This document describes a CFD simulation of fluid dynamics and biokinetic processes within an activated sludge reactor (ASR) operating under intermittent aeration. The CFD model considers fluid dynamics, oxygen transfer, and biological processes described by Activated Sludge Model No. 1 (ASM1). The model is used to evaluate two different aeration system configurations for an ASR in terms of their ability to satisfy effluent requirements with minimum energy consumption. Results show that modifying the air diffuser layout can improve energy consumption by 2.8%, and reducing the air flow rate per diffuser improves energy consumption by 14.5%. The model provides insight into aeration inefficiencies within ASRs.

1. CFD simulation of fluid dynamic and biokinetic processes within
activated sludge reactors under intermittent aeration regime
F. S
anchez a
, H. Rey b, c, *
, A. Viedma a
, F. Nicol
as-P
erez d
, A.S. Kaiser a
, M. Martínez c
a
Departamento de Ingeniería T
ermica y de Fluidos, Universidad Polit
ecnica de Cartagena, Dr. Fleming, s/n, 30202, Cartagena, Spain
b
Instituto de Ingeniería del Agua y Medio Ambiente, Universitat Polit
ecnica de Val
encia, Camino de Vera 14, P.O. Box 46022, Valencia, Spain
c
Prointec S.A., C/ De San Juli
an nº1, 28108 Alcobendas, Madrid, Spain
d
Lynx Simulations S.L., Calder
on de la Barca 31, 30180 Bullas, Spain
a r t i c l e i n f o
Article history:
Received 16 November 2017
Received in revised form
10 March 2018
Accepted 27 March 2018
Available online 28 March 2018
Keywords:
WWTP
CFD
ASM1
Intermittent aeration
a b s t r a c t
Due to the aeration system, biological reactors are the most energy-consuming facilities of convectional
WWTPs. Many biological reactors work under intermittent aeration regime; the optimization of the
aeration process (air diffuser layout, air flow rate per diffuser, aeration length …) is necessary to ensure
an efficient performance; satisfying the effluent requirements with the minimum energy consumption.
This work develops a CFD modelling of an activated sludge reactor (ASR) which works under inter-
mittent aeration regime. The model considers the fluid dynamic and biological processes within the ASR.
The biological simulation, which is transient, takes into account the intermittent aeration regime. The
CFD modelling is employed for the selection of the aeration system of an ASR. Two different aeration
configurations are simulated. The model evaluates the aeration power consumption necessary to satisfy
the effluent requirements. An improvement of 2.8% in terms of energy consumption is achieved by
modifying the air diffuser layout. An analysis of the influence of the air flow rate per diffuser on the ASR
performance is carried out. The results show a reduction of 14.5% in the energy consumption of the
aeration system when the air flow rate per diffuser is reduced. The model provides an insight into the
aeration inefficiencies produced within ASRs.
© 2018 Elsevier Ltd. All rights reserved.
1. Introduction
Wastewater treatment involves a combination of physical,
chemical and biological processes in order to remove the different
pollutants from the incoming wastewater. To achieve it, the process
of aeration is a must in the majority of wastewater treatment plants
(WWTP). However, it represents the largest proportion of the plant
energy consumption, ranging from 45 to 75% of the plant energy
expenditure (Reardon, 1995). It is interesting to analyse the aera-
tion system of biological reactors, since it is the most important
factor to achieve an optimal energy performance. One of the most
extended types of biological reactor configuration is the plug-flow,
very common in large plants, as well as in those where simplicity
and robustness is a priority. The application of accurate ON/OFF
aeration cycles (intensity and length) can diminish a significant
percentage of the energy costs. So an efficient aeration strategy,
analysed previously to be applied in plant, is convenient.
Most of the design and operation WWTP handbooks focuses on
the biological phenomena which occurs within the reactors,
without taking into account the fluid dynamic phenomena, since
biological modelling could provide enough information to satisfy
quality standards of the effluent. The series of Activated Sludge
Models, ASM (Henze et al., 2000), developed by the International
Water Association (IWA) are the most popular models for the
design of activated reactors. Some commercial software (i.e. WEST,
BioWIN) solves the ASM equations, assuming that the flow behaves
as a perfect mixed or plug flow. However, some authors (Ouedraogo
et al., 2016) point out the importance of fluid dynamics in the
pollutant removal of a biological reactor. A correct fluid dynamic
design improves the reactor efficiency, reducing operational costs.
In this context, Computational Fluid Dynamics (CFD) arises as an
adequate tool for modelling fluid dynamic and biological processes
which take place in WWTP reactors. One of the reasons of the
growth of the applications of CFD in WWTP analysis is its ability to
* Corresponding author. Instituto de Ingeniería del Agua y Medio Ambiente,
Universitat Polit
ecnica de Val
encia, Camino de Vera 14, P.O. Box 46022, Valencia,
Spain.
E-mail address: hecregon@upvnet.upv.es (H. Rey).
Contents lists available at ScienceDirect
Water Research
journal homepage: www.elsevier.com/locate/watres
https://doi.org/10.1016/j.watres.2018.03.067
0043-1354/© 2018 Elsevier Ltd. All rights reserved.
Water Research 139 (2018) 47e57

2. combine mathematical models of phenomena from different na-
ture (fluid dynamics, biological processes, etc.).
CFD has been widely employed to simulate the different hy-
draulic and biological phenomena which occurs within biological
reactors. Many authors have developed numerical models to
simulate the flow behaviour in biological reactors. Most of these
works employed the k-ε turbulence model (i.e. Yang et al., 2011;
Stamou, 2008), although some authors have also achieved good
agreement with experimental data by using k-u models (Gresch
et al., 2011). The multiphase (liquid-air bubbles) modelling has
been extensively investigated in the literature. There are two main
alternative approaches to model bubble columns reactors: Euler-
Euler (Gresch et al., 2011; Cockx et al., 1997) or Euler-Lagrange
(Gong et al., 2007). Although Euler-Lagrange approach allows the
calculation of each single bubble trajectory, it requires a high
computational cost. For this reason, Euler-Euler approach is more
common in bubble flow simulations. Residence Time Distribution
(RTD) analysis is commonly employed to evaluate the hydraulic
efficiency of biological reactors (Teixeira and Siqueira, 2008). A
mass of tracer is released at the reactor inlet; the RTD curve of the
reactor is obtained from the temporal evolution of tracer concen-
tration at the reactor outlet. There are some publications con-
cerning pulse-tracer RTD experiments to evaluate the hydraulic
performance of ASRs (S
anchez et al., 2016; Sarkar et al., 2017). The
RTD curve of a reactor can be calculated by CFD from the velocity
field previously obtained, using two different techniques: particle
tracking method (Stropky et al., 2007) or solving the transport
equation of a passive tracer (Talvy et al., 2011). Moullec et al. (2008)
compared both numerical RTD techniques, achieving a good
agreement between both numerical curves and the experimental
one, obtained in a bench scale reactor. One of the major challenges
in the activated sludge reactors (ASR) modelling is the calculation
of oxygen mass transfer from air bubbles to the liquid. The oxygen
mass transfer modelling requires the evaluation of the mass
transfer coefficient, KLa. Traditional ASR design software employs a
global KLa coefficient in each tank. However, CFD is able to calculate
Nomenclature
ci concentration of the component i
COD chemical oxygen demand
Csat oxygen saturation concentration
d dimensionless axial dispersion parameter
db bubble diameter
Def effective diffusivity
DL diffusion coefficient
eij
a relative error of the key variable
EðtÞ RTD curve
Fs safety coefficient, GCI method
g
!
gravitational acceleration
Hc Henry constant
Ipq
!
momentum exchange between two phases
k turbulent kinetic energy
KL mass transfer coefficient
KLa volumetric mass transfer coefficient
kp kinetic turbulent energy of the phase p
N apparent method order
N-NH4 ammonia concentration
N-NO3 nitrate concentration
P pressure in the bubble
Qd flow rate per diffusor
QextR external recirculation flow rate
Qin influent flow rate
QintR internal recirculation flow rate
Qt total flow rate
Qt average flow rate
R2 coefficient of regression
Ri reaction i of the ASM1
rij di=dj, GCI method
Sct turbulent Schmidt number
SNH ammonia nitrogen
SNO nitrates
SO dissolved oxygen
SS readily biodegradable substrate
t time
t0 averaged residence time
tON aeration subcycle length
TSS total suspended solids
up
!
velocity of the phase p
Vr air-liquid relative velocity
vtrO oxygen transfer rate from air to liquid
_
W average power consumption of the blower
XBA autotrophic biomass
XBH heterotrophic biomass
Xo volume fraction of oxygen in air (20.9%)
Greek symbols
aa air volume fraction
ap volumetric fraction of the phase p
Vp pressure gradient
Dt time step
Dp pressure jump in blower
dI size of the mesh I
ε dissipation rate of k
εp dissipation rate of the phase p
h blower efficiency
hset settler efficiency
mp laminar viscosity of the phase p
mt;p turbulent viscosity of the phase p
yef effective kinematic viscosity
ykj stoichiometric coefficient of the component i in the
reaction j
rj process rate
rp density of the phase p
tp viscous stress of the phase p
tt;p turbulent stress of the phase p
4 Key variable, GCI method
u specific rate of dissipation of k
Abbreviations
ADM Axial Dispersion Model
ASM1 Activated Sludge Model No. 1
ASR Activated sludge reactor
CFD Computational Fluid Dynamics
HRT Hydraulic Residence Time
IWA International Water Association
GCI Grid Convergence Index
PID Proportional e Integral - Derivative
RTD Residence Time Distribution
SOTE Oxygen Transfer Efficiency
WWTP Wastewater treatment plant
F. S
anchez et al. / Water Research 139 (2018) 47e57
48

3. a KLa field within the reactor, from the local values of air volume
fraction obtained in the multiphase simulation. Cockx et al. (2001)
incorporated the penetration theory of Higbie (1935) equation to
his CFD model to predict the local values of KLa, the numerical re-
sults were numerically validated in an airlift pilot plant. Fayolle
et al. (2007) also employs Higbie equation to calculate numeri-
cally the oxygen mass transfer, the results were experimentally
validated and show a notable influence of the fluid dynamics on the
oxygen mass transfer. Apart from fluid dynamics and mass trans-
ference, CFD is able to incorporate biokinetic reactions into the
reactor model. Few authors have integrated the biological pro-
cesses, by means of ASM, into CFD models. By this way, it is possible
to analyse the influence of geometry, wastewater flow rate, air
diffuser layout or aeration regime on the removal of pollutants.
Glover et al. (2006) developed a complete CFD-ASM1 model of an
ASR and used it to evaluate the performance of the reactor. Moullec
et al. (2010a) developed and experimentally validated a complete
CFD-ASM1 modelling of an activated sludge channel reactor under
pseudo-steady state, one of the conclusion of the work was that a
compromise between precision and grid size has to be found, since
the coupling with biokinetics is computationally expensive. Lei and
Ni (2014) developed a complete numerical modelling (CFD-ASM1)
of an oxidation ditch, considering three different phases (sewage,
sludge and air bubble). The numerical results showed a good
agreement with the experimental data. More recently, Yang et al.
(2016), carried out a CFD modelling of a lab-scale membrane
bioreactor (MBR). They employed a simplified version of the ASM1
model and considered a three-phase flow. The results showed
pretty good consistency with the experimental data and the model
was employed to evaluate the performance of a full-scale MBR
(Yang et al., 2017). A complete review about the applications of CFD
tools to ASRs analysis can be found in the work of Karpinska and
Bridgeman, (2016). In the review of Ho et al. (2017), there are
some examples of the application of CFD-ASM models for the
analysis of waste stabilization pond systems.
All the above-mentioned works correspond to CFD models
where the biological simulation is carried out under steady con-
ditions. However, many ASRs operate under intermittent aeration
regime, switching on and off the aeration system. The aim of this
work is to develop a CFD modelling capable to simulate the bio-
kinetics within an ASR under intermittent aeration regime. The CFD
modelling also takes into account fluid dynamic and oxygen
transfer phenomena. The modelling is applied to the design of an
ASR. The modelling is employed to find the aeration system which
satisfies the standard quality effluent with the minimum energy
consumption. An analysis of the influence of the air flow rate per
diffusor on the aeration energy consumption is also carried out.
2. Problem description
An old WWTP needs to be restored in order to treat the
wastewater of a village (2500 population) before its discharge into
the sea. One of the most critical conditions imposed for the
enlargement of the WWTP is to transform the actual storm tank
(440 m3
) into the biological reactor of the plant (modified Ludzack-
Ettinger system).
There are two identical parallel ASRs. Each one is formed by two
zones: anoxic chamber (91 m3
) and oxic chamber (128 m3
). They
are separated by a partition wall which allows the pass of the water
over it. The anoxic chamber has a mixer impeller (Sulzer-XRW210)
and the floor of the oxic chamber is covered by air diffusers. A
fraction of the ASR effluent (internal recirculation of mixed liquor)
is returned to the anoxic chamber, and the rest flows into a clarifier,
where the sludge is separated from the water. Most of the sludge
from the settler is directly recycled to the ASR inlet (external
recirculation), however a small sludge fraction is conduced to the
thickener and centrifuge, where the liquid part is extracted and
leaded to the WWTP inlet. Fig. 1 shows the secondary treatment
diagram of the considered WWTP.
Two aeration configurations are considered for each one to the
two ASRs. Configuration 1 (C1) consists of 48 air diffusers ABS-
PIK300 (7 Nm3
/h/diff), distributed uniformly on the floor of the
oxic chamber (8 rows, 6 air diffusers per row). Fig. 2a shows the
spatial distribution of the air diffusers in C1. Configuration 2 (C2)
involves 80 air diffusers ABS-KKI215 (4 Nm3
/h/diff) with a hetero-
geneous layout; the spatial distribution is formed by 10 rows, with
8 air diffusers per row. According to the specifications of the
manufacturer, the representative bubble diameter is 2 mm in both
devices. At the beginning of the chamber, where the oxygen de-
mand is higher, the rows are closer than at the end of the chamber.
Fig. 2b shows the spatial distribution in C2.
Although C2 needs a higher initial investment, some manuals
(EPA, 1989) point out that aeration efficiency improves by
increasing air diffusers density (more air diffusers per m2
for the
same total air flow rate) and by avoiding a uniform layout (more air
diffusers in the first part of the chamber, where the oxygen demand
is higher). It is worthwhile to evaluate the improvement in the
process performance and the savings produced by this aeration
configuration.
Regarding the operating conditions and effluent requirement
parameters, the wastewater flow rate treated in the reactor is
Qin ¼ 500 m3
/d. The characteristics of the influent are
COD ¼ 525 mg/l, N-NO3 ¼ 0.85 mg/l, N-NH4 ¼ 45.9 mg/l and
TSS ¼ 209 mg/l. According to the European Council Directive 91/
271/EEC, transposed to the Spanish legislation (RD 11/1995),
applied for the local hydrographic confederation, the effluent
quality requirements are: COD 50 mg/l, N-NO3 10 mg/l, N-
NH4 1.5 mg/l and TSS 20 mg/l.
In order to satisfy the effluent requirements, as observed in
Fig. 1, the internal recirculation is set to QintR ¼ 500.1 m3
/d per lane,
while the recycled sludge flow rate from the settler is
QextR ¼ 490 m3
/d. The settler is assumed to have an efficiency,
defined as fraction of non-settleable solids, of hset ¼ 0.995. Thick-
ener and centrifuge efficiency are 0.96, while their dry solids frac-
tion are 3.4% and 21%, respectively.
During the process, the aeration PID controller, based on an
ammonia set-point, switches on and off the air blower. A complete
aeration cycle involves the ON and the OFF sub-cycles. The
controller is set to keep an average effluent ammonia concentration
of 1.4 mg/l (below the standard requirement, 1.5 mg/l). If the
average effluent ammonia concentration during a cycle is above
Fig. 1. Diagram of the WWTP secondary treatment where the ASR is located.
F. S
anchez et al. / Water Research 139 (2018) 47e57 49

4. 1.4 mg/l, the PID controller increases the ON sub-cycle length, and
vice-versa. An excessive number of starts/stops per hour can reduce
the blower motor lifespan; on the other hand, the more time the
aeration is activated, the less efficiency of the oxygen transfer
process (oxygen concentration increases and therefore oxygen
transmission rate decreases). A compromise between motor life-
span and process efficiency is adopted: the complete aeration cycle
(ON þ OFF sub-cycles) is set to have 12 min length. Although the
total duration of the aeration cycle is fixed, the PID controller reg-
ulates the sub-cycles duration in order to keep the ammonia set-
point.
3. Numerical modelling
3.1. Domain
The geometry of the numerical modelling includes one of the
two identical lanes. The geometry is formed by two chambers,
separated by a partition wall which allows the pass of the liquid
over it. The raw wastewater comes into the anoxic zone, the domain
outlet corresponds to the outlet weir of the oxic zone. The mixer
impeller of the anoxic zone includes the fix part where the engine is
located (treated as a standard wall) and the blades, which as in the
work of Yang et al. (2011) are modelled as a plane area where a
pressure jump occurs. The air diffusers of the oxic chamber are
included on the geometry. A general view of the geometry of the
problem can be observed in Fig. 2, where the mixer impeller and
the air diffusers are also shown.
3.2. Governing equations
Two phases are considered in the numerical modelling:
continuous phase (mixed liquor) and dispersed phase (air bubbles).
In a more rigorous approach, the flocs phase should be approxi-
mated to another phase. However, a three-phase flow simulation
would increase considerably the computational cost, making
impossible to simulate the intermittent aeration regime conditions.
In a conventional WWTP, there should not be sludge settlement in
the ASR, it should be produced within the settlers, so the two-phase
flow assumption can be taken. Nevertheless, it is important to
mention that some hydraulic inefficiencies (e.g. stagnant zones)
could generate sludge sedimentation and there could be zones
where the two-phase flow assumption would not hold. The
multiphase flow is modelled by a Euler-Euler approach. A set of
continuity and momentum conservation equations is applied to
each phase p.
V,
aprp up
!
¼ 0; (1)
V,
aprp up
!
up
!
¼ apVp þ V,ap
tp þ tt;p
þ aprp g
!
þ Ipq
!
;
(2)
where ap is the volumetric fraction of the phase p (
P
i
ai ¼ 1). The
term Ipq
!
stands for the momentum exchange between the both
phases: drag and virtual mass forces. Drag forces are modelled by
the Schiller-Naumann model for spherical particles (Schiller and
Naumann, 1935). The term tp is the viscous stress and tt;p is the
turbulent stress tensor, which are defined as:
tp ¼ mp
V up
!
þ V up
!T
2
3
mpV, up
!
I; (3)
tt;p ¼ mt;p
V up
!
þ V up
!T
2
3
kp þ mt;pV, up
!
I; (4)
being mp and mt;p the laminar and turbulent viscosity of the phase,
respectively. The term kp is the kinetic turbulent energy. The
mixture k-ε turbulence model (Behzadi et al., 2004) is employed to
solve the closure problem. This turbulence model solves a unique
transport equation of kinetic turbulent energy k and a unique
transport equation of its dissipation rate ε for the mixture formed
by the two phases. The physical properties of the mixture are
calculated by the weighted-average sum of the properties of each
phase, according to its volume fraction. The value of the turbulent
viscosity is calculated from the turbulent variables mt;p ¼
0:09rpðk2
p=εpÞ.
The evaluation of the fluid dynamic efficiency of the ASR is done
by means of the simulation of a pulse RTD experiment. A mass of
passive tracer is released at the ASR inlet, and the evolution of the
tracer concentration at the ASR outlet provides the RTD curve. The
RTD analysis is done numerically by solving the unsteady tracer
transport of the passive tracer ct on the continuous phase:
vct
vt
þ V,
ui
!
ct
¼ V,
Def Vct
; (5)
where Def is the effective diffusivity, sum of laminar and turbulent
diffusivity Def ¼ Dm;t þ mt=ðrSctÞ. The velocity and turbulent vis-
cosity used in the transport equation are taken from the fluid dy-
namic simulation. The turbulent Schmidt number Sct is set to 0.7.
The biological processes within the ASR are calculated from the
velocity, air fraction and turbulence fields obtained in the fluid
dynamic simulation (Eqs. (1) and (2)). Biological model ASM1
(Henze et al., 2000) is employed to model the biochemical re-
actions. A transport equation is solved for each one of the 12 bio-
logical components ck considered.
vck
vt
þ V,
ui
!
ck
¼ V,
Def Vck
þ
X
ykjrj; (6)
being ykj is the stoichiometric coefficient of the component ck in the
reaction j, whose kinetic velocity (process rate) is rj. The turbulent
Schmidt number is set to 0.7, similarly to other CFD-ASM1 models
(Le Moullec et al., 2010b; Karpinska, 2013). As with the passive
transport equation (Eq. (5)), the value of the turbulent diffusivity
field within the ASR (above 103
m2
/s) is considerably greater than
the molecular diffusivity of the components (about 109
m2
/s), so
the influence of molecular diffusion can be neglected. Special
attention is given to the component dissolved oxygen (SO). The SO
transport equation has an additional source term vtrO, which
mimics the oxygen transfer from the air bubbles to the mixed li-
quor. The oxygen transfer ratio between air bubble and water is
evaluated as:
vtrO ¼ KL
6aa
dbð1 aaÞ
ðCsat SOÞ; (7)
where aa is the air volume fraction, db the average bubble diameter,
Csat the oxygen saturation concentration in water and KL the mass
Fig. 2. Air diffuser layout of the two aeration configurations considered: a) C1; b) C2.
The mixer impeller is located in the anoxic chamber.
F. S
anchez et al. / Water Research 139 (2018) 47e57
50

5. transfer coefficient, which is evaluated by means classical pene-
tration theory (Higbie, 1935):
KL ¼ 2
ffiffiffiffiffiffiffiffiffiffi
DLVr
pdb
s
; (8)
being Vr the relative velocity between the phases and DL the
diffusion coefficient (at 20 C). The alpha correction factor (Asselin
et al, 1998) is applied to transform the clean water kL to wastewater
kL. The oxygen saturation concentration Csat is calculated from the
Henry's law (de Gracia, 2007):
Csat ¼ HcXoP; (9)
being HC the Henry coefficient (at 20 C), XO the volume fraction of
oxygen in the air bubbles (20.9%) and P the pressure in the bubble.
Although the pressure in the bubble is the sum of hydrostatic and
surface tension pressure, the contribution of surface tension is
much smaller than hydrostatic pressure and it is not taken into
account.
3.3. Solver settings and boundary conditions
The CFD simulation of the ASR is performed in two stages, using
a freezing technique. The first stage involves the resolution of the
steady fluid dynamic equations (multiphase flow with turbulence
model). The fluid dynamic simulation is carried out under two
different aeration regimes: with aeration and with the air diffusers
disconnected, so two velocity, turbulent viscosity and air fraction
fields are obtained. Once the fluid dynamic simulation is
completed, the biological simulation is done from the fields ob-
tained. The biological simulation must calculate the evolution of
each one of the ASM1 components during the aeration cycle. The
aeration cycle includes two sub cycles: one with air diffusers
running and another without aeration. This issue is taken into ac-
count by switching the fluid dynamic fields used in the ASM1
transport equations (Eq. (6)). A set of steady velocity, turbulent
viscosity and air fraction field is employed when the aeration is
running, the other set of fluid dynamic fields is employed when air
diffusers are not running. This procedure assumes that fluid dy-
namic fields remain steady during each aeration sub cycle, an
instantaneous switch of the fields is produced when aeration is
connected or disconnected. However, in the actual ASR, the “no-air”
velocity field takes a lapse of time to evolve to the “air” velocity
field when air diffusers activates. In order to evaluate that “trans-
formation” time, a transient CFD simulation of the multiphase flow
has been carried out, starting from the “no-air” state. It has been
found that, due to the low depth of the ASR (1.5 m), the trans-
formation time is approximately 10 s, far less than the cycle dura-
tion (720 s), so the assumption of only two fluid dynamic states
during the aeration cycle can be taken. A virtual aeration controller
is included in the CFD model for the biological simulation, the
solver changes the fluid dynamic state according to the criteria
specified in section 2. The transient simulation is running until
reaching a pseudo-steady state: until the evolution of the ASM1
variables repeats from one cycle to the next one. The results pre-
sented in this paper correspond to the pseudo-steady state, since
during the previous cycles (from initial conditions to pseudo-
steady state) the ammonia set-point criteria specified in section 2
is not accomplished.
The CFD modelling was developed by means of the software
OpenFOAM V4.1 (Weller et al., 1998). The “SIMPLE” algorithm is
used in the fluid dynamic simulation under “no-air” conditions,
since it is a steady state. The “PIMPLE” algorithm (Holzmann, 2017),
hybrid of “SIMPLE” and “PISO” is employed for the fluid dynamic
simulation under “air” conditions. Since this algorithm is intrinsi-
cally unsteady, the simulation is running until finding and steady
averaged flow over time, which will be used in the biokinetic
simulation”. The transient biokinetic simulation is carried out by
solving sequentially the transport equation of each ASM1 compo-
nent for each time step. The well-known second-order up-wind
differencing scheme is applied for the convective term of fluid
dynamic simulations, whereas second order “central-difference”
schemes are employed for the biological variables. Respecting the
numerical convergence, the normalized residuals for pressure, ve-
locity, k and ε equations had to be below 104
, while in the bio-
kinetic simulation, the convergence criterion for each time step was
that the normalized residuals for each ASM1 component below
105
, using a time step Dt ¼ 0:5 s. As above mentioned, the bio-
logical simulation finished when the pseudo-steady state is
reached.
The boundary conditions for the fluid dynamic simulation
where the following: non-slip wall for the ground and side surfaces,
the mixed liquor comes into the domain by its inlet with uniform
velocity; a constant air phase velocity and air fraction is fixed on the
surfaces of the air diffusers. The pressure of the outlet surface is set
to the ambient pressure. Slip wall condition is selected for the
upper surfaces. Degassing boundary condition is employed on the
upper surfaces. The concentration of each ASM1 component at the
ASR inlet is not a steady function, it depends on the ASR outlet
concentrations (see Fig. 1). The inlet concentration of each
component is the weighted sum of raw water ASM1 concentration,
internal recirculation concentration, recycled sludge concentration
(calculated by the settler efficiency) and sludge return concentra-
tion (from thickener and centrifuge).
3.4. Mesh details and GCI
The mesh is cartesian and structured. In order to ensure the
mesh independence of the numerical results, a grid dependence
study was performed by comparing the results obtained with three
different meshes (A, B, C; 2.42, 1.56 and 1 millions of elements,
respectively). It was found that meshes A and B provide the same
results, so mesh A was selected to ensure the grid independence of
the numerical results. The size of the mesh is about 0.05 m in the
core of the domain, decreasing until 0.02 m in the zones where a
higher spatial gradient is expected (around mixer impeller, air
diffusers, partition wall and ASR outlet).
A numerical uncertainty estimation based on the Grid Conver-
gence Index (Roache, 1997) was performed. The three meshes with
different representative sizes previously presented were employed
for the estimation. The representative size of meshes A,B and C
were dA ¼ 45 mm ðfineÞ; dB ¼ 52 mm ðmediumÞ and dC ¼
60 mm ðcoarseÞ, respectively. Two GCI test are made (dA against dB,
dB against dC). The uncertainty of the pair of meshes involved in
each test is:
GCIfine
i
¼
ðFsÞe
ij
a
rN
ij
1
; GCIcoarse
j ¼
ðFsÞ
rN
ij
e
ij
a
rN
ij
1
; (10)
where Fs is a safety coefficient (1.25 in this case), e
ij
a is the relative
error of the key variable 4 (average residence time in this case),
e
ij
a ¼
j4i4jj
4i
, rij is the fraction of the two mesh sizes involved on the
test, rij ¼ dj=di, N is the apparent method order. Table 1 shows the
GCI values obtained after the two tests carried out. Note that the
mesh selected in the grid dependence study (mesh A) has an un-
certainty value GCI
fine
A ¼ 4.2%.
F. S
anchez et al. / Water Research 139 (2018) 47e57 51

6. 4. Description of the flow within the ASR
Flow behaviour has a notable influence on the biological pro-
cesses that occurs within the ASR, since the transport of the ASM1
components is made by velocity field (convective term in Eq. (6))
and the mixing level is determined by turbulence (diffusive term in
Eq. (6)). As previously explained, each complete numerical simu-
lation (fluid dynamics and biokinetics) involves the simulation of
the ASR under two different flow conditions: with and without
aeration.
The effect of the mixer impeller has a notable influence on the
flow pattern of the anoxic chamber. As observed in Fig. 3a, the
water jet generated by the impeller goes from the impeller (left side
of the chamber) to the opposite wall, where the jet impacts and
rebounds. As a result, the mixing in the zone is considerably high
and there are hardly any zones with very low velocity.
The flow behaviour in the oxic chamber is mainly due to the
effect of the aeration. Bubble columns, which arises from the air
diffusers, produces a vertical drag to the liquid, resulting in a
characteristic flow pattern of aeration zones: upward velocity in
the area of the columns, downward velocity in the space between
columns (see Fig. 3b). The aeration produces an acceptable degree
of mixing, avoiding stagnant zones. However, when air diffusers
stop running, as there are no other moment sources in the chamber,
velocity decreases, as well as the mixing degree, generating a ve-
locity field with velocities in the range 0e0.2 m/s.
An evaluation of the hydraulic efficiency of the ASR is carried
out. The fluid dynamic efficiency is evaluated by means of the
Residence Time Distribution (RTD) curve, by a pulse-tracer simu-
lation (Teixeira and Siqueira, 2008). A mass of tracer is released at
the inlet of the ASR at time t ¼ 0; the RTD curve of the ASR is
calculated from the evolution of the tracer concentration at the
reactor outlet. Fig. 4 shows the RTD curve (commonly known as E
curve) of the RTD under two aeration regimes: with and without
aeration. Although the velocity field in the oxic chamber is very
different under both regimes, there is hardly any difference be-
tween the RTD curves of both regimes.
In order to check the validity of the model, the RTD axial
dispersion model equation of Levenspiel, ADM (Levenspiel,1999), is
compared to the curves provided by the CFD model. ADM is used to
characterize reactors whose flow behaviour is between plug and
mixed flow, its equation is:
EðtÞ ¼
1
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
4pdt0t
p e
ðtt0Þ2
4dt0t
; (11)
being t0 the averaged residence time within the reactor and d the
dimensionless axial dispersion parameters. A least squared fitting is
carried out to find the pair of parameters of the numerical curves
under the two different aeration regimes. The fitting is excellent in
both cases, with coefficients of regression, R2
¼ 0.982 and
R2
¼ 0.979 for non-aeration and aeration cases, respectively. Table 2
shows the coefficients calculated for the numerical curves. It is
found that aeration reduces slightly the ASR hydraulic efficiency,
since it increases the dispersion and decreases the averaged
residence time. Active volume (renewed volume) is calculated by
dividing t0 by the ideal hydraulic residence time (HRT ¼ Q/vol).
The employed dimensionless RTD parameters (i.e. active volume
and dispersion) are useful to characterize the fluid dynamic per-
formance of an ASR, regardless of its size (they are universal pa-
rameters). The value of the dimensionless RTD parameters depend
on the flow behaviour within the reactor, which is determined by
its geometry and operating conditions. For instance, Burrows et al.
(2001) carried out a pulse-tracer experiment in a full-scale oxida-
tion ditch. They concluded that an oxidation ditch may be consid-
ered nearly a complete mixed reactor, which means active volume
close to 100% and a high value of dispersion parameter (ideally
infinite). Those results are very different from the ones obtained in
this work, as expected from such a different geometry. A pulse-
tracer RTD experiment of a full-scale ASR with a configuration
similar to the one analysed in this work: anoxic and oxic chambers
separates by partition walls with impellers in the anoxic zone and
air diffusers in the oxic chambers was carried out in the work of
S
anchez et al. (2016). That experiment provided a value of the
dimensionless parameters d and active volume of 0.25 and 70.5%,
Table 1
GCI values provided by the GCI tests. Note that the mesh selected for the CFD
modelling has an uncertainty value of 0.6%.
Test dA;dB GCIfine
A
4.2%
GCIcoarse
B 6.1%
Test dB;dC GCIfine
B
6.2%
GCIcoarse
C 9.1%
Fig. 3. a) Velocity field within the ASR. The flow in the anoxic chamber is characterized
by the mixer impeller jet. b) Velocity field on the air diffusers area. The bubble column
generates an upward force on the mixed liquor.
Fig. 4. RTD curves provided by the CFD model for the two considered aeration
regimes.
Table 2
RTD parameters for the two aeration regimes considered. The fluid dynamic effi-
ciency improves slightly when aeration is not connected.
Flow regime t0 (minutes) d Active Volume (%)
No aeration 211 0.29 66.9
Aeration 209 0.33 66.1
F. S
anchez et al. / Water Research 139 (2018) 47e57
52

7. respectively. Both of them similar to the ones obtained in this study.
5. Biological processes within the ASR. Validation of the CFD
model
The biokinetic simulation of the ASR (with C1 aeration system)
is carried out from the fluid dynamic fields previously obtained.
According to the ASM1 model, wastewater is characterized in terms
of 13 different components (6 particulate, 6 dissolved and alka-
linity), which are involved in 8 kinetic processes. From these 8
processes, three are related to the growth of heterotrophic (R1, R3)
and autotrophic (R2) organisms, two describe the biomass decay
(R4, R5), and three are related to hydrolysis (R6, R7, R8). Special
attention should be paid to processes R1 (aerobic growth of het-
erotrophs, XBH), R2 (anoxic growth of heterotrophs, XBH) and R3
(aerobic growth of autotrophs, XBA), since they command the rates
of pollutants removal.
The anoxic chamber, fed for three different streams (influent,
external and internal recirculation), will experiment a brief devel-
opment of the reactions R1 and R3 in the first part of the chamber.
The remnants of dissolved oxygen (SO) which comes with the in-
ternal recirculation from the oxic chamber and part of the soluble
substrate (SS) from the raw wastewater are consumed. Once SO has
been consumed, R2 process is performed within the rest of the
chamber, consuming SS as well as nitrates (SNO).
Unlike the anoxic chamber, the biological behaviour within the
oxic chamber is highly influenced by the aeration cycle. When the
ON sub-cycle begins, after a period of no aeration, it is found a high
concentration of ammonia nitrogen (SNH) and low values of SO, so
R1 and R3, do not take place. Both processes, R1 and R3, start to take
place just SO appears in the mixed liquor, and its rate is regulated
depending on the amount of the different components at expense
they develop. As the concentration of SO increases, the concentra-
tion of SNH diminishes, as R3 consumes it, generating SNO. At the
same time, R1 consumes SS as well as a little of SNH. At the end of
the ON sub-cycle, the concentration of SO in the ASR effluent has
grown until values of 5 mg/l (see Fig. 5a). When the OFF sub-cycle
starts, the So concentration in the chamber starts to decrease, since
the mixed liquor from the anoxic chamber, with low SO concen-
tration, advances through the bottom of the chamber (see Fig. 5b).
As a result, the rate of R1 and R3 also decreases, allowing R2 to
reproduce the anoxic growth of heterotrophs, which use SNO to
remove SS, in absence of SO.
It has been detected a deficient mixture within the oxic chamber
during the OFF sub-cycle. The mixed liquor from the anoxic
chamber (with high SNH and low SO) drops into the oxic chamber
over the weir of the partition wall. This “ammonia” plume reaches
the bottom of the chamber and advances through it (see Fig. 6), in
absence of SO (without R1 or R3). When air diffusers switch on, the
bubble columns drag the ammonia plume to the top of the cham-
ber, reaching the ASR outlet in few seconds. As a result, at the
beginning of the ON sub-cycle, a fleeting SNH rise in the effluent is
detected. This undesirable phenomenon is due to the absence of
mixing in the oxic chamber when air diffusers are not working (see
section 4).
Fig. 7 compares the evolution (during a complete aeration cycle)
of the total mass of SO within the reactor, SO concentration at the
ASR outlet and the Oxygen Transfer Efficiency (SOTE). It is shown
that at the beginning of the ON sub-cycle, since the SO concentra-
tion in the chamber is very low, the SOTE value (see Eq. (7)) is
relatively high (16.0%). However, as the cycle continues, SO con-
centration increases and SOTE value decreases until 10.8%. The air
diffuser datasheet provides, under these conditions (wastewater,
1.5 m depth) SOTE ¼ 19%. At the end of the ON sub-cycle, the SO
concentration in the effluent has been increased until 4.8 mg/l (see
Fig. 5a). This fact shows an inefficiency in the aeration perfor-
mance: part of the oxygen transferred from the bubbles to the
water leaves the ASR without having been consumed. This is
because in the last part of the chamber, SNH and SS concentration
are so low that there is hardly any SO demand (low values of R1 and
R3). This inefficiency can be avoided by modifying the air diffuser
layout (EPA, 1989): by increasing air diffuser density in the first part
of the oxic chamber, where SO demand is higher (high SNH and SS
concentration) and decreasing it in the final part of the chamber.
The average values of COD, TSS, N-NH4þ and N-NO3- obtained in
the WWTP effluent (after the settler) are 40.79, 16.33, 1.40 and
7.77 mg/l, respectively. All of them accomplish with clearance the
effluent requirements (50, 20, 1.5 and 10 mg/l, respectively). Note
that the virtual controller set the length of the ON/OFF sub-cycles to
305 and 415 s, respectively. By doing this, it satisfies the two pro-
posed requirements: cycle duration of 720 s, keeping the ammonia
nitrogen value in 1.40 mg/l. A longer length of the OFF sub-cycle
would have produced a higher ammonia nitrogen concentration,
even greater than the maximum allowed.
In order to check the validity of the biological results provided
by the CFD model, a model of the ASR under the same operational
parameters was implemented in the simulation platform WEST,
using the ASM1Temp model. Two perfect mixed zones are consid-
ered (anoxic and oxic chamber, see Fig. 1). The simulation is carried
out under intermittent aeration conditions, taking the global KLa
value and sub-cycle lengths from the CFD simulation (the value of
KLa is set to zero during the OFF sub-cycle). The sequence ON-OFF
sub-cycles is repeated until a stationary behaviour is reached.
Table 3 shows the comparison between the ASM1 component
concentrations provided by the two models (CFD and WEST) for the
effluent of the two considered chambers. The differences, in both
chambers, are relatively low. Special mention deserves SO in the
oxic chamber, which shows a relatively high difference, 0.71 mg/l
(26.2%). This difference can be due to the fact that WEST model
assumes a uniform SO concentration within the oxic chamber.
However, the biokinetic CFD simulation provides a non-uniform SO
contour in the chamber (see Fig. 5). Although the SO concentration
at the ASR outlet in the CFD model is 2.71 mg/l, the average value in
the chamber during a cycle is lower. The WEST simulation is not
able to detect the accumulation of SO in the last part of the oxic
chamber previously explained.
6. Assessment of ASR efficiency under different aeration
regimes
The CFD modelling is employed to evaluate the aeration energy
consumption of the two aeration systems presented in section 2.
After selecting which one of the two aeration systems will be
installed in the ASR, an analysis of the influence of air flow rate per
diffuser on the aeration efficiency is carried out.
Aeration system selection
The two aeration systems analysed were presented in section 2
(see Fig. 2):
/ Conf. 1 (C1): 48 diffusers ABS-PIK-300 (7 m3
/h/diff) distrib-
uted uniformly in 8 rows along the oxic zone.
/ Conf. 2 (C2): 80 diffusers ABS-KKI-215 (4 m3
/h/diff) distrib-
uted in 10 rows with variable distance between them.
Both configurations provide a similar total air flow rate (336 m3
/
h and 320 m3
/h), although in C2, air diffusers density is higher, in
order to keep a lower SO concentration in the bubble column and
improve the SOTE (see Eq. (7)). C2 layout produces a higher oxygen
injection at the beginning of the oxic chamber, where the SO de-
mand is higher, and a lower oxygen injection at the end of the
F. S
anchez et al. / Water Research 139 (2018) 47e57 53

8. chamber, where the demand is low. On the other hand, the initial
economic investment of C1 is lower. As presented in, the simulation
is performed within two steps (fluid dynamics and biokinetics),
controlling the aeration length to keep the average effluent
SNH ¼ 1.4 mg/l with an aeration length (ON þ OFF sub-cycles) of
12 min.
The average power consumption of the blower can be calculated
by multiplying the air flow rate by the pressure jump produced and
diving it into the blower efficiency:
_
W ¼
Qt,Dp
h
; (12)
Dp is the sum of hydrostatic pressure, pressure losses in pipes and
pressure drop in diffusers. The hydrostatic pressure on the surface
of the diffusers (1.5 m depth) is independent of the aeration
configuration. The pressure drop in the diffusers for a specific air
flow rate is taken from the datasheets of the devices. The blower
efficiency is assumed to be h ¼ 60%.
Table 4 shows the numerical results with both configurations. It
is observed that C2 is able to satisfy the effluent requirements by
injecting 6.3% less air flow rate by the blower, with 2.8% less power
consumption.
This efficiency improvement is mainly due to two reasons.
/ The heterogeneous air diffusers distribution, with less dif-
fusors in the last part of the chamber, provides a more effi-
cient oxygen consumption by the biomass. It is reflected in
Fig. 5. Dissolved oxygen concentration in the oxic chamber at different times: a) At the end of the ON aeration sub-cycle, when the concentration is maximum; b) during the OFF
sub-cycle.
Fig. 6. Ammonia concentration within the oxic chamber at the end of the OFF sub-cycle. The absence of moment sources in the chamber avoid the mixing.
Fig. 7. Effluent So concentration, SOTE and total mass SO in the ASR during a complete
aeration cycle.
F. S
anchez et al. / Water Research 139 (2018) 47e57
54

9. the maximum value of SO registered in the effluent (see
Fig. 8): 4.0 mg/l, lower than the 4.8 mg/l registered in C1 (see
Fig. 5a)). It is also observed that C2 layout provides a SO
concentration more homogeneous than C1.
/ Since the air flow rate per diffuser is lower in C2, the SO
concentration in the bubble plume is lower, increasing the
oxygen transfer from the bubbles to the mixed-liquor, and
consequently the SOTE. Fig. 9 shows the comparison be-
tween SOTE provided by C1 and C2 (C2a in the figure), it is
observed that the SOTE parameter in C2 during the ON sub-
cycle is approximately 1% higher than with C1.
Influence of the air flow rate per diffuser on the aeration
energy consumption
An analysis of the influence of the air flow rate per diffuser on
the power consumption is carried out. A complete simulation (fluid
dynamics and biokinetics) is performed with C2 aeration system,
but with the diffusers running at 62.5% load (C2b, 2.5 Nm3
/h/diff)
instead at 100% load run (C2a, 4 Nm3
/h/diff). Table 5 compares the
results provided by C2a and C2b. Besides needing less average air
flow rate, C2b has a lower pressure drop through the diffusers. As a
result, C2b satisfies the effluent requirements with 14.5% less po-
wer consumption.
The reasons for this improvement in the ASR performance are
the same that in the comparison between C1 and C2a: the oxygen is
injected gradually and properly adapted to the SO demanded by R1
Table 3
Comparison between the mean ASM1 concentrations provided by the CFD model and WEST software at the effluent of the two ASR chambers. Both results are very similar.
Anoxic Chamber effluent Oxic Chamber effluent
CFD (mg/l) WEST (mg/) Diff (mg/l) Diff (%) CFD (mg/l) WEST (mg/) Diff (mg/l) Diff (%)
SS 19.19 19.67 0.48 2.50 3.47 3.73 0.26 7.49
XS 142.26 143.45 1.19 0.84 33.33 34.70 1.37 4.11
XBH 1337.9 1325.2 12.72 0.95 1381.2 1368.2 12.96 0.94
XBA 69.89 70.51 0.62 0.89 71.58 72.19 0.61 0.85
XP 1405.5 1401.9 3.53 0.25 1414.2 1410.7 3.54 0.25
SO 0.01 0.00 0.01 83.3 2.71 2.00 0.71 26.2
SNO 0.04 0.08 0.04 100.0 7.77 8.50 0.73 9.40
SNH 12.28 12.30 0.02 0.16 1.40 1.35 0.05 3.57
SND 0.33 0.35 0.02 6.06 1.10 1.07 0.03 2.73
XND 9.30 9.30 0.00 0.00 2.54 2.59 0.05 1.97
SI 21.00 21.00 0.00 0.00 21.00 21.00 0.00 0.00
XI 375.71 375.71 0.00 0.00 375.71 375.71 0.00 0.00
Table 4
Aeration results obtained for the two aeration system simulated. C2 needs 6.3% less air flow rate to achieve the same effluent requirements.
Conf. Air flow rate
Qd (Nm3
/h/dif)
Total flow rate
Qt (Nm3
/h)
Aeration subcycle length
tON (s)a
Average total flow rate
Qt (Nm3
/h)
Dp (kPa) _
W
(W)
Energy
Consum.
(kWh/m3
)
C1 (48 diff) 7 336 305 142.3 29.5 1944 0.188
C2 (80 diff) 4 320 300 133.3 30.6 1889 0.181
a
The duration of the complete cycle (ON þ OFF) is 720 s.
Fig. 8. SO concentration in the oxic zone at the end of the ON sub-cycle with C2 (100% load).
Fig. 9. Comparison between SOTE and SO at outlet in the three cases simulated.
F. S
anchez et al. / Water Research 139 (2018) 47e57 55

10. and R3 processes. As a result the registered value of SO in the oxic
chamber remains far from the dissolved saturation point, providing
a higher SOTE value during the sub-cycle (see Fig. 9, C2b). Fig. 10
shows the value of the effluent SO concentration at the end of the
ON sub-cycle, which reaches a maximum of 2.9 mg/l, lower than
the concentrations provided by C1 and C2a.
7. Conclusions
A CFD modelling of an ASR which works under intermittent
aeration regime is carried out. The CFD model simulates the evo-
lution of the biological components concentration within the
reactor during a complete aeration cycle (ON and OFF sub-cycles).
The modelling involves the simulation of two steady fluid dy-
namic regimes, with and without aeration. In the transient bio-
kinetic simulation, the fluid dynamic fields employed for the
transport and oxygen transfer equations changes based on if air
diffusers are switched on or off.
The CFD model is used for the selection of the aeration system of
an ASR. Two aeration system are simulated: C1 and C2. The model
evaluates the power consumption necessary to satisfy the effluent
requirements under both scenarios. According to the numerical
results, C2 configuration, which have a heterogeneous diffuser
layout and more diffuser density than C1, needs 6.3% less air than
C1. An analysis of the influence of the air flow rate per diffuser is
carried out. It is found that decreasing the air flow rate (from 100%
load to 62.5% load), it is possible to increase the efficiency of the
aeration system, with a 14.5% less power consumption, since the
oxygen injection is properly adapted to the demand by R1 and R3
biological processes.
In conclusion, this work develops a CFD modelling capable to
evaluate the power consumption of the ASR aeration system for a
certain effluent requirements, under intermittent aeration regime.
Acknowledgments
The authors would like to thank Dr. Andr
es Zornoza, Alexander
L. Skaug and Dr. M
onica de Gracia for their suggestions to improve
the quality of the paper. This research is sponsored by the Seneca-
Agency for Science and Technology of the Region of Murcia, Spain
(Exp 19778/FPI/15).
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Total flow rate
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Aeration subcycle length
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