Impact of Fluid Dynamic Effects on Granular Activated ... · Velocimetry (PIV), Particle Tracking Velocimetry (PTV), Laser Doppler Anemometry (LDA) and micro Particle Image Velocimetry

Impact of Fluid Dynamic Effects

on Granular Activated Sludge

Einfluss von fluiddynamischen Effekten

auf granularen Belebtschlamm

Der Technischen Fakultät der Universität Erlangen-Nürnberg

zur Erlangung des Grades

DOKTOR-INGENIEUR

vorgelegt von Bogumiła Ewelina Zima–Kulisiewicz

Erlangen, 2008

Als Dissertation genehmigt von der Technischen Fakultät der Universität Erlangen-Nürnberg

Tag der Einreichung: 6.5.2008 Tag der Promotion: 1.8.2008 Dekan: Prof. Dr.–Ing. Johannes Huber Berichterstatter: Prof. Dr.–Ing. Antonio Delgado Prof. Dr.rer.nat. Harald Horn

II

VORWORT

Die vorliegende Arbeit entstand während meiner Tätigkeit als wissenschaftliche Mitarbeiterin

am Lehrstuhl für Fluidmechanik und Prozessautomation der Technischen Universität München

von November 2003 bis März 2006 und am Lehrstuhl für Strömungsmechanik der Friedrich-

Alexander Universität Erlangen-Nürnberg von April 2006 bis August 2008 unter der Leitung von

Prof. Dr.-Ing. Antonio Delgado, Inhaber der beiden Lehrstühlen. Allen die zur Entstehung dieser

Doktorarbeit beigetragen haben möchte ich meinen ganz herzlichen Dank aussprechen.

Zuallererst möchte ich meinem Doktorvater Herrn Prof. Dr.-Ing. Antonio Delgado für die

Möglichkeit an seinem Lehrstuhl zu promovieren, seine Zuversicht aber auch für die Zeit die er

für zahlreiche wissenschaftliche Gespräche geopfert hat danken.

Herrn Prof. Dr.-Ing. Christoph Hartmann danke ich für sehr hilfreiche Anregungen, Geduld und

Nachsichtigkeit in der Anfangsphase meiner Promotion.

Darüber hinaus gilt mein ganz besonderer Dank Herrn Prof. Dr.-Ing. Wojciech Kowalczyk,

meinem direkten Ansprechpartner und Betreuer, der immer Zeit für mich hatte und mir geholfen

hat die strömungsmechanischen Effekte in Mehrphasenströmung zu verstehen.

Des Weiteren danke ich den Prüfern meiner Dissertation, Herrn Prof. Dr. rer. nat. Harald Horn

und Herrn Prof. Dr.-Ing. Johann Jäger für das Interesse an der Arbeit sowie Herrn Prof. Dr. rer.

nat. Rainer Buchholz für die Übernahme des Prüfungsvorsitzes.

Ich darf meine Familie nicht vergessen, die trotz der Entfernung immer bei mir war. Dafür

herzlichen Dank. Nicht zuletzt danke ich auch meinem Mann für seine große Unterstützung und

Dabeisein.

Erlangen, August 2008

Bogumiła Ewelina Zima-Kulisiewicz

III

ABSTRACT

Increasing water consumption, urbanization and industrialization as well as decreasing environmental quality demand effective wastewater treatment plants. Aerobic granulation is a novel technology in the biological purification of wastewater. Granular Activated Sludge (GAS) is described as aggregates of microbial origin, with better settling ability than Conventional Activated Sludge (CAS), which do not coagulate under reduced hydrodynamic shear. Moreover GAS, in comparison with CAS, has a denser, compacter structure and higher biomass retention. Due to those properties, GAS has a great application potential in biological purification of wastewater. However, biogranulation is a complex process and its mechanism is not yet fully understood. Many factors influence granule formation and destruction. Hitherto, several researchers have focused on the biochemical aspects. However, little information concerning hydrodynamic effects is available. Thus, the current work concerns fluid mechanical investigations of multiphase flow (water, air, granules) in a Sequencing Batch Reactor (SBR) with the help of optical in situ techniques which allow the spatial distribution of momentum transport to be described including local velocity, stress and particle collision, for the first time. Particle Image Velocimetry (PIV), Particle Tracking Velocimetry (PTV), Laser Doppler Anemometry (LDA) and micro Particle Image Velocimetry (μ–PIV) are implemented to describe the influence of fluid dynamic effects on the granulation process on the micro and macro scales. Moreover, basic theory is presented including fundamental conservation laws of mass and momentum enabling a theoretical understanding of the process.

For a clear interpretation of experimental investigations and a reduction in the number of parameters, results are represented in a dimensionless way. Fluid dynamic investigations show a characteristic flow pattern in the aeration phase of bioreactor operation. At the bottom of the laboratory scale SBR a large vortex exists and in the upper part smaller eddies appear. PIV data reveal that fluid velocity and normal and shear strain are higher in the upper part of the SBR. Furthermore, these parameters decrease close to the SBR wall. LDA experiments confirm an increasing tendency of fluid velocity with increasing vertical coordinate, wall distance and aeration flow rate. However, PTV results show that the velocity of granules decreases with increasing vertical coordinates. Fundamental fluid dynamic forces and the effect of collisions are also addressed in the current study. Obviously, different process parameters (especially aeration rate), inducing specific flow conditions, influence the granulation process. In this respect, the comparison of granulation with different aeration rates reveals fatigue effects and hydrodynamic selection of microorganism species. μ–PIV studies indicate an enormous role in the granulation process played by protozoa (ciliates) living on the biogranules. A methodology of investigation of micro–flow induced by these microorganisms including correct seeding is elaborated within the present study.

The current work contributes to the understanding of the bio granulation process based on fluid mechanical aspects. Finally, general guidelines regarding the design and operation of the SBR in respect of optimal flow conditions are derived.

IV

ZUSAMMENFASSUNG

Der ansteigende Wasserverbrauch, die zunehmende Urbanisierung und Industrialisierung und eine abnehmende Umweltqualität erfordern effektiver Abwasserbehandlungsanlagen. Aerobe Granulation stellt eine neuartige Technologie im Bereich der biologischen Abwasserreinigung dar. Dabei wird der granulare Belebtschlamm (Granular Activated Sludge, GAS) als Aggregate mikrobiologischen Ursprungs beschrieben, die im Vergleich zu konventionellem Belebtschlamm (Conventional Activated Sludge, CAS), der unter reduzierter hydrodynamischer Scherung nicht koaguliert, eine verbesserte Absetzfähigkeit aufweist. Ferner zeigt GAS im Vergleich zu CAS eine dichtere, kompaktere Struktur sowie einen höheren Biomasszurückhaltung auf. Diese Eigenschaften begründen das hohe Anwendungs-potential im Bereich der biologischen Abwasserbehandlung. Die Biogranulation ist jedoch ein komplexer Prozess, dessen Mechanismus bisher nicht vollständig erfasst wurde. Viele Faktoren beeinflussen Bildung und Zerstörung der Granula. Bisher haben sich mehrere Forscher auf die biochemischen Aspekte fokussiert. Es steht allerdings wenig Information zu hydrodynamischen Effekte zur Verfügung. Die vorliegende Arbeit beschäftigt sich daher mit fluidmechanischen Untersuchungen der Mehrphasenströmung (Wasser, Luft, Granula) in einem Sequencing Batch Reactor (SBR) mittels optischer in situ Techniken, die erstmalig eine Beschreibung der räumlichen Verteilung des Impulstransports, einschließlich lokaler Größen wie Geschwindigkeit, Spannung und Partikelkollision ermöglichen. Particle Image Velocimetry (PIV), Particle Tracking Velocimetry (PTV), Laser Doppler Anemometry (LDA) und micro Particle Image Velocimetry (μ–PIV) werden zur Beschreibung des Einflusses fluiddynamischer Effekte auf den Granulationsprozess sowohl in Mikro– als auch in der Makroskala implementiert. Überdies wird eine grundlegende Theorie dargestellt, die die fundamentale Masse– und Impulserhaltungsgesetze einschliesst und so ein tcheoretisches Verständnis des Prozesses ermöglicht.

Um eine eindeutige Interpretation der experimentellen Untersuchungen und eine Reduzierung der Parameteranzahl zu gewährleisten, werden die Ergebnisse dimensionslos dargestellt. Fluiddynamischen Untersuchungen zeigen in der Aerationsphase des Bioreaktorbetriebs ein charakteristisches Strömungsmuster. Am Boden des Labormaßstabs–SBR existiert ein großer Wirbel und in dem oberen Teil treten kleinere Wirbel auf. PIV–Daten lassen erkennen, dass sowohl Fluidgeschwindigkeit als auch Normal– und Scherspannungen im oberen Teil des SBR größer sind. Außerdem nehmen diese Parameter nahe der SBR–Wand ab. LDA Experimente bestätigen eine zunehmende Tendenz der Fluidgeschwindigkeit mit ansteigender vertikaler Koordinate, ansteigendem Wandabstand und ansteigender Belüftungsrate. PIV Ergebnisse zeigen jedoch, dass die Geschwindigkeit der Granula mit ansteigenden vertikalen Koordinaten abnimmt. Zudem werden grundlegende fluiddynamische Kräfte und die Auswirkung von Kollisionen in der vorliegenden Studie angesprochen. Offensichtlich beeinflussen verschiedene Prozessparameter (besonders die Belüftungsrate) den Granulationsprozess, indem sie spezifische Strömungsbedingungen hervorrufen. In dieser Hinsicht lässt der Vergleich der Granulation bei

V

verschiedenen Belüftungsraten Ermüdungseffekte und eine hydrodynamische Selektion von Mikroorganismenspezies erkennen. μ–PIV Studien deuten auf eine herausragende Bedeutung im Granulationsprozess hin, die auf den Granula lebende Protozoen (Ciliaten) spielen. Eine Methodik zur Untersuchung der von diesen Mikroorganismen ausgelösten Mikroströmung, einschließlich der korrekten Seedings wird innerhalb der vorliegenden Studie eraibertet.

Die aktuelle Arbeit trägt zum Verständnis des Biogranulationsprozesses basierend auf fluidmechanischen Aspekten bei. Abschließend werden allgemeine Richtlinien bezüglich des Designs und der Prozessführung des SBR hinsichtlich optimaler Strömungsbedingungen abgeleitet.

VI

TABLE OF CONTENTS

1. INTRODUCTION ________________________________________________________ 1

1.1 Aerobic and anaerobic granulation ________________________________________ 1

1.2 Fluid dynamics of multiphase flow _______________________________________ 14

1.3 Work objectives______________________________________________________ 23

2. SOME BASIC THEORETICAL CONSIDERATIONS __________________________ 24

2.1 Basic equations of fluid dynamics________________________________________ 24

2.2 Mechanical forces in multiphase flow_____________________________________ 29

3. MATERIALS AND METHODS ____________________________________________ 45

3.1 Experimental setup ___________________________________________________ 45

3.2 Optical in situ techniques with He–Ne Laser and video lamp___________________ 47

3.2.1 Particle Image Velocimetry (PIV) _________________________________ 48

3.2.2 Particle Tracking Velocimetry (PTV) ______________________________ 49

3.3 Laser Doppler Anemometry (LDA) ______________________________________ 49

3.4 Microscopic investigations _____________________________________________ 51

3.4.1. Microscopic analysis____________________________________________ 51

3.4.2 Micro Particle Image Velocimetry _________________________________ 52

4. RESULTS AND DISCUSSION_____________________________________________ 54

4.1 Dimensionless representation of results ___________________________________ 54

4.2 Particle Image Velocimetry_____________________________________________ 56

4.2.1 Fluid velocity distributions_______________________________________ 56

4.2.2 Normal strain rate______________________________________________ 65

4.2.3 Shear strain rate _______________________________________________ 68

4.3 Particle Tracking Velocimetry___________________________________________ 71

4.4 Laser Doppler Anemometry ____________________________________________ 73

4.4.1 Velocity distribution____________________________________________ 73

VII

4.4.2 Energy spectrum analysis________________________________________ 77

4.5 Fluid dynamic forces __________________________________________________ 81

4.6 Microscopic observations ______________________________________________ 86

4.6.1 Microscopic analysis ___________________________________________ 86

4.6.2 Micro Particle Image Velocimetry _________________________________ 88

5. CONCLUSIONS ________________________________________________________ 98

6. APPENDIX____________________________________________________________ 105

7. REFERENCES _________________________________________________________ 106

VIII

SYMBOLS

Latin letters

a Distance between particle centres m

A Hamaker constant -

AC Acceleration number -

AP Cross–section of spherical particle m2

b Average roughness height of sphere m

CA Virtual mass coefficient -

Cd Dynamic friction coefficient -

CD Drag coefficient -

CLR Lift coefficient -

CR Rotational coefficient -

CS Static friction coefficient -

D Particle diameter m

eR Restitution coefficient -

fr

Body force per unit volume N

AFr

Added mass forces N

BFr

Basset force N

fC Collision frequency s-1

DFr

Drag force N

EFr

Electrostatic force N

GFr

Buoyancy force N

MFr

Magnus force N

SFr

Saffman force N

WFr

Van der Waals force N

gr Vector of gravitational acceleration m/s2

Hmax Maximum liquid level m

IX

m Mass kg

n Sample number -

nr Unit normal vector directed from particle 1 to 2 -

ni Relative number of particles with diameter DPi -

nj Relative number of particles with diameter Dj -

Nij Particle–particle collision rate s-1

Pr

Surface force per unit volume N

q Particle charge C

ReP Particle Reynolds number of translation -

ReR Particles Reynolds number of rotation -

ReS Particles Reynolds number of shear -

t Time s

tr

Unit vector in tangential direction of particle contact point -

Tr

Lift torque Nm

RTr

Lift rotational force N

ur Velocity vector m/s

V Volume m3

v Mean axial velocity m/s

wi Weighting factor -

Greek letters

δ Kronecker unit tensor -

Λ Viscosity coefficient -

ε& Normal strain rate s-1

oε Dialectric constant -

γ& Shear strain rate s-1

λ Wavelength nm

X

μ Dynamic viscosity Pas ρ Density kg/m3

σ Normal stress Pa

τ Tangential stress Pa

τC Averag time between collision s

τP Particle response time s

ωr Fluid rotation s-1

Ωr

Relative rotation s-1

Sub/superscripts

0 Initial condition

1 First particle number

2 Second particle number

P Particles

R Reactor

W Liquid

X Horizontal direction

Y Vertical direction

Z Horizontal direction

Abbreviations

ACF Autocorrelation function

BOD Biochemical oxygen demand

CAPRT Computer–automated radioactive particle tracking

CAS Conventional activated sludge

CFD Computational fluid dynamics

CMTR Completely mixed tank reactor

COD Chemical oxygen demand

CT Computed tomography

DLVO Theory of Derijaguin, Landau, Verwey, Overbeek

XI

DO Dissolved oxygen

DPM Differential pressure measurement

ECM Electrical conductivity measurement

ECT Electrical capacitance tomography

EDM Electrodiffusion measurement

EPS Extracellular Polymeric Substances

GAS Granular activated sludge

GTL Gas to liquid technology

HFA Hot film anemometry

HRT Hydraulic retention time

HWA Hot wire anemometry

LDA Laser Doppler anemometry

OLR Organic loading rate

PBC Packed–bubble concurrent upflow reactor

PIV Particle image velocimetry

PSD Power spectral density

PTV Particle tracking velocimetry

SBR Sequencing batch reactor

SC Slot correlation

SGV Superficial gas velocity

SRT Sludge residence time

TBR Trickle bed concurrent downflow

TDR Time domain reflectometry

TSS Total suspended solids

UASB Upflow anaerobic sludge blanket reactor

INTRODUCTION

1

1. INTRODUCTION

Water is one of the most important human needs. The total volume of water on the Earth is

estimated at 1386 million cubic kilometres, only 2.5% is fresh water and somewhat less than

one–third of this is available for human use. More than two–thirds of fresh water is frozen in

glaciers and polar ice caps (Postel et al., 2006). Additionally, over half of available fresh

water supplies are already used for human activities and the use is increasing with residential

demands, industrial and agricultural growth (Postel et al., 2006, Vorosmarty and Sahagian,

2000). Moreover, worldwide human water consumption increased three–fold in the last 50

years from 1382 km3/yr in 1950 to 3973 km3/yr. According to Clarke and King (2004), the

increase will continue up to 5235 km3/yr in 2025. By that time, 5 out of 8 people will live in

conditions of water stress and scarcity (Arnell, 1999). Fresh water is essential in human

conurbations, agriculture and industry (Ganoulis, 1994). However, water pollution caused by

human activities is one of the main threats to fresh water supplies. Due to increasing

industrialization and urbanization, the environmental quality is declining as more and more

wastewater appears. William and Musco (1992) estimated that the cost for running the

municipal water supply and waste water systems is € 14 billion per year in the EU. In order to

face the problems of future water demand, ameliorate growing pollution, effective wastewater

treatment investigations are needed. One of the attractive technologies is aerobic granulation

in a Sequencing Batch Reactor (SBR), a recent innovation in biological purification of

wastewater. However, it is very complex process and its mechanisms are not well understood.

Thus, the current study is aimed to carrying out fluid dynamic investigations in an SBR for

a better understanding of this process.

1.1 Aerobic and anaerobic granulation

Granulations is a self–immobilization process in which biological solids or more general

condensed matter agglomerate and develop into dense and compact granular biomass under

controlled operating conditions. Granular Activated Sludge (GAS) in comparison with

Conventional Activated Sludge (CAS) has better settling ability and higher capacity for

biomass retention, which permits the easy separation of the granules from the purified water.

INTRODUCTION

2

Granules have an ellipsoidal form with diameter up to 5 mm and density ca. 1.05 g/mL

(Etterer and Wilderer, 2001, Tay et al., 2001). Due to those properties granulation is

a promising biotechnology for wastewater treatment. Figure 1 illustrates the differences

between CAS and GAS.

Granule formation is very complex process which includes physical, chemical and

biological phenomena. Granulation can be described as a four–step procedure. At the

beginning, a physical movement initiates contact between bacteria and bacterial attachment to

a solid surface is recognized. During this phase, the diffusion, gravity and hydrodynamic and

thermodynamic forces (e.g. Brownian movement) play a crucial role. Additionally, cell

mobility has a decisive influence on the initial interaction and movement along the surface. In

the second step, the initial attractive forces maintain a stable bacteria solid surface and

multicellular contacts are observed. Here, physical, chemical and microbiological forces

effect significant granule formation (Liu and Tay, 2002). In the case of physical forces,

hydrophobicity of the bacterial surface has an important role at the beginning of granule

formation (Van Loosdrecht et al. 1987). Taking into account thermodynamic theory, it can be

noted that increasing hydrophobicity of the cellular surface would cause a decrease in the

excess Gibbs free energy of the surface, which promotes cell–to–cell interaction and further

serves as a driving force for bacteria to self–aggregate out of the liquid phase (hydrophilic

phase). Here, filamentous bacteria, by linking together individual cells, play a crucial role in

the growth of a three–dimensional structure (Liu and Tay, 2002). Taking into account

chemical forces, the formation of ionic pairs and triplets must be considered. In the case of

microbiological forces, cellular surface dehydratation and membrane fusion seem to be

essential in initiating self–immobilization of anaerobic bacteria (Tay et al., 2000). In the third

Figure 1: Comparison of Conventional Activated Sludge (left) and Granular Activated Sludge (right) (source: Tay et al., 2001)

INTRODUCTION

3

step of granule formation, microbial forces play a decisive role in the construction of attached

bacteria and aggregated bacteria mature. During this phase, production of extracellular

polymers and the growth of cellular clusters take place (Hartmann et al., 2007,

Kowalczyk et al., 2007, Petermeier et al., 2007, Zima et al., 2007a). At the end of the process

(fourth step), a steady–state three–dimensional structure of the microbial aggregate appears.

Hydrodynamic forces, especially shear forces, have a decisive task in forming a structured

community (Liu and Tay, 2002). Although the effect of shear stress is well studied in the

literature, the role of normal stress has been poorly investigated. Elongation flow influences

biological material more effectively than pure shear flow. The wall collision effect may be

determined by particle mass loading, particle shape and wall roughness, combination of

particle and wall material and hydrodynamic interactions. Relative motion between particles

is crucial for inter–particle collision (Esterl et al., 2002, Höfer et al., 2004, Nirschl and

Delgado, 1997, Zima et al., 2007).

Aerobic and anaerobic granulation are distinguished among biogranulation phenomena.

For a better understanding of the granule formation, a short comparison of both processes is

presented. It includes both anaerobic and aerobic granule characteristics, different theories on

the granulation process and factors affecting their formation. The anaerobic process,

extensively studied for over 25 years, is currently the main process operated by hundreds of

wastewater treatment plants (Alves et al., 2000, Murnleitner et al., 2002). Experimental

investigations present the Upflow Anaerobic Sludge Blanket Reactor (UASB) as an

appropriate system for the growth of anaerobic granules. However, the anaerobic process has

some disadvantages. A long start–up period (at least 2–4 months) together with a long

operation time and unsuitability for low–strength organic wastewater are the most significant.

Moreover, nutrient removal (nitrogen, phosphorus) from wastewater does not take place in

this system. In order to overcome these weaknesses, novel investigations under aerobic

conditions (Liu and Tay, 2004) have been implemented. Aerobic granulation represents

a new, not fully understood field, where further scientific investigations are required. In the

present work, fluid dynamic investigations in an aerobic Sequencing Batch Reactor (SBR) are

carried out.

Anaerobic technology was reported for the first time in 1969 by Young and McCarty.

Further investigations were made in Dorr´Oliver Clarigesters in the context of agro–industrial

effluent treatment in South Africa (1979). Moreover, granular sludge was discovered in

INTRODUCTION

4

a 6 m3 pilot plant at the CSM sugar factory in Breda (the Netherlands) in 1976. A report

concerning this work shows the great importance of granulation process in wastewater

treatment (Lettinga et al., 1977). However, a large gap in the understanding of this process

recommends further investigations.

Structure of anaerobic granules. Microscopic investigations carried out by

MacLeod et al. (1990) and Guiot et al. (1992) show a multilayer microstructure of anaerobic

granules. In the inner part, methanogens, which may act as nucleation centres, appear.

H2–producing and H2–utilizing bacteria dominate in the middle layer. In the outer section,

a mix of species including rods, cocci and filamentous bacteria is observed. Immunological

and histological methods (Achring et al., 1993), dynamic models (Arcand et al., 1994),

studies with microelectrodes (Santegoeds et al., 1999) and other investigations have

confirmed the multilayer structure of anaerobic granules. However, granules with

a homogenous, monolayer form can be also observed (Fang et al. 1995). In this case,

filamentous organisms dominate.

The diameter of anaerobic granules ranges from 2 to 5 mm and their density varies

between 1.033 and 1.065 g/mL. Due to those properties, they settle rapidly, which allows the

separation of liquid and solid phases. The optimal properties in the case of industrial

wastewater include granules with a size of 1–2 mm (Pereboom and Vereijken, 1994).

Additionally, the high strength of anaerobic granules results in granule stability, which is

desired in industrial applications (Quarmby and Forster, 1995).

It is also well known that cell surface hydrophobicity plays a crucial role in both aerobic

and anaerobic granulation processes (Liu et al, 2003). Microorganisms with high surface

hydrophobicity form dense aggregates which remain in the bioreactor. Factors such as

starvation, oxygen level, selection pressure and ionic strength of the medium influence cell

surface hydrophobicity.

Several theories of granule formation have been developed within the past 20 years (Liu

and Tay, 2004). One of them is the physical theory (Hulshoff et al., 1983, Pereboom, 1994).

In this case liquid, SGV, suspended solid in the effluent and seed sludge, attrition and removal

of excess sludge from the reactor belong to the most important granulation factors. Pressure

selection (Hulshoff et al., 1983) can be estimated as the sum of the hydraulic loading rate and

gas loading rate. Under high–pressure selection, dispersed and light sludge is washed out

whereas heavier flocs remain in the bioreactor. The first granules obtained are fluffy, but with

INTRODUCTION

5

increasing process time become denser due to bacterial growth on the outside and inside of

the aggregates. Filamentous granules which are met in the first stages of the process become

denser with increasing time of the process. In the second case, under a low selection pressure,

bulking sludge can be observed. According to Pereboom (1994), growth of colonized

suspended solids significantly influences anaerobic granulation. Moreover, he postulated that

the granule size increases due to growth of microbial colonies and in consequence concentric

layers observed on sliced granules are related to small fluctuations in growth conditions.

The second approach describing anaerobic granulation is the microbial theory. Among

microbial theories the physiological approach is distinguished. The production of extracellular

polymers by microorganisms under certain conditions seems to affect granule formation

significantly (Dolfing, 1987). This influence has been observed by several authors. For

example, the Cape Town Hypothesis (Sam-Soon et al., 1987) shows that granulation depends

on Methanobacterium strain AZ, an organism which uses H2 as its individual energy source

and can produce all its amino acids, with the exception of cysteine. In the presence of a high

H2 partial pressure, cell growth, excess substrate and amino acid production is activated. If

Methanobacterium strain AZ cannot produce the essential amino acid, than cell synthesis is

limited by the rate of cysteine supply. The presence of ammonium causes a high production of

other amino acids which Methanobacterium strain AZ secretes as extracellular polypeptide,

binding Methanobacterium strain AZ and other bacteria together to form granules. However,

it is considered that other anaerobic bacteria can be similar to Methanobacterium strain AZ

and also contribute to the granulation process.

According to the Spaghetti model proposed by Wiegant (1987), granule formation can be

divided into two phases: precursor formation and granule growth from them. The first step is

treated as the limiting stage in granulation. Agitation of liquid, generated by gas production,

causes the formation of small aggregates by Methanothrix bacteria. Individual bacteria growth

and the entrapment of non–attached bacteria lead to granule formation from precursor

particles. The presence of mechanical forces has an influence on the spherical shape of

granules. During this phase granules still have a filamentous form, and can be compared to

a ball of spaghetti formed by very long Methanothrix loose and bundled filaments.

Subsequently, due to an increase in density of the bacterial growth, rod–type granules are

formed.

The last approach among microbial theories is the ecological concept. Several studies have

INTRODUCTION

6

been carried out in this field. One of them suggests bridging of microflocs by Methanothrix

filaments. Microscopic investigations and activity measurements carried out by Dubourgier et

al. (1987) indicated a crucial role of Methanothrix in granule strength by forming a network

which stabilizes their structure. Here cocci and rod colonies cover filamentous Methanothrix,

forming microflocs of 10–50 µm. Subsequently, Methanothrix filaments, due to their

particular morphology and surface properties, can establish bridges between several

microflocs creating larger granules, larger approximately than 200 µm.

The third approch defining anaerobic granulation is the theromodynamic theory.

According to Schmidt and Ahring (1996), the granulation process in UASB reactors can be

divided into four steps. First, transport of cells to the surface of an uncolonized inert material

or other cells takes place. Cells can be moved by different mechanisms such as diffusion

(Brownian motion), advective (convective) transport by fluid flow, sedimentation or gas

flotation. Subsequently, initial reversible adsorption by physicochemical forces to the

substratum commences. This adsorption is described by DLVO theory (from the names of the

authors Derijaguin, Landau, Verwey and Overbeek) (Hulshoff et al., 2004). DLVO explains

microbial adhesion using calculations of adhesion free energy changes. The latter states that

the total long–range interaction over a distance of more than 1 nm is a result of van der Waals

and Coulomb (electrostatic) interactions. Here, three different situations can occur: repulsion

when electrostatic interactions dominate, weak attraction when cells are located within

a certain distance from each other or strong irreversible attraction if van der Waals forces are

the principal factor. Physicochemical forces such as hydrogen, ionic and dipolar bonds and

hydrophobic interactions also influence the adsorption strength. The third step affecting

biofilm formation is irreversible adhesion of the cells to the substratum by polymers. This

phenomenon can occur due to specific bacterial characteristics such as cell surface structures

or polymer appendages (Van Loosdrecht and Zehnder, 1990, Schmidt and Ahring, 1996). At

the end of the process, cells are multiplied and granules appear. After adherence of bacteria

colonisation takes place.

The proton translocation–dehydration theory presented by Tay et al. (2000) describes the

granulation process as following four steps: dehydration of bacterial surfaces, embryonic

granule formation, granule maturation and post–maturation.

Finally, it should be added that the presence of nuclei or bio–carriers for microbial

attachment improves significantly granule formation from suspended sludge. Cell attachment

INTRODUCTION

7

to particles can be concluded as the initiation step for granule growth. In the second stage,

formation of a dense and thick biofilm on the cluster of the inert carriers takes place (Hulshoff

et al., 2004). According to Yu et al. (1999), inert materials which enhance sludge granulation

should have a high specific surface area, good hydrophobicity, spherical shape and specific

gravity similar to that of anaerobic sludge.

Several factors influence granule formation and destruction. One of them is the upflow

liquid velocity and hydraulic retention time (HRT). Alphenaar et al. (1994) observed that

a high upflow liquid velocity and short HRT lead to washout of nongranulation component

bacteria and promote sludge granulation. Usually, the effects of upflow liquid velocity on

anaerobic granulation are explained by the selection pressure theory (Hulshoff et al., 1988).

Moreover, optimal anaerobic granulation takes place only under appropriate temperature.

Methanogenic bacteria, being the core of the microbial component of anaerobic granules,

grow very slowly at low temperature, and their activity is reduced when the temperature is

below 30°C (Bitton, 1999). Successful granulation in a UASB is assured at temperatures from

30 to 35°C. It is well known that high temperatures encourage the growth of suspended solids;

however, extremely high temperatures inhibit bacterial growth (Bitton, 1999, Liu and

Tay, 2004).

Granules can be effectively grown only under optimal pH condition. GAS with acidogenic

bacteria can be obtained when the pH is between 5.0 and 6.0. Methane–producing bacteria

grow in a very narrow pH range of 6.7–7.4 (Bitton, 1999).

Feed solution is another key factor influencing the composition and structure of anaerobic

granules. Anaerobic granulation takes place in different types of wastewaters. However, due

to the extremely low growth rate of anaerobic bacteria, a sufficient energy content in the

substrate is required for anaerobic granulation. Substrate complexity exerts a pressure

selection on the microbial diversity in anaerobic granules, which may significantly affect the

formation and microstructure of granules (Liu and Tay, 2004).

The role of added polymers or cations should not be forgotten. Both synthetic and natural

polymers have been used in coagulation and flocculation processes. They promote particle

agglomeration and enhance the formation of anaerobic granules. El–Mamouni et al. (1998)

found that addition of the polymer chitosan improves anaerobic processes in UASB reactors.

The above studies have briefly covered anaerobic process. However aerobic granulation,

INTRODUCTION

8

which in contrast is not fully developed, especially from the fluid dynamic point of view, is

the main object of the present work, and investigations in an aerobic SBR are presented

below.

A general description of aerobic granules was presented during the first Aerobic

Granular Sludge IWA Workshop in 2004 in Garching (Germany). A definition was

formulated by de Kreuk et al. (2005) as follows “granules making up aerobic granular sludge

are to be understood as aggregates of microbial origin, which do not coagulate under reduced

hydrodynamic shear, and which settle significantly faster than activated sludge flocs”.

Aerobic granulation is a novel technology; the first aerobic investigations were performed by

Mishima and Nakamura not earlier than in 1991, in a continuous aerobic upflow sludge

blanket reactor. Science than, a lot of scientific work has been carried out and is still

continuing on this topic.

Aerobic granule morphology is completely different to flock–like sludge. Granules can

be treated as a metropolis of microbes containing millions of individual bacteria. By using

molecular biotechnology techniques, heterotrophic, nitrifying, denitrifying, P–accumulating

and glycogen–accumulating bacteria can be recognized in aerobic granules. Granular

Activated Sludge (GAS) has a spherical shape with a very clear outline (Tay et al., 2001a,

Zhu and Wilderer, 2003). Microscopic investigations indicate a multilayer structure. The

aerobic ammonium–oxidizing bacterium Nitrosomonas appears at a distance of 70–100 µm

from the granule surface. In the next layer (400 µm below the granule surface),

polysaccharides are seen. In sequence, the anaerobic bacterium Bacteroides appears

(800–900 µm). Up to a depth of 900 µm below the granule surface, many pores and channels

which allow transport of oxygen and nutrients into and metabolites out of the granules are

observed. Layers of dead microbial cells are located at a depth of 800–1000 µm (Tay et al.,

2002). Another mushroom–like structure of granules with high ratios of nitrogen/chemical

oxygen demand (N/COD) was recognized by Liu et al. (2004). Here, at a depth of 70–100 µm

from the granule surface, a nitrifying population is located. Biofilms of mixed bacterial

communities form thick layers of differentiated mushroom–like structures which are similar

to the structure observed in aerobic granules (Costerton et al., 1981).

The average diameter of granules ranges from of 0.2 to 5 mm. The balance between

growth and abrasive detachment due to strong mechanical forces in an aerobic reactor impacts

significantly on the granule size. Settleability similar to anaerobic conditions is a very

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9

important factor which determines the efficiency of solid–liquid separation. It reaches values

from 30 to 70 m/h and is comparable to anaerobic granules from a UASB reactor but at least

three times higher than Conventional Activated Sludge (CAS). The high settling allows high

biomass retention in the reactor, faster degradation of pollutants and finally compact reactor

dimensions. The aerobic granule density varies from 1.004 to 1.065 g/mL (Etterer and

Wilderer, 2001). Moreover, GAS has high physical strength, which protects against high

abrasion and shear. As indicated above, cell surface hydrophobicity significantly influences

granule stability.

Aerobic granule growth can be regarded as a special case of biofilm development (Liu

and Tay, 2002). Microbial granulation, which is fundamental in biology and cell aggregation,

can be explained as a gathering together of cells to form a fairly stable, multicellular

association under physiological conditions (Calleja, 1984). According to Weber et al. (2006),

granules development with the aid of ciliates takes place in three different phases (see

Figure 2).

At the beginning, ciliates settle on other organisms or particles (Figure 2A). Then, bulky

growth of ciliates is recognized (e.g. Epistylis sp.) (Figure 2B). Stalks and zooids are

Figure 2: Granule growth (source: Weber et al., 2006)

swarming cellC

1 mm

A 100 µ m

B1 mm

D 1 mm

C C C 1 mm

A A A

B1 mm BB1 mm

D 1 mm D D1 mm

swarming cell

50 µm

100 µm

50 µm E

50 µm

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10

colonized by bacteria. In the second phase, the granule grows and the core zone is developed.

Here, many ciliate cells are completely overgrown by bacteria and die. A dense core of

bacteria and remains of ciliate stalks is formed (Figure 2C). Subsequently, a mature granule is

developed. Finally, granules are composed of two zones [core zone (red part) and loose

structured fringe zone (grey part), see Figure 2E] and serve as a new substrate for swarming

ciliates (Figure 2D).

Almost all aerobic granules are cultivated in a Sequencing Batch Reactor (SBR)

(Al–Rekabi et al., 2007). SBRs have been successfully used all over the world since the

1920s. However, their popularity increased after Irvine and Davis (1971) described the

operation of SBRs. The SBR is a modified design of the Conventional Activated Sludge

(CAS) plant. The CAS system requires the application of multiple tanks (aerated and anoxic)

with the recycling of various mixed liquors to obtain high concentrations of microorganisms,

nitrate and degradable organics in anoxic reactors. Consequently, appropriate space and large

capital investment are required. All economic and space problems can be solved in

single–stage biological wastewater treatment plants with high biomass concentration and

bioactivity. The SBR is the optimal method for granule growth with good settling properties,

solid–liquid separation and the accumulation of high amounts of active biomass (Liu and Tay,

2004). This system can be operated successfully to enhance the removal of nitrogen,

phosphorus, ammonia, Total Suspended Solids (TSS) and carbonaceous Biochemical Oxygen

Demand (BOD). High–quality BOD and TSS effluents contain 5–15 mg/L of CBOD5 and

10–30 mg/L of TSS (EPA, 1999). The SBR is operated in repetitive cycles, each containing

five phases: fill, react, settle, draw, idle (see Figure 3).

Figure 3: Scheme of the Sequencing Batch Reactor (SBR) cycle

STATIC FILL REACT SETTLE DRAW IDLE (aeration/mixing)

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11

Each part consists of different chemical and biological processes. During the fill phase,

denitrification and phosphorus removal take place. In the next step (react phase), phosphorus

uptake, BOD release and nitrifications occur. Then, during the settle phase nitrate is removed

by endogenous denitrification. Finally (draw and idle), nutrients (phosphorus, nitrogen) are

removed through biological activity (Zhu et al., 2006). Successive SBR processes can be

achieved by the control system, which consists of a combination of level sensors,

microprocessors and timers. Up to now almost all aerobic systems have been operated in

laboratory–scale bioreactors. However, first investigations with an aerobic pilot plant reactor

were implemented by de Bruin et al. (2005). This reactor has a height of 6 m, a diameter of

0.6 m and a hydraulic capacity of 5 m3/h, depending on the applied load.

Factors affecting granule formation. Aerobic granulation, similarly to anaerobic

granulation is a very complex process, in which many factors affect the structure and

composition of granules. The causes and mechanisms of granulation are not yet exactly

understood. According to Guiot et al. (1992), a selective pressure created by the upflow

velocity in a bioreactor can contribute to the formation of easily settleable granules. Another

aspect is the hypothesis that methagenic microorganisms found in granules exhibit natural

tendencies to aggregate, being the cause of granule formation (Kosaric and Blaszczyk, 1990).

Additionally, the substrate type and its composition have a significant influence on the

formation of granules. Dolfing et al. (1987), Lettinga et al. (1980), van der Hoek (1987),

Etterer and Wilderer (2001) and Wang et al. (2005) carried out experiments with different

carbon sources. The results of these investigations illustrate that granulation can be achieved

only with certain carbon sources of a typical concentration. Using acetate, caproic acid and

glycerol, granulation is observed after 20 days of operation. These granules have

a nonfilamentous and very compact bacterial structure. However, the fastest granulation is

obtained with glucose and peptone as a carbon source. As shown by Zhu and Chunxin (1999),

granule formation can be obtained after 12 days. Glucose–fed granules have a filamentous

structure. Moreover, investigations by Van Loosdrecht et al. (2005) indicated the importance

of growth rate on biofilm on granule morphology. With decreasing maximal growth rate of

organisms in aerobic granules, their surface becomes smoother. According to van Loosdrecht

et al. (2005), with some substrates it is not possible to achieve granules with higher growth

potential. For example, it is easier to obtain compact structures on methanol than acetate

because of the different growth rates of organisms on these two substrates.

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12

Furthermore, the influence of feast–famine and settling time on species selection was

investigated by McSwain et al. (2005). A high feast–famine regime with pulse feeding is

necessary for compacted granule formation.

The Superficial Gas Velocity (SGV) is indirectly one of the most important parameters for

granule formation and structure. According to investigations carried out by de Kreuk et al.

(2005a), granules reach a maximal diameter at an SGV of 2 cm/s. With lower and higher gas

velocities, their diameter becomes smaller. The significant role of SGV in the granulation

process was confirmed also by Tay et al. (2001). They investigated the operation of three

bioreactors with the same geometric configuration (height 800 mm and diameter 60 mm) and

working volume (2.0 L). The granulation process was compared for different SGVs of 0.3,

1.2 and 2.4 cm/s, which are equivalent to flow rates of 0.5, 2 and 4 L/min, respectively.

During these experiments, no granulation was observed with the lowest flow rate. In contrast,

aerobic granule formation occurred at higher velocities, where they had a more regular and

rounded shape. Additionally, SGV generates substantial hydrodynamically induced

mechanical stress (Tay et al. 2001a). This stress can be classified as shear stress due to

relative motion between particle and fluid (Henzler, 2000), normal stress due to pressure

(gradients) and velocity gradient, which can act in both normal and tangential directions with

respect to the relative motion between the granular particles and surrounding water (Zima et

al., 2007). Trinet et al. (1991), Oashi et al. (1994) and Tay et al. (2004) reported that high

hydrodynamic forces can stimulate the production of Extracellular Polymeric Substances

(EPS). EPS acts as a kind of glue substance between the microorganisms of an aggregate.

According to Tay et al. (2001a), this substance also plays an important role in the formation

and maintenance of aerobic granules. Indeed, there is general agreement that flow–induced

forces have a significant impact on the structure and metabolic activity of granule formation

(Mikkelsen and Keiding, 1999, Biggs and Lant 2000, Mikkelsen, 2001, Liu and Tay, 2002,

Di Iaconi et al., 2004, Zima et al., 2007).

The optimal settling time which belongs to one of the phases of the process is also very

important for granulation. This factor selects the growth of fast settling bacteria and sludge

with poor settling ability is washed out (Liu, Y et al., 2004). According to Qin et al. (2004),

successful aerobic granulation can be obtained with a settling time under 5 min. This short

time can improve the cell surface hydrophobicity.

Hitherto, it was shown that the hydraulic retention time (HRT), defined as the ratio of

INTRODUCTION

13

discharged effluent volume and working volume of the SBR, significantly affects granule

formation. As short HRT decreases the growth of suspended solids and improves the

granulation process. However, its duration should be long enough for microbial growth and

accumulation.

Aerobic starvation in the SBR plays a decisive role in the microbial aggregation process,

leading to stronger and denser granules. According to Bossier and Verstraete (1996), under

starvation conditions bacteria become more hydrophobic and in consequence adhesion or

aggregation is simplified.

Additionally, aerobic granulation can be obtained with an optimal reactor configuration

(Beun et al., 1999, Liu and Tay, 2002). Up to now granules have mainly been grown in

column–type upflow reactors. This type of reactor, in comparison with a completely mixed

tank reactor (CMTR), has a different hydrodynamic behaviour in terms of interactions

between microbial aggregates and flow. Homogeneous circular flow in a column bioreactor

created by liquid or air upflow forces the microbial aggregates to take on a regular shape with

minimum surface free energy. Moreover, a high ratio of reactor height to diameter (H/D)

improves the selection of granules by the difference in settling velocity. In contrast, in the

CMTR microbial aggregates move with dispersed flow in all directions. Under those

conditions, granules cannot be obtained and only flocs with an irregular shape and size

appear.

In contrast to anaerobic granulation, the organic loading rate (OLR), dissolved oxygen

(DO) concentration, pH and temperature are not so decisive factors in the formation of

aerobic granules. Granules mainly grow at pH around 7.0 ± 0.5 (de Kreuk et al., 2005a,

MsSwain et al., 2005, Wang et al., 2005). It was shown that aerobic granules can be obtained

over a wide range of organic loading rates from 2.5 to 15 kg chemical oxygen demand

(COD)/m3day (Moy et al., 2002). However, OLR affects the physical characteristic of aerobic

granules. As shown by Liu et al. (2003), the mean size of aerobic granules increases from 1.6

to 1.9 mm with increasing OLR from 3 to 9 kg COD/m3day. DO concentration influences the

operation of aerobic wastewater treatment systems. Successful aerobic granulation can be

obtained at lower DO concentrations of 0.7–1.0 mg/L (Peng el al., 1999) and also higher than

2 mg/L (Tay et al., 2002). Mostly aerobic GAS formation takes place at room temperature,

between 20 and 25°C. However, as shown by de Kreuk et al. (2005a), temperature changes

can significantly affect granule formation. Starting up a reactor at a low temperature (8°C) led

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14

to the presence of organic COD during the aerobic phase. Under those conditions, filamentous

organisms with irregular structures appeared, causing washout of the biomass. Due to the

instability of the laboratory–scale bioreactor, the experiment was terminated. However,

investigations with decreasing temperature of a steady–state operated reactor from 20 to 15

and to 8°C until steady–state operation was reached again depict different situations. Aerobic

granulation can be effectively operated at low temperatures 15 and 8°C only if is started at

a higher temperature (20°C). Due to the increased oxygen penetration depth at low

temperatures, nitrification rates are influenced to only a limited extent. The increased

penetration depth of oxygen leads to a decreased nitrogen–removing capacity of aerobic

granules at low temperature. These investigations showed that aerobic granular sludge

reactors should preferentially be started up in warm seasons (spring and summer).

From the statements above, it can be seen that aerobic granulation is a complex process

and what factors influence granule formation are not yet fully understood. Several

investigations were carried out from different point of view. However, the main aim of the

present work was to study fluid dynamic effects on Granular Activated Sludge formation.

Thus, in the current work, fluid dynamic investigations of multiphase flow in an SBR with

optical in situ techniques were applied. Particularly, the influence of global flow parameters

(e.g. aeration flow rate, solid phase concentration) on the local fluid dynamic effects

(velocities, shear and normal stresses as well as particle–particle and particle–wall collision)

was studied in terms of its impact on granules formation and destruction.

1.2 Fluid dynamics of multiphase flow

Multiphase flow occurs widely in nature and engineering processes. It can be met in the

biochemical, chemical, food, electronic, pharmaceutical, agricultural, petroleum and power

generation industries. The inherent complexity of multiphase flow causes problems from both

experimental and theoretical points of view. Moreover, a fundamental knowledge of

multiphase flow is still not complete. Three main reasons have influenced this state. One of

them is the complex physical phenomenon of multiphase flow, which consists at least of two

phases (gas–solid, gas–liquid, liquid–solid, gas–liquid–solid, etc.). Within each flow type

several possible flow regimes can exist, such as annular flow, slug flow, jet flow and bubbly

flow. The inherent oscillatory behaviour of multiphase flow requires costly non–stationary

INTRODUCTION

15

solution algorithms. Additionally, numerical methods for solving equations of multiphase

flows are very complicated. Complex physical laws and mathematical treatments of

phenomena occurring in two– and three–phases flows such as coalescence, break–up, drag and

interface dynamics are not well developed. Furthermore, a lack of appropriate experimental

results prevents efficient simulation (Van Wachem and Almstedt, 2003). The hydrodynamics

of gas–liquid and gas–liquid–solid system have been intensively studied over the past two

decades (Chen et al., 1999, Dziallas et al., 2000, Li and Prakash, 2000, Liu et al, 2001,

Schallenberg et al., 2005). However, there is still lack of detailed physical understanding and

appropriate tools for the design and optimization of such system (Cui and Fan, 2004).

The complexity of fluid dynamics in multiphase systems (bubble column reactors, airlift

reactors, stirred vessels, fluidization systems, etc.) needs to be well understood owing to its

application in the chemical and bioprocess industries. Many parameters control the flow of

solid, liquid and gas phases in the bioreactors, where the relative buoyancy of each discrete

form is the major driving force applied to the flow regime. Coalescence, surface tension,

viscosity, pressure effects and bubble disruption affect complex flow phenomena. These

parameters can influence the size, shape and volume fraction of the dispersed phase. The

hydrodynamics of multiphase reactor influences the efficiency of biochemical production

rates through transport processes such as inter–phase oxygen transfer and mixing of nutrients

and reactants. Because the majority of biochemical reactions occur at a supported organism

and flocculating microbe, transport of the solid phase plays a crucial role (Glover and

Generalis, 2004).

Recently, computational fluid dynamics (CFD) has become an important tool for

multiphase flow simulation. Anderson and Jackson (1967) carried out CFD investigations and

presented continuum equations of motion for gas–particle flow. Computations of bubble

behaviour in a particle bed were reported by Garg and co–workers (1975). After those

investigations, researchers improved gas–solid flow models. Subsecutively, Ishii (1975)

developed fluid–fluid governing equations and improved models for different gas–liquid

conditions. Up to the 1980s, mainly the Eulerian model for continuous and dispersed phases

was used for computational models of multiphase flow, where, both dispersed and continuous

phases are described as a continuous fluid with appropriate closures. With improved

computational methods, the dispersed phase can be computed separately by using

a Lagrangian formulation. However, the amount of dispersed particles and droplets is still

INTRODUCTION

16

limited in those calculations (van Wachem and Almstedt, 2003). Therefore, improvement of

CFD models is necessary for a better understanding of hydrodynamic phenomena. Local and

global flow properties such as the velocity field of phases and flow structures can be

quantified by using novel methods such as computer automated radioactive particle tracking

(CAPRT) (Chen et al., 1999), particle image velocimetry (Raffel et al., 1998), hot film

anemometry (HFA) (Franz et al., 1984), laser Doppler anemometry (Brenn et al, 2006) and

electrical capacitance tomography (ECT) (Warsito and Fan, 2001). These techniques will be

described later more in detail.

Multiphase bioreactors. As indicated above, multiphase flow is very complex physical

phenomenon which takes place in different systems. Because the main object of the present

work is to show the impact of fluid dynamic effects on GAS which grows in an SBR, fluid

dynamics investigations will focus on bioreactors, especially bubble columns. Multiphase

bioreactors are divided into two groups: fixed beds with two–phase flow and reactors with

a moving catalyst. In the first case, among packed–bed reactors, trickle–bed concurrent

downflow (TBR), trickle bed countercurrent flow and packed–bubble flow concurrent upflow

reactors (PBC) are distinguished. Different fluid dynamics investigations such as flow regime

statement, pressure drop and liquid holdup, gas–liquid interfacial areas and interphase mass

transfer coefficients were considered. Reactors with moving catalyst, bubble columns, slurry

bubble columns and three–phase fluidized bed reactors are considered.

Bubble column reactors, which belong to multiphase reactors, can be characterised as

a cylindrical vessel with a gas distributor at the bottom where air is dispersed into a liquid or

solid–liquid suspension. They can be used as contactors and as reactors in the chemical,

petrochemical, biochemical and metallurgical industries (Dagaleesan et al. 2001, Kantarci et

al. 2005). Moreover, multiphase bioreactors are typical for chemical processes based on

different reactions such as oxidation, chlorination, alkylation, polymerization and

hydrogenation, in biochemical processes such as biological wastewater treatment,

fermentation and in the manufacture of synthetic fuels by gas conversion processes (Prakash

et al. 2001, Kantarci et al. 2005). It should be pointed out that bubble column reactors have

a simple design and operation principle. However, this mechanically simple setup goes hand

in hand with very complex flow structures inside the vessel (Michele and Hempel, 2002). Due

to the wide application area and huge industrial importance, hydrodynamic investigations of

bubble columns have been carried out for over 30 years. The research interests concern gas

INTRODUCTION

17

holdup studies, flow regime investigations, local and average heat transfer measurements,

mass transfer studies and computational fluid dynamics studies (Anabtawi et al., 2002, Li and

Prakash, 1999, Verma and Rai, 2003). The effect of operating conditions, superficial gas

velocity, type and concentration of solids and column dimensions were examined in those

studies. The fluid dynamic characterization of bubble column reactors has a crucial effect on

their operation and performance. Three different flow regimes can be distinguished in bubble

column reactors: homogeneous (bubbly flow), heterogeneous (churn turbulent) and slug flow

(Hyndman et al., 1997). Moreover, a foaming regime can be present. Under low SGV (less

than 5 cm/s) in watery dispersion, in semibatch columns, the bubbly flow regime

(homogeneous) is recognised. It can be characterized by a uniform small size and rise velocity

of bubbles. In this case, bubble coalescence and break–up almost not exist. Furthermore,

investigations by Kawagoe et al. (1976) showed that gas holdup in bubbly flow increases with

increasing superficial gas velocity. In the second case, for SGV higher than 5 cm/s, the

churn turbulent regime exists. Here, a disturbed form of the homogenous gas–solid system

due to enhanced turbulent motion of gas bubbles and liquid recirculation appears. Due to high

gas throughputs, unsteady flow patterns and large bubbles with a short residence time are

formed. The average bubble size is determined by coalescence and break–up, which are

controlled by the energy dissipation rate in the bulk (Throat and Joshi, 2004). Moreover,

investigations by Matsuura and Fan (1984) in churn–turbulent flow showed a diversity of

large bubbles with diameters from a few millimetres to a few centimetres. This type of

bioreactor is well developed on the industrial scale. Slug flow regime bubble columns are

operated in small–scale laboratory columns (with diameters up to 15 cm) at high gas flow

rates (Hyndmann et al., 1997). Figure 4 illustrates the above flow regimes.

Figure 4: Flow regimes in bubbly columns

perfect bubbly imperfect bubbly (or bad bubbly)

churn slug flow

homogeneous heterogeneous

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18

From the above, it must be concluded that hydrodynamic investigations under different

flow regimes are urgently required. Thorat and Joshi (2004) showed the dependence of gas

velocity on column dimensions, physical properties of the system and sparger design.

However, the effects of those parameters have not been fully investigated. For a better

understanding of those phenomena, further detailed experimental and numerical

investigations are necessary. Fluid dynamic work mainly concerns global parameters and

local time–dependent hydrodynamic measurements are limited (Mudde et al., 1997). A lot of

modelling work (Ueyama and Miyauchi, 1979, Clark et al., 1987) has been carried out with

one–dimensional, time–invariant flow fields based on the investigations of Hills (1974) on

velocity and gas fraction profiles. However, this method goes only in the single parameter

direction. A priori knowledge of the gas fraction distribution is essential. Computational Fluid

Dynamics (CFD) is an effective tool for solving this problem. Nevertheless, many questions

still remain unanswered, particularly modelling of the phase interactions and turbulence in

bubbly flow. An understanding of flow from the physical point of view and simulation

validation are possible by implementing experiments.

Flow structures in two–phase bubble columns were characterized for the first time by

hot film anemometer investigations carried out by Franz et al. (1984). Those experiments

show a complexity of flow structure. Helical upward flow in the centre and downflow region

close to the wall containing vertical structures is observed. Moreover, the fluctuating nature of

the flow field was discovered by Groen et al. (1995). In this case, dominating up– and

downward velocities are seen to change with time. PIV investigations in 2D and 3D carried

out by Chen and Fan (1992) made a contribution. Here, three regimes, the dispersed bubble

flow regime at low superficial gas velocities, the vertical–spiral flow regime and the turbulent

regime at higher gas flow rates, are distinguished. The above experiments described the flow

and also selected the flow field in terms of stresses and parameters of the vertical coordinates.

Reese et al. (1993), Reese and Fan (1994) and Mude et al. (1997) explained the use of PIV to

study flow in 2D and 3D in bubble columns. Their experiments permitted recording of

instantaneous velocity, holdup fields and turbulent stresses in 2D columns and furthermore

showed good agreement with computational fluid volume predictions. Additionally, extensive

correlations for bubble rise velocity and size as a function of the operating conditions were

developed. Another method, hot wire anemometry (HWA), enables velocity and the turbulent

stress field in three–dimensional bubble columns up to gas velocity of 8 cm/s to be obtained

(Menzel et al., 1990). Moreover, the computer–automated radioactive particle tracking

INTRODUCTION

19

(CARPT) technique with neutrally buoyant radioactive particles present in the liquid phase

(Devanathan et al., 1990, Kumar et al., 1994, Yang et al., 1992) allows studies of the flow

field in bubble columns. Here, no limitations concerning the transparency of the system are

met. The CARPT method permits mapping of Lagrangian tracer particle trajectories

throughout the column. Following from those trajectories, instantaneous velocities,

time–averaged flow patterns, turbulent stresses and turbulent kinetic energy due to measured

fluctuating velocities can be obtained. In CARPT, the position of a single radioactive particle

is continuously monitored by a series of pre–calibrated detectors. Analysing the motion of

solids in slurries or fluidized beds, the radioactive particle is of the same size and mass as

particles in the investigated system. Motion up to frequencies of 20–30 Hz can be followed.

A combination of CARPT and computed tomography (CT) (CARPT–CT) shows unique

capabilities for flow field mapping in the whole column. Moreover, this system provides an

important view of the time–averaged flow field and gas holdup distribution. Average liquid

velocities and eddy diffusivities determined by CARPT and time–averaged holdup profiles

obtained by CT can be implemented in the convection diffusion model to predict the

residence time distribution of the liquid tracer (Degaleesan, 1997). The above investigations

show differences between radial and axial mixing. However, CARPT and PIV are limited in

their frequency resolution for turbulence analysis. The laser Doppler anemometry (LDA)

system overcomes those difficulties. This method allows probing of the high–frequency

contents above 100 Hz. It should be pointed out that LDA is now a standard technique for

single–phase flows. Experiments carried out by Mudde et al (1997) concerned fluid dynamics

analysis in a two–phase bubble column. Reduced transparency of the bubbly system due to

the presence of the dispersed bubbles, which act as scatters for the laser system, makes the

LDA experiment more complicated. Furthermore, it is not clear if the velocity of the liquid

phase or bubbles is measured. It is found that in the backscatter mode, the data rate can be

sufficiently high if the liquid flow is seeded with small seeding particles. In the mentioned

work, alumina–coated spherical polyethylene particles of 4 µm diameter were implemented.

Thereby, a 2D LDA system was studied in which axial and tangential velocity components

could be measured simultaneously. Results with a high data rate of 1000 Hz could be obtained

close the wall. With increasing distance from the wall, the frequency decreased due to the

high probability of interference of the bubbles with the laser beams (Mudde et al. 1997).

Three–phase flow in bubble columns. The methods described above concern only one–

and two–phase flow. Recently, gas to liquid (GTL) technologies with gas–liquid–solid

INTRODUCTION

20

systems have been taken into consideration. Two–phase flows (gas–liquid) in bubble columns

consist of several processes occurring at different time and space. The presence of a third

phase causes higher instability of the system. The operating parameters (gas flow rate, solid

loading, sparger and reactor configuration) and system design are related to unsteady fluid

dynamics. In the case of two–phase flow, homogeneous and heterogeneous flow can be

distinguished from each other. Taking into account three–phase flow, this distinction is often

not possible. As was observed in several studies (Khare and Joshi, 1990, Schallenberg et al.,

2005, Li and Prakash, 2000, Dziallas et al., 2000), the presence of a third phase can lead to

different effects in respect of coalescence and gas holdup in multiphase flow. For example

due to solid–phase coalescence or dispersion of bubbles, the gas holdup is influenced

(Schallenberg et al., 2005). A study carried out by Khare and Joshi (1990) showed that small

particles can accumulate at the bubble interface and reduce their coalescence and increase gas

holdup. However, Dziallas et al. (2000) and Liu and Prakash (2000) reported that the presence

of a third phase may reduce gas holdup in comparison with two–phase flow. Moreover,

a solid phase leads to coalescence and a larger diameter of bubbles, hence increased bubble

rise velocity and decreased gas holdup can be observed. On the other hand, a decreased

diameter of large bubbles, as a consequence their dispersion, can be caused by a third phase

(Li and Prakash, 2000). It should also be taken into account that bubbles influence the

suspension of solid particles. With a small density difference between continuous

liquid–phase and solid particles, particles are fluidized due to momentum transfer from the

liquid and gaseous phase (Li and Prakash, 2000, Liu et al, 2001). In order to find an

appropriate reactor design, as in previous cases (one– and two–phases flow) computational

simulation models are required (Rampure et al, 2003). Numerous models of gas–liquid flows

have been developed with time–averaged flow features (Ranade, 1997) where unsteady

properties were lost. However, studies by Buwa and Ranade (2003) concerning the role of

unsteady flow structures of the liquid phase in bubble columns showed that 3D unsteady

simulations are necessary for appropriate prediction of mixing times. It must be added that

previous work on unsteady gas–liquid flows was mainly carried out with small, rectangular

bubble columns (Buwa and Ranade, 2003, Becker et al., 1994). Experimental and numerical

studies of fluid dynamics in cylindrical bubble columns are necessary for a better

understanding of bubble columns on both the laboratory and industrial scales. Hitherto,

cylindrical bubble column investigations were carried out to measure and predict

time–averaged velocity and gas holdup profiles (Ranade, 1997). Experimental (Becker et al.,

INTRODUCTION

21

1999) and numerical (Pfleger and Becker, 2001) fluid dynamic studies of two–phase flow

(liquid, gas) still reveal some misunderstandings. Moreover, the influence of the solid phase

on multiphase flow is poorly investigated and understood. Two– and three–phase

experimental and numerical investigations carried out by Rampure et al. (2003) in bubble

column reactors provide a basis for understanding gas–liquid–solid flows and for the further

development of both methods. Local gas and solid holdups in a three–phase pilot plant–sized

bubble column operated at solid loadings up to 10% and a gas holdup of 20% were

determined by a measurement technique involving the combination of differential pressure

measurements (DPM), electrical conductivity measurements (ECM) and time domain

reflectometry (TDR) (Dziallas, 2000, Dziallas et al., 2000). By using the above methods,

detailed investigations of the influence of SGV, sparger geometry, solid loading, local gas and

solid holdups, fluidization and mixing phenomena were carried out.

Velocity measurements in three–phase bubble columns operated at high gas and solid

holdups are a serious challenge. Investigations carried out by Cui and Fan (2004) report LDA

system to be an attractive tool for turbulence analysis in gas–liquid–solid flow. However, due

to the presence of a dispersed phase (particles and gas bubbles), application of LDA is limited

to low gas holdup and solids loading conditions. The liquid velocity in a bubble column

system can be obtained if certain requirements are met, e.g. backscatter mode with proper

seeding (Mudde et al., 1998). In this case, experiments with gas holdup up to 20% and

solids loadings of 4% were carried out. Additionally, LDA can be implemented to measure

the velocity of the solid phase. The above investigations showed a huge influence of solid

particles on the liquid–phase turbulence which depends on the solid properties and gas

velocity (Cui and Fan, 2004). Extension of LDA to cover three–phase flow (extended phase

Doppler anemometry, EPDA) was reported by Braeske et al. (1998) as a suitable method for

liquid visualization. However, in this system the optical properties of the dispersed phase

need to be known. Moreover, a new invasive technique called electrodiffusion measurement

(EDM) allows high–quality visualization of the liquid phase (Onken and Hainke, 1999). This

method is based on mass transfer at a probe surface being influenced by the liquid velocity

close to the surface. During investigations, a high constant voltage is applied between the

silver wire electrode surface and platinum reference electrode. Increasing liquid flow velocity

causes a decreasing boundary layer thickness at the electrode surface, leading to increased

mass transfer and subsequently increased electric current at constant voltage.

Two–dimensional liquid velocities up to 2 m/s can be obtained in this system. Solid particles

INTRODUCTION

22

hitting the electrode have a slight polishing effect on the surface and in consequence they

protect it from slow degradation. A special post–processing algorithm is responsible for

filtering bubble signals. Furthermore, liquid and bubble rise velocity measurements in 2D and

3D three–phase bubbly columns can be carried out by using the PIV system (Chen et al.,

1994, Fan, 1989, Tzeng et al., 1993, Reese et al., 1993). Moreover, as with two–phase flows,

CARPT and CT have been implemented for velocity distribution and holdup field

investigations (Larachi et al., 1997, Moslemian et al., 1992).

Finally, it must be emphasized that aerobic granulation in a Sequencing Batch Reactor

(SBR) is a complex, multiphase phenomenon where factors influencing granule formation are

not fully understood. This is primarily connected to the novelty of the technique. Furthermore,

several researchers have focused on the investigation of chemical, biological, microbiological

and physical aspects. In contrast, hitherto, only very little information concerning

hydrodynamic effects has become available. It can be supposed that mechanical forces caused

by particle–wall and inter–particle collisions and normal and tangential strains significantly

affect both granule formation and destruction. The wall collision effect may be determined by

the particle mass loading, particle shape and wall roughness, combination of particle and wall

materials or hydrodynamic interactions. Relative motion between particles is crucial for

inter–particle collision. There are some factors which influence relative motion, e.g. laminar

or turbulent fluid shear and particle inertia in the flow (Sommerfeld, 2000). The mechanical

stresses acting on granules can be divided into normal and tangential stress (Esterl et al.,

2002). The shear stress acting on particles is due to the relative velocity between the particles

and fluid (Henzler, 2000). Although the effect of shear stress has been well studied, the role of

normal stress has been poorly investigated (Höfer et al., 2004). Elongation flow can influence

biological materials more effectively than pure shear flow (Nirschl and Delgado, 1997, Zima

et al., 2007). Moreover, flow induced by ciliates plays a crucial role in the granulation process

(Hartmann et al., 2007, Kowalczyk et al., 2007, Petermeier et al., 2007, Zima et al., 2007a).

Therefore, in the current work, fluid dynamic investigations of multiphase flow in an SBR

with different optical in situ techniques were applied.

INTRODUCTION

23

1.3 Work objectives

The main aims of the present work were multiphase flow studies in a Sequencing Batch

Reactor (SBR). As indicated in section 1.1, Granular Activated Sludge (GAS) due to its good

settling ability, is very useful in wastewater treatment. However, the aerobic granulation

mechanism is not fully developed and especially there is lack of information concerning fluid

dynamic effects. For a better understanding of this process, the following questions should be

answered:

• Which global and local flow conditions allow granules formation? Does the flow

condition influence GAS size? Here, fluid mechanical characterization is required.

• Why do granules take a regular form?

• Which fluid mechanical forces affect granules? Work should be mainly concentrated

on normal and shear stress analysis.

• Do microorganisms develop a protective mechanism?

The above problems can be solved most effectively with appropriate in situ investigations

in combination with theoretical and numerical considerations. Here, for the first time, Particle

Image Velocimetry (PIV), Particle Tracking Velocimetry (PTV) and Laser Doppler

Anemometry (LDA) allow the flow type and structures in SBR and forces affecting granules

to be recognized. However, the granulation process is a multiscale phenomenon (macro– and

micro–scale). Therefore, in addition to the above–mentioned macro–scale, µ–PIV studies

should also be taken into account. Microscopic investigations permit the analysis of

microorganisms at different flow rates and observation of the flow field induced by them.

Fluid dynamic equations provide a basis for a theoretical understanding of multiphase

phenomena in an SBR.

In the present work, one of the most prominent results is the assertion of the effect not only

of shear strain but also of normal strain on granule formation and destruction. Moreover, it

was corroborated that the granulation process takes place only under appropriate global flow

conditions, e.g. aeration flow rate and granule concentration. As will be shown in the results

section, granules influence significantly the analysed flow pattern. Furthermore, ciliates which

live on the GAS surface play a crucial role in their formation.

SOME BASIC THEORETICAL CONSIDERATIONS

24

2. SOME BASIC THEORETICAL CONSIDERATIONS

General multiphase theory is very complex and comprehensive (see e.g. Brenn et al., 2003,

Crowe et al., 1998). In the present chapter, only brief introductions to chosen subjects are

given according to specific requirements of the problems concerned in this work.

2.1 Basic equations of fluid dynamics

Understanding of the fluid dynamics in bubble column reactors is necessary due to the

wide applications in the chemical and bioprocess industries. In order to describe fluid motion

affected by certain forces, the characteristic equations are required. Here, two basic

conservation laws, mass and momentum, are taken into account. In the present work, due to

the isothermal character of the flow, energy conservation is not considered. The total volume

of the dispersed phases is small in comparison with the volume of the flow domain.

In the analysed system, the liquid phase has a similar viscosity to pure water (1.035 mPas

over a wide range of shear rate). This allows the liquid phase to be treated as a Newtonian

fluid. Furthermore, it is assumed that the liquid phase is a homogeneous solution of a number

of chemical components (predominantly water). This means particularly that the fluctuations

of chemical composition resulting from, for example, biochemical reactions (such as the

metabolism of microorganisms inhabiting the system) are not considered in the fluid dynamic

balancing aspect. As a result, for the continuous phase only a single equation of mass

conservation and a single equation of momentum conservation are sufficient to express the

respective balances. However, if the single chemical components of the fluid should be

considered separately as substrates taking part in chemical reactions governed by respective

chemical balances, another approach would be necessary. This means specifically the

application of mass and momentum conservation equations separately for each of the

components of the mixture. These balancing equations would have to take into account the

generation of sink terms according to the governing biochemical reaction laws. However, as

explained above, this approach is not used in the current study and common equations of mass

and momentum conservation are given.


25

Mass conservation. The continuity equation is a statement about mass conservation. It

describes that in a volume element, all the mass flowing in and out per unit time must be

equal to the change in mass due to change in density per unit time (Schlichting and Gersten,

2003). The definition for unsteady flow is

0=udiv+DtDρ rρ , (2.1)

where ρ represents density, t time and ( )wvuu ,,=r velocity vector. In our case, fluid is

incompressible (ρ = const) and therefore equation 2.1 can be written as

0=udiv r . (2.2)

Momentum conservation. The second equation, the basic law of fluid mechanics states

that mass times acceleration is equal to the sum of the acting forces. Its general form is

PfDt

uD rrr+=ρ . (2.3)

DtuD r

is the substantial acceleration, which consists of the local acceleration tu∂∂r

and convective

accelerationdtud r , which is equal to:

u)gradu(dtud rrr= . (2.4)

On the right–hand side, fr

is the body force per unit volume (e.g. grρ , where gr is the vector of

gravitational acceleration) and Pr

the surface force per unit volume. External forces describe

the body forces. The surface forces determine the state of stress on a volume element and

depend on the state of deformation of the fluid.

The state of stress is determined by nine scalar quantities which form stress tensor:

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

=

zzzyzx

yzyyyx

xzxyxx

στττστττσ

σ . (2.5)

The normal stress component due to molecular momentum transfer in the fluid τij results as


26

the difference between the total normal stress σij and the pressure p, which is always negative.

Thus, it can be written

,pxxxx +=στ pyyyy +=στ , pzzzz +=στ . (2.6)

From equations (2.3), (2.5) and (2.6), the momentum equation can be written as

τρ divgradpfDt

uD+= -

rr, (2.7)

whereby, the pure diffusive (often also called molecular or viscous) momentum transfer is

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

=

zzyzxz

yzyyxy

xzxyxx

τττττττττ

τ . (2.8)

Moreover, the motion of fluid element causes a deformation. The rate of deformation depends

on the relative motion between two points, which can be described by (Raffel et al., 1998)

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

∂∂

∂∂

∂∂

∂∂

∂∂

∂∂

∂∂

∂∂

∂∂

=

zw

zv

zu

yw

yv

yu

xw

xv

xu

xdudr

r. (2.9)

The deformation tensor can be decomposed into symmetric and antisymmetric parts:


27

.

yw

zv

xw

zu

zv

yw

xv

yu

zu

xw

yu

xv

zw

yw

zv

xw

zu

zv

yw

yv

xv

yu

zu

xw

yu

xv

xu

xdud

⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

−∂∂

⎟⎠⎞

⎜⎝⎛

∂∂

−∂∂

⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

−∂∂

⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

−∂∂

⎟⎠⎞

⎜⎝⎛

∂∂

−∂∂

⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

−∂∂

+

+

⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

∂∂

⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

+∂∂

⎟⎠⎞

⎜⎝⎛

∂∂

+∂∂

⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

+∂∂

∂∂

⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

+∂∂

⎟⎠⎞

⎜⎝⎛

∂∂

+∂∂

⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

+∂∂

∂∂

=

021

21

210

21

21

210

21

21

21

21

21

21

r

r

(2.10)

It can be observed that the symmetric tensor consists of normal and shear strains, whereas the

antisymmetric tensor includes only the vorticity components.

As shown in Chapter 4, the present work gives all PIV results in a two–dimensional

representation. Taking into account 2D analysis, the normal strain can be expressed as

yv

xu

∂∂

+∂∂

=ε& . (2.11)

Moreover, the shear strain can be written as

xv

yu

∂∂

+∂∂

=γ& (2.12)

and vorticity as

yu

xv

∂∂

−∂∂

=ω . (2.13)

From the above explanation, it can be concluded that the viscous stress tensor components τij

depend on the spatial velocity xu∂∂ …

zw∂∂ in suitable scalar sums. This relation is formulated

by the following equivalences:


28

,zwudiv

,yvudiv

,xuudiv

zz

yy

xx

∂∂

+=

∂∂

+=

∂∂

+=

μΛτ

μΛτ

μΛτ

2

2

2

v

r

r

(2.14)

.xw

zu

,zv

yw

,yu

xv

xzzx

zyyz

yxxy

⎟⎠⎞

⎜⎝⎛

∂∂

+∂∂

==

⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

+∂∂

==

⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

+∂∂

==

μττ

μττ

μττ

(2.15)

According to the Stokes hypothesis, the second or volume viscosity coefficient (Λ) can be

expressed as

μΛ32

−= , (2.16)

where μ is dynamic viscosity.

Taking into account equations (2.14) and (2.15), and the Stokes hypothesis (2.16), the viscous

stress tensor is given by

)udiv( rδεμτ322 −= . (2.17)

where δ is the Kronecker unit tensor (δij = 1 for i = j, δij = 0 for i ≠ j) and ε depicts the

strain–rate tensor.


29

2.2 Mechanical forces in multiphase flow

In multiphase flow systems, the interaction between the continuous and dispersed phases

plays a crucial role (Brenn et al., 2003). In order to describe the motion of particles and

bubbles in the continuous phase in SBR, all the affecting forces should be taken into account.

Forces controlling particle motion can be divided into three groups:

• forces through the interface between fluid and particles,

• forces due to interactions between particles and walls and

• forces caused by external fields.

Taking into account the first group, drag force, Basset force, added mass force, Saffman force

and Magnus force, should be considered. In the second group (particle–wall, particles

interactions), collision forces, van der Waals force and inter–particle electrostatic force are

distinguished. Magnetic force, electric force and buoyancy force belong to the forces imposed

by external fields (Johnson, 1998). For a better understanding of the influence of these forces

on the flow pattern, a more detailed analysis is presented below.

Forces between fluid and particles

Drag force. One of the most important forces in a fluid–particle system is the drag force,

which consists of a friction and form drag (Clift et al., 1978). The drag force can be expressed

by the equation

( )PWPWW

pDD uuuuACF rrrrr−−=

2ρ , (2.18)

where CD determines the drag coefficient, which depends on the particles Reynolds number:

W

PWPWP

uuDRe

μρ rr

−= , (2.19)

where ρW is liquid density, DP particle size and μW liquid dynamic viscosity. The subscript P

means bubbles or solid particles.

In equation (2.18), AP denotes the cross–section of a spherical particle:


30

2

4 PP DA π= , (2.20)

Wur is fluid velocity and Pur particle velocity.

As mentioned before, the drag coefficient depends on Reynolds number. Experimental

investigations by Schlichting et al. (1965) identified several regimes correlated with the flow

characteristic around the sphere:

• At low Reynolds number (Rep < 0.5) viscous effects dominate and no separation between

fluid flow and particle occurs. In this case, the drag coefficient is calculated with the

equation proposed by Stokes (1851):

PD Re

C 24= , (2.21)

well known as the Stokes regime.

• For higher Rep, 0.5 < Rep < 1000 (transition region), the importance of inertial effects

increases. Although several correlations have been proposed (Clift et al., 1978,

Crowe et al., 1998), that suggested by Schiller and Naumann (1933) is best known:

( )P.

PD Re.

ReC 6870150124

+= . (2.22)

• For ReP > 1000, the Newton regime is observed. Here the drag coefficient remains almost

constant up to the critical Reynolds number and thus

440.CD = . (2.23)

• When 51052 ⋅= .ReP (critical Reynolds number), the drag coefficient decreases drastically.

This effect is caused by the transition from a laminar to turbulent boundary layer around

the particle.

• For very high ReP, ReP > 4.0·105 (supercritical region), the drag coefficient increases

continuously.

It should be pointed out that several factors, such as turbulence of the flow, particle shape,

fluid compressibility, particle surface roughness and wall effects, influence the drag

coefficient.


31

Basset force appears due to accelerating or decelerating action of the particles in the fluid.

According to Reeks and McKee (1984), the Basset force including initial velocity can be

expressed by the following equation:

( ) ( )⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡−

+−

−= ∫

tPW'

'

PW

WWPB tuu

dttt

uudtd

DF0

02

23

rrrrv

μπρ , (2.24)

where (t – t’) represents the time elapsed since past correlation from 0 to t and ( )0PW uu rr− is the

initial velocity difference.

Acceleration (or deceleration) of particles in the fluid requires acceleration (or

deceleration) of the fraction of surrounding fluid, leading to an additional resistance of the

fluid against the particle motion. This effect is named added mass force:

( )PWP

PWAA uu

dtdmC.F rrr

−=ρ

ρ50 , (2.25)

where CA, called the virtual mass coefficient, was obtained by experimental investigations by

Odar and Hamilton (1964)

120132012 2 .A..C

CA +

−= . (2.26)

The acceleration number (AC) is given by

dtuud

D

uuA

PWP

PwC rr

vr

−−

=2

. (2.27)

Lift forces act on particles due to their rotation. Particle rotation can be caused by

a velocity gradient of fluid (Saffman lift forces) in addition to particle contact and rebound

from a wall (Magnus force). A brief explanation of both forces is given below.

Saffman force can be explained as a lift force, perpendicular to the flow direction, which

affects particles in a shear flow. This phenomenon takes place due to non–uniform relative

velocity over the particle and a non–uniform pressure distribution (Sommerfeld, 2000). The

slip shear lift force for freely rotating particles in two–dimensional flow at low Reynolds


32

number was expressed by Saffman (1965, 1968) as

( )PWW

WWP

S uuy

uD.F rrr−

∂∂

= μρ4

4662

, (2.28)

where y

uW

∂∂ is the shear rate.

Taking into consideration a higher particle Reynolds number, Saffman force is expressed by

( )[ ]WPWW

WWPS uuD.F ωω

μρrrr

rr

×−=1611 2 , (2.29)

where Wωr is the fluid rotation:

WFW uurot rrr×∇==ω . (2.30)

Saffman analysis concerns conditions when the particle Reynolds number (ReP) is less than

the shear Reynold number (ReS). The latter is given by the equation

W

WPWS

DRe

μωρr2

= . (2.31)

Magnus force is a lift force resulting from particle rotation due to, e.g., particle–wall

collisions and particle–particle collisions. This phenomenon causes a deformation of the flow

field around particles: the velocity on one side of the particles increases whereas the velocity

on the other side decreases. Rubinow and Keller (1961) defined the slip–rotation lift force for

a rotating sphere at low Reynolds number (of the order of unity) as

( )[ ]PWWPM uuDF rrrr−×= Ωρπ 3

8, (2.32)

where Ωr

is the relative rotation, and Pωr the angular velocity of the particle:

.u PW ωΩrrr

−×∇=21 (2.33)

For moderate Reynolds number, the slip–rotation lift force may be expressed by


33

( )Ω

Ωρ r

rrrrrr

PWPWWPLRM

uuuuACF −×−=

21 , (2.34)

where CLR is the lift coefficient.

For lower Reynolds numbers, the lift coefficient can be computed by using Rubinow and

Keller’s (1961) calculations:

P

R

PW

PLR Re

Reuu

DC =

−= rr

rΩ

, (2.35)

where ReP is the particle Reynolds number of translation and ReR the particle Reynolds

number of rotation:

W

PWR

DRe

μ

Ωρr

2

= . (2.36)

However, calculation of the lift coefficient for higher Reynolds numbers is not

straight–forward, and experimental information is needful. Taking into account the available

literature and experimental data, Oesterlé and Bui Dinh (1998) presented the lift coefficient

equation for ReP < 140:

( )3040056840450450 .P

.R

P

RLR ReRe.exp.

ReRe.C ⋅⋅−⎟⎟

⎠

⎞⎜⎜⎝

⎛−+= . (2.37)

Lift torque is due to the shear stress distribution on the particle surface. Similarly to the

lift force calculations for low particle Reynolds number, Rubinow and Keller (1961) derived

an equation for torque acting on particles:

PPW DT ωπμrr

3−= . (2.38)

Taking into account higher Reynolds numbers, torque can be calculated with the relation

ΩΩρ rrrR

PW CDT5

22⎟⎠⎞

⎜⎝⎛= . (2.39)

However, in this case the rotational coefficient CR should be known.


34

For the low particle Reynolds numbers (ReR < 32), CR can be computed by the expression

(Rubinow and Keller, 1961)

RR Re

C π64= . (2.40)

For higher particle Reynolds numbers (32 < ReR < 1000), experimental data of Sawatzaki

(1970) and numerical simulations by Dennis et al. (1980) allow the determination of CR:

R.

RR Re

.Re

.C 412891250 += (2.41)

In the present work, lift rotational force is calculated by dividing lift torque by granule

diameter.

Forces due to particle–particle and particle–wall interactions

One of the most important forces in this group is due to particle–particle and particle–wall

collisions.

In order to describe particle collisions, two different models can be used (Crowe et al.,

1998). In the hard sphere model, the relationship between pre– and post–collisional

velocities can be obtained with the help of the restitution coefficient and friction coefficient.

This model is based on impulse equations. In the formulation, the instantaneous deformation

of the particles is not investigated. The soft sphere model describes the relationship between

pre– and post–collisional velocities and the instantaneous motion during the whole collision.

Here, the whole process of collision is solved with the use of differential equations of motion

and constutive equations of the particle material.

Particle–wall collisions influence significantly the particle motion in multiphase flow.

Several factors, such as particle response time, particle mass loading, particle shape, particle

inertia and wall and particle materials affect the wall collision frequency (Sommerfeld, 2000).

The wall collision frequency influences directly the pressure drop in a considered flow. Each

collision is connected with a momentum exchange of particles. Other factors such as wall

roughness and particle size, are of importance in the particle–wall collision process

(Sommerfeld, 1992, Sommerfeld and Huber, 1999). For small particles (<100 µm), the

influence of the wall roughness is limited to the near–wall region. Those particles after


35

rebound will quickly adapt to the flow. The large particles can cover several roughness

structures during wall collision. However, more time to match to the flow after rebound is

needed due to their high inertia.

Given the requirement for detailed analysis of particle material properties, which is often

infeasible, a fortiori in the case of biological material (being inherently irregular and

heterogeneous), the applicability of the soft sphere model is very limited. Therefore, the hard

sphere model will be considered. In this case, three types of collisions are distinguished:

• the particle stops sliding in the compression period

• the particle stops sliding in the recovery period

• the particle slides along the wall during the wall collisional process.

The static coefficient of friction, the velocity of the particle surface relative to the contact

point and the restitution ratio of the normal velocity component determine the type of

collision. The collision is considered based on the momentum conservation equation and the

Coulomb law of friction. Figure 5 shows schematically the analysed situation.

The non–sliding collision (first and second types) takes place when the following condition is

met (Sommerfeld and Huber, 1999):

( ) 00 127 νRSR eCu +≤v , (2.42)

Figure 5: Particle–wall collision

y

x

αo α

u0

u ω0 ω


36

where the subscript 0 expresses the situation before impact, u0R is the velocity of the particle

surface relative to the contact point (see equation 2.43), eR is the restitution coefficient

relating the normal velocity component after collision to that before collision, CS is the static

coefficient of friction and v is the particle linear velocity in Y direction.

2

00

2

000 22⎟⎠⎞

⎜⎝⎛ −+⎟

⎠⎞

⎜⎝⎛ += X

PZ

PR

DwDuu ωωr , (2.43)

where DP is the particle diameter, u is the particle linear velocity in the X direction and Z0ω

and X0ω are the angular velocity of the particle in the Z and X directions. In order to calculate

the change in particle velocities, the following equations should be considered:

( )00571 ωPDuu −= , (2.44)

0vev R−= , (2.45)

( )XPDww 00571 ω+= , (2.46)

PX D

w2=ω , (2.47)

XY 0ωω = , (2.48)

PZ D

u2=ω . (2.49)

Taking into account sliding collision (third type), the following condition is obtained:

( ) 00 127 νRSR eCu +≥

r , (2.50)

In this case, velocity components are calculated in accordance with the equations

00 1 v)e(Cuu XRd ε++= , (2.51)

0vev R−= , (2.52)


37

( ) 00 1 veCww ZRd ε++= , (2.53)

PZRdX D

v)e(C 00 15 εωω +−= , (2.54)

YY 0ωω = , (2.55)

( )P

XRdZZ DveC 0

0 15 εωω ++= , (2.56)

where Cd is the dynamic friction coefficient and

R

ZP

X u

Du

0

00 2v

ωε

+= , (2.57)

R

XP

Z u

Dw

0

00 2v

ωε

−= (2.58)

are the direction of the motion of the particle surface with respect to the wall.

Particle–particle collisions mainly depend on the particle concentration, particle size and

fluctuating motion of the particles (Sommerfeld, 2001). Crowe (1981) distinguished the

boundary between dilute and dense systems based on the ratio of particle response time τP to

the average time between collisions τC. Two characteristic regimes can be observed:

• dilute two–phase flow:

1⟨C

P

ττ (2.59)

• dense two–phase regime:

1⟩C

P

ττ . (2.60)

In the first case, the particle motion can be caused by fluid dynamic transport effects: drag

force, lift forces and turbulence. For the dense two–phase regime, high collision frequencies


38

between particles are mainly influenced by inter–particle interactions. The average time

between inter–particle collisions (τC ) is related to the average collision frequency by

CC f

1=τ . (2.61)

Taking into account the kinetic theory of gases, the collision frequency of one particle with

diameter DPi and velocity iur with all other particles with diameter DPj and velocity jur can be

calculated as

( ) jjiPjiP

N

ji

ijC nuuDD

nN

fclass rr

−+== ∑=

2

1 4π , (2.62)

where ni and nj are the relative numbers of particles with diameter DPi and DPj, respectively.

Dartevelle (2003) proposed three different cases for particles interactions (see Figure 6). In

the dilute regime, particles randomly fluctuate and translate. Here, kinetic stress can be

observed. For more concentrated situation, grains besides fluctuations can collide. This

phenomenon is named kinetic–collisional stress, which is dependent of the rate of

deformation. At very high concentrations (more than 50% in volume), sliding and rubbing

contacts can be observed. Here, frictional stress appears.


39

kinetic

Collisional & kinetic

frictional

It should be pointed out that the relative motion between the particles has a crucial role in

inter–particle collisions. Different factors such as mean drift between particles of different

size, laminar and turbulent fluid shear and particle inertia in turbulent flow affect the relative

motion.

Several equations for different situations (Brownian motion, collision rate due to turbulent

shear or due to particle inertia in turbulent flow) can be used to calculate the collision rate.

The collision rate due to a mean drift between particles of different size can be obtained from

the kinetic theory of gases with the equation (see Sommerfeld, 2001)

( ) jijiPjPiij uunnDDN rr−+= 2

4π . (2.63)

It must be mentioned that based on the kinetic theory of gases, several authors described

multiphase flow using the kinetic theory of granular flow (Huilin et al., 2003, Jenkins and

Mancini, 1989). The latter developed theoretical descriptions of suspensions with more than

one particle size employing the kinetic theory of granular flow. Moreover, Gidaspow et al.

(1996) extended the kinetic theory of dense gases to binary granular mixtures with different

granular temperatures between particles. In the present work, due to different particles size in

SBR, equation (2.63) will be used to describe inter–particles collision. However, several

Figure 6: Particle interactions (source: Dartevelle, 2003)


40

1

2

u01

u02 u2

u1

ω01

ω02

restrictions apply in the real process. Detailed analysis of inter–particle collisions in SBR will

be discussed in Chapter 4.

Similarly to the previous section, the hard sphere model is considered. The situation when

two particles with different velocities ( 0201 u,u rr ) colloide is shown in Figure 7. During

collision, they lose kinetic energy due to the inelastic property of the material and become

new velocities ( 21 u,u rr ), where subscripts 1 and 2 define two particles. Particle–particle

interactions will also be analysed for two situations, when particles are sliding and when they

stop sliding. In order to calculate velocities changes, the momentum equation and Coulomb

law of friction must be taken into account (Crowe et al., 1998).

The case when the particles continue to slide during the collision process is described by

the equation (Crowe et al., 1998)

( ) RRSRC uneCu 00 127 rrr

⋅+⟩ , (2.64)

where RCu0r is the tangential component of the relative velocity at the contact point before

collision. The latter is given by the equation

Figure 7: Particle–particle collision


41

( ) nDnDnnuuu PPRRRC

rrvrrrr×+×+⋅−= 20

210

1000 22

ωω , (2.65)

where nr is the unit normal vector directed from particle 1 to particle 2. The relative velocity

between particle centres before collision Ru0r is calculated as (see Figure 8)

02010 uuu Rrrr

−= . (2.66)

All the post–collisional velocities can be deduced as

( )( )( )21

20011 1

mmmeuntCnuu RRS +

+⋅−−=rrrrrr , (2.67)

( )( )( )21

10022 1

mmmeuntCnuu RRS +

+⋅−+=rrrrrr , (2.68)

( )( ) ( )21

20

1011 15

mmmeCtnun

D RSRP +

+×⋅⎟⎟⎠

⎞⎜⎜⎝

⎛−=

rrrrrrωω , (2.69)

( )( ) ( )21

10

2022 15

mmmeCtnun

D RSRP +

+×⋅⎟⎟⎠

⎞⎜⎜⎝

⎛−=

rrrrrrωω . (2.70)

where m depicts particles mass and tr

is the unit vector in the tangential direction of the

contact point.

An illustration of normal ( nr ) and tangential ( tr

) unit vectors from particle 1 to particle 2 is

shown in Figure 8.

Particles stop sliding if the following equation is satisfied:

( ) RRSRC uneCu 00 1

27 rrr

⋅+⟨ . (2.71)

For this situation, all post–collisional velocities can be written as

( )( )21

200011 7

21mm

mtununeuu RCRR +⎥⎦⎤

⎢⎣⎡ +⋅+−=

rrrrrrr , (2.72)


42

( )( )21

100022 7

21mm

mtununeuu RCRR +⎥⎦⎤

⎢⎣⎡ +⋅+−=

rrrrrrr , (2.73)

( )21

20

1011 7

10mm

mtnuD RC

P +×−=rrrrr

ωω , (2.74)

( )21

10

2022 7

10mm

mtnuD RC

P +×−=rrrrr

ωω . (2.75)

Equivalent time–averaged collisional force. In order to estimate the impact of collisional

forces on the granules, the equivalent time–averaged collisional force being the ratio of

average momentum change of a single granule due to collision to the average time between

collisions (particle–particle, particle–wall), is introduced. Here, the collisions are assumed to

be predominantly inelastic with restitution factor close to zero. This assumption can be treated

as correct taking into account the soft structure of granulated sludge.

E

PPC t

umFrr

= , (2.76)

where tE is the average time between collisions.

Van der Waals forces appear due to molecular interactions between solid surfaces. They

gain increased importance if very smooth particles contact each other. The magnitude of van

der Waals forces can be obtained by taking into account the contribution of molecules

constituting the surfaces.

Figure 8: Relative motion of two spheres

1

2

t

n


43

Investigations of various geometries were carried out by Hamaker (1937). Equation 2.77

gives the force between two infinite flat plates with separation z:

36 zAFW π

=r

, (2.77)

where FW is the force per unit area and A is the Hamaker constant.

The force between two spheres is given by

212zAdFW =

r, (2.78)

where the geometric parameter d is expressed by the equation

21

21

PP

PP

DDDDd+

= , (2.79)

where DP1 and DP2 are the particle diameters.

In the above equations, the roughness of the sphere surface is not considered. However,

Czarnecki and Dabros (1980) modified the van der Waals force relation taking into account

the roughness of the surfaces, leading to

( )212 bzAdFW +

=r

, (2.80)

where b represents the average roughness height .

Electrostatic force

221

41

aqqF

oE πε=

r (2.81)

is the force between charged particles which is proportional to the product of charges and

inversely proportional to the square of the distance between them, where q1 and q2 denote the

particles charges, εo is the dielectric constant and a is the distance between particle centres

(Crowe, 2005).


44

Forces imposed by external fields

Buoyancy force. In the analysed multiphase flow, the analysis of forces imposed by

external fields can be restricted just to the action of gravitational force since no electric and

magnetic fields are present. A direct implication of the gravitational force is the buoyancy

force acting on the dispersed phases. Given the assumption of constant temperature, the

density of the continuous phase (liquid) is considered to be constant. Then the buoyancy force

acting on the dispersed phase (granules) is given by

)(gF PWG ρρ −=rr

. (2.82)

Summary. The above theoretical consideration of multiphase flow allows a partial

understanding and quantitative description of this process. Two basic conservation laws (mass

and momentum) describe fluid motion affected by certain forces. Moreover, the interaction

between the continuous and dispersed phases plays a significant role in the multiphase

system. The motion of particles and bubbles in the continuous phase in SBR can be described

taking into account all the affecting forces, such as forces through the interface between fluid

and particles, forces due to interaction between particles and walls and forces caused by

external fields.

The subsequent chapters present the applied experimental methods and findings achieved

within the current work with regard to the theoretical background presented above. Hence the

underlying theory described here provides a basis for structuring, understanding and

evaluating the experimental results.

MATERIALS AND METHODS

45

3. MATERIALS AND METHODS

In this chapter, a laboratory–scale bioreactor that permits granule formation is presented.

Further, the applied optical in situ techniques facilitating multiphase flow visualisation are

described. As mentioned in the Introduction, the granulation process is a multiscale

phenomenon (different characteristic size and time scales). Concerning the observed size

scale range, the laboratory–scale SBR has a 1000 mm height, whereas sessile protozoa

(Opercularia asymmetrica), which live on the granule surface, reach a maximum size of up to

100 µm. Considering different time scales, aerobic granule formation is observed after 2–4

weeks. However, flow induced by ciliates (protozoa) reaches a maximum velocity of

120 µm/s. Analysing the above phenomena, experiments on the macro– and micro– scales

must be carried out. In order to investigate multiphase flow on the macro–scale, Particle

Image Velocimetry (PIV), Particle Tracking Velocimetry (PTV) and Laser Doppler

Velocimetry (LDA) are employed. Consideration of the flow induced by ciliates requires the

implementation of micro Particle Image Velocimetry (µ–PIV).

3.1 Experimental setup

Experiments are carried out in a laboratory–scale Sequencing Batch Reactor (SBR). The

SBR used is based on the bioreactor of McSwains et al. (2004), which is constructed from

a Plexiglas cylinder with 90 mm diameter and 1000 mm high, filled with 4 L of fluid.

Granules are grown from a municipal wastewater treatment plant (initial Mixed Liquor

Suspended Solids 2.5 g/L) in the McSwains et al. (2004) bioreactor. Inoculated GAS is

transported to our laboratory SBR. Biofilm growth is discarded every day. The wasted

volume is

SRTVV R

Day/W = (3.1)

where VR is the reactor volume and SRT is the sludge residence time. After 40 days of

bioreactor operation, VW/Day amounts 100 mL wasted/day.

The reactor is operated in four cycles (sequences) per day of 6 hours each. Every cycle


46

contains five steps. First, the SBR is filled within 10 minutes with 2 L of synthetic wastewater

food with glucose, peptone and nutrients. The composition of the applied synthetic

wastewater was selected in closed cooperation with the group of Prof. Wilderer and Prof.

Horn and is given in the Appendix. The second step, aeration, takes 320 minutes. Here, the

most important interaction between granules, air and fluid takes place. Thus, the optical

investigations are carried out at this stage. Subsequently, settling occurs (2 minutes). After

this step, effluent is extracted from the half–height of the fluid part (7 minutes). The whole

process is completed with idling, which takes 21 minutes. All cycles are controlled

automatically.

A porous stone connected to a compressed air pipeline is placed at the bottom of the

bioreactor. By this means, aeration of the reactor is provided. In order to establish the optimal

conditions for granule formation, different air flow rates (4, 6, 8 L/min) are applied.

Furthermore, LDA investigations with 2, 3 and 4 L/min aeration flow rates are carried out.

These experiments allow a comparison of the velocity distribution under different flow

conditions. However, the very high granule concentration and the presence of bubbles make

LDA studies impossible at air flow rates of 6 and 8 L/min.

A cylindrical shape of the bioreactor is appropriate for practical purposes in waste water

cleaning but inconvenient for optical investigations due to light reflection effects. Optical

accessibility of the SBR interior is improved by a Plexiglas rectangular prism which

Figure 9: Experimental setup

extracting pump

cuboid filled with water

porous air stone

dosing pump

influent effluent

air

D

635

mm

(4 l)

250 mm

120

mm

1000

mm

liquid level velocity 13.05 D bioreactor diameter


47

He – Ne laser

surrounds the SBR. The gap between bioreactor and cuboid is filled with water. A scheme of

the experimental setup is depicted in Figure 9.

3.2 Optical in situ techniques with He–Ne laser and video lamp

For the experimental analysis of the multiphase flow pattern in the bioreactor, optical

in situ techniques are employed. An He–Ne laser and video lamp are used as two independent

light sources applied for two different measurements techniques. The plane of the He–Ne

laser light sheet is arranged perpendicular to the camera optical axis. Because of the high

granule concentration, the laser plane is placed close to the bioreactor wall. Moreover, due to

the local character of the flow pattern and also for detailed visualisation of the analysed flow,

experiments with different intervals from the SBR wall (Z/D = 0.06, 0.09, 0.11) are carried

out. The principal idea of measurements with the He–Ne laser is shown in Figure 10. The

video lamp is situated ahead of the bioreactor.

Images are acquired by a high–speed CCD camera (Mikrotron GmbH) with

a macro–zoom objective allowing a maximum speed of 520 frames/s. In the present case, the

images of flow patterns with a size of 860 x 1024 pixels are taken with two different speeds,

Figure 10: Optical system with He–Ne laser

Z

90°

light sheet

CCD

image plane Y

X


48

namely 54 and 65 frames/s. Because of the local character of the flow pattern, the experiments

are carried out in different subdomains of the SBR. The frames from the CCD camera are

directly transferred and recorded on a PC.

Both artificial tracer particles and granules themselves are employed to visualise the flow

pattern. During experiments with the video lamp, granules allow the visualisation of fluid and

solid flow patterns. Hollow glass spheres with a density of 1.1 g/cm3 and a diameter of

2–20 μm (Dantec Dynamics) are used for experiments with the He–Ne laser as flow tracer

particles. Due to the complexity of the three–phase flow, the velocity distribution of granules

and fluid are calculated separately. PIV is implemented to visualise the flow field of the

continuous phase (fluid) and the velocity distribution of the dispersed phase (granules) is

determined using PTV.

3.2.1 Particle Image Velocimetry (PIV)

The calculation of the fluid velocity is carried out with the help of the software PIVview2C

(PIVTEC GmbH), developed by Raffel et al. (1998). The PIV technique consists in

comparing two images with known time spacing. The cross–correlation mode is used to

extract particle displacement (Quenot et al., 1998). The differences in position of the tracers in

the first and second pictures represent the displacement. The knowledge of the time interval

between two recordings permits computations of the liquid velocity uW (Lindken et al., 1999).

This procedure can be repeated manually or in batch mode for numerous pairs of images.

PIV investigations are carried out by the multiple–pass interrogation algorithm which is

built in the PIVview2C software. This method increases the data yield due to the higher

number of matched particles and reduces the bias error (Westerweel et al. 1997). In the

present work, the interrogation window size is chosen as 32 x 32 pixels and the grid size is

16 x 16 pixels. Sub–pixel displacement of the correlation peak is obtained by a 3–point Gauss

fit. This selects the four closest neighbours of a correlation maximum and fits a three–point

Gaussian curve for each of the major axes (Willert and Gharib, 1991). Subsequently, velocity

data from PIVview2C are further processed with TECPLOT (Amtec Engineering).

It should be pointed out that PIVview2C also allows the calculation of normal strain, shear

strain and vorticity. All data are obtained by using classical equations (see Section 2.1,

equations 2.11–2.13).


49

3.2.2 Particle Tracking Velocimetry (PTV)

The flow pattern of granules is evaluated with use of OPTIMAS (Media Cybernetics,

L. P.). Similarly to the PIV case, PTV is based on comparing two images with known time

spacing. The points (granules) for which the velocity is to be determined are marked manually

on both images. The difference in position of the markers represents the displacement of

granules, which for the known time interval between images can be recalculated as solids

velocity uG. The procedure is repeated for different images sequences. Data obtained from

OPTIMAS are further analysed with TECPLOT (Amtec Engineering).

3.3 Laser Doppler Anemometry (LDA)

The laser Doppler anemometry (LDA) system (TSI), another non–intrusive technique is

implemented for liquid velocity measurements in SBR. One–component LDA is operated in

backscatter mode, which means that optical lens works as transmitter and receiver. The LDA

equipment consists of an argon–ion laser (300 mW) and Colorburst multicolour beam

separator Model 9201. Measurements are carried out with green light (wavelength

λ = 514.5 nm). The light is transmitted through a fiber–optic cable and probe. Two split laser

beams meet in the region of intersection (LDA measurement volume). Here, two laser beams

interfere producing light intensity variations (parallel planes with a Gaussian intensity

distribution). Subsequently, the scattered light is detected through the same probe and

processed by signal processor (Nobach, 1999). The whole system is depicted schematically in

Figure 11. As seeding particles, hollow glass spheres of diameter 2–20 µm and density

1.1 g/cm3 (Dantec Dynamics) are applied. Investigations are carried out for two–phase (water,

air) and three–phase (water, air, granules) flow under different aeration flow rates 2, 3 and

4 L/min. Moreover, experiments are performed in three different vertical positions Y/Hmax

(0.32, 0.44, 0.65) and various distance from the SBR wall chosen in 3 mm steps (Z/D = 0.03).

Because of the high granule concentration, the LDA measurement volume is placed close to

the bioreactor wall. Every measurement point is observed for 180 s. The mean velocity Wv and

velocity variance Wv ' are obtained with the weighting technique:


50

∑

∑

=

=

+ ⎟⎠⎞

⎜⎝⎛ +

= n

ii

n

ii

ii

W

w

wvv

v

1

1

1

2 , (3.2)

( )

∑

∑

=

=+−

= n

ii

n

iiiW

'W

w

wvvv

1

1

21

, (3.3)

where n is the sample number, vi the discrete value of fluid velocity and wi the weighting

factor.

Turbulence power spectra analysis from LDA is carried out by implementing a novel slot

correlation (SC) algorithm with the help of a kern program developed by Nobach’s group

(Nobach et al., 1998, Benedict et al., 2000, Gjelstrup et al., 2000, Nobach, 2000). Here, the

autocorrelation function (ACF) is estimated from flow velocity fluctuations. Subsequently,

Fourier transformation of this symmetrical function under the assumption of local isotropy of

turbulence produces the power spectral density (PSD). The important advantageous

characteristic of the SC algorithm is that it enables turbulence spectra to be obtained at low

data rates, which is the case in the present work.

Figure 11: LDA system

detector

signal processor

PC

probe

beam separator

argon-ion laser


51

3.4 Microscopic investigations

As mentioned above, the granulation process is a multiscale phenomenon. In addition to

investigations on the macro–scale, studies on the micro–scale are also needed. In order to

analyse the microorganism species which inhabit granules, microscopic analyses are carried

out. Ciliates (protozoa), Opercularia asymmetrica, with an average length from 50 to 100 µm

are observed to dominate on the granule surface (see Figure 12). Characteristic

microorganismic flow generated by cilia beats of Opercularia asymmetrica during their

feeding is proved to influence granules formation significantly. In order to analyse this

characteristic flow, micro Particle Image Velocimetry (µ–PIV) investigations are carried out.

3.4.1 Microscopic analysis

Microscopic observations are done by using an Axiotech 100 microscope (Carl Zeiss) with

10–, 20– and 50– fold optical magnification. Here, the GAS probe removed from the SBR

with a certain amount of fluid is placed on the glass plate. The prepared sample is covered

with a cover–plate. Subsequently, the probe is analysed under a microscope. Described

microscopic experiments are carried out with GAS samples coming from the SBR operated

with different aeration flow rates, 4, 6 and 8 L/min.

Figure 12: Ciliates on granules surface

granula surface

ciliates


52

3.4.2 Micro Particle Image Velocimetry

The principal idea of micro Particle Image Velocimetry is the same as Particle Image

Velocimetry. However, the clear difference is the different scale (see Section 3.2.1).

Micro–fluid flow is observed by using an Axiotech 100 microscope (Carl Zeiss) with 10–, 20–

and 50– fold optical magnification. Samples are prepared in the same way as described in

Section 3.4.1.

However, for µ–PIV investigations, seeding is necessary in order to provide the

displacement tracers. This aspect becomes an especially complex issue in the case of

a biological system such as is concerned here. In order to obtain reliable results, reflecting the

natural behaviour of protozoa, their habitat during the experiment must not be altered. That

includes also the requirement of biocompatibility of tracer particles. Investigations carried out

by Petermeier et al. (2006), Hartmann et al. (2007), Kowalczyk et al. (2007) and Zima et al.

(2007a) show that biocompatibility of the measurement technique in microorganismic flow

belongs to the most important issues. Artificial tracers (polystyrene particles of 4.8 µm from

Microparticles GmbH, Germany) are instantaneously detected by microorganisms.

Contraction mechanisms which are activated by ciliates rejecting the artificial material

significantly influence the flow field (Kowalczyk et al., 2007, Petermeier et al., 2007, Zima et

al., 2007a). As a result, the recorded behaviour does not correspond to the natural behaviour.

Effective results can be obtained only with appropriate seeding biotracers.

Therefore, in the present work, no artificial tracer particles are used. Instead,

a biocompatible seeding approach is developed (Kowalczyk et al., 2007, Zima et al., 2007a).

Yeast cells (Saccharomyces cerevisiae, dimensions approx. 3–10 µm) and milk, being an

emulsion with scattering particles (fat and proteins, dimensions 0.3–3 µm), are implemented

as tracers for flow visualization. They are well recognized by ciliates as nutrients and do not

disturb the natural behaviour of zooids. Investigations are carried out with different aqueous

solutions of yeast cells of 1:100, 1:200 and 1:300 (yeast to distilled water) and milk of 1:1,

1:2, 1:3, 1:4 and 1:5 (milk to distilled water).

For the same reason of biocompatibility, the intensity of the illumination applied must not

exceed a certain level acceptable by the microorganisms. Otherwise, the viability of the

protozoa is drastically reduced (Petermeier et al., 2007). Therefore, laser light, often used in

PIV experiments (Hartmann et al., 2007), is inapplicable in the present case. Instead, built–in


53

microscope white light illumination with moderated intensity is applied as a light source.

Similarly to the PIV investigations, pictures are recorded by a high speed CCD camera

(Mikrotron GmbH) with two different speeds, 25 and 65 frames/s. Images have a resolution of

860 x 1024 pixels, the interrogation window size is chosen as 32 x 32 pixels and the grid size

as 20 x 20 pixels. Figure 12 depicts the µ–PIV system.

The recorded images are analysed with the same software and with the same algorithms as

in PIV studies (PIVview2C, PIVTEC GmbH). Further, the velocity data obtained are

processed with TECPLOT (Amtec Engineering).

Sludge granulation within the SBR is a multiscale process involving a wide range of time and

length scales. Therefore, in situ investigations on both the macro–scale (PIV, PTV and LDA)

and in micro–scale (µ–PIV) are necessary for proper characterisation of the process, especially

with respect to flow visualization in a multiphase system. The experimental techniques

applied for complementary investigations of the fluid mechanical effects within the process

were described above. The results obtained are described in the following chapter.

Figure 13: µ–PIV system

CCD

RESULTS AND DISCUSSION

54

4. RESULTS AND DISCUSSION

4.1 Dimensionless representation of results

The results and descriptions of figures are presented in dimensionless form.

Dimensionless analysis allows a reduction in the number of parameters and a clear

understanding of the experimental results.

The dimensionless time is defined as the ratio of time ( t ) to the characteristic settling time

of granula (tS).

The dimensionless fluid velocity (uW) and horizontal and vertical fluid velocity

components (uW, vW) are calculated as the ratio of fluid velocity ( Wu ) or fluid velocity

components ( Wu , Wv ) to the reference velocity (uref). As the reference velocity, a superficial

gas velocity (SGV) of 0.0105 m/s is used. The dimensionless velocity of granules (uG) is

represented in the same way as for the continuous phase.

Moreover, dimensionless shear and normal strain rates are calculated as the ratio of shear

or normal strain rate to the experimentally obtained maximum strain rate

( 11 2628 −− == s,s maxmax εγ && ).

In order to show the forces acting in the multiphase flow in the SBR in dimensionless

form, the dominating buoyancy force ( GFr

) is taken as the reference force ( refFr

). Thus, any

analysed fluid dynamic force in its dimensionless form is represented as the ratio of the force

in question to the buoyancy force.

The length scales in the X, Z (horizontal) and Y (vertical) directions are depicted in the

following way: the X– and Z–axes are marked as the ratio of experimental position in the

horizontal direction X or Z to the bioreactor diameter D. The Y–axis is defined as the ratio of

the vertical coordinate of the SBR Y to the maximum liquid level Hmax.

As written before, the granulation process is a multiscale phenomenon and the present

work concerns a number of scales. Here, the micro–scale results must also be presented in

dimensionless form. In this case, as reference velocity the maximal velocity (umax = 132 μm/s)


55

observed within the series of experiments is taken.

The dimensionless representation of the results with all parameters considered above is

given in Table 1.

Parameters Dimensionless representations

Time

Sttt =

Liquid velocity SGVu

u WW =

Liquid velocity components SGVu

u WW = ,

SGVv

v WW =

Granule velocity SGVu

u GG=

Shear strain rate

maxγγ

γ&

&& =

Normal strain rate

maxεεε&

&& =

Forces

G

ii F

FF r

rr=

Liquid velocity on micro–scale

maxuuu =

X–axis DX

Y–axis

maxHY

Z–axis DZ

Table 1: Dimensionless representation of the results


56

4.2 Particle Image Velocimetry

In this section, PIV results are presented. PIV data enable the characteristic flow pattern in

the SBR to be observed. Moreover, the characteristic fluid velocity tendency is shown. Based

on the velocity distribution, normal and shear strain rate analysis is possible.

4.2.1 Fluid velocity distributions

At the beginning of this section, the results with a video lamp as a light source are

presented. They enable the typical flow structure and the velocity distributions in bioreactor to

be recognized.

Flow pattern. The fluid flow patterns observed in the bioreactor on three different levels

are shown in Figure 14. Analysing the experimental data, characteristic flow patterns can be

detected in three different zones of the SBR. Close to the bottom, a vortex with

a characteristic length approximately equal to the cylinder diameter can be seen. A low

velocity of the fluid uw = 0.01 is observed in the area below this large vortex, i.e. for

Y/Hmax = 0.08 and X/D = 0.66. Moving to the upper part, the velocity of water increases to

uw = 1.8 for Y/Hmax = 0.11 and X/D = 0.55. With increasing vertical coordinate, the typical

eddy size decreases. In this subdomain, i.e. Y/Hmax = 0.58 and X/D = 0.50, the liquid velocity

amounts to uw = 8.0.

From Figure 14, it can be stated that the fluid velocity increases with higher vertical

coordinate of the SBR. This statement can be confirmed by the following dimensionless

velocity results in different positions of the SBR. At a height Y/Hmax = 0.09, the

dimensionless velocity of water reaches a value uw = 3.81, whereas, for example at

Y/Hmax = 0.29, Y/Hmax = 0.47 and Y/Hmax = 0.55 the velocities uw = 5.24, uw = 5.90 and

uw = 6.76 are observed, respectively.


57

Non–stationarity of the flow. Comparison of the flow pattern in the first subdomain of

SBR at different moments of time, at the initial moment of the experiment and after

dimensionless time t = 0.03 shows a crucial difference. As shown in Figure 15, the examined

flow has a different structure after dimensionless time t = 0.03 (right) than at the beginning

(left).

Figure 14: Fluid flow pattern in three characteristic zones

X/D

0.33 0.44 0.56 0.67 0.43

0.46

0.49

0.52

0.55

0.58

0.61

0.28

0.31

0.34

0.37

0.40

0.43

0.25

0.33 0.44 0.56 0.67

Y/Hmax

Y/Hmax

0.05

0.08

0.11

0.14

0.17

0.20

0.22

0.33 0.44 0.56 0.67

TO

P O

F T

HE

SB

R

MID

DL

E P

AR

T

OF

TH

E S

BR

B

OT

TO

M

OF

TH

E S

BR

Y/Hmax

uw= 5

uw= 5

uw= 5

X/D

X/D


58

On the left, two vortices can be observed. With increasing time, the flow pattern changes

considerably. From the right–hand picture, it is difficult to recognize even one large vortex.

Moreover, the range of liquid velocity distribution has slightly different values. At the

beginning, the dimensionless velocity alters between 0.48 and 11.05, whereas after

dimensionless time t = 0.03 the velocity changes between 0.67 and 9.33. Additionally, at the

same measurement point but at a different experimental time, the fluid velocity is different.

For example, at height Y/Hmax = 0.12 and horizontal position X/D = 0.56, the dimensionless

velocity of water at the beginning of the experimental time reaches a value uw = 4.40 wheras

for the same measurement point but with increasing process time uw = 8.70. Differences can

be also observed in other points, e.g. for Y/Hmax=0.14 and X/D = 0.35, the liquid velocity is

equal to uw = 7.60 (left picture) and uw = 1.43 (right) for Y/Hmax = 0.15 and X/D = 0.50,

uw = 11.05 (left) and uw = 2.86 (right), and for Y/Hmax = 0.21 and X/D = 0.60, uw = 1.71 (left)

and uw = 9.05 (right). From this short comparison for the first bioreactor subdomain, crucial

velocity differences can be seen. From the above studies, the highest velocity difference over

the experimental time can be recognized for the third measurement point (Y/Hmax = 0.15 and

X/D = 0.50) and is equal to ΔuW = 8.19.

It should be also pointed out that as in the lower vertical coordinates, also for the higher

Figure 15: Dimensionless velocity distribution at two subsequent times (dimensionless time step t = 0 and t = 0.03)

X/D

velocity

10.48

9.33

8.19

6.95

5.81

4.67

3.52

2.29

1.14

0.33 0.44 0.56 0.67

0.08

0.11

0.14

0.17

0.20

0.23

Y/Hmax

mag0.1100.0980.0860.0730.0610.0490.0370.0240.0120.000

X/D 0.33 0.44 0.56 0.67

0.08

0.11

0.14

0.17

0.20

0.23

0.05

Y/Hmax

velocity 10.48

9.33

8.19

6.95

5.81

4.67

3.52

2.29

1.14

0


59

bioreactor subdomains the velocities change significantly for different measurement times.

Influence of wasting. As mentioned in Section 3.1, in order to obtain compact granules

with stable shape and volume, the biofilm growth is discarded every day. Comparing the flow

patterns and liquid velocity distributions before and during wasting (e.g. 14 days of wasting),

crucial differences are obvious. For illustration of the alteration, the images before

(Figure 16a) and during wasting (Figure 16b) are presented. In Figure 16a, larger granules,

situated close to each other are observed, whereas in Figure 16b smaller granules can be seen.

Moreover, analysing both situations, different velocity distributions are observed. From

Figure 17, it can be seen that the characteristic fluid velocity is lower before wasting than

during wasting. This is due to the higher concentration of the granules. Significant differences

in liquid velocity are observed in the first subdomain of the bioreactor, i.e. up to a height of

Y/Hmax = 0.27. At Y/Hmax = 0.10, the dimensionless velocity before wasting reaches uw = 4.03

and increases to uw = 6.00 during wasting. In contrast, in the upper region of the SBR, the

differences in the liquid velocity are much smaller. For example, at a height Y/Hmax = 0.58 the

fluid velocity amounts to uw = 5.68 before wasting and uw = 6.27 during wasting. Based on

this comparison, it can be stated that the concentration of the GAS significantly influences the

flow patterns and consequently fluid dynamic effects in the bioreactor.

Figure 16: Comparison of flow patterns (a) before and (b) during wasting

a) b)


60

For better illustration of analysed problem of wasting, velocity distributions and flow patterns

for two different bioreactor subdomains are shown in Figures 18 and 19. Figure 18 presents

the fluid pattern in the lowest bioreactor subdomain. For both pictures the flow pattern varies.

It can be clearly observed that the liquid velocity is lower before wasting (case a). Here, the

dimensionless velocity varies between 0 and 8.10. Taking into account the wasting results

(Figure 18b), it can be seen that the liquid velocity is significantly higher. In this case, the

liquid velocity changes varies 0 and 12.40. More detalied liquid velocity studies are given for

different measurement points. For example, for X/D = 0.65 and Y/Hmax = 0.14, the

dimensionless liquid velocity before wasting reaches uW = 3.81, whereas during wasting

uW = 11.24. Large differences can be also seen for X/D = 0.38 and Y/Hmax = 0.20. In this case

the liquid velocity before wasting is uW = 1.80 and, as in the previous case, the velocity during

wasting is considerably higher, uW = 10.76. The same velocity tendencies are recognized for

higher vertical coordinates (Y/Hmax up to 0.43). As shown in Figure 19a, the dimensionless

liquid velocity varies between 0 and 13.00. Considering the wasting experiments, the liquid

velocity alters between 0 and 17.14. Detailed velocity studies show that the dimensionless

liquid velocity is lower before wasting, e.g. for X/D = 0.44 and Y/Hmax = 0.39, uW = 2.86 and

Figure 17: Dimensionless velocity before and during wasting

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 2 4 6 8

u/urel

Y/H

max

u (before wasting) u ( during wasting)uW

uW (during wasting) uW (before wasting)


61

for X/D = 0.57 and Y/Hmax = 0.35, uW = 0.38, whereas in wasting investigations, for the same

measurement points, the dimensionless liquid velocity is higher, uW = 15.24 and 16.76,

respectively.

The above study reveals that in the higher vertical coordinates the flow pattern before wasting

differs from that during wasting.

Figure 18: Velocity distribution (a) before and (b) during wasting– comparison for bottom part of the SBR

Figure 19: Velocity distribution (a) before and (b) during wasting – comparison for the middle part of the SBR

a)

mag0.1100.0980.0860.0730.0610.0490.0370.0240.0120.000

17.14

15.24

13.33

11.43

9.52

7.62

5.71

3.81

1.90

0

velocity

X/D 0.33 0.44 0.56 0.67

0.28

0.31

0.34

0.37

0.40

0.43

0.25

Y/Hmax

X/D

b)

0.33 0.44 0.56 0.67

0.28

0.31

0.34

0.37

0.40

0.43

0.25

Y/Hmax

a)

0.33 0.44 0.56 0.67

0.08

0.11

0.14

0.17

0.20

0.23

0.05

Y/Hmax

X/D

b)

mag0.1100.0980.0860.0730.0610.0490.0370.0240.0120.000

12.38

11.00

9.63

8.26

6.88

5.50

4.12

2.75

1.33

0

X/D 0.33 0.44 0.56 0.67

0.08

0.11

0.14

0.17

0.20

0.23

0.05

Y/Hmax

velocity


62

Influence of the wall distance. Detailed analysis of the flow pattern using the He–Ne laser

as light source confirms the tendency observed during experiments with the video lamp, i.e.

the fluid velocity increases with higher vertical coordinate. This is particularly observed in the

first part of the SBR, Y/Hmax up to 0.51. Because of the non–stationary flow character, this

tendency is not stable. Additionally, the velocity increases with increasing distance from the

SBR wall. Figure 20 presents velocity distribution results for different vertical coordinates

and various distances from the SBR wall. In Figure 20a, results close to the wall (wall interval

Z/D = 0.06), in the first experimental subdomain (Y/Hmax up to 0.14), are presented. Figure

20b shows data for higher vertical coordinate (up to Y/Hmax = 0.37) and greater wall distance

(Z/D = 0.11). It is clearly seen that fluid velocity is higher in the second case, for higher SBR

subdomain and greater wall distance. In this case, the dimensionless velocity is in range

between 0 and 12.38. For lower vertical coordinate, the liquid velocity varies between 0 and

4.76. More detailed analysis reveals precise differences. For example, taking into account

Figure 20a (Y/Hmax up to 0.14) for X/D = 0.67 and Y/Hmax = 0.07, the dimensionless liquid

velocity is uW = 2.57, whereas for X/D = 0.50 and Y/Hmax = 0.10, the velocity is slightly

higher, uW = 2.86. The highest value (in the present analysis) of uW = 4.19 is obtained for

X/D = 0.76 and Y/Hmax = 0.12. From Figure 20b, it can be seen that for X/D = 0.32 and

Y/Hmax = 0.28, uW = 9.57.

Figure 20: Dimensionless velocity distribution at different vertical coordinates and different distance from the SBR wall: (a) Z/D = 0.06, (b) Z/D = 0.11

mag0.1300.1160.1010.0870.0720.0580.0430.0290.0140.000

0.88 0.78

Y/Hmax

0.02

0.03

0.04

0.06

0.08

0.12

0.14

0.22

0.11

0.10

0.33 0.44 0.56 0.67 X/D

Y/Hmax

0.22 0.33 0.44 0.56 0.67 0.88 0.78

0.25

0.26

0.28

0.29

0.31

0.36

0.37

0.34

0.33

0.11

12.38

11.00

9.63

8.26

6.88

5.50

4.12

2.75

1.33

0

a) b)

velocity

X/D


63

Higher velocities are obtained for higher vertical coordinates, e.g. for X/D = 0.60 and

Y/Hmax = 0.29, uW = 13.20 and for X/D = 0.22 and Y/Hmax = 0.31, uW = 10.52.

The above results show how the liquid velocity changes with various vertical coordinates

and different distances from the SBR wall. However, for a better understanding of the liquid

velocity changes with different wall distances but at the same level, a comparison is shown in

Figure 21. The pictures are analysed in the same range of Y/Hmax from 0.70 up to 0.86 but at

various distances from SBR wall (Z/D = 0.06, 0.09, 0.11). It can be clearly observed that

liquid velocity increases with increasing distance from the SBR wall. The lowest velocities

are obtained for Z/D = 0.06. Here, the dimensionless liquid velocity varies between 0 and

9.26. With increasing distance from the bioreactor the wall velocity is higher; for Z/D = 0.09

the velocity varies between 0 and 10.48 whereas for Z/D = 0.11 the liquid velocity is in the

range from 0 to 13.05. Observing velocity changes for different wall distances at various

measurements points reveals precise differences. Taking into account Z/D = 0.06, for a height

Y/Hmax = 0.73 and X/D = 0.22, the dimensionless liquid velocity is uW = 5.81, whereas for

Y/Hmax = 0.84 and X/D = 0.44, uW = 7.81. With Z/D = 0.09, for Y/Hmax = 0.75 and X/D = 0.35

and Y/Hmax = 0.84, X/D = 0.37 the liquid velocities reach uW = 9.52 and uW = 10.76,

respectively. Moving to the last measured plane (Z/D = 0.11 wall distance), the highest

velocities are observed. At the height Y/Hmax = 0.78 and X/D = 0.67, uW = 13.17 and for

Y/Hmax = 0.83 and X/D = 0.61, uW = 11.85.


64

Figure 21: Dimensionless velocity distribution at different distances from the SBR wall

0

0

0

0

0

0

0

0

0

0

Y/Hmax

0.70

0.73

0.76

0.83

0.86

0.79

X/D 0.22 0.33 0.44 0.56 0.67 0.89 0.78 0.11 1.0

Z/D

= 0

.06

Z/D

= 0

.09

Y/Hmax

0.70

0.73

0.76

0.83

0.86

0.79

X/D 0.22 0.33 0.44 0.56 0.67 0.89 0.78 0.11 1.0

Z/D

= 0

.11

Y/Hmax

0.70

0.73

0.76

0.83

0.86

0.79

X/D 0.22 0.33 0.44 0.56 0.67 0.89 0.78 0.11 1.0

mag0.1370.1220.1070.0910.0760.0610.0460.0300.0150.000

velocity

13.05

11.64

10.19

8.67

7.24

5.81

4.38

2.86

1.00

0

mag0.1370.1220.1070.0910.0760.0610.0460.0300.0150.000

mag0.1370.1220.1070.0910.0760.0610.0460.0300.0150.000

velocity

13.05

11.64

10.19

8.67

7.24

5.81

4.38

2.86

1.00

0

velocity

13.05

11.64

10.19

8.67

7.24

5.81

4.38

2.86

1.00

0


65

4.2.2 Normal strain rate

Similarly as for the liquid velocity distribution, the experimentally obtained normal strain

rate results are analysed with the use of two different light sources, the video lamp and He–Ne

laser.

Normal strain rate tendency. First, investigations with the video lamp are presented. As

indicated in the Introduction, the normal strain rate and shear strain rate seem to have

significant influence on the granule structure. As shown in Figure 22, the elongation rate

obtained with the help of the PIVview2C program reaches a relatively high value, up to

ε& = 26 s-1, which corresponds to ε& = 1. Red fields mark the highest values. It is observed that

with increasing vertical coordinates, more fields with the highest normal strain rates are

found. In the first bioreactor subdomain, a normal strain rate with a value of 0.04 (green

fields) dominates. However, higher normal strain rates can be also found, e.g. at the height

X/D = 0.40 and Y/Hmax = 0.07, the dimensionless normal strain rate is 0.22, whereas for

X/D = 0.50 and Y/Hmax = 0.14 and for X/D = 0.34 and Y/Hmax = 0.16, ε& = 0.57 and 0.38,

respectively. With increasing vertical coordinates (Y/Hmax from 0.25 to 0.43) low normal

strain rate values still prevail. Nevertheless, somewhat more spots with higher strain rate

values appear. For those areas, exemplary dimensionless normal strain rates are given; for

X/D = 0.39 and Y/Hmax = 0.27, X/D = 0.50 and Y/Hmax = 0.32 and X/D = 0.38 and

Y/Hmax = 0.37, the normal strain rates are equal to 0.21, 0.40 and 0.73, respectively. In the

third SBR subdomain, the situation is different. Here, fields with ε& < 0.1 occupy half part of

normal strain rate profile whereas the other part is dominated by higher values reaching

a maximum of ε& = 1 at some spots. At the height X/D = 0.40 and Y/Hmax = 0.45, ε& = 0.18.

For higher vertical coordinates X/D = 0.43 and Y/Hmax = 0.56, the dimensionless normal

strain rate amounts 0.62 whereas for X/D = 0.45 and Y/Hmax = 0.57 ε& reaches 1. Taking into

consideration investigations carried out by Höfer et al. (2004) with CAS, where significant

elongation of the flocs appeared at ε& = 0.12, it can be concluded that the strain rates observed

in the present study substantially affect the granulation process. These high elongation rates

prevent the growth of fluffy flocs and influence their breakdown in an early state of growth.


66

Figure 22: Normal strain rate and velocity distribution in different bioreactor subdomains

Y/Hmax X/D

nstrain15.00011.111

7.2223.333

-0.556-4.444-8.333

-12.222-16.111-20.000

1

0.74

0.5

0.22

-0.04

-0.03

-0.6

-0.8

-1.07

-1.3

uW=10

nstrain15.00011.111

7.2223.333

-0.556-4.444-8.333

-12.222-16.111-20.000

1

0.74

0.5

0.22

-0.04

-0.03

-0.6

-0.8

-1.07

-1.3

uW=10

nstrain15.00011.111

7.2223.333

-0.556-4.444-8.333

-12.222-16.111-20.000

1

0.74

0.5

0.22

-0.04

-0.03

-0.6

-0.8

-1.07

-1.3

uW=10

0.33 0.44 0.56 0.67

0.08

0.11

0.14

0.17

0.20

0.05

BO

TT

OM

OF

TH

E S

BR

X/D 0.33 0.44 0.56 0.67

0.28

0.31

0.34

0.37

0.40

0.43

0.23

Y/Hmax

MID

DL

E P

AR

T O

F T

HE

SB

R

0.43 0.33 0.44 0.56 0.67

0.46

0.49

0.52

0.55

0.58

0.61 Y/Hmax

TO

P O

F T

HE

SB

R

nstrain rate

nstrain rate

nstrain rate

X/D


67

Influence of the wall distance. Investigations with the He–Ne laser allow a more detailed

analysis of normal strain rate. Comparing all results with different distances from SBR wall

and with different vertical coordinates, it is observed that the normal strain rate increases with

increasing wall distance. Moreover, an increasing tendency is observed with increasing SBR

height. For a better understanding of the analysed situation, Figure 23 is shown.

Example (a) presents the dimensionless normal strain rate in the lowest SBR subdomain

(Y/Hmax from 0.02 to 0.14) and close to the wall (Z/D = 0.06) and (b) depicts the situation for

higher vertical coordinates (Y/Hmax in the range 0.35–0.51) at a distance Z/D = 0.11 from

SBR wall. Analysing both cases, it can be clearly observed that for the first case, the

dimensionless normal strain rate is significantly lower than for the second case. Here, the

major part of the normal strain field is characterized by a low value, e.g. at the height

X/D = 0.60 and Y/Hmax = 0.10, the dimensionless normal strain is ε& = 0.006. However,

higher values of normal strain can be also observed, e.g. for X/D = 0.56 and Y/Hmax = 0.08,

ε& = 0.16. However, fields with ε& > 0.11 occupy a minor part of the domain (see Figure 23a).

Considering the higher SBR subdomain and a wall distance of Z/D = 0.11 (Figure 23b), more

fields with elevated ε& are observed, e.g. for X/D = 0.56 and Y/Hmax = 0.42, the dimensionless

normal strain rate is equal to ε& = 0.19, for X/D = 0.78 and Y/Hmax = 0.44, ε& = 0.39 and for

Figure 23: Dimensionless normal strain rate at different vertical coordinates and different distances from the SBR wall: (a) Z/D = 0.06 and (b) Z/D = 0.11

a) b)

nstrain15.00011.111

7.2223.333

-0.556-4.444-8.333

-12.222-16.111-20.000

nstrain rate

0.58

0.42

0.27

0.11

-0.02

-0.15

-0.31

-0.46

-0.62

-0.77

Y/Hmax

0.35

0.37

0.39

0.41

0.43

0.49

0.51

0.47

0.45

X/D 0.22 0.33 0.44 0.56 0.67 0.89 0.78 0.11 1.0

uW=10 uW=10

Y/Hmax

0.02

0.03

0.04

0.06

0.08

0.12

0.14

0.11

0.10

X/D 0.22 0.33 0.44 0.56 0.67 0.89 0.78 0.11 1.0


68

X/D = 0.56 and Y/Hmax = 0.48, the dimensionless normal strain can reach ε& = 0.94. It should

not be forgotten that lower values of normal strain exist but on a minor scale, e.g. ε& = 0.05

for X/D = 0.56 and Y/Hmax = 0.37.

4.2.3 Shear strain rate

Shear strain rate tendency. Similarly as for normal strain rate investigations, in order to

analyse the shear strain rate tendency video lamp results are shown (see Figure 24). It can be

clearly observed that with increasing vertical coordinate, higher values of shear strain rate are

found. In the lowest SBR subdomain, the lowest value of the shear strain rate dominates, e.g.

at a height X/D = 0.44 and Y/Hmax = 0.11, the dimensionless shear strain rate

amountsγ& = 0.04. Fields with higher shear strain rates also appear. However, high γ& areas

occupy a minor part. Precise studies allow detailed analysis for different fields, e.g. for

X/D = 0.60 and Y/Hmax = 0.13, the dimensionless shear strain rate is γ& = 0.16, whereas for

X/D = 0.35 and Y/Hmax = 0.14 and for X/D = 0.48 and Y/Hmax = 0.16, γ& = 0.36 and 0.47,

respectively. Taking into consideration the second examined bioreactor part (Y/Hmax from

0.25 to 0.43), more fields with medium γ& appear, e.g. for X/D = 0.39 and Y/Hmax = 0.32,

γ& = 0.19. At a height X/D = 0.44 and Y/Hmax = 0.38, the dimensionless shear strain rate is

γ& = 0.38, and for X/D = 0.39 and Y/Hmax = 0.38, γ& = 0.58. For the higher SBR subdomain

(Y/Hmax in the range 0.43–0.61), fields with γ& > 0.2 occupy half part of the examined shear

strain rate profile. However, lower values of the dimensionless shear strain are still met; at

a height X/D = 0.44 and Y/Hmax = 0.46, γ& = 0.06. Analysing higher shear strain rate values,

e.g. for X/D = 0.59 and Y/Hmax = 0.50, for X/D = 0.43 and Y/Hmax = 0.50 and for X/D = 0.48,

and Y/Hmax = 0.57, γ& = 0.16, 0.29 and 1, respectively.


69

Figure 24: Shear strain rate and velocity distribution in different bioreactor subdomains

Y/Hmax X/D

nstrain15.00011.111

7.2223.333

-0.556-4.444-8.333

-12.222-16.111-20.000

sstrain rate

0.54

0.39

0.25

0.11

-0.02

-0.16

-0.30

-0.43

-0.57

-0.71

nstrain15.00011.111

7.2223.333

-0.556-4.444-8.333

-12.222-16.111-20.000

sstrain rate

0.54

0.39

0.25

0.11

-0.02

-0.16

-0.30

-0.43

-0.57

-0.71

0.33 0.44 0.56 0.67

0.08

0.11

0.14

0.17

0.20

0.05

BO

TT

OM

OF

TH

E S

BR

X/D 0.33 0.44 0.56 0.67

0.28

0.31

0.34

0.37

0.40

0.43

0.25

0.23

Y/Hmax

MID

DL

E P

AR

T O

F T

HE

SB

R

nstrain15.00011.111

7.2223.333

-0.556-4.444-8.333

-12.222-16.111-20.000

0.54

0.39

0.25

0.11

-0.02

-0.16

-0.30

-0.43

-0.57

-0.71

sstrain rate

uW=10

uW=10

0.43 0.33 0.44 0.56 0.67

0.46

0.49

0.52

0.55

0.58

0.61

Y/Hmax

TO

P O

F T

HE

SB

R

uW=10


70

Influence of the wall distance. He–Ne laser investigations allow more detailed shear

strain rate analysis. The results obtained show a similar tendency to studies with the use of the

video lamp. It can be observed that with higher vertical coordinate, the shear strain rate

increases. Moreover, similarly as for velocity distributions and normal strain rate, the

dimensionless shear strain rate increases with greater wall distance.

Two representative illustrations for different vertical coordinates and two distances from the

SBR wall are given in Figure 25. Example (a) presents the lower bioreactor subdomain

(Y/Hmax from 0.02 to 0.14) and small wall distance (Z/D = 0.09) and (b) shows studies for

higher vertical coordinates (Y/Hmax from 0.25 to 0.37) and distance from the bioreactor wall

Z/D = 0.11. Comparing the two shear strain rate profiles, crucial differences are observed. In

the lower SBR subdomain, close to the wall the dimensionless shear strain rate is significantly

lower than in the second case. For example, green and yellow colours marking low shear

strain rate values dominate. At a height X/D = 0.78 and Y/Hmax = 0.09, the dimensionless

shear strain rate is γ& = 0.04, whereas for X/D = 0.56 and Y/Hmax = 0.09, the shear strain rate is

slightly higher, γ& = 0.16. On the other hand, in the analysed shear strain rate profile small

fields with elevated γ& values can also be found. For example, at X/D = 0.50 and

Y/Hmax = 0.10, the dimensionless shear strain rate is γ& = 0.27. The highest normal strain rate

Figure 25: Dimensionless shear strain rate at different vertical coordinates and different distance from the SBR wall: (a) Z/D = 0.09, (b) Z/D = 0.11

a) b)

nstrain15.00011.111

7.2223.333

-0.556-4.444-8.333

-12.222-16.111-20.000

sstrain rate 0.54

0.39

0.25

0.11

-0.02

-0.16

-0.30

-0.43

-0.57

-0.71

Y/Hmax

0.02

0.03

0.04

0.06

0.08

0.12

0.14

0.11

0.10

X/D 0.22 0.33 0.44 0.56 0.67 0.89 0.78 0.11 1.0

Y/Hmax

0.25

0.26

0.28

0.29

0.31

0.36

0.37

0.34

0.33

X/D 0.22 0.33 0.44 0.56 0.67 0.89 0.78 0.11 1.0

uW=10 uW=10


71

is obtained for X/D = 0.60 and Y/Hmax = 0.12, whereγ& becomes 0.53. Figure 25b represents

a different situation, with more spots showing high γ& values, e.g. for X/D = 0.56 and

Y/Hmax = 0.29, the dimensionless shear strain rate is γ& = 0.36, whereas for X/D = 0.56 and

Y/Hmax = 0.36, γ& = 0.65. In the analysed picture, γ& can even reach a value of 0.96 at a height

X/D = 0.30 and Y/Hmax = 0.29. A lower γ& value of 0.17 is observed for X/D = 0.08 and

Y/Hmax = 0.28 (yellow field). The lowest dimensionless shear strain rate appears at different

points, e.g. γ& = 0.02 for X/D = 0.60 and Y/Hmax = 0.26.

Shear stress. For a better understanding of the influence of tangential velocity gradient

(γ& ) on the granulation process, the shear stress within the liquid phase is computed with the

equation

γμτ &= , (4.1)

where μ is the dynamic viscosity of the fluid. The calculation of the maximum shear stress for

the above investigations provides absolute values up to τ = 0.029 Pa. In accordance with the

literature, this magnitude of shear stress appears not to be high enough to destroy the

granules. The critical tangential stresses that can alter or destroy agglomerates lie beyond

τ = 10 Pa (Esterl et al., 2002). Experimental results yield much lower values. Nevertheless,

tangential velocity gradients can reach larger values close to the granule surface, which it is

not possible to detect within the spatial resolution of the PIV experiment. The latter effect can

induce much higher shear stresses than observed away from the surface. Hence, the possibility

cannot be excluded that shear stress may influence the structure of granules.

4.3 Particle Tracking Velocimetry

Tendency of granule velocity. Application of the PTV method shows that the

dimensionless velocity of granules decreases slightly with increasing vertical coordinate in the

bioreactor. An example of the dimensionless velocity distribution of the solid phase is

presented in Figure 26. According to this illustration, higher velocities are observed close to

the bottom of the bioreactor. In this case, the average dimensionless velocity magnitude

reaches uGav = 9.62. As shown in Figure 26b, in the upper part of the SBR the characteristic

velocity is lower, i.e. uGav = 7.3.


72

Influence of granule concentration. Furthermore, similarly as for the liquid phase, the

concentration of granules has an important influence on the granule velocity distribution. In

our investigations, different GAS concentrations, 13% and 15.9% are applied. In the first

case, a higher average velocity can be observed. With increasing concentration, the velocity

of the dispersed phase decreases, which is caused by increased momentum exchange

interaction. As a representative example, the first subdomain of the bioreactor (height up to

Y/Hmax=0.27) is shown in Figure 27. For the lower GAS concentration, the granules reach

a velocity uGav = 12, whereas for a 15.9% concentration, the dimensionless velocity is

uGav = 8.

Figure 26: PTV results with solid dimensionless velocity distribution: (a) lower position in the bioreactor, uGmin= 4.8, uGmax= 21.8, uGav= 9.62; (b) higher position in the

bioreactor, uGmin= 2, uGmax= 14.29, uGav= 7.3

a) b)

uG=10 uG=5


73

4.4 Laser Doppler Anemometry

4.4.1 Velocity distribution

Granules influence on the flow pattern. In this section, the velocity distribution of three–

and two–phase flows at different aeration flow rates is presented. Current comparisons show

the influence of the granules on the flow pattern. Part of a typical time series of the axial

velocity component measured with LDA for two– and three–phase flow is shown in

Figure 28a and b, respectively. Both examples show that bubbles and granules slightly

interfere with the measurement, causing gaps in the signal. Similar results for air–water

bubbly column investigations were observed by Mudde et al. (1998). From Figure 28, it is

difficult to identify which axial velocity is higher. However, comparing the liquid

dimensionless mean velocity (calculated with the help of equation 3.2), it is observed that Wv

Figure 27: PTV results with solid dimensionless velocity distribution: (a) 13% granule concentration, uGav= 12; (b) 15.9% granule concentration, uGav= 8

a) b)

0.33 0.56 0.78 1.0 0.25

0.27

0.29

0.31

0.33

0.35

0.37

0.33 0.56 0.78 1.0 0.25

0.27

0.29

0.31

0.33

0.35

0.37

X/D

Y/Hmax Y/Hmax

X/D

uG=10 uG=5


74

is significantly higher for two–phase flow, Wv =8.92, whereas for three–phase flow Wv = 3.10.

Studies with different aeration flow rates. Analysis of the influence of granules on the

flow pattern can be done by comparison of the axial velocity distribution of two– and

three–phase flows with different aeration flow rates. The characteristics of the axial velocity

can be seen in Figure 29 for an exemplary measurement point Y/Hmax = 0.43. From the

analysis of two– and three–phase flow occurrences, it is observed that the liquid velocity is

lower in the latter case. As might be supposed, the velocity increases with increasing aeration

flow rate for both cases. Taking into account three–phase flow, it can be seen that the highest

dimensionless velocity is obtained for a wall interval Z/D = 0.10. For this measurement point

with a 2 L/min aeration flow rate, the mean dimensionless axial velocity is Wv = 6.03,

whereas with a 3 L/min aeration flow rate, the mean axial velocity is Wv = 7.13. The highest

value of Wv = 10.29 is observed with a 4 L/min aeration flow rate. From the above

comparison, an increasing tendency of axial velocity with increasing aeration flow rates is

distinguished. This effect is also observed for the case of two–phase flow. Here, the analysis

is done for the same characteristic point Z/D = 0.10. The lowest axial mean velocity is

observed at a 2 L/min aeration flow rate, where Wv = 8.10. For a 3 L/min aeration flow rate,

Figure 28: Typical time series of the liquid velocity measured with the LDA technique (Y/Hmax = 0.44, wall distance X/Z = 0.07): (a) two–phase flow and

(b) three–phase flow

a) b)

t

v

10 20 30-0.4

-0.2

0

0.2

0.4

t

v

50 100 150

-0.2

0

0.2

0.4

38

19

-19

-38

38

19

-19


75

0

2

4

6

8

10

12

14

0.00 0.02 0.04 0.06 0.08 0.10

Z/D

v w

2 phase flow 2 l/min aeration rate 3 phase flow 2 l/min aeration rate2 phase flow 3 l/min aeration rate 3 phase flow 3 l/min aeration rate2 phase flow 4 l/min aeration rate 3 phase flow 4 l/min aeration rate

the dimensionless velocity reaches Wv = 10.95, and for 4 L/min, Wv = 12.38. The above

comparisons evidently show that the presence of the granules (third phase) significantly

influences the flow pattern. The dimensionless mean axial velocity is lower for three–phase

flow and reaches the highest value of Wv = 10.29 with a 4 L/min aeration flow rate, whereas

the axial velocity for two–phase flow with a 4 L/min aeration flow rate for the same

measurement point is Wv = 12.38.

Influence of the wall distance. The liquid velocity increases slightly with increasing

distance from the SBR wall. Close to the centre, due to the large influence of bubbles and also

of granules, the velocity decreases. However, this result can be influenced by the decrease in

measured LDA data rate with increasing distance from the SBR wall, e.g. close to the wall

(with Z/D = 0.03) a data rate as high as 460 Hz (two–phase flow) is observed, whereas with

a larger distance (Z/D = 0.10) only 280 Hz can be registered for two–phase flow and 16 Hz

Figure 29: Axial liquid velocity in two– and three–phase flows for different flow rates obtained from LDA


76

(Z/D = 0.10) for three–phase flow.

In order to show clearly the influence of the third phase, i.e. granules, on the flow, the

velocity difference between two– and three–phase flows is computed (see Table 2). Contrary

to the highest dimensionless velocity for wall interval Z/D = 0.10, the calculated velocity

difference is lowest for this measurement point, e.g. for 4 L/min the aeration rate equals 2.10.

High velocity differences are observed close to the SBR wall, at Z/D = 0.03. In this case, the

velocity difference between two– and three–phase flows is equal to 3.82, 4.68, 6.48 for 2,

3 and 4 L/min aeration rates, respectively.

Dimensionless velocity difference Wall distance

X/D 2 L/min 3 L/min 4 L/min

0.03 3.82 4.68 6.48

0.07 6.17 3.50 3.52

0.10 2.07 3.82 2.09

Table 2: Velocity difference between two– and three–phase flows with different aeration

flow rates

Liquid axial velocity tendency. Further LDA studies show that the liquid velocity

increases with increasing height for two– and three–phase flows. These studies confirm the

results presented in Section 4.1 for PIV investigations. Figure 30 illustrates axial velocity Wv

profiles for three–phase flow with a 4 L/min aeration rate. It can be observed that Wv

increases with increase in height, e.g. for Z/D = 0.03 the dimensionless mean axial velocity is

Wv = 0.75 for Y/Hmax = 0.44, whereas for Y/Hmax = 0.65, Wv = 2.95. The same tendency is

observed for subsequent points. An exception is the third measurement point (wall distance

Z/D = 0.07), where in the higher bioreactor subdomain (Y/Hmax = 0.44) the velocity is slightly

lower. However, this difference can appear due to the non–stationary character of the

analysed flow.


77

4.4.2 Energy spectrum analysis

Turbulence studies in different vertical coordinates. Laminar or turbulent character of

the flow can be recognized by power spectra density (PSD) analysis. Energy spectrum studies

are implemented for two– and three–phase flows, different vertical coordinates and various

wall distances. First, the one–dimensional power spectra density for three–phase flow with

a wall distance of Z/D = 0.07 is given in Figure 31. A comparison for three different vertical

coordinates does not indicate the existence of turbulent flow. It can be clearly seen that the

Kolmogorov law, requiring an energy spectrum having slope of –5/3, is not obeyed. Hence, it

can be concluded that of the most important mechanisms of turbulence, i.e. converting energy

from the main flow to the turbulence dissipations, does not occur. Therefore, without this

mechanism viscous dissipation will dominate the flow, which can then be considered as being

laminar in principle.

0

1

2

3

4

5

6

7

8

0.00 0.03 0.07 0.10

Z/D

v w

Y/Hmax=0.44 Y/Hmax=0.65

Figure 30: Liquid velocity for different vertical coordinates, obtained from LDA


78

Figure 31: Measured three–phase flow power spectra density for different vertical coordinates (a) Y/Hmax = 0.32, (b) Y/Hmax = 0.44 and (c) Y/Hmax = 0.65 for distance

Z/D = 0.07 wall

1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+001.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03

frequency (Hz)

PSD

(m2 /s)

measured spectrum Kolmogorov's (-5/3) slope

1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+001.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03

frequency (Hz)

PSD

(m2 /s)


1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+001.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03

frequency (Hz)

PSD

(m2 /s)


a)

b)

c)


79

Similar studies were carried out for two–phase flow. Figure 32 depicts the PSD for different

vertical coordinates and wall distance Z/D = 0.07. Similar as in previous case, current result

indicates laminar character of two phase flow.

Figure 32: Measured two–phase flow power spectra density for different vertical coordinates (a) Y/Hmax = 0.32, (b) Y/Hmax = 0.44 and (c) Y/Hmax = 0.65 for Z/D = 0.07 wall

distance

1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+001.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03

frequency (Hz)

PSD

(m2 /s)


1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+001.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03

frequency (Hz)

PSD

(m2 /s

)


1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+001.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03

frequency (Hz)

PSD

(m2 /s)


a)

b)

c)


80

Influence of the wall distance. PSD investigations for different wall distances also

indicate the laminar character of the analysed flow. As an example, the power spectra density

of three–phase flow with different wall distance Z/D = 0.03, 0.07 and 0.10 is given

(in Figure 33).

Figure 33: Measured three–phase flow power spectra density for different wall distances (a)Z/D = 0.03, (b) Z/D = 0.07 and (c) Z/D = 1.0 for exemplary vertical coordinate

Y/Hmax = 0.32

1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+001.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03

frequency (Hz)

PSD

(m2 /s)


1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+001.00E-02 1.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03

frequency (Hz)PS

D (m

2 /s)


1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+001.00E-02 1.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03

frequency (Hz)

PSD

(m2 /s)

measured spectrum Kolmogorov's slope (-5/3)

a)

b)

c)


81

From Figure 33, it can be deduced that the spectra obtained do not agree with Kolmogorov’s

–5/3 slope law. Furthermore, as can be seen in Figure 34, the courses of the spectra do not

differ significantly for three– and two–phase flow. The presence of the third phase, i.e.

granules, reduces the velocity but does not change the turbulence energy significantly. Both

curves, dark blue (three–phase) and light blue (two–phase) show very similar tendencies.

4.5 Fluid dynamic forces

As shown in the theoretical part, fluid mechanical forces play an important role in

multiphase flow. At the beginning of this section forces, through the interface between fluid

and particles, equivalent time–averaged collisional force and van der Waals forces from the

group of interactions between particles and walls and buoyancy forces belonging to the forces

1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+001.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03

frequency (Hz)

PSD

(m2 /s

)

3 phase 2 phase Kolmogorov's (-5/3) slope

Figure 34: Turbulence power spectra for two– and three–phase flows in the lower vertical coordinate (Y/Hmax = 0.32)


82

caused by external fields will be considered. Due to the impossibility of calculating granule

charges, electrostatic force is not analysed.

Using typical physical properties of solid and liquid phases such as density, viscosity,

average granule diameter, average velocity of granules and fluid, average vorticity and

characteristic angular velocity of the particles, the fluid dynamic forces can be determined.

The physical parameters that are used in calculations of fluid dynamic forces are shown in

Table 3.

Physical properties (25°C) Granules Liquid

Density [kg/m3]

Dynamic viscosity [Pas]

1044

-

997

0.001035

Table 3: Physical properties of solid and liquid phases

The analysis of the magnitude of the considered fluid dynamic forces is given in Table 4.

Absolute representation of the forces shows that buoyancy force reaches the highest order of

magnitude of 10-6 N. The equivalent time–averaged collisional force and drag force have

a lower order of magnitude of 10-7 N, followed by a decreasing tendency for the Saffman

force, Magnus force, added mass force, Basset force, lift rotational force and van der Waals

force with orders of magnitude of 10-7, 10-7, 10-8, 10-8, 10-8 and 10-16 N, respectively. The

above analysis clearly shows that buoyancy is the dominant force. However, for a better

estimation of the order of magnitude of the above forces and to show their importance,

dimensionless representation is introduced (see the last column in Table 4). In this case, every

analysed force is presented as the ratio of examined force to buoyancy force. Dimensionless

representation of the forces allows their rapid analysis, e.g. it can be clearly observed that the

equivalent time–averaged collisional force and drag force are smaller by one order of

magnitude than the buoyancy force.


83

Force

Absolute representation of the forces

Dimensionless

representation

of the forces

Drag force

( ) 7103572

−×=−−= .uuuuACF PWPWW

pDDrrrrr ρ N

( ) ( )

8

0

02

10571

23

−×=

=⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡−

+−

−= ∫

.

tuu

dttt

uudtd

DFt

PW''

PW

WWPB

rrrrv

μπρ

( ) 81079150 −×=−= .uudtdmC.F PW

P

PWAA

rrr

ρρ N

( )[ ] 72 106741611 −×=×−= .uuD.F WPWW

WWPS ωω

μρrrr

rr

( ) 7105812

−×=−×

−= .uuuuACF PWPW

WPLRM Ω

Ωρr

rrrrrr

N

85

1082322

−×=⎟⎠⎞

⎜⎝⎛= .CDT R

PWR ΩΩρ rrr

N

110311 −×= .FF

G

Dr

r

310422 −×= .FF

G

Br

v

310742 −×= .FF

G

Ar

r

210177 −×= .FF

G

Sr

r

210432 −×= .FF

G

Mr

r

310865 −×= .FT

G

Rr

r

Equivalent time averaged collisional force Van der Waals force

710589 −×== .tumF

E

PPC

rrN

16

2 1075112

−×== .z

AdFW

rN

110471 −×= .FF

G

Cr

r

1110692 −×= .

FF

G

Wr

r

Buoyancy force

610516 −×=−= .)(gF PWG ρρrrN

1=G

G

FFr

r

Table 4: Fluid dynamic forces in absolute and dimensionless representations

Forc

es b

etw

een

fluid

and

par

ticle

s

Basset force

Added mass force

Saffman force

Magnus force

Lift rotational force

Forc

es b

etw

een

pa

rticl

es a

nd w

alls

Fo

rces

cau

sed

by

exte

rnal

fiel

ds

N

N


84

Particle–particle collisions and particle–wall collisions demand separate analysis.

Collisions certainly account for an additional load acting on the granules involved. Here, to

describe inter–particles collisions, a model based on the kinetic theory of gases with several

restrictions is applied, according to Huilin et al. (2003). Here, two dominant kinds of particles

in the SBR with two different diameters, 2 and 4 mm, are analysed. Similarly as

experimentally observable, three different flow structures in the bioreactor (vortices of

different size – see Section 4.2.1) and also three SBR subdomains with different granules

concentrations are distinguished. Thus, the bioreactor is divided imaginarily into three zones

with an estimated distribution of the granules in each zone.

The highest GAS concentration is recognized in the lowest bioreactor subdomain (Y/Hmax up

to 0.25). Here reside around 60% of the overall number of granules of 4 mm diameter and

approximately 50% of the overall number of the granules of 2 mm diameter. With increasing

vertical coordinate, decreasing granule concentration is observed. The second SBR

subdomain (Y/Hmax from 0.25 up to 0.50) is occupied by ca. 25% of the overall number of 4

mm granules and around 30% of the 2 mm granules. In the highest bioreactor subdomain

(Y/Hmax from 0.50 up to 1.00), ca. 15% of the overall number of larger granules (4 mm) and

Figure 35: Particle–particle collisions in different SBR subdomains

60% 50%

25%

30%

15%

20%

42447690 collisions/s/m3



0

0.25

0.50

1.00 Y/Hmax


85

ca. 20% of the number of smaller granules (2 mm) can be found. The collision rate for the

above different domains is calculated with the help of equation 2.63. For the lowest bioreactor

subdomain, the collision rate is equal to 42447690 collisions/s/m3, in the middle part,

10609900 collisions/s/m3, and in the highest SBR subdomain, 318190 collisions/s/m3.

Analysing the above results, a decreasing tendency of collision rate with increasing vertical

coordinate is observed (see Figure 35).

In this section, the particle–wall collision rate in the SBR is obtained with the help of

experimental investigations. Similarly as in the previous case, the SBR is divided into three

subdomains with different collisions rates resulting from the observed inhomogeneous

distribution of granules within the bioreactor volume. In order to estimate the particle–wall

collision rate, in every SBR subdomain a small wall area is marked. Subsequently, for every

marked area a number of repetitions of the experiment are carried out consisting of visual

observation and counting the particle–wall collisions appearing during 100 s. Based on the

arithmetic average of the results of all counting series, the particle–wall collision rate can be

obtained. However, due to practical difficulties in visually distinguishing granules with

different sizes, no differentiation of granule diameters is done. Thereby, in the lowest

bioreactor subdomain, the collision rate is equal to 6030 collisions/s/m2, for Y/Hmax from 0.25

up to 0.50 is 4890 collisions/s/m2 and in the highest part 3990 collisions/s/m2. Similarly as in

previous case (particle–particle collisions), the collision rate decreases with increasing vertical

coordinate. This tendency appears due to the decreasing concentration of granules in the

higher SBR subdomain. It is worth mentioning, that despite the obviously non–stationary

nature of the flow (see Section 4.2.1), the reproducibility of the time–averaged collision rate is

very high. The standard deviation within a measurement series does not exceed few percent.

Figure 36 shows the analysed situation.

The inter–particle and particle–wall collision rates obtained above are based on

experimental and computational estimations of the granule number. Nevertheless, it should

not be forgotten that granulation is a biological process and as such features inhomogeneities

and fluctuations of characteristic parameters. Furthermore, many factors influence granule

formation, e.g. water quality and mineral content, temperature, pH, illumination. Hence, it is

impossible to determine absolutely accurate and reproducible particle numbers and collision

rates.


86

Based on the average time between collisions (particle–particle, particle–wall) given in

Table 4, the equivalent time–averaged collisional force ( CFr

) is computed (see Section 2.2).

4.6 Microscopic observations

In this section, microscopic analysis, which enables the typical microorganisms living on

the granules and also flow induced by ciliates to be recognized, is presented. The present

studies allow optimal conditions for granule formation to be found. Moreover, µ–PIV

indicates crucial differences between the flow field induced by one ciliate and by a colony.

Furthermore, investigations with different seeding particle concentrations give proper

biocompatibility conditions.

4.6.1 Microscopic analysis

As indicated in Section 3.1, in order to observe granule behaviour under different flow

mechanical conditions, three bioreactors with different air flow rates are compared.

Figure 36: Particle–wall collisions in different SBR subdomains




0

0.25

0.50

1.00 Y/Hmax


87

Figure 37: Microscopic analysis for different aeration flow rates

6 L/min

8 l/min

50µm

Ciliates

200 µm

Rotatoria

filaments

8 L/min

4 L/min

50µm


88

The microscopic analysis (Carl Zeiss Axiotech 100 microscope) reveals a great diversity of

microorganisms for different flow conditions. With a 4 L/min aeration flow rate, where

granulation process takes place a lot of ciliates (Peritricha) colonies are seen. For those flow

conditions the best settling ability of granules is observed. By higher flow rates, where the

granulation does not appear, no ciliates are observable. Moreover, with increasing flow rate,

the settling ability of the sludge decreases. With a 6 L/min aeration rate, ciliates in

a small tree colony and dominating Rotatoria are observed. For 8 L/min, only filaments are

seen. Hence, it can be supposed that biochemical and hydrodynamic selection of

microorganisms takes place. Comparing three bioreactors operating with different aeration

flow rates, it can be seen that optimal conditions for granule formation are met at 4 l/min.

During the whole process, granules are spherical and compacted. In the second and third cases

(for 6 and 8 L/min), granules are gradually destroyed with increasing process time. After

start–up of the bioreactor, spherical and compacted granules are observed. This indicates that

granules are not destroyed immediately with higher shear and elongational forces. With

increasing process time, the consistency of the granules is changed. For 6 L/min granules are

destroyed and split into floc–aggregates with substantially decreased settling ability. The SBR

with an aeration flow rate of 8 L/min presents the worst conditions. After around 2 weeks of

operation, fluffy flocs appear with very long settling times. The diversity of the

microorganismic structures on the granule surface for different aeration flow rates is shown in

Figure 37. The above analysis indicates the important role of ciliates for the granule formation

process. The present investigations confirm studies carried out by Weber et al. (2007)

showing ciliates as a granules backbone. Furthermore, it can be supposed that additional to

biochemical phenomena fatigue effect takes place. Investigations carried out by Brück (1997),

Arnold et al. (1999), Lutz (1999), Esterl et al. (2002), Höfer et al. (2004) for different

biomaterials show that influence of mechanical forces on the biofilm structure not only

depends on the load magnitude but also duration of the process.

4.6.2 Micro Particle Image Velocimetry

As shown in the previous section, ciliates play an important rule in the structural formation

of microbial granules derived from activated sludge. Investigations by Hartmann et al (2007),

Kowalczyk et al. (2007), Petermeier et al. (2007) and Weber et al. (2007) came to the same

conclusions. Cilia beats of ciliates provide continuous nutrient flux enhancing the


89

colonization of bacteria on the Peritrichia stalks. Thus, in this section flow induced by ciliates

is analysed.

Analysing µ–PIV investigations of the fluid flow in the vicinity of the granule surface,

a characteristic micro flow pattern with two vortices generated by cilia beats can be observed

(see Figure 38). The μ–PIV experiments are carried out in compliance with the condition of

biocompatibility. That concerns, among others, application of biotracers for flow

visualization. As described in the Section 3.4.2, a special methodology for μ–PIV seeding was

elaborated in the present work, whereby a suspension of yeasts (Saccharomyces cerevisiae)

and alternatively an aqueous solution of milk (Zima–Kulisiewicz et al., 2007) is applied for

the purpose of micro–flow tracing.

Considering experiments with yeast cells as tracer particles, an aqueous solution of 1:100

(yeast to water) is required for the present investigations. In the case of milk solution, the best

results are obtained for several concentrations, 1:1, 1:2, 1:3 and 1:4 (milk to water). Higher

concentrations of tracer substance can influence the investigated flow.

Analysis with different seeding particles. Exemplary experiments with yeast cells

(1:100) and with milk (1:3) for 20– and 50–fold optical magnifications are compared. Figure

39 illustrates µ–PIV measurements with 20–fold optical magnification for yeast cells (a, c)

and milk (b, d). Examples (a) and (b) depict the situation for a single ciliate, and (c) and (d)

for a colony of these microorganisms. The results reveal some significant differences in the

Figure 38: Characteristic flow pattern generated by one ciliate observed at 50–fold magnification

0.08 50 µm


90

fluid flow visualization. Although the order of magnitude of fluid velocity remains at the

same level, more detailed presentation of the fluid flow field concerning visualization

artefacts is obtained for the probe with milk solution. The maximal dimensionless velocities

obtained in the investigations with yeast cells are slightly lower than those with milk,

amounting to 0.19 for the former and 0.21 for the latter.

Figure 39: Velocity field observed by 20–fold optical magnification: (a) one ciliate (yeast cells), (b) one ciliate (milk), (c) colony (yeast cells) and (d) colony (milk)

0.08 0.08 50µm 50µm

c) d)

a) b)

0.08 50µm 0.08 50µm


91

The region with the highest velocity is located in the vicinity of zooids. Comparing Figure

39b and d, the smoothing effect of milk on the results of flow visualization can be clearly

seen. Additionally, vortex structures caused by ciliary beating for both one ciliate and

a colony can be better recognized.

Figure 40: Velocity field observed by 50–fold optical magnification: (a) one ciliate (yeast cells), (b) one ciliate (milk), (c) colony (yeast cells) and d) colony (milk)

0.08 50 µm 0.08 50 µm

a) b)

0.08 50 µm 0.08 50 µm

c) d)


92

In order to visualize and analyse the fluid flow close to the ciliate in more detail, PIV

measurements with the same seeding particles concentration as above but with 50–fold optical

magnification are carried out. Figure 40 shows the velocity distribution as in Figure 39. In

Figure 40, it can be seen that investigations with yeast cells at higher optical magnification are

strongly limited. It is not possible to obtain the flow field with sufficient quality and quantify

the velocity. There are many spurious vectors due to the low density and large dimensions of

the tracer particles. Calculated values of the velocity (the maximal velocity amounts 0.07) are

underestimated and loaded with a high error. Although in Petermeier et al. (2007) and

Kowalczyk et al. (2007) novel, powerful algorithms for correcting image artefacts including

spurious vectors automatically by using a priori knowledge of the flow field are suggested,

here another way is preferred. In contrast, application of milk allows detailed visualization of

the flow close to the body of ciliates. Although the maximal noted dimensionless velocity is

0.34, many vectors have values in the region of 0.15. Moreover, the characteristic vortex

structures built by ciliates during the feeding phase are evidently documented.

Investigations with different ciliate number. As shown above, detailed flow

visualization is possible for higher optical magnification with milk solution as seeding

substance. Thus, further investigations will be presented for 50–fold optical magnification

with admixture of milk. Figure 41 depicts a comparison of the velocity distribution for one

ciliate, two ciliates and a colony with a milk to water concentration of 1:1. Here, an increasing

tendency of velocity distribution with increasing ciliate number is observed. The highest value

of the maximal dimensionless velocity distribution of umax = 0.86 is observed for the colony

and the lowest value of umax = 0.20 appears for the single ciliate. The above analysis clearly

shows that cooperative colony work influences significantly the velocity distribution

displaying a bio–synergetic effect. Moreover, comparing the three situations, the

characteristic two vortices can be seen only for the first case with a single organism. In the

second and third cases instead of typical flow each ciliate produces one vortex. Additionally,

synergetic vortex belonging partially to two different ciliates appears.


93

Figure 41: Velocity distribution observed by 50–fold optical magnification for (a) one and (b) two ciliates and (c) colony

synergetic vortex

10 μm/s 50 µm

10 μm/s 50 µm

10 μm/s 50 µm

synergetic vortex

umax = 0.20

umax = 0.48

umax = 0.86

a)

b)

c)


94

Investigations with different seeding substance concentrations show crucial

differences. Figure 42 depicts the flow induced by one ciliate for different milk to water ratios

of 1:1, 1:2 and 1:4. An increasing velocity with increasing dilution of seeding milk is

observed, e.g. for the highest milk concentration (1:1) the velocity has the lowest value of

umax = 0.17 and for the lowest concentration (1:4) the velocity reaches the highest value of

umax = 1.00.

Figure 42: Velocity distribution observed by 50–fold optical magnification with different milk concentration is (a) 1:1, (b) 1:2, (c)1:4

0.08 50 µm

0.08 50 µm

0.08 50 µm

a)

b)

c)

umax = 0.17

umax = 0.54

umax = 1.00


95

It should be pointed out that the seeding concentration is not arbitrary. μ–PIV

investigations with dilutions higher than 1:4 are impossible, e.g. studies with a milk to water

ratio of 1:5 show that low tracer particle numbers prevent correct recognition of the flow

structure.

Kinetic energy investigations. Flow induced by ciliates on the micro–scale is in fact an

efficient way of nutrient transport to the biofilm surface from the bulk liquid with minimum

energy requirement (Hartmann et al., 2007, Kowalczyk et al., 2007, Petermeier et al., 2007).

As a result, the action of ciliates resembles the work of micro pumps or micro mixers

providing purification of the bulk liquid and aggregation of impurities on the biofilm surface.

The present study extends previous investigations showing that flow generated by ciliates

living on the granule surface plays an important role in granule formation. Due to the

characteristic motion generated by ciliates seeking nutrients, flocs and microorganisms are

pulled together and as a result compacted GAS appears.

Moreover, µ–PIV studies described in the previous section reveal that the cooperative fluid

transport induced by ciliates living in colonies or groups is more effective than in the case of

a single organism. That is confirmed by the investigation of the convective kinetic energy

produced by living protozoa. Figure 43 presents the calculated two–dimensional spatial

distribution of the kinetic energy generated by these microorganisms. Ciliates living in

a colony produce more kinetic energy per single organism than a single ciliate. The synergy

factor amounts to approximately 1.7. In order to emphasize the efficient work of ciliates in

a colony, a comparison of energy dissipation is given. In contrast to kinetic energy, the

dissipated energy during the mixing process is very low, i.e. for one ciliate it varies between

0.2 x 10-4 and 3.6 x 10-4 W/m3. The synergy factor for a single ciliate and a colony is 1.2. The

above situation is analysed for a 1:3 milk concentration (see Figure 43). Further kinetic

energy investigations show its increasing tendency with increasing milk dilution. As an

example, the flow induced by one ciliate is given. Comparing different seeding milk to water

ratios of 1:1, 1:2 and 1:4, the highest value of the kinetic energy is found for the lowest milk

concentration (1:4). On the other hand, the highest milk concentration (1:1) yields the lowest

kinetic energy (see Figure 44).


96

Comparison of the above data with the velocity distribution for different seeding

concentrations results in the same tendency. The seeding concentration influences the velocity

distribution significantly and consequently the kinetic energy of the fluid. With increasing

milk dilution, both the velocity and convective kinetic energy are clearly higher. The highest

values are observed for a milk concentration of 1:4. Experimental investigations indicate a

ratio of milk to sewage of 1:4 as a maximal dilution for μ–PIV studies. A lower concentration

of the seeding (e.g. 1:5) prevents efficient nutrient transport and also makes μ–PIV analysis

impossible due to an insufficient number of flow tracers. Moreover, comparisons of velocity

distributions and convective kinetic energies for one ciliate and a colony reveal a higher

efficiency of cooperative work of the ciliate colony.

Figure 43: Kinetic energy generated by one ciliate (left) and a colony (right)

μW/m3K

inetic energy [μW/m

3]

Kinetic energy

Kinetic energy [μW

/m3]

Kinetic energy

μW/m3

Kin

etic

ene

rgy

[μW

/m3 ]

Kin

etic

ene

rgy

[μW

/m3 ]


97

Figure 44: Kinetic energy produced by one ciliate with different milk proportions: (a) 1:1, (b) 1:2, (c) 1:4

x [um]

0100

200300y [um]

0100

200300

Ekinconv

-80000

-60000

-40000

-20000

0

20000

40000

60000 Ekinconv

5.00E+044.07E+043.14E+042.21E+041.29E+043.57E+03

-5.71E+03-1.50E+04-2.43E+04-3.36E+04-4.29E+04-5.21E+04-6.14E+04-7.07E+04-8.00E+04

x [um]

0100

200300

y [um]

0100

200300

Ekinconv

-400000

-320000

-240000

-160000

-80000

0

80000

160000 Ekinconv

1.23E+058.92E+045.54E+042.16E+04

-1.21E+04-4.59E+04-7.97E+04-1.14E+05-1.47E+05-1.81E+05-2.15E+05-2.49E+05-2.82E+05-3.16E+05-3.50E+05

a)

b)

c)

Ekinmax= 7853 μW/m3



x [um]

0100

200300y [um]

0100

200300

400

Ekinconv

-10000

-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

10000Ekinconv

8.00E+036.86E+035.71E+034.57E+033.43E+032.29E+031.14E+030.00E+00

-1.14E+03-2.29E+03-3.43E+03-4.57E+03-5.71E+03-6.86E+03-8.00E+03

a)

Kinetic energy [μW

/m3]

Kinetic energy [μW

/m3]

Kinetic energy [μW

/m3]

Kinetic energy

μW/m3

Kinetic energy

μW/m3

μW/m3

Kinetic energy

Kin

etic

ene

rgy

[μW

/m3 ]

Kin

etic

ene

rgy

[μW

/m3 ]

Kin

etic

ene

rgy

[μW

/m3 ]

CONCLUSIONS

98

5. CONCLUSIONS

Increasing water consumption and its decreasing quality demand efficient wastewater

treatment technologies. Here, aerobic sludge granulation provides a wide range of possibilities

in the biological purification of wastewater. So far, anaerobic techniques are the main

processes operated by hundreds of wastewater treatment plants. Although, the anaerobic

process is well established in wastewater treatment plants, it has some disadvantages: a long

start–up period (up to 4 months) and long operation time and unsuitability for low–strength

organic wastewaters are the most significant. Moreover, nutrient removal (nitrogen,

phosphorus) from wastewater does not take place in this system. In order to overcome these

weaknesses, a system under aerobic conditions (Liu and Tay, 2004) can be implemented,

providing biogranulation of the sewage. The first aerobic investigations were done by

Mishima and Nakamura in 1991, using a continuous aerobic upflow sludge blanket reactor.

However, due to the novelty of the aerobic biogranulation technologies, various investigations

are still necessary. A number of scientific investigations with different points of view have

been carried out and presented in the literature, e.g. Buen et al. (1999), Etterer et al. (2001),

de Kreuk et al. (2005a), McSwain et al. (2005). However, a complete understanding of the

aerobic sludge granulation process has still not been achieved. In the present work, for better

comprehension of the complexity of the biogranulation phenomena in an operating aerobic

Sequencing Batch Reactor (SBR), the process is studied, for the first time, from a fluid

mechanical point of view. The multiphase flow effects taking place in an SBR are considered

with respect to different characteristic length and time scales ranging from microorganism

nutrition up to granulation effects lasting several days.

Complete analysis of a multiphase flow is very complex and as such demands appropriate

experimental methods. Nowadays, Electrical Conductivity Measurement (ECM), Time

Domain Reflectometry (TDR), Computer Automated Radioactive Particle Tracking (CART),

Computer Tomography (CT), Hot Wire Anemometry (HWA), Laser Doppler Anemometry

(LDA), Particle Tracking Velocimetry (PTV) and Particle Image Velocimetry (PIV) allow the

appropriate treatment of multiphase flow in bubble columns. In the present work, the

questions given in the Introduction, such as: which global and local conditions allow granule

formation?, why do granules take a regular form?, which fluid mechanical forces affect

granules? and do microorganisms develop protective mechanism? could be answered only by

CONCLUSIONS

99

using suitable optical in situ experimental methods. Thus, in order to recognize typical flow

structures of the liquid phase, velocity distributions and the influence of normal and shear

strain rates on the granulation process, PIV investigations are implemented. In this case, two

different light sources, i.e. a video lamp and a He–Ne laser, are used. The velocity distribution

of the solid phase is obtained with a help of PTV with a video lamp as a light source.

Moreover, LDA permits the recognition of velocity profiles of two– and three–phase flows

and the fluid mechanical character of multiphase flow in an SBR. Because the granulation

process is a multiscale phenomenon, the current work concerns a number of scales.

Investigations include the flow induced by protozoa living on the biogranules. Here,

information about the flow structure and character is obtained by using µ–PIV.

In the present work, fluid dynamic equations providing a basis for the theoretical

understanding of the granulation process in an SBR are given. Two basic conservation laws,

i.e. mass and momentum conservation equations, describe fluid motion affected by certain

forces. However, for explanation of the motion of dispersed phases (bubbles and particles)

within the continuous phase, the forces acting on the interface between fluid and particles and

between particles and walls and also forces caused by external fields are considered.

As explained in Chapter 4, to reduce the number of parameters and obtain a clear

understanding of experiments, all results are shown in dimensionless form. PIV investigations

with a video lamp reveal the characteristic flow pattern in an SBR during the aeration phase.

On the bottom, a large vortex exists and in the upper part of the bioreactor, smaller eddies

appear. Numerical investigations on the same system carried out simultaneously by

Díez et al. (2007) depict identical flow characteristics. Analysing all PIV data, it can be

observed that the fluid velocity increases with increasing vertical coordinate. Moreover,

comparison of the flow patterns in the SBR at different moments in time and space indicates

the non–stationary character of the analysed flow. Further investigations reveal a strong

influence of granule concentration on both the liquid velocity and flow pattern. Thereby, the

liquid velocity before wasting has significantly lower values than during wasting. This

tendency is especially observed in the lowest bioreactor subdomain where the granule

concentration is the highest. The calculated tangential and normal strains are 128 −= sγ& ,

126 −= sε& which correspond to dimensionless values of γ& = 1 and ε& = 1, respectively. The

latter seems to be a crucial factor influencing granulation. As shown by Höfer et al. (2004),

significant elongation of the flocs appears atε& = 0.12. In the present work, the elongation rate

CONCLUSIONS

100

is approximately ten times higher, which means that the normal strain rate substantially

affects the granulation process, preventing the growth of fluffy flocs and influencing their

breakdown in the early stage of growth. Taking into account the shear stress results, it is

observed that the maximum shear stress is τ = 0.029 Pa, whereas, as shown by Esterl et al.

(2002) the critical tangential stress destroying agglomerates is significantly higher, τ = 10 Pa.

However, as explained in Section 4.2.3, such high values of τ close to the granule surface are

merely possible but cannot be detected within the used spatial resolution of PIV measurement.

Moreover, strain rate studies reveal increasing values of strain rate with increasing vertical

coordinates. A similar tendency is shown by PIV studies with illumination provided by a

video lamp and by a He–Ne laser. Moreover, the latter investigations permit more detailed

analysis. Thereby, measurements for different wall distances indicate that both the liquid

velocity and normal and shear strain rates increase with increasing wall distance. In contrast

to the liquid phase, the velocity of the solid phase decreases with increasing vertical

coordinate. Here, the concentration of granules affects the granule velocity, i.e. with

increasing concentration, the velocity of the dispersed phase decreases. This effect (similar to

that for the liquid phase) is mainly caused by frictional interactions and collisions.

Complementary to the field measurements (PIV), one–point LDA studies are also carried out.

Here an increasing tendency of axial velocity with rising aeration flow rate, increasing

vertical coordinate and distance from the SBR wall is noted too. Concerning the latter, the

liquid velocity increases up to Z/D = 0.10. A significant influence of granules and bubbles

causes a decrease in the axial liquid velocity close to the SBR centre. Furthermore, comparing

two– and three–phase flows, LDA measurements reveal a large influence of the third phase

(granules) on the flow pattern. As shown in Section 4.4.1, the dimensionless mean axial

velocity is higher for two–phase flow, e.g. the maximal value of the axial velocity for

three–phase flow with a 4 L/min aeration rate is 2910.vW = , whereas for two–phase flow for

the same conditions the velocity reaches 3812.vW = . For this case, the velocity difference

between two– and three–phase flows is 092.vW =Δ . However, a significantly higher velocity

difference can be observed, reaching 486.vW =Δ for a wall distance of Z/D = 0.03.

Additionally, the PSD analysis of the LDA signal indicates the existence of laminar flow. The

Kolmogorov –5/3 PSD slope law is not obeyed for different vertical coordinates and various

wall distances for two– and three–phase flow. Comparisons of PSD for two– and three–phase

flows indicate that granules reduce the velocity but do not change the turbulence energy.

CONCLUSIONS

101

In order to achieve an estimation of the overall load acting on the granulated sludge,

dimensional analysis of the fluid dynamic forces within the three phase–flow in an SBR is

carried out. The dimensionless representation of the forces indicates the buoyancy force to be

dominant. The equivalent time–averaged collisional force and drag force also play a crucial

role in the SBR; however, their dimensionless values are one order of magnitude lower,

=G

C

FFr

r

1.47 x 10-1 and =G

D

FFr

r

1.31 x 10-1, than the buoyancy force. Although it must be

emphasized that the calculated equivalent time–averaged collisional force represents the

time–averaged impact of the transient forces due to collisional interactions between granules

themselves as in addition to granules and the walls of the bioreactor. Therefore, it can

definitely be stated that the actual value of the transient force acting in the moment of

collision exceeds the given value of equivalent time–averaged collisional force. Nonetheless,

the latter permits valuable estimation of the impact of collisions on the structure of

biogranules. Lift forces such as the Magnus force =G

M

FFr

r

2.43 x 10-2 and Saffman force

=G

S

FFr

r

7.17 x 10-2 also act to a certain extent on GAS. However, the van der Waals force,

reaching the dimensionless value of =G

W

FFr

r

2.69 x10-11, is in fact irrelevant for granule

formation. Moreover, collisional forces play a powerful role in the granulation process. Based

on the kinetic theory of gases and experimental investigations, a decreasing tendency of both

particle–particle and particle–wall collision rates with increasing vertical coordinates is

observed. The latter effects are merely caused by the granule concentration decreasing with

height. As shown in Section 4.5, the highest particle–particle collision rate at the bottom of

the SBR is equal to 42447690 collisions/s/m3, whereas the highest particle–wall collision rate

in the lowest bioreactor subdomain is 6030 collisions/s/m2.

Given that biogranulation is a multiscale process and the micro–scale effects are surely

reflected in the macroscopic phenomena, the present work includes a microscopic analysis of

the granulated sludge and specifically an analysis of the micro–flow generated by

microorganisms living on the granule surface. Microscopic analysis for different aeration flow

rates clearly shows that granulation takes place only under appropriate hydrodynamic

conditions. In the present study, granules appear only for a 4 L/min aeration rate,

CONCLUSIONS

102

corresponding to SGV = 1.05 cm/s, whereupon many ciliates colonies live on the GAS

surface. Comparing the occurrence of granules for different aeration flow rates, hydrodynamic

selection of microorganisms and biomechanical fatigue effects are detected. As shown in

Section 4.6.2, micro–flow induced by protozoa seeking nutrients plays a key role in the

structural formation of sludge granules. Generally, micro–flow induced by ciliates can be

treated as an efficient means of nutrient transport with minimum energy requirement. For the

purpose of the present study, a unique methodology for investigations of the flow induced by

microorganisms is developed with respect to biocompatible conditions and specific seeding of

the flow. μ–PIV studies indicate the existence of two characteristic vortices generated by cilia

beats. Moreover, studies with two different tracer substances, yeast cells and milk, show that

a more detailed representation of the fluid field is possible by using milk. Comparisons of

velocity distributions for one ciliate and a colony reveal efficient cooperative group work of

more than one ciliate. Thereby, with increasing ciliate number, higher velocity values are

seen. Moreover, the flow structure induced by one ciliate differs considerably from that

induced by a colony. Investigations with different milk concentrations indicate increasing

liquid velocity with increasing seeding dilution. Additionally, kinetic energy investigations

demonstrate its increasing tendency with higher milk dilution. However, as shown in the

results part, milk seeding dilution is limited up to a ratio of 1:4 (milk to water).

The fluid mechanical investigations of an operating SBR presented here for the first time

enable some so far open questions to be answered. The results achieved clearly show that

granulation takes place only under appropriate flow conditions. The magnitudes of both

normal and shear strain rates, buoyancy, drag, lift and collisional forces play a crucial role in

the formation of granules with a regular, compact shape. Moreover, it is shown that the

micro–flow induced by protozoa (ciliates) living on the surface of granulated sludge

contributes considerably to the granulation process. In fact, ciliates living on GAS can be

treated as a kind of “compacting system”, enabling efficient granule growth and providing

their robust structure.

Considering the practical side of the present study, a number of guidelines regarding the

design and operation of SBRs can be derived on the basis of the results achieved. The

guidelines are mainly based on the fluid mechanical issues accounting for the layout of

optimal flow conditions on the micro– and macro–scale. Thus, the following points should be

taken into consideration:

CONCLUSIONS

103

I. Optimal magnitude of fluid dynamic forces must be provided. An insufficient

mechanical load acting on biogranules (resulting from too low values of

hydrodynamic strains) inhibits the formation of compacted GAS. Instead of

granules, fluffy flocs are formed with very poor settling ability and biomass

retention capacity. In turn, too high a value of the mechanical load leads to tearing

and destruction of biogranules as a consequence of fatigue effects (see Section

4.6.1). An optimal granulation effect is observed for typical flow elongation rates

around 15 −= sε& but not exceeding a value of 126 −= sε& and typical shear rates

around 15 −= sγ& but not higher than 128 −= sγ& . These values correspond to SGV in

the range 1–1.1 cm/s and this applies as a fundamental fluid mechanical design

guideline.

II. Homogeneous aeration of the liquid domain of the SBR must be provided. Aeration

(the upflow of air bubbles) is the main source of mechanical energy in an SBR.

Therefore, homogeneous aeration of the whole liquid batch is important not only for

providing oxygen for biochemical processes and aerobic microorganisms but also in

order to ensure uniform mechanical treatment and mixing of GAS. Special attention

must be paid to avoiding the formation of “dead zones” in the flow patters with

liquid at rest or separated vortices not mixing with the bulk of liquid.

III. Sufficient wasting of the formed GAS must be provided in order to keep the solid

fraction in the bioreactor at an optimal level. The concentration of granules

influences significantly the velocity distribution and flow strain rates in an SBR.

Hence an optimal level of granule fraction is crucial for maintaining fluid

mechanical conditions in the required range (see Section 4.2 and 4.3). The optimal

solid fraction is estimated to be approximately 10% of volume, which corresponds

to the rule that the height of the settled granulated sludge amounts to approximately

10–20 % (depending on the granule size distribution) of the overall liquid column

in the SBR.

IV. Sufficient height of the bioreactor must be provided in order to enable unconstrained

fall and settling of the granulated sludge floating in the liquid sewage and separation

of GAS from effluent.

V. An appropriate habitat for the microorganisms must be provided in terms of

CONCLUSIONS

104

temperature, illumination, pH and also fluid dynamic conditions. Hydrodynamic

selection of microorganisms is observed, which means that in case of too high an

aeration rate and resulting in too high SGV and hydrodynamic forces, extinction of

ciliates takes place (see Section 4.6.1). The ciliates, however, contribute greatly to

the development of biogranules and are necessary to provide a robust structure of

GAS. Here applies the indication for SGV from point I.

Finally, it must be emphasized that aerobic granulation in SBRs is becoming a worldwide

interesting technology in wastewater treatment. Considerable advantages of biogranules in

wastewater treatment such as good settling ability, a compact, strong structure and no need for

carrier material (Etterer and Wilderer, 2001, Liu and Tay, 2002, McSwain et al., 2005) make

GAS an object of intensive research activities and are attracting the interest of industry.

However, as yet almost all aerobic granulation studies with different classes of wastewater of

industrial, municipal and artificial origin are carried out on the laboratory scale. The first

pilot–scale tests have been implemented out by de Bruin et al. (2005). Obviously, pilot–scale

investigations still require scale–up for industrial applications. Moreover, some crucial

questions concerning the operation of an SBR in potentially unstable outside laboratory

conditions remain open. Hence, common work by scientists in different fields of competence,

e.g. microbiology, chemistry, fluid dynamics and process control, is needed in the future for

practical usage of the aerobic granulation process.

APPENDIX

105

6. APPENDIX

Feed solution (after McSwain et al., 2004)

Stock solutions:

FeCl3 6H2O 12.4 g/L

NaCl 201.6 g/L

(NH4)2SO4 201.6 g/L

KH2PO4 140.8 g/L

NaHCO3 95.5 g/L (saturated solution)

10 L of concentrate

110 g Glucose

25 g Peptone

50 mL FeCl36H2O

100 mL NaCl

200 mL (NH4)2 SO4

100 mL KH2PO4

50 mL NaHCO3

stock solution

106

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Lebenslauf

Bogumiła Ewelina Zima-Kulisiewicz Persönliche Daten Geburtsdaten Familienstand Nationalität Ausbildung Seit April 2006 November 2003 - März 2006 Juni 2003 Oktober 1998 - Juni 2003 Mai 1998 September 1994 - Juli 1998

11. Dezember 1979 Tarnόw (Polen) verheiratet polnisch Vorbereitung der Promotion am Lehrstuhl für Strömungsmechanik Friedrich-Alexander-Universität Erlangen-Nürnberg Vorbereitung der Promotion am Lehrstuhl für Fluidmechanik und Prozessautomation Technische Universität München Studienabschluss Magister Inżynier Studium Fakultät für Bergbaugeodäsie und Umwelttechnik Fachrichtung für Umweltschutz in der Planung und Verwaltung Berg- und Hüttenakademie namens Stanisław Staszic in Krakόw Abitur Lyzeum namens Kazimierz Brodziński in Tarnόw

Berufserfahrung Wissenschaftliche Angestellte und Doktorandin Seit April 2006 November 2003 - März 2006

Wechsel mit Prof. Delgado an den Lehrstuhl für Strömungsmechanik Friedrich-Alexander-Universität Erlangen-Nürnberg Wissenschaftliche Mitarbeiterin am Lehrstuhl für Fluidmechanik und Prozessautomation Technische Universität München

Documents

Impact of Fluid Dynamic Effects on Granular Activated ... · Velocimetry (PIV), Particle Tracking Velocimetry (PTV), Laser Doppler Anemometry (LDA) and micro Particle Image Velocimetry