Post on 08-Nov-2018
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
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
Autor Nuno Miguel Fernandes Reis
e-mail nunoreis@deb.uminho.pt
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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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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).
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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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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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
7.2 Materials and Methods ........................................................... 183
7.3 Results and discussion ........................................................... 191
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
8.3 Results and discussion ........................................................... 217
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
9.2 Materials and Methods ........................................................... 235
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
9.3 Results and discussion ...........................................................237
9.4 Conclusions............................................................................241
9.5 References .............................................................................241
Chapter 10 Lysozyme Dilution Refolding..................................................245
10.1 Introduction............................................................................246
10.2 Materials and methods ...........................................................249
10.3 Results and discussion ...........................................................252
10.4 Conclusions............................................................................258
10.5 References .............................................................................258
Chapter 11 General conclusions and suggestions for future work.............261
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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.
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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)
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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;
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
■ 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;
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
(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
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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).
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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]
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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]
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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
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PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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
PhD dissertation Novel Oscillatory Flow Reactors for Biotechnological Applications
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
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
Chapter 1 Introduction
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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.
Chapter 2 Literature review
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)
Pneumatic oscillator
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)
Oscillatory Flow Reactor
Continuous OFR Continuous production of sterols in an ester saponification reaction.
Harvey et al. (2001)
Oscillatory Flow Reactor
Oscillatory Baffled Batch Crystallizer (OBBC)
Crystallization of Paracetamol Chew et al. (2004a)
Oscillatory Flow Reactor
Pulsed Baffled Tubular Photochemical Reactor
Treatment of wastewater (photocatalytic oxidation)
Fabiyi and Skelton (1999; 2000a; 2000b), Gao et al. (2003)
Reciprocating plates column
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)
Pneumatic oscillator
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
Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Table 2-3: (Continued)
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
Table 2-3: (Continued)
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 plates column
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)
Reciprocating plates column
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
Table 2-3: (Continued)
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Chapter 2 Literature review
-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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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)
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Chapter 2 Literature review
Table 2-5: (Continued)
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)
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N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
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Chapter 2 Literature review
(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).
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N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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).
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Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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).
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Chapter 2 Literature review
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).
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N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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)
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Chapter 2 Literature review
( ) ⎥⎦⎤
⎢⎣⎡
∂∂−
∂∂+
∂∂−
∂∂−=⎟
⎠⎞
⎜⎝⎛
∂∂+
∂∂+
∂∂+
∂∂
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.
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N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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).
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Chapter 2 Literature review
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.
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N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
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Chapter 2 Literature review
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).
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N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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
Chapter 2 Literature review
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
N. Reis Novel Oscillatory Flow Reactors for Biotechnological Applications
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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