nuno miguel fernandes reis novel oscillatory flow reactors for...

Abril de 2006

Escola de Engenharia

Nuno Miguel Fernandes Reis

Novel Oscillatory Flow Reactors for Biotechnological Applications

Tese de Doutoramento Doutoramento em Engenharia Química e Biológica

Trabalho efectuado sob a orientação dos Doutor António A. Vicente Professor Doutor José A. Teixeira

ii

PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications

Autor Nuno Miguel Fernandes Reis

e-mail [email protected]

Telf. +351 253604400

BI 11382186

Título da tese

Novel oscillatory flow reactors for biotechnological applications

Orientadores

Doutor António A. Vicente

Professor Doutor José A. Teixeira

Ano de conclusão 2006

Doutoramento em Engenharia Química e Biológica

É AUTORIZADA A REPRODUÇÃO INTEGRAL DESTA TESE/TRABALHO APENAS PARA EFEITOS DE INVESTIGAÇÃO, MEDIANTE DECLARAÇÃO ESCRITA DO INTERESSADO, QUE A TAL SE COMPROMETE.

Universidade do Minho, 10 de Abril de 2006

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Acknowledgements

I would like to acknowledge the important role that my supervisor, Dr. António

Vicente, has played in this thesis, somehow operating as my mentor and my

godfather! I also would like to thank to my co-supervisor, Prof. José Teixeira,

for his precise guidelines and support on publications.

My thankfulness to Polymer Fluids Group, at the University of Cambridge, UK,

for hosting me within the group during two research training periods at

Cambridge, in particular to Dr. Adam P. Harvery (now at the University of

Newcastle upon Tyne), to Mingzhi Zheng, and for last (but not the least) to

Prof. Malcom R. Mackley, the group leader, for his supreme guidelines, for the

pleasant collaboration kept throughout these years, and for the full support of

my research.

Many thanks to Cassilda, for being such an outstanding wife, my orientation

on earth and my breath in the time I got down; I really fill this thesis also

belongs to her! To all my family for such consideration of my work and for

bringing a smile when it could not exist… I am also grateful to all my friends

for the good leisure times we shared together throughout these years, in

particular to Diana and Eduarda for seeding together the scientific research

when we were just undergraduate students.

After all, I am very grateful to my mother, whom too early has left me, for her

fully support, love and unique energy, which I saw irreversibly fading

throughout the first two and half years of this thesis…

Thanks are also due to Fundação para a Ciência e a Tecnologia (FCT) for

financial support by means of scholarship SFRH/BD/6954/2001.

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In memory of my mother, Georgina.

“…acredito que nada do que é importante se perde verdadeiramente. Apenas

nos iludimos, julgando ser donos das coisas, dos instantes e dos outros.

Comigo caminham todos os mortos que amei, todos os amigos que se

afastaram, todos os dias felizes que se apagaram. Não perdi nada, apenas a

ilusão de que tudo podia ser meu para sempre.”

Miguel Sousa Tavares

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Summary

This thesis explores the biotechnological applications of two novel scale-down

oscillatory flow reactors (OFRs). A micro-bioreactor (working mostly in batch) and a

continuous meso-reactor systems were developed based on a 4.4 mm internal

diameter tube with smooth periodic constrictions (SPC), both operating under

oscillatory flow mixing (OFM).

The first part is dedicated to the flow characterisation in the novel SPC geometry. Flow

patterns within SPC geometry were experimentally studied using Particle Image

Velocimetry (PIV) technique at different combinations of fluid oscillation frequency (x0)

and amplitude centre-to-peak (x0), and afterwards used for validation of numerical

simulations via Computational Fluid Dynamics (CFDs). CFD simulations were run with

2-D axisymmetric and 3-D laminar models as wells as using a turbulent Large Eddy

Simulation (LES) model using Fluent (New York, USA) software.

Mixing times of the micro-bioreactor were determined for batch operation at f and x0 of

0 to 20 Hz and 0-3 mm, respectively, and correlated using a newly defined mixing

coefficient (km).

The control of fluid dispersion in the novel SPC geometry was studied for continuous

operation of both the micro-bioreactor and the meso-reactor at different combinations

of f, x0 and fluid net flow rates (v). Macroscopic flow patterns were studied through the

residence time distribution (RTD) and the non-ideal tracer response was modelled by

four single-phase flow models, allowing the prediction of conversion ( X ) in the novel

SPC tube geometry. Further RTD experiments were performed in the presence of a

steady, continuous flow rate (at various values of v) and their results were compared

with those obtained from CFDs simulations.

Flow patterns within this novel SPC geometry were found to be very dependent of both

x0 and f. In particular, km, RTD and X have demonstrated to be manipulated by the

OFM conditions, as a result of a controlled fluid convection and dispersion within the

SPC tube through vortex rings detachment. It is possible to drive the macroscopic flow

patterns within both the micro-bioreactor and the meso-reactor towards the ideal flow

cases of plug flow reactor (PFR) or completely back-mixed reactor (or a continuous

stirred tank reactor, CSTR), being the convection maximized in relation to fluid

dispersion mainly at smooth OFM conditions (i.e. x0 ≤ 1 mm and f ≤ 10 Hz). A 2-D

axisymmetric laminar model was found to match the flow patterns at small values of f

and x0 (where flow has demonstrated to match the PFR) while a 3-D laminar model

was required to simulate non-axisymmetric flow patterns (as those found in a CSTR).

The 3-D laminar model was highly grid-dependent, but numerical simulations with 3-D

LES were found to overcome such grid dependency.

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Amongst the four single-phase models used in the modelling of macroscopic flow

patterns by means of the analysis of RTD results, the tanks-in-series model with

backflow is highly recommended due to the physical analogy with the SPC geometry

(several interconnected stages – the cavities) and for considering the existence of a

backflow rate, G, between the cavities.

The second part of this work is focused on exploring both the micro-bioreactor and the

meso-reactor in three main biotechnological applications: i) aerobic and anaerobic

growth of Saccharomyces cerevisiae in the micro-bioreactor; ii) biotechnological

production/screening of γ-decalactone in the micro-bioreactor; iii) dilution refolding of

lysozyme for batch (micro-bioreactor) and continuous (meso-reactor) operation.

Beforehand, mass transfer within the micro-bioreactor was studied by assessing the

oxygen mass transfer rates in a gas-liquid system. The effect of f and x0 on the oxygen

mass transfer coefficient (kLa) and on the gas hold-up (ε) were studied at a fixed gas

flow rate vgas of 0.28 mL/min. An empirical correlation was developed for kLa and

related with the flow patterns observed by PIV and numerically simulated with CFDs.

Gas-liquid mass transfer in the micro-bioreactor was shown to be enhanced in relation

to other scale-down systems, as values of kLa up to 0.05 s-1 were obtained through

OFM (f = 0 - 20 Hz and x0 = 0 - 3 mm) at a small value of vgas = 0.28 mL/min. Such

improved oxygen mass transfer was suggested to be responsible for an 83 %

improvement of yield of biomass growth on glucose (YX/S), obtained in the aerobic

growth of S. cerivisiae in comparison with the value of YX/S obtained for a stirred tank

reactor (STR). Also the 50 % reduction of the time needed for maximum γ-decalactone

production with the strictly aerobic yeast Yarrowia lipolytica suggested improved mass

transfer rates in the four-phase system as result of an improved contact between the

different phases.

It has been shown that the reciprocating nature of OFM (backflow) enhances the

interaction between fluid elements. This lead to the conclusion that both the micro-

bioreactor and the meso-reactor present design limitations for lysozyme dilution

refolding, mainly when applied to continuous refolding (with the meso-reactor). In fact,

an intensive protein aggregation was observed, leading to the suggestion that the

meso-reactor could be used as a scale-down system for production of bio-aggregates

and nano-particles. In summary, the two novel scale-down platforms are ready to

contribute to accelerate the bioprocess design, by allowing the running of high-

throughput screening experiments at reproduced and well-controlled conditions.

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Tese de Doutoramento Novos Reactores Oscilatórios para Aplicações Biotecnológicas

Resumo

Este trabalho explora as aplicações biotecnológicas de dois novos reactores de fluxo

oscilatório (RFO) de pequena-escala: um micro-reactor e um meso-reactor contínuo,

compostos por um tubo (diâmetro interno = 4.4 mm) com constrições suaves na

parede (CSP), sujeitos a mistura por fluxo oscilatório (MFO).

A primeira parte visa a caracterização do fluxo na nova geometria CSP. Analisaram-se

os padrões de fluxo recorrendo à Velocimetria por Imagem de Partícula (VIP), para

diversos valores de frequência (f) e amplitude centro-ao-pico (x0) de oscilação, os quais

foram posteriormente utilizados na validação de simulações numéricas por Dinâmica

de Fluidos Computacional (DFC). As simulações foram realizadas com o software

Fluent (Nova Iorque, EUA), com base em modelos do tipo 2-D com simetria axial, 3-D

laminar e 3-D com Simulação directa de Grandes Vórtices (SGV).

Determinaram-se tempos de mistura para um funcionamento do micro-bioreactor por

partidas, a diversas combinações de f e x0 (0 - 20 Hz e 0 – 3 mm, respectivamente),

tendo-se encontrado uma correlação empírica para os tempos de mistura com base

num novo parâmetro: o coeficiente de mistura (km).

O controlo da dispersão nos dois novos reactores foi analisado para várias

combinações de f, x0 e caudais de líquido (v), tendo-se analisado os padrões de fluxo

macroscópicos através da distribuição dos tempos de residência (DTR). A resposta

não-ideal do traçador foi modelada por quatro modelos hidrodinâmicos e permitiu

prever a conversão ( X ) na nova geometria SCP. Realizaram-se experiências

complementares para a situação de um caudal contínuo e estacionário (a diversos

valores de v), tendo-se comparado os resultados com os previstos pelas simulações

por DFC.

Concluiu-se que os padrões de fluxo na nova geometria SCP são bastante

dependentes quer de f quer de x0. Os parâmetros km, kLa, ε, RTD e X são

manipulados pelas condições de MFO graças a um controlo efectivo sobre a

convecção e dispersão do fluído no interior da geometria CSP por geração de anéis de

vórtices. Em concreto, os padrões de fluxo macroscópicos no interior do micro-

bioreactor e do meso-reactor podem ser aproximados aos casos ideais de um reactor

de fluxo pistão (RFP) ou de reactor perfeitamente agitado (RPA), sendo que a

convecção é maximizada (relativamente à dispersão do fluido) essencialmente a

baixos valores de MFO (p. ex., x0 ≤ 1 mm e f ≤ 10 Hz). O modelo 2-D laminar com

simetria axial é capaz de prever os padrões de fluxo a baixos valores de f e x0 (p. ex.,

RFP), mas um modelo 3-D laminar é indispensável para prever a assimetria axial do

fluxo (situação de um RPA).

viii

Tese de Doutoramento Novos Reactores Oscilatórios para Aplicações Biotecnológicas

As simulações numéricas efectuadas com o modelo 3-D laminar apresentaram-se

bastante dependentes do espaçamento da grelha, enquanto que o modelo 3-D com

SGV permitiu ultrapassar tal dependência.

Entre os quatro modelos hidrodinâmicos utilizados para a modelação dos padrões de

fluxo por análise de DTR, o modelo ‘tanques em série com retro-fluxo’ é altamente

recomendado visto existir uma analogia física com a geometria CSP (diversas

unidades perfeitamente agitadas e interligadas - as cavidades) e por considerar a

existência de uma taxa de retrodispersão (G) entre as várias cavidades.

A segunda parte do trabalho focou a aplicação do micro-bioreactor e do meso-reactor

a três bioprocessos: i) crescimento aeróbio e anaeróbio da Saccharomyces cerevisiae

no micro-bioreactor; ii) optimização da produção biotecnológica da γ-decalactona no

micro-bioreactor; iii) renaturação da lisozima por diluição por partidas (no micro-

bioreactor) ou em contínuo (no meso-reactor). Primeiramente, estudou-se a

transferência de massa no micro-bioreactor por medição das taxas de transferência

de oxigénio (kLa) num sistema gás-líquido. Averiguou-se o efeito de f e x0 sobre kLa e a

fracção de gás (ε) para a um valor fixo de caudal volumétrico de gás vgas = 0.28

mL/min, o que permitiu encontrar uma correlação empírica para o kLa e relacionar kLa

com os padrões de fluxo quer experimentalmente observados (por VIP), quer

numericamente simulados (por DFC).

Os estudos de kLa demonstraram que a transferência de massa de um sistema gás-

líquido no micro-bioreactor é superior à obtida em outros sistemas de pequena-escala:

kLa = 0.05 s-1 (para f = 0 - 20 Hz e x0 = 0 – 3 mm) a vgas = 0.28 mL/min. O aumento

de kLa foi apontado como responsável pelo aumento em 83 % do rendimento de

crescimento de biomassa em glucose (YX/S ) para a situação de crescimento aeróbio

de S. cerevisiae, em comparação com os valores de YX/S obtidos num RPA. De igual

modo, a diminuição em 50 % do tempo necessário para máxima produção de γ-

decalactona com a levedura aeróbica restrita Yarrowia lipolytica sugere taxas de

transferência de massa incrementadas neste sistema de quatro-fases, graças a um

aumento da área de contacto entre as diversas fases.

A natureza recíproca na MFO aumenta a interacção entre os elementos do fluido. Por

isso, quer o micro-bioreactor quer o meso-reactor apresentam limitações na

renaturação de lisozima por diluição, essencialmente quando em contínuo (com o

meso-reactor). A intensa agregação proteica observada sugere que o meso-reactor

poderá ser eficazmente utilizado como um sistema de pequena-escala para a

produção contínua de bio-agregados e nano-partículas. Em suma, os dois novos

sistemas de pequena-escala contribuirão, certamente, para acelerar o processo de

projecto de bioprocessos, permitindo realizar experiências como elevada

selectividade, reprodutibilidade e condições bem controladas.

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Table of contents

Acknowledgements....................................................................................... iii

Summary ......................................................................................................v

Resumo ...................................................................................................... vii

Table of contents.......................................................................................... ix

List of publications ..................................................................................... xiii

List of abbreviations ....................................................................................xiv

List of figures .............................................................................................. xv

List of general nomenclature..................................................................... xxvii

List of tables.............................................................................................. xxix

Chapter 1 Introduction ................................................................................ 1

Chapter 2 Literature review......................................................................... 5

2.1 Types and applications of oscillating devices............................... 6

2.1.1 Types of oscillating devices .................................................... 6

2.1.2 Industrial applications of oscillating reactors........................... 8

2.2 The Oscillatory Flow Reactor (OFR) ........................................... 11

2.3 The Oscillatory Flow Mixing (OFM) ............................................ 18

2.3.1 Parameters governing the OFM............................................ 19

2.3.2 The effect of geometrical parameters ................................... 24

2.3.3 Effect of f and x0 in the flow patterns..................................... 27

2.3.4 Power input ......................................................................... 27

2.3.5 Numerical simulation........................................................... 28

2.4 Further studies regarding oscillatory flow mixing ....................... 30

2.5 Tools in reactor engineering ..................................................... 31

2.5.1 Measuring techniques.......................................................... 31

2.5.2 Flow visualisation by Particle Image Velocimetry................... 35

2.5.3 Assessment of the non-ideal flow ......................................... 37

2.5.4 Computational flow modelling .............................................. 40

2.6 Biotechnological process engineering ....................................... 42

2.6.1 Application areas ................................................................. 42

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2.6.2 Bioreactors and bioprocesses...............................................43

2.6.3 Bioreactor engineering .........................................................46

2.6.4 Bioprocesses monitoring ......................................................48

2.6.5 Continuous cultures .............................................................50

2.6.6 Biotechnological applications of OFM....................................51

2.7 Scale-down of bioprocesses ......................................................52

2.8 Conclusions..............................................................................55

2.9 References ...............................................................................56

Chapter 3 The novel oscillatory flow reactor designs ..................................75

3.1 The novel SPC tube geometry ...................................................76

3.2 The novel micro-bioreactor........................................................76

3.3 The novel continuous oscillatory flow meso-reactor....................77

Chapter 4 Fluid mechanics and catalyst particle suspension within the novel

micro-bioreactor .........................................................................................79

4.1 Introduction..............................................................................80

4.2 Materials and methods .............................................................81

4.3 Results and analyses ................................................................89

4.4 Discussion and conclusions ....................................................107

4.5 Nomenclature.........................................................................109

4.6 References .............................................................................110

Chapter 5 Mixing times and residence time distribution of liquid phase within

the SPC geometry.....................................................................................113

5.1 Introduction............................................................................115

5.2 Experimental ..........................................................................117

5.2.1 RTD of liquid phase in the novel micro-bioreactor................117

5.2.2 RTD of liquid phase in the novel meso-reactor ....................124

5.2.3 Mixing times for batch operation of the novel micro-bioreactor

126

5.2.4 Numerical simulations of RTD for steady flow in the SPC tube

geometry.........................................................................................127

5.3 Results and discussion ...........................................................127

5.3.1 Analysis of RTD of liquid phase in the micro-bioreactor .......127

5.3.2 Analysis of RTD of liquid phase in the meso-reactor ............141

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5.3.3 Determination of mixing times fro batch operation of micro-

bioreactor at different combinations of f and x0................................. 145

5.3.4 Matching of numerical simulations of RTD in the SPC tube for

steady flow...................................................................................... 147

5.4 Conclusions ........................................................................... 149

5.5 Notation................................................................................. 150

5.6 References............................................................................. 152

Chapter 6 Correlating the macroscopic fluid mixing and axial dispersion with

the fluid mechanics of the micro-bioreactor............................................... 155

6.1 Introduction ........................................................................... 156

6.2 Materials and Methods ........................................................... 157

6.3 Results and discussion ........................................................... 160

6.4 Conclusions ........................................................................... 175

6.5 References............................................................................. 176

Chapter 7 Oxygen mass transfer rates for gas-liquid flow in the micro-

bioreactor................................................................................................. 179

7.1 Introduction ........................................................................... 180



7.4 Conclusions ........................................................................... 203

7.5 Notation................................................................................. 204

7.6 References............................................................................. 206

Chapter 8 Aerobic and anaerobic growth on glucose of Saccharomyces

cerevisiae in the micro-bioreactor.............................................................. 209

8.1 Introduction ........................................................................... 210

8.2 Materials and methods........................................................... 212


8.4 Conclusions ........................................................................... 227

8.5 References............................................................................. 228

Chapter 9 Biotechnological production of γ-decalactone by the strict aerobic

yeast Yarrowia lipolytica in the micro-bioreactor......................................... 233

9.1 Introduction ........................................................................... 234


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9.4 Conclusions............................................................................241

9.5 References .............................................................................241

Chapter 10 Lysozyme Dilution Refolding..................................................245

10.1 Introduction............................................................................246

10.2 Materials and methods ...........................................................249


10.4 Conclusions............................................................................258

10.5 References .............................................................................258

Chapter 11 General conclusions and suggestions for future work.............261

xiii


List of publications

Reis N, Vicente AA, Teixeira JA, Mackley MR. 2004. Residence times and

mixing of a novel continuous oscillatory flow screening reactor. Chemical

Engineering Science 59(22-23):4967-4974.

Reis N, Harvey AP, Vicente AA, Teixeira JA, Mackley MR. 2005. Fluid

Mechanics and Design Aspects of a Novel Oscillatory Flow Meso-Reactor.

Chemical Engineering Research & Design 83(A4):357-371.

Reis N, Vicente AA, Teixeira JA. forthcoming. The Control of Liquid Axial

Dispersion in a Small-Scale Tube through Oscillatory Flow Mixing. Aiche

Journal.

Reis N, Mackley MR, Harvey AP, Vicente AA, Teixeira JA. in progress. The

correlation between the macroscopic flow patterns and the deviation from

ideal flow for a Smooth, Periodically Constricted Tube.

Reis N, Vicente AA, Teixeira JA. forthcoming. Enhanced mass transfer rates in

a novel oscillatory flow screening reactor. Chemical Engineering Science.

Reis N, Gonçalves CN, Teixeira JA, Vicente AA. forthcoming. Proof-of-concept

of a Novel Micro-bioreactor for Fast Development of Industrial Bioprocesses.

Bioengineering & Biotechnology.

Reis N, Gonçalves CN, Águedo M, Gomes N, Teixeira JA, Vicente AA.

forthcoming. Application of a novel oscillatory flow micro-bioreactor to the

production of γ-decalactone in a two immiscible liquid phase medium.

Biotechnology Letters.

xiv


List of abbreviations

OFM Oscillatory Flow Mixing

OFR Oscillatory Flow reactor

POF Pure Oscillatory Flow

RTD Residence Time Distribution

PIV Particle Image Velocimetry

CFD Computational Fluid Dynamics

HTP High throughput

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List of figures

Figure 1-1. Multidisciplinary nature of biotechnology (Moo-Young et al. 1985).1

Figure 2-1. Technical drawing of a Van Dijck’s US Patent (1935). .................. 6

Figure 2-2. Examples of oscillating vessels: reciprocating plates - (A) and (B) –

and oscillating piston – (C) and (D). (A) from Prochazka and Rod (1974). (B)

from Ni (2002)(2002). (C) from Prochazka and Rod (1974), (D) from

Hounslow and Ni (2004). See references for numbering details..................... 7

Figure 2-3. Number of publications out coming from a global search in ISI Web

of Knowledge (http://portal.isiknowledge.com/portal.cgi) using keywords

“oscillatory flow”. All citation databases, document types and languages were

considered in the search. ............................................................................. 8

Figure 2-4. Schematic representation of cross section in an OFR. di – reactor

internal diameter, L – baffles spacing, d0 – orifice diameter, δ - baffle

thickness.................................................................................................... 12

Figure 2-5. Mechanism of oscillatory flow mixing (OFM) in an OFR, according

to Fitch et al. (2005). (A) Start of Up Stroke. (B) Maximum velocity in Up

stroke, i.e. flow reversal. (C) Start of Down stroke. (D) Maximum velocity in

Down stroke. .............................................................................................. 18

Figure 2-6. The net flow in a plain tube. ...................................................... 19

Figure 2-7. Oscillatory motion superimposed onto a net flow........................ 21

Figure 2-8. The oscillatory (baffled) flow. ..................................................... 22

Figure 2-9. Exemplification of sinusoidal movement of a piston (displacement,

x, velocity, v, and acceleration, a) for w = 0.62 rad/s (i.e., 0.1 Hz), and x0 = 5

mm............................................................................................................ 23

Figure 2-10. Particle flow pattern in a batch OFR. Tracer = pollen particles of

25 µm in diameter, bulk fluid = water, f = 2.5 Hz, x0 = 6mm, d = 50 mm, L =

1.5d, α = 36 %, δ = 3 mm (from Ni et al. 2002a). ...................................... 29

Figure 2-11. Overview of PIV technique. (A) Schematic representation of the

flow field illumination in a PIV system. (B) PIV interrogation analysis. (C)

xvi


Evaluation of the image density. Only build up of 2-D velocity vector maps is

exemplified (adapted from dantecdynamics 2002). .....................................36

Figure 2-12. Factors that influence the performance of a bioprocess and the

complexity of interactions between them. Only some interactions are shown

for illustrative purposes. The factors are grouped under three system

properties, namely, physical, chemical and biological (adapted from

Vaidyanathan et al (1999))..........................................................................44

Figure 2-13. A schematic of the approaches to measurement in bioprocesses

(adapted from Vaidyanathan et al., (1999). .................................................49

Figure 2-14. Main stages crossing the bioprocess development....................53

Figure 2-15. Examples of commercially available HTP screening bioreactor

systems. (A) Infors Profors – 16 x 400mL, sparged column reactors. (B)

DasGIP Fedbatch-pro – 16 x 300mL stirred tank reactors. (C) Infors Sixfors –

6 x 500mL, stirred tank reactors. ................................................................54

Figure 3-1. Novel SPC tube geometry. All dimensions are in mm. ................76

Figure 3-2. Geometry of the SPC tube composing the novel micro-bioreactor.

..................................................................................................................77

Figure 3-3. Simplified scheme of the novel continuous oscillatory flow meso-

reactor. ......................................................................................................78

Figure 4-1. Experimental setup used in experimental PIV. A. Laser source. B.

Laser sheet. C. Optical box made of Perspex. D. CCD camera. E. captured

image-pair. F. SPC tube. G. Oscillation unit. The optical box (C) and the jacket

of SPC tube (F) were filled with glycerol to avoid optical distortions. .............82

Figure 4-2. Mesh for 3-D numerical simulations (units are radii of the tube, R).

A detail of mesh in zones a, b and c may be found in Table 4-2...................84

Figure 4-3. Instantaneous velocity vector maps at Reo = 348, x0 = 1.1 mm, f =

11.1 Hz coloured by absolute velocity magnitude (mm/s) and different phase

angles (black vortex rings and arrows added to aid visualization):.................91

Figure 4-4. Instantaneous velocity vector maps at Reo = 1,350, x0 = 4 mm, f =

12.1 Hz and different phase angles (blue vortex rings and arrows added to aid

visualization):..............................................................................................92

xvii


Figure 4-5. Influence of the grid size on the recirculation strength of steady

state solution, wmax - wwall, for continuous net flow, based on 2-D planar model

results (Re = 100). ..................................................................................... 94

Figure 4-6. Simulated flow patterns for Reo = 11, x0 = 0.2 mm, f = 2 Hz, no net

flow, using a 2-D axisymmetric laminar model, after 2 simulation cycles.

Contours of stream functions (kg s-1) at: ...................................................... 95

Figure 4-7. Simulated flow patterns for Reo = 348, x0 = 1.1 mm, f = 11.1 Hz,

using a 2-D axisymmetric laminar model, after 12 simulation cycles, no net

flow. Velocity vectors coloured by velocity magnitude (m/s) at: .................... 96

Figure 4-8. Simulated flow patterns for Reo = 348, x0 = 1.1 mm, f = 11.1 Hz,

using a 3-D laminar model, after 26 simulation cycles. Velocity vectors

coloured by velocity magnitude (m/s), on plane z = 0, (black arrows added to

aid visualization) at:.................................................................................... 97

Figure 4-9. Comparison of the total areas occupied by the vortices ( ) in

different instants of the oscillation cycle (Reo = 348, x0 = 1.1 mm, f = 11.1 Hz;

no net flow, i.e. Ren = 0) for: ....................................................................... 98

Figure 4-10. Average of axial velocities through the oscillation cycle at Reo =

348, f = 12.1 Hz, x0 = 1.2 mm, using a) experimental data from PIV, b) data

from numerical modelling using a 2-D laminar axisymmetric model and c) data

from numerical modelling using a 3-D laminar model. ( ) global averaged

axial velocity; ( ) average of positive values of axial velocity; ( ) average of

negative values of axial velocities. ............................................................... 99

Figure 4-11. Average of radial velocities through the oscillation cycle using a)

experimental data from PIV, b) data from numerical modelling using a 2-D

laminar axisymmetric model and c) data from numerical modelling using a 3-D

laminar model using cells at plane z = 0. ( ) global averaged radial velocity;

( ) average of positive values of radial velocity; ( ) average of negative

values of radial velocities. Connection lines just intend to represent a

tendency. Reo = 348; f = 12.1 Hz, x0 = 1.2 mm......................................... 100

Figure 4-12. a) maximum concentration of ion exchange resin particles

completely suspended at different fluid oscillations frequencies and

amplitudes for a vertically fixed SPC tube; b) minimum u(t)max (maximum

xviii


oscillation velocities) for complete suspension of particles, at different fluid

oscillation frequencies. .............................................................................103

Figure 4-13. Instantaneous velocity vector maps of fluid phase at Reo = 990, x0

= 3 mm, f = 12.1 Hz and 45º of tube position in the presence of a small

amount of ion exchange resin particles:.....................................................105

Figure 4-14 Complete suspension of 40 % v/v of ion exchange resin particles

at varying angles and similar oscillation conditions: (a) vertical position, f =

12.1 Hz, x0 = 4 mm; (b) 45º, f = 12.1 Hz, x0 = 4 mm; (c) 10º, f = 12.1 Hz, x0 =

3 mm; (d) horizontal position, f = 12.1 Hz, x0 = 3 mm. In b), c) and d), the

right hand side corresponds to the bottom of the tube. ..............................106

Figure 4-15. Proposed “in series” configuration for a single screening reactor

unit. .........................................................................................................108

Figure 5-1. Experimental setup. A: Peristaltic pump; B: Reservoir; C: Electric

motor; D: Piston pump; E: 350-mm-long SPC tube; F: Micro transmission dip

optical probe; G: Reflection optical probe; H: Aluminium foil; I: In-line cell; J:

Tungsten halogen light source; K: 475 nm LED light source; L: Multi-channel

fibre optic spectrometer; M: Personal computer; N: Tracer injection; O: Optical

path of reflection probe; P: Optical path of transmission probe (2 mm); Q:

section of dye injection; R: detail of SPC geometry (all units are in millimetres);

S: inlet tube; T: outlet tube........................................................................118

Figure 5-2. Relation between absorbance (A = log (P0/P)) measured by optical

micro-probes and the tracer concentration (x)............................................120

Figure 5-3. (a) Response of micro-probes during the consecutive phases of a

complete RTD experiment; (b) comparison of generated Laplace step-down

function (‘generated inx ’) found by deconvolution of x measured by micro-

probe 1 (located downstream the injection point) with a perfect Laplace input

step function (‘perfect inx ’). Example is given for an experiment at steady,

continuous flow (Reo = 0 and v = 1.94 ml/min). I: system cleaning; II: feeding

of the system with the tracer; III: stabilisation of concentration in the system

through the recirculation and oscillatory mixing; IV: RTD experiment running. ○

micro-probe 1 response, micro-probe 2 response. Line in (b) represents:

‘generated inx ’ = ‘perfect inx ’. .................................................................121

xix


Figure 5-4. Schematic of the SPC configuration for RTD experiments, as seen

in Laplace’s domain. ................................................................................ 123

Figure 5-5. Experimental apparatus used for RTD studies in the novel

continuous meso-reactor. ......................................................................... 125

Figure 5-6. Light absorbance (A = log (P0/P)) measured by micro-probes 3, 4

and 5 at dye tracer concentration (x) of 0 to 10 mg/L. .............................. 126

Figure 5-7. (a) Reduced RTD curves for different superficial liquid tube

velocities (uLs) (in cm/s) and comparison with the pure-convective flow

(Danckwerts 1953); (b) mean tracer residence time ( t ) as a function of inlet

liquid flow rate (v)..................................................................................... 128

Figure 5-8. Tracer response curves at the outlet of a 350-mm-long SPC tube at

f = 20 Hz and v = 1.94 ml/min. (a) Repeatability of two different experiments

(x0 = 1 mm); (b) Experimental data for x0 of 0, 0.5, 1.0, 2.0 and 3.0 mm... 129

Figure 5-9. Average mean residence times of the tracer in a 350-mm-long SPC

tube as a function of fluid oscillation frequency (f) and amplitude(x0) for a net

flow rate (v) of 1.94 ml/min. .................................................................... 131

Figure 5-10. Details of best-fitting of cumulative dimensionless concentration of

tracer (Fθ-diagram) and transfer function g(T) to single-flow models. (a) and (b)

shows parameters Ntis and DP estimated through direct comparison of Fθ-

diagrams (Levenspiel 1972) and using best-fitting criteria of Equation (5-5); (c)

and (d) shows model parameters estimated by best-fitting (with Equation (5-6)

of transfer function gout(T) to transfer function g(T) derived by mass balance of

single-phase models. Fluid oscillated at: (a) and (c) 3 Hz and 0.3 mm; (b) and

(d) 20 Hz and 3 mm. Net flow rate of 1.94 ml/min. Note that some curves are

coincident. The fitting range refers to the integration intervals in Equation (5-

6)............................................................................................................. 133

Figure 5-11. Effect of fluid oscillation frequency (f) on the dimensionless

number IPD for constant fluid oscillation amplitudes (x0), using values of DP

estimated by different methods. ■ 0 mm, □ 0.5 mm, ▲ 1.0 mm, ♦ 2.0 mm,

○ 3.0 mm; (a) IPD found by the moments method; (b) IPD found by fitting of

gout(T) to g(T); (c) IPD found by direct nonlinear regression of the analytical

equation of axial dispersion model presented by Levenspiel (1972) Vertical

xx


error bars represent spread (standard deviation) of values for different

experiments. Net flow rate of 1.94 ml/min. Area not shaded corresponds to

the region where an improvement of RTD is achieved, i.e. where dispersion

becomes less significant than convection. .................................................134

Figure 5-12. Cross-correlation of dimensionless axial dispersion number (PD)

with the backmixing (G), the number of tanks-in-series (Ntis) and the volume-

fraction of ideal PFR (Vp/V), according the values of parameters estimated

through different methods. (a) Linear plot of G vs. 1/PD; (b) linear plot Ntis vs.

PD; (c) log-plot of VP/V vs. PD; model parameters estimated through fitting of

moments, fitting of gout(T) to g(T) of the model. × fitting of Fθ-diagrams

(Levenspiel 1972). Line in (a) represents the theoretical relation G + 0.5 =

Nsw/PD (Mecklenburgh and Hartland 1976); continuous line in (b) represents

the theoretical (Westerterp et al. 1963) relation: Ntis = 0.5 PD + 1; dash line in

(b) shows relation of Ntis with the values of PD estimated by fitting of

experimental Fθ-diagram to that given by the Levenspiel’s equation (Levenspiel

1972).......................................................................................................137

Figure 5-13. Predicted deviation on conversion ( X ) in a 350-mm-long SPC

tube (micro-bioreactor) for a homogeneous, isothermal chemical reaction as

determined directly from the Eθ-diagram in the SPC tube, at v = 1.94 ml/min.

................................................................................................................140

Figure 5-14. Reduced RTD curves at three axial distances of the meso-reactor,

operated at eight combination of hydraulic mean residence times (τ) and

oscillatory flow Reynolds number (Reo). M – micro-probe 3; S1 – microprobe

4; S2 – microprobe 5; (a) τ = 60 min, steady flow (i.e. Reo = 0); (b) τ = 60

min, x0 = 1 mm, f = 10 Hz, Reo = 312; (c) τ = 60 min, x0 = 2 mm, f = 10 Hz,

Reo = 625; (d) τ = 60 min, x0 = 3.5 mm, f = 6 Hz, Reo = 657; (e) τ = 10 min,

Reo = 0; (f) τ = 10 min, x0 = 1 mm, f = 10 Hz, Reo = 312; (g) τ = 10 min, x0 =

2 mm, f = 10 Hz, Reo = 625; (h) τ = 60 min, x0 = 3.5 mm, f = 6 Hz, Reo =

657. Note that θ = t/ t , where t was determined from tracer response in

micro-probe 3 (i.e. located at the higher axial distance). ............................142

Figure 5-15. Number of tanks-in-series (Ntis) estimated by direct comparison of

Eθ-curve of micro-probe 5 response for increasing values of net flow Reynolds

number (Ren). ○ Steady flow, i.e. Reo = 0; ● x0 = 3.5 mm, f = 6 Hz, Reo = 312;

xxi


■ x0 = 2 mm, f = 10 Hz, Reo = 625; ♦ x0 = 1 mm, f = 10 Hz, Reo = 657. Error

bars shows standard deviation of values extracted for different experiments.

................................................................................................................ 144

Figure 5-16. a) determination of mixing time t90 parameter from experimental

data at 20 Hz and 1 mm; b) comparison of experimental t90 parameter with

estimated values with Eq. (5-14). .............................................................. 145

Figure 5-17. Variation of the mean values of mixing coefficient km with fluid

oscillation (a) frequency and (b) amplitude at different oscillation conditions.

................................................................................................................ 147

Figure 5-18. Effect of net flow rate over a) tracer mean residence time and b)

backmixing, g, assuming a perfect step input at steady flow (no fluid

oscillations). A comparison is presented between experimental (■) and

simulated values (●) using a 2D-axisymmetric model. ............................... 148

Figure 6-1. Illustration of the procedure applied to the determination of

instantaneous-average (Vradial, Vaxial) and cycle-average velocities ( axialV , radialV ).

................................................................................................................ 160

Figure 6-2. Maps of instantaneous-average radial velocity (Vnegradial in left hand

side of figures and Vposradial in right hand side) and of axial velocity (Vaxial), through

three complete fluid oscillation cycles, when the fluid is oscillated in batch

mode at: (a) 4.1 s-1 and 1 mm, Reo = 117; (b) 5.1 s-1 and 1 mm, Reo = 203; (c)

10.1 s-1 and 1 mm, Reo =259; (d) 11.1 s-1 and 1 mm, Reo = 348; (e) 15.1 s-1

and 1 mm, Reo = 430; (f) 20.1 s-1 and 1 mm, Reo = 630............................ 162

Figure 6-3. Maps of standard deviation of radial velocities (σVx) and axial

velocities (σVy) as obtained from PIV velocity vector maps for three complete

fluid oscillation cycles, when the fluid is oscillated in batch mode at: (a) 4.1 s-1

and 1 mm, Reo = 117; (b) 5.1 s-1 and 1 mm, Reo = 203; (c) 10.1 s-1 and 1 mm,

Reo = 259; (d) 11.1 s-1 and 1 mm, Reo = 348; (e) 15.1 s-1 and 1 mm, Reo =

430; (f) 20.1 s-1 and 1 mm, Reo = 630. White dots represents the cycle-

average parameter Rs = σradial/σaxial, while the sloping-dashed line represents the

relationship σVx/σVy = Ld = 0.294. .......................................................... 164

Figure 6-4. Cycle-average velocity vector maps as seen in PIV measurements.

(a) Reo = 117, x0 = 1 mm, f = 4.1 s-1; (b) Reo = 203, x0 = 1.4 mm, f = 5.1 s-1;

xxii


(c) Reo = 259, x0 = 0.9 mm, f = 10.1 s-1; (d) Reo = 348, x0 = 1.1 mm, f = 11.1

s-1; (e) Reo = 430, x0 = 1.0 mm, f = 15.1 s-1; (f) Reo = 630, x0 = 1.1 mm, f =

20.1 s-1; (g) Typical flow patterns in a stirred tank reactor (side and bottom

view) when a propeller and wall baffles are used (adapted from J. H. Rushton

and J. Y. Oldshue, Chem. Eng. Prog., 49, 161 (1953))..............................166

Figure 6-5. Effect of f on the best-fitting of G, PD and Ntis dispersion parameters

at a constant fluid oscillation, x0 = 1 mm. (a) Effect of f on RV, RS, PD and Ntis

(white dots in PD and Ntis curves represent interpolated data); (b) Correlation of

PD and G with the products fRV . Axial dispersion data is from Reis et al.

(2004), using a net flow rate of 1.94 ml min-1. ...........................................168

Figure 6-6. Illustration of the effect of RV on the RTD (net flow rate of 1.94 ml

min-1) at increasing f (from 0 to 15 s-1) and x0 = 1 mm. (a) Oscillatory velocity

(in mm s-1) at input; (b) Cycle-average axial and radial velocities within the

cavities of SPC tube; (c) Experimental F(θ)-diagram (comparison with ideal

plug-flow and stirred tank).........................................................................170

Figure 6-7. Effect of Reo on mixing coefficient km and comparison with the effect

of Reo on the product fRS . ....................................................................172

Figure 6-8. Correlation between the product s fRS and the products fRV .

................................................................................................................173

Figure 6-9. Cycle-average axialV and radialV as a function of Reo at different

combinations of f and x0, i.e. Reo. (a) cycle-average axial velocities, axialV . (b)

cycle-average radial velocities, radialV . ( ) experiments at constant x0 of ∼1

mm, i.e. run a); ( ) experiments at different combinations of f and x0, i.e. run

b). Vertical dashed line represents the critical Reo where break of flow

symmetry was detected. ...........................................................................174

Figure 7-1. Typical flow patterns within a SPC-tube’s geometry (Reis et al.,

2005).......................................................................................................183

Figure 7-2. Geometry of a 350-mm-long SPC tube (SPC1 – micro-bioreactor)

and a 75 mm length tube (SPC2); details of SPC geometry. All distances are

in mm. .....................................................................................................184

xxiii


Figure 7-3. a) Illustration of the variation of the dynamic O2 method used in this

work; b) experimental time profiles of O2 dissolved saturation level using the

proposed modification of the dynamic method ( SPC1 tube, x0 = 1 mm and f =

3 to 20 s-1)................................................................................................ 186

Figure 7-4. Experimental setup used in kLa studies. ................................... 187

Figure 7-5. Effect of OFM on the mean value of O2 saturation levels at the

outlet of SPC2 tube: a) effect of f; b) effect of x0......................................... 192

Figure 7-6. Estimated kLa values for the SPC2 tube. a) 3-D representation of

the effect of f and x0; b) plot of kLa regimes................................................ 193

Figure 7-7. Comparison of kLa values obtained with SPC2 tube and with the

work of Oliveira and Ni (2004) using a conventional 50 mm internal diameter

OFR for similar fluid oscillation conditions. ................................................ 194

Figure 7-8. Effect of f on εG when operating the SPC1 tube under OFM and a) a

continuous fluid net flow (v = 1.58 ml min-1) or b) in batch mode (i.e. v = 0 ml

min-1)........................................................................................................ 196

Figure 7-9. Comparison of experimental kLa values with estimated ones, using:

a) the semi-empirical correlation shown in Eq. (7.12); b) the coarse correlation

presented in Eq. (7.8). The solid line represents y = x. .............................. 199

Figure 7-10. Correlation between the experimental kLa values and of the best-

fitted backmixing parameter (G) of liquid phase (from Reis et al., 2004) for f in

regime II (7.5 to 15 s-1). ............................................................................ 200

Figure 7-11. Variation of kLa with εG at different f. Dotted lines represents the

general tendency. 0 to 7.5 s-1: regime I; 7.5 to 15 s-1: regime II; 15 to 20 s-1:

regime III. ................................................................................................ 201

Figure 7-12. Schematic representation of bubbles behaviour in the three

identified regimes, in the studied range of f............................................... 202

Figure 7-13. Ten frames sequence showing the bubble breakage phenomenon

under OFM at 12 s-1 and 4 mm................................................................. 203

Figure 8-1. (A) Experimental setup used in batch fermentations of S.

cerevisiae: A- rotary motor; B- piston pump; C- gas inlet; D- gas outlet; E- fluid

heating inlet; F- fluid heating outlet; G- SPC tube; H- purging port; I- sampling

xxiv


port. (B) Detail of SPC (Smooth Periodic Constricted) tube geometry, which

composes the novel, designed oscillatory flow Micro-bioreactor. All dimensions

are in mm. ...............................................................................................214

Figure 8-2. Time course of glucose concentration (S), cell dry weight (X) and

ethanol concentration (P) in batch aerobic-growth on glucose of S. cerevisiae

(bioprocess IIIa and IIIb – see Table 8-1). Fermentations in the 5-L stirred tank

bioreactor (A), with an aeration rate of 1.1 vvm and in the micro-bioreactor (B)

with an aeration rate of 0.064 vvm............................................................219

Figure 8-3. Time profiles of cell dry weight, X (log scale) in aerobic-batch

glucose-growth of S. cerevisiae (bioprocesses I to IV). Fermentations in the 5-L

stirred tank (ST) bioreactor (A) and in the micro-bioreactor (B) with an aeration

rate of 1.1 vvm for the 5-L ST and 0.064 vvm for the micro-bioreactor. (C)

Time profiles of dry cell weight in two replicates of S. cerevisiae growth in a

shake flask (SF) starting with a glucose concentration of 20 g/L (bioprocess

IVc – see also Table 8-1); yeast was cultivated at 27 ºC and agitated in an

orbital shaker at 150 rpm (these experiments correspond to the seed culture’s

growth).....................................................................................................220

Figure 8-4. Time profiles of residual glucose concentrations (S) in the aerobic

batch growth on glucose of S. cerevisiae in bioprocesses I to IV (see Table

8-1). Fermentations running in the 5-L stirred tank (ST) bioreactor (A) and in

the micro-bioreactor (B). ...........................................................................221

Figure 8-5. Specific growth rates (µ) for batch growth on glucose of S.

cerevisiae at 25 ºC and different initial glucose concentrations (S0) in the 5-L

stirred tank (ST) bioreactor and in the micro-bioreactor. The specific growth

rate presented for the SF was the averaged µ found for the seed culture

growth, incubated at 27 ºC and 150 rpm. .................................................222

Figure 8-6. Increase in dry cell weight, ∆X = X – X0, obtained until complete

depletion of glucose in the aerobic batch growth of S. cerevisiae on glucose in

bioprocesses I to IV, for initial glucose concentrations S0 of ∼ 5 - 20 g/L. ...223

Figure 8-7. Time course of anaerobic batch growth on glucose (expressed as a

relative function of the OD) of S. cerevisiae in the 2-L stirred tank (ST) reactor

and in the micro-bioreactor. Experiments were run in parallel and started with

glucose concentrations of 5 g/L (A), 10 g/L (B), 15 g/L (C) and 20 g/L (D).

xxv


No seed culture was prepared and fermentation temperature was controlled at

25 ºC. Note that OD was turned dimensionless with the OD peak obtained at

the end of growth phase (i.e. at the instant of glucose depletion, as indicated

from pH measurements). ......................................................................... 226

Figure 9-1. Experimental setup used in batch biotransformations. 1- rotary

motor; 2- piston pump; 3- air inlet; 4- air outlet; 5- fluid heating inlet; 6- fluid

heating outlet; 7- SPC tube; 8- purging port; 9- sampling port. ................... 236

Figure 9-2. Concentration of γ-decalactone experimentally obtained with a SPC

tube in the four biotransformations carried out in this study (details given in

................................................................................................................ 237

Figure 9-3. Evolution of the number of cells (n) of Y. lipolytica in suspension

within a SPC tube in the four biotransformations carried out in this study

(details given in ........................................................................................ 238

Figure 9-4. Evolution of the specific rate of production of γ-decalactone (υ) with

the oscillatory mixing intensity (i.e. oscillatory Reynolds number, Reo). ....... 239

Figure 10-1. Simplified kinetic scheme showing first-order refolding competing

with higher-order aggregation, where kr is the refolding rate constant and ka is

the aggregation rate constant (Hevehan and Clark 1997). ......................... 248

Figure 10-2. Relation between lysozyme concentrations (before dilution with

TFA) and slope of decrease on absorbance (450 nm) of a cell suspension

(0.15 g/l Micrococcus lysodeikticus)......................................................... 251

Figure 10-3. Refolding yield (Yref) of lysozyme in a batch, unstirred Falcon tube

(denatured-reduced lysozyme added with a sharp micropipette stroke and

solution briefly mixed); ● this work; □ results from Buswell & Middelberg

(2003). .................................................................................................... 253

Figure 10-4. Refolding yield (Yref) of lysozyme in a batch, small stirred beaker

(refolding initiated with a sharp addition of denatured lysozyme); two parallel

experiments are shown. Vertical bars represent standard deviation of Yref.. . 254

Figure 10-5. Refolding yield (Yref) of lysozyme in a batch, small stirred beaker;

refolding initialled through a slow addition (30 s) of denatured-reduced

lysozyme solution (average injection rate = 0.7 ml/min)............................ 255

xxvi


Figure 10-6. Refolding yield (Yref) of lysozyme in a batch, 350-mm-long SPC

tube at a constant x0 = 3 mm and varying f and injection procedures. ○ f = 10

Hz, sharp injection; ♦ f = 10 Hz, injection time = 4 min; □ f = 3 Hz, injection

time = 2 min; ▲ f = 10 Hz, injection time = 2 min and OFM stopped at t = 4

min. .........................................................................................................256

Figure 10-7. Comparison of refolding yields (Yref) of lysozyme in the continuous

meso-reactor (along the residence time, t) with the values of Yref in batch

dilution refolding. ● batch refolding in a small stirred beaker, with injection

time >> 0 s; ○ continuous refolding in a meso-reactor at x0 = 1 mm, f = 1 mm,

Reo = 30; □ batch refolding in the SPC tube, x0 = 3 mm, f = 3 Hz, injection

time = 2 min. ...........................................................................................257

xxvii


List of general nomenclature

Symbol

A acceleration [L T-2]

Cd orifice discharge coefficient dimensionless

d tube diameter [L]

D’ characteristic dimension of effective width of obstacle [L]

d0 orifice diameter [L]

di reactor internal diameter [L]

f oscillation frequency [T-1]

fv frequency of vortex shedding [T-1]

H reactor or column height [L]

h half channel width [L]

hmax maximum channel width [L]

kLa oxygen mass transfer coefficient [T-1]

L baffles spacing [L]

N number of baffles per unit length [L-1]

P power input [M L2 T-2]

p pressure drop [M L-2]

Reo oscillatory Reynolds number dimensionless

Ren net-flow Reynolds number dimensionless

Reob obstacle Reynolds number dimensionless

Rep pulsating Reynolds number dimensionless

u mean superficial flow velocity [L T-1]

u∞ liquid free velocity [L T-1]

up pulsating velocity [L T-1]

upeak peak velocity at the maximum channel width [L T-1]

xxviii


v velocity [L T-1]

S object to image scale factor dimensionless

Sc Schimdt number dimensionless

Sh Sherwood number dimensionless

St Strouhal number dimensionless

Stf Strouhal number (by Sobey (1980)) dimensionless

t time [T]

V reactor volume [L3]

Vr fluid velocity at cylindrical coordinate r [L T-1]

vs superficial gas velocity [L T-1]

Vz fluid velocity at cylindrical coordinate z [L T-1]

Vθ fluid velocity at cylindrical coordinate θ [L T-1]

w angular velocity [T-1]

x displacement [L]

x0 oscillation amplitude (centre-to peak) [L]

Greek symbols

α free baffle area dimensionless

δ baffle thickness [L]

δ’ Stokes layer thickness [L]

µ viscosity [M L-1 T-1]

µ0 normal laminar viscosity [M L-1 T-1]

µt turbulent viscosity [M L-1 T-1]

ρ density [M L-3]

υ kinematic viscosity [L2 T]

xxix


List of tables

Table 2-1: Examples and applications of oscillating devices since the 1970’s. 9

Table 2-2: Summary of main USA patents related with oscillating systems. f

and x0 are the fluid oscillation frequency and amplitude, respectively ........... 10

Table 2-3: Experimental studies and applications of oscillatory flow reactor

(OFR) in the last 12-15 years. f and x0 are the fluid oscillation frequency and

amplitude, respectively ............................................................................... 13

Table 2-4: Summary of works concerning the fundamental study of OFM in

OFR’s......................................................................................................... 20

Table 2-5: Relevant studies concerning the research of OFM and the effect of

constrictions............................................................................................... 32

Table 2-6: Some of the applications of Biotechnology (Lee 1984) ................ 43

Table 2-7: Summary of the main features of reactor classes (Cabral et al.

2001) ........................................................................................................ 46

Table 4-1: Experimental Conditions ............................................................. 83

Table 4-2: Details of mesh used for 3-D numerical simulations presented in

Figure 2 ..................................................................................................... 85

Table 4-3: Minimum critical Reo observed for the screening reactor and

comparison with some reported values for the conventional OFR................. 93

Table 4-4: Comparison of cycle average axial, radial (and tangential) velocities

measured by PIV with the results from numerical simulations, using 2-D

axisymmetric and 3-D laminar models; Reo = 348, f = 11.1 Hz, x0 = 1.1. Ren =

0.............................................................................................................. 102

Table 4-5:. Comparison of measured mixing intensity by PIV with the results

from numerical simulations, using 2-D axisymmetric and 3-D laminar models;

Reo = 348, f = 11.1 Hz, x0 = 1.1 mm......................................................... 102

Table 5-1: Conversion between mean flow rate (v), superficial liquid velocity

(uLS), net flow Reynolds number (Ren) and hydraulic residence time (τ) for the

meso-reactor experiments. ....................................................................... 125

xxx


Table 5-2: Correspondence between superficial liquid velocity (uLS), liquid flow

rate (v) and net flow Reynolds number (Ren). All values are based on d = 4.4

mm. .........................................................................................................127

Table 5-3: Equations for cross-correlation of dimensionless axial dispersion

number (PD) with the backmixing (G), the number of tanks-in-series (Ntis) and

the volume-fraction of PFR (Vp/V), using the values of parameter estimated

through various techniques. ......................................................................138

Table 5-4: Summary of maximum and minimum values of dimensionless

parameters INtis, IPD, IG and IVp/V obtained with the introduction of OFM, towards

the ideal convective or dispersive systems, respectively. * Ideal flow case for

convective flow is a PFR; ideal case for dispersive flow is a CSTR...............139

Table 5-5: Predicted conversions and respective deviations from conversion in

a PFR, calculated from the experimental RTD (Eθ-diagrams) in the 350-mm-

long SPC tube, for a continuous, homogeneous, isothermal first-order reaction,

at v = 1.94 ml/min...................................................................................141

Table 6-1: Experimental conditions (f, x0 and Reo) used for PIV measurement of

flow patterns in SPC tube (from Reis et al. 2005). Experiments run (a) are

those performed at a constant value of x0 ≈ 1 mm, while experimental run (b)

comprises experiments performed at further values of x0. upeak is the maximum

theoretical axial velocity of the fluid (i.e. equal to 2 π f x0), while theo,axialV is

the (theoretical) cycle-average axial velocity throughout a complete oscillation

cycle (i.e. ( ) ftfcosxfVf/

f/theo,axial 21 2 2

45

430∫= ππ ); these values will be

used for comparison of experimental data in Figure 6-2 and Figure 6-9......157

Table 7-1: Dimensions and constants used in the experiments with tubes

SPC1 and SPC2 .......................................................................................184

Table 7-2: Comparison of performance of the SPC geometry for O2 mass

transfer in a gas-liquid system with further reported works in literature.......198

Table 8-1: Averaged yields of biomass on substrate (YX/S) and specific substrate

uptake rate (qs = µ/YX/S) during the exponential phase of the aerobic growth of

S. cerevisiae in bioprocesses I to IV and in three different small-scale vessels:

5-L stirred tank (ST) bioreactor, micro-bioreactor and shake flask (SF). S0 is the

xxxi


initial glucose concentration, as measured after inoculation with 10 % v/v of

seed culture ............................................................................................. 224

Table 9-1: Fluid oscillation conditions used in the four biotransformations

carried out in this work, at different combinations of fluid oscillation frequency

(f) and amplitude (x0) (expressed as centre-to-peak); Reo is the oscillatory

Reynolds number and is a measure of mixing intensity.............................. 235

1

Chapter 1 Introduction


Biotechnology is a multidisciplinary field having its roots in the

biological, chemical and engineering sciences (Figure 1-1) leading to a

host of specialities, e.g. molecular genetics, microbial physiology,

biochemical engineering (Moo-Young et al. 1985).

Figure 1-1. Multidisciplinary nature of biotechnology (Moo-Young et al.

1985).

2

N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications

About 160 biopharmaceuticals have recently gained medical approval and several hundred are in the

pipeline (Walsh 2005). Biopharmaceuticals (recombinant therapeutic proteins, monoclonal antibody-based

products used for in vivo medical purposes and nucleic acid-based medicinal products) actually represent

approximately one in every four genuinely new pharmaceuticals (Walsh 2003). But the successful

commercialization of novel processes/products developed by pure scientists requires the development of

large-scale processes which are both technologically viable and economically efficient.

Biochemical engineering is focused in conducting biological processes to the industrial scale. The role of

the biochemical engineers has become more important in recent years due to the dramatic developments

of biotechnology (Lee 1992). They actually play an important function on the commercialisation of

biotechnology, linking the biological sciences with the chemical engineering design. Nowadays, the

challenge for the biochemical engineer is enhanced. To carry out a bioprocess at large scale the engineer

needs:

a) to work together with biological scientists;

b) to obtain the best biological catalyst (microorganism, animal cell, plant cell, or enzyme) for a

desired process;

c) to create the best possible environment for the catalyst, by designing the bioreactor and

operating it in the most efficient way;

d) to separate the desired products from the reaction mixture in the most economical way.

The biological reactor (bioreactor) is of such importance in biological processes as the heart on a live

body. A bioreactor can be understood as “a vessel where a biological reaction or change takes place,

usually a fermentation or biotransformation, including tank bioreactors, immobilised cell bioreactors,

hollow fibre and membrane bioreactors and digesters” (Bains 1998). The design of biological reactors is

an integral part of biotechnology. Especially when designing bioreactors, integration of biological and

engineering principles is essential (Cabral and Tramper 1993).

Proteomics research as a result of the human genome project demanded many recombinant constructs

with potentially beneficial therapeutic products to be designed and needing to be tested for efficacy of

expression (Betts et al. 2005). This calls for the performance of a vast number of development

fermentations. In order to speed up this process, the use of controlled high-performance parallel (scale-

down) reactor systems is required.

3


The preceding tasks involve process design and development including the bioprocessing at a small-scale,

which is familiar to chemical engineers for the chemical processes. Techniques which have been applied

successfully in chemical processes can be used in bioprocesses with small modifications (Lee 1992).

Biochemical conversions with the aid of biological catalysts differ from purely chemical processes in a few

numbers of ways (Atkinson 1974). In both cases, the best possible environment must be created by

designing efficient reactors.

A wide range of bioreactor classes may be identified, attending to their design, power source and number

of phases (Crueger 1987). One particular design has gained increasing interest in the last decade: the

oscillatory flow reactor (OFR). It is basically a column provided with periodic sharp constrictions (baffles)

and operating under oscillatory flow mixing (OFM). The formation and dissipation of eddies has proved to

result into significant enhancement in processes such as heat transfer (Mackley and Stonestreet 1995;

Mackley et al. 1990), mass transfer (Hewgill et al. 1993; Ni et al. 1995a; Ni et al. 1995c), particle mixing

and separation (Mackley et al. 1993), liquid-liquid reaction (Ni and Mackley 1993), polymerization (Ni et

al. 1998b; Ni et al. 1999), flocculation (Gao et al. 1998) and crystallization. Research has been further

extended to include: flow patterns (Brunold et al. 1989; Mackley and Ni 1991; Mackley and Ni 1993),

local velocity profiles and shear rate distribution (Ni et al. 1995b), residence time distribution (Dickens et

al. 1989; Mackley and Ni 1991; Mackley and Ni 1993; Ni 1994), dispersion (Howes 1988; Howes and

Mackley 1990), velocity profiles (Liu et al. 1995) and scale-up correlations (Ni and Gao 1996).

Unlike conventional tubular reactors, where a minimum Reynolds number must be maintained, mixing in

an OFR is independent of the net flow, allowing long residence times to be achieved in a reactor of greatly

reduced length-to-diameter ratio. For example, OFR is able to achieve a required product specification in a

saponification process with a residence time one- eigth th that of a full-scale batch reactor (Harvey et al.

2001). In this case, OFR comes in line with the process intensification that is redirecting the reactor

engineering (Harvey et al. 2003; Mackley 2003). Overall, the Oscillatory Flow Mixing (OFM) is presented

as a “technology ready to deliver” (Harvey and Stonestreet 2001).

The aim of this thesis is to present and characterise innovative configurations of oscillatory flow reactors

for biotechnological applications. Key areas of interest are the scale-down of OFRs for fast upstream

development of biotechnological processes, from a single (liquid) phase to a multi- (four) phase (gas-liquid-

liquid-solid) system. Such novel designs may be very useful in some stages of bioprocesses development,

4


while selecting and optimising biotransformation media and operational conditions, as well in bioprocess

design.

The text is organised in eleven chapters. Chapters of experimental results (chapters 4 to 10) are provided

with a specific introduction and a specific list of references. Chapter 2 presents an overview of reactor

designs based on OFM, and more particularly the research on OFR, introducing some concepts and

dimensionless groups very important in designing oscillatory reactors. The different designs and

applications of OFRs in several previous studies are deeply reviewed. A state-of-the-art of reaction

engineering tools is presented as well as a review of the main applications in biotechnology and of the

main topics of bioprocess design. Conventional bioreactor designs are classified and examined and the

main issues in reactor’s scale-down are explained.

A novel tube geometry is introduced in Chapte 3 (Materials and Methods Section) and two scale-down

reactor configurations (micro-bioreactor and meso-reactor) developed during the running time of this thesis

are presented. In Chapter 4 the fluid mechanics generated within this particular tube geometry are

investigated and consequently used in the validation of numerical simulations carried out with CFD’s

technique. Chapter 5 assesses the deviation of both micro-bioreactor and meso-reactor from the ideal flow

cases of ideal plug flow reactor and completely back-mixed reactor, while the steady flow was matched

with numerically predicted flow backmixing using CFDs. Also, batch mixing in the micro-bioreactor is

considered, thus mixing times results are presented. Chapter 6 shows a statistical correlation of deviations

from ideal mixing/flow (summarised in Chapter 5) with the flow patterns observed in the tube geometry

from (Chapter 4). The aeration capacity in the small-scale geometry was studied in Chapter 7. Chapters 8

to 10 are dedicated to the biotechnological applications of both micro-bioreactor and meso-reactor. In

particular, chapters 8 and 9 test the two scale-down platforms with two workhorse microorganisms:

Saccharomyces cerevisiae and Yarrowia lipolytica¸respectively, while Chapter 10 assesses the dilution

refolding of lysozyme. Overall conclusions and suggestions of future work are presented in Chapter 11.

Chapter 2 Literature review

5


The application of external energy in the pulsing form (oscillatory flow

mixing - OFM) has, for a long time, been a common practice to improve

reaction performance, namely mass transfer rates in chemical

engineering units. The general principles associated with the pulsing

column were established by Van Dijck (1935), at the Royal Dutch/Shell

Laboratory in Amsterdam, in the 1930’s (Figure 2-1). Since then, a

number of techniques, based on several principles, have been

developed and adapted for their applications to very different fields

(Lema et al. 2001).

6


Figure 2-1. Technical drawing of a Van Dijck’s US Patent (1935).

2.1 Types and applications of oscillating devices

2.1.1 Types of oscillating devices

In general, oscillating equipment may be classified in two main types (Lema et al. 2001):

a) Alternating motion of some intrinsic elements of the column. It is worth mentioning the

reciprocating plate columns (Figure 2-2A and 3B), in which the pulsation is generated by


7

means of an upwards-downwards motion of plates (e.g. Baird and Rao (1988); Skala and

Veljkovic,(1988b)) and the columns with oscillating piston (Figure 2-2C and 3D), where a plug

is coupled to the bottom of the column (Harrison and Mackley 1992).

b) Oscillation is generated by the hydraulic transmission of a perturbation to the liquid contained

in the column. This perturbation is typically generated by e.g. systems using positive

displacement pumps (plug or membrane) to introduce the feed into the column (Mak et al.

1992) and the pneumatic oscillating systems. In the latter example, the oscillation is

generated by means of a pressurized gas which propels the liquid contained in a parallel

branch to the column (Murthy et al. 1987). The self-propelled oscillators are based on a

different concept. In this case, fluid oscillating is the result of the liquid entering the columns,

through a pulsation chamber. Once the pressure in the chamber is high enough, the

membrane covering the feeding tube injects the liquid into the column; this membrane then

closes the inlet tube again, which creates a cyclical feed system. In contrast with the previous

pulsators, in this system, the motion of the liquid in the column is always generated in the

upward direction (Baltar 1972).

Figure 2-2. Examples of oscillating vessels: reciprocating plates - (A) and (B) – and oscillating piston – (C)

and (D). (A) from Prochazka and Rod (1974). (B) from Ni (2002)(2002). (C) from Prochazka and Rod

(1974), (D) from Hounslow and Ni (2004). See references for numbering details.

8


2.1.2 Industrial applications of oscillating reactors

The oscillating reactors were firstly used in separation processes in order to enhance the contact between

the phases and, consequently, to improve mass transfer rates. Since then, they have been applied to a

number of systems, either chemical or biochemical, under several configurations. Table 2-1 summarises

some applications of oscillating vessels since the 1970’s.

In the last three decades, the number of publications resulting from the study of OFM has increased

several times, as seen in Figure 2-3, which demonstrates that this is a technology creating an increasing

interest in the scientific community. Several patents are currently protecting novel oscillating devices’

designs and/or their commercial applications.

Table 2-2 summarises the main registered patents in the US Patents Office. Very different types of

oscillator systems were coupled to several types of unit operations and processes (Table 2-1 and Table

2-2).

0

10

20

30

40

50

60

70

80

90

1970 1975 1980 1985 1990 1995 2000 2005

Publication year

# of

pub

licat

ions

Figure 2-3. Number of publications out coming from a global search in ISI Web of Knowledge

(http://portal.isiknowledge.com/portal.cgi) using keywords “oscillatory flow”. All citation databases,

document types and languages were considered in the search.


9

Table 2-1: Examples and applications of oscillating devices since the 1970’s

Oscillating reactor Designation Application Reference

Plug oscillator Column of perforated plates

Production of SCP Serieys et al. (1978)

Pneumatic oscillator

Packed-bed column with perforated plates

Anaerobic treatment of waste-water

Brauer and Sucker (1978)


Packed-bed column with perforated plates

Alcoholic fermentation Navarro and Goma (1980)

Membrane oscillator

Column of perforated plates

L-L extraction Golding and Lee (1981)

Alternating motion pumps

Packed-bed column S-L extraction Goebel and Fortuin (1986)

Reciprocating plates column

Column of perforate plates

Absorption Skala and Veljkovic (1988a)

Oscillating pump Ultrafiltration unit Clarification of juices Finnigan and Howell (1989)

Membrane oscillator

Anaerobic Filter Anaerobic treatment of wastewater

Etzold and Stadlbauer (1990)

Oscillating piston Batch bioreactor Production of biodegradable plastic

Harrison and Mackley, (1992)

Pulsative Pumping System

High-Efficiency Membrane Oxygenator

Animal trials Bellhouse et al.(1973)

Oscillatory Flow Reactor

Continuous OFR Process intensification of biodiesel production

Harvey et al. (2003)


Continuous OFR Continuous production of sterols in an ester saponification reaction.

Harvey et al. (2001)


Oscillatory Baffled Batch Crystallizer (OBBC)

Crystallization of Paracetamol Chew et al. (2004a)


Pulsed Baffled Tubular Photochemical Reactor

Treatment of wastewater (photocatalytic oxidation)

Fabiyi and Skelton (1999; 2000a; 2000b), Gao et al. (2003)


Novel pilot scale gas–liquid reciprocating plate column

Counter-current gas–liquid contacting

Gomaa and Al Taweel (2005)

Oscillating piston Novel oscillatory flow reactor

Protein refolding Lee et al. (2002; 2001)

Pulsed reactor Pulsed reactor Pulse combustion: dehydration, decomposition reactions, oxidation

Begand et al. (1998)

Reciprocating plate Reciprocating plate agitator

Fluid mixing Masiuk (1999)

Oscillating pistons (moving against two pump bags)

Vortex wave membrane bioreactor

Aeration of high density mammalian cell culture

Millward et al. (1996)

Oscillating piston Oscillatory baffled reactor (OBR)

Polymer production Ni et al. (2002c)


Pressure swing operated reactor

Bioconversion in a solid immobilised system

Lee and Fan (1999)

10

Table 2-2: Summary of main USA patents related with oscillating systems. f and x0 are the fluid oscillation frequency and amplitude, respectively

Oscillating equipment Reactor designation Settings Patented application Reference

Reciprocating plates column Column of perforated plates f, x0, N and L are determined in each particular case Improve efficiency of (immiscible) liquid-liquid washing or extraction process

Van Dijck (1935)

Reciprocating plates column or oscillating piston

Large-diameter vibrating or/and pulsating column

Different kinds of perforated trays L defined as proportional to di Vibratory trays or fluid pulsating

Apparatus for bringing fluid phases (including gases) into mutual contact

Prochazka and Rod (1974)

Reciprocating piston or diaphragm

Tubular continuously flow reactor

Oscillating motion is superimposed on the linear (laminar) flow of reactants in order to maintain turbulent flow throughout; f and volume liquid displaced adjusted to particular reaction situation; peak instantaneous Reynolds number > 3,000 ; reciprocating piston or diaphragm

Method to prevent the solids deposits in the walls of tubular reactors by pulsed flow

Soubrada and Galvez (1981)

Compressed air Pulsed fluidised bed f is determined by the rotation speed of a disc valve; 1-50 Hz is effective; 1-15 Hz seems adequate; 8-10 is optimum in several cases

Processing materials in a batchwise or continuous fluidised bed, such as a drier; improvement is higher for particulate solids of a non-uniform size

Kudra et al. (1999)

Piston oscillator Continuous oscillatory baffled reactor;

Tubular tube, may be operated vertically or horizontally, temperature controlled d = 0.1 – 5 m, L = 1.8d, f = 0-10 Hz, x0 = 0-20 mm, α = 21 % Further possible dimensions: L = 1.2 – 2.0d (preferably 1.5d); α = 10 – 40 % (preferably 21 %), d = 0.1 – 5 m

Continuous phase-separated synthesis of particulates; Continuous polymerization

Ni (2002)(2002)

Reciprocating plates column Batch oscillatory baffled reactor (Premixer reactor)

Vertical tube, f and x0 adjustable Premixer of monomer with an initiator Ni (2002)(2002)

11


2.2 The Oscillatory Flow Reactor (OFR)

In the late 1980s, research aiming at generating unsteadiness in a laminar flow showed that when a

periodically reversing flow exists in a tube fitted with orifice-type baffles mounted transverse to the flow and

equally spaced, vortex rings are formed downstream of the baffles. On each flow reversal the vortices are

swept into the central region of the tube and the cycle of vortex formation, growth and ejection results in a

state of ‘chaotic’, advected mixing in each inter-baffle cavity (Brunold et al. 1989; Dickens et al. 1989).

This marked the birth of the oscillatory flow reactor (OFR).

The application of periodic fluid oscillations to a cylindrical column containing evenly spaced orifice baffles

is the basic concept of OFR. A schematic representation of an OFR is shown in Figure 2-4. The OFR can

be operated batchwise or continuously in horizontal or vertical tubes. The liquid or multiphase fluid is

typically oscillated in the axial direction by means of diaphragms, bellows or pistons, at one or both ends

of the tube (Ni et al. 2002a). The sharp edged baffles are fixed and distributed along the tube at a regular

spacing (L). Another system for generation of flow oscillations is also common and has already been

described (reciprocating plates column), which works by moving a set of baffles up and down from the top

of the tube.

The mixing within an OFR is an efficient mechanism, where fluid moves from the walls to the centre of the

tube. The intensity of this movement is affected by the oscillation frequency, f, and amplitude, x0. The flow

becomes progressively more complex as the oscillation frequencies and amplitudes increase. These

results are consistent for a 25-mm (Brunold et al. 1989; Dickens et al. 1989) and also for a 50-mm

internal diameter tube (Ni et al. 1995c), indicating that the fluid mechanical conditions in an OFR can be

linearly scaled up, as demonstrated later on by Ni et al. (1996).

12


Figure 2-4. Schematic representation of cross section in an OFR. di – reactor internal diameter, L – baffles

spacing, d0 – orifice diameter, δ - baffle thickness.

Table 2-3 summarises the most frequent OFR design and operational settings used in past experimental

research works.

One particularly advantageous application area of OFR is for performing 'long' (usually over 10 minutes)

reactions in configurations which are substantially more compact than batch reactors, and which have

substantially smaller length to diameter ratios than conventional tubular reactors. A novel methodology for

design of continuous OFRs is based on mixing, as presented by Stonestreet and Harvey (2002)(2002).

13

Table 2-3: Experimental studies and applications of oscillatory flow reactor (OFR) in the last 12-15 years. f and x0 are the fluid oscillation frequency and amplitude,

respectively

Oscillation equipment Reactor designation Settings Characterisation/application Reference(s) Oscillating piston Baffled tubes di = 12 cm, length: 2 x 1,0 m, 55 orifice baffles

f = 3-14 Hz, x0 = 1-6 mm Operated Horizontally

Heat transfer measurements for pulsative flow Operation fluid: lubrificating oil at 60 ºC

Mackley et al. (1990)

Oscillating piston (2 x external)

Baffled tube di = 12 cm, length = 1.0 m f = 0-10 Hz, x0= 1-7 mm Operated horizontally

Heat transfer and associated energy dissipation measurements for oscillatory flow in baffled tubes, using mineral oil

Mackley and Stonestreet (1995); (same vessel as Baird and Stonestreet (1995))

Oscillating piston Pulsed baffled tube photochemical reactor (PBTPR)

di = 75.6 mm, length: 910 mm, L = 0.5d f =: 0-11 Hz, x0 = 0-4.5 mm UV lamp in its central axis

Photocatalysed mineralization of methylene blue in a continuous flow operation

Fabiyi and Skelton (1999; 2000a; 2000b)

Oscillating piston Pulsed baffled tube bundle di = 25 mm, length = 1.0 m, L = 1.5d f = 0.5-9 Hz Operated vertically

Experimental flow pattern and associated residence time distribution measurements

Ni (1994) Mackley and Ni (1993) – multitube arrangement

Oscillating piston Pulsed baffled bioreactor di = 50 mm, length: 500 mm, L = 1.5d f = 1-12 Hz, x0 = 0-14 mm

Mass transfer measurement in yeast culture Ni et al. (1995c)

Oscillating piston Baffled tube di = 25 mm, length = 1 m, L = 1.5d f = 0.5-9 Hz, x0 = 1-10 mm Operated horizontally

Fluid dispersion and concentration profile measurement

Ni (1995)

Oscillating piston Batch pulsed baffled bioreactor

di = 50 mm, length = 500 mm, L = 1.5d f = 0.5-9 Hz, x0 = 1-10 mm f = 1-12 Hz, x0 = 0-14 mm Operated vertically

Study of mass transfer of oxygen in yeast re-suspension and yeast culture

Ni et al. (1995a)

Oscillating piston Double-pass tube Section 1 : di = 38 mm, length = 2 m Section 2 : di = 43.5 mm, length = 2 m, L = 1.5d f = 0.5-10 Hz Operated horizontally

Measurement of velocity of single particles for steady and oscillatory flows in plain and baffled tubes

Liu et al. (1995)

Oscillating piston (2x) Pulsed baffled reactors

Reactor 1: di = 50 mm, length = 525 Reactor 2: di = 100 mm, length = 875 mm f = 1-10 Hz, x0 = 1-12 mm Operated vertically

Scale-up correlation for based on mass transfer measurements in two pulsed baffled reactors, with different diameters but in which the water level is maintained constant

Ni and Gao (1996)

14

Table 2-3: (Continued)

Oscillation equipment Reactor designation Settings Characterisation/application Reference(s) Oscillating piston Pulsed baffled reactor di = 50 mm, length: 800 mm

f = 1-10 Hz, x0 = 0-15 mm Operated vertically

Determination of degree of oil-water dispersion by two methods: sampling technique and the visualization method; surfactants effects on dispersion

Zhang et al., (1996)

Oscillating piston Pulsed baffled reactor di = 50 mm, height: 525 mm, L = 1.8D f = 1-10 Hz, x0 = 0-12 mm Operated vertically

Effect of surfactants on mass transfer of oxygen into water glycerol solutions; KLa measurements;

Ni et al (1997)

Oscillating piston Modified pulsed baffled reactor

di = 50 mm, H = 1 m, L = 35-100 mm f = 1-6 Hz, x0 = 5-25 mm Operated vertically

Experimental flow visualisation Gough et al, (1997)

Oscillating piston Batch oscillatory-baffled column

di = 50 mm, H = 750 mm f = 1-10 Hz, x0 = 1-15 mm Operated vertically

Droplet size and size distribution in methylmethacrylate suspension Correlation of particle size with droplet size in suspension polymerisation of methylmethacrylate

Ni et al. (1998b) Ni et al. (1999)

Oscillating piston Oscillatory-baffled column di = 50 mm, d = 950 mm f = 1-10 Hz, x0 = 1-15 mm Operated vertically

The effect of gap size between baffle outer diameter and tube inner diameter on the mixing characterists

Ni and Stevenson (1999)

Oscillating piston Oscillatory baffled column di = 50 mm, H = 500 mm, L = 75 mm f = 0.2-10 Hz Operated vertically

The measurement of stains rate using particle image velocimetry (PIV)

Ni et al (2000a)

Oscillating piston Novel continuous oscillatory baffled tube

di = 40 mm, total length = 25 m, L = 1.8d f = 1-4 Hz, x0 = 1-20 mm Operated vertically

Study of parameters affecting fluid dispersion. This new reactor consist on 14 glasses tubes vertically disposed and connected by a straight U-bends

Ni and Pereira (2000)

Oscillating piston Batch oscillatory baffled flocculator

di = 50 mm, H = 500 mm f = 0.2-10 Hz, x0 = 1-12 mm Operated vertically

Flocculation of bentonite and Alcaligenes eutrophus; the measurement of mean stains rates and their distribution using digital particle image velocimetry

Gao et al. (1998)

Oscillating piston Gassed oscillatory baffled column

di = 50 mm, H = 1.5 m, L =1.5d f = 1-5 Hz, x0 = 2-8 mm Operated vertically

Gas hold-up and bubbles diameters Oliveira and Ni (2001)

Oscillating piston Continuous oscillatory baffled reactor (COBR)

di = 40 mm, total length = 25 m f = 0-5 Hz, x0 = 0-60 mm Operated vertically

Droplet size distribution in the absence of surfactants and coalescence inhibitors

Pereira and Ni (2001)

15


Oscillation equipment Reactor designation Settings Characterisation/application Reference(s) Oscillating piston Oscillatory baffled reactor di = 50 mm, H = 1 m

Operated vertically Polymer product engineering: particles production with controlled size and morphology in batch and continuous mode

Ni et al. (2002c)

Oscillating piston Batch oscillatory baffled column

di = 50 mm, H = 950 mm, L = 1.5f f = 1-10 Hz, x0 = 1-15 mm Operated vertically

The effect of tracer density (tracer solution of potassium nitrite) on axial dispersion; comparison with both “Tank-in-series” and “Plug flow with axial dispersion” models; mechanical energy empirical correlations establishment

Ni et al. (2002b)

Oscillating piston Oscillatory baffled column di = 50 mm, H = 500 mm f = 0.2-10 Hz Operated vertically

Computation fluid dynamics (CFD) modelling of flow patterns; 3-D numerical simulation of oscillatory flow in a baffled column

Ni et al. (2002a)

Oscillating piston Baffled tube d = 26 mm, length = 1.08 m, L = 1.5d f = 0-8 Hz, x0 = 0-6 mm Operated vertically

Gas-liquid (air/water) mass transfer enhancement determination and visualisation using oscillatory flow in a baffled tube

Hewgill et al. (1993)

Oscillating piston Periodic baffled tube arrays in two different configurations: serial and parallel (multitube)

di = 26 mm, length = 5 x 1.0 m, L = 1.5d f = 0.5-9 Hz Operated vertically

Flow pattern and associated residence time distribution measurements

Mackley and Ni (1993)

Oscillating piston Novel oscillatory flow reactor

d = 2.4 cm, d0 = 1.2 cm, H = 28 cm, baffled w = 22 rad-1 and x0 = 3 mm, or w = 4.09 rad-1 and x0 = 1 mm

Refolding of denatured-reduced lysozyme Lee et al. (2002; 2001)

Oscillating piston (column 1) Reciprocating plates (columns 2 and 3)

Oscillatory baffled columns Column 1: d = 50 mm, H = 950 mm Column 2: d = 50 mm, H = 990 mm Column 3: d = 90 mm, H = 730 mm f = 1-10 Hz, x0 = 1-20 mm Operated vertically

Study of the effects of geometrical parameters on mixing time; the effect of tracer concentration, baffle spacing and free baffle area

Ni et al., (1998a)

Oscillating piston (electromagnetic)

Glass baffled tube di = 23 mm, length = 1 m, L = 1.5d w = 0-125 rad/s, x0 = 0-4 mm Operated vertically

Mixing and separation of particle suspension using oscillatory flow in baffled tubes

Mackley et al. (1993)

Oscillating piston (external)

Baffled tube di = 12 cm, length =1.0 m, 55 orifice baffles f = 3-14 Hz, x0 = 1-6 mm Operated horizontally

Determination of energy dissipation Operation fluid: light oil

Baird and Stonestreet (1995))

16


Oscillation equipment Reactor designation Settings Characterisation/application Reference(s) Oscillating piston Baffled tube d = 25 mm, length = 1.08 m, 28 cylindrical baffles

f = 0.5-9 Hz Operated vertically and horizontally

Observations on the dispersion of fluid; local profile measurements

Mackley and Ni (1991)

Reciprocating plates column (x2)

Reciprocating baffled-plate column

Column 1: di = 19.4 cm, H = 90 cm f = 0.6-3.0 Hz, x0 = 5-20 mm Column 2: di = 15.0 cm (nominal), H = 3.0 m f = 0.6-3.0 Hz, x0 = 1-10 mm

Power dissipation and flow patterns determined with water Different plates distance and configuration

Bair and Rao (Baird and Rao 1995)


Reciprocating baffled-plate column

di = 15.0 cm (nominal), H = 3.96 m f = 2.0-5.0 Hz, x0 = 1-10 mm

Time-average power dissipation rates and hold-up determination

Column description: Hafez and Baird (1978); Work: Baird et al (1996)


0.38 m diameter oscillatory baffled column

di = 0,38 m, H = 2 m, Only 2 baffles f= 0-1 Hz, x0 = 60-200 mm Operated vertically

Flow patterns and oil-water dispersion in a 0.38 m diameter OBC

Ni et al. (2000b)

Reciprocating plates columns

Novel self-aerating pilot scale oscillating baffle column

di = 19 cm, H = 0.9 m f = 0.25-2 Hz, x0 = 0-4.2 mm Operated vertically

Mass transfer measurements of self-aerating system for oxygenation of water

Mackley et al. (1998) Same vessel as Baird and Rao (1995)

Oscillating piston (electromagnetic)

Oscillatory Baffled Batch Crystallizer (OBBC)

di = 30 cm, L = 1.5d mm, d0 = 15 mm, δ = 2 mm f = 1-20 Hz, x0 = 1-4 mm Operated vertically

Crystallization of paracetamol Chew and Ristic, Chew et al (2005; 2004a)

Oscillating piston Oscillatory baffled column di = 50 mm, H = 1.5 m, d0 = 28 mm, δ = 3 mm, L = 1.5d f = 0.2-10 Hz, x0 = 1-10 mm Operated vertically

Oxygen mass transfer rates Oliveira and Ni (2004)

Oscillating piston Pulsed sieve plate column (PSPC)

di = 39.6 mm, Length = 800 mm, L = 25, 50 or 100 mm, α = 22.3 % f = 0-4.5 Hz, x0 = 5-25 mm

Analysis of axial dispersion in an oscillatory-flow continuous reactor

Palma and Giudici (2003)

Oscillating pistons U-tube di = 24 mm, d0 = 12 mm, L = 1.5d 2 vertical-interconnected operated tubes

Heat transfer performance Stephens and Mackley (2002) Same geometry as Mackley and Stonestreet (1995)

17


Oscillation equipment Reactor designation Settings Characterisation/application Reference(s) Oscillating piston Pulsed baflled tubular

photochemical reactor (PBTPR),

di = 75 mm, Total length: 1,500 mm, L = 70 mm, δ = 122 mm, ratio of L/di = 1.41 high f and x0 UV lamp in its central axis

Photooxidation of a model pollutant (salicylic acid) Gao et al.(2003)

Oscillating piston Oscillatory baffled column di = 50 cm, H = 0.5 m f = 0.5-10 Hz, x0 = 2-6 mm Operated vertically

Effect of fluid viscosity on mixing in an OFR Fitch et al. (2005)

18


2.3 The Oscillatory Flow Mixing (OFM)

The mechanism of oscillatory flow mixing (OFM) can be understood with the help of Figure 2-5. The

essential feature is that sharp edges are presented perpendicular to a periodic and fully reversing flow.

The flow patterns of OFM exhibit a complicated eddy mixing pattern due to the presence of wall baffles.

Two half cycles can be identified, each containing flow acceleration and deceleration, corresponding to a

sinusoidal velocity-time function. On each acceleration, vortex rings are formed downstream of the baffles.

A peak velocity is reached and then as the flow decelerates, the vortices are swept into the bulk, and

consequently unravel with bulk flow acceleration in the opposite (axial) direction. It is the radial velocities,

arising from the repeating cycles of vortex formation, and of similar magnitude of the axial ones, which

create a uniform mixing in each inter-baffle zone and cumulatively along the length of the column (Brunold

et al. 1989; Mackley and Ni 1991; Mackley and Ni 1993).

Figure 2-5. Mechanism of oscillatory flow mixing (OFM) in an OFR, according to Fitch et al. (2005). (A)

Start of Up Stroke. (B) Maximum velocity in Up stroke, i.e. flow reversal. (C) Start of Down stroke. (D)

Maximum velocity in Down stroke.

The study of OFM within OFRs has steadily grown in the last decade. The areas of research now include

several aspects related to OFR characterisation and applications. In recent years, the science of the OFR

19


has increasingly been applied to various industrial processes, such as suspension polymerisation,

crystallisation, paint dispersion, flocculation and fermentation. Several scientific articles and industrial

applications demonstrate that OFR is an exciting type of reactor and can be a process technology with

major commercial applications (Ni and Gough 1997). Table 2-4 compiles the fundamental studies on of

OFR’s in the last decade. Several science aspects will be discussed with more detail in the forthcoming

sections.

The concepts and key developments of OFM enhancement through pulsation and oscillation are reviewed

by Ni et al. (2003). This configuration can generate high heat and mass transfer rates in both batch and

continuous modes of operation, whereas potential applications may include pipes, mixers, (bio)reactors,

filtration units and crystallizers (Mackley 1991).

2.3.1 Parameters governing the OFM

The dynamical nature of OFM may be presently characterised by a few fundamental dimensionless

groups, namely: the classical Reynolds number, Ren, the oscillatory Reynolds number, Reo, and the

Strouhal number, St. In addition, two dimensionless geometrical parameters contribute to describe the

fluid mechanics within OFRs: the interbaffle spacing defined as L/di, and the baffle free area, α, defined

as: d0/di (Ni and Pereira 2000). A brief definition of each dimensionless group is presented below.

a) Net-flow Reynolds number, Ren

In flow in pipes the Reynolds number, Ren, is the dimensionless number used as the indicator of the type

of flow in question and captures all the parameters shown in Figure 2-6.

Figure 2-6. The net flow in a plain tube.

20


Table 2-4: Summary of works concerning the fundamental study of OFM in OFR’s

Science aspect of OFR Reference(s)

Axial Dispersion / RTD’s Howes (1988), Howes and Mackley (1990), Ni et al. (2002b), Palma and Giudici (2003), Takriff and Masyithah (2002), Dickens et al. (1989), Mackley and Ni (1991; 1993), Ni (1994)

Bioprocessing Ni et al. (1995a), Lee et al. (2002; 2001), Fabiyi and Skelton (1999; 2000a; 2000b), Gao et al. (2003; 1998), Lee et al. (2002; 2001)

Chemical reaction Ni and Mackley (1993)

Crystallisation Chew et al. (2004a), Chew and Ristic (2005) Dispersion Mackley and Ni (1991; 1993), Ni (1995), Ni , (2000)Ni et al.

(2002a),(2000) Ni and Pereira (Ni and Pereira 2000); Palma and Giudici (2003), Fitch and Ni (2003), Ni and Stevenson (1999), Ni et al., (1998a)

Fluid mechanics Baird and Rao (1995), Ni (1994), Liu et al.(1995), Fitch et al. (2005), Gao et al. (2003), Mackley and Ni (1991; 1993), Ni et al. (1995b; 2000b), Gough et al.(1997), Brunold et al. (1989), Ni et al. (2002a), Chew et al.(2004b), Mackley et al. (1996), Gao et al. (1998)

Gas-liquid systems Oliveira and Ni (2001), Oliveira et al. (2003a; 2003b), Hewgill et al. (1993), Baird et al. (1996), Mackley et al.(1998)

Heat transfer Mackley et al. (1990); Mackley and Stonestreet (1995), Stephens and Mackley (2002)

Liquid-liquid systems Hounslow and Ni (2004), Ni et al (1998b); Ni et al. (1999); Ni et al. (2002c); Ni et al. (2002b); Harvey et al. (2003), Zhang and Ni (1996), Pereira and Ni (2001)

Mass transfer Hegwill et al. (1993); Ni et al. (1995a); Ni and Gao (1996) (Ni et al. 1995a), Oliveira and Ni (2004), Lau et al. (2004)

Numerical simulations Howes (1988), Howes et al.(1991), Jian and Ni (2003), Roberts and Mackley (1996), Mackay et al. (1991); Chew et al.(2004b); Ni et al (2002a)

Particle suspension Mackley et al.(1993); Liu et al. (1995)

Power input Mackley and Stonestreet (1995), Baird and Stonestreet (1995), Baird and Rao (1995), Baird et al. (1996)

Scale-up Ni and Gao (1996), Ni (2001)

Fluid viscosity Fitch et al. (2005)

The Reynolds number is defined as follows

υduRen = (2.1)

21


where d is the tube diameter, υ the kinematic viscosity of fluid and u the mean superficial flow velocity.

b) The oscillatory Reynolds number, Reo

When an oscillatory motion is superimposed onto the net flow (Figure 2-7) an additional dimensionless

group is often needed to characterise such a motion, in conjunction with the above defined Ren.

Figure 2-7. Oscillatory motion superimposed onto a net flow.

The characterisation of such a pure oscillatory flow (POF) can be backdated in the 1940’s (e.g. Binnie

(1945)). Since then, oscillatory flow was studied by several tube arrangements (see Ni and Gough (1997),

for references). In all the published works, the characterisation of POF was achieved by using a

dimensionless group called the pulsating Reynolds number, Rep, defined as:

υdu

Re pp

= (2.2)

where up is the pulsating velocity. In most cases, up was taken as the product of x0w, Rep describes the

oscillatory motion applied to the system, and Ren (as defined in Eq. (1)) gives a measure of the state of

flow in question. However, other authors used different definitions for up. Sarpkaya (1966), for example,

defined it as the amplitude of the periodic component of the cross-sectional mean velocity

(= tubepiston A/Axf 0π ), where Apiston and Apipe are the cross-sectional areas of the piston and tube,

respectively. No reason was given why ‘πf’’ was used instead of ‘2πf’. Sinada and Karim (1984a; 1984b),

used a different approach: they replaced up by u and d by the Stokes layer thickness defined

as )w/(' υδ 2= in Eq. (2.2), when working with a special application, using a fixed stroke length.

The situation is more complex when an oscillatory motion is imposed into a net flow in the presence of

baffles (Figure 2-8).

22


Figure 2-8. The oscillatory (baffled) flow.

Following previous studies, Brunold et al. (1989) defined the first of the two dimensionless groups

controlling the fluid mechanics of OFR: the oscillatory Reynolds number, Reo:

υdxw

Reo 0= (2.3)

Rep and Reo for both POF and OFR are basically identical. However, they describe different states of flow

since, at certain oscillatory conditions, the fluid mechanics in Figure 2-7 will predominately be axial, while

in Figure 2-8 will be complex and chaotic with similar magnitudes for both axial and radial velocity

components.

Since the oscillator normally operates sinusoidally, the variations in time of displacement, x, velocity, v,

and acceleration, a, take the forms of (Ni and Gough 1997; Ni et al. 2002a):

( )twsinxx 0= (2.4)

( )twcoswxv 0= (2.5)

( )twsinwxa 20−= (2.6)

where w is the angular piston velocity and x0 is the oscillation amplitude, measured as centre-to-peak. The

maximum velocity during the oscillation cycle is ‘x0w’, as seen in Eq. (5) when ‘cos (w t) = 1’. An example

is given in Figure 2-9.

23


-6.E-03

-4.E-03

-2.E-03

0.E+00

2.E-03

4.E-03

6.E-03

0 1 2 3 4 5 6 7 8 9 10

TIME [s]

x [m

], v

[m/s

], a

[m/s

2 ]

x0va

Figure 2-9. Exemplification of sinusoidal movement of a piston (displacement, x, velocity, v, and

acceleration, a) for w = 0.62 rad/s (i.e., 0.1 Hz), and x0 = 5 mm.

From extensive studies, there is now a solid understanding of the mixing nature in an OFR. At low Reos of

100-300, it exhibits plug flow characteristics: the vortices are axisymmetrically generated within each

baffled cavity (plug flow mode). When Reo increases further, the symmetry is broken and flow becomes

intensely mixed and chaotic; flow achieves the mixing mode, as defined by Ni et al. (1999; 2002b).

c) The Strouhal number, St

The description of POF develops further when tube inserts or varying tube shapes are incorporated. Sobey

(1980) introduced another dimensionless number, apart from Rep, when working in a flow through a

furrowed channel to account for the additional parameters involved. This was named the Strouhal number,

Stf:

peakf u

hfSt = (2.7)

24


where h is the half channel width and upeak the peak velocity at the maximum channel width, hmax. The

physical meaning of such dimensionless group was just given in Sobey’s later work of flow past an

indentation in a channel (Sobey 1985) as the ratio of the channel length scale to the scale of the fluid

particle displacement. Since then the characterisation of various structures in oscillatory flows has

followed a similar line (e.g., Nishimura et al. (1985)).

At the end of the 1980’s, Brunold et al. (1989) followed Sobey’s examples and definitions and reported

the second dimensionless group to define the fluid mechanics in OFR’s, referring to it as the Strouhal

number St: it represents a measure of the effective eddy propagation and is defined as the ratio of column

diameter to the stroke length:

0 4 xdSt

π= (2.8)

This re-definition of St is actually the most used. In a simplified form, St represents the ratio of orifice

diameter to oscillation amplitude (Ni and Gough 1997).

2.3.2 The effect of geometrical parameters

The recent advances in the OFR research have suggested the introduction of a term that involves either

the orifice diameter (d0) or the baffle spacing (L), since they play an important role in OFR and do not

participate either in Reo or St numbers. For example, L influences the shape of eddies while d0 controls the

width of the vortices within each baffled cavity, either of which affects the onset of fluid mixing within OFR

(Ni and Gough 1997).

In the presence of sharp edges, Knott and Mackley (1980) and Brunold et al. (1989) have reported that

eddies’ interaction is optimal for a baffle spacing of 60 % in a tube with a di of 25 mm. Since these two

studies, the effect of the geometrical parameters in OFRs was intensively explored, mainly weighted by the

residence time and liquid-liquid dispersion characteristics, in single horizontal (e.g. Dickens et al. 1989),

vertical (e.g. Mackley and Ni 1993) or an array of tubes (e.g. Pereira and Ni 2001). Reproducible and

consistent results have shown that the introduction of OFM, coupled with periodically spaced baffles,

greatly enhances fluid mixing even at laminar flow conditions. Each baffle cavity acts as a continuously

stirred tank, in which the radial velocity components are comparable to the axial ones. The events at the

25


walls are similar to the events at the centre. This resulted, for example, in a six-fold increase in the mass

transfer of oxygen into water was reported for oscillatory flow in a baffled tube with an air-water system (Ni

et al. 1995a).

a) The effect of free baffle area, α

Several studies considered the effect of α = d02/d2 on mixing time or axial dispersion. Ni et al. (1998b)

studied the effect of α (11 to 51 %), δ (1 to 48 mm) and L (d to 2.5d). The lowest value of α tested (11 %)

exhibited the best mixing and, consequently, required shorter mixing times, presumably due to a higher

power input, or by the increased mixing efficiency due to the higher dispersion rate. Gough et al. (1997)

found L = 0.57d, α = 0.63 as the optimized sizes to achieve efficient mixing of a polymerization

suspension (for d = 50 mm, L = 0.7 – 3.3d and d0 = 0.51 – 0.69d tested). For the smallest orifice

diameter tested (with a corresponding α = 0.26) small symmetrical eddies were formed at the sharp

edges of the baffles and the vortex rings did not encompass the entire column cross-section, nor the

complete length of the entire-baffle region. Thus stagnant regions between eddies were identified. For a

higher α = 0.32, eddies extended to the reactor walls covering a greater area of the section. Vortex rings

were still symmetrical along the centre line (axisymmetric) and displaying small interaction. At α = 0.40,

the axisymmetry was lost and the intense interaction between eddies led to the disappearance of the

stagnant regions within the baffled cavity, inducing characteristics of plug flow when in continuous

operation. For the maximum α tested (0.47), a high degree of channelling through the baffle orifice was

observed and the formation of eddies was destroyed by the predominant axial movement, thus low mixing

could take place (Gough et al. 1997).

Research on liquid-liquid dispersions by Zhang et al. (1996) was consistent with Gough’s observations. A

minimum value of α tested (0.19) showed to be the most appropriated value for dispersion of liquid-liquid

solutions, leading to the use of the lowest minimum f (on average) to achieve the complete dispersion.

The rate of increase of the degree of oil-water dispersion with the oscillatory component of velocity is

greater for lower values of d0 than for higher ones defined before, as reported by Ni et al. (2000b).

b) The effect of baffles spacing, L

Several authors have suggested that different values of L may result into different flow behaviours. The

baffle spacing is a key design parameter in an OFR as it influences the shape and length of eddies within

26


each baffle cavity, for a given x0 (e.g. Brunold et al. 1989; Knott and Mackley 1980). However, it is usually

not included in the dimensionless groups in OFRs. Some authors are of the opinion that it should

participate in the equations characterising the mixing in OFRs. Since in the usual flow regimes both L and

d0 are close enough to pipe diameter (L = 1 - 2d; (1-α2)/α2 = 26 - 35 %), both parameters are not

independent in the dimensionless groups (Ni and Gough 1997). Mackley et al. (1993) used a new

dimensionless group called the Stroke ratio, intending to classify the flow in terms of the relation between

x0 and L.

The optimal L should ensure a full expansion of vortex rings generated behind baffles so that vortices will

spread effectively throughout the entire inter-baffle zone. At a small value of L, the generation of vortices is

strongly suppressed. This effectively restrains the growth of the vortices and reduces the required radial

motion within each baffled cell. Conversely, if the baffles are spaced too far apart, the opposite effect

occurs. The vortices formed behind baffles cannot effectively cover the entire inter-baffle regions. In this

case, it is most likely that stagnant plugs will be created, into which the vortices disperse and diminish.

This demonstrates that vortex rings generation is not independent of L. Brunold et al. (1989) reported the

optimal L as 1.5d for flow visualisation studies. However, L = 1.8d was suggested by Ni and Gao (1996) in

their mass transfer studies. Ni et al. (1998a) reported that the maximum L/d ratio tested (2 to 2.5) is the

one minimizing the mixing time. But L seems to have little effect on oil-water dispersions, as the relation

L/d is linearly scaled-up, as repeated by Ni et al. (2000b).

c) The effect of baffle thickness, δ

The generation of vortices in each baffle of an OFR is similar to that of vortices formed in a fluid flowing

around an object. Each eddy needs an edge to cling on for and has an optimal time of processing of

shedding (Ni and Gough 1997). As there should be an optimal δ, Ni et al. (1998b) also investigated the

effect of δ on the mixing time, for top and bottom injection locations. Six values of δ (between 1 and 48

mm, for a d of 50 mm) were tested. Mixing time decreased with the increase of f or x0. Overall, the results

suggest that the thinner baffles (i.e. low δ) favoured the generation of vortices. If vortices attach to baffle

edges for too long prior to shedding, their shape can distort somewhat, thereby affecting mixing time. The

higher values of δ resulted in higher mixing times, in the order of five-fold greater than those of the

thinnest baffles.

27


2.3.3 Effect of f and x0 in the flow patterns

The oscillation frequency (f) and amplitude (x0) are the most important operational parameters in OFR. At a

given L and d0 changing the combination of f and x0 allows control of the generation of eddies and

produces a range of fluid mechanical conditions as broad as required, as reported by e.g. Gough et al

(1997) from their work in application of polymerisation suspensions. Research reported by Zhang et al.

(1996) on oil-water dispersions demonstrated that both x0 and f have a significant effect on the minimum

frequency for complete dispersion in liquid-liquid extraction processes; a 50 % reduction occurred when x0

was increased from 6 to 12 mm. Ni et al. (1998b) found that the mixing time decreased as f or x0

increased (valid for L = 1 - 2.5d)

A similar work of Ni et al. (2000b) on oil-water dispersions in a scaled-up OFR (di = 380 mm) illustrated

that f and x0 affect the nature of mixing much more than design parameters, such as d0 and L. The degree

of dispersion increased linearly with the oscillatory velocity until a complete dispersion is achieved. The

oscillatory f and x0 were also found to affect the mass transfer measurements (for wall baffles) in a yeast

cell suspension (Ni et al. 1995c). The oxygen mass transfer coefficient, kLa, increased with the increasing

of f (from 3 to 12 Hz) for all the tested values of x0 (4 to 14 mm), in a 25 mm internal diameter OFR.

Changes in x0 affected kLa more than changes in the f, meaning that x0 controls the length of eddy

generated in the column.

For some applications an optimum f or x0 may be identified. For example, Dickens et al. (1989) identified

x0 = 1 mm as the minimum value for full axial dispersion in a pulsed packed bed.

2.3.4 Power input

There are essentially two models for estimation of the power consumption in an OFR: i) the quasi-steady

flow model (Jealous and Johnson 1955), and ii) the eddy acoustic model (Baird and Stonestreet 1995).

The quasi-steady flow model was originally derived for packed columns and subsequently used by Baird

and Garstang (1967) for pulsed columns. This method is based on a quasi-steady assumption to calculate

the pressure drop and power density for oscillating flow. By applying Bernoulli’s equation between two

planes adjacent to a baffle, the pressure drop across the orifice plate can be obtained and an

instantaneous power density can be calculated (Hewgill et al. 1993; Ni and Mackley 1993). By integrating

28


this over a cycle and allowing for a number of orifice plates, it gives a time-averaged power density defined

as (Ni et al., 1998b):

3202

2

2 1

3 2 wxCN

VP

D αα

πρ −= (2.10)

where N is the number of baffles per unit length, ρ is the density of fluid and CD is the orifice discharge

coefficient (usually equal to 0.7). For small values of α the term (1-α2)/α2 increases, and Eq. (2.10)

predicts high mixing intensity and a reduced mixing time. This suggests the existence of a threshold in the

uniformity of mixing in OFRs. The decrease in α would have a similar effect on mixing time as the product

‘f x0’ is increased (Ni et al., 1998b). But the power input for this model is valid for high x0 and low f, i.e. 5 -

30 mm and 0.5 - 2 Hz (Baird and Stonestreet 1995). The eddy acoustic model (Baird and Stonestreet

1995) is based on acoustic principles and uses the concept of eddy viscosity with reasonable accuracy.

The power input for this model appears to be justified for conditions of low x0 and high f, i.e. 1 - 5 mm, 3 -

14 Hz, where the quasi-steady model was shown to be inappropriate for predicting the power dissipation

of oscillatory flow (Baird and Stonestreet 1995). The eddy acoustic model relates the frictional resistance

to the acoustic resistance of a single orifice in a thin plate and assumes that the eddy kinematic viscosity

is a function of f and of a mixing length corresponding to the average distance travelled by turbulent

eddies. Through several experiments, the mixing length was shown to be equal to the orifice diameter d0.

2.3.5 Numerical simulation

Although technological applications of oscillatory flow to pursuit enhancements in unit operations have

been reported since the early 1930s (Van Dick 1935), numerical simulations for oscillatory flow in baffled

geometry were not cited until 1980. Sobey was perhaps the first one reporting his extensive 2-D numerical

studies (Sobey 1980; 1983) followed by Ralph (1986), while in different situations. These studies revealed

that the vortex mixing mechanism was the key factor responsible for high mixing efficiency of the system.

Based on those works, Howes (1988) developed a numerical code for studying dispersion of unsteady flow

in baffled tubes. Following on, Roberts (1992) extended Howes’ work to 2-D baffled channel flows. A solver

based on finite difference axisymmetrical, time dependent Navier-Strokes equation plus a stream function

and vorticity was used. Even with some assumptions such as a flow spatial periodicity, with flow in each

29


cell being identical and the formation of axisymmetric vortices (Howes 1988),(Mackay et al. 1991);

Roberts (1992), these models were successfully applied to many fields, namely to predict the onset of

chaotic motions, and they evaluated concentration gradients by incorporating transport such as heat and

mass transfer (Roberts and Mackley 1995) and provided fluid particle motion simulations (Neves-Saraiva

1998) up to a critical Reo.

The first numerical study taking d0 into consideration into POF was done by Jones and Bajura (1991), by

carrying out a numerical analysis on a pulsating laminar flow through a pipe orifice while considering two

Reynolds numbers: the numerical Reynolds number, Ren, and the orifice Reynolds number, Reop.

The flow characteristics of a POF are dominated by the axial velocity components, but thanks to the

contribution of numerical studies, there is nowadays a good understanding of the nature of OFM. It is

known that at low values of Reo of 100 - 300, the OFR exhibits plug flow characteristics, where the vortices

are axisymmetrically generated within each baffled cavity (referred to as the plug flow mode). On the other

hand, for high values of Reo, the symmetry condition is no more valid and flow becomes intensely mixed

and chaotic (referred to as the mixing mode). Depending upon the column geometry and the viscosity of

the fluid, these critical values of the oscillatory Reynolds number may vary. When the Reo number

increases beyond such critical values, the generation of vortices is no longer axisymmetrical, as show in

Figure 2-10.

Figure 2-10. Particle flow pattern in a batch OFR. Tracer = pollen particles of 25 µm in diameter, bulk fluid

= water, f = 2.5 Hz, x0 = 6mm, d = 50 mm, L = 1.5d, α = 36 %, δ = 3 mm (from Ni et al. 2002a).

30


Recently, Chew et al. (2004b) used the Computational Fluid Dynamics (CFD) technique to model spatial

and temporal behaviour of flow patterns in an OFR (L = 48 mm, di = 30 mm, d0 = 15 mm). Large eddy

simulation (LES) was found suitable for simulations of OFM at two combinations of f and x0, respectively:

10 Hz – 3 mm and 10 Hz – 5 mm. The volume-averaged shear rate was found to be of one order of

magnitude larger than that of an impeller-driven stirred tank and a marked distinction between the

temporal shear rate distributions was observed. The modelling also showed that particles in an OFR spend

most of their residence time in high shear regions.

The effect of fluid viscosity on OFM was qualitatively assed by numerical simulations (further validated with

experimental measurements) by Fitch et al. (2005). A ratio of the plane-averaged axial over the radial

velocity was defined to quantify such viscosity effects. For the given geometry the velocity ratio approached

to 2 very quickly at increased the values of Reo, regardless of Newtonian and non-Newtonian fluids. An

empirical critical value of velocity ratio equal to 3.5 was identified, below which the system mixed

sufficiently.

Jian and Ni (2003) tested the modelling of turbulence with the traditional Reynolds Averaged Navier-Stokes

(RANS) model. Results are sufficiently good for simulating flows in stirred tank reactors but the RANS

turbulence models showed a poor prediction of turbulence in periodic flows in an OFR as the methodology

of averaging in time in RANS has effectively removed the turbulence. As in OFR eddies of various sizes are

the main ingredient for mixing, the large-eddy simulation (LES) is particularly suitable for such type of

flows (Jian and Ni 2003).

Outside the OFR field, Komoda et al. (2001) carried out CFD simulations in a reciprocating disk cylindrical

vessel. Simulations were experimental validated (with laser Doppler anemometry velocity measurements)

and represented well the flow patterns and the force acting on the disk during the oscillation cycle.

2.4 Further studies regarding oscillatory flow mixing

In complement to many studies regarding the industrial application of OFM, many further studies were

carried out in relation to the science of OFM and the effect of tube constrictions. A survey is presented in

Table 2-5. Apart from the works in OFR, fundamental studies (fluid mechanics and numerical simulations)

govern the major part of publications of OFM (e.g. Bolzon et al. 2003). Several authors also seek the

31


understanding of control of mixing/dispersion (e.g. Crittenden et al. 2005) or the science behind the

enhancement of mass/heat transfer rates (Nishimura et al. 2000). More recently (since 2004), oscillatory

flow was scaled-down to microfluidics applications (e.g. Morris and Forster 2004).

2.5 Tools in reactor engineering

Reactor engineering activity is related to the engineering of (chemical or biochemical) transformations.

Such transformations can occur only if the reactant molecules are brought into short contact (mixed)

under the appropriate environment (temperature and concentration fields, catalysts/biocatalysts) for and

adequate time. The process vessel (reactor) must provide the necessary conditions to favour the desired

reaction and allow for removal of products. To describe a reactor’s behaviour it is necessary to

characterise it in terms of flow patterns and mixing, eventually for the different phases in presence.

Recently, CFD tools appear to make a substantial contribution in establishing the best way to carry out a

desired transformation, as on accelerating the reactor engineering tasks (Ranade 2002).

2.5.1 Measuring techniques

The description and design of multiphase (gas–liquid, gas–liquid–solid and gas-liquid-liquid-solid) reactors

still relies to a large extent on empirical rules and correlations, which in turn are based on measurements

made under conditions as relevant as possible to industrial practice. This is true for the classical chemical

engineering approach, where such quantities as liquid hold-up (fraction) or pressure drop are predicted via

empirical correlations based on data as numerous and precise as possible. Nevertheless, more modern

approaches appeared in the last years to help in the design of multiphase reactors, such as CFD. Even in

this case, the physical models used require information on local and transient flow characteristics (e.g.

turbulence characteristics, wake coefficients, etc.), since ab initio calculations are up to now impossible.

Reliable measuring techniques are therefore needed for the rational description and the design of

multiphase reactors. Different types of measurements are required depending on the aim of the analysis.

Measurement techniques can be classified according to different criteria. A first classification distinguishes

between ‘time-averaged’ and ‘transient’ measurements and between ‘local’ and ‘global’ measurements.

32


Table 2-5: Relevant studies concerning the research of OFM and the effect of constrictions

Main research subject Study description Reference

Bioprocesses Oscillatory flow in a cone-and-plate bioreactor Chung et al. (Chung et al. 2005)

Bioprocesses Differential responses of the Nrf2-Keap1 system to laminar and oscillatory shear stresses in endothelial cells

Hosoya et al.(Hosoya et al. 2005)

Bioprocesses Tissue factor activity is upregulated in human endothelial cells exposed to oscillatory shear stress

Mazzolai et al.(2002)

Bioprocesses An harmonic analysis of arterial blood pressure and flow pulses

Voltairas et al.(2005)

Dispersion/simulations Simulation of concentration dispersion in unsteady deflected flows

Hwu et al. (1997)

Dispersion Oscillatory flow and axial dispersion in packed beds of spheres

Crittenden et al.(2005)

Dispersion Effect of turbulence on Taylor dispersion for oscillatory flows

Ye and Zhang (2002)

Dispersion/diffusion Augmented longitudinal diffusion in grooved tubes for oscillatory flow

Ye and Shimizu (2001)

Fluid mechanics Linear stability analysis of flow in a periodically grooved channel

Adachi and Uehara (2003)

Fluid mechanics Birth of three-dimensionality in a pulsed jet through a circular orifice

Bolzon et al.(2003)

Fluid mechanics Asymmetric Flows and Instabilities in Symmetric Ducts with Sudden Expansions

Cherdron et al (1978)

Fluid mechanics Characterisation of impeller driven and OFM Chew et al.(2004b) Fluid mechanics Bifurcation phenomena in incompressible

sudden expansion flows Drikakis (1997)

Fluid mechanics Nonlinear Flow Phenomena in a Symmetric Sudden Expansion

Fearn et al. (1990)

Fluid mechanics Characteristics of laminar flow induced by reciprocating disk in cylindrical vessel

Komoda et al.(2001)

Fluid mechanics Instability in three-dimensional, unsteady, stenotic flows

Mallinger and Drikakis (2002)

Fluid mechanics 3-D analysis of the unidirectional oscillatory flow around a circular cylinder

Nehari et al. (2004)

Fluid mechanics Three-dimensionality of grooved channel flows at intermediate Reynolds numbers

Nishimura and Kunitsugu (2001)

Fluid mechanics Flow around a short horizontal bottom cylinder under steady and OFM

Testik et al.(2005)

Heat transfer Cooling of micro spots by OFM Chou et al. (2004) Heat transfer Convective heat transfer enhancement in a

grooved channel using cylindrical eddy promoters

Herman and Kang (2001)

Heat transfer Effect of oscillating interface on heat transfer Chen et al. (1997)

33



Main research subject Study description Reference

Heat transfer Local heat transfer in the presence of a single baffle within a channel

Chen and Chen (1998)

Heat transfer The effects of gas-liquid interfacial movement on heat transfer using oscillations

Chen et al. (1997)

Heat transfer Effect of the distance between a single baffle and the solid wall on the local heat transfer in a rectangular channel due to an oscillatory flow

Chen and Chen (1998)

Mass transfer Enhancement of liquid phase adsorption column performance by means of oscillatory flow

Lau et al. (2004)

Mass transfer Oscillatory flow of droplets in straight capillary tubes

Graham and Higdon (2000a)

Mass transfer Oscillatory flow of droplets constricted in capillary tubes

Graham and Higdon (2000b)

Mass transfer A comparison between the enhanced mass transfer in boundary and pressure driven oscillatory flow

Thomas and Narayanan (2002a)

Mass transfer Influence of x0 and f on mass transfer enhancement of grooved channels

Nishimura et al. (2000)

Microfluidics DNA molecules in microfluidic oscillatory flow Chen et al. (2005) Microfluidics Oscillatory flow in microchannels Morris and Forster

(2004) Microfluidics Numerical simulation of micromixing by

pulsative micropump Kim et al (2003)

Mixing Mixing performance by reciprocating disk in cylindrical vessel

Komoda et al. (2000)

Mixing/dispersion Interstage backmixing in oscillatory flow in a baffled column

Takriff and Masyithah (2002)

Mixing/Microfluidics Chaotic mixing in cross-channel micromixers Tabeling et al. (2004) Mixing/Numerical simulations Simulation of mixing in unsteady flow trough a

periodically square obstructed channel Howes and Shardlow (1997)

Particle suspension Influence of wall proximity on the lift and drag of a particle in an oscillatory flow

Fischer et al. (Fischer et al. 2005)

Rheology Vibrational flow of non-Newtonian fluids Deshpande and Barigou (2001)

Rheology Viscous dissipation of a power law fluid in an oscillatory pipe flow

Herrera-Velarde et al. (2001)

Suspension Response of concentrated suspensions under large x0 oscillatory shear flow

Narumi et al. (2005)

Suspension The use of pulsative flow to separate species Thomas and Narayanan (2002b)

Suspension/fluidisation Using pulsed flow to overcome defluidization Wang and Rhodes (2005)

34


Since the classification between local and global measurements is not always possible other classification

has been preferred by Boyer (2002), relying on the physical basis of the measurement, thus distinguishing

between ‘invasive’ and ‘non-invasive’ measuring techniques as follows:

a) Non-invasive techniques

(a) Global techniques

i. Time-averaged pressure drop

ii. Measurement and analysis of signal fluctuations

iii. Dynamic gas disengagement technique (DGD)

iv. Tracing techniques

1. Tracing of the liquid

2. Tracing of the gas-phase

3. Tracing of the solid (coloured tracers, magnetic tracers, fluorescent

tracers)

v. Conductimetry

vi. Radiation attenuation techniques

1. X-ray, γ-ray or neutron absorption radiography

2. Light attenuation

3. Ultrasound techniques

(b) Techniques yielding local characteristics

i. Visualisation techniques

1. Photographic techniques

2. Radiographic techniques

3. Particle image velocimetry

4. NMR imaging

ii. Laser Doppler anemometry and derived techniques

iii. Polarographic technique

iv. Radioactive tracking of particles

v. Tomographic techniques

1. Tomography by photon attenuation measurement

2. Electrical tomographic system

3. Ultrasonic tomography

b) Invasive techniques

35


(a) The so-called ‘needle probes’ (optical probes, resistive or conductive probes, or

‘impedance probes’)

(b) Heat transfer probes

(c) Ultrasound probes

vi. Ultra-sound transmittance technique (UTT)

vii. Pulse echo technique

(d) Pitot tubes.

A detailed analysis of time and space resolution as well some examples of the use of measuring

techniques with industrial constraints in the petrochemical and refinery industry is also presented by Boyer

(2002).

2.5.2 Flow visualisation by Particle Image Velocimetry

The Particle Image Velocimetry (PIV) has become quite classical for the determination of velocity fields

essentially in single-phase flow (e.g. Boyer et al. 2002). While large-scale turbulence structures have been

recognised historically by fluid dynamicists as significant phenomena, most of today’s fluid dynamics

measurements are made with point-based techniques. The PIV system, on the other hand, provides

practical quantitative whole-field turbulence information and thus has the potential to give a new

perspective on flow phenomena. The PIV measurement process usually involves (dantecdynamics 2002):

a) Seeding the flow: seed particles are suspended in the fluid to trace the motion and give a

visible reflection for the cameras.

b) Flow field illumination: when a thin slice of the flow field is illuminated by a light-sheet (of laser

light), the illuminated seeding scatters the light. This is detected by a camera placed at right

angles to the light-sheet. The light-sheet is pulsed (switched on and off very quickly) twice at a

known intervals (∆t) (Figure 2-11A).

c) Image acquisition: the first pulse of the laser freezes images of the initial positions of the

seeding particles (at time t) onto the first frame of the camera. The camera frame is advanced

and the second frame of the camera is exposed to the light scattered by the particles from the

second pulse of laser light (at time t + ∆t). There are thus two camera images, the first

showing the initial positions of the seeding particles and the second their final positions after

an interval of time equal to ∆t due to the movement of the flow field (Figure 2-11B).

36


d) Vector processing: the two camera frames are then processed to find the velocity vector map

of the flow field. This involves dividing the camera frames into small areas called interrogation

regions. In each interrogation region, the displacement of groups of particles between frame 1

and frame 2 (∆x) is measured using correlation techniques. The velocity vector, v, of this area

in the flow field is then calculated using the equation

txSv

∆∆= (2.11)

where S is the object to image scale factor between the camera’s CCD chip and the measurement

area (Figure 2-11C).

This is repeated for each interrogation region to build up the complete (2-D) velocity vector map.

Figure 2-11. Overview of PIV technique. (A) Schematic representation of the flow field illumination in a PIV

system. (B) PIV interrogation analysis. (C) Evaluation of the image density. Only build up of 2-D velocity

vector maps is exemplified (adapted from dantecdynamics 2002).

37


The PIV technique has been successfully used in the study of fluid mechanics within an OFR. The first

study of this kind was performed by Ni et al. (1995b), thus demonstrating that it is possible to directly

measure velocity vector fields and strain-rate distributions in an OFR using time-resolved PIV. It also

allowed finding a correlation between the strain rate and the power dissipation generated within OFRs, as

seen in Ni et al. (2000a). More recently, Fitch et al (2005) used the PIV technique to validate CFD

simulations and concerning the effect of fluid viscosity on mixing in a OFR: Gao et al. (2003) used PIV

measurements to assist in obtaining the design optimum oscillatory flow conditions for catalyst dispersion

(in photochemical oxidation of organic compounds) whilst avoiding the possible side effects of strong

scattering or reduction of quantum yield. The PIV studies showed that uniform mixing can be readily

achieved at low Reo (i.e. at Reo above 2,000).

2.5.3 Assessment of the non-ideal flow

The most extensively used concept in reactor engineering is that of an ‘ideal’ reactor. The simplest

reactor, whose performance is governed by the so-called ‘zero dimensional’ equation, is the ‘completely

mixed reactor’. The key assumption is that mixing in the reactor is complete, so that the properties of the

reaction mixture are uniform in all parts of the reactor and are, therefore, the same as those of the ‘exit’

stream. The other ideal reactor concept, known as ‘plug flow reactor’ is based on a ‘one dimensional’

approximation of the material and energy balance equations. In an ideal plug flow reactor, unidirectional

flow through the reactor is assumed (similar to the flow through a pipe) (Ranade 2002).

It is of extreme importance to evaluate the consequences of the assumptions involved in the concepts of

ideal reactors to estimate the behaviour of an actual reactor, as the mixing may deviate significantly from

the ideal flow cases. This deviation can be caused e.g. by channelling of fluid, by recycling of fluid or by

the formation of stagnant regions within the reactor (e.g. Levenspiel 1972). The mixing of a phase may be

experimentally characterised by tracing techniques (e.g. Boyer et al. 2002).

The residence time distribution (RTD) is an important concept used for analysis of reaction engineering

with idealised models. RTD, as the name suggests, indicates the spread of residence time experienced by

different fluid elements while flowing through the reactor. The response data or measurements of the

variation of reactor outlet concentration of a substance for the known change of inlet concentration of that

substance can be used to estimate the RTD of a given reactor. The completely segregated (assuming no

38


mixing between fluid elements of different ages) and completely mixed fluid elements constitute the two

limiting solutions. Obtaining the RTD of an actual reactor and applying these two limiting assumptions to

obtain the bounds of the performance of the reactor is a practical method for reaction engineering analysis

(Ranade 2002). RTD affects heat transfer rates, interphase mass transfer rates and the conversion and

selectivity of chemical and biochemical reactions (Briens et al. 1995).

Several sophisticated techniques and data analysis methodologies have been developed to measure the

RTD of reactors. Measuring the RTD of a tracer dissolved in the liquid phase is a well-known technique to

evaluate the mixing of the liquid phase. This technique is easy to apply but may present some pitfalls, as

demonstrated by (Briens et al. 1995). Main tracer types are (Boyer et al. 2002):

a) Tracer dissolved in the liquid phase, e.g.:

(a) Salt tracer

(b) Coloured tracers

(c) Radioactive isotope tracer

b) Particle tracking technique, i.e. neutrally buoyant solid particles followed by electromagnetic

means.

Various different types of models have been developed to interpret RTD data (tracer concentration versus

time) and to use it further to predict the influence of non-ideal behaviour on reactor performances. Most of

these models use ideal reactors as building blocks. In simple case, a two-parameter model (the mean

residence time and the axial dispersion coefficient) may be sufficient to yield an adequate description of

the global flow behaviour of a reactor: In more complex cases, models with more parameters have to be

used (Levenspiel 1972). A flow model representing the actual flow patterns and mixing within a reactor is

necessary for the realistic description of reactor behaviour (Ranade 2002).

Another important issue in RTD studies is the physical boundaries of the reactor in study: closed or open

type. When the flow patterns are disturbed across a boundary (e.g., a measurement point), such boundary

is classified as being open. If flow patterns are not disturbed along the boundary it is classified as closed.

Most academic and practically all industrial tracer studies are conducted with open boundary conditions,

using the "imperfect pulse method". A pulse of tracer is injected upstream of the reactor and the resulting

tracer concentration peaks are detected at two different locations in the reactor. Then, the residence time

distribution between these locations is obtained by deconvolution (Briens et al. 1995).

39


Care must be taken when measuring the RTDs in reactors with one or two open boundaries. In such cases

tracer measurements do not provide RTD but a ‘transient response function’ from which RTD may only be

obtained if separate experiments provide more information (Nauman and Buffham 1983). The

measurement of the tracer concentration can be performed by three different techniques (Briens et al.

1995):

a) mixing-cup

b) local concentration (e.g. as the measured by an effective fibre-optical probe or a conductivity

probe)

c) through-the-wall, along a diameter of a cross-section (e.g. conductivity meters or scintillation

systems).

Only the mixing-cup concentration provides the true RTD (Nauman and Buffham 1983).

A second important concept in reactor engineering analysis, mainly for batch operating vessels, is the

‘mixing time’. This is briefly the time required to reach a specified degree of uniformity the system being

then said to be ‘mixed’. Practical mixing times can be measured by a variety of experimental tracing

techniques, similarly to those applied to obtain RTDs (Harnby 1992):

a) acid/base/indicator reactions

b) electrical conductivity variations

c) temperature variations

d) refractive index variations

e) light-absorption techniques.

In each case it is necessary to specify the manner of tracer addition, the position and number of recording

points, the sample volume of the detection system, and the criterion for deciding the cut-off point of the

end of the experiment (Harnby 1992). An example of how to determine the mixing times in a process

vessel used in biopharmaceutical manufacturing is presented by Ram et al. (2000). In such case an acid

reaction was monitored by pH probes.

Studies in OFRs have shown that OFM coupled to a net flow (of the correct magnitude) gives high fluid

mixing and narrow residence time distribution (e.g. Dickens et al. 1989; Howes and Mackley 1990;

Mackley and Ni 1991; Mackley and Ni 1993). The baffle edges promote the formation of eddies, which

increase the radial mixing in the tube (Ni and Pereira 2000).

40


2.5.4 Computational flow modelling

Computational Fluid Dynamics (CFD) is an engineering-numerical tool which has gained large popularity

during the last years. As opposed to the semi-empirical models (e.g. those use for modelling of RTDs),

CFD aims at solving the (complete or simplified) fundamental physical equations that describe a flow

phenomenon. The most general form of these equations has been given by Navier and Stokes more than

150 years ago, therefore the set of equations that has been applied are named Navier-Stokes equations.

These equations encompass mass, momentum and energy balances; they have to be adapted to the

specific problem under consideration by additional closure laws. Also the subsidiary sets of reaction

equations can be used in case of having reacting species.

While CFD has been very popular among car manufacturers and in the air and space industry, chemical

engineers have only recently become aware of the large potential it bears for the development and

improvement of process equipment. This is mainly due to the fact that with modelling flow around a car

body or an airplane wing, only single-phase flow has to be considered while in most applications in

chemical reactors two- and three-phase flows are common. This poses a wealth of new questions and

brings about serious difficulties in modelling and numerics.

CFD simulations did bring some advances to move forward in the numerical simulations of OFRs in

comparison to previous works. The stream function approach (Howes et al. 1991) was abandoned and the

3-D Navier–Stokes equations are solved directly, as described below.

a) Model equations

In most of CFD packages (e.g. Fluent 5 – Fluent Inc., Paris, France) the governing equations are solved in

cylindrical coordinates, as follows (Ni et al. 2002a):

Momentum equations:

( ) ⎥⎦⎤

⎢⎣⎡

∂∂+−

∂∂+

∂∂−

∂∂−=⎟

⎟⎠

⎞⎜⎜⎝

⎛

∂∂+−

∂∂+

∂∂+

∂∂

zrrr

rrrp

zVV

rVV

rV

rVV

tV rzr

rrr

zrr

rr ττττ

θρ θθθθθ 112

(2.12)

( ) ⎥⎦

⎤⎢⎣

⎡∂

∂−∂

∂+∂∂−

∂∂−=⎟

⎠⎞

⎜⎝⎛

∂∂+−

∂∂+

∂∂+

∂∂

zrr

rrp

rzVV

rVVV

rV

rVV

tV z

rzr

rθθθ

θθθθθθθ τ

θττ

θθρ 111 2

2 (2.13)

41


( ) ⎥⎦⎤

⎢⎣⎡

∂∂−

∂∂+

∂∂−

∂∂−=⎟

⎠⎞

⎜⎝⎛

∂∂+

∂∂+

∂∂+

∂∂

zrr

rrzp

zVVV

rV

rVV

tV zzz

rzz

zzz

rz τ

θττ

θρ θθ 11 (2.14)

Continuity equations:

( ) 011 =∂

∂+∂∂+

∂∂

zVV

rrV

rrz

r θθ (2.15)

where

( )⎥⎦⎤

⎢⎣⎡ ∇−

∂∂−= V

rVr

rr 322µτ (2.16)

( )⎥⎦

⎤⎢⎣

⎡∇−⎥⎦

⎤⎢⎣⎡ +

∂∂−= V

rVV

rr

3212

θµτ θ

θθ (2.17)

( )⎥⎦⎤

⎢⎣⎡ ∇−

∂∂−= V

zVz

zz 322µτ (2.18)

⎥⎦

⎤⎢⎣

⎡⎟⎠⎞

⎜⎝⎛

∂∂+

∂∂−==

rV

rrV

rr

rrθ

θθ θµττ 1 (2.19)

⎥⎦⎤

⎢⎣⎡

∂∂+

∂∂−==

zVV

rz

zzθ

θθ θµττ 1 (2.20)

⎥⎦⎤

⎢⎣⎡

∂∂+

∂∂−==

zV

rV

rrz

rzzr1µττ (2.21)

where

( ) ( )z

VVr

rVrr

V zr ∂

∂+∂

∂+∂∂=∇

θθ11 (2.22)

where Vr, Vθ and Vz are the fluid velocities (m/s) at r, θ and z coordinates respectively, p is the pressure

drop (Pa). The viscous term in Eq.s (2.16) - (2.21) takes the form of µ = µ0 + µt, where µ0 is the nominal

laminar viscosity (kg m-1 s-1) and µt the turbulent viscosity (kg m-1 s-1). For laminar flow simulation, µt, = 0,

and Vr, Vθ and Vz are the laminar velocity components. For turbulence simulation, µt, is included and Vr, Vθ

and Vz are averaged velocities. For 2-D simulations, all variables in the third direction (θ) are treated as

constants, thus simplifying the above equations accordingly.

42


Fluent (Fluent Inc., Paris, France) is one of the CFD software products commercially available in the

market. It solves numerically the Navier-Stokes equations to find the flow pattern in the reactor. Three

main steps are involved in numerical simulations with Fluent:

a) designing the geometry & meshing (descritisation of domain into finite elements)

b) defining the fluid properties

c) boundary conditions.

For complex geometries, its designing and meshing are usually performed in a Fluent’s CAD tool, the

‘Gambit’ software package.

2.6 Biotechnological process engineering

In 1989, the European Federation of Biotechnology proposed, in General Assembly, the following

definition: “Biotechnology is the integration of natural and engineering sciences in order to achieve the

application of organisms, cells, parts thereof and molecular analogues for products and services” (EFB

General Assembly, 1989).

Contrary to its name, Biotechnology is not a simple technology. Rather, it is a group of technologies that

share two things in common: they manipulate living cells and their molecules, and they have a wide range

of practical uses that can improve our lives. Simply defined, then, Biotechnology is a collection of scientific

techniques that use living cells and their molecules to make products or solve problems (ncbiotech 2002).

2.6.1 Application areas

The applications of biotechnology are so broad, and the advantages so compelling, that virtually every

industry is using the technology. Several examples are listed in Table 2-6. Biotechnology is enabling these

industries to make new or better products, often with greater speed, efficiency and flexibility. The

consumers are beginning to see the benefits in the foods they eat, the clothes they wear, the medicines

they take, and the environment they live in, etc (ncbiotech 2002).

43


Table 2-6: Some of the applications of Biotechnology (Lee 1984)

Application field Main products Pharmaceuticals Antibiotics, antigens, diagnostics, endorphin, gamma globulin, human

growth hormone, human serum albumin, immune regulators, insulin, interferon, interleukins, lymphokines, monoclonal antibody, neuroactive peptides, tissue plasminogen activator, vaccines, etc.

Animal agriculture Products similar to those being developed in the pharmaceutical industry; development of disease-free seed stocks and healthier, higher-yielding food animals

Plant agriculture Transfer of stress-, herbicide-, and pest-resistance traits to important crop species; development of plants with the increased abilities of photosynthesis or nitrogen fixation; development of biological insecticides and nonice nucleating bacterium

Specialty chemicals Amino acids, enzymes, vitamins, lipids, hydroxylated aromatics, and biopolymers

Agricultural chemicals Pesticides, fungicides, herbicides Environmental applications Mineral leaching, metal concentration, pollution control, toxic waste

degradation, and enhanced oil recovery Foods and beverages Alcoholic beverages, sweeteners, single-cell protein Commodity chemicals Acetic acid, acetone, butanol, ethanol, and many other products from

biomass processes Bioelectronics Biosensors, biochips

2.6.2 Bioreactors and bioprocesses

The commercialisation of biotechnology developments requires the scale-up of biological processes. To

successfully design biological reactors (bioreactors) it is demanding to understand the bioprocesses

mechanism/kinetics. There are several factors affecting the performance of a bioprocess and, in

consequence, the operation of a biological reactor. They can be grouped in three systems, such as

physical, chemical and biological properties, as listed in Figure 2-12 (Vaidyanathan et al. 1999). A

complex network of interactions might exist in a bioprocess.

44


Figure 2-12. Factors that influence the performance of a bioprocess and the complexity of interactions

between them. Only some interactions are shown for illustrative purposes. The factors are grouped under

three system properties, namely, physical, chemical and biological (adapted from Vaidyanathan et al

(1999)).

The bioreactor is the ‘heart’ of biological processes and basically must display the following settings

(Blenke 1985):

a) a well-defined spatial distribution of all components (i.e., a good mixing, no concentration

gradients)

b) a good dispersion of all phases (gaseous, liquid and solid)

c) avoid cell damage

d) a high heat transfer rate

45


e) an easy design and construction of high dimension bioreactors (volumes up to 100 m3) at low

construction cost

f) easy operation: good sterility and possibility of keeping sterile conditions, low mechanical

management, low power requirements and possibility to operate on high volume reactors

g) easy set of operation conditions on a high range of temperature, concentration, viscosity, etc.,

(batch mode), or flexibility in production, (for continuous mode)

h) design and operation performance must be easily appointed and proper to scale-up.

As mentioned earlier in this text, mixing is of paramount importance in the bio/chemical process industry

as it is determinant on heat/mass transfer, reaction performance and product uniformity. Engineers often

require reactors with well defined residence times and good fluid mixing, while also searching for devices

that exhibit near plug-flow behaviours, in some cases.

Despite of all these issues, bioreactor scale-up may be indeed the biggest challenge in biotechnology due

to mixing, oxygen transfer and shear stress restrictions. These parameters are often interrelated. Aeration

should be as low as possible to avoid excessive shear stress, but must also ensure adequate oxygenation

of the cells. Cells are delicate and their culture and processing invariably exposes them to intense

hydrodynamic forces at some stage. A sufficiently intense force will destroy cells outright, while forces of

lower magnitude may induce various physiological responses, without necessarily causing any obvious

physical damage (Chisti 2001). Nowadays, little is known about shear fields in bioreactors but it is

definitely desirable to know the maximum shear rates (usually near the walls) rather than averaged values,

as this can be critical for the application of a reactor to a biotechnological process.

An attempt to classify biological reactors in two main groups according to the source of power and the

degree of homogeneity was made by the Working Party of Bioreactor Performance of the European

Federation of Biotechnology (Crueger 1987). While doing that, authors noticed that many of the bioreactor

designs attempt to keep the whole of their volume homogenous. Nevertheless, in the most stirred

volumes, for example, total homogeneity becomes progressively more difficult to archive as the scale

increases (Cabral and Tramper 1993).

Biological reactor types may be summarised in a few number of classes (see Table 2-7). Some innovative

designs appeared in the last decade. Most innovations addressed either oxygen transfer, shear induced by

stirring, control of water activity in organic phase systems or waste biotreatment. An extensive review is

presented by Deshussest et al. (1997).

46


Table 2-7: Summary of the main features of reactor classes (Cabral et al. 2001)

Biological reactor type Main features Stirred tank, ST Cylindrical vessel, equipped with a stirrer, baffles

and aeration Continuous flow stirred-tank reactor, CSTR A refined design of the stirred tank (provided with

ports for inlet and outlet) Packed-bed Vertical mounted settled bed of particles,

continuous, upward or downwards feeding Fluidized reactor Upward fluid feeding; flow rate must assure

fluidisation of bed of particles Bubble column reactor, BC Attractive alternative to stirred reactor for aerobic

processes, continuous or batch operation Air-lift loop reactor, ALR Similar to BC, but where the hydrodynamic flow

pattern is well described and controllable Novel reactor designs Mainly at laboratory scale, e.g.: membrane and

liquid-impelled loop reactor; liable for scale-up; integrated in downstream process

For a long time, stirred tank (ST) was the most used reactor for chemical applications and for aerobic

fermentations (Chisti 1989). While it still being the most used reactor in industrial applications, the

growing attention on processes development at industrial scale brought into evidence that ST is not the

most suitable for microorganisms’ culture. Several reasons exist for this statement, namely: cell damage is

very intense, essentially due the high shear stress caused by the stirrer; sterility is very difficult to assure;

they present low energetic efficiency, often involving heat removal by temperature control; usually high

construction cost (Chisti 1989).

The knowledge of such disadvantages of STs, namely the issue of excess shear stress and low energetic

efficiency has fomented the investigation of other types of reactors, namely oscillating bioreactors.

Recently, new reactor designs have been developed, but further development of innovative bioreactors

remains a high priority, as a single bioreactor configuration will never provide a universal solution

(Deshusses et al. 1997).

2.6.3 Bioreactor engineering

Producing more, faster, with higher yields and more reliably have been the main driving forces behind the

evolution in bioreactor designs. It has been pointed out that mixing, oxygen transfer and shear stress

47


remain the biggest challenges as far as the scale-up to industrial size bioreactors is concerned. These

parameters are generally linked, and compromises need to be made, for instance, in aeration to avoid

excessive shear stress. The latest developments in bioreactors for better mixing, oxygen transfer and lower

shear stress are reviewed by Deshussest et al. (1997)

One of the particularities of biotransformations is their polyphasic composition (gas-liquid-solid or gas-

liquid-liquid-solid). Consequently, the mass transfer of nutrients (carbon and energy sources, organic

nitrogen and oxygen) is more complex than for chemical processes thus controlling the performance of

bioreactors (Galaction et al. 2004).

a) Oxygen mass transfer rates

The oxygen supply constitutes one of the decisive factors in submerged microbial cultures and can play an

important role in the scale-up and economy of aerobic biosynthesis systems. The aeration efficiency

depends on oxygen solubilisation and diffusion rate into the liquid-phase. The amount of dissolved oxygen

in a culture is limited by its solubility and mass transfer rate, as well as by its consumption rate by cells’

metabolic pathways (Galaction et al. 2004).

The oxygen mass transfer can be described and analyzed by means of the mass transfer coefficient, kLa. It

represents the most important parameter implied on the design and operation of mixing–sparging

equipment of bioreactors. The correct measurement and estimation of kLa is a crucial step in the design

procedure of the bioreactors (Puthli et al. 2005). The kLa values are affected by several factors, such as

geometrical and operational characteristics of the vessels, media composition, type, concentration and

microorganisms morphology, biocatalyst properties (particle size, porosity, etc.) (e.g. Chisti and Jauregui-

Haza 2002).

Numerous mathematical correlations have been proposed for kLa, either as functions of adimensional

groups, such as

( ),...ScRe,fSh = (2.23)

or using specific mechanical power input and superficial air velocity:

⎟⎟⎠

⎞⎜⎜⎝

⎛= ,...v,

VP

fak sL (2.24)

48


The second relation is preferred, being more useful in practical applications or for fermentation scale-up

using oxygen mass transfer efficiency criteria.

A survey of measurement techniques to assess kLa in bioreactors is presented by Gogate and Pandit

(1999). Main techniques may be summarised as follows:

a) Dynamic methods

(a) Dynamic oxygen electrode method

(b) Start-Up method

b) Steady state sulphite method

c) Dynamic pressure method (DPM)

d) Peroxide method

e) Response methods

Several drawbacks and errors (up to 100 %) may be associated with any of these methods. Depending on

the range of the variables i.e. (P/V) and vg (superficial gas velocity), the most appropriate method needs to

be chosen (Gogate and Pandit 1999).

2.6.4 Bioprocesses monitoring

Bioprocesses monitoring is crucial in bioreactors operation. Preferably, on-line and real-time information of

bioprocesses kinetics should be obtained with the final aim of process full control. Measurements of

bioprocesses may occur at different levels, as presented in Figure 2-13. For many years, the approach to

the measurement of non-physical variables (as shown in Figure 2-12) has been to perform those

measurements outside and away from the reactor (‘off-line’), principally due to a lack of appropriate

technologies with which to obtain the values directly from the reactor. However, for some years now

measurement techniques have been applied increasingly ‘by’ (at-line) and where possible ‘on’ (on-line),

and even ‘in’ (in situ) the bioreactor vessel or flow stream (Vaidyanathan et al. 1999). Ideally, in situ

approaches are desirable.

49


Figure 2-13. A schematic of the approaches to measurement in bioprocesses (adapted from Vaidyanathan

et al., (1999).

Measurement of physical variables, such as temperature, pressure, agitation speed, and flow rates, are

not considered here as they can today be reliably made in situ, provided that appropriate maintenance

schedules are maintained. Basically, the chemical and biological variables can be the measured using

(Vaidyanathan et al. 1999):

a) Optical Sensors

(a) Light Absorption/Scattering Measurements

(b) Fluorescence Measurements

(c) Vibrational Spectroscopy

(d) Image Analysis

(e) Flow Cytometry

b) Flow-Injection Analysis — Biosensor Systems

(a) Flow-Injection Analysis (FIA)

(b) Biosensors

c) Chromatography

d) Mass Spectrometry

e) Dielectric Spectroscopy

f) Nuclear Magnetic Resonance (NMR) Spectroscopy

g) Calorimetry

Further techniques (essentially ‘off-line’) have found specific utility: steric sedimentation field-flow

fractionation, electrophoresis, etc.

50


2.6.5 Continuous cultures

Microbiological processes have been largely developed through batch-processing methods, i.e. one batch

of material is completely processed in a given vessel before the next batch is started. This situation arose

partly because of the operational problems involved (e.g. aseptic operation and the maintenance of a

particular strain of microorganisms). The complex relationships between substrate consumption, microbial

growth and product formation are also significant factors. One of the advantages of batchwise operation is

that the capital cost is less then for a continuous process and, for this reason, it is frequently favoured for

new and untried processes, which may be converted to continuous operation at a more advanced stage of

development (Atkinson 1974).

These factors are slowly being overcome as the tendency to large-scale continuous process inevitably

continues. Examples of applications are illustrated by developments in beer production (Branyik et al.

2002) and in the production of cellular material for use as protein (Jung 2006), or enzyme recovery

(Papamichael and Hustedt 1994). The reasons why continuous processes are eventually adopted in

almost all large-scale operations are (Atkinson 1974)

a) diminished labour costs (repeated filling and emptying operations of batch vessels are

eliminated; in situ medium sterilisation)

b) ease of application of automatic control to continuous processes (also leading to the reduction

of labour costs)

c) more stable bioreactor conditions, and hence greater steadiness in the quality of product

(product recovery is also facilitated)

d) homogeneous environmental conditions, leading to a reduced range of by-products

e) products produced only during a very brief transient growth phase can only be produced in

quantity in continuous mode under well-defined conditions

f) a steady load on the services required by the process, e.g. air and steam.

In fact, the continuous culture experiments offer a number of advantages over the conventional batch

method. Batch cultures are traditionally used in biological experiments because of easy handling. But

many often the interpretation of the results in batch culture is difficult because concentrations of

substrates and products change constantly, pH varies, and osmotic pressure and redox potentials change

(Nielsen and Villadsen 1992). In continuous culture, under steady-state conditions, the environment is

well-controlled and defined and the results obtained (e.g. kinetic parameters and yield coefficients) may be

51


more reliable and reproducible (Sipkema et al. 1998). Therefore, the cause/effect relationships are more

easily determined in a continuous culture than in batch cultures. Continuous fermentation experiments

can provide details and valuable information about a biological system and certainly it is the option for

determining specific characteristics that are difficult to observe with non-continuous culture techniques.

For example, pulse and medium shift experiments (Fiechter et al. 1981; Sipkema et al. 1998; Zhang and

Greasham 1999) and accelerostat operation (Paalme et al. 1995) can be used in continuous fermentation

for the preparation and the optimization of the chemical and physical environment to which an organism is

exposed.

2.6.6 Biotechnological applications of OFM

Good aeration, mixing and mass transfer are important for biotechnological processes which require an

efficient and adequate supply of oxygen to aerobic microorganisms. There are many different designs and

methods to obtain gas dispersion. Some devices are quiescent, such as bubble columns and trickle beds.

Others employ dynamic (mechanical) agitation, such as gas sparged stirred tanks widely used at industrial

scale (Linek et al. 1991; Schugerl 1982), multiple impeller vessels (Linek et al. 1996), cascade reactors

with rotating or axially moving mixing elements, and mechanical surface aerators.

All such devices use constant “steady” mixing, such as superficial velocities or fixed agitation speed. The

use of OFM is an alternative, with the relative periodic motion of fluid (usually, sinusoidal). OFM is found to

significantly enhance mass transfer rates in bubble columns (Hewgill et al. 1993) and more efficient than

mechanical (stirred) agitation with respect to gas hold-up (Baird and Garstang 1967).

The development of high efficiency bioreactors has been an important research objective in the field of

bioprocesses. Appropriate selection and design could greatly improve the efficiency of the overall process.

Several bioreactor configurations (fixed/fluidized-bed, gas-lift, membrane fermentors, reciprocating

bioreactors) have been considered (Chamy et al. 1990; Chisti 1989; Mehaia and Cheryan 1984). In many

cases, gas-lift, fluidized and reciprocating bioreactors is better suited to particular applications (Brauer

1991; Gilson and Thomas 1993).

Many reports concerning the successful application of OFM to bioengineering can be found in the

literature. Several fermentations and enzymatic processes where improved with fluid oscillations (gas or

52


liquid) either by preventing operational problems or by facilitating the improvement of efficiency and

control of multiphase bioreactors. Enhancement of mass transfer rate using pulsation has been achieved

by Baird and Garstang (1967), applying f from 1.09 to 1.35 Hz and x0 up to 9.4 mm, to a 76 mm

diameter column packed with random rings of 12.5 mm. The introduction of pulsation gave a three-fold

increase in gas hold-up. Bellhouse et al. (1973) used oscillations in furrowed channels to enhance blood

oxygenation. Serieys et al. (1978) also reported that in a reciprocating column with perforated plates the

gas hold-up was slightly higher than a turbine agitator, but lower than with airlift bioreactors. However, the

kLa values obtained were much higher than those published for any other technology. Beeton et al. (1991)

applied fluid oscillations to a membrane and achieved at least a five-fold enhancement in mass transfer

over flat membranes. Mass transfer of oxygen into water was reported for OFM in a baffled tube (Hewgill

et al. 1993), and a six-fold increase in kLa was measured as compared with those for a bubble column.

Measurement of kLa into yeast re-suspension and yeast cultures (Ni et al. 1995a) revealed on average a

75 % increase in kLa values in a OFR over those obtained for a ST bioreactor, explained by the better shear

rate distribution inside the vessel, leading to averaged thinner liquid films (hence increasing the kL term).

Another application of OFM is of consideration. The potentially lethal bubble break-up at the gas-liquid

interface was minimized by the development of a vortex wave membrane bioreactor by Millward et al

(1996). The vortex wave generates a very effective mixing under laminar flow conditions by generating,

expanding and transporting vortices in an oscillatory flow field (Millward et al. 1996). Significant mass

transfer enhancement has been achieved under laminar flow conditions, without a major increase in

power dissipation. The low shear rate indicated that such vortex wave design may be an effective

alternative to conventional bioreactors for shear-sensitive systems.

2.7 Scale-down of bioprocesses

During the development of a microbial cell cultivation process there are four key stages (Steven et al.

2004), as represented in Figure 2-14. Throughout a development process, many native and modified cell-

lines are created and many operating conditions are considered and therefore large numbers (>100) of

experiments are usually performed (Chartrain et al. 2000). Since development time is precious to

commercial success, approaches that increase the rate at which these experiments can be carried out are

53


of great value and therefore high throughput (HTP) screening methodologies are of increasing interest (Lye

et al. 2003). The elements in an ideal high throughput approach are:

a) experiments can be performed in parallel

b) experiments can be operated at a small-scale

c) experiments can be automated (or online monitored).

Figure 2-14. Main stages crossing the bioprocess development.

The application of small-scale reactors to the early stages of the bioprocesses can effectively contribute to

the integration of biocatalyst, medium and bioprocess designs (Weuster-Botz et al. 2005). For such

reasons, the micro-scale processing techniques are rapidly emerging as a means to increase the speed of

bioprocess design and reduce material requirements (Lye et al. 2003).

Biocatalysis is also a key technology in the synthesis of optically pure fine chemicals and pharmaceuticals

(Schmid et al. 2001). Drugs developed with the incorporation of biocatalytic steps in their syntheses are

now involved in the treatment of diseases such as HIV, heart disease, cancer, diabetes, flu and bacterial

infections including tuberculosis (McCoy 1999). More than 150 industrial bioconversion processes are

currently in operation or have been used for the manufacture of kilogram quantities of materials (Liese et

al. 2000). The implementation of a new bioconversion process requires careful consideration of several

competing biocatalyst and process options. Biocatalyst and process decisions has historically been

collected at the 1.5 – 2.0 l scale, which can be time consuming and often requires significant quantities of

expensive synthetic substrates.

The most commonly used cultivation vessel in process development is the shaken flask (Buchs 2001;

Maier and Buchs 2001). Erlenmeyer flasks (100 – 2000 ml), filled with low volumes of medium (10 – 25

% of the total capacity) are shaken to promote mixing and mass transfer via surface aeration.

Unfortunately shaken flasks cannot be easily automated and the number of simultaneous experiments is

Strain

selection

Strain

enhancement

Process

optimization Scale-up

54


limited to several tens. Thus, recently several authors presented alternative designs to the shaken flask. A

critical review is presented by Lye (2003). Also some HTP screening systems are commercially available,

but limited to a small number (10 - 20) of bio-transformations in parallel (Figure 2-15).

Figure 2-15. Examples of commercially available HTP screening bioreactor systems. (A) Infors Profors –

16 x 400mL, sparged column reactors. (B) DasGIP Fedbatch-pro – 16 x 300mL stirred tank reactors. (C)

Infors Sixfors – 6 x 500mL, stirred tank reactors.

55


Characterisation of the engineering environment in a small-scale system may be complex. The generation

of quantitative process design data at the micro-litre scale first requires an understanding of the underlying

mixing and mass transfer phenomena. The establishment of key parameters, such as kLa, is necessary to

enable scale-up, which also implies the study of the influence of well design and methods of agitation or

aeration. On the other side, the lower scale requires the development of miniaturised techniques (e.g.

Gernot T. John 2003).

2.8 Conclusions

The OFM is increasingly finding more applications and is now introduced as ‘a technology ready to deliver’

(Harvey and Stonestreet 2001). Since the 1930’s many authors were reporting the enhancement of

chemical processes by operating under OFM conditions. The nuclear industry was the first one beneficing

with the OFM following Van Dijck’s work (1935).

The several studies with OFRs (essentially from the 1990’s) brought a deep knowledge of the OFM nature.

CFD simulation tools developed in the last years offered the change to successfully predict the fluid

mechanics within OFRs. The linear scale-up of lab-scale OFRs was successfully demonstrated as well as

its enhanced capacity to deal with multiphase systems.

Previous numerical simulations of OFM had some problems of validation (e.g. Howes 1988) due to the

inexistence of appropriate experimental techniques. Thus, the simulation work in OFR has been at

standstill since the mid 1990s. In recent years, novel high-resolution techniques appear as a validation

tool, such as the digital Particle Image Velocimetry (PIV) technique. It is now possible to continue and

extend the previous numerical research in this field with quantitative validation (Ni et al. 2002a).

Although several micro-bioreactor designs are found in literature, few of them support a continuous

operation. This is a gap that needs to be repaired. One exception is the work of Akgun (2004). It is

basically a 250 ml shake flask provided with two inlet ports (one for gas supply and another for medium

inlet) and one combined outlet on the side of the flask for the exhaust of gas and culture liquid, thus

supporting the continuous growth.

Continuous culture experiments with conventional fermentation technology (e.g. ST bioreactors) are very

time- and material-consuming, and laboratory setup is complex. It is clear the challenge opportunity for a

56


biochemical engineer in the design of scale-down platforms supporting the application of HTP screening in

a continuous operation mode.

The last decade brought several studies on multiphase systems, with promising results achieved in terms

of mass transfer rates and particle suspension in OFRs, suggesting that the OFR could be a successful

biological vessel. On the other hand, a scale-down of OFR (to less than 10 millilitres scale) was never tried

before. The high demand of bioprocesses for reactor engineering asks for a systematic study and

characterisation of OFRs for application to biotechnological processes.

Several areas for novel reactor designs based on OFM technology may be identified in early stages of

biotechnological processes development. The enhanced fluid mixing, heat and mass transfer rates

highlight an opportunity for applications of such novel oscillatory reactor designs e.g. at the strain selection

stage, allowing the parallel screening of strains and fermentation media. Batch HTP screening is allowed,

assuring essentially small operation volumes and reduced reagent costs and waste generation. But the

narrow RTDs found in OFRs forecast a chance to develop novel OFR configurations suiting the continuous

process optimization, allowing keeping the same environment conditions (essentially the fluid mechanics).

Anticipating the scale-up of OFRs, such novel scale-down reactor designs may complement the OFR and,

together with the metabolic and genetic engineering work, concretise the two novel concepts in bioprocess

development: integration and intensification.

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