Faculty of Bioscience Engineering

Academic year 2013 – 2014

Model-based analysis of aeration in lab and full-scale

activated sludge systems

Giacomo Bellandi

Promoter: Prof. dr. ir. Ingmar Nopens

Tutor: Ing. Youri Amerlinck

Master’s dissertation submitted in partial fulfillment of the requirements for

the degree of

Master in Environmental Sanitation

Faculty of Bioscience Engineering

Academic year 2013 – 2014

Model-based analysis of aeration in lab and full-scale

activated sludge systems

Giacomo Bellandi

Promoter: Prof. dr. ir. Ingmar Nopens

Tutor: Ing. Youri Amerlinck

Master’s dissertation submitted in partial fulfillment of the requirements for

the degree of

Master in Environmental Sanitation

ad Ivaldo

04-06-2014

De auteur en de promotor geven de toelating dit afstudeerwerk voor consultatie beschikbaar te

stellen en delen ervan te kopiëren voor persoonlijk gebruik. Elk ander gebruik valt onder de

beperkingen van het auteursrecht, in het bijzonder met betrekking tot de verplichting uitdrukkelijk

de bron te vermelden bij het aanhalen van resultaten uit dit afstudeerwerk.

The author and the promoter give the permission to use this thesis for consultation and to copy

parts of it for personal use. Every other use is subject to the copyright laws, more specifically the

source must be extensively specified when using results from this thesis.

The promoter, The tutor, The author,

Prof. dr. Ir. Ingmar Nopens Youri Amerlinck Giacomo Bellandi

i

Contents

Contents ................................................................................................................................................ i

Acknowledgments............................................................................................................................... iii

Abstract ............................................................................................................................................... iv

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

1. Introduction and literature review ................................................................................................ 1

1.1. Brief history of wastewater treatment.............................................................................................................. 1

1.2. Wastewater treatment plants ................................................................................................................................ 3

1.2.1. The UCT (University of Cape Town) configuration .......................................................................... 4

1.2.2. Submerged aeration systems ....................................................................................................................... 6

1.3. Modelling of wastewater treatment plants..................................................................................................... 6

1.3.1. Mathematical models ....................................................................................................................................... 6

1.3.2. Activated sludge model ................................................................................................................................... 7

1.3.3. Modelling Oxygen transfer ............................................................................................................................ 7

1.3.4. Predictions of oxygen transfer in presence of surfactants ......................................................... 10

1.3.5. Bubble column tests ....................................................................................................................................... 12

2. Materials and methods ................................................................................................................ 16

2.1. The Eindhoven WWTP............................................................................................................................................. 16

2.2. The Eindhoven wastewater treatment plant model in WEST® .......................................................... 17

2.3. Experimental data collection................................................................................................................................ 19

2.3.1. Off-gas measurements ................................................................................................................................... 19

2.3.2. Bubble column ................................................................................................................................................... 21

2.3.3. Imaging and bubble size measurements .............................................................................................. 23

3. Results ........................................................................................................................................ 27

3.1. Bubble column ............................................................................................................................................................. 27

3.1.1. Rheological measurements ......................................................................................................................... 27

3.1.2. Bubble size measurements ......................................................................................................................... 28

3.1.2.1 Clean water bubble size measurements ............................................................................................... 28

3.1.2.2 Effects of salt addition (50 mg/l) ............................................................................................................. 30

3.1.2.3 Effects of salt addition (100 mg/l) .......................................................................................................... 33

ii

3.1.2.4 Effects of viscosity variation (0.2 g/l Xanthan TER) ...................................................................... 36

3.1.2.5 Effects of viscosity variation (0.8 g/l Xanthan TER) ...................................................................... 39

3.1.3. Lab scale oxygen transfer measurements ........................................................................................... 42

3.2. Full-scale aeration efficiency ................................................................................................................................ 45

3.2.1. Off-gas measurements ................................................................................................................................... 45

3.2.2. The Eindhoven aeration model performances on αSOTE prediction ................................... 50

3.2.2.1 Predictions of DO and NH4 using full scale αSOTE measurements ........................................ 53

4. Discussion ................................................................................................................................... 59

4.1. Bubble column tests .................................................................................................................................................. 59

4.2. Off-gas measurements ............................................................................................................................................. 60

4.3. Aeration model of Eindhoven WWTP .............................................................................................................. 60

5. Conclusions and perspectives ..................................................................................................... 62

References .......................................................................................................................................... 64

iii

Acknowledgments

First and foremost, I would like to thank my academic promoter Ingmar Nopens for his always kind

availability, contagious positive energy and strength which always characterize his person and

motivate who is in contact with him. I have a secret question to ask myself in the hard times, “What

would Ingmar do?!”, and that’s the answer too.

In the same warm way I would like to thank very much my tutor Youri who sustained me through

the long forest of the bad results, through which every student has to pass sooner or later (in my

case quite some time later), with stimulating ideas, precious organization and motivation.

There are so many people that have been important in this period and I would like to thank all and

each one of them. First of all Tinne (before I forgot like the last time!) for the precious help and

source of noise in the lab, Thomas who’s my very good friend and excellent colleague, Stijn who has

always been keeping my back, Andreia who came directly from Portugal to help me, Daan for all the

loughs and the motivational playlist, Sophie for her kind attitude and surprising skills with the

ADCP, Michael for the excellent learning curve on Italian statements to catch girls, Robin for the

memorable time in the BIOMATH weekend, and last but not least Chaim and Stijn for all the

respirometer parties in the lab. Nonetheless, all the BIOMATH family to which I’ll always owe a lot

of gratitude. Thanks Elena, Ivo, Ashish, Usman, Wouter, Séverine, Timothy, Stijn, Wim, Andres,

Katrijn and everyone that passed through the BIOMATH walls.

Ora, dopo tutti i discorsi di rito, vorrei però arrivare al dunque. Voglio ringraziare la mia famiglia, la

forza che mi ha sostenuto e mi sostiene tutti i giorni. Babbo e mamma, vi voglio un bene che scoppio

e vi voglio ringraziare per tutto il sostegno e la fiducia che mi avete dato, spero siate fieri di me

almeno un millesimo di quanto lo sono io di voi. Gianluca e Elena, che sono tutto il mondo per me, li

voglio ringraziare per essere cosi bellini che li mangerei, ma soprattutto per essere sempre nei miei

pensieri vicini vicini. Ci sono stati e ci saranno sempre momenti duri, e questa tesi non è uno di

quelli, e se c’è qualcosa che mi da la forza per tirare avanti anche un carro armato in quei momenti,

quella cosa e il pensiero di Gianluca, Elena e Annalisa. Grazie Na’, che ci sei sempre come un pilastro

in ogni cosa che faccio, e ci portiamo sempre insieme.

Linda, semplicemente la persona piu bella del mondo. La persona che mi fa ridere e innamorare,

arrabbiare e scompisciare. Mi regali ogni giorno quella scintilla che rende le giornate piene di vita.

Grazie, senza di te questa tesi non l’avrei mai fatta.

Sicuramente prima di finire voglio ringraziare la mia altra parte della famiglia, Massimo, la Tiziana,

Simone (Toxy) e Simone (Gloxy), che per me sono importanti come parti del corpo, e che per me

son tutti fratelli (Tiziana compresa), vi voglio bene. E grazie a tutti i letterati che allietano le serate

al barre con poemi e sonetti, ma soprattutto grazie a Grazia, Graziella e Grazialcazzo (che tanto i

ringraziamenti non li legge mai nessuno ☺).

iv

Abstract

Aeration in activated sludge treatment is used to supply the amount of oxygen needed in order to

accomplish the oxidation of part of the nutrients and pollutants unwanted in our river systems. As

the most energy intensive step in wastewater treatment, oxygen supply needs to be performed

efficiently, facilitating the transport of oxygen from the gas to the liquid phase. To do so, the

available surface for exchange needs to be maximized and the coalescence process hindered.

Moreover, the chemical and physical properties of the liquid play an important role affecting the

surface tension and ultimately the diffusion coefficient of oxygen. In the framework of the

development of a sound aeration model, this work studied the (i) effects of salt addition and

viscosity variation on oxygen transfer in a lab scale bubble column, along with (ii) aeration

efficiency measurements performed at a full scale wastewater treatment plant and (iii) the

evaluation and analysis of the prediction performances of one available aeration model. The bubble

column tests provided precious informations on the role singularly played by typical wastewater

characteristics (i.e. low electrolyte concentrations help the oxygen transfer process while

increasing viscosity not necessarily hinders it) and were a valuable tool for the interpretation of the

off-gas measurements performed at the full scale municipal facility of Eindhoven. However, further

experiments should be carried out to rigorously answer some unsolved questions. The model

results confirm the need of a better understating and finer representation of those dynamics

important in the oxygen transfer process, and of the definition of new dependencies for the

accomplishment of robust predictions.

v

Summary

A first introduction and presentation of the concept of wastewater treatment along with its

development throughout history is provided in this thesis in order to stress the importance and

impact that this process, in its different applications, has on our society and on the environment

that hosts it. A brief overview of the different wastewater treatment technologies available is given

with particular attention to conventional activated sludge systems and their most energy intensive

step, i.e. aeration. The focus is then concentrated on submerged aeration systems, on the processes

characterizing oxygen transfer and on the available models that allow its description and

prediction. The materials and methods section provides a thorough description on the practical and

theoretical tools used and on their application. The evolution of bubble sizes and oxygen dissolution

dynamics, studied in lab scale bubble column, are presented in the results along with full scale

measurements of oxygen transfer at the Eindhoven wastewater treatment facility and the outputs

of the currently available model for the same plant. Interpretation of the results is finally discussed

interconnecting the findings for a complete interpretation and understanding of important

dynamics occurring during the aeration process and important clues are provided on the

possibilities for further development of aeration models.

1

1. Introduction and literature review

1.1. Brief history of wastewater treatment

Improved sanitation and adequate water quality availability are the basis for ensuring a healthy

population and primary tools to counteract a vast number of diseases. This was evident already

since the prehistoric period but disposal problems were primarily limited because of the very small

nomadic communities. The first stable civilizations of the Mesopotamian Empire (3500-2500 B.C.)

showed the need to address the problem of sanitation within the growing communities using

connected drainage systems (Lofrano and Brown, 2010). Nonetheless, the Indus civilization left

proves of extensive knowledge in the urban planning with high priority to hygiene. A sewage

network under paved streets dated 14th century B.C., directly connected to sanitary facilities, was

discovered in the Greek island of Santorini (Angelakis et al., 2005) and is only one of the many

examples proving the organized sanitation of the Ancient Greeks. Later the Romans showed an

outstanding improvement in engineering developing these systems further. The construction of a

colossal aqueduct net for irrigation and drinking water supply brought fresh drinking water from

the surrounding hills in the whole city center, and a massive sewage system serving the whole

metropolis ensured a regular cleaning of the streets and waste removal (Henze et al., 2008). The

Cloaca Maxima (6th century B.C.) represents the greatest expression of this milestone of sewage

network.

Figure 1 - View of a branch of the Cloaca Maxima in Rome

Thanks primarily to this very efficient water management system and a well-organized water

supply and waste collection, ancient Rome reached the population of 1,200,000-1,700,000

inhabitants around half of the 2nd century A.D. gaining the title of “Regina Aquorum” (queen of

waters) (Henze et al., 2008). Interestingly, parts of the ancient water supply net and of the Cloaca

Maxima are nowadays still functional (Lofrano and Brown, 2010). However, the collected sewage

did not undergo any treatment and the wastewater was usually discharged directly into the Tiber.

After the Roman empire collapsed, the entire sanitary concept was abandoned (at least in Europe)

and a long period (also known as the sanitary dark age) had to pass before the first collection

Introduction and literature review

2

systems appeared (1800 A.D.). In the early 19th century in Europe a special cart travelling around

the city, gathering the so called “humanure” appeared. The latter was used as fertilizer in the

agricultural fields without any treatment. However, the presence of this cart was not very welcome

in the cities as its inevitable smell bothered the population. Only at the beginning of the 20th century

the first use of microorganisms and the first implementations of the activated sludge (AS) process

were seen.

Firstly observed in the UK, the sludge was believed to be activated similarly to activated carbon and

as such named “activated sludge” by Ardern and Locket in their publication dated 1914.

Approaching the mid-20th century research started to increase significantly and the understanding

of the treatment process gained depth with the characterization of the substrate used by the

microbial communities. The concepts of dissolved oxygen (DO) and biochemical oxygen demand

(BOD) were used in the first river system models to understand the maximum loading a river

stream could handle preventing the DO to fall below a threshold limit (Phelps, 1944). In the second

half of the 20th century the problem of eutrophication began to rise due to the rapid growth of the

population and the increasing discharge of nitrogen (N) and phosphorous (P) in the natural river

streams from both growing cities and enhanced usage of fertilizers. With the general awareness

and agreement that nitrogen and phosphate also needed to be removed from the waste stream, the

application of a mathematical model, developed for understanding the kinetics of continuous

cultures (Monod, 1950), helped to apprehend that nitrification was dependent on the maximum

specific growth rate of autotrophic nitrifying organisms. In order to achieve nitrification, the

retention time of the sludge had to be sufficiently long to ensure the growth of these communities.

However, for the sake of eutrophication prevention, nitrogen had to be completely removed from

the water stream and the discovery of some heterotrophic bacteria capable of converting nitrate to

nitrogen gas (McCarty, 1964) led to the nitrification-denitrification concept of the AS process. Right

after this, the implementation of the pre-denitrification step followed and the combination of the

two processes with the introduction of the recycle flow was a successful improvement also

regarding biological phosphorous removal (Barnard, 1973). The strong increase in population of

the 1970s was the cause of two main issues for the sanitation field. Firstly, the necessity of city

areal expansion caused the incorporation of many treatment plants, initially built outside the urban

area, inside the residential space. Therefore, when the consequential need of increasing the

treatment capacity of these plants became tangible, space efficient technologies such as membrane

bioreactors (MBRs) successfully entered the market. Secondly, the energy crisis pushed the

attention of the research towards the development and improvement of anaerobic processes (e.g.

upflow anaerobic sludge blanket reactors (UASB)), and towards the optimization and further

understanding of the most energy demanding steps in the WWT such as aeration and pumping.

The evolution of sanitation was recently elected from the readers of the British Medical Journal as

the greatest medical advance since 1840 preceding closely the discovery of antibiotics and the

development of anesthesia (Ferriman, 2007). Water and sanitation were defined as two key

components of the right to an adequate standard of living (Amnesty Intenational, 2010). From an

economical point of view, investments for improved sanitation in developing countries are

estimated to generate a revenue between 5 and 29 US$ for each US$ invested (Hutton and Haller,

2004). Although this seems a rather well established concept for most of the industrialized

Introduction and literature review

3

countries, in 2011 just 64% of the world population relied on improved sanitation and the United

Nations Millennium development Goal of 75% by 2015 seems still far (WHO and UNICEF, 2013).

Despite the evidences brought up from numerous scientific publications and the efforts of several

international institutions to rise general awareness on this fundamental issue, proper sewage

treatment not always takes place and progresses in meeting sanitation targets are slow. In this

framework, it is however not only important to provide suitable technologies but also to

sustainably develop long lasting and low energy demanding solutions.

1.2. Wastewater treatment plants

The wastewater treatment plant (WWTP) is currently conceived as an industrial structure in which

one or multiple technologies are applied with the purpose of removing a number of (bio-)chemical

compounds from a water stream. Generally a WWTP is composed of an initial step called primary

treatment where the coarse and easily settable solids can be removed from the wastewater stream

entering the facility. For this purpose, sand grids and primary clarifiers are normally the default

devices used in the primary treatment of a WWTP. Then follows the biological treatment, in which

AS removes the biologically degradable fraction from the wastewater stream. In this step, at least

one tank needs to be provided with dissolved oxygen (DO) from aeration devices in order to ensure

the aerobic metabolism of the microbial communities responsible for the oxidation of organic

matter (OM). The mixed liquor (AS plus wastewater) is retained for a sufficiently long time in the

bioreactor in order to ensure adequate removal of the compounds which are unwanted in the

effluent. As a final step of the treatment, the separation between AS flocs and the treated water is

performed by means of a gravity separator (secondary clarifier). The overflow of the secondary

settler is therefore the purified water that can be discharged or reused, while the underflow is a

concentrated stream of AS. The underflow of the secondary clarifier is recycled to the bioreactor

with the exception of a small fraction called waste sludge. This wasted AS fraction is the extra

sludge generated from the biomass growth and can be collected along with the underflow of the

primary clarifier for anaerobic digestion (energy recovery) and eventual disposal (Tchobanoglous

et al., 2003).

To the present date a large number of different technologies and configurations exist for the

treatment of wastewater streams. The choice of the application of a specific technology depends

mostly on the environmental constraints (temperature, light availability, water scarcity…), type of

wastewater, purpose of the treatment. However, the local, national or international legislation on

discharge limits can impose stringent standards for the water composition exiting the plant. The

European Council Directive 91/271/EEC aims to protect the environment from urban and

industrial wastewater discharges. Therefore, the Directive sets limits for effluent concentration of

organics, nutrients and suspended solids, and defines the required methods for collection,

treatment and discharge of wastewater. To a more general level, the European Water Framework

Directive 2000/60/EC extends the water management to all surface and ground water.

Nonetheless, the selection of a suitable technology often primarily depends on economic aspects.

From here the need of economically suitable solutions which are environmentally sustainable also

from an energetic point of view.

Introduction and literature review

4

1.2.1. The UCT (University of Cape Town) configuration

This configuration was developed in South Africa where groundwater overexploitation, seawater

intrusion and water scarcity were the main driving forces stimulating innovation in the water

sector. Therefore, from the main necessities of water re-use and nutrient removal, a WWTP

configuration capable of removing not only nitrogen but also phosphorous along with providing a

high quality effluent was conceived. The UCT-configuration (Tchobanoglous et al., 2003; Wentzel et

al., 2008) is composed of at least three bioreactors in which the biomass undergoes a sequence of

anaerobic, anoxic and aerobic conditions (Figure 2).

Figure 2 - Schematic representation of the UCT-layout. Blue lines represent the mainstream

of wastewater and mixed liquor with the two internal recirculations, recycle A is a fundamental step for P removal while recycle B plays a crucial role for N removal. Purple

lines represent underflow streams from the two settlers for AS recirculation and waste.

The anaerobic reactor is, for this layout, the first step of the biological treatment and provides an

environment in which neither DO nor nitrogen oxides (NOx) are present. In the anoxic tank that

follows oxygen is absent in the form of DO but there is presence of nitrate (NO3-) and nitrite (NO2

-).

In the last step of the biological treatment DO is present ensuring aerobic conditions for the

oxidation of organic compounds. This sequential alternation is the basis for the accomplishment of

the three main objectives of the system which are carbon, nitrogen and phosphorous removal.

The oxidation of OM is mainly accomplished in the aerobic reactor by heterotrophic bacteria which

oxidize carbonaceous compounds to CO2, water and minerals in order to get energy for their

growth. The fraction of carbon in the wastewater that can be oxidized biologically is usually

identified as BOD5 and can be measured via a closed bottle test (APHA et al., 2012). The chemically

oxidizable carbon (COD) includes both biochemically removable and inert carbon sources. In this

process the availability of a substantial amount of oxygen in solution is of crucial importance for

ensuring the presence and activity of the heterotrophic biomass responsible for the oxidation of

ammonium and OM. At the same time the dissolution of oxygen requires an energy investment

which needs to be carefully balanced with a control strategy, being the most energy demanding step

of the treatment sequence (Reardon, 1995).

The removal of nitrogen is accomplished via the intercurrence of two processes, referred to as

nitrification and denitrification. The nitrogen, usually present in the wastewater under the form of

Introduction and literature review

5

ammonium (NH4+) and organic nitrogen (nitrogen contained in OM), is firstly oxidized to NO3

-

under aerobic conditions and then reduced to nitrogen gas (N2) in anoxic conditions. The chemical

reactions can be summarized as follows for NH4+:

NH4+ + 3 O2 � NO3

- + 2 H+ + H2O (1)

NO3- + C-source � CO2 + N2 + H2O + OH- (2)

In the aerobic tank the oxidation of NH4+ to NO2

- is accomplished by ammonia oxidizing bacteria

(AOB) and subsequently nitrite oxidizing bacteria (NOB) complete the reaction to NO3-. These two

bacterial consortia are autotrophic since they use CO2 as carbon source. The recirculation of the AS

from the aerobic reactor to the anoxic reactor ensures the denitrification step (Equation 2) where

heterotrophs use the oxidation potential of NO3- to oxidize the incoming organic carbon, thus

reducing NO3- to NO2

- and finally to N2 which is free to escape as gas into the atmosphere. The

application of a denitrification step before the nitrification has been of particular importance for

energy recovery since the oxygen contained in the nitrogen oxides can be reused to oxidize part of

the incoming organic matter.

A more complex mechanism, from a conceptual and technical point of view, is the removal of

phosphorus which can be accomplished by chemically provoking its precipitation or favoring the

growth of polyphosphate accumulating organisms (PAOs). The latter process is referred to as

enhanced biological phosphorus removal (EBPR). Providing an anaerobic phase, a consortia of

facultative heterotrophs are able to ferment readily biodegradable OM into volatile fatty acids

(VFA) which are then stored by PAOs in their biomass under the form of polyhydroxyalkanoates

(PHAs). PHAs are energy dense storage compounds and are used by PAOs as their energy source in

anaerobic conditions by hydrolyzing internal polyphosphate reserves and consuming glycogen.

However, this latter process causes release of phosphorus into solution. When PAOs are brought

under anoxic or aerobic conditions they start oxidizing PHA as carbon sources for their growth and

for glycogen regeneration. Since PAOs incorporate more phosphorus in the aerobic/anoxic phase

(up to 24% of their dry weight) than what they release in the anaerobic phase, a net P-removal can

be achieved. Therefore, fundamental steps to be ensured in a P-removal oriented AS process are an

anaerobic phase to advantage PAOs growth and sufficient concentration of readily biodegradable

OM to be converted into VFA (Wentzel et al., 2008).

As described so far, besides ensuring appropriate environmental physical-chemical conditions in

the bioreactors, the recirculation of a well-defined fraction of mixed liquor between the tanks is of

crucial importance for stimulating the growth of specific microbial consortia in a certain section of

the WWTP. Recycle stream flow rate and placement are thus to be carefully planned in the design

step. As schematized in Figure 2, recycle B ensures a constant flow of nitrates to the anoxic tank so

that denitrification can be accomplished. Additionally, recycle A guarantees the return of PAOs into

the anaerobic selector and is located usually at the end of the anoxic tank in order to minimize the

amount of nitrates to be sent in the anaerobic tank. Finally, the flow of return sludge from the

secondary sedimentation tank is maneuvered so to maintain the needed amount of biomass in the

AS system for a successful biological removal of carbon and nutrients.

Introduction and literature review

6

1.2.2. Submerged aeration systems

A number of solutions exist to provide the necessary oxygen to an AS system (Figure 3). Surface

aerators increase the contact area at the gas-liquid interface by agitation of the water surface,

shearing the liquid into small droplets under very turbulent conditions. Although these devices

have easier maintenance (primarily in terms of accessibility), submerged solutions are currently

the most widely used applications due to their higher efficiency and, hence, lower energy

requirements.

Figure 3 – Examples of three different aeration strategies. Surface aeration (a), coarse

bubble aeration (b) and fine bubble aeration (c).

In submerged systems, oxygen is brought into solution by bubbling air or pure oxygen into the

liquid through nozzles or porous material placed at the tank bottom. These devices are called

diffusers and can be divided in two classes, coarse bubble and fine bubble diffusers, depending on

the size of the generated bubbles. Coarse bubble systems generally have orifices larger than 6 mm

and release bubbles up to 50 mm in diameter, whereas a bubble is considered fine when having a

diameter less than 5 mm (WEF, 2010). Rising coarse bubbles are classified as high-flow regime

interfaces due to their high interfacial velocity at the gas-liquid interface, while fine bubbles present

lower interfacial velocity and are therefore grouped as low-flow regime interfaces (Rosso and

Stenstrom, 2006a; Wagner et al., 2002). This is of particular importance for the renewal of the

oxygen concentration in the gas phase at the interfacial layer.

1.3. Modelling of wastewater treatment plants

1.3.1. Mathematical models

A mathematical model is the description of a real-life situation (natural process or behavior) using

mathematical concepts and language. As a theoretical representation of the reality it can only

describe a process to some extent of accuracy but the evolving knowledge and available

computational power make it nowadays possible to solve very complex problems in biological

systems like a WWTP. Especially useful for including the available knowledge, unravelling

underlying processes and making predictions of processes, mathematical models can be used for a

variety of applications such as system optimization, design, development of control strategies,

scenario simulation, etc. (De Pauw, 2005). In general a model transforms a certain input supplied

Introduction and literature review

7

by the user into an output by means of one or multiple (coupled) equations. Therefore, the output

represents the answer the user is interested in.

Mathematical models can be firstly divided in deterministic or stochastic (also called non-

deterministic). The output variable of a stochastic model is obtained probabilistically and is

therefore affected by a certain variability generated by a probability function. On the other hand, a

model is defined deterministic when the selected set of assumptions and equations “determine” a

specific output or response. A deterministic model is composed of a set of algebraic and/or

differential equations containing variables, constants and parameters. A constant always has a fixed

value (e.g. π). On the other hand, a parameter value can vary between simulations but does not

change during a simulation. A variable is a model constituent that can change based on e.g. time or

space. Other than input and output variables, the state variable is the third type of model

constituent and contains the informations used to evaluate the future state of the system. Models

with input variables evolving over time are called dynamic models, otherwise are called static or

steady-state. The linearity or non-linearity of a model depends on how their variables and

parameters appear in the model structure, but more generally a linear model can be defined as

solvable analytically and therefore without the use of numerical techniques (De Pauw, 2005;

Dochain and Vanrolleghem, 2001).

1.3.2. Activated sludge model

In 1982 the International Association on Water Pollution Research and Control (IAWPRC), now

International Water Association (IWA), created a task group on mathematical modelling for design

and operation of activated sludge processes and assigned them the objective of combining the

available knowledge on modeling nitrogen-removal AS plants for developing a model with

minimum complexity. This collaboration resulted in the activated sludge model No. 1 (ASM1). The

ASM is composed of a series of differential equations describing the behavior of AS and is currently

the basis of a common working platform for wastewater treatment modelers. Several extensions

were developed in the following years including more details of the AS process such as storage

properties of the biomass (i.e. ASM3). Furthermore, ASM2 and ASM2d were developed to include

respectively EBPR and the denitrifying capacity of PAOs (Henze et al., 2000).

1.3.3. Modelling Oxygen transfer

For any AS process aeration is a fundamental step since it provides the biomass with the necessary

oxygen in the aerobic section. The oxygen is transferred by shearing the water surface or bubbling

air through macroscopic pores or porous material, always trying to create the maximum oxygen

exchange rate between the gas and liquid phase. Obviously this represents a major energy

demanding process for a WWTP and was estimated to be ranging between 45 to 75 % of the plant’s

energy expenditure (Reardon, 1995). The increasing cost of energy since 1970 awoke the interest

of researchers towards a more in depth understanding of aeration design, specification and

operation, and hence modelling and optimization.

Generally, for a gas bubble immersed into a liquid, the exchange of oxygen per unit of time between

the gas phase and the liquid phase can be described by Equation 3.

Introduction and literature review

8

����� = �� �

(��∗ − ��) (3)

�� = 2 ∙ � ��∙�� (4)

where kL (m/s) is the oxygen transfer coefficient, Cs* (mg/l) is the dissolved oxygen in clean water

at saturation, V (m3) the volume of the liquid phase, A (m2) the surface of the gas-liquid interface

and Ci (mg/l) the dissolved oxygen concentration in the bulk liquid at time t. The oxygen transfer

coefficient is function of the diffusion coefficient D (m2/s) and the mean bubble residence time te

(s). Being D, te and A not easily measurable, an overall oxygen transfer coefficient can be defined as

kLa (h-1).

��� = �� � (5)

The speed with which the oxygen is dissolved in water is function of the difference between the

actual DO concentration and the saturation concentration, but is also strongly dependent of the

physical and geometrical properties of the control volume.

Standardized testing methods for measuring full-scale oxygen transfer were developed by the U.S.

Environmental Protection Agency and the American Society of Civil Engineers (Stenstrom et al.,

2006). The results of about three years of testing lead to the selection of the most influencing

parameters affecting oxygen transfer and therefore aeration systems performance, combined in the

alpha (-), beta (-) and theta (-) factors (Stenstrom and Gilbert, 1981).

The alpha factor is defined as the ratio between the overall oxygen transfer coefficient in

wastewater (kLa) and the one in clean water (kLa*) (Equation 6).

� = ������∗ (6)

This difference between the two kLa is influenced by the presence of surface active agents

(surfactants) and others contaminants affecting the shape of the bubbles and the gas flow at the

interface (Hebrard et al., 2000; Rosso and Stenstrom, 2006b; Stenstrom and Gilbert, 1981).

Ultimately, also the physical properties of the liquid together with its flowing regime can influence

the kLa as coalescence or breakage can vary the bubble sizes and thus the available area for gas

transfer (a coefficient). Viscosity in particular was observed to affect the shape of a bubble plume

(Figure 4) and thus increasing the chances that a bubble has to collide with a neighboring one

(Fabiyi and Novak, 2008; Ratkovich et al., 2013).

Introduction and literature review

9

Figure 4 – Differences in bubble characteristics in a low (a) and high (b) viscosity system.

The mean bubble size in the high viscosity system is much larger (Fabiyi and Novak, 2008).

The beta factor, β, is defined as the ratio between the saturation DO concentration in wastewater

(Cs) and the saturation concentration in clean water (Cs*) (Equation 7).

� = ����∗ (7)

Therefore, beta is affected by several environmental and process conditions having an effect on the

maximum saturation level of DO, among which salinity, temperature, pressure, suspended and

dissolved matter (Stenstrom and Gilbert, 1981; Vogelaar et al., 2000).

The theta factor, also known as geometric temperature correction coefficient, is used to relate mass

transfer coefficients to a standard temperature (Equation 8). Generally a value of 1.024 should be

used unless differently specified and strongly supported by consultants and manufacturers.

Moreover, it is also recommended to limit the temperature correction lower than 10°C, although it

is well known that greater corrections are often needed (Stenstrom and Gilbert, 1981).

���(�) = ���(20°) ∙ "#$%& (8)

Alpha, beta and theta factors are important parameters to standardize aeration performances and

avoid bias due to site-specific environmental and process conditions. In general, for submerged

aeration devices, the oxygen transfer efficiency (OTE, %) is defined by Equation 9 and can be

measured with the off-gas method (Iranpour et al., 2000; Redmon et al., 1983).

'�( = ()*,�,$)*,-./))*,�, (9)

Introduction and literature review

10

where O2,in and O2,out are respectively the mole fractions (%) of oxygen in the gas stream going in

and out of the aerated tank. Typically, for clean water applications, results are reported as standard

oxygen transfer efficiency (SOTE, %), referring to zero DO, zero salinity, 20°C and 1 atm. In order to

correct for process water conditions the alpha factor is used and results are normally shown as

αSOTE (Equation 10).

�0'�( = '�( ∙ �1*2∗(3∙�14∗ $��) ∙ "(%&$#) (10)

This method allows to standardize results of OTE calculating the oxygen saturation concentration in

clean water at 20°C (Cs*20) and the saturation concentration for clean water at half depth (Capela et

al., 2004) in process temperature conditions (Cs*

T).

Additionally, it has to be considered that also the air flow rate has an effect on oxygen transfer

(Figure 5). In fact, an increase in air flow rate is observed to initially decrease steeply the SOTE until

reaching a minimum plateau (Gillot and Heduit, 2000).

Figure 5 - Standard oxygen transfer efficiency (SOTE) as function of the collected air flow rate (Gillot and Héduit, 2008).

1.3.4. Predictions of oxygen transfer in presence of surfactants

Surfactants in wastewater are typically oil, soaps and detergents. These compounds are generally

composed of a hydrophobic tail and an hydrophilic head, and have three peculiar behaviors in

water: the formation of micelles (the hydrophobic tails of multiple molecules attract to each other

forming an agglomerate), the accumulation at the water surface (bringing the hydrophobic tail out

of the liquid) and the accumulation on the bubble surface. This accumulation on the gas-liquid

interface induces two major problems: the increase in rigidity of the interface and a decrease in the

internal gas circulation of the bubble which have a direct effect on the diffusion coefficient and

therefore on the kLa (Ferri and Stebe, 2000; Rosso and Stenstrom, 2006a).

Introduction and literature review

11

Figure 6 - α factors at different flow regimes (defined by the Reynolds (Re) number) for

different aerator types. Adapted from (Rosso and Stenstrom, 2006b).

Figure 6 shows values of α factors measured for different aeration devices with regard to the

respective interfacial flow regimes (expressed by means of the Reynolds (Re) number). In the

region of fine bubble aerators operation, diffusional transport is the driving force for mass

exchange and the gas transfer is controlled by surfactant interfacial migration. In this range of flow

an increase in Re leads to increased surfactants transport to the interface which decreases the α

factor. With regard to coarse bubble diffusers and high shear aerators (surface aerators and

turbines), operating in the turbulent flow domain, an increase in Re results in an enhanced surface

renewal rate and therefore in higher α values (Garner and Hammerton, 1954; Rosso and Stenstrom,

2006a). However, it must be pointed out that the variability in α factor for a given Re value is

considerably high, meaning that other mechanisms are in play.

The mean cell retention time, or sludge retention time (SRT), was observed to be related to the

evolution of the α value. In fact, SRT comprehends in some way the degree of degradation of

contaminants in the wastewater, and therefore also of surfactants. The α value was observed to

increase with increasing SRT showing that, allowing the biomass for a higher contaminants

degradation, ameliorated the oxygen transfer. However, some discrepancies were observed

between the two parameters for plants working with comparable SRT (Groves et al., 1992; Rieth et

al., 1995; Rosso et al., 2005; Wagner, 1999). Interestingly, the parameter χ (s2) and the regression

coefficients for the α factor and OTE prediction (Equation 11, 13 and 14) were defined after a

regression analyses of a large dataset of OTE collected with the off-gas technique over a period of

fifteen years (Rosso et al., 2005).

5 = 67#89�: (11)

;��< = �=7��>�?∙@A∙B (12)

� = 0.172 ∙ FGH5 − 0.131 (13)

Introduction and literature review

12

�0'�( = 5.717 ∙ FGH5 − 6.815 (14)

where AFR (m3/s) is the air flow rate, aspec (m2) is the diffuser specific area, Nd is the total number of

diffusers, Z (m) is the diffusers submergence and Qair (s-1) is the resulting normalized air flux. Figure

7 shows the efficiency parameters α and αSOTE (reported per meter of tank depth) in function of

Qair and SRT (reported as MCRT) for different aerator types, a subset of data used in the design of

the aeration model just described (Equation 11-14).

Figure 7 - Efficiency parameters in function of the normalized air flow rate and mean cell retention time (MCRT or SRT). CDi: ceramic discs; CDo: ceramic domes; CP: ceramic plates;

MD: membrane discs; Tu: ceramic, plastic and membrane tubes; MP: membrane panels (Rosso et al., 2005).

Apparently, up to 30 % of the variability in the α value cannot be explained due to the several

interactions taking place in the mass transfer process and to the lack of knowledge regarding the

effect of aerator submergence (Gillot and Héduit, 2008). In order to take into account the effect of

diffuser submergence the equivalent contact time (ECT) was included in the prediction of α along

with SRT and airflow rate (Gillot and Héduit, 2008). Although ECT seems to combine most of the

generally known factors having an effect on mass transfer, for this method a calculation or

estimation of the kLa*(20°) is necessary a priori complicating the application in predictive aeration

models for WWTP.

1.3.5. Bubble column tests

Bubble columns have been extensively used in the evaluation of aerator’s performance and for the

definition of useful design parameters. Their major advantages are the ease in controlling the

Introduction and literature review

13

process conditions and the relatively simple construction. In bubble columns specific process

conditions, such as temperature or pressure, can be finely controlled and the effect of specific

parameters can be isolated and characterized. Also for wastewater applications bubble columns

represent an important source of informations. Predictive models for oxygen transfer have been

developed using dimensional analysis methods to define the correlation coefficients linking a

selected number of operational parameters influencing kLa and therefore mass transfer (Gillot et al.,

2005; Pittoors et al., 2014; Rosso and Stenstrom, 2006a). However, these studies focus more on the

effect of contaminants on the mass exchange process not fully considering the physical properties

of a liquid and its effects on bubble size. Coalescence and breakup can play in fact an important role

on the bubble size distribution and therefore on the total available surface for mass exchange.

Bubble column tests combined with image analysis showed that presence and concentration of

electrolytes promote to some extent bubble coalescence when salinity is not sufficiently high to

have an influence on viscosity (Lessard and Zieminski, 1971; Ruen-ngam et al., 2008). Moreover,

similar tests revealed that bubble coalescence is also affected by temperature (Figure 8) and a

critical velocity exists, specific for a given liquid at a given temperature but not depending on the

bubble diameter, from which bubbles approaching with a higher speed will bounce back rather

than coalesce (Ribeiro and Mewes, 2006).

Figure 8 - Relative velocity (urel) and the results of individual collisions as a function of

bubble diameter (Ribeiro and Mewes, 2006).

Interestingly, as temperature increases, also the critical velocity increases indicating an

enhancement in bubble coalescence. These latter findings were correctly predicted only by

coalescence models assuming immobile or partially immobile interfaces (also in pure liquids)

suggesting that the liquid thinning process leading to coalescence follows a viscosity dependent

mechanism (Ribeiro and Mewes, 2006). Finally, also the presence of solids, known to affect the

viscosity of the medium (Rosenberger et al., 2002), was observed to influence hydrodynamics along

with mass transfer. In particular, for tests performed in an air-lift with AS sludge at different

concentrations, as the solids content of the mixed liquor increased both the gas holdup and the

mass transfer coefficient decreased (Jin et al., 2006).

Computational fluid dynamics (CFD) simulations are a useful tool to understand the hydrodynamics

taking place in a process using one or multiple fluids and have been extensively used to study the

Introduction and literature review

14

events occurring in bubble columns. The two fluids model, considering both the liquid and gaseous

phase, has been of crucial importance for the study of aerated systems. However, this model was

observed to perform well only in the homogeneous flow domain where the narrow bubble size

distribution can be sufficiently well described by the assumption of a constant bubble diameter. In

order to be able to describe the hydrodynamics in the heterogeneous flow, where bubbles often

show a bimodal distribution (Ribeiro and Lage, 2004; Ruen-ngam et al., 2008), the assumption of a

constant bubble diameter is no longer sufficient and the use of a population balance model (PBM) is

required. In this model, population balance equations (PBEs) are used to describe the evolution of a

certain population as a result of external forces and interactions creating a pressure on their

distribution. The two models (CFD and PBM) are coupled using the gas holdup and the kinetic

energy dissipation calculated with the CFD to solve the PBM equations and get the bubble size

distribution necessary to calculate interphase forces and turbulence modifications of the CFD

model (Wang and Wang, 2007). The use of an integrated CFD-PBM model significantly improved

the insights on the processes driving the coalescence and breakup of bubbles by coupling the

dynamic interactions between the physical properties and the hydrodynamic behavior of a fluid

with the size distribution of the dispersed gas phase. In particular, it was observed that increasing

both the gas velocity and the liquid viscosity resulted in a change in bubble size distribution from

unimodal to bimodal and an increase in viscosity accelerated this process significantly increasing

the average bubble size (Xing et al., 2013). Interestingly, for the range of viscosity usually occurring

in AS processes, i.e. in the range of 10 and 100 mPa s-1 (Ratkovich et al., 2013; Rosenberger et al.,

2002), increasing viscosity was observed to decrease the total gas holdup and the volume fraction

of small bubbles in the CFD-PBM model (Xing et al., 2013). The use of a coupled model led also to

the definition of five different mechanisms for bubble breakup and coalescence (Figure 9). The

breakage can be procured by turbulence and collision, by shear and by interfacial instability.

Concerning coalescence, the effect of turbulent eddies was observed to be much larger as compared

to the other coalescing processes.

Figure 9 - Different mechanisms leading to either bubble coalescence or breakup (Wang, 2011; Xing et al., 2013).

Recent progresses showed that important factors to be considered in the CFD-PBM coupled model

are the effects of liquid viscosity, surface tension (and therefore surfactants and electrolytes

presence), solids concentration, temperature and pressure, and therefore further development is

needed (Wang, 2011).

Introduction and literature review

15

Based on these observations it is evident that the importance of an accurate description and

understanding of both chemical and physical processes occurring during the oxygen transfer is of

crucial importance for the correct quantification of its efficiency. The evidences reported in

literature describing the processes affecting oxygen transfer must not be ignored. The inclusion of

the knowledge coming from the CFD-PBM model could substantially reduce the observed

variability in the prediction of the efficiency parameters based on SRT and airflow since the bubble

size has a direct relation with the kLa coefficient.

16

2. Materials and methods

2.1. The Eindhoven WWTP

The WWTP of Eindhoven (The Netherlands) is the third largest in the country and is operated by

Waterboard De Dommel. Designed to treat the wastewater of 750,000 inhabitant equivalents (IE)

with a load of 136 gCOD/d/IE, the plant is composed of three parallel treatment lines equipped

with one primary settler, one bioreactor and four secondary sedimentation tanks (Figure 10). The

treated effluent is then discharged in the river Dommel.

Figure 10 - Aerial view of the WWTP of Eindhoven and its main process units.

Each bioreactor (Figure 11) is designed according to the UCT layout and consists of one anaerobic

tank (inner ring), one anoxic tank (middle ring) and one aerobic/anoxic tank (outer ring), all

operating in plug-flow configuration. The pre-settled wastewater enters the inner (anaerobic) ring

of the bioreactor and is directed around four sub-divisions ensuring its plug-flow operation. After

the fourth compartment of the inner ring, the mixed liquor is directed to the middle (anoxic) ring

through an opening at the bottom of the tank. At this point the AS is circulated, with a retention

time of 3.5 h, by means of impellers. An overflow located at the outer wall of the middle ring is

feeding the outer (aerobic/anoxic) ring of the bioreactor, while a recirculation pump returns a

fraction of the mixed liquor (recycle A) to the inner ring for P removal. In the outer ring, alternated

aerobic and anoxic zones are maintained. Three pairs of impellers located on three bridges around

the outer ring ensure a minimum of 0.25 m/s mixed liquor flow velocity in order to prevent settling

of the AS flocs (Bosma et al., 2007). The AS exits the outer ring via an underflow located at its outer

wall after the summer package (cascade outflow) while a fraction of the mixed liquor is recycled

back into the middle ring for denitrification.

Materials and methods

17

Figure 11 - Scheme of a bioreactor. The full black arrows show the mixed liquor direction

and the dotted arrows show the recirculation flows throughout the different compartments.

Aeration to the biomass is provided in the outer ring by plate aerators divided in two sections, a

continuously active summer package and a winter package. The winter package is used only

occasionally to increase the aerated volume in the tank (e.g. when low temperatures decrease the

bacterial activity or during rain events when the influent load increases). On the other hand, the

summer package is always active and its airflow is controlled by an ammonia-DO feedback cascade

control which reduces the airflow when the effluent ammonia from the bioreactor is below 1 mg/l.

Additionally, a feedforward control takes action when the incoming flow rate to the plant is above

11,000 m3/h. When this happens, the DO set point is increased to 6 mg/l and both summer and

winter packages are used in order to ensure nitrification.

Thanks to the very advanced Supervisory Control and Data Acquisition (SCADA) system the WWTP

of Eindhoven disposes of high quality dataset of influent, effluent and process data. Therefore, time

series with the resolution of a minute were used in this work for the model simulations.

2.2. The Eindhoven wastewater treatment plant model in WEST®

The multi-platform modelling and experimentation system WEST® (World-wide Engine for

Simulation, Training and automation, MIKEbyDHI, USA) was developed in BIOMATH and used also

for the implementation of the Eindhoven WWTP model (Amerlinck et al., 2013). This software was

developed for incorporating simulation in the design and optimization of WWTPs and provides an

extensive model library. More generally, it allows the construction of models of any kind of system

representable with differential-algebraic equations (Benedetti et al., 2008). The first model of the

Eindhoven WWTP was composed of a single treatment line and used one Continuous-flow Stirred-

Tank Reactor (CSTR) for each of the biological treatments. Over the years several improvements

were included in the model structure in order to most accurately describe the behavior of the

treatment line in all (bio)physical-chemical aspects. Particularly for the biological treatment, the

use of multiple tanks in series was adopted to describe zones of the same ring with different

Materials and methods

18

characteristics and the presence of recirculation flows. Still at the present, the latest version of the

model configuration still considers the three parallel lines of the Eindhoven WWTP as one single

line having the total volume of the plant (Figure 12).

Figure 12 - Layout of the Eindhoven WWTP model in WEST14. The black boxes indicate the

subdivision of the three main compartments of the bioreactors.

However, the three rings of the biological treatment are modelled separately using a number of

tanks in series, each one of them being a CSTR. This configuration enables to mimic the plug flow

character of the three rings of the bioreactors and to separate areas of the same tank having

different conditions (e.g. aerobic/anoxic tank). The inner ring is composed of four tanks resembling

the different compartments of the anaerobic treatment. Two tanks represent the middle ring where

only anoxic conditions are maintained. Because of the higher complexity and heterogeneity of the

outer ring in terms of flow and aerated zones, a more detailed subdivision is needed separating its

volume in seven tanks. Two of these tanks are aerated and represent the summer and winter

package respectively. The remaining five tanks composing the outer ring resemble the four main

anoxic areas in the biological tank and a cascade collecting the outflow towards the secondary

clarifier. In order to resemble the carrousel type character of the middle and outer ring the

recirculation flows are also included. The recycle A and recycle B, necessary for a robust

representation of the UCT layout are implemented by controllers resembling very closely the

actions described in the WWTP process manual. Finally, the return flow of AS (RAS) from the

secondary settler and the wasted fraction are determined by their online full-scale measurements.

Two controllers are implemented in the model for the summer and winter package. The aeration

model used for these controllers (see section 1.3.4) assumes a constant SOTE and calculates the kLa

Materials and methods

19

needed in the ASM resulting in variable α with time. The value of α varies depending on a dynamic

relation with SRT and AFR. The SRT is calculated from the predicted mass of AS in the bioreactor

and the wasted fraction from the secondary settler.

For the biological process, the use of the ASM2d modified model (Gernaey and Jørgensen, 2004)

guarantees the inclusion of both nitrogen and biological phosphorous removal. This model requires

the fractionation of the influent total COD and soluble COD.

The influent data used in the model consists of influent water flow rate (m3/d) and concentrations

(mg/l) of total and soluble COD, total suspended solids (TSS), total phosphorous (TP) and ammonia

(NH). Also necessary as input to the model are the data of AFR (m3/d) to the summer and winter

packages, air and water temperature (°C), pressure (Pa), along with flow rates (m3/d) of aluminum

salts dosing, RAS and waste AS. Despite an excellent data quality and availability, sensor failures are

always a present threat and during the month of July and the first eight days of August very few

data are available. For this reason, for the simulations of the month of August, only the data from

the period 8th – 31st of August were used.

2.3. Experimental data collection

2.3.1. Off-gas measurements

Redmon et al. (1983) stated “The off-gas measurement technique may be a tool for obtaining more

useful design data for aeration systems”. A decade later, an official protocol for process water

testing was developed based on this technique (ASCE, 1997). Today this technique is an established

and reliable measurement for the evaluation of the efficiency performance of submerged aeration

systems and was used in this work to monitor the aerated zone of the biological tank number two

(ATII) of the Eindhoven WWTP. The off-gas equipment was composed of a reinforced polyethylene

hood floating on the wastewater surface (1.5 x 1.5 x 0.3 m, LxWxH). The hood was connected to an

off-gas analyser (evolution of the analyser in Leu et al., 2009) through a flexible hose of 40 mm in

diameter (Figure 13).

Figure 13 - Off-gas analyser (right) and floating hood (left).

Materials and methods

20

In the off-gas analyser, a vacuum pump diverges a small fraction of the off-gas from the main hose

to a desiccator unit in order to remove water vapour. The spilled air flow is then circulated inside a

zirconium oxide fuel cell (AMI Model 65, Advanced Micro Instruments, USA) to measure oxygen

partial pressure. Ambient air was sampled by means of a three-way valve at the start and end of

each experiment as reference for the efficiency evaluation. A schematic representation of the off-gas

analyser is given in Figure 14.

Figure 14 - Off-gas analysis system scheme

When the humidity is stripped out of the gas stream, only the knowledge of the CO2 content is

necessary in order to calculate the actual mass fraction of oxygen (Redmon et al., 1983). With this

purpose, the CO2 content of both the ambient air and the off-gas stream was measured with a

photo-acoustic infrared gas analyser (X-Stream, Emerson). Knowing the CO2 content of the gas

stream, the partial pressure of oxygen and its ratio with inerts were calculated using the following

equations.

MNO/� = QR1−QR−Q�'2R

(15)

MNOS/� = T-UV$T-U$TWX*-U (16)

where MNO/� and MNOS/� represent the molar ratio of oxygen to inerts in the inlet and off-gas

respectively. Q< and QOS are the mole fractions of water vapor in the inlet and off-gas, while Q�)*<

and Q�)*OS are the mole fractions of CO2. Finally, OTE can be calculated with Equation 17 similarly

to Equation 9 considering the dynamic CO2 content in the off-gas.

'�( = Y7-/�$Y7-U/�Y7-/� (17)

The floating hood was equipped with an LDO probe (Hach-Lange) and DO data were acquired in

order to correct for variable DO gradients during the oxygen transfer process and relate the

efficiency results to standard conditions with Equation 10. Data of DO and oxygen content in the

off-gas were acquired with a data acquisition card (DAQ-card USB-6341, National Instruments)

using a graphical user interface developed in LabView (National Instruments, USA). Adjustments

Materials and methods

21

for CO2 content in the off-gas were performed in a post processing step when both the data from the

off-gas analyser and the X-Stream were available.

The off-gas measurements were performed in the month of August 2012 during an extensive

measurement campaign in three locations of ATII (Figure 15), namely the beginning, the middle

and the end of the summer package according to the flow direction.

Figure 15 - Picture of the hood placed at the beginning of the summer package (s.p.) of the ATII (right) and schematic view of the three hood locations (right, red dots).

Dynamics of OTE were monitored during daylight from the 3rd of August until the 24th with the

exception of Saturdays and Sundays when the plant was not accessible. From the morning of the

25th until the early morning of the 27th of August the data were recorded continuously leaving the

instrumentation operating on the site during night hours. The location at the beginning of the

summer package was monitored from August 3rd until August 10th, while the location in the middle

was monitored between August 13th and 17th and the last location hosted the hood from August 20th

to the 27th . The monitoring of the three locations did not have the same time duration due to the

fact that aeration efficiency monitoring was not the sole purpose of the measurement campaign.

2.3.2. Bubble column

A Plexiglas cylindrical column (Figure 16) was used to monitor the fate of air bubbles generated

from a fine pore diffuser in different conditions of salinity and viscosity mimicking typical

characteristics of AS. The column is 160 cm high with an inner diameter of 38 cm. The water level

was maintained in all the experiments at 150 cm resulting in a total liquid volume inside the

column of 170.1 litres. The diffuser used was a disc aerator OXYFLEX®-MT 300 (Supratec,

Germany) with a diameter of 30 cm mounted on a metal plate at the bottom of the column. The

membrane of the aerator was located 20 cm from the bottom of the column. The column was

equipped with a membrane DO probe (Mettler Toledo) to monitor DO dynamics for the evaluation

of the kLa.

Materials and methods

22

Figure 16 - An image of the bubble column (left) and a scheme of the setup used to monitor

bubble sizes and oxygen transfer (right).

For the estimation of the kLa the DO present was stripped by means of N2 gas and, when the DO was

as low as 0.6 mg/l, compressed air was injected until the saturation level was almost reached. In

this way the kLa could be calculated by integrating the re-aeration curve as follows:

Z V��∗$�� [��

�\�2 = ��� Z []�\

�2 (18)

ln(��∗ − ��) = −��� ∙ ] + ln(��∗ − �&) (19)

where �& is the DO concentration at the beginning of the curve, �a is the DO concentration at the

end of the curve, ]& is the initial time and ]a is the final instant of the re-aeration curve. A plot of

ln(��∗ − ��) over time returns a straight line whose slope is -kLa.

Additionally, the off-gas of the column was captured and the oxygen content monitored with the

same analyser used in the full-scale measurements. Softened water was used as the reference clean

water solution. Sodium chloride (NaCl) and Xanthan TER (Colltec GmbH) were used to vary salinity

and viscosity of the medium respectively in order to approach the AS sludge characteristic as much

as possible. The salts concentrations were chosen based on typical wastewater characteristics

(Tchobanoglous et al., 2003). For mimicking the rheological behaviour of AS with a concentration of

10 gMLSS/l, the Xanthan TER concentration was obtained from published results (Rosenberger et

al., 2011). On the other hand, for the AS concentration of 5 gMLSS/l the same literature reported

only the viscosity profile. Therefore, by means of a rotational rheometer (conical concentric

cylinder AR 2000, TA Instruments US) similar in the working principle to the one used by

Rosenberger et al. (2011), the wanted concentrations of Xanthan TER were found fitting the

published results.

Materials and methods

23

Table 1 summarizes the NaCl, Xanthan TER concentrations and the air flows tested in this

experiment. Knowing the value of the kLa in clean water at the different air flows, the relative

effects of salinity and viscosity variations on the α value were evaluated (Equation 6).

Table 1 List of parameters tested and respective values used in the experiments. Viscosity

values report the Xanthan TER concentration and the corresponding AS concentration.

Parameter Values tested

Air flow rate (l/min) 2 – 4 – 6 – 8

Xanthan TER (g/l [gMLSSS/l]) 0 [0] – 0.2 [5] – 0.8 [10]

NaCl (mg/l) 0 – 50 – 100

Additionally, at the different air flows and for all the solutions tested, the gas holdup of the column

was measured manually so that the amount of air trapped inside the liquid from the various

solutions could be monitored.

2.3.3. Imaging and bubble size measurements

A high speed camera (i-SPEEDLT, Olympus) was used to capture high frequency images in order to

evaluate the bubble size distribution (BSD) along the height of the column at the different

conditions of air flow, NaCl and Xanthan TER additions. The camera was equipped with a zoom lens

of focal length 12.5 – 75 mm and aperture f/1.8 to f/22 (Pentax). After a trial period the most

suitable settings were found using a focal length of 75 mm and aperture of f/8, placing the camera

88 cm from the centre of the column, and using an outdoor light of 500W located at the opposite

side of the column (5cm from the outer edge). In order to provide a diffused source of light for a

homogeneous image background, a paper foil was placed on the column surface in front of the light.

These settings provided a reasonably narrow focal plane and a sufficiently bright image to ease the

image processing in the recognition of the bubbles edges. Seven filming locations along the height

of the column were established, namely at 5 cm above the aerator, 20, 40, 60, 80, 100 and 120 cm.

This resolution allowed a very thorough monitoring of the dynamics along the height. For each

location, and for each measurement, three movies were recorded and at least the first 1000 images

of each movie were sampled for the image processing making each BSD the result of the analysis of

about 3000 RBG images in jpeg format. All the settings described for the use of the high speed

camera were used in all the filming with the exception of the paper foil which was not used in

presence of the Xanthan solution. Due to the turbidity of the Xanthan solution the paper foil was not

needed to obtain diffused light.

The image obtained from the high speed camera with the settings described above, allowed to have

a resolution of 0.095 (+/- 0.005) mm/pixel with image dimensions of 600x800 pixels. Having the

resolution far below the millimetre is an important feature to thoroughly detect size variations in

fine bubble systems.

The image analysis was implemented in Matlab® (MathWorks) during this thesis. This code is the

result of an empirical selection of processing methods which were the best performing for the

Materials and methods

24

purpose of an accurate bubble detection. Hereunder, the main steps characterizing the image

analysis are listed and an accurate description of each step follows.

• Image loading and selection of the red channel

• Edge detection and objects filling

• Removal of open edges and of objects touching the image border

• Objects labelling and measurement

• Filtering for circularity reciprocal and convexity

• Final bubble size distribution

The script selects only the red channel of the image as it provides the best contrast in general

(Figure 17, left). Firstly, the Canny method (Canny, 1986) is applied on the black and white image,

for which it calculates the Gaussian derivative on the pixels space detecting the magnitude and the

orientation of the black and white gradients. This allows the enhancement of local maxima of the

black and white gradients and the suppression of the remaining values. Finally, high and low

gradient thresholds refine the bubble detection and the identified edges are composing the output

binary gradient mask (Figure 17, right).

Figure 17 - Red channel of the original image (left) and the binary gradient mask (right) resulting from the edge detection function

After this step the dilate function helps enclosing the edges and the filling of closed objects is

performed while open lines are removed from the image. The borders of the images are cleared

from any object touching the four pixels around the frame perimeter so that only entire objects are

analysed. The rough edges of the filled objects are then eroded of one pixel to smooth the surface.

Consequently, a recursive identification number is given to every object present on the binary

image (i.e. to every “island” of ones) and for each object the values of perimeter, area and convex

area (this latter is the area of the convex regions around the object) are provided. With these shape

parameters, the circularity reciprocal (1/C, -) (Equation 20) and the convexity (-) of each object are

calculated. The circularity reciprocal returns value one for a circle, decreases to zero for ellipsoidal

shapes and increases above one when the perimeter of the object is irregular. Unlike circularity,

convexity is more strictly a measure of roughness and is calculated as the ratio between the internal

area of an object and the area of the imaginary elastic band around it. Therefore, convexity returns

Materials and methods

25

a value of one for e.g. a circle or an ellipse and its value decreases towards zero for more irregular

shapes. Circularity and convexity are thus filtering criteria especially useful to eliminate those blobs

formed by multiple bubbles so that only single bubbles are considered in the final size distribution

(Figure 18). For this last step to work properly, the cut-off criteria were carefully defined with some

preliminary experiments. A maximum of 1.6 and a minimum of 0.2 were optimal values for

circularity, while a maximum of 0.92 was optimal for convexity.

1/� = bc<�dc�c<*e∙�∙�<c� (20)

Figure 18 - Outlined original image before (left) and after (right) the application of the filter

based on circularity and convexity.

The equivalent diameter of each of the remaining bubbles is then stored in memory. The equivalent

diameter, or equivalent projected area diameter, is the size of a circle with the same area as the

detected bubble and is calculated as:

[cf = g�∙e� hi* (21)

where A is the area of the bubble projected on the image. At the end of the image analysis the

number based distribution of the selected bubbles is built dividing the sizes in ten classes with

range of 0.2 mm from 0 to 20 mm. The frequency of occurrence in each of the size classes is then

expressed in number percentage and results in the final number based distribution.

The number based distributions obtained from the image analysis were used, along with the

column gas holdup, to separately calculate the factor kL and a (Baz-Rodríguez et al., 2014; Ruen-

ngam et al., 2008). The value of a can be estimated as:

� = j∙kU�l*(V$kU) (22)

were εg (-) is the fractional gas holdup and d32 (mm) is the Sauter mean diameter. From the gas

holdup (ΔV) and the height of the liquid (VL), it is possible to calculate εg as:

Materials and methods

26

mS = nno� (23)

Concerning the d32, the number based distributions obtained from the image analysis can be used

as follows:

[p% = ∑r���l∑r���* (24)

where ni is the occurrence frequency number of the equivalent area diameter di. Finally, the value

of kL can be calculated as:

�� = ���� (25)

The separate evaluation of kL and a can help in understanding whether a change in kLa is mainly

due to bubble size and amount of surface for exchange or to physical-chemical properties at the

gas-liquid interface.

27

3. Results

3.1. Bubble column

In this section the results are presented of the experimental work performed with the bubble

column in order to understand the effect of a change in electrolyte concentration and viscosity on

the oxygen transfer mechanism. The measurements were first performed in clean water (softened

tap water) and then repeated using two concentrations of NaCl (i.e. 0.05 g/l and 0.1g/l) and two

concentrations of Xanthan TER (i.e. 0.2 g/l and 0.8 g/l). Image analysis tools were used for bubble

size measurements and the gassing out method was used to evaluate the kLa of the system.

3.1.1. Rheological measurements

The concentrations of Xanthan TER were chosen in order to mimic the rheological behaviour of AS

at the concentrations of 10 gMLSS/l (i.e. typical value used in MBR systems) and of 5 gMLSS/l (i.e.

closer to the conditions of conventional AS plant) according to the measurements of Rosenberger et

al. (2011). The profiles of apparent viscosity measured for a set of different concentrations of

Xanthan TER were plotted on the published results (Figure 19). The apparent viscosity profile for

the concentration of 0.8 g/l of Xanthan TER was successfully reproduced (Figure 19, left, red

triangles versus white squares) despite the few differences in the instrumentation used. The major

difference was that Rosenberger et al. (2011) used a double gap cylinder while in this work a single

gap cylinder was used. As expected, decreasing the concentration of Xanthan TER, the apparent

viscosity also decreased showing more and more a similar behaviour to the one of Newtonian fluids

(i.e. constant apparent viscosity over shear rate).

The measurements were performed only once due to time constraints and limited availability of the

instrument. However, for the concentration of 0.4 g/l of Xanthan TER, two measurements were

performed (Figure 19, 0.4 and 0.4b) and the two apparent viscosity profiles matched very closely.

Interestingly, all the curves measured present a rising tail when approaching the highest values of

shear rate differing from the measurements of Rosenberger et al. (2011). Also, the length of the

rising tail, is increasing with decreasing Xanthan TER concentration. A possible explanation for this

observation can lay in the differences between the equipment. The rising tail of a viscosity profile is

usually observed when turbulent flow conditions start to form due to the high speed of the

cylindrical rotor. Since the viscosity measurements have as first assumption that laminar flow

conditions prevail, the part of the measurements in which the viscosity is increasing cannot be

considered reliable.

The Xanthan TER concentration chosen by Rosenberger et al. (2011) matches closely the relative

AS profile in the shear rate around 100 s-1 (Figure 19, left), which is the range of shear occurring in

aerated AS systems (Rosenberger et al., 2011, 2002). Therefore, throughout all the concentrations

tested in this work, the 0.2 g/l Xanthan TER seems to reproduce better than the others the AS

concentration of 5 gMLSS/l of the published results (Figure 19, right).

Results

28

Figure 19 – Measurements of apparent viscosity from Rosenberger et al. (2011) (background black and white graphs) and the rheological measurements performed in this work for

different Xanthan TER concentrations (coloured triangles, no difference between right and left)

3.1.2. Bubble size measurements

In order to have an estimate of the accuracy of the image analysis tool, three sub-samples of a

regular set of 3000 images, used for one of the measurements, were analysed separately and

reported in Figure 20. The use of a sub-sample of 1000 images acquired in sequence resulted in

about 9000 measured diameters composing the final distribution. As expected, the number based

distributions show the highest sensitivity at smaller diameters, while they appear not so sensitive

to variations in the number of large particles. At the value of 50% the distributions differ for

0.08mm, while for lower percentages the gap increases to 0.3mm. The use of the full set of 3000

images is therefore recommended to increase the robustness of the analysis method.

Figure 20 – Number-based cumulative distribution plot of three sub-samples (1000 images each) of the same set of images (3000).

3.1.2.1 Clean water bubble size measurements

The bubble size analysis in clean water shows the effect of height and air flow on the equivalent

diameter of the bubbles (Figure 21 and Figure 22). The bubbles dimension increases with height

due to the (hydrostatic) pressure drop they experience on their surface, causing the internal gas

volume to expand. The effect of pressure was detectable from the image analysis at all air flow rates

(Figure 21 a, b, c and d) showing very similar smooth, continuous shifts in the distribution of the

diameters..

0 1 2 3 40

20

40

60

80

100

deq

(mm)

Num

ber

%

123

Results

29

Figure 21 – Number-based cumulative bubble size distributions in clean water at different heights. The imposed air flow is reported in the title of the graphs.

Beside the pressure effect, also bubble coalescence and breakup can potentially take place during

the rising path along the column height. These effects can be better observed in Figure 22 where

the distributions are plotted with respect to the airflow rate. Close to the aerator (Figure 22, a, b

and c) all the air flows studied produced very similar distributions with the majority of the bubbles

below 1 mm. Further away from the aerator, at 60 cm (Figure 22, d), the difference between the

lowest and highest air flow becomes considerably larger suggesting the occurrence of a higher

coalescence with increasing air flow rate. However, when the bubbles reach 80 and 100 cm from

the aerator (Figure 22 e and f respectively) the difference between the lowest and the highest air

flow seems to slightly decrease. Reaching the highest observation point, at 120 cm from the air

diffuser (Figure 22, g), the difference between the 2 and 8 l/min air flows increases again, i.e. at the

highest flow rate 50% of the bubbles are 0.4 mm bigger than at the lowest air flow rate.

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

a

2 l/min

deq

(mm)

Num

ber

%

5 cm20

40

60

80

100120

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

b

4 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

c

6 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

d

8 l/min

deq

(mm)

Num

ber

%

Results

30

Figure 22 – Number-based cumulative bubble size distributions for clean water at different air flows. The height (in cm) above the aerator is given in the figure title.

3.1.2.2 Effects of salt addition (50 mg/l)

The addition of 50 mg/l of NaCl resulted in very similar bubble size distributions, along the height

and among the air flow rates, to what was observed in clean water (Figure 23 and Figure 24). The

four tested flow rates generated bubbles below the millimeter and crossed the 1.5 mm size when

reaching 120 cm from the aerator.

0 1 2 3 40

20

40

60

80

100

a

5 cm from aerator

deq

(mm)

Num

ber

%

2 l/min

46

8

0 1 2 3 40

20

40

60

80

100

b

20 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

c

40 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

d

60 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

e

80 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

f

100 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

g

120 cm from aerator

deq

(mm)

Num

ber

%

Results

31

Figure 23 – Number-based cumulative bubble size distributions for 50 mg/l NaCl solution at

different heights. The respective air flow is reported in the title of the graphs.

Close to the aerator (Figure 24, a) the distributions at all air flow rates are very close and below the

millimeter, suggesting that, regardless of the air flow, the bubbles have very similar dimensions.

Already at 20 cm from the aerator (Figure 24, b) the discrepancy between the lowest and the

highest air flow increases, and this is particularly visible in the larger diameter range where a

difference of more than 0.2 mm can be found at 80% of the cumulative distribution. This

discrepancy increases with increasing height, at 40 cm from the aerator (Figure 24, c), suggesting a

predominant coalescence process in this first section of the bubbles rising path with increasing air

flow rate. This coalescence zone seems however to have an earlier end in the NaCl solution

compared to the clean water case since, at 60 cm from the aerator (Figure 24, d), the discrepancy

between distributions appears to stop or even reduce. However, all the distributions are still

shifting to the right side of the graphs with increasing height, and in this movement both the effect

of pressure and coalescence overlap. For this reason, in order to make a good comparison with the

clean water case at the net of the pressure effect, the distributions of the NaCl solution are shown in

terms of deviation from the respective clean water distributions (Figure 25).

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

a

2 l/min

deq

(mm)

Num

ber

%

5 cm20

40

60

80

100120

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

b

4 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

c

6 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

d

8 l/min

deq

(mm)

Num

ber

%

Results

32

Figure 24 – Number-based cumulative bubble size distributions for 50 mg/l NaCl solution at

different air flows. The height (in cm) from the aerator is given in the figure title.

The distributions in the NaCl solution do not differ substantially from the clean water

measurements (Figure 25, a) for the air flow rate of 2 l/min, and maximum deviations, or

oscillations, are in the range of +/-3%. Increasing the air flow rate to 4 l/min (Figure 25, b), the

differences become more pronounced and close to the aerator (5 cm) the NaCl solution shows a

drop of more than 8% for the size of 0.8 mm and an increase of about 4% for the diameters around

1.5 mm with respect to the clean water sample. However, this difference fades out with height, and

at 60 cm the NaCl solution shows a smaller bubble size than the clean water sample with a 4%

increase of the sizes closer to 1 mm and a small drop in the 2 mm range (Figure 25, b). Increasing

the air flow to 6 l/min (Figure 25, c), a similar drop in the size distribution below 1 mm can be

noticed but to a smaller extent (i.e. -6%), which again fades out along the height of the column

resulting in more similar bubble size distributions of the NaCl solution compared to the clean water

case. Close to the aerator, an inversion of this trend can be observed when reaching the 8 l/min air

flow (Figure 25, d), where the diameters around 0.5 mm show an increase of 5.5% coming from a

reduction of 6% in the number of bubbles of 1 mm. Although to a lower extent, this can partially be

visible already at 6 l/min (Figure 25, c). This increase in small diameters seems to disappear

already at 20 cm from the aerator where an increase is shown in the sizes around 1.5 mm. This

latter observation suggests that, even though smaller bubbles are generated by the diffuser in the

0 1 2 3 40

20

40

60

80

100

a

5 cm from aerator

deq

(mm)

Num

ber

%

2 l/min

46

8

0 1 2 3 40

20

40

60

80

100

b

20 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

c

40 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

d

60 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

e

80 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

f

100 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

g

120 cm from aerator

deq

(mm)

Num

ber

%

Results

33

NaCl solution at 8 l/min of air flow, a strong coalescence takes place already before 20 cm and

continues along the height. However, in general, the distributions at 120 cm for the NaCl seem

always not to deviate more than 3% from the respective clean water case, suggesting that, even

though differences are present close to the diffuser, , the dimensions tend to find a common

equilibrium in both solutions.

Figure 25 – Number-based graphs of the 50 mg/l NaCl solution in percentage of deviation

from the clean water solution. The respective air flow is given in the figure title.

3.1.2.3 Effects of salt addition (100 mg/l)

The increase of NaCl concentration to 100 mg/l presents similar trends over height in the results of

the bubble size analysis (Figure 26) suggesting that the effects of pressure on the bubble size in

both cases are comparable. However, at 2, 4 and 6 l/min of air flow rate (Figure 26, a, b and c) the

difference from the relative change in size due to the height is lower than what is observable at 8

l/min (Figure 26, d), i.e. for 2, 4 and 6 l/min at 50% of the distribution there is a difference of 0.6

mm between the location at 5 cm and the one at 120 cm from the diffuser, while this difference

increases to 0.9 mm for 8 l/min.

0 0.5 1 1.5 2 2.5 3 3.5 4

-8

-6

-4

-2

0

2

4a

2 l/min

deq

(mm)

Num

ber

%

5 cm20

40

60

80

100120

0 0.5 1 1.5 2 2.5 3 3.5 4

-8

-6

-4

-2

0

2

4b

4 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 4

-8

-6

-4

-2

0

2

4c

6 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 4

-8

-6

-4

-2

0

2

4d

8 l/min

deq

(mm)

Num

ber

%

Results

34

Figure 26 – Number-based cumulative bubble size distributions for 100 mg/l NaCl solution

at different heights. The respective air flow is reported in the title of the graphs.

Grouping the distributions according to the air flow rate (Figure 27) it can be firstly noticed how

the increase in air flow rate, in the range investigated, does not influence the bubble size at 5 cm

from the aerator in the same solution (Figure 27, a). The large majority of the generated bubbles

are below 1 mm at the point of generation. However, already at 20 cm above the aerator, the effect

of air flow rate becomes visible in the distributions and at 50% of the cumulative distribution a shift

of 0.15 mm can be observed from the lowest to the highest air flow (Figure 27, b). This discrepancy

increases with height until 80 cm from the diffuser (Figure 27, c) where the gap between the

distributions seems to find an equilibrium range of variation up to the highest location (Figure 27,

e, f and g).

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

a

2 l/min

deq

(mm)

Num

ber

%

5 cm20

40

60

80

100120

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

b

4 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

c

6 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

d

8 l/min

deq

(mm)

Num

ber

%

Results

35

Figure 27 – Number-based cumulative bubble size distributions for 100 mg/l NaCl solution

at different air flows. The height (in cm) from the aerator is given in the figure title.

Figure 28 allows to visualize the effect of 100 mg/l NaCl addition on the bubble size distributions

with respect to the clean water measurements. What can be noticed at first sight, is the peak that

recursively occurs in the distributions for 2, 6 and 8 l/min at the location 5 cm far from the aerator

(Figure 28 a, c and d). However, this does not appear for the air flow of 4 l/min (Figure 28, b) which

instead shows very low deviations from the clean water measurements. In Figure 28 (a), all

locations show a similar trend of increased percentages in the diameter range below 1 mm caused

by a decrease in the bigger sizes. However, apart from the locations at 5, 60 and 80 cm above the

diffuser, the deviations are close to 2% or even lower. In general, it seems that NaCl addition of 100

mg/l promoted the formation of considerably smaller bubbles compared to clean water but along

the bubble rising path this difference gradually disappears due to the coalescence effect.

0 1 2 3 40

20

40

60

80

100

a

5 cm from aerator

deq

(mm)

Num

ber

%

2 l/min

46

8

0 1 2 3 40

20

40

60

80

100

b

20 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

c

40 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

d

60 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

e

80 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

f

100 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

g

120 cm from aerator

deq

(mm)

Num

ber

%

Results

36

Figure 28 – Number-based graphs of the 100 mg/l NaCl solution in percentage of deviation

from the clean water solution. The respective air flow is given in the figure title.

3.1.2.4 Effects of viscosity variation (0.2 g/l Xanthan TER)

The concentration of 0.2 g/l Xanthan TER was used to mimic the rheological conditions of AS at the

concentration of 5 g MLSS/l.

The combined effect of pressure and coalescence on the bubble size along the height of the column

in presence of the 0.2 g/l Xanthan TER solution is still measurable although the deviations from the

5 to the 120 cm locations are considerably smaller than what was observed in clean water (Figure

29). Interestingly, the smaller bubbles recorded are rarely below 1 mm and almost never smaller

than 0.5 mm. .

0 0.5 1 1.5 2 2.5 3 3.5 4

-10

-8

-6

-4

-2

0

2

4

6

8

10 a

2 l/min

deq

(mm)

Num

ber

%

5 cm20

40

60

80

100120

0 0.5 1 1.5 2 2.5 3 3.5 4

-10

-8

-6

-4

-2

0

2

4

6

8

10 b

4 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 4

-10

-8

-6

-4

-2

0

2

4

6

8

10 c

6 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 4

-10

-8

-6

-4

-2

0

2

4

6

8

10 d

8 l/min

deq

(mm)

Num

ber

%

Results

37

Figure 29 – Number-based cumulative bubble size distributions for 0.2 g/l Xanthan TER

solution at different heights. The respective air flow is reported in the title of the graphs.

Also in the 0.2 g/l Xanthan TER solution the sizes of the generated bubbles do not substantially vary

with the air flow rate (Figure 30, a) but show a maximum difference of 0.25 mm at 50% of the

distribution. Increasing the distance to 20 and even 40 cm from the diffuser gap among the

distributions at the different air flows increases almost exclusively for the bigger diameters (Figure

30, b and c). At 80 cm above the diffuser the difference from the distribution curve obtained with 2

l/min and the distribution obtained with 8 l/min of air flow seems to reach its maximum extent

(Figure 30, d) and this gap appears to be maintained, with some fluctuation, throughout all the

successive locations towards the water surface (Figure 30, e, f and g).

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

a

2 l/min

deq

(mm)

Num

ber

%

5 cm20

40

60

80

100120

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

b

4 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

c

6 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

d

8 l/min

deq

(mm)

Num

ber

%

Results

38

Figure 30 – Number-based cumulative bubble size distributions for 0.2 g/l Xanthan TER

solution at different air flows. The height (in cm) from the aerator is given in the figure title.

The deviations of the distributions in Xanthan TER solution from the distributions in clean water

show very similar trends for all the air flows (Figure 31). In all the measurements of this

experiment the Xanthan TER solution resulted in sensibly bigger bubbles with respect to the clean

water case. The air flow of 2 l/min (Figure 31, a) shows the most gradual trend along the height, but

also for higher flow rates the same can be observed. At 5 cm from the aerator, more than 20% less

bubbles below 1mm in diameter were registered and a 20% increase was observed around 1.5 mm

(Figure 31, a). Increasing the distance from the diffuser, the range in which these changes occur

remains similar but the magnitude changes. Approaching the water surface, the negative difference

shown around 1 mm levels off to 10%, while the positive peak at 1.5 mm reaches 30% at 80 cm

from the aerator and decline again towards the outlet of the column (Figure 31, a). Increasing the

air flow rate to 4, 6 and 8 l/min, the highest (positive and negative) peaks are always registered at 5

cm from the diffuser and the deviation lines smoothen down in the +/-10% range progressively

towards the surface.

0 1 2 3 40

20

40

60

80

100

a

5 cm from aerator

deq

(mm)

Num

ber

%

2 l/min

46

8

0 1 2 3 40

20

40

60

80

100

b

20 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

c

40 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

d

60 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

e

80 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

f

100 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

g

120 cm from aerator

deq

(mm)

Num

ber

%

Results

39

Figure 31 – Number-based graphs of the 0.2 g/l Xanthan TER solution in percentage of

deviation from the clean water solution. The respective air flow is given in the figure title.

3.1.2.5 Effects of viscosity variation (0.8 g/l Xanthan TER)

The solution of 0.8 g/l of Xanthan TER increased the viscosity of the liquid to that similar conditions

found in MBR systems operated at a concentration of about 10 gMLSS/l of AS. The image analysis

revealed very homogeneous bubble size distributions throughout the height of the column as

compared to the rest of the experiments performed, i.e., in Figure 32, all the distributions within the

same graph are laying in the same size range showing no particular variation with increasing

distance from the diffuser. ). The effects of pressure and coalescence along the height of the column

are much less apparent from the distributions with a small exception for the set of curves obtained

with 2 l/min of air flow (Figure 32, a). In the same graph, the curves relative to the distances of 5

and 80 cm from the diffuser, show a rather high number percentage of bubbles in the range of 0.5 to

1 mm (10 to 20%) which differs from what is observed from the rest of the curves. In order to

verify the reliability of these curves, the images relative to the locations at 5 and 80 cm from the

aerator were visually checked and the image analysis was further tested on these images. No failure

was found in the software and bubbles in the range of 0.5 to 1 mm were actually observed in both

sets. The shape of the initial part of the distribution at 5 cm from the diffuser can be explained by

the presence of few pores generating very small bubbles, and the shape of the initial part of the

0 0.5 1 1.5 2 2.5 3 3.5 4

-20

-10

0

10

20

30a

2 l/min

deq

(mm)

Num

ber

%

5 cm20

40

60

80

100120

0 0.5 1 1.5 2 2.5 3 3.5 4

-20

-10

0

10

20

30b

4 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 4

-20

-10

0

10

20

30c

6 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 4

-20

-10

0

10

20

30d

8 l/min

deq

(mm)

Num

ber

%

Results

40

distribution at 80 cm from the aerator could be the result of breakage occurring along the height.

However, the absence of this initial shape in the rest of the distributions, also for the experiment

with the lower concentration of Xanthan TER, is not clearly explainable and needs further

confirmation through repetitive measurements (outside scope of this work). At higher flow rates (4,

6 and 8 l/min) the distributions are very close and similar to each other in shape (Figure 32, b, c

and d).

Figure 32 – Number-based cumulative bubble size distributions for 0.8 g/l Xanthan TER solution at different heights. The respective air flow is reported in the title of the graphs.

Grouping the distributions according to the air flow rates tested, the diameters of the bubbles at 5

cm from the aerator do show significant variations, suggesting again that, regardless of the air flow

tested, the generated bubbles have similar initial size (Figure 33, a). Getting further in height from

the diffuser, it can be noticed that increases in the air flow rate produced a slightly higher

percentage of smaller bubbles (Figure 33, b). At 40 cm from the aerator (Figure 33, c) this

discrepancy between the air flows increases and reaches its maximum, showing a gap of 0.3 mm, at

50% of the cumulative distribution (i.e. at the air flow of 8 l/min, 50% of the bubbles are at least 0.3

mm smaller than those generated at 2 l/min). This trend is maintained at all heights further than 5

cm from the aerator suggesting that coalescence is counteracted by the increased flow rate.

However, as observed from Figure 32, nor the effect of coalescence neither the effect of pressure

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

a

2 l/min

deq

(mm)

Num

ber

%

5 cm20

40

60

80

100120

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

b

4 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

c

6 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 40

10

20

30

40

50

60

70

80

90

100

d

8 l/min

deq

(mm)

Num

ber

%

Results

41

can be detected along the height. Interestingly, at the highest viscosity more variation can be

observed in the smaller sizes than in the bigger sizes, the opposite of what was observed in the

other experiments.

Figure 33 – Number-based cumulative bubble size distributions for 0.8 g/l Xanthan TER

solution at different air flows. The height (in cm) from the aerator is given in the figure title.

When comparing the 0.8 g/l Xanthan TER solution to the clean water results, all the air flow rates

tested produce significantly bigger bubbles in the viscous system than in clean water (Figure 34).

The bubbles with equivalent diameter in the range of 0.7 to 1.5 mm are, along the height, 30 to 15%

less in the viscous system, while the bubbles with size of 1.5 to 2 mm considerably increased (40%)

(Figure 34, a). The air flow of 4 l/min (Figure 34, b) resulted in a similar deviation profile

concerning the location at 5 cm from the aerator, while the amount of bigger bubbles diminishes

gradually with the height but remaining above 10%. The percentage of deviation for the location at

5 cm from the aerator maintains a similar extent and shape also at 6 and 8 l/min of air flow, while

the rest of the locations result in a similarly shaped curve but with a lower maximum and minimum.

Hence, the impact of viscosity, is especially important at low flow rates while higher flows

counteract its effect.

0 1 2 3 40

20

40

60

80

100

a

5 cm from aerator

deq

(mm)

Num

ber

%

2 l/min

46

8

0 1 2 3 40

20

40

60

80

100

b

20 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

c

40 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

d

60 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

e

80 cm from aerator

deq

(mm)

Num

ber

%

0 1 2 3 40

20

40

60

80

100

f

100 cm from aerator

deq

(mm)N

umbe

r %

0 1 2 3 40

20

40

60

80

100

g

120 cm from aerator

deq

(mm)

Num

ber

%

Results

42

Figure 34 – Number-based graphs of the 0.8 g/l Xanthan TER solution in percentage of

deviation from the clean water solution. The respective air flow is given in the figure title.

3.1.3. Lab scale oxygen transfer measurements

Oxygen transfer measurements were performed in all the solutions and the different air flow rates

considered in this study. The kLa of the column in clean water varies from 4.36 (+/-0.09) 1/h at the

lowest air flow rate, up to 12.55 (+/- 0.05) 1/h at 8 l/min (Figure 35). The dissolution of 0.05 g/l

NaCl is initially not having a relevant effect on the kLa for the air flows of 2 and 4 l/min.

Interestingly, when further increasing the air flow rate, the kLa of the 0.05 g/l NaCl solution

becomes about 9.9% and 4.9% higher than in clean water respectively under 6 and 8 l/min air

flows. A further improvement is provided by the dissolution of 0.1 g/l NaCl which enhances the kLa

in the entire range of air flows giving a maximum increment with respect to clean water of 23.4% at

an air flow of 6 l/min. Regardless of the air flow, the α factor in the 0.1 g/l solution is always greater

than 1 (Figure 36). On the other hand, the addition of 0.2 g/l of Xanthan TER, caused a general

decrease in the oxygen transfer by to 20.29% in kLa with respect to clean water and an α value of

about 0.8 for all the air flows. A further increase in viscosity (0.8 g/l Xanthan TER solution) caused

the kLa to drop even further (-32.4%) at 2 l/min of air flow, registering the lowest kLa measured

(2.95 +/-0.22 h-1) . However, increasing the air flow to 4 l/min under the highest viscosity, resulted

in a steeper increase in kLa than in all the other solutions tested, making the kLa to jump over the

0 0.5 1 1.5 2 2.5 3 3.5 4-30

-20

-10

0

10

20

30

40 a

2 l/min

deq

(mm)

Num

ber

%

5 cm20

40

60

80

100120

0 0.5 1 1.5 2 2.5 3 3.5 4-30

-20

-10

0

10

20

30

40 b

4 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 4-30

-20

-10

0

10

20

30

40 c

6 l/min

deq

(mm)

Num

ber

%

0 0.5 1 1.5 2 2.5 3 3.5 4-30

-20

-10

0

10

20

30

40 d

8 l/min

deq

(mm)

Num

ber

%

Results

43

value of 6 1/h. The kLa of the 0.8 g/l Xanthan solution increases with the air flow more steeply than

for the rest of the solutions, and at 6 l/min the α factor crosses the value of 1 (Figure 36). At the

highest air flow, the kLa of the 0.8 Xanthan TER solution is 25.1% bigger than the respective clean

water value.

Figure 35 – Measured kLa of the system at the different airflows and liquid compositions.

Figure 36 – Effect of air flow and solution characteristics on the measured α. The red

horizontal line is the reference for α=1.

The gas holdup was measured in all the solutions and for each air flow rate in order to have an

estimate of the volume of air trapped in the liquid phase. As expected, the values of the gas holdup

for the clean water and the two solutions with NaCl presented no differences (Figure 37, right). On

the other hand, the solution of 0.2 g/l Xanthan TER retained on average 0.16 (+/-0.03) liters more

of air than the clean water. The highest viscosity retained the same amount of air at the air flow of 2

l/min compared to the 0.2 g/l Xanthan TER (i.e. 0.31 l). However, increasing the flow rate in the

higher viscosity medium, also increased the retained fraction of air recursively arriving to a

maximum of 0.91 l of gas holdup. The Sauter diameter of the measured bubbles in the different

Results

44

solutions revealed similar values between the NaCl solutions and the clean water test (Figure 37,

right). On the other hand, the lowest Xanthan TER concentration showed a 10% increase in the

Sauter diameter as compared to the clean water sample for 6 and 8 l/min of air flow rate. The

Sauter diameter for the highest Xanthan TER concentration presented a different profile than for

the other solutions, showing a constant, or slightly decreasing, value over the air flow rate range

(ca. 0.04 mm drop). This observation can explain, along with the increase in gas hold up, the steeply

increasing kLa profile with air flow rate for the Xanthan TER solution. However, the separated

values of kL and a in Figure 38, give a more detailed picture of what process takes place in more

extent (i.e. if either the available surface area for exchange or the diffusion properties at the

interface play a more important role).

Figure 37 – Measured gas holdup (left) and Sauter diameter (right) in function of the air flow

for the different solutions.

Among the clean water and NaCl solutions the calculated a factors show no relevant difference

throughout the air flow rate range (Figure 38, right), while deriving kL puts in evidence the

remaining effects on the oxygen transfer due to gas-liquid interface characteristics (i.e. residence

time and diffusion coefficient). For the 0.05 g/l NaCl solution the kL is lower than the one in clean

water at the air flow of 2 l/min but increasing the air flow to 4 l/min the kL value reaches the one of

the respective clean water sample. (Figure 38, left). Further increasing the airflow the kL reaches its

maximum (and with it also the kLa) at 6 l/min.

Figure 38 – Effect of air flow rate on the calculated a factor (left) and on kL (right) in the different solutions.

Results

45

For the highest NaCl concentration, the kL is constantly bigger than the relative clean water value.

Moreover, although the clean water kL seems to have a descending trend (from 1.42 m/h at 2 l/min

to 1.22 m/h at 8l/min), the kL at the highest NaCl concentration seems not to be influenced by the

air flow rate and oscillates around 1.45 m/h.

The Xanthan TER solutions show higher a and lower kL relatively to the clean water and NaCl

solutions (Figure 38). The a factor for the 0.2 g/l Xanthan TER solution increases less steeply

though with respect to air flow rate than the clean water sample (Figure 38, left), but at the same

time, its kL increases with the air flow (Figure 38, right) making the resulting α value rather

constant. Increasing the viscosity to 0.8 g/l of Xanthan TER, at the air flow of 2 l/min the a factor

results lower than the relative value at lower viscosity, but still higher than the clean water sample.

Increasing the air flow the a value increases with a higher rate than for all other solutions reaching

almost the value of 17 m2/m3 of available area for mass transfer (Figure 38, left). On the other hand,

the kL also increases but is, at all the air flows, sensibly lower than the clean water sample, and

lower than the unity (Figure 38, right). This latter observation makes the a factor, i.e. the available

area for oxygen transfer, the major responsible for the steep increase in the α factor.

3.2. Full-scale aeration efficiency

In this section the results of the aeration efficiency measurements performed in the WWTP of

Eindhoven during the month of August 2012 are reported. Additionally, the off-gas measurements

were used in the plant model in WEST to monitor and improve the performances of the currently

used aeration model.

3.2.1. Off-gas measurements

The OTE performances of the summer package aeration system used in ATII of the Eindhoven

WWTP were monitored using a floating hood and an oxygen analyzer. The monitoring took place

during the month of August 2012, from the 3rd until of the 27th. The 3rd of August the hood was

placed at the beginning of the summer package. However, this was the start-up of the campaign

and, due to the testing of the equipment, the data availability is limited and not of relevant

importance. The measurements officially started the 6th of August, which is taken as day 1 of the

campaign. Table 2 summarizes the subdivision of the days along the summer package during the

measurement campaign.

Table 2 – Locations of the hood on the summer package of ATII during the off-gas

measurement campaign

Beginning Middle End

Day 1 to 5 Day 6 to 10 Day 11 to 17

During day 1 and day 2, the αSOTE fluctuated around 25% in a range of 7% maximum variation

giving an overall constant profile from the morning till the evening of both days (Figure 39). The DO

profile was also rather stable showing a slight increase towards 1 mg/l when approaching 5PM. The

same initial profile can be observed in Day 3 (Figure 39) where, before reaching 5PM the αSOTE

fluctuated around 25%. The NH4 sensor located at the end of the summer package in ATII was

Results

46

under maintenance from day 1 until day 4 and the data are only available from day 5. The increase

in air flow rate during day 3 was also observable at the plant and resulted in both the increase in

DO above 1 mg/l (around 6PM) and the relative drop in αSOTE.

Figure 39 – αSOTE and DO dynamics measured with the hood at the beginning of the summer

package. Data of NH4 (green dashed line) and air flow are obtained from the SCADA system and relative to ATII.

Higher fluctuations in both αSOTE and DO can be observed during day 4 where, for part of the

morning and the afternoon, the efficiency fluctuated around the value of 20% and even lower.

During day 5, the αSOTE profile was again rather stable, around 25%, similarly to day 1, 2 and 3.

The fluctuations observable in all the air flow profiles, with the exception of day 2, are due to a yet

not solved issue with the controllers of the aeration packages.

12PM 1PM 2PM 3PM 4PM 5PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)

time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 1

12PM 3PM 6PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)

time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)

0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 2

12PM 3PM 6PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)

time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)

0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 3

9AM 12PM 3PM 6PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)

time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)

0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 4

12PM 3PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)

time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)

0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 5

Results

47

Moving the hood to the middle of the summer package (Figure 40), clearly higher DO values can be

noticed. At the same time, although to a lower extent, also an general increase in the αSOTE profiles

of about 2 to 3% is observable. Day 6 was characterized by a slightly decreasing αSOTE due to the

gradual increase in air flow rate caused by the high NH4 levels. During day 7, the αSOTE showed a

decrease only in the second part of the afternoon when the increasing NH4 caused the controller to

increase the flow rate of air.

Figure 40 – αSOTE and DO dynamics measured with the hood in the middle of the summer

package. Data of NH4 (green dashed line) and air flow are obtained from the SCADA system and relative to ATII.

More constant, although presenting slight variations, is the αSOTE profile that was recorded on day

8. Despite the NH4 increase after 3PM, the increase in air flow rate was not yet considerable to

influence the αSOTE. A different dynamic was observed on day 9, when instead of the usual

constant or slightly decreasing αSOTE, the low NH4 allowed the controller to lower the air flow in

12PM 3PM 6PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)

time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)

0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 6

9AM 12PM 3PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)

time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)

0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 7

12PM 3PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)

time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)

0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 8

9AM 12PM 3PM 6PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)

time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 9

12PM 3PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)

time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)

0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 10

Results

48

the late afternoon in order to save energy. As a consequence, an increase in αSOTE up to 30% was

recorded. During the last day in which the middle of the summer package was monitored (day 10),

the αSOTE returned to fluctuate around the value of 25% showing a gentle decrease in the

afternoon due to the increased air flow rate generated by the rising NH4.

Figure 41 - αSOTE and DO dynamics measured with the hood at the end of the summer

package. Data of NH4 (green dashed line) and air flow are obtained from the SCADA system

and relative to ATII.

At the last location of the summer package (Figure 41), the efficiencies observed are often above

30%. During day 11, NH4 increased again in the late afternoon which increased the air flow rate and

decreased the αSOTE slightly below 30%. However, on day 11, the NH4 peak was a bit delayed as

compared to day 12 and 13 and further decreases in αSOTE might have happened. In fact, from day

11 to 15, all the variables show very similar relative and absolute behavior. Interestingly, when the

NH4 peak starts to cross the value of 1 mg/l (around 3PM), the controller of the air flow begins to

12PM 3PM 6PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)

time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)

0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 11

12PM 3PM 6PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)

0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 12

12PM 3PM 6PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)

time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)

0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 13

12PM 3PM 6PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)

time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)

0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 14

12PM 3PM 6PM0

5

10

15

20

25

30

35

40

αS

OT

E (%

)

time (hh:mm)

0

1000

2000

3000

4000

5000

6000

7000

8000

Air

flow

rate

(m3/h

)

0

1

2

3

4

5

DO

and

NH 4

(mg/

l)

Day 15

Results

49

not properly respond, and gives the high fluctuations with the extent or 2000 or 3000 m3/h in a

very short time frame. This particular behavior was observed each day from day 11 to 15 (Figure

41) and in some of the days when the hood was in the middle location (Figure 40).This behavior

might have occurred as well when the hood was located at the beginning of the summer package

but it is not possible to state this with certainty due to the lack of NH4 data.

The last two days of the campaign (day 16 and 17) the αSOTE and DO profile were monitored

continuously. Similarly to what was observed in the previous figures, the NH4 first rose in in the

afternoon of day 16 provoking once more the fluctuations in the air flow due to the controller.

However, day 16 differs from the previous days in the last location due to the higher NH4 peak (i.e.

4 mg/l) which caused the air flow to jump to 8000 m3/h. This sudden increase in air supply caused

the DO to reach 5 mg/l and the αSOTE dropped about 5%. Around 7PM of day 16 the NH4 was

completely depleted and the controller could lower the air flow rate. Interestingly, another case of

high fluctuations was registered around 9PM when the NH4 was probably not the cause. After

midnight of day 16 a long steady rain event started (2.87 cm of rain fell in the area of Eindhoven in

about 12 hours) that lasted until the end of the measurement campaign. Around 7AM of day 17 the

feedforward control of the plant detected the increase in influent flow and enhanced the air flow in

order to have enough nitrification capacity in the bioreactor. As a consequence, the relative

increase in air flow and DO and the drop in αSOTE (of about 10%) are observable in Figure 42.

Interestingly, when the peak in NH4 was almost depleted, the fluctuation in the air flow occurred

again (3PM of day 17).

Figure 42 – αSOTE and DO dynamics measured continuously at the end of the summer

package during the last two days of the campaign (i.e. from 9AM of day 16 until late night of day 17).

The αSOTE monitored along the length of the summer package showed some variability especially

due to the periods of high fluctuation caused by the controller. It can be noticed that these

fluctuations in αSOTE were more visible towards the end of the summer package. The reason for

this trend is that the fluctuations in the DO, due to the jumping air flow, are not pronounced at the

beginning of the summer package as they are towards the end. However, despite the variability in

the dataset of αSOTE, an increasing efficiency in oxygen transfer can be observed from the

12PM 3PM 6PM 9PM 12AM 3AM 6AM 9AM 12PM 3PM 6PM 9PM 12AM 3AM0

5

10

15

20

25

30

35

40

45

50

αSO

TE

(%

)

time (hh:mm)

0

1

2

3

4

5

6

DO

and

NH 4 (

mg/

l)

Day 16 and 17

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Air

flow

rat

e (m

3 /h)

Results

50

beginning towards the end of the aeration package of ATII (Figure 43). In particular, although the

standard deviation is rather wide, the beginning of the summer package shows on average about

2% and 6% less efficiency than the middle and end locations respectively.

Figure 43 – Average values of αSOTE at the three locations monitored on the summer

package of ATII

3.2.2. The Eindhoven aeration model performances on αSOTE prediction

In this section the data collected during the off-gas measurements are compared with the modeled

results of αSOTE in order to evaluate the prediction performances of the aeration model.

Additionally, an evaluation of the relative effects on the NH4 predictions is given. Figures are

presented according to the time reference used for the off-gas measurement campaign.

The model was run with the data collected by the SCADA system of the WWTP of Eindhoven during

the month of August so that the results could be compared with the off-gas measurements

performed in the same period. Unfortunately, due to a multiple sensor failure that lasted for the

whole month of July and part of August, it was only possible to run the dynamic simulations starting

from day 6 of the off-gas measurement campaign. Therefore, comparisons with the measured

αSOTE from the beginning of the summer package were not possible. As input for the steady state

simulations, average data from the months of May and June were used, hereby only selecting dry

periods of normal operation.

The modeled results are 4 to 6% lower than the measured ones when the hood was placed in the

middle of the summer package (Figure 44). During day 6, the modeled αSOTE seems to follow with

a similar slope the descending measured profile. The following day the measured αSOTE decreases

more sharply towards its end but the modeled curve seems not to follow the same behavior

although it is decreasing. An increasing measured αSOTE profile during day 8 is initially followed by

the modeled results which, however, starts to decrease earlier. The high presence of noise in the

measured αSOTE as compared to the model profile is due to the high variability of the air flow rate

which, in the case of the input data used for the model, was first filtered.

Beginning Middle End0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

αS

OT

E (

%)

Results

51

Figure 44 – Comparison between the modeled αSOTE and the αSOTE measured in the middle

of the summer package.

The profiles of measured DO from the sensor placed on the hood and from the SCADA system are

about 0.5 mg/l off (Figure 45) due to their location, the hood was placed directly on the aerated

zone while the DO sensor of ATII is located 20 m after the end of the summer package. The modeled

results follow sufficiently the profiles of measured DO only for some of the descending parts. When

the DO is increasing the predictions tend to underestimate with the exception of the last peak at the

end of day 8. The persistent difference between the modeled data and the DO from the hood is

deriving from the gap in αSOTE observed in Figure 44.

Figure 45 – Comparison between the modeled DO, the DO logged by the SCADA system and the DO sensor on the floating hood (middle of summer package).

The NH4 predictions are underestimating the actual values in almost all the cases (Figure 46).

However, the peaks in the simulation seem to gradually increase, whereas the measured NH4 peaks

maintain at a rather constant level. The sudden increase in both measured and modeled NH4 is due

to the arrival of a peak in the influent from a rain event during the night between day 8 and 9.

6 6.5 7 7.5 8 8.5 916

18

20

22

24

26

28

30

αS

OT

E (%

)

time (d)

αSOTE Modeled

αSOTE from hood

6 6.5 7 7.5 8 8.5 90

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

DO

(mg/

l)

time (d)

Modeled DODO from SCADADO from hood

Results

52

Figure 46 – Comparison between the NH4 profile modeled and the NH4 data logged by the SCADA system.

Unfortunately, day 8 was the last possible comparison with the measured data from the middle of

the summer package due to a rain event that changed the influent conditions. The model used is

only calibrated for dry weather (i.e. influent flow of about 85000 m3/d) and the rain that fell in the

night between day 8 and 9 brought the influent flow above 120000 m3/d which heavily disturbs the

predictions.

In the last location, at the end of the summer package, the recorded efficiency was the highest of the

aeration package. Therefore, when comparing the αSOTE predictions of this period with the off-gas

measurements, the gap observed reaches 8 to 12% (Figure 47). However, the profiles of both data

sets show similar trends within the days with the exception of day 12 which reports a decrease of

10% in the measured αSOTE. Looking at the general trend, the prediction of αSOTE slightly

decreases from day 10 to 15 while the measured profiles of αSOTE maintain a rather constant

trend.

Figure 47 - Comparison between the modeled αSOTE and the αSOTE measured at the end of the summer package.

Similarly to what was observed for the location in the middle of the summer package, the modeled

DO profile resembles rather well the behavior of the measured DO (Figure 48). However, the gap

between the modeled profile and the measured DO at the hood location increased with respect to

that observed in the middle of the summer package. This is due to the higher DO values at the end

6 6.5 7 7.5 8 8.5 90

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

NH

4 (m

g/l)

time (d)

Modeled NH4

NH4 from SCADA

10 11 12 13 14 15 1616

18

20

22

24

26

28

30

32

34

36

αS

OT

E (%

)

time (d)

αSOTE Modeled

αSOTE from hood

Results

53

of the aeration package, where the oxygen dissolution reaches its maximum in the tank. The peaks

of the predicted DO seem to gradually decrease while this is not the case for the measured DO.

Figure 48 - Comparison between the modeled DO, the DO logged by the SCADA system and

the DO sensor on the floating hood (end of summer package).

Also for the end of the summer package the predicted NH4 profile is initially underestimating the

real value and presents an increasing trend which was not observed in the measured NH4 profile

(Figure 49). The reason for this response of the model might be linked to the decreasing DO

predicted which is lowering the oxidation capacity of the volume.

Figure 49 - Comparison between the NH4 profile modeled and the NH4 data logged by the

SCADA system.

3.2.2.1 Predictions of DO and NH4 using full scale αSOTE measurements

The measured αSOTE during the off-gas campaign was now used as input for the CSTR representing

the summer package, to run the new predictions of DO and NH4. The simulations were initiated for

steady state with the same dataset used in the previous section. The dynamic simulations were run

in the same way as in the previous section until the measurements of αSOTE were available. From

that moment direct measurements of αSOTE could be used as input for the model. The dynamic

simulation was re-initiated with the last dynamic results and the new prediction of DO and NH4 was

performed with the measured αSOTE.

10 11 12 13 14 15 160

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

DO

(m

g/l)

time (d)

Modeled DODO from SCADADO from hood

10 11 12 13 14 15 160

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

NH

4 (m

g/l)

time (d)

Modeled NH4

NH4 from SCADA

Results

54

Running the simulation for day 6, the new DO profile, resulting from the use of the measured

αSOTE, gave an initially better prediction of the DO from the SCADA system than for the previous

simulation (Figure 50, left). However, the value of the new DO increases much faster and reaches

the values of DO measured from the hood. This higher DO concentration in the new profile results

in even lower concentrations of NH4, far below the SCADA measurements (Figure 50, right).

Figure 50 – Model performances for DO (left) and NH4 (right) predictions using αSOTE measurements as model input. Middle of summer package for day 6.

From the simulation of day 7, the new DO profile suddenly detaches from the old simulated DO

values and matches almost perfectly the measurements of the hood (Figure 51, left). Also in this

case, the high new value of DO results in a much more underestimated NH4 concentration as

compared to the old prediction (Figure 51, right).

Figure 51 - Model performances for DO (left) and NH4 (right) predictions using αSOTE

measurements as model input. Middle of summer package for day 7.

The last day of measurements on the middle location of the summer package does not show a very

close fit of the new DO profile with any of the measurements on the site (Figure 52, left). The

prediction of the new DO based on measured αSOTE falls initially in between the measurements of

6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 70

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

DO

(mg/

l)

time (d)

New DO prediction

Old DO prediction

DO from SCADA

DO from hood

6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 70

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

NH

4 (m

g/l)

time (d)

New NH4 prediction

Old NH4 prediction

NH4 from SCADA

7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 80

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

DO

(mg/

l)

time (d)

New DO prediction

Old DO prediction

DO from SCADA

DO from hood

7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 80

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

NH

4 (m

g/l)

time (d)

New NH4 prediction

Old NH4 prediction

NH4 from SCADA

Results

55

the SCADA system and the measurement from the hood, and finishes above all. Once more, given

the elevated DO, the new prediction of the NH4 profile results much lower than the measured value.

Figure 52 - Model performances for DO (left) and NH4 (right) predictions using αSOTE measurements as model input. Middle of summer package for day 8.

The simulations ran using the measured αSOTE from the end of the summer package were

performed with the same methodology. The measured αSOTE of day 11 caused the new simulated

DO to reach and surpass the DO concentrations measured with the hood, and therefore reach a

much higher value than the DO from the SCADA system (Figure 53, left). As a consequence, the new

predicted NH4 concentration is almost not visible in the graph (Figure 53, right).

Figure 53 - Model performances for DO (left) and NH4 (right) predictions using αSOTE measurements as model input. End of summer package for day 11.

Passing to the simulation of day 12, the new DO is also up to the level, or even higher than the

measurements from the hood location (Figure 54, left) and, as a result, the NH4 concentration is

also in this case extremely low (Figure 54, right).

8 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 90

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

DO

(mg/

l)

time (d)

New DO prediction

Old DO prediction

DO from SCADA

DO from hood

8 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 90

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

NH

4 (m

g/l)

time (d)

New NH4 prediction

Old NH4 prediction

NH4 from SCADA

11 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9 120

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

DO

(mg/

l)

time (d)

New DO prediction

Old DO prediction

DO from SCADA

DO from hood

11 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9 120

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

NH

4 (m

g/l)

time (d)

New NH4 prediction

Old NH4 prediction

NH4 from SCADA

Results

56

Figure 54 - Model performances for DO (left) and NH4 (right) predictions using αSOTE

measurements as model input. End of summer package for day 12.

Similarly as for the latter simulation, the new DO prediction is far above the old simulation and

even higher than the measurements at the hood location (Figure 55, left). Moreover, the new NH4

predicted profile is again merely visible (Figure 55, right)

Figure 55 - Model performances for DO (left) and NH4 (right) predictions using αSOTE measurements as model input. End of summer package for day 13.

The measured αSOTE used for the new simulation of Day 14 resulted in a considerably high initial

value of the new DO, which then descended again to meet the DO hood measurements (Figure 56,

left). The new NH4 simulation remains noticeably lower than the old NH4 simulation and the NH4

data from the SCADA system (Figure 56, right).

12 12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 12.9 130

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

DO

(mg/

l)

time (d)

New DO prediction

Old DO prediction

DO from SCADA

DO from hood

12 12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 12.9 130

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

NH

4 (mg/

l)

time (d)

New NH4 prediction

Old NH4 prediction

NH4 from SCADA

13 13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9 140

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

DO

(mg/

l)

time (d)

New DO prediction

Old DO prediction

DO from SCADA

DO from hood

13 13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9 140

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

NH

4 (m

g/l)

time (d)

New NH4 prediction

Old NH4 prediction

NH4 from SCADA

Results

57

Figure 56 - Model performances for DO (left) and NH4 (right) predictions using αSOTE

measurements as model input. End of summer package for day 14.

On day 15, although it experienced a relatively high αSOTE as compared to the other days in the

location at the end of the summer package, the new DO prediction results lower than the value

measured with the hood (Figure 57, left). At the end of the new simulation the DO profile matches

the DO data from the SCADA system. Despite the lower new predicted DO with respect to the one

measured at the hood location, the NH4 profile still tends to very low concentrations far from the

SCADA measurements.

Figure 57 - Model performances for DO (left) and NH4 (right) predictions using αSOTE measurements as model input. End of summer package for day 15.

In order to have a better fit of the results concerning NH4 prediction the relative half saturation

constant was increased from the previously calibrated value of 0.05 mg/l to the ASM2d default

value of 1 mg/l reporting the results in Figure 58.

14 14.1 14.2 14.3 14.4 14.5 14.6 14.7 14.8 14.9 150

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

DO

(mg/

l)

time (d)

New DO prediction

Old DO prediction

DO from SCADA

DO from hood

14 14.1 14.2 14.3 14.4 14.5 14.6 14.7 14.8 14.9 150

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

NH

4 (mg/

l)

time (d)

New NH4 prediction

Old NH4 prediction

NH4 from SCADA

15 15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8 15.9 160

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

DO

(mg/

l)

time (d)

New DO prediction

Old DO prediction

DO from SCADA

DO from hood

15 15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8 15.9 160

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

NH

4 (mg/

l)

time (d)

New NH4 prediction

Old NH4 prediction

NH4 from SCADA

Results

58

Figure 58 - Model performances for DO (left) and NH4 (right) with the change in half

saturation constant for NH4

6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 70

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

DO

(mg/

l)

time (d)

New DO prediction

Old DO prediction

DO from SCADA

DO from hood

6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 70

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

NH

4 (mg/

l)

time (d)

New NH4 prediction

Old NH4 prediction

NH4 from SCADA

59

4. Discussion

4.1. Bubble column tests

The use of a bubble column allowed a better understanding of fine bubble aerator performance in

different conditions of salinity and viscosity. Four air flow rates were tested using clean water, with

the addition of NaCl and with Xanthan TER. Two concentrations were used for each of the two

compounds, i.e. 0.05 and 0.1 g/l for NaCl, 0.2 and 0.8 g/l for Xanthan TER. The concentrations of

both compounds were chosen based on the available literature (Rosenberger et al., 2011) in order

to recreate, at least in part, typical conditions of wastewater and AS. The NaCl concentrations were

chosen in order to resemble typical variations of electrolyte content in wastewater with respect to

clean water, and hence their effect on the oxygen transfer. On the other hand, two Xanthan TER

concentrations were chosen to mimic the rheological features of two typical AS concentrations, i.e.

5 and 10 g MLSS/l. The separate effects of the change in electrolyte concentration and viscosity on

the bubble size and oxygen transfer could be successfully evaluated and quantified.

The use of a high speed camera made it possible to acquire a large quantity of images with a pixel

resolution of 0.095 mm. The image analysis tool developed was able to detect variations in bubble

sizes due to the effect of hydrostatic pressure and medium composition.

The addition of the higher concentration of NaCl resulted in smaller bubbles generated close to the

aerator. This was also visible for the lower concentration of NaCl but only from the 6 l/min of air

flow. However, the coalescence process seems to take place in the first 20 to 40 cm from the

diffuser and at lower depths the bubbles in the NaCl solution had a similar size distribution as to

what was observed in clean water. The effect on kLa was visible just on the kL factor and the

observations were in good agreement with the literature since the effect of electrolyte

concentrations has been seen to enhance the exchange of oxygen by lowering the surface tension

(Baz-Rodríguez et al., 2014; Ruen-ngam et al., 2008). However, for the lower NaCl solution, also the

air flow seems to increase the kLa (higher than the clean water kLa only at 6 and 8 l/min) which can

be due to the gradual decrease in bubble size, but also to enhanced turbulence (and thus increased

surface renewal) and lowered surface tension.

All the bubble size measurements in Xanthan TER solutions resulted in significantly larger bubbles

compared to the clean water case. However, the highest differences in bubble size were observed

close to the aerator for all the air flow rates tested. Values of α always lower than 1 were observed

consistently for the lowest Xanthan TER concentration which, despite the higher gas holdup with

respect to clean water, presented a low kL. This leads to the observation that, at low viscosity,

diffusion limitations are prevailing on the higher surface available for exchange and, even for higher

air flows (with which kL increases due to higher turbulence and surface renewal), the kLa remains

constantly lower than in clean water. The increase in viscosity with the highest concentration of

Xanthan TER presented a steeply increasing kLa profile over the air flow rate

Discussion

60

range which started below the clean water value and ended with the highest kLa registered in this

work. This increase can be addressed to the relative augmentation of a, and therefore in the

available surface for exchange with increasing air flow. Also the kL was observed to increase with

air flow, supposedly due to enhanced surface renewal, but remained significantly lower than the

respective clean water value suggesting that turbulence did not play a major role in the increase in

kLa. In fact, the higher concentration of Xanthan TER, despite the sensibly bigger size of the bubble

with respect to clean water, showed α values greater than one for a sufficiently high air flow but

mainly due to the considerably higher capacity in trapping the gas bubbles, i.e. increased gas

holdup.

4.2. Off-gas measurements

Measurements of oxygen transfer efficiency were performed on the ATII of the WWTP of Eindhoven

at the beginning, in the middle and at the end of the summer package using a floating hood

equipped with a DO probe and an off-gas oxygen analyser. The main findings of the campaign were

the observation of an increasing αSOTE from the beginning towards the end of the summer package

(following the flow direction) and the relatively high efficiency of this system as compared to other

similar applications. This difference in αSOTE between the locations, considering that the entire

aeration package provides homogeneously the air flow, is addressable to the DO gradient along the

aeration package (i.e. the water entering the aerated section has DO concentrations close to zero

since it is coming from the anoxic zone) and to the gradual contaminant oxidation occurring (Rosso

and Stenstrom, 2006a). Fluctuations in αSOTE due to the air flow rate were observed almost every

day of measurements in concomitance with the rise of NH4 above 1 mg/l indicating an imperfect

operation of the controller. These variations could not be really explained from the results of the

bubble column, however, tests in controlled conditions at higher flow rates than the ones tested in

the lab, could give an answer to the occurrence and extent of these changes in efficiency. These

fluctuations were more and more visible on the αSOTE profile towards the end of the summer

package since the effect of air flow on DO was more pronounced. A longer period of continuous

measurements in the last two days of the campaign, revealed that these fluctuations occurred also

in moments in which the air flow was being decreased after the occurrence of the NH4 peak. A

possible explanation could be still a problem with the air flow controller linked to a certain range of

air flow.

4.3. Aeration model of Eindhoven WWTP

The model of the WWTP of Eindhoven was used to run simulations with the data made available

from the SCADA system of the plant for the same period in which the off-gas measurements

campaign was performed. In this way the performances of the aeration model were compared with

the measurements of off-gas in terms of αSOTE. The aeration model for both middle and summer

package returned lower αSOTE and DO values than those measured from the hood. However, there

was relatively higher accuracy in terms of αSOTE in the prediction for the middle of the summer

package rather than for the end location. For this reason, the aeration model is potentially better

predicting the aeration efficiencies and DO values from the location at the beginning of the summer

Discussion

61

package. However, the lack of influent data needed to run the model for the initial period of the off-

gas measurement campaign is still an obstacle.

A reasonably accurate prediction resulted when the modeled DO was compared with the one

measured by the SCADA system. However, the modeled DO profile for the end location showed a

decreasing trend in the DO peaks and therefore a constant loss of nitrification capacity. Regarding

the precision in the NH4 prediction, the model almost always underestimated the actual value

measured by the plant sensor and the NH4 profile showed, in both middle and end locations, an

increasing trend of the peaks in time.

The αSOTE measurements represented an important source of informations on the performances

of the aeration model and were also tested as direct model input for DO and NH4 prediction. As a

consequence of the use of a higher αSOTE with respect to the previous simulation results, the DO

predictions often overestimated the SCADA measurements and went to match the measurements

from the hood. As expected the NH4 was even further depleted as compared to the previous

simulations and therefore more distant from the measured NH4. These observations might lead to

consider a re-evaluation of the model parameters linked to NH4 depletion as was illustrated by

lowering the NH4 affinity. Doing so, as observed in the last simulation shown, is probably going to

improve the predictions of the modeled DO profile in matching the measured values, since a higher

amount of DO will be used for nitrification.

62

5. Conclusions and perspectives

From the bubble column tests one important observation can be drawn which is the importance of

the presence of a dynamic α factor in an aeration model. Different compositions of the liquid

medium respond in a different manner in terms of α and therefore on the ultimate aeration

efficiency. Therefore, it is of crucial importance that a dynamic α is considered in an aeration model.

However, by nature kL and a, are influenced by both physical and chemical properties of the system,

nonetheless by hydrodynamic conditions which are heavily depending on air flow rate in the

aerated zones. Therefore, the speed with which oxygen is dissolved is depending not only on the

amount and type of electrolytes and surfactants in the liquid phase, but also on the physical

properties of the liquid, such as viscosity, and their interplay with a variable air flow rate on the

speed of oxygen dissolution.

The bubble column tests showed how both bubble size and kLa measurements are important and

complementary for a satisfying description of the oxygen transfer process. Further studies should

be performed in order to understand how the interaction between electrolyte dissolution and

viscosity resolves in the final kLa measurement. This is of crucial importance, as the establishment

of new dependencies and the development of more detailed relations can help filling the need for

better predictive models for aeration performances (and from here more precise energy demand

and carbon footprint predictions). The coalescence process was observed and monitored in the

different medium tested and its extent of occurrence could be measured revealing that viscous

mediums at low air flow present less coalescence than clean water. However, for higher flow rates

than the ones tested in this work, this latter conclusion might not be valid anymore since stronger

hydrodynamic forces can play a more important role.

The off-gas measurements were a valuable tool to gather informations on the performances of the

aeration system used in the Eindhoven WWTP. A more in depth understanding of the dynamics

occurring in the different sections of the summer package was possible and provided good insights

on the capabilities of this systems. The aeration efficiency increases along the length of the summer

package in the direction of the flow due to the gradient in DO from the beginning to the end location

but probably also to the change in wastewater characteristics (i.e. the gradual oxidation of

contaminants affecting the surface tension) (Rosso and Stenstrom, 2006a).

The αSOTE measurements from the off-gas campaign were used to evaluate the performance of the

aeration model used in the Eindhoven WWTP model. The predictions ran with the influent data

acquired by the SCADA system of the plant, resembled reasonably well the DO concentrations

measured in the aeration tank. However, comparisons with data measured from the hood (αSOTE

and DO) revealed very similar trends but with a consistent gap. The predictions of NH4

concentration in the bioreactor as compared to the actual measurements were not accurate, which

suggests the need of a revaluation of the parameters responsible for NH4 dynamics. Further off-gas

measurements should be performed again in the location at the beginning of the summer package

which might show more similar results to the aeration model output.

Conclusions and perspectives

63

Possible future improvement of the aeration model could consider the subdivision of the summer

aeration package in three tanks in series resembling more accurately the three locations monitored

at the Eindhoven WWTP and their local aeration efficiency. Moreover, a step forward in the model

development, would be represented by the inclusion of a PBM considering the evolution of bubble

size and relative available surface for exchange along the height, throughout the air flow rate range

and the influent flow rates. However, as hydrodynamics play a crucial role in the oxygen transfer

process, CFD modeling is a necessary and crucial tool to be coupled with PBM. Additionally, a more

consistent link between the influent composition and the relative effect on oxygen transfer could

also represent a turning point in the development of a sound aeration model. This work is a first

step towards this direction.

64

References

Amerlinck, Y., Flameling, T., Maere, T., Weijers, S., Nopens, I., 2013. Practical application of dynamic process models for wastewater treatment plant optimization: work in progress, in: WEFTEC. p. 5.

Amnesty Intenational, 2010. Haki zetu, ESC rights in Practice. Amnesty International Netherlands, Amsterdam.

Angelakis, A.N., Koutsoyiannis, D., Tchobanoglous, G., 2005. Urban wastewater and stormwater technologies in ancient Greece. Water Res. 39, 210–20.

APHA, AWWA, WEF, 2012. Standard Methods for the Examination of Water & Wastewater. American Public Health Association, New York.

ASCE, 1997. ASCE Standard: Standard Guidelines for In-Process Oxygen Transfer Testing (18-96). ASCE, New York.

Barnard, J.L., 1973. Biological nutrient removal without the addition of chemicals. Water Res. 9, 485–490.

Baz-Rodríguez, S. a., Botello-Alvarez, J.E., Estrada-Baltazar, A., Vilchiz-Bravo, L.E., Padilla-Medina, J. a., Miranda-López, R., 2014. Effect of electrolytes in aqueous solutions on oxygen transfer in gas–liquid bubble columns. Chem. Eng. Res. Des. In press.

Benedetti, L., Bixio, D., Claeys, F., Vanrolleghem, P. a., 2008. Tools to support a model-based methodology for emission/immission and benefit/cost/risk analysis of wastewater systems that considers uncertainty. Environ. Model. Softw. 23, 1082–1091.

Bosma, A., Dalstra, G., Verhoeven, M., Reitsma, B., 2007. Meten en modelleren van het stromingsgedrag in de actief slibtanks van de rwzi Eindhoven met CFD. Afvalwaterwetenschap 6, 115–128.

Canny, J., 1986. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–98.

Capela, S., Heduit, A., Roustan, M., 2004. Influence of water depth on fine bubble aeration efficiency in the presence of surfactants, in: Preprints of the 3rd IWA World Water Congress.

De Pauw, D., 2005. Optimal experimental design for calibration of bioprocess models: a validated software toolbox. Ghent University.

Dochain, D., Vanrolleghem, P.A., 2001. Dynamical modelling and estimation in wastewater treatment processes. IWA Publishing.

References

65

Fabiyi, M.E., Novak, R., 2008. Evaluation of the factors that impact succesful membrane biological reactor operation at high solids concentration. Membr. Technol. 2008, 503–512.

Ferri, J., Stebe, K., 2000. Which surfactants reduce surface tension faster? A scaling argument for diffusion-controlled adsorption, Advances in colloid and interface science.

Ferriman, A., 2007. BMJ readers choose sanitation as greatest medical advance since 1840. Br. Med. J. 334, 111.

Garner, F.H., Hammerton, D., 1954. Circulation inside gas bubbles. Chem. Eng. Sci. 3, 1–11.

Gernaey, K. V., Jørgensen, S.B., 2004. Benchmarking combined biological phosphorus and nitrogen removal wastewater treatment processes. Control Eng. Pract. 12, 357–373.

Gillot, S., Capela-Marsal, S., Roustan, M., Héduit, a, 2005. Predicting oxygen transfer of fine bubble diffused aeration systems--model issued from dimensional analysis. Water Res. 39, 1379–87.

Gillot, S., Héduit, a, 2008. Prediction of alpha factor values for fine pore aeration systems. Water Sci. Technol. 57, 1265–9.

Gillot, S., Heduit, A., 2000. Effect of air flow rate on oxygen transfer in an oxidation ditch equipped with fine bubble diffusers and slow speed mixers. Water Res. 34, 1756–1762.

Groves, K.P., Daigger, G.T., Simpkin, T.J., Redmon, D.T., Ewing, L., 1992. Evaluation of oxygen transfer efficiency and apha-factor on a variety of diffused aeration systems. Water Environ. Res. 64, 691–698.

Hebrard, G., Destrac, P., Roustan, M., Huyard, A., Audic, J.M., 2000. Determination of the water quality correction factor α using a tracer gas method. Water Res. 34, 684–689.

Henze, M., Gujer, W., Mino, T., van Loosdrecht, M., 2000. Activated sludge models ASM1, ASM2, ASM2d and ASM3. IWA Publishing.

Henze, M., van Loosdrecht, M.C.M., Ekama, G.A., Brdjanovic, D., 2008. Biological Wastewater Treatment. IWA Publishing.

Hutton, G. (World H.O., Haller, L. (World H.O., 2004. Evaluation of the Costs and Benefits of Water and Sanitation Improvements at the Global Level.

Iranpour, R., Magallanes, A., Zermen, M., Varsh, V., Abrishamchi, A., Stenstrom, M.K., 2000. Assessment of aeration basin performance efficiency: sampling methods and tank coverage. Water Res. 34, 3137–3152.

Jin, B., Yin, P., Lant, P., 2006. Hydrodynamics and mass transfer coefficient in three-phase air-lift reactors containing activated sludge. Chem. Eng. Process. Process Intensif. 45, 608–617.

References

66

Lessard, R.R., Zieminski, S.A., 1971. Bubble coalescence and gas transfer in aqueous electrolytic solutions. Ind. Eng. Chem. Fundam. 10, 260–269.

Leu, S.-Y., Rosso, D., Larson, L.E., Stenstrom, M.K., 2009. Real-Time aeration efficiency monitoring in the activated sludge process and methods to reduce energy consumption and operating costs. Water Environ. Res. 81, 2471–2481.

Lofrano, G., Brown, J., 2010. Wastewater management through the ages: a history of mankind. Sci. Total Environ. 408, 5254–64.

McCarty, P.L., 1964. Thermodynamics of biological synthesis and growth, in: Procs. 2nd Int. Conf. on Water Pollution Control. pp. 169–199.

Monod, J., 1950. Technique for continuous culture - theory and application, Ann. Inst. Pasteur.

Phelps, E.B., 1944. Stream sanitation, John Wiley and Sons Inc.

Pittoors, E., Guo, Y., Van Hulle, S.W.H., 2014. Oxygen transfer model development based on activated sludge and clean water in diffused aerated cylindrical tanks. Chem. Eng. J. 243, 51–59.

Ratkovich, N., Horn, W., Helmus, F.P., Rosenberger, S., Naessens, W., Nopens, I., Bentzen, T.R., 2013. Activated sludge rheology: a critical review on data collection and modelling. Water Res. 47, 463–82.

Reardon, D.J., 1995. Turning down the power. Civ. Eng. 65, 54–56.

Redmon, D.T., Boyle, W.C., Ewing, L., 1983. Oxygen transfer efficiency measurements in mixed liquor using off-gas techniques. Water Pollut. Control Fed. 55, 1338–1347.

Ribeiro, C.P., Lage, P.L.C., 2004. Experimental study on bubble size distributions in a direct-contact evaporator. Brazilian J. Chem. Eng. 21, 69–81.

Ribeiro, C.P., Mewes, D., 2006. On the effect of liquid temperature upon bubble coalescence. Chem. Eng. Sci. 61, 5704–5716.

Rieth, M.G., Chiesa, S.C., Polta, R.C., 1995. Effects of operational variables on the oxygen transfer performance of ceramic diffusers. Water Environ. Res. 67, 781–787.

Rosenberger, S., Helmus, F.P., Krause, S., Bareth, a, Meyer-Blumenroth, U., 2011. Principles of an enhanced MBR-process with mechanical cleaning. Water Sci. Technol. 64, 1951–8.

Rosenberger, S., Kubin, K., Kraume, M., 2002. Rheology of Activated Sludge in Membrane Bioreactors. Eng. Life Sci. 2, 269–275.

Rosso, D., Iranpour, R., Stenstrom, M.K., 2005. Fifteen years of off-gas tranfer efficiency measurements on fine pore aerators: key role of sludge age and normalized air flux. Water Environ. Res. 77, 266–273.

References

67

Rosso, D., Stenstrom, M.K., 2006a. Surfactant effects on alpha-factors in aeration systems. Water Res. 40, 1397–404.

Rosso, D., Stenstrom, M.K., 2006b. Economic Implications of Fine-Pore Diffuser Aging. Water Environ. Res. 78, 810–815.

Ruen-ngam, D., Wongsuchoto, P., Limpanuphap, A., Charinpanitkul, T., Pavasant, P., 2008. Influence of salinity on bubble size distribution and gas–liquid mass transfer in airlift contactors. Chem. Eng. J. 141, 222–232.

Stenstrom, M.K., Gilbert, R.G., 1981. Effects of alpha, beta and theta factor upon the design, specification and operation of aeration systems. Water Res. 15, 643–654.

Stenstrom, M.K., Leu, S.-Y. (Ben), Jiang, P., 2006. Theory to Practice: Oxygen Transfer and the New ASCE Standard, in: WEFTEC. pp. 4838–4852.

Tchobanoglous, G., Burton, F.L., Stensel, H.D., 2003. Wastewater Engineering: Treatment and Reuse, Engineering, McGraw-Hill series in civil and environmental engineering. McGraw-Hill.

Vogelaar, J.C.T., Klapwijk, A.M., Van Lier, J.B., Rulkens, W.H., 2000. Temperature effects on the oxygen transfer rate between 20 and 55°C. Water Res. 34, 1037 – 1041.

Wagner, M., 1999. Factor influencing the magnitude of alpha-values of fine bubble aeration systems, in: WEFTEC.

Wagner, M., Cornel, P., Krause, S., 2002. Efficiency of different aeration systems in full scale membrane bioreactors, in: WEFTEC. pp. 434–443.

Wang, T., 2011. Simulation of bubble column reactors using CFD coupled with a population balance model. Front. Chem. Sci. Eng. 5, 162–172.

Wang, T., Wang, J., 2007. Numerical simulations of gas–liquid mass transfer in bubble columns with a CFD–PBM coupled model. Chem. Eng. Sci. 62, 7107–7118.

WEF, 2010. Design of Municipal Wastewater Treatment Plants. WEF Press, McGraw-Hill Professional, and ASCE.

Wentzel, M.C., Comeau, Y., Ekama, G.A., van Loosdrecht, M.C.M., Brdjanovic, D., 2008. Enhanced biological phosphorus removal, Biological waste water treatment. Principles, modelling and design. IWA Publ. 155–170.

WHO and UNICEF, 2013. Progress on sanitation and drinking-water.

Xing, C., Wang, T., Wang, J., 2013. Experimental study and numerical simulation with a coupled CFD–PBM model of the effect of liquid viscosity in a bubble column. Chem. Eng. Sci. 95, 313–322.