Faculty of Bioscience Engineering
Academic year 2015 – 2016
Off-gas O2 and CO2 monitoring to assess activated sludge
performance
Andreas Vromant
Promoter:
Prof. dr. ir. Eveline Volcke
Tutor:
Hong Quan Le
Master’s dissertation submitted in partial fulfillment of the requirements for the
degree of Master of Science in Bioscience Engineering: Chemistry and
Bioprocess Technology
The author and the promoters give the authorization to consult and to copy parts of this work for personal use
only. Every other use is subject to the copyright laws, more specifically the source must be extensively
specified when using from this thesis. Permission to reproduce any materials contained in this work should be
obtained from the author.
Author
Andreas Vromant
Promoter
Prof. dr. ir. Eveline Volcke
Ackowledgments
Without a doubt, completing my Master’s thesis has been the most intense study activity I have lived through.
Not only has completing this dissertation provided me an insight in what it takes to perform scientific research,
this experience has also given me the opportunity to learn much about myself good and bad. Completing this
thesis wouldn’t have been possible without the much appreciated support and help by others.
It is a genuine pleasure to give my gratitude to my promoter, Prof. Eveline Volcke. Thank you, Professor Volcke
for your endless enthusiasm, your never-ending support, and your willingness to push me to keep doing better.
Your candor and personality have really impressed me.
My sincere thanks also go out to my tutor throughout this process: Hong Quan Le. Quan, my utmost and
greatest of thanks for always supporting me and help me out whenever I needed it. To provide interesting
discussions, which really helped me get a hold on my thesis subject.
The colleagues at the faculty also have my thanks, for providing relieve and understanding when needed.
Last but not least, my thanks go out to my family. To my parents who have given me the chance to complete
these studies. To my brother and my sister who have always supported me. And to my girlfriend, Alex, who has
really supported me with her sunshine-like personality.
Andreas Vromant
Ghent, 03 June 2016
List of abbreviations
ASM
Activated sludge model
BOD
Biological oxygen demand
BSM
Benchmark simulation model
COD
Chemical oxygen demand
CPR
Carbon dioxide production rate
CTR
Carbon dioxide transfer rate
GHG
Greenhouse gas
OTR
Oxygen transfer rate
OUR
Oxygen uptake rate
Q
Flow rate
RQ
Respiratory coefficient (ratio of CO2 over O2 gas phase components)
SRT
Sludge retention time
TOC
Total organic carbon
VSS
Volatile suspended solids
WWTP
Wastewater treatment plant
Summary
The activated sludge systems is a state-of-the art wastewater treatment process. There’s a general
trend that demands on water quality become more and more stringent, requiring more advanced
treatment systems able to comply with tighter standards. The introduction of new and alternative online methods may offer a solution to this posed problem. Most monitoring of activated sludge occurs in
the liquid phase, with the exception of oxygen transfer and more recently the increasing interest in
monitoring for greenhouse gasses. With the exception of the latter two little attention has been given
to the potential of off-gas to monitor a WTTP for performance assessment. Especially the monitoring
of off-gas CO2 is not practiced often. The reason for this, is that the bicarbonate equilibrium of CO2
complicates straightforward off-gas monitoring.
In this thesis, a previously calibrated model was reconstructed to assess the performance of an
activated sludge process through simulation of monitoring the off-gas O2 and CO2. A calibrated model
provided by Leu et al. (2010) was taken as a starting point and subsequently adapted to be applied for
different case-studies. Adaptations account for influence of seasonal variations, influence of pH,
influence of COD/TOC on OTR (oxygen transfer rate) and CTR (carbon dioxide transfer rate), and the
estimation of the biomass production rate through OTR and CTR monitoring. The objective of this
thesis was to evaluate certain theoretical postulates proposed, such as: the potential of off-gas O2 and
CO2 to estimate the COD/TOC ratio, and the potential to estimate biomass production rate through
off-gas O2 and CO2 monitoring.
It was confirmed that the O2 and CO2 gas phase composition can be successfully modelled and can
be used in combination to predict ammonia discharge.
Variable influent pH causes an overestimation of CTR for a lower pH of the influent compared to the
pH at which the activated sludge process is controlled, whereas the reverse is true for a higher pH
influent. Additionally, it was found that the alkalinity in the form of bicarbonate influences the
magnitude of the CTR significantly. If a WTTP is controlled at constant pH, the CTR can be
successfully simulated. The simulated model was found to be applicable for high alkalinity wastewater
plants, or WTTP with low ammonia concentration.
The average off-gas emission is relatively constant throughout the year, this is because the SRT of a
WTTP is varied throughout the year to keep the WTTP performance optimal.
COD/TOC ratio, which is related to the substrate affinity, has a significant influence on the OTR/CTR
ratio, much more than the COD/N ratio. As such, it seems feasible to use O 2/CO2 measurements for
determining on-line COD/TOC ratios of the substrate. Additionally, the variance of the OTR/CTR in a
dynamic simulation is an indication for the substrate affinity. The higher the substrate affinity, the more
the OTR/CTR ratio fluctuates.
When OTR are CTR are monitored separately, it seems feasible that the biomass production rate can
be monitored. To monitor the biomass production rate, the knowledge of the gas flow rate is a
requirement. This is not a requirement when monitoring the OTR/CTR ratio. CTR showed the same
trend as the biomass production rate on a consistent basis. OTR is a measure for the total microbial
activity.
Table of contents
1.
INTRODUCTION ................................................................................................................................. 1
Introduction .............................................................................................................................. 1
Thesis outline........................................................................................................................... 2
2.
LITERATURE REVIEW ....................................................................................................................... 3
Biological wastewater treatment .............................................................................................. 3
Nitrification and denitrification ................................................................................................... 3
Off-gas ..................................................................................................................................... 4
Off-gas definition....................................................................................................................... 4
Off-gas composition .................................................................................................................. 4
Off-gas measurement for process monitoring ......................................................................... 5
Oxygen transfer ........................................................................................................................ 5
Greenhouse gas emissions ...................................................................................................... 5
Monitoring CO2 off-gas ............................................................................................................. 6
Influence of microbial activity on off-gas O2 and CO2 composition ......................................... 7
How to measure off-gas .......................................................................................................... 7
The off-gas method................................................................................................................... 8
Gas transfer .............................................................................................................................. 8
Carbonate/bicarbonate equilibrium ........................................................................................... 9
Sampling location ................................................................................................................... 11
Sampling time ......................................................................................................................... 11
Thesis objectives ................................................................................................................... 12
3.
MATERIALS AND METHODS ..........................................................................................................13
Model setup ........................................................................................................................... 13
Overview ................................................................................................................................. 13
Modelling biological processes ............................................................................................... 13
Modelling gas transfer ............................................................................................................ 15
Modelling bicarbonate equilibrium .......................................................................................... 15
Reactor setup ........................................................................................................................ 16
Simulation .............................................................................................................................. 17
Mass balance of dissolved oxygen ......................................................................................... 17
Mass balance of carbon dioxide ............................................................................................. 17
Model validation ..................................................................................................................... 22
Model application ................................................................................................................... 23
Seasonal variation of CTR:OTR ratio ..................................................................................... 23
Influence of pH ........................................................................................................................ 23
Influence of COD/TOC ratio on OTR/CTR ratio ..................................................................... 24
Estimation of the biomass production rate by CO2 and O2 measurements............................ 26
RESULTS AND DISCUSSION ..........................................................................................................27
4.1
Model evaluation.................................................................................................................... 27
4.1.1
Steady-state analysis.............................................................................................................. 27
4.1.2
Dynamic simulation................................................................................................................. 28
4.2
Effect of process conditions on OTR/CTR ............................................................................ 31
4.2.1
Effect of pH ............................................................................................................................. 31
4.2.2
Seasonal trends of OTR and CTR.......................................................................................... 32
4.2.3
Influence of COD/TOC ratio on OTR/CTR ratio ..................................................................... 33
4.2.4
Estimation of biomass production rate ................................................................................... 36
5.
GENERAL CONCLUSIONS AND PERSPECTIVES ........................................................................39
6.
REFERENCE LIST ............................................................................................................................41
7.
APPENDIX………………………………………………………………………………………………45
1. INTRODUCTION
Introduction
The aim of wastewater treatment is to convert wastewater into an effluent that can be returned to the
water cycle with minimal environmental issues. One way to quantify the environmental impact of a
wastewater treatment plant (WTTP) is by comparing the physical and chemical parameters of the
effluent to the standards described in the appropriate regulations, such as the 91-271 EEC directive.
Traditional quality parameters for a WTTP are Biological Oxygen Demand (BOD), Chemical Oxygen
Demand (COD), Total Organic Carbon (TOC), and Total Suspended Solids (TSS) (Bourgeois et al.,
2001). In the last few years, more specific parameters, such as total nitrogen, total phosphorus,
polycyclic aromatic hydrocarbons, etc., and a list of dangerous substances have appeared, e.g. in the
context of the Water Framework Directive (2000/60/EC). Overall, there’s a general trend that demands
on water quality become more and more stringent, requiring more advanced treatment systems able to
comply with tighter standards not only for organic carbon, but also for nitrogen and phosphorus
nutrient levels (Vanrolleghem et al., 2003).
The introduction of new and alternative on-line methods may offer a solution to this posed problem.
The development of such methods has been complicated due to the harsh environment and problems
such as fouling, reproducibility and reliability of the available sensors arise in such conditions.
Monitoring of traditional quality parameters have been designed as off-line methods (Bourgeois et al.,
2001). Other parameters such as nitrogen and phosphorus nutrient levels can be measured on-line by
placing sensors in the liquid phase (Vanrolleghem et al., 2003). However, with the exception of
measurement of oxygen transfer rate (OTR), monitoring of greenhouse gasses (GHG) and some
case-studies monitoring CO2 gas phase emissions (Hellinga et al., 1996; Weissenbacher et al., 2007;
Leu et al., 2010; Guz et al., 2015) little attention has been given to the potential of off-gas to monitor a
WTTP for performance assessment.
Off-gas analysis has many advantages over measurements in the liquid phase towards overall
performance estimation of the reactor, pre-treatment of the samples, and costs for maintenance of the
sensors (Hellinga et al., 1996; Schuchardt et al., 2005). Literature suggests that monitoring off-gas
O2/CO2 to assess the performance of WTTP, and more specifically activated sludge, is an interesting
method to supplement the activated sludge processes. This thesis will investigate certain postulates
1
made by previous authors concerning the monitoring of off-gas O2 and CO2 in relation to performance
assessment, in order to gain insight in the possibilities of combined monitoring of these off-gasses.
Thesis outline
In this thesis, a previously calibrated model was reconstructed to assess the performance of an
activated sludge process through simulation of monitoring the off-gas O2 and CO2.
Chapter 2 introduces the reader to off-gas related to biological wastewater treatment. A general
composition of off-gas in the activated sludge processes is discussed and some applications of
monitoring certain substances comprising this off-gas are given, more specifically: oxygen transfer,
GHG monitoring, and lastly some case-studies are presented concerning the monitoring of off-gas
CO2. An explanation follows how the microbial activity relates to the off-gas composition of O2 and
CO2. Additionally, the off-gas measuring procedure is explained with its related implications and
obstacles, such as: gas transfer, bicarbonate equilibrium, and sample location and time. This chapter
is concluded by the thesis objectives.
Chapter 3 describes the mathematical model used for simulation. A calibrated model provided by Leu
et al. (2010) was taken as a starting point and subsequently adapted to be applied for different casestudies. Adaptations account for influence of seasonal variations, influence of pH, influence of
COD/TOC on OTR and CTR, and the estimation of the biomass production rate through OTR and
CTR monitoring.
Chapter 4 evaluates the constructed model and discusses the case-studies that are set up in
Materials and methods (Chapter 3).
Chapter 5 closes this thesis with conclusions and suggestions for future research.
2
2. LITERATURE REVIEW
Biological wastewater treatment
Conventional wastewater treatment consists of a combination of physical, chemical, and biological
processes and operations to remove solids, organic matter and nutrients from wastewater. The
activated sludge process, an aerobic treatment process with suspended biomass, is the most
commonly applied process for treating municipal or industrial wastewater. Other traditional biological
treatment processes concern trickling filters or biofilters, and rotating biological contactors (RBC).
Usually biological treatment processes are preceded by primary treatment for the removal of coarse
solids and other large materials, and the removal of settleable or floating organic and inorganic solids
by sedimentation, skimming or other physical operations. The conventional activated sludge process
consists of an aeration tank where microbial activity ensures the biological degradation of the organic
impurities, followed by a secondary clarifier. The types of bacteria or microorganisms that are involved
in the degradation of organic impurities in a given wastewater strongly depend on the operating
conditions of the bioreactor. As such, the presence of oxygen has a strong influence on the type of
microorganisms that are active. Aerobic treatment processes use oxygen to assimilate organic
impurities i.e. convert them in to carbon dioxide, water and biomass.
Nitrification and denitrification
Nowadays state of the art, nitrification/denitrification (NDN) processes are the most widely used
technique to remove nitrogenous pollutants from municipal wastewater. Where nitrification is an
aerobic treatment process that happens simultaneously in the aeration tank with the degradation of
other organic compounds, denitrification is an anaerobic process. Nitrification is the biological
oxidation of ammonia or ammonium to nitrite (NO2-) followed by the oxidation of the nitrite to nitrate
(NO3-). Denitrification is an anaerobic process of nitrate reduction that may ultimately produce
molecular nitrogen gas (N2). Anaerobic treatment processes take place in the absence of oxygen by
those microorganisms (also called anaerobes) which do not require oxygen to assimilate organic
impurities. The final products of organic assimilation in anaerobic treatment are methane and carbon
dioxide and biomass. Both carbon dioxide and methane can occur in the gas phase. The conversion
from NO3- and NO2- to N2 occurs over several reactions and several intermediate products, namely
N2O and NO are also formed during NDN. If the biological nitrogen conversion is incomplete then
nitrous oxide can escape the wastewater treatment plant (Kampschreur et al., 2009).
3
Off-gas
Off-gas definition
Off-gas is defined as a gas that exits an industrial process or chemical process as a byproduct. This
off-gas can either be produced during the process, or can be a residual gas used for process
operation. In this case the process discussed is the activated sludge NDN process . The composition
of off-gas depends mainly on the process at hand.
Off-gas composition
As mentioned in section 2.1, microbial activity (either aerobe or anaerobe) consumes oxygen and
causes the production of gaseous compounds, such as carbon dioxide, methane and nitrogen gas.
These however, are not the only gaseous compounds present in off-gas. The composition of the offgas of a WTTP is diverse and consists of many different substances. An outline of the possible
substances present in off-gas is discussed below.
During aerobic treatment, the bioreactor is aerated which causes gaseous oxygen to dissolve in the
liquid phase. The oxygen present in the water is consumed by the aerobes, to obtain carbonaceous
substrate oxidation and nitrification. The residual oxygen gas that is still present after microbial
consumption is present in the off-gas.
In the previous section the production of methane, carbon dioxide and nitrous oxide was mentioned.
These are all greenhouse gasses (GHG). The importance for monitoring these gasses will be
discussed in the following section.
The management and operation of WTTP usually involve the release into the atmosphere of
malodorous substances with the potential to reduce the quality of people living nearby. Some of the
major odor causing chemicals are sulfide containing substances such hydrogen sulfide (H2S) and
dimethyl sulfide (CH3)2S, and nitrogen containing substances such as skatole (C 9H9N) or dimethyl
amine (C2H7N). The major odor causing substance of concern is H 2S, which has the distinct smell of
rotten eggs.
From the chemicals listed above in this section it can be seen that off-gas from WTTP can range from
substances that have operational value such as the monitoring of oxygen for aeration efficiency, to
greenhouse gasses, to malodorous substances such as H2S or volatile organic compounds (VOC).
4
Off-gas measurement for process monitoring
Off-gas analysis has many advantages over measurements in the liquid phase, e.g. easier sample
pre-treatment, no chemical reagents, lower investment costs and less maintenance (Schuchardt et al.,
2005). Monitoring off-gas for activated sludge process assessment is thus an interesting addition to
liquid phase monitoring.
Oxygen transfer
The off-gas method has its origin as a method for measuring oxygen transfer in subsurface aeration
systems. Oxygen transfer is an important part of wastewater treatment and accounts for as much as
60% of the energy consumption for the activated sludge process (Stenstorm et al., 2006). Since its
development (Redmon et al., 1983), the method has helped increase accuracy and precision in
designing and quantifying aeration systems, improving energy efficiency of the WTTP significantly
(Rosso et al., 2005). The off-gas method has been extensively used to monitor aeration efficiency,
oxygen uptake rate (OUR), dissolved oxygen (DO) and has become the method of choice for
measuring oxygen transfer (Rosso et al., 2005; Schuchardt et al., 2005; Stenstorm et al., 2006).
Greenhouse gas emissions
Together with N2O and methane, carbon dioxide is a greenhouse gas. Carbon dioxide that is emitted
directly during the microbial conversion of organic matter is short-cycle carbon, and therefore it does
not contribute to the increased carbon dioxide concentrations in the atmosphere. Wastewater
treatment also consumes fossil-fuel derived energy and synthetic chemicals, with indirect emission of
carbon dioxide emissions as a consequence. The processes leading to the indirect emission of carbon
dioxide are well understood, and the magnitude of the emission can be estimated by life cycle
assessment (Daelman, 2014). Nitrous oxide and methane however are GHG’s with a relative potency
of respectively 300 CO2-equivalents and 25 CO2-equivalents (IPCC, 2007). In the context of the rising
concern for greenhouse gas emissions, measuring the off-gas methane and nitrous oxide provides a
framework for the estimation of the anthropogenic emissions from WTTP. The concern for these offgasses also fuel the search for innovative ways for off-gas monitoring, such as a novel method for
monitoring N2O developed by Mampaey et al. (2015). The method can also be applied to measure
other dissolved gasses, such as methane.
5
Monitoring CO2 off-gas
In contrast to the wide spread measurement of oxygen off-gas, and the rising interest in monitoring
greenhouse gas emissions, measurement of gaseous carbon dioxide concentrations are not widely
practiced. For carbon dioxide gas phase measurement, the main reason for it not being widespread, is
that CO2 is only partially transferred to the gas phase due to microbial activity, the influence of pH and
the carbonate/bicarbonate equilibrium. However, some case-studies suggested that monitoring CO2
off-gas can be used to assess the performance of the activated sludge process.
Weissenbacher et al. (2007) successfully demonstrated a practical method to correct measured offgas CO2 as an indicator of biological activity by taking into account pH shifts and changes in influent
alkalinity, thus effectively accounting for the influence of the bicarbonate system on the liquid-gas
transfer of CO2. Especially under limited oxygen conditions like under simultaneous nitrificationdenitrification (SND) where respirometric measurements are not applicable.
Leu et al. (2010) demonstrated the possibility to predict nitrification performance in activated sludge
processes by monitoring off-gas O2/CO2. Since carbon dioxide is produced by carbonaceous oxidizing
organism and not by nitrifiers (see next section), it is possible to use the off-gas carbon dioxide mole
fraction to estimate nitrification performance independently of the oxygen uptake rate (OUR). This
paper used off-gas data with a dynamic model to estimate nitrifying efficiency for various activated
sludge process conditions. The relationship among nitrification, oxygen transfer, carbon dioxide
production, and pH change was also investigated. The results of this study showed measurable
differences in OUR and carbon dioxide transfer rate (CTR) and the simulations successfully predicted
the effluent ammonia by using the measured CO 2 and O2 contents in off-gas as input signal. Carbon
dioxide in the off-gas thus proved as a useful technique to monitor nitrification rate.
Hellinga et al. (1996) described a theoretical model by which continuous CO 2 and O2 measurements in
the exhaust gas of WTTP had been simulated to study their significance for fast process monitoring.
The simulations show that exhaust gas phase concentration measurements of CO2 and O2 can be
used for various purposes. These measurements are sufficient to calculate an observed respiratory
coefficient (RQ value) that is primarily indicative of the COD/TOC ratio of the converted substrate. If
oxygen measurements are combined with gas flow measurements, (rapid changes in) overall
oxidation activity can be monitored on-line. If N-removal (nitrification-denitrification) is determined,
6
biomass production can be estimated on-line using these gas measurements, which may be beneficial
for sludge level control.
Influence of microbial activity on off-gas O2 and CO2 composition
During the activated sludge process microorganisms assimilate organic matter and either use oxygen
or another electron acceptor to convert it mainly into carbon dioxide or other smaller inorganic
compounds, biomass and water. In this section the microbial reactions for carbon removal and
nitrogen removal illustrate their influence on CO2.
During aerobic treatment the following reaction denotes the carbon removal by heterotrophic bacteria:
1 𝐶𝐻1.6 𝑂0.3 + 0.0498 𝑁𝐻3 + 0.989𝑂2 → 0.249 𝐶𝐻1.8 𝑂0.5 𝑁0.2 + 0.751 𝐶𝑂2 + 0.651 𝐻2 𝑂
(1.1)
In order to clearly separate the COD from the nitrogen load, the total Kjeldahl nitrogen (TKN) is here
assumed to be present as 𝑁𝐻3 . The stoichiometric coefficients in the formula above were calculated
by Hellinga et al. (1996).
During nitrification a part of the carbon dioxide is removed along with oxygen by autotrophic bacteria
according to the following equation (Hellinga et al., 1996):
1 𝑁𝐻3 + 0.098 𝐶𝑂2 + 1.858 𝑂2 → 0.098 𝐶𝐻1.8 𝑂0.5 𝑁0.2 + 0.980 𝐻𝑁𝑂3 + 0.922 𝐻2 𝑂
(1.2)
Carbon removal also occurs during denitrification by heterotrophic bacteria. However, as the
denitrification process is an anoxic process, NO3- and NO2- is used as an electron acceptor.
The consumption of oxygen and production of carbon dioxide, these being soluble gaseous
compounds, is a first indication that microbial activity has an effect on the exhaust gas composition
from a wastewater treatment plant.
How to measure off-gas
Like any form of monitoring, off-gas detection has some technical aspects that should be explained in
order to gain insight in its mechanism and general working. In this section the original off-gas method
is discussed and some technical characteristics of the method are discussed.
7
The off-gas method
The major components of the original off-gas monitoring system include: a hood to capture the gas, a
hose to conduct the gas to, an analytical circuit for monitoring off-gas composition, temperature,
pressure, and volumetric gas flow rate, and a vacuum source to draw the gas from the hood through
the analytical circuit. (Redmon et al. 1983). An outline of the general installment can be seen in the
figure below.
A number of instruments that can are used for the measurement of gas-phase
components include: paramagnetic analyzers, infrared absorption photometers, gas chromatographs,
mass spectrometers, flame ionization detectors, and amperometric and potentiometric sensors
(Heinzle et al., 1990). The off-gas analyzer is commonly used to measure only oxygen mole fraction,
but can be used to measure other gas fractions (nitrogen, carbon dioxide, water vapor, volatile organic
chemicals). The CO2 mole fraction can be easily measured if an additional analyzer, such as a CO2
absorption tube or infrared sensor is used (Leu et al., 2010).
Figure 2.1 Off-gas test equipment, showing hood, analyzer, DO meter and aeration tank (Stenstrom et
al., 2006).
Gas transfer
It is not straightforward to predict the corresponding concentration in the liquid phase through gas
phase measurements which, after all, represents the environment of the organisms. One of the causes
of discrepancy between the liquid and gas phase concentration of a gas is that the molecule of interest
should transfer from one phase to another. The classic theory of gas transfer considers the movement
of gas from sparged gas to the biomass via dissolution into the bulk liquid. The dissolved gas deficit
between the two phases provides the driving force for transfer. The most commonly used theory for
8
gas transfer dynamics is the two film theory, proposed by Lewis and Whitman (1924). The mass
transfer dynamics can be expressed by the following equation:
𝑑𝐶
= 𝛼𝐾𝐿 𝑎(𝛽𝐶 ∗ − 𝐶𝑙 ) − 𝑟𝑚
𝑑𝑡
𝐾𝐿 𝑎 = the mass transfer coefficient (‘a’ is the interfacial area per unit volume and ‘K L’ the liquid phase
mass transfer coefficient for oxygen); 𝐶 ∗ , 𝐶𝑙 = the saturation and actual bulk liquid concentrations of the
gas of interest; 𝛼 = the ‘alpha factor’ = ratio of process water 𝐾𝐿 𝑎 to the clean water 𝐾𝐿 𝑎 for a given
system; 𝛽 = ratio of process water 𝐶 ∗ to the clean water 𝐶 ∗ for a given system, usually close to 1 for
municipal waters (WPCF, 1988); 𝑟𝑚 = the continuous removal of the gas from the liquid by the microorganisms (Harris et al., 1996).
The saturation concentration 𝐶 ∗ can be calculated using Henry’s law:
𝐶 ∗ = 𝐾𝐻 𝑝𝑔
Where 𝐾𝐻 and 𝑝𝑔 equals Henry’s constant and the gaseous partial pressure of the gas of interest
respectively. Also the 𝐾𝐿 𝑎-value is dependent on the nature of the substance: the ratio of two 𝐾𝐿 𝑎values for two different substances equals the square root of the ratio of the diffusion coefficients of
those substances (Noorman et al., 1994).
Pauss and Guiot (1993) observed that, in the case of hydrogen, hydrodynamic conditions of the liquid
can have a great effect on the concentration discrepancy between the two phases. Estimation of gas
transfer rate is not trivial, however, being a function of basin geometry, aerator type (in the case of
oxygen), gas flow rate (power input when aeration is present), wastewater characteristics, process
conditions, temperature, and diffuser clogging (in case of aeration) (Boyle et al., 1989). Therefore, gas
composition measurements should only be used with great care, especially under dynamic conditions
(Vanrolleghem et al., 2003).
Carbonate/bicarbonate equilibrium
In an activated sludge system, microorganisms consume oxygen and produce carbon dioxide.
Monitoring off-gas carbon dioxide can thus be indicative for the microbial activity in such a WTTP.
However, measuring of the carbon dioxide gas phase composition in dynamic processes gives biased
results when seen directly as a change due to the biological production of carbon dioxide. This is
because the solubility of carbon dioxide, and thus its transfer to the gas phase, depends on the pH of
the liquid phase. Mineral carbon in the liquid can occur as several different species due to equilibrium
9
reactions, i.e., free carbon dioxide, bicarbonate, and carbonate forms, assuming that carbonic acid is
completely dissociated (Ho et al., 1987; Ishizaki et al., 1971; Minkevich and Neubert, 1985; Nyiri and
Lengyel, 1968; Royce, 1992; Smith et al., 1990). Sperandio et al. (1997) stated that for the usual
conditions in microbial systems, pH in the 6 to 8 range, the dissociation of bicarbonate to carbonate
ions is negligible (Ho et al., 1987; Roques, 1990; Royce, 1992; Schneider and Frischknecht, 1977)
and complexation with amine groups of protein molecules can be neglected (Royce and Thornhill,
1991). The high dependence on pH of these equilibria has been described by several investigators
such as Roques (1990). When pH changes, dissolved carbon dioxide acts as a buffer and shifts the
fraction between carbonic acid (H2CO3) and bicarbonate (HCO3-) to consume or release hydrogen
ions. Since CO2 transfer relates to the concentration of carbonic acid, increasing the fraction of
bicarbonate creates a supersaturated condition until the dissolved CO 2 can be stripped. Stripping of
CO2 also leads to an increase in pH, which shifts the equilibrium towards bicarbonate and is similar to
the response of receiving high alkalinity influents (Leu et al., 2010). A schematic representation of the
different carbon dioxide states from the cell to the gas phase is given in the figure below.
Figure 2.2 Schematic representation of the different carbon dioxide states from the cell to the gas
phase (Sperandio et al., 1997).
10
Sampling location
The sampling location and size of the area sampled by the hood in full scale basins is of critical
importance in providing precise and accurate estimates of the measured off-gas due to spatial and
temporal variability of the emissions. Accurate and precise estimation of the instantaneous mass flow
rate, provided that the concentration and volumetric gas flow rate can be adequately measured, is only
possible if activated sludge tanks are completely covered (Daelman, 2014). The off-gas method has
been used for various measurements, most prominently for the estimation of aeration efficiency and
the monitoring of greenhouse gas (GHG) emissions. With the exception of a few (e.g. Fred et al.
2009), most case-studies conducted, resorted to the use of a floating gas hood (e.g. Leu et al., 2009;
Leu et al., 2010; Desloover et al., 2010). The gaseous emissions from a bioreactor can be very
heterogeneous across its water surface. As an example, the difference of emission rate of fugitive
compounds can be significant between aerated and non-aerated zones. Floating gas hoods especially
pose a problem with surface aeration since the major part of emission originates from the aerator zone
while it is impossible to use a hood in the immediate vicinity of a surface aerator (Ye et al., 2014;
Stenstrom et al., 2006). In general, hydrodynamic effects play a major role in concentration profiles
across an activated sludge basin, particularly in plug flow reactors (Daelman, 2014). It may be clear
that especially in the case of a covered WTTP, off-gas analysis is worthwhile to be considered as an
addition to liquid phase analysis (Hellinga et al., 1996).
Sampling time
The frequency and duration at which off-gas data points are assimilated can play a critical role in
assessing the temporal variability of off-gasses. Off-gas emissions can vary widely throughout time;
causes for this include varying influent load and temperature differences. When interested in the
seasonal variability of off-gasses, one has to sample at least one year to cover the entire temperature
range. On a short term basis the diurnal temperature differences also have an effect on off-gas
emissions. Influent load is generally less during the night for domestic activities, or during weekends
and holidays for some industries. Daelman et al. (2013) studied the influence of sampling strategies on
the estimated emission of nitrous oxide by analysing the variability of an extensive data set resulting
from a long-term online monitoring campaign at a full-scale municipal wastewater treatment plant.
They concluded that short-term sampling is inadequate to accurately estimate the average nitrous
oxide emissions from a particular WTTP, while online monitoring is indispensable to capture the short-
11
term variability (diurnal dynamics). An argument can be made that such variability may also be
applicable for other off-gasses.
Thesis objectives
It was suggested by several authors that the off-gas measurement of CO2 combined with O2
monitoring in activated sludge systems has the potential to be a viable method for monitoring the
process performance. Hellinga et al. (1996) described a theoretical model by which continuous CO2
and O2 measurements were simulated. The RQ proved to be primarily indicative of the COD/TOC ratio
of the converted substrate. Hellinga et al. (1996) also state that if N-removal (nitrificationdenitrification) is determined, biomass production can be estimated on-line using these gas
measurements. However, the statements made by these authors were based on a theoretical model
based on mass balance equations and the model not take into account any dynamic response, nor the
factor of time. It should be clear from literature that the dynamic influence is an important factor, to be
considered.
A dynamic model will be set up for this thesis, to investigate the relationship between O 2 and CO2 gas
phase emissions to the performance of an activated sludge system. Additionally, it will be investigated
if the theoretical postulates made by Hellinga et al. (1996) still hold for this dynamic model.
Objectives of this thesis are:

Establishing a mathematical model based on an available model in literature to simulate the
temporal concentrations of the major components in wastewater and off-gas

Evaluate off-gas O2/CO2 to predict nitrification performance in activated sludge processes

Evaluate the statements, concerning influence of COD/TOC on off-gas O2 and CO2, and the
estimation of biomass production rate through off-gas O2 and CO2 monitoring, made by
Hellinga et al. (1996) for a dynamic model
12
3. MATERIALS AND METHODS
In this chapter the model used to predict microbial activities by using O 2 and CO2 measurement is set
up. The model consists of an activated sludge model adapted from ASM3 with additional processes of
the bicarbonate equilibrium and the gas transfer for oxygen and carbon dioxide. Subsequently, the
validation of the model and the manner the model was applied to obtain results for different casestudies will be discussed.
Model setup
Overview
First the general form of the adapted activated sludge model will be explained. Next, a more detailed
use of the proposed model structure will be explained by constructing the mass balance equations for
dissolved oxygen (𝑆𝑂 ) and dissolved carbon dioxide (𝑆𝐻2𝐶𝑂3∗ ), building up from liquid activity (biological
activity, as well as the bicarbonate equilibrium), including the gas transfer equations.
The kinetics and the simulated components are depicted in a Petersen matrix (Table 3.1). The mass
balance equations can be developed by adding the product of the coefficients listed in the column and
the process rate of each reaction in the right end column. The coefficients in each column are
calculated from stoichiometry, and Monod functions are used to describe various cell growth.
Modelling biological processes
The activated sludge model is based on the model proposed by Leu et al. (2010), which is similar to
the carbonaceous and nitrification parts of the general Activated Sludge Models No. 3 (ASM3, Gujer et
al., 1999). One difference between the ASM3 and the approach of Leu et al. (2010) is a pathway for
direct growth of active biomass from soluble substrate, which provides for simultaneous growth from
substrate and stored mass, which Krishna and van Loosdrecht (1999) have suggested better
describes the real conditions of the growth of heterotrophic bacteria. To define the yield coefficients of
direct heterotrophic growth (𝑌𝐻,𝑆 ), substrate, substrate storage (𝑌𝑆𝑇𝑂 ), and heterotrophic growth (𝑌𝐻,𝑆𝑇𝑂 )
on stored mass, the parameter, δ, (Sin et al., 2005) was used as:
𝒀𝑯,𝑺 =
4𝛿 − 2
4.2
4𝛿 − 2 4.5
4.5𝛿 − 0.5 4.2
×
, 𝒀𝑺𝑻𝑶 =
×
, 𝒀𝑯,𝑺𝑻𝑶 =
×
4.2𝛿 + 4.32
4
4.5𝛿
4
4.2𝛿 + 4.32 4.5
(3.1)
13
The use of one parameter, δ, also facilitates calibration of the model. The yield for the nitrification
reaction was set to 0.24 g COD biomass.g-1 N-NO3, used for both ASM1 and ASM3 (Henze et al.,
2000).
The model simulates the temporal concentrations of carbonaceous substrate (𝑆), stored mass (𝑋𝑠𝑡𝑜 ),
heterotrophic (𝑋𝐻 ), autotrophic (𝑋𝑁 ), and inert biomass (𝑋𝐼 ), in the biological phase; dissolved oxygen
(𝑆𝑂 ), carbon dioxide (𝑆𝐻2𝐶𝑂3∗ ), alkalinity (𝑆𝐻𝐶𝑂3− ) and ammonia (𝑆𝑁𝐻 ) in the liquid phase; and oxygen (O 2)
and carbon dioxide (CO 2) contents in the gas phase. To accurately calculate the mass
production/consumption of each simulated compound, stoichiometry was derived for all reactions (Eq.
3.2-3.6). In this model, all forms of carbonaceous substrates (rapidly degradable COD, slowly
degradable COD, or stored COD) are expressed as 𝐶𝐻𝑥 𝑂𝑦 with constant 𝑥 and 𝑦. Stored mass is
𝐶2 𝐻3 𝑂, and cell formula 𝐶5 𝐻7 𝑁𝑂2 is used for all bacteria species (heterotrophic or nitrifying bacteria).
When defined, the value of 𝑥 and 𝑦 also determines the COD:TOC ratio of a compound. For organic
materials, COD may analytically approximate the Theoretical Oxygen Demand (ThOD), which is
commonly used in the development of process stoichiometry for ASM3.
The five biological reactions, which occur simultaneously in the reactor are: (1) storage of
carbonaceous substrate; (2) the direct growth of heterotrophic on substrate; (3) growth of
heterotrophic on stored substrate; (4) nitrification by nitrifiers; and (5) biomass decay. The
stoichiometry of each reaction can be expressed as follows:
Storage of carbonaceous substrate:
𝐶𝐻𝑥 𝑂𝑦 + (1 +
𝑥 𝑦 9
𝑥 3
− − 𝑌̅ ) 𝑂 → 𝑌̅𝑆𝑇𝑂 𝐶2 𝐻3 𝑂 + (1 − 2𝑌̅𝑆𝑇𝑂 )𝐶𝑂2 + ( − 𝑌̅𝑆𝑇𝑂 ) 𝐻2 𝑂
4 2 4 𝑆𝑇𝑂 2
2 2
(3.2)
Direct growth of heterotrophic bacteria:
𝐶𝐻𝑥 𝑂𝑦 + 𝑌̅𝑁𝐻4+ + (1 +
𝑥 𝑦
𝑥
− − 5𝑌̅) 𝑂2 → 𝑌̅𝐶5 𝐻7 𝑁𝑂2 + (1 − 5𝑌̅)𝐶𝑂2 + ( − 2𝑌̅) 𝐻2 𝑂 + 𝑌̅𝐻 +
4 2
2
(3.3)
Growth of heterotrophic bacteria on stored mass:
𝐶𝐻1.5 𝑂0.5 + 𝑌̅ 𝑁𝐻4+ + (1.125 − 5𝑌̅)𝑂2 → 𝑌̅𝐶5 𝐻7 𝑁𝑂2 + (1 − 5𝑌̅ )𝐶𝑂2 + (0.75 − 2𝑌̅ )𝐻2 𝑂 + 𝑌̅𝐻 +
(3.4)
Nitrification:
𝑁𝐻4+ + 5𝑌̅𝑁 𝐶𝑂2 + (2 − 7𝑌̅𝑁 )𝑂2 → 𝑌̅𝑁 𝐶5 𝐻7 𝑁𝑂2 + (1 − 𝑌̅𝑁 )𝑁𝑂3− + (2 − 𝑌̅𝑁 )𝐻 + + (1 − 3𝑌̅𝑁 )𝐻2 𝑂
(3.5)
14
Endogenous respiration or biomass decay:
𝐶5 𝐻7 𝑁𝑂2 + 5𝑂2 + 𝐻 + → 5𝐶𝑂2 + 2𝐻2 𝑂 + 𝑁𝐻4+
(3.6)
Modelling gas transfer
Apart from biological activities mention above (Eq. 3.2-3.6), dissolved oxygen (𝑆𝑂 ) and dissolved
carbon dioxide (𝑆𝐻2𝐶𝑂3∗ ) also depend on the oxygen transfer rate (OTR), carbon dioxide transfer rate
(CTR) and bicarbonate equilibrium in the reactor.
3.1.3.1
Modelling oxygen transfer rate
In the model, OTR represents the gas transfer capacity of aeration system, which is based on the wellknown two film theory (Lewis and Whitman, 1924), which can be expressed as:
𝑂𝑇𝑅 = 𝛼𝐾𝐿 𝑎 × 𝑉 × (𝑆𝑂𝐼𝑁 − 𝑆𝑂 )
3.1.3.2
(3.7)
Modelling carbon dioxide transfer rate
CTR is simulated in a similar fashion as OTR and the mass transfer coefficient (𝛼𝐾𝐿 𝑎𝐶𝑂2 ) was
calculated based on the 𝛼𝐾𝐿 𝑎 of 𝑂2 . The saturated concentration 𝑆𝐻∞2𝐶𝑂3∗ can be calculated by Henry’s
Law. All relationships for the simulation of CTR can be expressed as:
The ratio of 0.91 for
𝛼𝐾𝐿 𝑎𝐶𝑂2
𝛼𝐾𝐿 𝑎
𝐶𝑇𝑅 = 𝛼𝐾𝐿 𝑎𝐶𝑂2 × 𝑉 × (𝑆𝐻∞2𝐶𝑂3∗ − 𝑆𝐻2𝐶𝑂3∗ )
(3.8)
𝛼𝐾𝐿 𝑎𝐶𝑂2 = 0.91 ∙ 𝛼𝐾𝐿 𝑎
(3.9)
𝑆𝐻∞2𝐶𝑂3∗ = 𝐻𝐶𝑂2 𝐶𝑂2
(3.10)
in Eq. (3.9) was proposed by Spérandio and Paul (1997). The 𝛼𝐾𝐿 𝑎 is
simulated through the control of the dissolved oxygen.
Modelling bicarbonate equilibrium
Simulation of carbon dioxide transfer is more complex than oxygen due to the bicarbonate equilibrium.
In addition to the reaction kinetics described by the bacteria activities, such as carbon dioxide uptake
for autotrophic growth (CUR), production due to heterotrophic growth and biomass decay (CPR), an
15
additional mass balance and reaction term is needed, namely 𝑟𝐻𝐶𝑂3− . 𝑟𝐻𝐶𝑂3− is dependent not only on
𝑆𝐻2𝐶𝑂3∗ , but also on 𝑆𝐻𝐶𝑂3− .
𝑟𝐻𝐶𝑂3− = 𝑉[(𝑘1 + 𝑘2 × 10𝑝𝐻−14 ) ∙ 𝑆𝐻2𝐶𝑂3∗ − (𝑘−2 + 𝑘−1 × 10−𝑝𝐻 ) ∙ 𝑆𝐻𝐶𝑂3− ]
(3.11)
Spérandio and Paul (1997) estimated the transformation rate of bicarbonate (𝑟𝐻𝐶𝑂3− ) using the 𝐶𝑂2
transformation kinetics (𝑘1 , 𝑘2 ) and the 𝐶𝑂2 dissociation constants (𝐾1 , 𝐾2 ). Furthermore: 𝑘−1 = 𝑘1 /𝐾1
and 𝑘−2 = 𝑘2 /𝐾2 . The concentration of bicarbonate and dissolved 𝐶𝑂2 (𝐻2 𝐶𝑂3∗ ) changes with pH. If pH
shift is too significant, off-gas 𝐶𝑂2 measurements may be upset. Fortunately, biological treatment
processes are normally controlled at constant pH, the changes of 𝐶𝑂2 transfer due to pH shifting
should be small. Based on this assumption, modeling efforts can be applied to correct the off-gas
measurements of 𝐶𝑂2 . In the study of Leu et al. (2010), pH changes associated with periodical plant
alkalinity measurement was used as input signal to adjust the 𝐶𝑂2 simulation.
Reactor setup
The model assumed a continuous stirred tank reactor (CSTR) in which the heterotrophic and
autotrophic turnover proceed simultaneously. The reactor is assumed to be perfectly mixed and
simulation of hydraulic conditions was not required. This reactor is preceded by a predenitrification
tank from which it receives its wastewater. A biomass recycle rate in accordance with the simulated
solid retention time was also implemented. The data used for simulation is provided by Leu et al.
(2010). Influent was characterized for plant flow, total COD, soluble COD, ammonia, organic-nitrogen
and carbon dioxide. The wastewater composition in the reactor was characterized for biomass
equivalent MLVSS. In table 3.4 the wastewater composition was adapted to fit the model state
variables.
The air flow in the reactor was controlled by a conventional constant-DO control system The 𝛼𝐾𝐿 𝑎 is
simulated through the control of the dissolved oxygen. Dissolved oxygen is controlled by the default
controller of the Benchmark Simulation Model no. 1 (BSM1) implemented in Simulink. The suggested
controller is of the PI type with an anti-windup to prevent integration wind-up.
16
Simulation
Mass balances of components such as carbonaceous substrate (𝑆), stored mass (𝑋𝑠𝑡𝑜 ), heterotrophic
(𝑋𝐻 ), autotrophic (𝑋𝑁 ), and inert biomass (𝑋𝐼 ) followed conventional setup by adding the product of the
coefficients listed in the column and the process rate of each reaction in the right end column.
Particularly, the mass balance equations of dissolved oxygen and carbon dioxide depend not only on
the uptake or production rate of the substance due to heterotrophic growth and decay, and nitrification
but also depend on gas transfer capacity of aeration system which expressed in term of OTR and
CTR. Moreover, the carbon dioxide mass balance equations also depends on the bicarbonate
equilibrium. To exemplify this approach, these two mass balances are briefly discussed.
Mass balance of dissolved oxygen
The mass balance of DO can be expressed as:
𝑉
𝑑𝑆𝑂
= 𝑄𝑙 (𝑆𝑂𝐼𝑁 − 𝑆𝑂 ) + 𝑂𝑇𝑅 − 𝑂𝑈𝑅𝑁 − 𝑂𝑈𝑅𝐶 − 𝑂𝑈𝑅𝐷
𝑑𝑡
(3.12)
OUR is the mass oxygen consumed to degrade certain substrate per unit volume due to biological
activity, i.e. nitrification (OURN), oxidation of carbonaceous substrate (OURC), and cell decay (OURD).
The procedure for constructing this mass balance is similar to the construction of the mass balance for
𝑆𝐻2𝐶𝑂3∗ as discussed in the following section.
Mass balance of carbon dioxide
The relationships among dissolved carbon dioxide (𝑆𝐻2𝐶𝑂3∗ ), bicarbonate (𝑆𝐻𝐶𝑂3− ), and the off-gas CO2
can be derived as:
𝑉
𝑑𝑆𝐻2𝐶𝑂3∗
𝑑𝑡
= 𝑄𝑙 (𝑆𝐻𝐼𝑁2𝐶𝑂3∗ − 𝑆𝐻2𝐶𝑂3∗ ) + 𝐶𝑇𝑅 − 𝐶𝑈𝑅𝑁 + 𝐶𝑈𝑅𝐶 + 𝐶𝑈𝑅𝐷 − 𝑟𝐻𝐶𝑂3−
𝑉
𝑑𝑆𝐻2𝐶𝑂3−
𝑑𝑡
𝑉
= 𝑄𝑙 (𝑆𝐻𝐼𝑁2𝐶𝑂3− − 𝑆𝐻2𝐶𝑂3− ) + 𝑟𝐻𝐶𝑂3−
𝑑𝐶𝑂2
= 𝑄𝑔 (𝐶𝑂2𝑖𝑛 − 𝐶𝑂2𝑖𝑛 ) + 𝐶𝑇𝑅
𝑑𝑡
(3.13)
(3.14)
(3.15)
Biological activity using 𝑆𝐻2𝐶𝑂3∗ such as the nitrification process in Eq. (3.5) is denoted as CURN.
Biological activity producing 𝑆𝐻2𝐶𝑂3∗ such as oxidation of carbonaceous substrate and cell decay are
17
denoted as CURC and CURD respectively. The latter two comprise the carbon production rate. The
uptake or production of CO2 when a unit mass of substrate is consumed, is calculated by multiplying
the reaction kinetics with appropriate stoichiometric yields. Table 3.1 and 3.2 show all the required
parameters and the following example is given to illustrate how the CUR (in this case for nitrification) is
written as:
𝑁𝐻
𝐶𝑈𝑅𝑁 = 𝑌𝐶𝑂
𝜇
2
𝑆𝑁𝐻
𝑆𝑂
×
𝑋 𝑉
𝐾𝑁𝐻 + 𝑆𝑁𝐻 𝐾𝑂 + 𝑆𝑂 𝑁𝐻
(3.16)
𝑁𝐻
In the equation above 𝑌𝐶𝑂
is the carbon demand of ammonia oxidation by nitrifiers and is derived from
2
𝑁𝐻
Eq. (3.5). The relationship between 𝑌𝐶𝑂
(g CO2.(g COD nitrifying biomass)-1) and molar yield ̅̅̅
𝑌𝑁 can
2
be expressed as:
𝑁𝐻
𝑌𝐶𝑂
=
2
The first term
̅̅̅̅
5𝑌
𝑁
̅̅̅̅
𝑌𝑁
̅̅̅
5𝑌
44
1
𝑁
×
×
̅̅̅
113 1.42
𝑌𝑁
(3.17)
follows directly from the stoichiometry of Eq. (3.5), the second term is the ratio of the
molar masses of 𝐶𝑂2 and 𝐶5 𝐻7 𝑁𝑂2 respectively, the third term is the reverse of g ThOD.g-1 biomass.
Note that, although CTR have positive operators in Eq. (3.8), the negative value of CTR is obtained
and thus simulates gas stripping. This is due to the supersaturation of 𝑆𝐻2𝐶𝑂3∗ . In reality carbon dioxide
is produced during heterotrophic growth and decay, and as such the terms CUR C and CURD have
positive operators.
The model simulates six microbial, two gas transfer and onebicarbonate equilibrium reactions (Table
3.1) by using C-MEX S-function in Matlab 8.4 (MathWorks, Natick, Massachusetts) with a fourth-order
correct, variable time- step Runge-Kutta technique (function ode45) to integrate the mass balance
ordinary differential equations (ODEs).
18
Table 3.1 Stoichiometric and kinetic expressions for biological conversion processes for the model
State variables →
Processes ↓
𝑆
𝑔 𝐶𝑂𝐷. 𝑚−3
1
Storage of carbonaceous
substrate
−1
𝑌𝑆𝑇𝑂
2
Direct synthesis of
heterotrophic bacteria
−1
𝑌𝐻,𝑆
3
Heterotrophic growth on
stored mass
𝑆𝑁𝐻
𝑔 𝑁. 𝑚−3
𝑋𝑠𝑡𝑜
𝑔 𝐶𝑂𝐷. 𝑚−3
𝑋𝐻
𝑋𝑁
𝑋𝐼
𝑔 𝐶𝑂𝐷. 𝑚−3 𝑔 𝐶𝑂𝐷. 𝑚−3 𝑔 𝐶𝑂𝐷. 𝑚−3
𝐻,𝑆𝑇𝑂
−𝑌𝑁𝐻
−1
𝑆𝐻2 𝐶𝑂3∗
𝑆𝐻𝐶𝑂3−
Reaction kinetics (mg/L/day) ↓
𝑔 𝐶𝑂2 . 𝑚−3 𝑔 𝐶𝑂2 . 𝑚−3
−𝑌𝑂𝑆𝑇𝑂
𝑆𝑇𝑂
𝑌𝐶𝑂
2
𝑘𝑠𝑡𝑜 × 𝑀𝑂 × 𝑀𝑆 × 𝑋𝐻
1
−𝑌𝑂𝐻,𝑆
𝐻,𝑆
𝑌𝐶𝑂
2
𝜇𝑚𝑎𝑥,𝑆 × 𝑀𝑂 × 𝑀𝑆 × 𝑀𝑁𝐻 × 𝑋𝐻
1
−𝑌𝑂𝐻,𝑆𝑇𝑂
𝐻,𝑆𝑇𝑂
𝑌𝐶𝑂
2
1
𝐻,𝑆
−𝑌𝑁𝐻
𝑆𝑂
𝑔 𝑂2 . 𝑚−3
𝑌𝐻,𝑆𝑇𝑂
𝜇𝑚𝑎𝑥,𝑠𝑡𝑜 × 𝑀𝑂 × 𝑀𝑆 × 𝑀𝑁𝐻 ×
2
𝑓𝑠𝑡𝑜
𝐾𝑠𝑡𝑜2 + 𝑓𝑠𝑡𝑜 × 𝐾𝑠𝑡𝑜1
× 𝑋𝐻
4
Nitrification
−1
𝑌𝑁
5
Endogenous respiration
of heterotrophs
𝑋
𝑌𝑁𝐻
6
Endogenous respiration
of autotrophs
𝑋
𝑌𝑁𝐻
7
Oxygen transfer
8
Carbon dioxide transfer
−1
9
Bicarbonate equilibrium
−1
𝑌𝑂𝑁𝐻
𝑁𝐻
−𝑌𝐶𝑂
2
𝜇𝑁 × 𝑀𝑁𝐻,𝑂 × 𝑀𝑂,𝑁 × 𝑋𝑁
𝑓𝑋𝐼
−(1 − 𝑓𝑋𝐼 )
𝑋
𝑌𝐶𝑂
2
𝐾𝐷 × 𝑋𝐻
𝑓𝑋𝐼
−(1 − 𝑓𝑋𝐼 )
𝑋
𝑌𝐶𝑂
2
𝐾𝐷 × 𝑋𝑁
1
−1
−1
𝛼𝐾𝐿 𝑎(𝑆𝑂∞ − 𝑆𝑂 )
1
𝛼𝐾𝐿 𝑎𝐶𝑂2 (𝑆𝐻∞2𝐶𝑂3∗ − 𝑆𝐻2𝐶𝑂3∗ )
1
(𝑘1 + 𝑘2 × 10𝑝𝐻−14 ) ∙ 𝑆𝐻2𝐶𝑂3∗
− (𝑘−2 + 𝑘−1 × 10−𝑝𝐻 ) ∙ 𝑆𝐻𝐶𝑂3−
Note: 𝑓𝑠𝑡𝑜 = 𝑋𝑆𝑇𝑂 /𝑋𝐻 ; 𝑀𝑧 = 𝑆𝑧 /(𝐾𝑧 + 𝑆𝑧 ) 𝑤𝑖𝑡ℎ 𝑧 = 𝑂 ⌵ 𝑆 ⌵ 𝑁𝐻
19
Table 3.2. Stoichiometric coefficients
Sym.
Units
𝑌𝑆𝑇𝑂
Aerobic yield of stored product for
substrate
0.7826
g COD stored mass.g-1
COD substrate
𝑌𝑂𝑆𝑇𝑂
Oxygen demand for storage of
carbonaceous substrate
0.2778
g O2.g-1 COD stored mass
𝑆𝑇𝑂
𝑌𝐶𝑂
2
Carbon dioxide yield for storage of
carbonaceous substrate
0.3257
g CO2.g-1 COD stored
mass
𝑌𝐻,𝑆
Aerobic yield of heterotrophic biomass
for direct growth on substrate
0.5408
g COD biomass.g-1 COD
substrate
𝐻,𝑆
𝑌𝑁𝐻
Nitrogen yield for direct growth of
heterotrophic bacteria
0.0871
g N-NH4.g-1 COD biomass
𝑌𝑂𝐻,𝑆
Oxygen demand for direct growth of
heterotrophic bacteria
0.8468
g O2.g-1 COD biomass
𝐻,𝑆
𝑌𝐶𝑂
2
Carbon dioxide yield for direct growth of
heterotrophic bacteria
0.8627
g CO2.g-1 COD biomass
𝑌𝐻,𝑆𝑇𝑂
Aerobic yield of heterotrophic biomass
for growth on stored mass
0.6576
g COD biomass.g-1 COD
stored mass
𝐻,𝑆𝑇𝑂
𝑌𝑁𝐻
Nitrogen demand for growth of
heterotrophic bacteria on stored mass
0.0872
g N-NH4.g-1 COD biomass
𝑌𝑂𝐻,𝑆𝑇𝑂
Oxygen demand for growth of
heterotrophic bacteria on stored mass
0.5192
g O2.g-1 COD biomass
𝐻,𝑆𝑇𝑂
𝑌𝐶𝑂
2
Carbon dioxide yield for growth of
heterotrophic bacteria on stored mass
0.4822
g CO2.g-1 COD biomass
𝑋
𝑌𝑁𝐻
Nitrogen yield for endogenous
respiration
0.0875
g N-NH4.g-1 COD biomass
𝑁𝐻
𝑌𝐶𝑂
2
Carbon dioxide yield for endogenous
respiration
1.3750
g CO2.g-1 COD biomass
𝑌𝑁
Aerobic yield of autotrophic biomass for
nitrogen substrate
0.76
g COD biomass. g-1 COD
substrate
𝑌𝑂𝑁𝐻
Oxygen demand for ammonia oxidation
by nitrifiers
4.6018
g O2.g-1 COD biomass
𝑁𝐻
𝑌𝐶𝑂
2
Carbon dioxide demand for ammonia
oxidation by nitrifiers
1.3711
g CO2.g-1 COD biomass
Production of XI in endogenous
respiration
0.2
g COD inert mass.g-1 COD
biomass
𝑓𝑋𝐼
Ref.
Henze et al. (2000)
Value
See Note
Description
Note: YH,S, YH,STO, YSTO and derivatives were calculated for a delta value of 2.3, an x value of 1.5 and a
y value of 0.48.
20
Table 3.3 Kinetic parameter
Sym.
Units
Decay coefficient
0.06
day-1
Oxygen gas transfer coefficient
controlled
day-1
Carbon dioxide gas transfer coefficient
controlled
day-1
𝐾𝑆
Half-velocity coefficient, substrate
20
g.m-3
𝐾𝑁𝐻
Half-velocity coefficient, ammonia
0.1
g.m-3
Half-velocity coefficient, oxygen
0.5
g.m-3
𝑁
𝐾𝑁𝐻
Autotrophic half-velocity coefficient,
ammonia
1.05
g.m-3
𝐾𝑂𝑁
Autotrophic half-velocity coefficient, oxygen
1
g.m-3
𝜇𝑁
Autotrophic maximum growth rate
Season
bound
day-1
Heterotrophic maximum growth rate on
substrate
3
day-1
storage rate constant
1
day-1
Heterotrophic maximum growth rate on
stored mass
3
day-1
Regulation constant of biomass controlling
degradation rate of 𝑋𝑆𝑇𝑂 as function of 𝑓𝑆𝑇𝑂
0.102
g COD 𝑋𝑆𝑇𝑂 .g-1 COD
1.2 x 10-3
g COD 𝑋𝑆𝑇𝑂 .g-1 COD
𝐾𝐷
𝛼𝐾𝐿 𝑎
𝛼𝐾𝐿 𝑎𝐶𝑂2
𝐾𝑂
𝜇𝑚𝑎𝑥,𝑆
𝑘𝑠𝑡𝑜
𝜇𝑚𝑎𝑥,𝑠𝑡𝑜
𝐾𝑠𝑡𝑜1
𝐾𝑠𝑡𝑜2
A lumped parameter related to the affinity of
biomass to storage fraction of biomass i.e.
𝑓𝑆𝑇𝑂
𝑋𝐻
Ref.
Sin et al. (2005)
Value
Metcalf and Eddy Inc, (2003)
Description
𝑋𝐻
Forward rate constant of carbon dioxide
hydration with water
1560
day-1
𝑘2
Forward rate constant of carbon dioxide
hydration with hydroxide ion
3.96 x 108
m³.mol-1.day-1
𝑘−1
Backward rate constant of carbon dioxide
hydration with water
3.85 x 109
m³.mol.day-1
𝑘−2
Backward rate constant of carbon dioxide
hydration with water
9.76
day-1
Sperandio et al. (1997)
𝑘1
Note: The values referred to from Sin et al. (2005) are average values. However, these parameter
values are not sensitive towards oxygen and carbon dioxide transfer rate. The 𝐾𝐿 𝑎 values were
controlled by the DO-controller. The parameter 𝜇𝑁 , varied for each season and its values can be found
in Table 3.5.
21
Model validation
Data was used given by Leu et al. (2010) to validate the model. The model was constructed on the
basis of this article, and thus using the data given by the article is a good way to see if the model was
correctly implemented. It should be brought to the reader’s attention that no full data set was given in
this paper. Exact influent data was not mentioned in the paper of Leu et al. (2010). However, the
influent range and average influent data was sufficient to obtain the same dynamic trends as
discussed by Leu et al. (2010). BSM1 data was adapted for 𝑆, 𝑆𝑁𝐻 and 𝑄𝑖𝑛 to fit the range and
averages as illustrated in table 3.4 and 3.5 to take advantage of diurnal variation. This method is
affirmed by the fact the parameters for the model were also given in the paper or referred to from other
authors. The only exception was the assumption of influent alkalinity ( 𝑆𝐻𝐶𝑂3− ), which was not mentioned
in the paper of Leu et al. (2010).
The plant is a medium size NDN plant with capacity of approximately 31000 m³.day-1 .The aerobic
zone is 13450 m³, or 53% of the total aeration tank volume. The average influent total COD is
approximately 600 g.m -3 and the TKN concentration is approximately 65 g.m-3. Off-gas was measured
once every 2 hours in a certain period of time for four different seasons (Table 2). The influent data for
𝑆𝐻2𝐶𝑂3∗ and 𝑆𝐻𝐶𝑂3− were assumed. These values depend on the pK 1 of the bicarbonate equilibrium and
are thus temperature dependent. pH was controlled at 6.8 for the NDN plant.
The model of Leu et al. (2010) was used to predict nitrification performance by monitoring the O 2/CO2
ratio. The results simulated with the data below were compared to the results discussed by Leu et al.
(2010) as a means to evaluate the model (Chapter 4, section 4.1.2).
Table 3.4 Reactor biomass composition (initial value for ODE) and influent composition used for
simulation. (Leu et al., 2010).
𝑋𝑠𝑡𝑜
Reactor
biomass
𝑋
𝑋𝑁
𝑋𝐼
𝐻
composition
𝑆𝑂
𝒈 𝑪𝑶𝑫. 𝒎−𝟑
𝒈 𝑵. 𝒎−𝟑
𝒈 𝑶𝟐 . 𝒎−𝟑
Mean
𝑆𝑁𝐻
2.35
1200
100
80
600
65
Range
𝒈 𝑪𝑶𝑫. 𝒎−𝟑
𝑆
1.6 3
700 1500
85 115
55 105
467 - 808
52.6 78.4
0
0
Influent
𝑆𝐻2𝐶𝑂3∗
𝑆𝐻𝐶𝑂3−
𝑄𝑖𝑛
𝒈 𝑪𝑶𝟐 . 𝒎−𝟑
𝒈 𝑪𝑶𝟐 . 𝒎−𝟑
𝒎𝟑 . 𝒅−𝟏
84.5 (20°C)
308 (20°C)
32400
Constant (based on
alkalinity influent of BSM1)
13200 45600
22
Model application
The validated model then was used to investigate the followings:
Case study 1: Seasonal variation of OTR/CTR ratio
Case study 2: influence of pH
Case study 3: Influence of ratio of TOC and COD to OTR/CTR
Case study 4: Estimation of the biomass production rate by CO2 and O2 measurements.
Seasonal variation of CTR:OTR ratio
Performed simulation took into account the seasonal variation of parameters (Table 3.5). The
bicarbonate equilibrium were also interpolated for varying temperature using the following temperature
equation:
𝑘(𝑇) = 𝑘(20°𝐶) ∙ exp(𝜃𝑇 ∙ (𝑇 − 20°𝐶))
(3.18)
The parameters 𝜃𝑇 for 𝑘1 , 𝑘2 and either 𝐾1 or 𝐾2 were found to be -0.02356, 0.0167 and 0.0103
respectively.
Table 3.5 Season variable parameters (Leu et al., 2010).
Property
Temperature
Solid Retention Time
Symbol
Unit
Testing period
1 – May
2 – July
3 – Nov
4 - Jan
T
°C
19.8
21.5
17.1
14.0
SRT
day
7.9
8.0
11.0
15.5
Autotrophic growth rate
𝜇𝑁
day-1
1
1.1
0.6
0.38
Delta
𝛿
[]
1.7
2.3
2.3
2.8
g COD.g-1 C
3
3
2.7
1.7
g.m-3
1.4
1.5
1.6
1.7
COD/TOC
Average DO
𝑆𝑂
Influence of pH
To assess the influence of pH, the proton production was also modelled. The proton production in the
reactor is related to the required buffer capacity to keep a constant pH. Since pH was fixed at the the
Waβmannsdorf WTTP, which was operated at a pH of 6.8, proton modelling is not a necessity.
23
However, since the buffer capacity is dependent on pH, this relationship can be used to assess the
influence of pH. The influent pH is defined by the ratio of influent 𝑆𝐻𝐶𝑂3− and 𝑆𝐻2𝐶𝑂3∗ calculated with the
Henderson-Hasselbalch equation:
𝑝𝐻 = 𝑝𝐾1 + log (
𝑆𝐻𝐶𝑂3−
𝑆𝐻2𝐶𝑂∗3
)
(3.19)
The value of 𝑝𝐾1 is here equal to 6.4 which is defined as the negative log-value of the ratio 𝐾1 =
The values for 𝑘1 and 𝑘−1 can be found in table 3.3. Where the ratio
𝑆𝐻𝐶𝑂−
3
𝑆𝐻
∗
2 𝐶𝑂3
𝑘1
𝑘−1
.
defines the influent pH, the
concentrations of the influent 𝑆𝐻𝐶𝑂3− and 𝑆𝐻2𝐶𝑂3∗ define the buffer capacity. The buffer capacity of the
influent should be chosen, such that the proton production in the reactor is neutralized by the
bicarbonate buffer. The proton production was simulated by setting up a mass balance in the way
explained previously. The yield coefficients for proton are illustrated in the table below.
Table 3.6 Yield coefficients for proton production for a delta value of 2.3.
Value
𝒀𝑯,𝑺
𝑯
𝒀𝑯,𝑺𝑻𝑶
𝑯
𝒀𝑵𝑯
𝑯
𝒀𝑿𝑯
1.375
0.0062
1.1812
0.0063
g H+.g-1 COD biomass
Unit
Influence of COD/TOC ratio on OTR/CTR ratio
A case-study will be performed investigating the influence of COD/TOC ratio on the gas phase
composition. This research question is backed by Hellinga et al. (1996), who stated that the RQ, which
is the respiratory coefficient known as the ratio of the carbon dioxide production rate to the oxygen
consumption rate, is a strong measure for the COD/TOC ratio of the converted waste. In this casestudy, the assumption concerning the COD/TOC ratio will be investigated for the dynamic model.
Simulation was performed with parameter values in accordance with process conditions during spring
(see table 3.5), all parameters were kept constant to look at the effect of COD/TOC and COD/N
individually.
3.5.3.1
Theoretical principal concerning COD/TOC influence
The COD/TOC ratio is indicative for the reactivity of carbonaceous substrate (Hellinga et al., 1996).
More specifically, in this model, the influence of the COD/TOC ratio of a substrate manifests itself
when calculating the molar yields of carbonaceous substrate storage or the molar yields of direct
24
growth of heterotrophic bacteria on substrate (Eq. 3.2 & 3.3). The molar yield 𝑌̅𝐻,𝑆 , with unit g COD
biomass.g-1 COD substrate, can be calculated as followed:
𝑌̅𝐻,𝑆 = 𝑌𝐻,𝑆 ×
𝑔 𝐶𝑂𝐷 𝑠𝑢𝑏𝑠𝑡𝑟𝑎𝑡𝑒
𝑚𝑜𝑙 𝑏𝑖𝑜𝑚𝑎𝑠𝑠
𝑚𝑜𝑙 𝑏𝑖𝑜𝑚𝑎𝑠𝑠
×
=
𝑚𝑜𝑙 𝑠𝑢𝑏𝑠𝑡𝑟𝑎𝑡𝑒
𝑔 𝐶𝑂𝐷 𝑏𝑖𝑜𝑚𝑎𝑠𝑠 𝑚𝑜𝑙 𝑠𝑢𝑏𝑠𝑡𝑟𝑎𝑡𝑒
(3.20)
𝑌̅𝑆𝑇𝑂 is calculated in a similar way , but has the unit g COD stored mass.g -1 COD substrate. All
stoichiometric equations are calculated for C-moles of substrate, every substrate has thus a constant
TOC of 12 g C.mol-1 substrate. A varying COD/TOC ratio of the substrate thus means a varying
substrate COD, which in turn influences equation 3.20. Additionally the COD/TOC ratio influences the
values x and y as defined in section 3.1.2, which influences the stoichiometric equations.
The ratio of the biological production rate of carbon dioxide and the oxygen uptake rate for direct
growth on substrate of heterotrophic bacteria can be rewritten as followed (see section 3.1.2 and table
3.1):
𝑥 𝑦
1 + − − 5𝑌̅𝐻,𝑆
𝜇𝑚𝑎𝑥,𝑆 × 𝑀𝑂 × 𝑀𝑆 × 𝑀𝑁𝐻 × 𝑋𝐻
𝑓𝑂 𝑚𝑜𝑙𝑎𝑟 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛
𝑂𝑈𝑅𝐻,𝑆
4 2
=
× 2
×
𝐶𝑃𝑅𝐻,𝑆
𝑓𝐶𝑂2 𝑚𝑜𝑙𝑎𝑟 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝜇𝑚𝑎𝑥,𝑆 × 𝑀𝑂 × 𝑀𝑆 × 𝑀𝑁𝐻 × 𝑋𝐻
(1 − 5𝑌̅𝐻,𝑆 )
(3.21)
With 𝑓𝑂2 𝑚𝑜𝑙𝑎𝑟 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 and 𝑓𝐶𝑂2 𝑚𝑜𝑙𝑎𝑟 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 representing factors to convert from molar yield to the
yield coefficient in gram. These factors are independent of the substrate composition (see section
3.3.2). An important observation is that the last ratio cancels out. The ratio
𝑂𝑈𝑅𝐻,𝑆
𝐶𝑃𝑅𝐻,𝑆
is thus independent
of the reaction kinetics. This equation shows that the oxygen uptake rate and carbon dioxide rate are
strongly dependent on the COD/TOC ratio due to the parameters present in the first ratio of Eq. (3.21).
Due to the bicarbonate equilibrium, observation of
𝑂𝑈𝑅𝐻,𝑆
𝐶𝑃𝑅𝐻,𝑆
is not direct. However, it can be investigated
if the OTR/CTR is sensitive towards COD/TOC ratio as was theoretically postulated by Hellinga et al.
(1996).
3.5.3.2
Case-study setup
To gain insight into the influence of COD/TOC ratio on the OTR/CTR ratio, the influent COD value was
varied for the influent range of 𝑆 from 467 to 808 g COD.m -3 (Table 3.4). The influence of the nitrogen
load was also taken into account and the influent 𝑆𝑁𝐻 was varied for a diurnal range of 25 - 80 g N.m-3,
avoiding incomplete nitrification. For the variation of COD over TOC the range was chosen on the
basis of the values given in table 3.5 (1.7 – 3 g COD.g-1 C) and on the basis of the work of Rao (1978),
25
who reported COD/TOC ratios of 2.5 (in the effluent of a brewery treatment plant) up to 4.98 g COD.g1
C (influent of the Louvain-La-Neuve municipal wastewater treatment plant).
The attentive reader will notice that by calculating the substrate composition from the COD/TOC ratio,
not both x and y can be determined. The ASMx models are constructed using COD and N mass
balances. The models in their current form do not close the balance on C, H, O and N. Takács and
Vanrolleghem (2006) stated that to determine the C, O and H content for one particular component,
three independent measurements are required, e.g. COD, VSS, TOC. Data on VSS was not available
however for this case-study. However, it was found that the absolute values on x and y did not have a
significant effect on the OTR and CTR, rather the overall COD/TOC determined by an appropriate
random chose of x and y effected the OTR and CTR. It was assumed that generally the proton content
in a substrate is larger than the oxygen content of a substrate based on the average value of a
substrate suggested by Orhon and Artan (1994).
Estimation of the biomass production rate by CO2 and O2 measurements.
Hellinga et al. (1996) suggested that, if N-removal is determined in a different way, other than through
off-gas measurements (and maybe alkalinity tests are sufficient for that). Biomass production can be
estimated on-line using off-gas O2 and CO2 measurements. This may be beneficial for sludge level
control. In order to evaluate the validity of this statement the OTR and CTR were investigated in
relation to the biomass production rate.
The biomass production rate is defined as the reaction kinetics that directly result in the production of
biomass, these are defined for 𝑋𝐻 and 𝑋𝑁 respectively as followed:
𝑟𝑋𝐻 = 𝑘𝑠𝑡𝑜 × 𝑀𝑂 × 𝑀𝑆 × 𝑋𝐻 + 𝜇𝑚𝑎𝑥,𝑠𝑡𝑜 × 𝑀𝑂 × 𝑀𝑆 × 𝑀𝑁𝐻 ×
𝑟𝑋𝑁= 𝜇𝑁 × 𝑀𝑁𝐻,𝑂 × 𝑀𝑂,𝑁 × 𝑋𝑁
2
𝑓𝑠𝑡𝑜
× 𝑋𝐻
𝐾𝑠𝑡𝑜2 + 𝑓𝑠𝑡𝑜 × 𝐾𝑠𝑡𝑜1
(3.22)
(3.23)
These reaction kinetics can be found in Table 3.1.
26
RESULTS AND DISCUSSION
4.1
Model evaluation
Prior to applying the model for different case-studies, it is important to evaluate the validity of the
model. A steady-state analysis was conducted, evaluating the microbial activities in the liquid of the
adapted activated sludge model. The liquid activity was evaluated by analysing the effluent
wastewater composition. Also the steady-state gas phase composition was simulated and discussed.
Subsequently, to confirm the model dynamics, a dynamic simulation was conducted and the results of
this simulation were compared to the data given by Leu et al. (2010).
4.1.1
Steady-state analysis
Steady-state simulation was performed to understand the relationship between bacteria activities,
alkalinity balance, and gas transfer. The model was simulated for 25 days with a constant influent
using the mean values given in table 3.4 (Chapter 3, Section 3.4). The stoichiometric and kinetic
parameters used for this simulation can be found in tables 3.2 and 3.3 respectively (Chapter 3, Section
3.3). The season dependent parameters were chosen for a temperature of 21.5°C, with an SRT of 8
days and a delta value of 2.3, as illustrated in table 3.5 (Chapter 3, Section 3.4). The simulation results
for the steady-state case are denoted in the table below.
Table 4.1 Biological reactor influent and steady-state simulation.
𝑆
State variables
𝑋𝑠𝑡𝑜
𝑋𝐻
𝑋𝑁
𝑋𝐼
𝑔 𝐶𝑂𝐷. 𝑚−3
𝑆𝑁𝐻
𝑆𝐻2𝐶𝑂3∗
𝑔 𝑁. 𝑚−3
𝑆𝐻𝐶𝑂3−
𝑔 𝐶𝑂2 . 𝑚−3
OTR
CTR
𝑘𝑔 𝐶𝑂2
. ℎ𝑜𝑢𝑟 −1
0
576
Influent
600
0
0
0
30
65
84
308
𝑘𝑔 𝑂2
. ℎ𝑜𝑢𝑟 −1
0
Steady-state
2
17.3
4151.6
463.9
399
0.4
64
163.7
555
Unit
The removal efficiency for substrate and nitrogen, both above 99%, are well within the requirements
for effluent discharge, but the removal efficiency is slightly overestimated in comparison to the
Waβmannsdorf WTTP (Thunberg, 2007). Also the ratio
𝑋𝑁
𝑋𝐻
is 11.5%, which is higher than average
range of 3-5% for a full-scale wastewater plant given the fact that this is a nitrification tank. The
wastewater composition of the Waβmannsdorf wastewater treatment plant also had a mean MLVSS of
3600 g.m-3, the simulated steady-state TSS approximates this value with a value of 3670 g.m -3. The
TSS was calculated using the method suggested in the Benchmark Simulation Model no. 1 (BSM1):
27
𝑇𝑆𝑆 = 0.75 ∙ (𝑋𝑠𝑡𝑜 + 𝑋𝐻 + 𝑋𝑁 + 𝑋𝐼 )
(4.1)
The simulated total liquid phase CO2 (𝑆𝐻2𝐶𝑂3∗ + 𝑆𝐻𝐶𝑂3− ) was 227 g.m 3. and the saturated H2CO3* (𝑆𝐻∞2𝐶𝑂3∗ )
was 54.7 g.m3. These values confirm the measured pH (6.8) of the Waβmannsdorf plant. The
modelled supersaturation of 𝑆𝐻2𝐶𝑂3∗ is stripped resulting in the carbon dioxide in the gas phase. The
simulated OTR and CTR values accounted to 555 kg O 2.hour-1 and 576 kg CO2.hour-1-, which is only a
slight overestimation for the Waβmannsdorf WTTP.
4.1.2
Dynamic simulation
In this subsection the results obtained by using a dynamic influent (as discussed in Chapter 3, Section
3.4) was compared to measured data as a means to evaluate the constructed model.
The model was again simulated for 20 days with a constant influent using the mean values given in
table 3.4 (Chapter 3, Section 3.4). The stoichiometric and kinetic parameters and the season
dependent parameters were also chosen for the same setting as the steady-state
In the table below the average data for the dynamic influent are denoted for influent and average
effluent. Compared the steady state simulation results in Table 4.1, the average simulation results
denoted in Table 4.2 are slightly lower. In general it can be stated that the same average
characteristics are obtained as for the steady-state case.
Table 4.2 Biological reactor influent and average data simulation for an influent with diurnal variation.
State
variables
Unit
𝑆
𝑋𝑠𝑡𝑜
𝑋𝐻
𝑋𝑁
𝑋𝐼
𝑔 𝐶𝑂𝐷. 𝑚−3
𝑆𝑁𝐻
𝑔 𝑁. 𝑚−3
𝑆𝐻2𝐶𝑂3∗
𝑆𝐻𝐶𝑂3−
𝑔 𝐶𝑂2 . 𝑚−3
OTR
CTR
𝑘𝑔 𝐶𝑂2
. ℎ𝑜𝑢𝑟 −1
0
557
Influent
600
0
0
0
30
65
84
308
𝑘𝑔 𝑂2
. ℎ𝑜𝑢𝑟 −1
0
Average
effluent
2.1
13.86
2078
256
163
0.36
64
163.7
530
28
Figure 4.1 Simulated results of off-gas and nitrification status. Season dependent parameters were
chosen for a temperature of 21°C. A (left side): OTR OTR and 𝑆𝑁𝐻 influent load, B (right side): 𝑆𝑁𝐻 in
the effluent, C: OTR and CTR, D: ratio plot of OTR/CTR Note that the y-axis of the overlay plots were
expanded to emphasize the difference in CTR and OTR, and OTR and nitrogen load.
The plant performed full nitrification most of the time except for one day (day 13) when influent
nitrogen loading was extremely high (Figure 4.1C). The results of OTR basically follow the plant
loading except at high nitrogen loading period, with a consistent lag of a few hours (Figure 4.1C). The
DO control system also controls total air, flow; therefore it is reasonable OTR is proportional to the
total plant loadings when all pollutants are fully oxidized. An important observation between OTR and
nitrogen loadings is the nitrification limit. At high loading period (day 13), ammonia was discharged.
This observation can serve as a good example for nitrification performance prediction, since DO was
kept at a constant concentration of 1.5 g.m -3, and thus did not provide information about nitrification
shortfall.
The ratio of OTR/CTR changed with nitrification conditions (Figure 4.1 B). The simulated OTR and
CTR (Figure 4.1A) showed the differences between OTR and CTR at different nitrification
performance and degree of ammonia discharge (Figure 4.1D): when nitrogen loadings are normal and
29
ammonia is fully nitrified, CTR matches well with OTR; but during times of high ammonia loading, CTR
is lower than OTR (Figure 4.1A). Figure 4.2 shows the correlations between CTR and OTR at the fully
nitrified period versus the ammonia overloading period. This figure contains the simulations for all
seasons based on the parameter values from Table 3.5. Off-gas measured OTRs and CTR are
linearly correlated in fully nitrified periods, while the slope of OTR over CTR is higher at nitrogen
overloading conditions and hence confirms the higher OTR/CTR ratio during the ammonia overloading
period.
Figure 4.2: Correlations of off-gas measured CTR and OTR at ammonia overloading period and fully
nitrified period. Distinguishable higher OTR can be observed during the ammonia breakthrough period.
The original results from the paper of Leu et al. (2010), illustrated similarly as in figures 4.1and 4.2 are
added in the appendix for comparison.
In conclusion, steady-state simulation showed that the established activated sludge model, gives
potentially realistic results based on evaluation of the steady-state reactor effluent composition. The
average simulation results for the dynamic case, has the same conclusion. Additionally, dynamic
simulation showed that the model exhibits the same dynamic trends concerning the OTR/CTR ratio.
Additionally, this section confirms the method for using online off-gas O2 and CO2 monitoring to predict
nitrification performance. The ratio of OTR/CTR increases during incomplete nitrification as was
originally stated by Leu et al. (2010).
30
4.2
Effect of process conditions on OTR/CTR
The model was simulated for different process conditions. Since CO 2 gas phase transfer is strongly
dependent on the bicarbonate equilibrium, a sensitivity analysis was conducted for varying pH.
Subsequently, the simulations for all four seasons are discussed in detail. Additionally, the effects of
the season dependent parameters on CTR and OTR was discussed, since these parameters are the
most sensitive parameters for OTR and CTR simulation. Finally, a case-study will be performed
investigating the influence of COD/TOC ratio on the gas phase composition.
4.2.1
Effect of pH
In section 3.5.1 the relationship between the buffer capacity and the pH was explained. The yield
coefficients needed for proton simulation were also calculated. To investigate the effect of varying
influent pH the ratio of
𝑆𝐻𝐶𝑂−
3
𝑆𝐻
∗
2 𝐶𝑂3
was varied, since it defines the pH.
Simulation resulted in a proton production in the range 7 -10 g H+.m-3, with an average production of
7.8 g H+.m-3. Since the proton production equals the bicarbonate consumption for constant pH the
influent 𝑆𝐻𝐶𝑂3− can be estimated by multiplying the proton production with the carbon dioxide molar
mass. At constant pH, the influent buffer capacity has no effect on the biological activity of the
activated sludge model. Only the carbon dioxide total mass in the system is increased by increasing
the buffer capacity and this singularly and directly influences the CTR due to gas stripping. A
sensitivity analysis was performed to estimate the potential errors in CTR caused by changes of pH.
The pH was varied from -0.6 to +0.6 units and the change in CTR was observed for three different
influent’s pH (6, 7 and 8). Simulation results indicated that if the pH decreases the model generally
overestimates the CTR. For an increasing pH the model underestimates the CTR. This trend is
illustrated in Figure 4.4 A below. CTR can thus be calculated under the assumption that the change of
pH does not change dramatically in a short period. For the Waβmannsdorf WTTP, the pH was stable
and the online measurements showed maximum pH change from 6.95 to 6.80, ΔpH = -0.15. The
percent error in simulated and actual CTR due to this change of pH is small (~10%) and relatively
constant. The buffer capacity of bicarbonate of a wastewater influent directly influence CTR, but has
little influence on other variables of interest. This can be clearly seen in Figure 4.4 B, where the OTR
did not vary with a different buffer capacity influent. This can be explained by the fact that a varying
buffer capacity is modelled as none other than different concentrations for 𝑆𝐻2𝐶𝑂3∗ and 𝑆𝐻𝐶𝑂3− , but with
the same ratio. The low buffer capacity concentrations for 𝑆𝐻2𝐶𝑂3∗ and 𝑆𝐻𝐶𝑂3− were both a factor 10
31
smaller than the usual influent for 𝑆𝐻2𝐶𝑂3∗ and 𝑆𝐻𝐶𝑂3− , namely: 84 and 308 g CO2.m-3 respectively (see
Table 4.1). This low buffer capacity however is not a realistic order of magnitude, but was simply
simulated to illustrate the effect of the buffer capacity. The model was found to be applicable for high
alkalinity wastewater plants, or WTTP with low ammonia concentration. For these conditions, pH
variation is limited and the potential error on CTR estimation is also kept to a minimum.
Figure 4.3 A: Effects of deviating pH change on CTR simulations. B: simulated CTR and OTR for
varying buffer capacity.
4.2.2
Seasonal trends of OTR and CTR
The model was simulated for all four seasons with the parameter values given in Table 3.5 adjusted
for each season. In the table below the mean values for a 15 days simulation per season are given for
OTR, CTR and OTR/CTR ratio. Also the mean effluent amount of 𝑋𝐻 , 𝑋𝑁 and their ratio was listed in
the table.
Table 4.2 Mean values for four seasons for OTR, CTR, OTR/CTR ratio𝑋𝐻 , 𝑋𝑁 and
𝑋𝑁
𝑋𝐻
.
OTR
CTR
OTR/CTR
𝑿𝑯
𝑿𝑵
𝑿𝑵
𝑿𝑯
Unit
kg O2.hour1
kg CO2.hour1
-
g COD.m-3
g COD.m-3
-
Summer
596
540
1.104
1547
296
5.22
Spring
546
489
1.115
1709
290
5.89
Winter
553
937
0.589
2313
367
6.29
Fall
565
579
0.976
1961
333
5.88
Variable
32
The average off-gas data are relatively constant during the year, with the average OTR ranging from
546 to 596 kg O2.hour-1. The solids retention time (SRT) was changed in response to water
temperature changes to maintain nitrification and denitrification capacity of the plant. SRT increases
from approximately 8 days in summer to 15.5 days in winter to provide better nitrification. The ratio
𝑋𝑁
𝑋𝐻
stays rather constant throughout the year, which is due to the adapted SRT. An exception to this trend
can be seen during the winter. The average CTR is significantly high during the winter, with a value of
937 kg CO2.hour-1, compared to the other values of CTR in the other seasons, which ranged from 487
to 579 kg CO2.hour-1. The same story holds true for the OTR/CTR, which drops during the winter to a
value of 0.589 caused by the high CTR. The other OTR/CTR ratio range from 0.976 to 1.115. In brief,
the average off-gas data are relatively constant throughout the year due to the adequate adjustment of
SRT.
4.2.3
Influence of COD/TOC ratio on OTR/CTR ratio
In this case-study, the assumption concerning the COD/TOC ratio suggested by Hellinga et al. (1996)
and discussed in the previous chapter will be investigated for the dynamic model (section 3.5.2).
The figure below illustrates the influence of COD/N and COD/TOC on OTR/CTR, which is the reverse
of the OTR/CTR. It is shown that OTR/CTR is strongly influenced by the COD/TOC ratio and hardly
sensitive to the COD/N ratio. Especially at lower to moderate COD/N ratios the influence of COD/TOC
is more outspoken. We can see that for a constant, low COD/N ratio, the OTR/CTR can vary from 0.5
to 1.6 for varying COD/TOC ratios. The same variance is slightly less outspoken for a constant high
COD/N, where OTR/CTR can vary from 0.4 to 1.3. Conversely for a constant COD/TOC, OTR/CTR
only varies over a range of 0.3. This results means that it seems feasible to use OTR/CTR
measurements for determining on-line COD/TOC ratios of the substrate, hence to replace the
laborious COD/TOC measurements with OTR/CTR. An advantage of calculating the ratio of
OTR/CTR, rather than the separate OTR and CTR, is that the ratio measurement only requires O 2 and
CO2 measurements in the off-gas and not gas flow measurements, which is more practical (Hellinga et
al., 1996). The effect of incomplete nitrification discussed in the previous section is limited here,
because of the slighter effect of COD/N. The influence of varying COD/TOC substrate on the
dynamics was also investigated. The OTR/CTR ratio was simulated for three different COD/TOC ratios
to visualize the variance. The graph clearly shows a wider variance for a higher value of COD/TOC.
With a high COD/TOC substrate of 4.9, the OTR/CTR varied from 1.2 to 2.6 during normal diurnal
variance. With a low COD/TOC substrate of 1.7, the OTR/CTR varied between 0.55 to 0.7. It was
33
found that the varying substrate composition mainly influenced the CTR. To quantify the variance of
the CTR, the coefficient of variance was calculated for increasing COD/TOC. Figure 4.5B illustrates
that the variance of CTR decreases with increasing COD/TOC, thus causing a higher variance for the
OTR/CTR (Figure 4.5A). For a COD/TOC of 1.7, CTR had a coefficient of variance of 38%, whereas
the coefficient of variance went down to a value of 26% for a COD/TOC ratio of 4.9. This effect of high
variance is most pronounced during incomplete denitrification on day 7.
Figure 4.4 Gaseous respiratory coefficient values for varying COD/TOC and varying COD/N ratios.
In conclusion, despite the fact that the substrate composition can’t be calculated completely based on
COD/TOC alone, the model could simulate the same results as was theoretically proposed by Hellinga
et al. (1996). This in turn affirms the assumptions made by these authors. It was shown that the
OTR/CTR is very sensitive to COD/TOC variation, much less so than the influent COD/N. This implies
that it seems feasible to use O2/CO2 measurements for determining on-line COD/TOC ratios of the
substrate. Also the variance of the OTR/CTR itself is an indication for the substrate affinity.
34
Figure 4.5 a) Variance of OTR/CTR for three different influent substrate compositions, b) coefficient of
variance of CTR for different influent substrate composition.
35
4.2.4
Estimation of biomass production rate
Hellinga et al. (1996) suggested that, if N-removal is determined in a different way, other than through
off-gas measurements. Biomass production can be estimated on-line using off-gas O2 and CO2
measurements. In order to evaluate the validity of this statement the OTR/CTR ratio was investigated
in relation to the production rate of biomass.
Figuur 4.6 From top to bottom. A: Oxygen uptake rate (OUR) divided into its constituting components,
B: OTR/CTR, C: CTR, D: Biomass production rate for XH and XN .
Biomass productions in reactor (the sum of the reaction kinetics that result in the production of 𝑋𝐻 and
𝑋𝑁 ) are illustrated in Figure 4.6D. It is clear that for the days that reactor performed full nitrification
(except days 13), both the production of 𝑋𝐻 and 𝑋𝑁 follow closely the trend of OTR/CTR ratio (Figure
4.6B). A deviation of OTR/CTR could be clearly observed in day 13 (Figure 4.6B), when incomplete
nitrification occurs. This coincided with the peak production of 𝑋𝑁 meaning that activities of autotroph
were greatly influenced by overloaded incoming nitrogen which in turn resulted in much higher OTR
while CTR is not affected by increased nitrogen loading as explained in 4.1.2. Indeed, when oxygen
uptake rate (OUR) is deconstructed in its building blocks, namely: OUR C, OURN and OURD (Figure
4.6A), it is shown that only OURN rises significantly when incomplete nitrification occurs (day 13). In
36
contrast OURC and OURD or in other words, the biomass production of heterotroph are not
significantly influenced by the higher nitrogen load. Since CTR is not affected by higher nitrogen load,
and neither is the heterotrophic biomass, CTR is also proportional to the biomass productivity. It
follows the same trend as the biomass production rate at all times.
In conclusion, it can be seen that combined monitoring of OTR and CTR are indicative for the biomass
production rate. Note that to calculate the biomass production rate, the gas flow rate should be known
also. This is not a requirement when monitoring the OTR/CTR ratio. CTR showed the same trend as
the biomass production rate on a consistent basis. OTR is a measure for the total microbial activity.
37
38
5.
GENERAL CONCLUSIONS AND PERSPECTIVES
In this thesis, a calibrated activated sludge model was reconstructed to simulate the production of
oxygen transfer rate (OTR) and carbon dioxide transfer rate (CTR). The model was used to
investigate: the potential of OTR/CTR to predict nitrification performance; the influence of pH on the
OTR/CTR ratio; the seasonal variation of OTR/CTR ratio; the influence of COD/TOC ratio on the
OTR/CTR ratio; the potential of off-gas CTR and OTR monitoring to estimate biomass production rate.
Prediction of nitrification performance through CO2/O2 monitoring
It was confirmed that the CTR can be successfully modelled and can be used in combination with OTR
to predict ammonia discharge. Monitoring the OTR/CTR ratio can successfully predict the nitrification
performance of an activated NDN process. During incomplete nitrification the OTR/CTR ratio rises
significantly due to higher oxygen consumption. Off-gas monitoring thus provides useful information to
evaluate the nitrifcation status.
Influence of pH
Variable influent pH causes an overestimation of CTR for a lower pH of the influent compared to the
pH at which the activated sludge process is controlled, whereas the reverse is true for a higher pH
influent. Additionally, it was found that the alkalinity in the form of bicarbonate influences the
magnitude of the CTR significantly. If a WTTP is controlled at constant pH, the CTR can be
successfully simulated. The simulated model was found to be applicable for high alkalinity wastewater
plants, or WTTP with low ammonia concentration.
Seasonal variation of OTR/CTR
The average off-gas emission is relatively constant throughout the year, this is because the SRT of a
WTTP is varied throughout the year to keep the WTTP performance optimal.
39
Influence of COD/TOC ratio
COD/TOC ratio, which is related to the substrate affinity, has a significant influence on the OTR/CTR
ratio, much more than the COD/N ratio. As such, it seems feasible to use O 2/CO2 measurements for
determining on-line COD/TOC ratios of the substrate. Additionally, the variance of the OTR/CTR in a
dynamic simulation is an indication for the substrate affinity. The higher the substrate affinity, the more
the OTR/CTR ratio fluctuates.
Estimation of biomass production rate through OTR and CTR monitoring
When OTR are CTR are monitored separately, it seems feasible that the biomass production rate can
be monitored. To monitor the biomass production rate, the knowledge of the gas flow rate is a
requirement. This is not a requirement when monitoring the OTR/CTR ratio. CTR showed the same
trend as the biomass production rate on a consistent basis. OTR is a measure for the total microbial
activity.
Perspectives
Combined monitoring of CTR and OTR provides some interesting supplementary information on the
activated sludge process performance. CTR in itself is less informative than OTR monitoring, however
the combination of the two proves some interesting perspectives:
-
Conducting a case-study either estimating biomass production or COD/TOC ratio with a realtime dataset would be a logical next step following this thesis.
-
ASM in its current form does not close the balance on C, H, O and N. OTR and CTR can be
used to estimate the COD/TOC ratio. Additional determination of VSS can provide a way to
determine the biomass composition of simple molecules (No Sulphur or Phosphor).
-
Monitoring CTR can be an interesting way to asses biological WTTP performance in cases
where OTR is limited, for instance in anaerobic/anoxic conditions.
40
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43
44
7. APPENDIX
Simulated results from Leu et al. (2010)
45