Available via license: CC BY 4.0
Content may be subject to copyright.
Dynamic air supply models add realism to the evaluation
of control strategies in water resource recovery facilities
Pau Juan-García, Mehlika A. Kiser, Oliver Schraa, Leiv Rieger
and Lluís Corominas
ABSTRACT
This paper introduces the application of a fully dynamic air distribution model integrated with a
biokinetic process model and a detailed process control model. By using a fully dynamic air
distribution model, it is possible to understand the relationships between aeration equipment,
control algorithms, process performance, and energy consumption, thus leading to a significantly
more realistic prediction of water resource recovery facility (WRRF) performance. Consequently, this
leads to an improved design of aeration control strategies and equipment. A model-based audit has
been performed for the Girona WRRF with the goal of providing a more objective evaluation of energy
reduction strategies. Currently, the Girona plant uses dissolved oxygen control and has been
manually optimised for energy consumption. Results from a detailed integrated model show that the
implementation of an ammonia-based aeration controller, a redistribution of the diffusers, and the
installation of a smaller blower lead to energy savings between 12 and 21%, depending on
wastewater temperature. The model supported the development of control strategies that counter
the effects of current equipment limitations, such as tapered diffuser distribution, or over-sized
blowers. The latter causes an intermittent aeration pattern with blowers switching on and off,
increasing wear of the equipment.
Pau Juan-García
Atkins,
(The Hub) 500 Park Avenue, Aztec West,
Almondsbury, Bristol, BS32 4RZ,
UK
Pau Juan-García
Mehlika A. Kiser
Lluís Corominas (corresponding author)
Catalan Institute for Water Research (ICRA),
Scientific and Technological Park of the University
of Girona,
Emili Grahit 101, Girona 17003,
Spain
E-mail: lcorominas@icra.cat
Oliver Schraa
Leiv Rieger
inCTRL Solutions Inc.,
7 Innovation Dr., Suite 107, Dundas ON L9H 7H9,
Canada
Key words |aeration control, ammonia-based aeration control, dynamic aeration system model,
energy optimisation, full-scale
INTRODUCTION
Water resource recovery facilities (WRRFs, formerly waste-
water treatment plants) are among the main consumers of
energy in a municipality. Aeration energy consumption typi-
cally accounts for around 50% of a facility’s total operating
costs (Olsson ). The WRRF aeration system is therefore
an important target for reducing municipal energy demands.
Classic energy audits identify opportunities to reduce energy
use, typically based on the average energy consumption of
unit processes, which may include benchmarking with
similar plants or comparison against key performance indi-
cators. However, significant saving potential lies in the
adaptation of operational parameters to the variability of
wastewater characteristics and loads and the changing
wastewater temperature by using tailored process control
strategies. More meaningful energy audits should therefore
use dynamic models that integrate the treatment process,
mechanical equipment, and a detailed description of the
applied control strategies.
The use of dynamic mechanistic models to reduce
energy consumption in WRRFs is common in the waste-
water field, normally carried out using a combination of
experimental work and modelling studies. Such optimis-
ation often includes the implementation of a new control
strategy, as seen in works of Corominas et al. ()and
Thornton et al. (). Recent studies have been published
on control system design (Rieger et al. ;Odriozola
et al. ); yet current studies use simplified aeration
system models that include oxygen transfer and oxygen
This is an Open Access article distributed under the terms of the Creative
Commons Attribution Licence (CC BY 4.0), which permits copying,
adaptation and redistribution, provided the original work is properly cited
(http://creativecommons.org/licenses/by/4.0/).
1© 2018 The Authors Water Science & Technology |in press |2018
doi: 10.2166/wst.2018.356
Corrected Proof
Downloaded from https://iwaponline.com/wst/article-pdf/doi/10.2166/wst.2018.356/487013/wst2018356.pdf
by guest
on 05 October 2018
demand, but assume ideal air supply and distribution with
no equipment constraints. Not including these constraints
such as blower, valve, or diffuser limitations can hide extra
costs or even mask the inability of a control strategy to
reduce energy consumption. Oftentimes, optimisations car-
ried out with simplified aeration models overestimate the
potential for energy savings by 5 to 10%, due to missing
equipment constraints (Schraa et al. ).
Recent research focused on more detailed models of the
aeration system and its energy consumption (Schraa et al.
;Alex et al. ;Amerlinck et al. ;Amaral et al.
). Within this context, the work of Schraa et al. (,
) uses a fully dynamic model for the piping network,
which recalculates the system curve based on the changing
pressure drops throughout the system. Dynamically simulat-
ing the air distribution system enables the possibility of
troubleshooting analyses, and evaluating the components
and limitations of the system for different optimisation
options and load and temperature scenarios. Hence, inte-
grated models can be used to find solutions for energy and
process optimisations that are more realistic and tailored
to a specific facility.
In this study, the advanced aeration system model devel-
oped and implemented in SIMBA# (Schraa et al. ,)
was used to carry out a model-based process performance
and energy audit of the Girona WRRF (Spain), with the aim
of reducing energy consumption while maintaining effluent
quality. The Girona WRRF’s 2013 energy consumption
record shows that 63% of the plant’s energy consumption
was due to aeration. The existing DO control system has
already been optimised by the plant operators by trial and
error, but it was hypothesised that further optimisation using
the dynamic air supply model would lead to extra energy sav-
ings and an improved dynamic response to disturbances (i.e.
load peaks, temperature variation, operational settings).
The objective of this study was to model the dynamic
interactions between process performance, aeration system
equipment, controller settings, and energy consumption, to
gain understanding of the limitations of the current operat-
ing approach. After assessing the current performance
(Base Case SC0), three different optimisation scenarios
have been selected: SC1) ammonia-based aeration control
(ABAC) (Rieger et al. ), SC2) optimisation of the air dis-
tribution system, and SC3) installation of a smaller blower.
The optimisation scenarios were compared for three differ-
ent temperature variations and a stress test in the form of
an ammonia peak. This is the first published application of
the aeration system model library developed by Schraa
et al. (,) at a full-scale WRRF.
MATERIALS AND METHODS
Plant layout and operation
The Girona WRRF receives an average of 55,000 m
3
·d
1
of
domestic and industrial wastewater, and has the capacity
to serve 275,000 population equivalents. The plant is a con-
ventional activated sludge system in a five-stage Bardenpho
configuration. Even though such a configuration is designed
for biological removal of nitrogen and phosphorus, chemical
phosphorus removal is currently practised at the WRRF.
Legislation allows the discharge of a total nitrogen concen-
tration of 10 mg·L
1
(measured as 24-hour composite
samples).
The biological stage consists of two parallel treatment
lanes. Each lane has a total volume of 14,360 m
3
split into
seven zones, of which four are aerated, as shown in Figure 1.
Sludge is wasted from the return activated sludge (RAS)
streams and is anaerobically digested. Reject water from
the centrifuges is sent back to the headworks of the facility.
Afirst analysis showed that the load profile of the plant –
including nitrogen and phosphorus –is heavily dependent
on the schedule of the reject water, which varies depending
on the usage of the sludge dewatering centrifuges. On aver-
age, the reject water represents up to 11% of the incoming
nitrogen load and 0.5% of the phosphorus load. Although
this is seen as a significant optimisation potential, optimising
reject water dosage was not evaluated in this study due to
the absence of buffer tankage.
The aeration system consists of a main blower and two
support blowers that service a main header, which splits
into two header pipes, each controlled by an automatic
valve, and followed by four manual zone valves (Figure 1).
The list of air supply equipment can be found in Table 1.
The reactors are aerated using fine-bubble membrane disc
diffusers. The sensors for on-line measurements of dissolved
oxygen (DO) are currently placed at the end of the biological
reactors (AER4). The air supply (blower set) is controlled by
the average DO of both lanes by varying the speed and guide
vanes of the blowers (Figure 1: Signal). The DO measure-
ment in each lane is used to manipulate the positions of
the automatic main header valves. Table 2 shows an over-
view of the number of diffusers, valve diameter and
settings of each aerated reactor.
The WRRF in its current form has been in operation
since 2008. Since then, operators have been manually opti-
mising the aeration system by adjusting the manual valves
in each lane and changing the location of the DO sensors.
The manual valves for AER1 and AER2 have been adjusted
2P. Juan-García et al. |Dynamic air supply models for control strategies evaluation Water Science & Technology |in press |2018
Corrected Proof
Downloaded from https://iwaponline.com/wst/article-pdf/doi/10.2166/wst.2018.356/487013/wst2018356.pdf
by guest
on 05 October 2018
by the operators to reduce airflows to the first two aerated
reactors. Whereas this improves the balance of the incoming
load with the air supply (using a DO signal from AER4), it
also leads to increased aeration system pressure drops.
The DO sensor was finally placed in AER4, with a DO
setpoint of 2 mg O
2
·L
1
. Measurements from the SCADA
system and DO probes temporarily installed by ICRA
show that this results in concentrations around 1–2mg
O
2
·L
1
in reactors AER1 and AER2, and very low DO con-
centrations (0.5–1mgO
2
·L
1
) in reactor AER3.
Girona WRRF model
A model was built following the recommendations of
the IWA Guidelines for Using Activated Sludge Models
(Rieger et al. a) and using the advanced modelling plat-
form SIMBA#. First, mass balances on total suspended
solids, chemical oxygen demand (COD) and total phos-
phorus were conducted to verify that no gross errors were
present in the data. The model was built using SIMBA#’s
in-house activated sludge model ASM-inCTRL, and a
Figure 1 |WRRF configuration and aeration system. ANA: anaerobic, ANX: anoxic, AER: aerobic, WAS and RAS: waste and return activated sludge respectively.
Table 2 |Number of diffusers, pipe diameter and valve setting on each aerated reactor
Reactor Diffusers (#) Valve diameter (m) Valve opening (%)
AER1 480 0.25 0.325
AER2 366 0.2 0.45
AER3 180 0.15 1
AER4 144 0.15 1
Table 1 |List of air supply equipment modelled (technical specifications and SIMBA# model parameters)
Equipment Brand Num. units SIMBA# parameters
Blower Turbo-compressor ABS HST
9000 Sulzer
1 (2 standby) Efficiency curve, surge curve, max. and min. airflow
Automatic
valves
Butterfly, centred axis.
Belgicast
2K
v
values, max. and min. airflow, fitted with a quadratic equation
Manual valves Butterfly, centred axis.
Belgicast
8K
v
values, max. and min. airflow, fitted with a quadratic equation
Diffusers ABS Nopon disc diffuser
system PIK 300
2,280 number of diffusers per grid, pressure drop and SOTE
a
curves,
submergence, oxygen transfer parameters
Pipes Measured in on-site visits 2 lines
(8 reactors)
K (i.e. resistance) factors, pipe roughness, length, diameter, fittings
a
Standard oxygen transfer efficiency.
3P. Juan-García et al. |Dynamic air supply models for control strategies evaluation Water Science & Technology |in press |2018
Corrected Proof
Downloaded from https://iwaponline.com/wst/article-pdf/doi/10.2166/wst.2018.356/487013/wst2018356.pdf
by guest
on 05 October 2018
simplified anaerobic digester model developed by the Insti-
tut für Automation und Kommunikation (ifak ). To
build the layout of the aeration system, mechanistic
models were used for each actuator in Table 1. The air dis-
tribution system includes blowers, pipes, fittings, valves,
and diffusers. The piping system is modelled using the
Darcy–Weisbach equation with the friction factor calculated
using the Swamee & Jain ()equation. The pressure rise
across blowers and pressure drops across valves and diffu-
sers are calculated using polynomial functions based on
airflow rate, which have been calibrated using the manu-
facturer-supplied data. More information on the aeration
system models can be found in Schraa et al. (). Aeration
control was accounted for by means of continuous feedback
using proportional-integral or proportional-integral-deriva-
tive (PID) control algorithms. To model the behaviour of
the automatic valves, a control algorithm was implemented
that fixes the valve position of the lane with the highest
oxygen requirement at 70% open and allows the other
valve to vary to adjust the required airflow; 70% is the maxi-
mum valve opening currently set in the SCADA system.
After building the model, a steady-state calibration was
conducted to match the sludge production (using full-scale
data from January until December 2015). Dynamic cali-
bration was executed by using real dynamics from a period
between the 7th and the 13th of December 2015. It com-
prised a period of dry weather data, with detailed flow
measurements (every 15 min) and daily nutrient measure-
ments (one sample per day). Hourly concentration
dynamics were incorporated by scaling an hourly ammonia
profile gathered in February 2016 to the measured daily com-
posite measurements. For the dynamic calibration period,
DO concentrations from reactors AER1 and AER4 were
used, as well as measured blower airflow, system header
pressure, and valve positions. The calibration effort focused
on influent fractionation and the physical characteristics of
the plant and its equipment instead of adjusting biokinetic
parameters, which should improve the validity of the predic-
tions. A list of the calibrated parameters is provided in
Table 1. The goodness of fit of the airflow during the cali-
brated week can be seen in Figure 2 and will be discussed
in the results section ‘Base case SC0: current operation’.
Scenario analysis
Several options were evaluated in the virtual plant to reduce
energy consumption while maintaining or even improving
effluent quality. The options were grouped into three cumu-
lative scenarios of increasing financial investment. An
overview of the optimisation options and the scenarios is
shown in Table 3. To guarantee the robustness of the control
strategies, all scenarios were stress-tested by an artificial
ammonia peak, which increased the influent ammonia con-
centration from the average (∼40 mg N·L
1
) to 80 mg N·L
1
for 4 hours, starting on the eighth day of the simulation.
Variations in ammonia loading are commonplace in the
treatment plant, and the peak is the maximum concen-
tration registered in the plant’s historical data.
SC1. Ammonia-based aeration control (ABAC)
This refers to a cascade controller where ammonium as
primary loop modifies the DO setpoint in the secondary
loop. Following the implementation described in Rieger
et al. (), an ammonia probe measures the ammonia con-
centration in the last aerated reactor (AER4). The ammonia
measurement adjusts the DO setpoint through a PID con-
troller, in a range of 0.1–2.5 mg O
2
·L
1
. The DO sensor is
moved from AER4, where it was in the Base Case (SC0),
to AER2, in the middle of the main aerated reactors where
Figure 2 |Comparison of airflow results obtained from 1 week of real data and the modelled Base Case (SC0). Days 6 and 7 correspond to a weekend. Fast decreases around days 1.5, 4.5
and 5.5 correspond to the blower on–off behaviour.
4P. Juan-García et al. |Dynamic air supply models for control strategies evaluation Water Science & Technology |in press |2018
Corrected Proof
Downloaded from https://iwaponline.com/wst/article-pdf/doi/10.2166/wst.2018.356/487013/wst2018356.pdf
by guest
on 05 October 2018
most of the load is being removed. To reduce the pressure
drop, the valve in the lane that has more air requirement
is fixed at 100% open, and the valve in the controlled lane
is set to operate between 20% to 90% open.
SC2. Aeration system upgrade
To improve the airflow distribution in the system, the
number of diffusers was optimised and redistributed as fol-
lows: from 480 to 360 in AER1, from 366 to 360 in AER2,
from 180 to 360 in AER 3, and from 144 to 230 in AER4.
A second optimisation step was to adapt the diameter of
the pipes that feed AER3 from 0.15 to 0.2 m to minimise
the pressure drops and improve air distribution. The valves
feeding these pipes, which were designed for a maximum
airflow of 1,194 m
3
·h
1
were re-sized to allow for up to
2,111 m
3
·h
1
. These values are the result of a sensitivity
analysis of diffuser distributions and iterative simulations
to assess the design.
SC3. Blower downscaling
This scenario addresses the problem of not being able to
turn down the blower set to match the requested air
demand at minimum load conditions. It includes the pre-
vious strategies, plus replaces one of the blowers with a
lower capacity blower: TDS Turbo compressor type ABS
HST 9000 to type ABS HST 6000. This reduces the lower
limit of the aeration system’s airflow rate. The blower sche-
duling was adapted to the new configuration, and simulation
results showed that this setup was not prone to surge of the
smaller blower.
To assess the performance of each scenario, the model
was first initialised by a steady-state simulation with the
scenario’s conditions for 100 days, and then dynamically
simulated for 11 days including an ammonia peak on day
8 at 10 a.m.
RESULTS AND DISCUSSION
The first results are for the Base Case (SC0) and have been
used to calibrate and diagnose the current shortcomings of
the plant. The subsequent three scenarios are presented
and the results of the optimisation strategies are discussed
below. Finally, the effects of temperature in each scenario
are discussed, and an optioneering assessment is performed
to evaluate the strategies from an economic point of view.
Base Case SC0: current operation
The model describes the airflow dynamics of the system, as
illustrated in Figure 2. It also captures the behaviour of the
blower overshooting 1–2 times per week on average, and
the oscillatory on/off behaviour. A better fit of the blower
overshooting would be possible if real hourly measurements
for nutrients and COD at the inlet of the reactor were used.
The support blower starts 2–3 times a week during peak
moments and runs for short periods, even though this is
not accurately captured by the SCADA system recordings
(days 5–7). The importance of the effect of delayed ammonia
peaks during the weekends’nutrient profiles is appreciated
in the delayed airflow curve (days 6–7).
System shortcomings
The blowers are controlled based on the average DO of the
last reactor of the two lanes and operate with a fixed DO
setpoint of 2 mg O
2
·L
1
. The DO probe location in the
reaeration reactor (AER4) is outside of the internal nitrate
recycle loop (AER1–3 to ANX1) and therefore is somewhat
disconnected from the main aeration stage and will there-
fore miss some of the system dynamics. To minimise
pressure drops due to both control valves closing when
the minimum blower capacity is reached, the control algo-
rithm fixes the valve in the lane with more oxygen
Table 3 |Summary of optimisation options and scenarios (SC)
Ammonia-based aeration control (ABAC) þAeration system upgrade þBlower downscaling
DO
probe ABAC
Fixed valve at
100% open
Optimised pipe and
valve sizes
Diffuser
distribution
Downscaled
blower
Blower
scheduling
SC0 AER4
SC1 AER2 X X
SC2 AER2 X X X X
SC3 AER2 X X X X X X
5P. Juan-García et al. |Dynamic air supply models for control strategies evaluation Water Science & Technology |in press |2018
Corrected Proof
Downloaded from https://iwaponline.com/wst/article-pdf/doi/10.2166/wst.2018.356/487013/wst2018356.pdf
by guest
on 05 October 2018
requirement at 70% open, while the valve in lane 2 is con-
trolled to redistribute the air between the two lanes.
The facility has been designed with diffuser tapering and
manual control valves to each of the individual aeration grids.
The header pipes and the valve diameters decrease in
diameter from 0.25 m to 0.15 m. With all manual valves com-
pletely open, the highest airflow would therefore be delivered
to the first aerated zone and then diminish with each succes-
sive zone. To compensate for the mismatch between airflow
and load, the airflow distribution has been adjusted by the
operators by setting the first two manual valves on the reac-
tor’s grid to be partially open, at 32.5% and 45% of the
total opening capacity respectively. Consequently, the reac-
tors with the highest airflows have a high pressure drop
which results in increased system air pressure requirements
–the system usually operates at 1,650–1,800 mbar.
The loss of control authority leads to an oscillation of
the oxygen concentrations in reactors AER1–3, and the
response of the plant to stressors is slowed down as the
only sensor is located at the end of the lane (and after an
anoxic zone). The minimum blower turn-down is above
the minimum airflow requirements during low load periods.
So, with the current control algorithm, the blower switches
on and off intermittently when the load is low for extended
periods of time. This reduces the blower life and creates
instabilities in the DO concentration.
The Base Case scenario displays periods of insufficient
DO concentrations throughout the reactors (Figure 3(b):
Base Case SC0); AER4 maintains the setpoint, except
during peak loading. Reactors AER1 and AER2 vary
around 1–3mg O
2
·L
1
, depending on the load, and AER3
is lacking airflow capacity as can be seen from the DO con-
centration. Although most of the COD and ammonia load is
treated in AER1 and AER2, the aeration capacity is not suf-
ficient to maintain an acceptable DO concentration in
AER3. Only at low load situations does the DO in AER3
increase to around 1 mg O
2
·L
1
, which limits the ability of
the plant to fully realise its nitrification capacity.
Overall, the Base Case (SC0) results in an energy con-
sumption in the biological reactors of about 0.2 kWh·m
3
of treated wastewater. This is at the low end of typical
ranges, which are between 0.13 and 5.5 kWh·m
3
(Ener-
water ); however, the system is unable to maintain the
DO setpoint during influent peaks. In low load situations,
the blower turn-down limitation forces the air control
valves to close as much as possible, which results in the
blowers working against an increased system pressure and
therefore reduced efficiency. Another effect of having
Figure 3 |Performance evaluation of Scenario SC1 over the Base Case (SC0). (a) Power consumption; (b) oxygen profiles across reactors in Lane 1 for the Base Case; (c) oxygen profiles
across reactors in Lane 1 for Scenario SC1.
6P. Juan-García et al. |Dynamic air supply models for control strategies evaluation Water Science & Technology |in press |2018
Corrected Proof
Downloaded from https://iwaponline.com/wst/article-pdf/doi/10.2166/wst.2018.356/487013/wst2018356.pdf
by guest
on 05 October 2018
AER1 and AER2 at high DO concentrations to compensate
for low DO in AER3 is a higher energy consumption, as
oxygen transfer is more efficient at low airflow rates and
low DO concentrations (Rosso et al. ).
Model-based evaluation of the optimisation scenarios
Scenario SC1: ABAC
The implementation of ABAC results in both energy savings
of up to 7% and improved controller response. Savings are
obtained by the DO setpoint varying between 0.1 and
2.5 mg O
2
·L
1
, increasing the airflow when higher ammonia
loads enter the reactor, and saving aeration power other-
wise. The system’s improved reaction to ammonia peaks
can be appreciated in Figure 3(b) and 3(c). Right after the
‘Event start’mark, the ABAC controller reaches a high
DO concentration faster than the Base Case, despite
having a lower DO setpoint before the event. The ability
of the ABAC controller to increase the DO setpoint up to
2.5 mg O
2
·L
1
instead of 2 mg O
2
·L
1
at peak load con-
ditions allows the aeration system to draw more capacity
in moments of need.
By controlling the oxygen supply to maintain a mini-
mum ammonia concentration in the effluent of 1 mg·L
1
,
the load is distributed over the entire reactor causing a
more balanced oxygen demand. As can be seen in
Figure 3(c), the DO profile is now more uniform across reac-
tors, which indicates that the load is more equally
distributed. Nevertheless, AER3 still presents a critical
limitation in reaching the required DO concentration
(Figure 3(c): SC1 AER3), and the turn-down capacity of
the blower is reached in low load periods (day 7.5), which
causes short spikes in the DO concentration.
Despite the improvements with the ABAC scenario
(SC1), the manual valves in reactors AER1 and AER2 still
must remain partially closed to compensate for the flawed
airflow distribution generated by the tapering. Figure 4(a)
shows that the ABAC controller is properly working and
reacts quickly to the measured ammonia most of the time.
However, at low load situations the minimum blower turn-
down prevents the system from maintaining the low DO
concentrations requested by the ammonia controller. The
DO controller (Figure 4(b)) works well until the ammonia
controller is limited; then the DO concentration spikes in
the lane with the fully opened valve. The valve control
(Figure 4(c)) shows that the automatic valve in lane 2
closes to regulate the airflow. Yet, the valve in lane 1 must
be fixed at 100% open to prevent the blowers working
against closed valves. This would reduce blower efficiency
and may lead to blower surge. After this in-depth analysis
of the controller and actuator performance, the two main
Figure 4 |ABAC performance evaluation in Scenario 1. (a) Input–output signals (ammonia (NH
x
) in mg N·L
1
,DOinmgO
2
·L
1
); (b) oxygen profiles of each lane; (c) valve position in % 0–1.
7P. Juan-García et al. |Dynamic air supply models for control strategies evaluation Water Science & Technology |in press |2018
Corrected Proof
Downloaded from https://iwaponline.com/wst/article-pdf/doi/10.2166/wst.2018.356/487013/wst2018356.pdf
by guest
on 05 October 2018
system constraints can be identified: 1) the airflow
distribution and 2) the minimum blower turn-down. Overall,
the model confirms the benefits of ABAC reported in other
studies (Amand et al. ).
Scenario SC2: aeration system upgrade
Scenario SC2 upgrades the aeration system to overcome air-
flow limitations in reactor AER3 by (i) reducing the number
of diffusers in the first two reactors, (ii) increasing the
number of diffusers in the last two reactors, and (iii) resizing
the pipes and valves, as described in Table 3.Accordingto
model predictions, this allows the plant to operate with the
manual valves fully open, which translates into reduced
system pressure. Although some form of diffuser tapering
still exists, the limitation of delivering air to AER3 has
improved, and the reactor can now reach higher DO concen-
trations (Figure 5(b)). The airflow distribution changes were
simulated and analysed (see Supplementary information,
Figure S1). The diffuser distribution has been calculated with
respect to the current loading patterns but could change if
the loading patterns were to change significantly over time.
The model predicts that improving the airflow distri-
bution would increase energy savings up to 12%, due to
several reasons. Firstly, reducing the system pressure
allows the blowers to supply the same airflow with less
energy consumption. Secondly, by increasing the nitrifica-
tion capacity in AER3, there is more nitrite and nitrate
being recirculated to the anoxic reactors; thus more organic
matter is removed anoxically instead of aerobically. This
NOx was previously being produced in AER4, and thus
its oxidising capacity was lost. The improvement in
denitrification activity can be seen in Figure 6, which
shows the ratio of kWh per kg NH
x
-N removed and total
nitrogen removed. Both nitrification and denitrification
become more efficient with each scenario, which translates
to less blower usage (Figure 5(a)) and reduced effluent con-
centration of total nitrogen. Finally, a more balanced load
allows the plant to run with a lower DO setpoint overall,
which increases the oxygen transfer driving force.
The only remaining constraint was the minimum blower
turn-down. Upgrading the aeration system further lowered
the air demand during low peak periods. However, the
blower can only decrease its capacity to 40% of the full
capacity of one blower, which is already over the minimum
air demand. The turn-down capacity of the blower is
reached in low load periods, which causes DO spikes in
low peak periods in reactors AER3 and AER4 (Figure 5(b)).
There are three main solutions to this problem. The first
one is to implement intermittent blower operation, using
advanced control based on effluent ammonia as in Rieger
et al. (b). The second solution is to implement a blow-off
valve, and the third solution is to replace the main blower by
a smaller one. In this study we have explored the third sol-
ution, as it is considered the most efficient in terms of design.
Scenario SC3: blower downscaling
The last scenario solves the minimum blower turn-down
limitation by downscaling the main blower. The virtual
system now has a lower minimum airflow, which results in
energy savings at low peak periods (Figure 7(a) and Figure 8)
and improves the DO profile across the reactors (Figure 7(b)).
The system dynamics are also smoother.
Figure 5 |Performance evaluation of Scenario SC2 over Scenario SC1. (a) Power consumption; (b) oxygen profiles across reactors in Lane 1 for Scenario SC2.
8P. Juan-García et al. |Dynamic air supply models for control strategies evaluation Water Science & Technology |in press |2018
Corrected Proof
Downloaded from https://iwaponline.com/wst/article-pdf/doi/10.2166/wst.2018.356/487013/wst2018356.pdf
by guest
on 05 October 2018
Influence of temperature
The calibration data used for the Base Case is from Decem-
ber 2016, which had a recorded average temperature of
16.5 C. As the temperature in the WRRF varies between
15 C and 25 C the effect of temperature on energy savings
was evaluated for the different optimisation options
(Figure 8).
Figure 8(a) shows the energy consumption of the
blowers in each scenario for each temperature. All scenarios
are more energy-efficient as the temperature increases, both
in terms of raw energy savings and kWh per pollutant
removed (Figure 6). This is mainly due to increased bacterial
activity, which allows the reactor to be run at a lower sus-
pended solids concentration compared to winter (where it
needs to be raised up to 4,000 mg·L
1
) and lower DO set-
point. The Base Case at 25 C consumes 8% less energy
than at 16.5 C.
Figure 8(b) shows the percentage of savings of each
scenario and each temperature with respect to the Base
Case at the same temperature. Results show that not every
Figure 7 |Performance evaluation of Scenario 3 over Scenario 2. (a) Power consumption; (b) oxygen profiles across reactors in Lane 1 for Scenario 3.
Figure 6 |kWh consumed per kg of ammonia/total nitrogen removed in each scenario.
Figure 8 |Energy consumption and the percentage of energy savings of each scenario compared to the Base Case at three different temperatures. (a) Weekly aeration energy con-
sumption. (b) Per cent savings, normalised to the savings of the Base Case at the given temperature.
9P. Juan-García et al. |Dynamic air supply models for control strategies evaluation Water Science & Technology |in press |2018
Corrected Proof
Downloaded from https://iwaponline.com/wst/article-pdf/doi/10.2166/wst.2018.356/487013/wst2018356.pdf
by guest
on 05 October 2018
scenario behaves similarly with temperature changes.
Upgrading the aeration system (Scenario 2) shows less sav-
ings variation across temperatures, whereas the ABAC
(Scenario 1) is significantly more efficient at high tempera-
ture. Downscaling the blower (Scenario 3) provides no
increased savings at low temperature, but is more efficient
at high temperatures when the minimum capacity of the
blower is reached more often. This shows the trade-off
between flexibility and energy savings. The perfect design
would be running several ‘small’blowers in a wider capa-
bility range, allowing maximum process modularity and
improving energy savings.
Optioneering assessment
Table 4 summarises the results for the tested scenarios,
considering the effluent quality and system response to
ammonia peaks. Return of investment (ROI) for each
enhancement has been calculated using the following
equation:
ROI ¼Investment cost
Savings per year Maintenance cost per year
Investment cost and maintenance is calculated as
follows: i) maintenance costs for the ABAC controller
during a 10-year period amount to €25,000·yr
1
, ii) energy
price used to calculate savings is considered to be
€0.1·kWh
1
, iii) cost of piping, valves and installation is
estimated at €52’000 (values obtained from a personal
communication with the consultancy Banc Bedec (ITeC)),
iv) blower cost estimated at €155,000 (obtained through a
personal communication based on recorded data from the
Belgian water utility Aquafin).
Results indicate that Scenarios 1 and 2 are rec-
ommended as valid optimisation options to save energy.
Replacing a blower needs to be evaluated based on a
cost-benefit analysis; however, this highlights the impor-
tance of designs including low load scenarios, and the cost
of over-sized equipment. Results on mean effluent ammonia
and total nitrogen show that the Base Case was not making
use of the plant’s full denitrifying capacity, which is
improved when aeration is optimised.
The ammonia stress test is generated in a low-load
period, and thus the DO setpoint set by the ammonia con-
troller, right before the ammonia peak, was at the lower
end of the range. This is the most disadvantageous
moment for the ABAC system to receive an ammonia
peak. Still, all scenarios handled the ammonia peak and
maintained effluent quality (Table 4,NH
x
peak).
CONCLUSIONS
A model-based audit of the energy and treatment perform-
ance has been carried out for the Girona WRRF. The goal
was to evaluate energy reduction strategies by understand-
ing the relationships between process performance,
aeration equipment, control and energy consumption,
using a dynamic air distribution model together with a pro-
cess model and a control system model. Prior to the audit,
the plant’s aeration system was already controlled and con-
sidered to be optimised following a trial-and-error approach
by operators. Using the model, a set of strategies was
designed which reduced energy consumption by 12–21%,
while improving effluent quality. Furthermore, in the simu-
lated scenario with no equipment limitations, the DO
profile is the same across reactors and follows the DO set-
point in all aerated tanks, and the aeration system can
respond more rapidly to disturbances and draw more aera-
tion capacity when needed.
This study highlights the importance of considering
equipment constraints when designing control strategies.
Often the system constraints are hidden as they cannot or
are not measured, such as the positions of the manual
Table 4 |Summary of modelled cases and performance obtained
Scenario description
a
Energy savings
(%)
b
Effluent NH
x
daily
average (g N·m
3
)
Effluent NH
x
peak (g N·m
3
)
Effluent TN
mean (g N·m
3
)
Return of
investment (years)
SC0: Base Case –0.59 1.19 6,85 –
SC1: ABAC 6.8–16.3 0.99 1.40 6.22 0.88
SC2: Aeration system optimisation 11.6–19.4 0.98 1.29 6.20 6.29
SC3: Blower downscaled 10.8–21.2 1.02 1.33 6.12 15.64
a
Scenarios are cumulative. Each new scenario includes the optimisation options of the previous scenario.
b
The range of savings for each scenario is calculated over the Base Case for each temperature.
10 P. Juan-García et al. |Dynamic air supply models for control strategies evaluation Water Science & Technology |in press |2018
Corrected Proof
Downloaded from https://iwaponline.com/wst/article-pdf/doi/10.2166/wst.2018.356/487013/wst2018356.pdf
by guest
on 05 October 2018
valves, or the effect of pipes and valves sizing. The mechan-
istic dynamic air supply model accurately represents the
current system, showing airflow distribution and pressure
drops as they occur in the plant. This enables in-depth analy-
sis of the aeration together with the treatment performance
and the control system, diagnosing the main bottlenecks in
the existing aeration system (i.e. the piping size, diffuser dis-
tribution and blower minimum turn-down).
ACKNOWLEDGEMENTS
The authors would like to thank Trargisa S.A. for providing
the data and feedback on the WRRF of Girona, the funding
from the EU project R3water (contract no. 619093), the
Ministry of Economy and Competitiveness for the Ramon
y Cajal grant for Lluís Corominas (RYC-2013-14595) and
for the REaCH project (CTM2015-66892-R). ICRA was
recognised as a consolidated research group by the Catalan
Government with the code 2017024_SGR17-19_ICRA-
TECH. This work has been supported by the European
Union’s Horizon 2020 research and innovation programme
under the Marie Sklodowska-Curie grant agreement No
642904 - TreatRec ITN-EID project.
REFERENCES
Alex, J., Rieger, L. & Schraa, O. Comparison of advanced
fine-bubble aeration control concepts with respect to energy
efficiency and robustness. In: Proceedings of WEFTEC 2016,
New Orleans, pp. 136–147.
Amand, L., Olsson, G. & Carlsson, B. Aeration control –a
review.Water Sci. Technol. 67 (11), 2374–2398. doi:10.2166/
wst.2013.139.
Amaral, A., Schraa, O., Rieger, L., Gillot, S., Fayolle, Y., Bellandi,
G., Amerlinck, Y., Mortier, S., Gori, R., Neves, R. & Nopens,
I. Towards advanced aeration modelling: from blower to
bubbles to bulk.Water Sci. Technol. 75 (3), 507–517. doi:10.
2166/wst.2016.365.
Amerlinck, Y., De Keyser, W., Urchegui, G. & Nopens, I.
A realistic dynamic blower energy consumption model for
wastewater applications.Water Sci. Technol. 74, 1561–1576.
doi:10.2166/wst.2016.360.
Corominas, L., Sin, G., Puig, S., Traore, A., Balaguer, M., Colprim,
J. & Vanrolleghem, P. A. Model-based evaluation of an
on-line control strategy for SBRs based on OUR and ORP
measurements.Water Sci. Technol. 53, 161–169. doi:10.
2166/wst.2006.120.
Enerwater Deliverable 2.1. Study of Published Energy Data.
H2020-EE-2014-3-MarketUptake. Available at http://www.
enerwater.eu/download-documentation/ (accessed 15 May
2017).
ifak SIMBA Portal. Available at https://simba.ifak.eu/
(Accessed 11 May 2017).
Odriozola, J., Beltrán, S., Dalmau, M., Sancho, L., Comas, J.,
Rodríguez-Roda, I. & Ayesa, E. Model-based
methodology for the design of optimal control strategies in
MBR plants.Water Sci. Technol. 75, 2546–2553. doi:10.2166/
wst.2017.135.
Olsson, G. Water and Energy: Threats and Opportunities.
IWA Publishing, London, UK.
Rieger, L., Gillot, S., Langergraber, G., Ohtsuki, T., Shaw, A.,
Takacs, I. & Winkler, S. aGuidelines for Using Activated
Sludge Models: IWA Task Group on Good Modelling
Practice. Scientific and Technical Report No. 22, IWA
Publishing, London, UK.
Rieger, L., Takács, I. & Siegrist, H. bImproving nutrient
removal while reducing energy use at three Swiss WWTPs
using advanced control.Water Environ Res. 84 (2), 171–189.
doi:10.2175/106143011X13233670703684.
Rieger, L., Jones, R. M., Dold, P. L. & Bott, C. B. Ammonia-
based feedforward and feedback aeration control in activated
sludge processes.Water Environ Res. 86 (1), 63–73. doi:10.
2175/106143013X13596524516987.
Rieger, L., Alex, J. & Schraa, O. Model-supported design,
testing, and implementation of process control strategies.
In: Smart Water Utilities: Complexity Made Simple
(P. Ingildsen & G. Olsson, eds.). IWA Publishing, London,
UK, pp. 221–226.
Rosso, D., Iranpour, R. & Stenstrom, M. K. Fifteen years of
off-gas transfer efficiency measurements on fine-pore
aerators: key role of sludge age and normalized air flux.
Water Environ. Res. 77 (3), 266–273.
Schraa, O., Rieger, L. & Alex, J. A comprehensive aeration
system model for WRRF design and control. In: Proceedings
of WEFTEC 15,Chicago, Illinois, USA.
Schraa, O., Rieger, L. & Alex, J. Development of a model for
activated sludge aeration systems: linking air supply,
distribution, and demand.Water Sci. Technol. 75 (3),
552–560. doi:10.2166/wst.2016.481.
Swamee, P. & Jain, A. Explicit equations for pipe-flow
problems. Journal of the Hydraulics Division (ASCE) 102 (5),
657–664.
Thornton, A., Sunner, N. & Haeck, M. Real time control for
reduced aeration and chemical consumption: a full scale
study.Water Sci. Technol. 61, 2169–2175. doi:10.2166/wst.
2010.971.
First received 16 February 2018; accepted in revised form 2 August 2018. Available online 30 August 2018
11 P. Juan-García et al. |Dynamic air supply models for control strategies evaluation Water Science & Technology |in press |2018
Corrected Proof
Downloaded from https://iwaponline.com/wst/article-pdf/doi/10.2166/wst.2018.356/487013/wst2018356.pdf
by guest
on 05 October 2018