of bacteria and fungal micro-organisms, the power

consumption ranges from 1 to 3 kW m3.102,103

Shaken flasks

Although well established for bench-scale and above reactors,

only recently have dedicated efforts been made to provide a

suitable characterization of the volumetric power consumption in

miniaturized devices, and to take advantage of this parameter forthe engineering characterization of said devices. Most reports on

the experimental determination of power consumption in shaking

systems are by the group of Jochen Buchs at RWTH Aachen. Buchs

etal.104,105 measured the power consumption in shaken flasks at

high and low-medium viscosities. The experimental assembly was

a simplerotary shaking machine fixed to a frame,combinedwith a

torque sensorattached tothe powering drive.Torque andshaking

speed were monitored and correlated by the following relation:

P

V= Ne

N3d4

V23

= C3N3d4

V23

Re0.2 (23)

where Ne

is the modified Newton number for shake flasks.The correlation implies that the specific power consumption is

dependent on the shaking frequency according to P N2.8. Such

a correlation is typical of that found in unbaffled agitated tank

reactors. The model includes C3 as the only fitting parameter

using least-squares non-linear fitting for the description of all the

experimental results obtained in their study, having a value of

1.94.

The values for volumetricpower consumptioncalculatedby this

empirical correlation and the experimentally measured values fit

within a deviation range that does not exceed 30%. In the range

0.01 to 0.2 kW m3, with shaking frequency from 80 to 380 min1,

filling volumes of 4% to 20% of nominal flask volume and

shaking diameter from 2.5 to 5 cm, larger discrepancies are found

between calculated and measured values. The corresponding

data was gathered at low shaking frequency (80 to 120 min1)

and, therefore, at low Reynolds numbers (Re 500 to 5000),

possibly within the transition from laminar to turbulent flow, a

feature that could account for the increased deviation between

predicted and experimental values.

The discrepancy between experimental and predicted data at

low power consumption was overcome in liquids with viscosities

between 0.8 and200 mPa s,105 where a correlation was found that

took into consideration all the flow regimes:

Ne = 70Re1 + 25Re0.6 + 1.5Re0.2 (24)

This correlationconsists of a laminar (Re1), a transition (Re0.6),

and a turbulent term (Re0.2). From the power number variation

with the flask Reynolds number, two flow conditions were

identified: in-phase conditions, where the bulk of the liquid

in the flask circulates in phase with the shaking platform; and

out-of-phase conditions, where only a minor fraction of the

liquid is actually moving along the flask wall.67 The out-of-phase

conditions lead to a decrease in volumetric power consumption,

mixing gas/liquid mass transfer. In order to systematically describethe in-phase and out-of-phase conditions, a new non-dimensional

number, called the phase number (Ph), was derived based on an

analogy to a partially filled, rotating horizontal drum, and can be

expressed as

Ph =d0

d

1+ 3log10

(2N)

d2

4

1

14

V0.33

d

2

2

(25)

Buchs etal.98 established that all operating conditions where

Ph > 1.26arein-phasewhileout-of-phaseconditionsareobserved

for Ph < 1.26, which are prone to occur when large flasks or high

viscosityfluids are used.11 Nonetheless, out-of-phase conditions in

largeflasks at water-likeviscosities arepossibleif theflask is quickly

accelerated.106 This may occur, if (i) the shaking machine has a

strong engine, (ii) the load of the machine is low (small number of

flasks placed on the shaker table or (iii) the shaking flasks which

wereremovedfromtheshakingtable(e.g.forsampling)areplaced

back on the running machine.

This phenomenon of out-of-phase operating conditionsmay be

of great practical relevance, since working under such conditions

will significantly reduce the oxygen transfer and mixing intensity,having a strong impact on strain and medium development. The

flow in a shaking flask tends to be in-phase at large shaking

diameter, low viscosity, large filling volume, higher shaking

frequency and small number and size of baffles.

Kato etal.107 used a calorimetric method, based on the method

developed by Sumino etal.,108 for assessing power consumption

in larger shaken flasks (nominal volume up to 20 L). Despite the

increase in volume, the order of magnitude ofP/V is the same as

for smaller scale shaking flasks, up to a nominal volume of 2 L and

having different geometries.104

Similar studies have been conducted reporting a stronger

dependency between power consumption and shaking speed,

P N.5.75 Nonetheless, a narrower range of shaking frequencies,



9/15


between 100 and 200 rpm was used, compared with the

100 300 rpm range of Kato etal.1 This may also account for

the volumetric power consumption reported in either work, from

less than 2 kW m3 to 5 kW m3, respectively. Therefore, it can be

concluded that the increase in power consumption is much more

pronounced in the region of low shaking frequencies, between

100 and 200 rpm.

Raval etal.109 compared the torque method and temperature

methodforthedeterminationofpowerconsumptionindisposable

shaken bioreactors of 2 L polycarbonate (PC) bottles and 20 L

polypropylene (PP). Data points collected by both methods were

within30%tolerance range.Values of3.5 kW m3 wereobtained

in 2 L flasks with filling volume of 250 mL at 300 rpm (shaking

diameter 50 mm).

Another approach for estimation of the volumetric power

consumption was proposed by Zhang etal.103 Using CFD

techniques,theseauthorswereabletopredictpowerconsumption

when 250 mL shake flasks were operated between 100 and

300 rpm, with shaking diameters between 20 and 60 mm and

filling volumes between 25 and 100 mL. CFD models suggest that

P/V can be correlated with the filling volume or with the shaking

frequency according to P/V V0.7

and P/V N2.7

, respectively.Both correlationsare in agreementwith those suggestedby Buchs

etal.104 Nonetheless, for lower shaking diameters the exponent

in the shaking frequency correlation decreased to 1.6, suggesting

that the applicability of these correlationsare limited to a range of

amplitudes. Values predicted were between 40 and 1200 W m3.

The powerconsumptionestimates werefoundto be moresensitive

to changes in shaking amplitude than to frequency

Multi-well plates

Despite the widespread application of MWPs, currently there is

no experimental report on power consumption in these devices,

mainlyduetolackofcommercialtorquemetersorothermeasuring

methods withsufficient sensitivity to perform suchmeasurements.In order to overcome this situation, Zhang etal.8 proposed the use

of CFD to obtain estimates of power consumption. These authors

applied the methodology followed by Zhang etal.103 where the

local energy dissipation rate was obtained using

=v

(26)

where V is the viscous dissipation term. The power consumption

based on the fluid friction is calculated from

P

V

=

V0

vdV

V

(27)

where V, can be expressed in terms of shear rates:103

V = 2

u

x

2+

v

y

2+

w

z

2+

u

y+

v

x

2

+

v

z+

w

y

2+

u

z+

w

x

2(28)

where U(u,v,w) is the velocity vector(velocity component) andx(x,

y, z) is the moving grid spatial vector (spatial component).

The results showed that power consumption and energy

dissipation rates in the shaken microwells are strongly affected

by the size of the well and the filling volume, as well as by the

shaking frequency and amplitude. These effects are higher in 96-

well microtiter plates than in 24-well microtiter plates under the

same operating conditions.

Moreover, two situations can be distinguished:

1. The power consumption in the 24-well reactor does not

increase linearly as theshaking frequency is increasedat 3 mm

shaking diameter. Up to roughly 800 rpm, there is a decreasein power consumption, presumably due to out-of-phase

conditions. The estimated volumetric power consumption

is more sensitive to changes in shaking amplitude than to

frequency.

2. The volumetric power consumption at orbital shaking ampli-

tudes of both 3 mmand 6 mmincreased linearly with increase

in shaking frequency. Increasing the shaking amplitude from

3 mm to 6 mm, led to an increase in the volumetric power

consumption of 1000 2000 W m3 using shaking frequencies

in the range of 800 1000 rpm.

The volumetric power consumption was compared for a 24-well

microtiter plate, 96-well microtiter plate, 20 L stirred reactor, 6 mL

miniature stirred reactor110 and a 250 mL shake flask102 at feasibleoperating conditions.

The miniature stirred reactor closely matched the configuration

of conventional stirred reactors, only scaled down to 6 mL

scale.11 The P/V was similar in all reactors with the exception

of shaken flasks and the 24-well microtiter plates. The values used

for microtiter plates were obtained by CFD studies in specific

conditions; nonetheless similar values of P/V can be reached in

shaken systems.

Case studies: scale-up from shaken devices and miniaturestirredreactorsbasedin specificpowerconsumptionsimilarity

Aqueous systems

Despite the large number of studies on scale-up of fermentationprocesses andsome on bioconversion processes, examples on the

use of volumetric power consumption as key parameterare scarce.

Gill etal.7 developed a prototype of a microreactor that enables

parallel operation of 4 16 independently controlled experiments.

Each microreactorhas a maximum working volumeof 100 mL and

is equipped with a magnetically driven six-blade Rushton turbine,

with a stirring range of 100 2000 rpm. Growth ofE. coli TOP10

pQR239was scaled-upfromthismicroreactor toa 2 L reactor fitted

with two six-blade Rushton turbine impellers111 using as constant

the power consumption. P/V values used in this study were 657,

1487 and 2960 W m3.

At the lowest P/V value of 657 W m3, the 2 L reactor signif-

icantly underperforms compared with the miniature bioreactor,achieving a final biomass concentration of almost 3 g L1 less

(Xfinal in the miniature bioreactor was 5.6 g L1). This is likely to be

the result of operating at a reduced agitation rate, and the poor

gasliquid dispersion that was observed at the 2 L scale under

these operating conditions. Given the poor oxygen transfer at the

2 L scale, the dissolved oxygen tension reached zero much earlier,

and cell growth was clearly seen to become oxygen limited. The

performance of the 2 L reactor was improved at higher P/Vvalues

(>1000 W m3), with very similar max and final biomass con-

centration values obtained at both scales. The trends for oxygen

depletion during the exponential growth phase and for the time

taken forbothsystems tobecome oxygenlimited were similar and

reproducible at the twohigher P/Vvalues(1487and 2960 W m3).



10/15


The volumetric power consumption was also used to scale-up

the alginate production using Azotobacter vinelandii cells, from

500 mL shake flasks to 14 L reactor.112 The study showed that

when an initial power draw of 0.27 kW m3 was applied, a specific

growth rate of 0.16 h1 was obtained in a stirred reactor, which

roughly doubled the specific growth rate obtained in shake flasks

(0.09 h1). Moreover, differences in the broth viscosity, concentra-

tion profiles and molecular weight of the alginate were observed.

In order to overcome these, the initial P/V in the reactor or along

the time course of the cultivation was reduced, which ultimately

allowed oneto match the molecularcharacteristics of the alginate

obtained in shake flasks. This approach further highlights the

potential limitations of shaken vessels as starting platforms for

scaling-up when significant modifications in medium rheology

take place throughout the process. This study was based on the

theoretical analysis of power consumption in shake flasks. The

power consumption in shake flasks was estimated from extrapo-

lationof data reported by Buchs etal.104,105This is a key feature, as

thechangesin power inputaffect theOTR,which in turn affects the

molecular characteristics of alginate. Further work was performed

in order to gain insight into the power consumption along cell

growth for the same alginate-producing system. Pena etal.113

showed that the power consumption increased exponentially

during fermentation, achieving a maximum value of 1.4 kW m3

after 40 h cultivation. This increase was due to the increased

viscosity of the culture broth, which resulted from the increase

in the molecular mass of alginate and polymer concentration. In

this period, a maximal alginate concentration of 5 kg m3, with a

maximum molecular weight of 550 kDa was obtained.

Although the viscosity increasedin theperiod from 40 to 70 h, a

slight drop in the power consumption was observed, leading to a

value of 1.2 kW m3 at 70 h cultivation. This behavior waspossibly

due to out-of-phase conditions, which could also account for the

results obtained previously by Reyes etal.112

Pena etal.114 tried toreproduce themean molecularmassof the

alginates obtained in shake flasks, in a stirred reactor maintainingP/V constant. A 500 mL Erlenmeyer flask was used, containing

1/5 filling volume. The power consumption during alginate pro-

duction increased exponentially from 0.18 to 1.4 kW m3 during

the first 40 h of culture and remained practically constant during

the rest of the fermentation. The exponential profile of the power

consumption(from0.2to1.2 kW m3)wassimulatedalongthefer-

mentationina14 Lbenchreactor,containing10 LofBurkmedium,

by adequately controlling the agitation rate from 250 to 515 rpm.

Further dissemination of the evaluation of the validity of

constant P/V as criterion for scaling-up is dependent on the

availability of methodologies and tools for easily assessing this

parameter in miniaturized devices.

Studies in two-phasesystems

All the above examples are for aqueous systems. When substrates

that have low water solubility are involved, two-liquid phase

systems can be used. In these cases, P/V is particularly important

as drop diameter (and hence the interfacial area available for

mass transfer between phases) depends on the maximum energy

dissipation rate or volumetric power consumption. Moreover, for

efficient extraction it determines also the mean drop size and the

dispersion of the immiscible organic phase.115

In a stirred vessel dmax (maximum drop diameter) will be given

bydmax

T

We0.6T (29)

and

WeT =cN

2T3

(30)

where WeT is thestirred-tankWeber number and istheinterfacial

tension.116 To account for the effect of volume fraction on dmax, a

linear concentrationcorrection functionwiththe followinggeneral

form was used:

dmax

T= c1(1+ c2)We

0.6T (31)

where c1 and c2 are constants and is the volume fraction of

the dispersed phase.61 The constant c2 is considered equal to 3

when it accounts for turbulence damping at low dispersed-phase

concentrations, or higher than 3 for coalescing systems.117

The maximum drop diameter is also a key parameter since it is

generally considered proportional to the Sauter mean diameter

d32, although this has been questioned.118 The Sauter mean

diameter, commonly used in processes depending on interfacial

area, is defined as the ratio of the third to the second moment of

the drop size distribution:

d32 =

ki=1

nid3i

ki=1

nid2i

(32)

where kis the number of size classes, ni the number of drops and

di the diameter of drops in size class i.117119

These aforementioned equations predict an increase in drop

size with increasing dispersed phase volume fraction, up to

about 40%. At higher fractions (usually above 50%), a further

increasein thedispersedphase concentrationresultsin decreasing

drop size. This behavior is attributed to a change in the dropbreakage mechanism, from turbulent eddy at low concentrations,

to boundary layer at high concentrations.117,120

BuchsandZoels120 showedthatshakeflasksledtolowerlevelsof

hydro-mechanical stress, as power consumption was much more

evenly distributed than in stirred tanks. This is due to different

mixing mechanism present in either reactor. Thus, in stirred tank

reactors, the power drawn from the region close to the stirrer is

quite high comparedwith the average P/V,sinceasmallimpelleris

used to promote mixingin a large vessel. a relatively small impeller

agitates a relatively bulky tank, leading to higher power drawn

in the region adjacent to the stirrer compared to the measured

average or specific P/V. Onthe other hand, whenshakenflasks are

used, power is more homogeneously distributed throughout thevessel, since energy is introduced through a relatively large wall

area.121 These effects must be taken also into account if cultures

are used where shear stress influences physiological behavior,

such as filamentous organisms.

Cull etal.116 exploited the use of two-phase systems for

the whole-cell bioconversion of 1,3-dicyanobenzene (1,3-DCB)

to 3-cyanobenzamide.with resting cells of Rhodococcus R312.

Volumetric power consumption (or N3Di2 constant) and constant

tip speed (or NDi constant) were the two criteria tested for scale-

up, from a 3 L to a 75 L reactor. The system was composed

of a phosphate buffer phase, where the cells were dispersed,

and toluene, which contained 1,3-DCB and, throughout the

bioconversion, the 3-cyanobenzamide formed.



11/15


Sauter mean drop diameters and drop size distributions were

verysimilarforscale-uponthebasisofP/V, in geometrically similar

reactors. In all cases the drop size distributions obtained were log-

normal. The results presented in that work were obtained in

a specific phase system comprising 20% v/v toluene dispersed

in an aqueous buffer containing up to 10 gcell wet weight L1

of Rhodococcus R312 cells. Despite being system specific, this

work contributed to establish specific power consumption as a

suitable tool for scale-up of two-phase bioconversion systems.

Moreover, it is demonstrated that a scale increase of 25-fold is

possible maintaining system specificity. The use of smaller reactors

leads to cost reduction with reagents and overall process power

consumption, among others. Moreover, problems that might be

encountered at the large scale can be rapidly and efficiently

identified in smaller scale.

Another example of scale-up of a two-phase system based on

the volumetric power consumption is the production 6-pentyl-

-pyrone (6PP) performed by cells of Trichoderma harzianum.

Rocha-Valadez etal.121 reported a 20-fold scale-up, from 500 mL

shake flasks to a 10 L stirred tank reactor. 6PP production

followed a sigmoid-shaped relationship with P/V, regardless of

the production scale or impeller diameter. Synthesis of 6PP wastriggered earlier in the stirred reactor, suggesting a physiological

response mechanism of T. harzianum towards hydrodynamic

stress. Overall, at low P/V values (from 0.08 to 0.4 kW m3) a

gradual increase in P/V improved 6PP production; however, for

P/V > 0.6 kW m3, 6PP concentration was significantly reduced

due to higher hydrodynamic stress.

P/Vshiftinfluenced microbialgrowth in the stirred reactor since

the specific growth rate decreased from 0.052 h1 at 0.08 kWm3

to 0.033 h1 at 1.6 kWm3. On the other hand the specific growth

rate was practically unaffected by P/V in the shake flask system.

Higher shear rates involve drastic physiological changes including

cellular differentiation and conidiospore production. This work

showed thatphysiological changes observedduring process scale-

up (i.e. microbial growth, production rates and sporulation) weretriggeredas resultof differences in the shear conditions prevailing

in the two systems employed.

P/V provides a suitable scale-up criterion from miniaturized

devices when two-phase systems are involved. Further studies

would be welcome addressing particular cases where MWP are

used as the starting platforms.

OTHER SCALE-UP PARAMETERSExamplesoftheapplicationofotherscale-upparametersarescarce

due to the fact that both kLa and P/V incorporate information

contained in the Reynolds number and mixing time, among

others. Nonetheless, examples of such criteria are given.

Constant impeller tip speed

The impeller tip speed, vtip, is expressed as

vtip = NT (33)

Tip speed is used as a rule for scale-up when the relationship

between shear and morphology is far from well understood, as

happens in mycelial cultures.122 A rough rule of thumb suggests

that cell damage can occur at tip speeds above 3.2 m s1, but

the exact value is influenced by many factors such as broth

rheology. Calculated tip speeds are usually greater than 3 m s1

for production scale reactors.122 Although useful for estimating

the potential for hyphae breakage and thus alteration of broth

morphology when branched yeast, filamentous bacterial and

fungal fermentations are involved, tip speed is less useful for

single cell bacterial or yeast fermentations. If scale-up is carried

out using constant tip speed (with geometric similarity), then the

value ofP/V is often lowered, which can adversely affect aeration

efficiency. It is possible to overcome this drawback by using more

impellers in the larger vessel in such a way that both tip speed and

P/Vare kept constant. Tip speed influences impeller shear, which

is proportional to the product of impeller tip speed and impeller

diameter, NDi2, for turbulent flow conditions.122

Hiruta etal.123 demonstrated that maintaining impeller tip

speed of 270 m min1 allowed scaling-up the production of -

linolenic acid by Mortierella ramanniana mutant MM 15-1 from a

30 L to a 1 m3 reactor.

More recently, Dubey etal.124 scaled-up the demethylation

of colchicine and their derivatives using Bacillus megaterium

ACBT03 cells, from a 5 L to a 70 L reactor. Under optimum culture

conditions the key monitoring factors to scale-up the process of

demethylation were aeration rate of 2.5 vvm and impeller tip

velocity of 4710 cm min1

.

Similar Reynolds number

Reynolds number is expressed as:

Re =TN2

(34)

The use of constant Reynolds number is hardly ever used

for fermentation scale-up, since the effect of aeration on the

process is notincorporated,125 andthe Reynoldsnumber generally

increases for successful scale-up designs. Other dimensionless

groups have also been examined for scale-up with limited

success, often resulting in technically unrealistic equipment andoperatingparameters. As it is difficultto maintain all dimensionless

parameters constant upon scale-up, those most important to the

process must be identified accurately.122

Constant mixing time

The mixing time tm denotes the time required for the reactor

composition to achieve a specified levelof homogeneity following

additionof a tracerpulse at a singlepointin thevessel. Mixingtime

containsinformation about flowand mixing within the reactorand

can be useful forbiosynthesis processes scale-up. Themixing time

tm is defined as1

tm =V

NfNT3

(35)

where Nf is the pumping number. The mixing time is typically

measured in stirred vessels. Nonetheless, there is an increased

interest in determining this parameter in shaken reactors. Gerson

etal.126 used a mixing probe to determine fluid mixing in a 1 L

shaken flask with filling volume of 540 mL at different shaking

frequencies. The results demonstrated that it is possible to

make such measurements, and that the mixing intensity rises

monotonically with shaking frequency in both stirred reactors and

shake flasks. Also, the range of mixing intensities measured by the

device is similar in both systems over the range studied.

Recently, Nealon etal.127 developed a high-speed video

technique for the accurate quantification of jet macro-mixing

times in static microwell plates, which also enables visualisation



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of jet formation and liquid flow patterns within the wells. Three

microwell geometries were investigated: a single well from a

standard 96-round well plate and dimension modified 96-well

plate. A general correlation was found for the time to reach 95%

homogeneity (s):

t95 =2.60D1.5h0.5

u0dn(36)

where dn is the nozzle diameter, D is the well or vessel diameter, his the liquid height and u0 is the nozzle velocity.

CONCLUSIONSDespite the relevance of the scale-up issue in biotechnology,there

is no straightforward and uniform strategy to tackle this matter.

A suitable scale-up criterion is elaborated in accordance with the

individual product, process and the facility.

An overall scale-up strategy consists of (i) a comprehensive

and detailed process characterization, and (ii) an appropriate

process control and process design. Owing to the complex

nature of bioprocesses (biotransformation, bioconversion and

fermentation) successful scale-up in most cases will not be basedon a straightforward strategy, but rather is the outcome of an

independent optimization on each process scale. Nonetheless,

bioconversions and fermentations are tentatively scaled-up on

the basis of kLa or volumetric power consumption from lab- to

pilot-scale. With the increasing ability to incorporate in MWPs and

in miniature and microreactors devices that allow the monitoring

of key process variables, such as dissolved oxygen tension,

pH, optical density or protein production, the application of

such small scale reactors is gaining widespread use in process

optimization, rather than just in the early stages of screening.

This pattern, that places small scale reactors in later stages of

process development, puts more focus on the need for increasing

the accuracy of scaling criteria. This has clearly been taken into

consideration, as shown by the number of papers dedicatedto this matter that have been published recently. Accordingly

the number of case studies reporting the successful scaling

up from MWPs and similar devices to bench scale reactor

or above is increasing. The insight required to obtain more

knowledge on this matter has received significant contributions

from the developments in image analysis, data processing and

development of predictive models, namely through the use

of CFD. In spite of the many existing studies, no examples of

direct scale translation to industrial scale exist. There is a strong

likelihood that scaling from shaken vessels to plant scale will

not become a reality, given distinct hydrodynamic environments,

preventing fullreproducibility, hencecompromisingthe outcome.

The translation from miniaturized to production environment is

nevertheless possible by scaling-out/numbering-up rather than

scaling-up. In this approach, large numbers of microreactors,

operating in continuous mode, are assembled so that production

up to tons/year basis can be achieved.

ACKNOWLEDGEMENTSMPC Marques and P Fernandes thank Fundacao para a Ciencia

e Tecnologia (Portugal) for financial support in the form of a

PhD grant SFRH/BD/24433/2005 and programme Ciencia 2007,

respectively. This work was partially funded by research project

POCI/SAU-MMO/59370/2004 from Fundacao para a Ciencia e a

Tecnologia (Portugal).

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