Full-scale modelling of an ozone reactor for drinking water treatment
Wim T.M. Audenaert1,2, Manly Callewaert1, Ingmar Nopens2, Jan Cromphout³, Robert Vanhoucke³, Ann
Dumoulin1, Pascal Dejans1, and Stijn W.H. Van Hulle1,2
Corresponding author: Wim.Audenaert@Howest.be
1EnBiChem Research Group, Department of Industrial Engineering and Technology, University College West
Flanders, Graaf Karel de Goedelaan 5, 8500 Kortrijk
2BIOMATH, Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure
Links 653, 9000 Gent
3VMW, Flemish Water Supply Company, Beliardstraat 73, 1040 Brussels, Belgium
In 2003, the Flemish Water Supply Company (VMW) extended its drinking water production
site in Kluizen (near Ghent, Belgium) with a combined ozonation and biological granular
activated carbon filtration (BGAC) process. Due to this upgrade, biostability increased, less
chlorination was needed and drinking water quality improved significantly. The aim of this
study was to describe the full-scale reactor with a limited set of equations. In order to describe
the ozonation process, a model including key processes such as ozone decomposition, organic
carbon removal, disinfection and bromate formation was developed. Kinetics were
implemented in WEST®and simulation results were compared to real data. The predicting
performance was verified with a goodness of fit test and key parameters were determined
through a local sensitivity analysis. Parameters involving optical density (both rate constants
and stoichiometric coefficients) strongly affect model output Some parameters with respect to
bromate and bacteria showed to be only, but to a large extent, sensitive to their associated
concentrations. A scenario analysis was performed to study the system’s behavior at different
operational conditions. It was demonstrated that the model is able to describe the operation of
the full-scale ozone reactor, however, further data collection for model validation is
Keywords: advanced oxidation processes, ozone, kinetic model, organic contaminant, full
scale drinking water production
In order to produce high quality drinking water from surface water resources, a combination
of physical and chemical treatment steps is typically used. To achieve good bacteriological
quality, chlorine is often used as oxidising agent. However, it is well established that chlorine
can lead to many problems in the aquatic environment due to the formation of potential toxic
organochlorine compounds such as tetrachloroethene, trichloroethene and halo-acetic acids
. Besides this, emerging pollutants and endocrine disruptors (EDC’s) became important
contaminants in water systems during the last few decades. In order to minimize this by-
product formation and to remove harmful compounds, advanced oxidation processes (AOP’s)
have already proven to be effective technologies [2,3]. Besides the potential benefits in
drinking water production, AOP’s have a large potential for the treatment of different types of
water and waste streams originating from waste water, such as domestic and industrial
effluent, sludge and membrane concentrates, swimming pool water and process water. Both
low and high concentrated flows can be treated with AOP techniques [4,5]. Most techniques
are based on the formation of hydroxyl radicals which are the strongest oxidators that can be
used in water treatment systems. Hydroxyl radicals can be generated in water through
different combinations of oxidants, like ozone and hydrogen peroxide, or by combining a
single oxidant with UV radiation . AOP techniques such as ozonation are either used for
(1) the complete or partial oxidation of the organic contamination, (2) the oxidation of a
specific contaminant or (3) the removal of pathogens. However, it should be noted, that ozone
also produces disinfection by-products (DBP) such as bromate which is formed out of
bromide and considered to be a potential human carcinogen . Ozone is used for removal of
MIB (2-methylisoborneol) in drinking water production installations as this component is
responsible for odour and taste problems . The stage 1 and stage 2 rules, promulgated by
US EPA (Environmental Protection Agency), defined a MCL (maximum contaminant level)
of 10 µg l-1bromate for systems using ozone . In Europe a similar regulatory level is
applied since 1998 (98/83/EC). If the incoming water contains high bromide levels, bromate
formation can be restricted by e.g. adding hydrogen peroxide (H2O2) to the ozonation process,
. In this case, hydroxyl radicals become important players.
Despite the many advantages and added value of the AOP techniques, there still exist several
bottlenecks and research questions concerning these techniques. First, scale-up of lab-scale
research reactors to full-scale industrial reactors is often failing. Second, further research on
process control and optimisation is necessary. Third, the removal of organic components and
micro-organisms is not yet completely unravelled. Modelling of AOP processes offers an
elegant and cost-effective tool to tackle these research questions.
Many different attempts have already been made to describe ozone decomposition with or
without the presence of organic compounds [10,11]. Two general accepted deterministic
models for ozone decomposition in “pure water” have been developed in the early 80’s, both
based on the first model of Weiss . The model of Staehelin, Hoigné and Bühler, known as
the SHB model, was experimentally developed at acidic to neutral pH’s [13,14] while
Tomiyasu, Fukutomi and Gordon (TFG) developed their model at high pH values . A
comparision of both, together with some simulation results can be found in . On the other
hand, numerous empirical and semi-empirical studies describing ozone decomposition
[17,18], reactions with organic compounds [16,18], by-product formation  and
disinfection  were conducted the last decades. Lovato and co-workers extended the SHB
model with an empirical approach by relating one of the 18 kinetic constants to the solution
pH . Van der Helm described ozone decomposition and organic compound removal by
using UV absorbance at 254nm (UVA254) as surrogate for the NOM concentration . Sohn
et al. developed an empirical relation to predict bromate formation related to several
operational and water quality parameters .
In this contribution a simplified model is presented for the simulation of the full-scale ozone
reactor of the Flemish Water Supply Company (VMW) in Kluizen (Belgium). The aim of this
study is to describe this process with a limited set of equations, to determine the key
parameters and to perform different scenario analysis. The results will be used as a starting
point for further model development, especially in terms of extending the mechanisms of
organic compound removal and biodegradability enhancement. As such, the model might
contribute to answering the research questions stated above.
The 60,000 m³ day-1water treatment plant (WTP) which is subject of this study is fed with
raw water captured from lowlands. Until 2003, the treatment concept consisted of micro-
sieving, enhanced coagulation followed by sludge blanket clarification, oxidation with
chlorine, sand filtration and granular activated carbon filtration (GAC). A final disinfection
with chlorine was applied [21,22]. The intensive chlorination resulted in high trihalomethanes
(THM) levels and prevented biofilm growth on the activated carbon granules. Due to the
absence of biological activity and limited contact time in the GAC (15 minutes), NOM
removal was mainly by adsorption, which is limited to the first 10,000 bed volumes (BV, a
unit that expresses the volume of water that already passed through the filter as a multiple of
the volume of the filter bed). In 2000 the earthy-musty taste and odour compound MIB
appeared in the feed water with concentrations above the odour threshold value of 10 ng l-1,
caused by algal growth in the reservoirs. This, along with a lack of biostability of the water
due to high TOC levels, forced the drinking water company to search for an effective
oxidizing technique. An ozone production and mixing unit was introduced, together with
biological granular activated carbon (BGAC) filters. Ozone was implemented for both
disinfection and oxidation. Due to the excellent disinfection capacities, a first chlorination
step could be omitted. On the other hand, ozone was also implemented to enhance
biodegradability in favour of the biofilm present in the BGAC to remove MIB and NOM.
Only a final chlorination step remained [21,22].
2. Material and methods
Ozone is produced from oxygen with two Wedeco EFFIZON®ozone generation units, each
with a production capacity of 4000 g h-1(180 g ozone per Nm³ oxygen/ozone mixture). The
ozone generating elements consist of discrete borosilicate glass tubes with a diameter less
than 11 mm. A part of the main water stream is pumped up and pressured up with a high
pressure booster pump. The water is brought into a venture injector where ozonated gas is
introduced into the water. The side stream is then re-introduced to the main water-pipe prior
to a static mixer. The normally applied ozone dose in the water is 2.5 mg l-1. A schematic
overview of the gas transfer process is given in Figure 1. A sampling point is located after the
static mixer (before the activated carbon filters). After the mixing is completed, the water
proceeds to the BGAC filters. Gas flow as well as ozone concentrations in the gas are
continuously monitored in order to evaluate the ozone transfer efficiency. An off-gas ozone
destruction system converts any residual or non-dissolved ozone to oxygen so that the ozone
concentration in the treated off-gas is lower than 0.1 ppm [21,22].
Figure 1: Schematic overview of the ozone production and mixing unit
2.2.Biological granular activated carbon filters (BGAC)
The BGAC consists of ten pressure filters with a diameter of 6 meter and operational pressure
of 1,5 to 2 bar. GAC is operated as a two-stage filtration, the first filter stage operates between
25,000 and 50,000 BV. After 50,000BV (two years) the carbon is reactivated and the filter is
Main water pipe
switched to the second stage position, which operates between 0 and 25,000BV. A contact
time of 6 minutes is obtained in the filters, above the carbon bed. Non-dissolved gas is
collected in an upward tee above each GAC filter and led away to the ozone destruction
The ozone reactor of the Flemish Water Supply Company was implemented in the modelling
and simulation platform WEST®(MostforWater, Belgium) as 2 continuous stirred tank
reactors (CSTR’s) in series. In the first tank with a volume of 10.68 m³ the ozone is
introduced. This tank represents the part of the main water pipe before and with the static
mixer. A second reactor represents the water on top of the activated carbon filters where a
second reaction phase takes place. The simulation configuration as used in the software
program is given in Figure 2.
Figure 2: Implementation of the ozone reactor in the simulation platform WEST®
Four ozone reactions were implemented in WEST according to [7,11,20,23]
Reaction of optical density (OD) with ozone (organic carbon oxidation)
Bromide oxidation (bromate formation)
Ozone reactions in water can be classified as either direct or indirect [10,23]. E.g., the
disinfection reaction rate can be described by the following equation, where molecular ozone
as well as hydroxyl radicals contribute to the oxidizing capacity of the system:
ρ : reaction rate, in this example the inactivation rate of bacteria (CFU m-3s-1)
: second order rate constant for the direct reaction of molecular ozone with a
specific compound, in this example micro-organisms (m³ g-1s-1)
: second order rate constant for the indirect ozone reaction pathway of hydroxyl
radicals with micro-organisms (m³ g-1s-1)
c R : the ratio of the concentrations of hydroxyl radicals and ozone
[O3]: the concentration of ozone in solution (g m-3)
[Xbact]: the density of viable micro-organisms, in this model expressed as colony-
forming units per liter (CFU m-³)
When Rcis assumed to be constant and very little, both direct and indirect reactions can be
lumped into one:
However, if the indirect mechanism plays an important role in the oxidation of some
calculated species, predictive capabilities of the model will deteriorate because process
efficiency in that case highly depends on other process conditions such as scavenger
concentrations that are not included in the model. Probably radical reactions indeed occur in
the waterworks because MIB was removed after ozonation, while rate constants with regard
to the direct and indirect pathway are <10 M-1s-1and 3×109M-1s-1, respectively .
Direct ozone decomposition is modeled assuming that ozone follows a first order decay with a
rate constant of 0.000485 s-1. UV absorbance (optical density) at 254nm was used as a
surrogate for the amount of organic material that reacts with ozone [11,24]. However, it has to
be highlighted that this parameter represents a part of the organic pollution concentration as it
specifically gives a measure of the amount of aromatic and unsaturated compounds in water
. On the other hand, this parameter offers great opportunities for modelling and control as
it can be determined on-line and consequently a huge amount of real-time and accurate data
are available. Accordingly, OD might be a useful parameter in model-based control of
Model calibration was performed with historical data of the year 2008 over a period of 300
days. For kOD, which is the rate constant for the reaction of UVA254with ozone, an initial
value in the range of 0.1 m³.g-1.s-1was used , while after calibration through parameter
estimation a value of 0.0135 m³ g-1s-1was found. The stoichiometry or yield (Y) of the
reaction is represented by YO3/OD, with a numerical value of 0.22 . This implies that one
unit of OD (m-1) consumes 0.22 grams of ozone.
Disinfection kinetics were adapted from the classic Chick-Watson model  (and references
For kX, the inactivation rate constant for a particular microorganism (m³.g-1s-1), a value of 0.6
m³.g-1.s-1was applied after calibration, while the initial value was 1.72 m³.g-1s-1. The
stoichiometry of the reaction is represented by YO3/X, with a numerical value of 1.29 10-14
Bromide oxidation was incorporated as bromate is an important by-product of ozonation in
bromide containing waters. Bromide was assumed to be directly oxidized to bromate,
although in reality ozone first oxidizes bromide to form hypobromous acid and hypobromite.
The latter is further oxidized to bromite which finally forms bromate[23,26,27]. As such, one
rate constant for bromate formation was determined after calibration and the stoichiometric
coefficients from this process were derived from a reaction where 1 mole of bromate is
formed out of 1 mole of both ozone and bromide. The rate constant was found to be 0.00043
The kinetics and stoichiometric coefficients used in the model are presented in Table 1 as a
Petersen matrix. This matrix presentation offers a clear overview of the chemical reaction
mechanisms included in the model. Reaction rates are indicated in the right column. Matrix
elements are stoichiometry parameters.
Table 1: Petersen matrix representing the model used in this study
The temperature and pH influence was not accounted for in this study as these parameters
remain almost constant during the waterworks daily operation. Although, for example the
inactivation constant for bacteria, kX, is temperature dependent . With respect to pH, this
parameter has a slight effect on the ozone decomposition rate when lower than 7, but at higher
values, the rate increases significantly . For instance, von Gunten and Hoigné showed that
the half-life of ozone is 10 times higher at pH 10 than that at pH 11 . These researchers
also reported that less bromate is formed when lowering the pH. Further, no gas transfer
equations were included in the model. A dissolved ozone concentration in the influent of the
first tank was defined at the beginning of each simulation run. Finally, the activated carbon
present in the second compartment could have a catalytic effect on ozone decomposition.
However, this was not considered.
The goodness-of-fit between experimental and simulated values was quantified by calculating
Theil’s inequality coefficient (TIC) , which is expressed as follows:
yirepresents the simulated data points
ym,irepresenting the measured data points
A value of the TIC lower than 0.3 indicates a good agreement with measured data .
A sensitivity analysis was performed in WEST®to determine the most important model
parameters (those parameters that have a major influence on the model output). The relative
sensitivity function (RSF) was adopted to evaluate the sensitivity of the model output
Reaction of optical
density with ozone
-1 -1.66 2.66 kBr[O3][Br-]
(concentration of ozone, OD, CFU and bromate) to a change of model parameters (Rate
constants kO3, kOD, kXand kBrand the stoichiomtric coefficients YO3/ODand YO3/X)
The RSF was calculated out of the sensitivity function (SF) by the finite forward difference
method with a perturbation factor of 0.1% . This means that SF’s were calculated by
raising the nominal parameter value with 0.1% as shown in following equation:
j θ ) represents the output variable
j θ represents the nominal parameter value
ξ is the perturbation factor
RSF was calculated as following:
A RSF less than 0.25 indicates that the parameter is not influential. Parameters are moderately
influential when 0.25<RSF<1. When 1<RSF<2 and RSF>2, the parameter seems to be very
and extremely influential, respectively .
Ozone was measured spectrophotometrically with the indigo reagent method at 600 nm .
Optical density was constantly measured with a process integrated UV spectrometer at 254
nm. Bromide, bromate and total CFU were analysed according to Standard Methods . All
analysis were performed by the Flemish Water Supply Company.
3. Results and discussion
Model calibration was performed with historical data of the year 2008 over a period of 300
days. The influent flow rate and optical density are represented in Figure 3. The influent
bacteria and bromide concentration were on average 183 CFU l-1and 138 µg l-1, respectively.
The descending trend of the OD can be attributed to improved settling and flotation
performance in the pre-treatment steps during that period.
)() , (
Figure 3: Influent flow rate and optical density
As mentioned before, all samples were withdrawn before the activated carbon bed (after the
static mixer). Consequently, all simulation results are representing the effluent of the first
reactor in Figure 2. Figure 4 shows the measured and calculated optical density after the first
reactor. A good agreement was obtained as the TIC for this parameter is calculated to be
0.044 (<0.3). The deviation of the applied rate constant for kODin comparision with literature
reported values can be explained by differences in organic carbon content of the water.
Figure 4: Comparison of measured and calculated optical density
s i t y
On average, 25% of the OD was removed for both calculated and modelled values.
Concerning the number of CFU, an average log removal of 1.2 was calculated, which is in
accordance with the measured removal (1.1 log). Measured influent bacteria concentrations
vary between 80 and 200 CFU l-1while those of the effluent are in the range of 10 CFU l-1.
Calculated and measured bacteria removals are compared in Figure 5. As can be seen,
calculated values agree well with experimental ones. This is confirmed with a calculated TIC
of 0.084, although it has to be highlighted that more data points have to be collected in future
0 50 100 150 200250300
t i c a
s i t y
Influent OD Influent flow rate
t i c a
OD effluent measured
OD effluent predicted
Figure 5: Comparison of measured and calculated logarithmic bacteria removal
c t e
r i a
Bromate was only measured once (4 µg l-1). Therefore, the average calculated bromate
concentration and the measured value are presented in Figure 6. Based on the measured value,
it can be seen that model predictions are realistic. In future work, the validation process will
be repeated, especially for bromate formation. Measured and calculated values are far below
the regulatory level of 10 µg l-1.
Figure 6: Predicted and measured bromate concentration in the effluent
t r a
t i o
The initial parameter values used in the sensitivity analysis are shown in previous paragraphs.
It can be clearly noticed that parameters involving OD (kODand YO3/OD) strongly affect model
output compared to others (Table 2). kODhas a moderate effect on calculated ozone, bacteria
and bromate concentrations. There’s a smaller influence with respect to OD itself. The same
conclusions can be made for YO3/OD, although moderate effects are slightly higher. kXand kBr
only influence their associated concentrations [X] and [BrO3-]. However, due to the very
affecting character (RSF?????accurate estimation regarding these parameters will be necessary
to obtain realistic predictions of bacteria and bromate levels. kO3and YO3/Xdo not exert an
050 100150 200 250 300
( l o
r i t h
i c )
Average predicted concentration Measured value
influence on simulation output. Bacteria (X) form part of OCS (ozone consuming substances),
but they consume a negligible amount of ozone due to their extremely low concentrations
. This explains that YO3/X has no effect on the bacteria concentration as the ozone
requirement for this reaction is met under normal operational conditions.
Table 2: RSF values indicating the degree influence of model parameters on output
3.3. Scenario analysis
The effect of applied ozone concentration and flow rate on certain key variables was
evaluated. The normally operational ozone dose in the drinking water production centre is 2.5
mg l-1. Scenario’s were calculated for concentrations varying from 0 to 5 mg l-1. Flow rate
(and consequently hydraulic retention time) was varied within a range of 0 (batch reactor) to 1
m³ s-1while real influent flow rates are in the range of 0.4 to 0.5 m³ s-1.
Effects of ozone dose on effluent OD and bromate formation are shown in Figure 7. This
figure reveals that a compromise has to be made regarding ozone dose and flow rate to
comply with bromate levels without losing OD (and bacteria) removal goals. Figure 7 also
shows that the normally applied flow rates in this case are in a beneficial range. A significant
amount of OD is removed and the bromate level stays well below the standard of 10 µg l-1.
The influent OD concentration was 11.3 m-1.
Figure 7: Scenario analysis, effect of flow rate on OD and bromate concentrations
Flow rate (m³ s-1)
t r a
t i o
t i c a
s i t y
OD at 1ppm ozone OD at 2ppm ozoneOD at 3ppm ozone
BrO3 at 1ppm ozoneBrO3 at 2 ppm ozoneBrO3 at 3 ppm ozone
Figure 8: Scenario analysis, effect of ozone dose on OD and bromate concentrations
As can be deduced from Figure 8, the operational ozone concentration of 2.5 mg l-1is well
chosen. Again, sufficient OD is removed and the bromate guideline is met without problems.
3.4 Bromate formation
Sohn and co-workers stated that most of the models for predicting residual ozone and bromate
formation take empirical functional forms because the complexity of natural organic matter
restricts developing complete theoretically based chemical kinetic models . They
developed a multiple regression model that was compared to experimental data from the
VMW  (and references therein):
82 . 526. 16
[ 1055 . 1][
2028 . 0 73. 0
57 . 1
) 035. 1 (
Figure 9 shows this comparison. The model developed in this study was added to the graph.
The regression model seems to show a better prediction of the experimental data, as also
indicated when comparing the TIC values of both, the regression and the kinetic model with
values of 0.17 and 0.21, respectively. However, both models agree well with reality
(TIC<0.3). The over-estimation of bromate by the kinetic model can be caused by inaccurate
parameter estimation with insufficient data as kBr has proven to be very influential to
simulated bromate concentrations (see sensitivity analysis). The assumptions that were made
regarding the bromate mechanism, together with not included temperature effects can
contribute to less prediction accuracy, together with the fact that only 1 data point was
available for calibration. Regression models are able to describe experimental data very well
and temperature or reactor correction factors can be easily added . On the other hand,
models based on well defined mechanisms can give more substantiated insight in processes
and are easier to apply.
0,0 1,0 2,0
Ozone concentration (ppm)
t r a
t i o
t i c a
s i t y
OD_in 18OD_in 14OD_in 10 OD_in 6
BrO3 at OD_in 18BrO3 at OD_in 14 BrO3 at OD_in 10BrO3 at OD_in 6
Figure 9: comparison of predicting performance of a regression model and the kinetic
model developed in this study.
t r a
t i o
In this study, a simplified kinetic model describing ozone decomposition, organic carbon
removal, disinfection and bromate formation during ozonation applied in drinking water
production was developed. Calibration and simulation runs were based on historical data from
a full-scale ozonation system at the Flemish Water Supply Company waterworks in Kluizen,
Belgium. It was demonstrated that the developed model is able to predict excess ozone
concentration, OD removal, bacteria inactivation and bromate formation, although further
data collection and batch experiments will be necessary to further validate the model. A
sensitivity analysis revealed that parameters involving optical density (both rate constants and
stoichiometric coefficients) strongly affect model output. Some parameters with respect to
bromate and bacteria showed to be only, but to a large extent, sensitive to their associated
concentrations. OD seems to be a valuable parameter for the application of model-based
control and optimization strategies as it can be determined on-line and consequently a huge
amount of real-time and accurate data are available.
With drinking water standards becoming more stringent, models will become an important
tool to assess drinking water plant performance .This model will be used for further
scenario analysis (particularly effect of ozone dosage on reactor performance) and will play
an important role in the drinking water modelling studies in Flanders. The model will be used
as starting point for a more detailed model which includes radical reactions (i.e. model of
Staehelin, Hoigné and Bühler) [10,13,14] and a combination of AOP techniques (ozone, UV,
H2O2) to guarantee satisfactory model predictions and to improve the applicability and
The authors would like to thank the Flemish Water Supply Company (VMW) and in
particular Jan Cromphout and Robert Vanhoucke for the fruitful collaboration and making the
data of the water purification site available.
This research was partially funded by a University College West Flanders PhD Research
0,0 1,02,0 3,04,05,0
Ozone concentration (ppm)
Measured (data VMW)
Mode developed in this study
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