Content uploaded by Pau Juan García
Author content
All content in this area was uploaded by Pau Juan García on Aug 09, 2018
Content may be subject to copyright.
Juan-García et al.
1
Exploring the potential of dynamic air supply models to evaluate
control strategies: the experience at the Girona WRRF
Pau Juan-García1, 2, Mehlika A. Kiser2, Oliver Schraa3, Leiv Rieger3 and Lluís Corominas2*
1Atkins, (The Hub) 500 Park Avenue, Aztec West, Almondsbury, Bristol, BS32 4RZ, UK.
2Catalan Institute for Water Research (ICRA), Emili Grahit 101, Girona 17003, Spain.
3inCTRL Solutions Inc., Oakville, Ontario, Canada.
*Corresponding author: Pau.JuanGarcia@atkinsglobal.com
Abstract: A model-based treatment performance and energy audit of the Girona WRRF in Spain was
conducted using advanced and fully dynamic air supply models. The focus of the study was on the aeration
system, which represents 63% of the plant’s energy consumption. A dynamic model consisting of a process,
a realistic controller, and a very detailed air supply model, was developed and calibrated to be used as the
baseline for testing various scenarios. Results show that the implementation of an ammonia-based controller
and a redistribution of the diffusers led to energy savings between 12 and 21%, depending on wastewater
temperature. In addition, the model demonstrated that the current blower is too large, which causes an
intermittent behaviour, endangering the equipment and shortening its life-time, plus limiting the minimum
air-supply. The applied aeration system models enable engineers to identify bottle-necks by modelling
equipment constraints (e.g. blower turn-down). Ignoring the air supply side in an assessment could result in
an overestimation of energy savings or treatment performance and consequently in non-optimal control
solutions or equipment selection.
Keywords: Aeration control, dynamic modelling, full-scale, energy
INTRODUCTION
One of the largest urban consumers of energy are water resource recovery facilities (WRRFs,
formerly wastewater treatment plants). Within a WRRF, aeration energy consumption typically
accounts for 50% of the facility’s total operating costs (Olsson, 2012). WRRFs are therefore
important targets for reducing municipal energy demands. Energy audits, which identify
opportunities to reduce energy use, are typically based on the average energy consumption of a
facility; they include benchmarking with similar plants or comparison against standard performance
indicators. However, significant saving potential lies in the variability of wastewater treatment and
the resulting process dynamics. Current energy audits should therefore include dynamic models that
integrate water and sludge lines and their respective energy consumption.
The usage of dynamic mechanistic models to reduce energy consumption in WRRFs is common in
our field. One of the most successful examples is the combination of experimental and modelling
approaches for optimizing full-scale WRRFs (Ayesa et al., 2006; Rieger et al., 2012, Rieger et al.,
2012a; Rieger et al., 2016; Thornton et al., 2010). However, these former studies used simplified
aeration system models which include oxygen transfer and oxygen demand, but assume ideal air
supply and distribution with no equipment constraints. Not including these constraints such as blower,
pipe, or valve limitations can hide extra costs or even mask the complete failure of an energy audit to
reduce energy consumption. Typically, optimizations carried out with simplified aeration models
overestimate the potentials for energy savings by 5 to 10%, due to missing equipment constraints
(Schraa et al., 2016).
Recent research is focusing on a more detailed description of energy consumption of the aeration
system (Beltrán, 2015; Amaral et al., 2016; Amerlinck et al., 2016). However, only the work of
Oral ICA2017
2
Schraa et al. (2015) is using a fully dynamic model for the piping network, which recalculates the
system curve based on pressure drops throughout the system. This allows for simulating the air
distribution system dynamically, conducting troubleshooting analyses, and evaluating the
components and limitations of the system in a range of scenarios of different optimisation options.
Hence, fully dynamic models can be used to find solutions for energy and process optimizations that
are more realistic and tailored to a specific facility. In this study, the advanced model developed and
implemented in SIMBA# by Schraa et al. (2016) is 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 is due to aeration. We hypothesize that
further optimization using the dynamic air supply model would lead to extra energy savings and better
response to disturbances (i.e. load peaks, temperature variation, etc.). After assessing the current
performance, three different optimisation scenarios have been evaluated, including three different
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. (2016) at a full-scale
WRRF.
METHODOLOGY
Plant layout and operation
The Girona WRRF receives an average of 55,000 m3·d-1 of domestic and industrial wastewater, which
has the capacity to serve 275,000 population equivalents. The plant is a conventional activated sludge
system in a five-stage Bardenpho configuration. Even though such configuration is designed for
biological removal of nitrogen and phosphorus, currently phosphorus is removed chemically.
Legislation allows the discharge of a total nitrogen concentration 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
m3 split into 7 zones, of which 4 are aerated, as shown in Figure 1. Sludge is wasted from secondary
settlers and is anaerobically digested. Reject water from the centrifuges is sent back to the entrance
of the facility. A first analysis showed that the load profile of the plant is heavily dependent on the
schedule of the reject water, which varies depending on the usage of the sludge dewatering
centrifuges. On average, the reject water represents up to 11% of the incoming nitrogen load and
0.5% of the phosphorus load.
The aeration system consists of a main blower and a support blower that service two main header
pipes, each controlled by an automatic valve, and followed by 4 manual valves, one per aeration zone:
AER1 to AER4. The list of air supply equipment can be found in Table 1. The reactors are aerated
using fine-bubble diffusers. Air distribution is controlled by online measurements of dissolved
oxygen (DO) currently placed at the end of the biological reactor (AER4). The air supply is controlled
by the average DO of both lanes by varying the speed and guide vanes of the blowers (Figure 1:
Signal). The DO measurement in each lane manipulates the positions of the automatic main header
valves. A Most Open Valve (MOV) algorithm makes sure that at least one valve is kept open: the
valve in lane 1 is fixed at 70% opening, and the valve in lane 2 oscillates to redistribute the air between
the two lanes.
The facility has been designed with diffuser tapering and no automated control valves to each of the
individual aeration grids; the highest airflow is therefore delivered to the first aerated zone and then
diminishes with each successive zone. The number of diffusers in the aerobic zones are 480 in AER1,
Juan-García et al.
3
366 in AER2, 180 in AER3 and 144 in AER4, respectively. Likewise, the piping system also
decreases in diameter from 0.25m to 0.15m, as well as the valves’ maximum flow capacity. As a
result, the air distribution of the plant is significantly biased towards reactors AER1 and AER2.
The WRRF in its current form has been in operation since 2008. Since then, operators have been
manually optimizing the aeration system by adjusting the manual valves in each lane and changing
the location of the DO sensor. The DO sensor was finally placed in AER4, with a DO setpoint at 2
mg O2·L-1, which results in concentrations in reactors AER1 and AER2 around 1-2 mg O2·L-1.
However, reactor AER3 shows very low DO concentrations (0.5-1 mg O2·L-1).
Figure 1. WRRF configuration (water line) & aeration system. ANA: Anaerobic, ANX (Anoxic),
AER (Aerobic), WAS & RAS (Waste and return activated sludge respectively).
Table 1: List of air supply equipment modelled (technical specifications and SIMBA# model
parameters)
Equipment
Brand
Num. Units
SIMBA# parameters
Blower
Turbocompressor ABS
HST 9000 Sulzer
1 (2 standby)
Efficiency curve, surge curve, max.
and min. airflow
Automatic
valves
Butterfly, centered axis.
Belgicast
2
Kv values, max. and min. airflow
Manual valves
Butterfly, centered axis.
Belgicast
8
Kv curves, max. and min. airflow
Diffusers
ABS Nopon disc
diffuser system PIK
300
2280
# of diffusers per grid, pressure drop
and SOTE* curves, submergence,
oxygen transfer parameters
Pipes
Measured in on-site
visits
2 lines
(8 reactors)
K factor, roughness, length,
diameter, fittings
* Standard oxygen transfer efficiency
Oral ICA2017
4
Overall, this results in a low energy consumption in the biological reactors of about 0.2 kWh/m3
treated wastewater. This is at the lower end of typical ranges, which are between 0.13 and 5.5 kWh/m3
(Enerwater, 2015). 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. This results in the blowers working against an increased system pressure and therefore
reduced efficiency. The loss of control authority leads to an oscillation of the oxygen concentrations
in reactors AER1-3. The response of the plant to stressors is slowed down since the only sensor is
located at the end of the lane (and after an anoxic zone). Another effect of having AER1 and AER2
at high DO concentrations to compensate for low DO in AER3 is more energy consumption. Oxygen
transfer is more efficient at low airflow rates and low DO concentrations (Rosso et al., 2005).
Girona WRRF model
A model was built following the recommendations of the IWA Guidelines for Using Activated Sludge
Models (Rieger et al., 2012b) and using the advanced modelling platform SIMBA#. First, mass
balances on TSS, COD and TP were conducted to verify that no gross error was present in the data.
The model was built using SIMBA#’s in-house activated sludge model ASM- inCTRL, and a
simplified anaerobic digester model developed by the Institut für Automation und Kommunikation
(Ifak, 2017). In order to build the layout of the aeration system, mechanistic models were used for
each actuator from Table 1. Aeration control was accounted for by means of PI controllers. One valve
was fixed at 70%, then a MOV algorithm was implemented following the layout in Alex et al., (2016),
allowing the other valve to oscillate to adjust the required airflow.
After building the model, a steady-state calibration was conducted to fit the sludge production (using
full-scale data from January until December 2015). Dynamic calibration was executed by using real
dynamics from the period comprised between the 7th and the 13th of December 2015. It comprises a
period of dry weather data, with detailed flow measurements (every 15 min) and daily nutrient
measurements (1 sample per day). Hourly nutrient dynamics were incorporated by scaling an hourly
ammonia profile gathered in February 2016. For the dynamic calibration period we had available DO
concentrations in reactors AER1 and AER4, as well as blower airflow, system pressure and valve
positions. Calibration focused on equipment instead of biokinetics, which should improve the validity
of the predictions; a list of parameters calibrated is in Table 1. The goodness of fit of the airflow
during the calibrated week can be seen in Figure 2.
Figure 2: Comparison of airflow results obtained from one week of real data and the modelled base
case. Days 6 and 7 correspond to a weekend.
Juan-García et al.
5
Figure 2 shows that the model describes well the airflow dynamics of the system and the behaviour
of the blower overshooting 1-2 times per week on average entering and on/off behaviour. A better fit
of the blower overshooting would be possible if we would have had the real hourly measurements for
nutrients and COD at the inlet of the reactor. The support blower on the other hand 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).
Current shortcomings
The blower is controlled based on the average DO of the last reactor of the two lanes, with a fixed
DO setpoint of 2 mg O2·L-1. The MOV control maintains the automatic valve in one lane open at
70%, whereas the other valve oscillates to redirect the required oxygen to the other lane. On top the
facility has a highly tapered diffuser system, which has been optimised by the operators by setting
the first two manual valves on the reactor’s grid to be partially closed, at 32 and 45% of the total
opening capacity respectively. As a consequence, the reactors with the highest airflows have a very
high pressure drop which results in increased system air pressure. The system usually operates at
1650 – 1800 mbar.
This causes the up and down in DO concentrations throughout the reactor (Figure 3B). Indeed, AER4
maintains the setpoint, except during peak loading. The Reactors AER1 and AER2 oscillate around
1-3 mg O2·L-1, depending on the load, whereas AER3 is lacking airflow capacity as can be seen from
the DO concentration (Figure 3.B). Although most of the COD and ammonia load is treated in AER1
and AER2, the aeration capacity is not sufficient to maintain a DO concentration of above 1.5 mg
O2·L-1 in AER3. Only at low load situations does the DO in AER3 increase to around 1 mg O2·L-1.
This limits the ability of the plant to fully use its nitrification capacity.
Relatedly, the minimum blower turndown is above the minimum airflow requirements during low
load periods. With the current control algorithm, the blower switches on and off intermittently when
the load is low for long periods of time. This reduces the blower life time and creates instabilities in
the DO concentration.
Scenario analysis
Several optimisation options were evaluated to reduce energy consumption while maintaining or even
improving effluent quality. The options were grouped into three scenarios (from small to large
investment). An overview of the optimisation options and the scenarios is shown in Table 2. To
guarantee the robustness of the control strategy, all scenarios were stress-tested by an artificial
ammonia peak, which increased the influent ammonia concentration from the average (~40 mg N·L-
1) to 80 mg N·L-1 during 4 hours, starting on the 8th day of the simulation. Variations in ammonia
loading are commonplace in the treatment plant, and the peak is the maximum concentration
registered. Each new scenario builds on top of the previous one.
1. Ammonia-Based Aeration Control (ABAC). An ammonia probe is used to measure ammonia
concentrations in the last aerated reactor. The ammonia measurement is then used to adjust the DO
setpoint with a PID controller, in a range of 0.1-2.5 mg O2·L-1. The DO sensor is moved from AER4,
where it is located in the base case, to AER2, in the middle of the main aerated reactors where most
of the load is being removed. Concerning the MOV controller, we optimized the valve tuning by
fixing the valve in the lane that had less air demand to 100% open. The valve in the controlled lane
was allowed to open from 20% to 90%.
Oral ICA2017
6
2. Aeration system upgrade. To improve the airflow distribution in the system, first the total number
of diffusers was increased, and they were redistributed as follows: 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. Second, the diameter
of the pipes that fed AER3 in each lane had to be enlarged from 0.15 to 0.2 m to increase airflow
capacity. Likewise, the valves were re-sized to allow for the necessary airflow capacity. The
automatic valve control was tuned and optimized to the new configuration.
3. Blower downscaling. This scenario addresses the problem of not being able to turn down the main
blower enough. It includes the previous strategies, plus downscales the main blower to one with less
capacity: TDS Turbo compressor type ABS HST 9000 to type ABS HST 6000. This improves the
lower limit of the aeration system supply. We also readjusted the blower’s tuning to the new
configuration.
To assess the response of the controller, each scenario is first initialized by a steady state simulation
with the scenario’s conditions, and then 11 days of dynamic influent conditions, which include an
ammonia peak on day 8 at 10am.
Table 2: Summary of optimisation options and scenarios (Sc). 1Ammonia-based aeration control
ABAC1
+ Aeration system upgrade
+ Blower downscaling
DO probe
ABAC1
Automatic
valve tuning
Increasing pipe
and valve size
Optimize
diffusers
Downscaling
blower
Blower
tuning
Sc. 0
AER4
Sc. 1
AER2
X
X
Sc. 2
AER2
X
X
X
X
Sc. 3
AER2
X
X
X
X
X
X
RESULTS & DISCUSSION
Model-based evaluation of the different optimisation options
Scenario 1: ABAC. The implementation of ABAC results in both energy savings up to 7% and
improved controller response. Savings are obtained by the DO setpoint varying between 0.1 and 2.5
mg O2·L-1, increasing the airflow when higher ammonia loads enter the reactor, and saving aeration
power otherwise. The system’s improved reaction to ammonia peaks can be appreciated in Figure
3B-C; right after the “Event Start” mark, the ABAC controller reaches a high DO concentration faster
than the Base Case, despite having a much lower DO setpoint before the event.
By controlling the oxygen supply to maintain an ammonia setpoint in the effluent, the load is
distributed over the entire reactor causing a more balanced oxygen demand. As can be seen in figure
3C, the DO profile shows that the load is removed gradually in each reactor. With this configuration,
it is possible to draw more capacity of the aeration system in moments of need, although AER3 still
presents a critical limitation in reaching the required DO concentration (figure 3C S1 AER3).
Despite the improvements with the ABAC scenario, the manual valves in reactors AER1 and AER2
still have to remain partially closed to compensate for the flawed airflow distribution generated by
the tapering. Figure 4A shows that the ABAC controller is properly working and reacts fast to the
measured ammonia most of the time. However, at low load situations, the minimum blower turndown
Juan-García et al.
7
prevents the system to maintain low DO concentrations. The DO controller (Figure 4B) works
perfectly until the ammonia controller is limited, and then the DO concentration spikes in the lane
with the fully opened valve. The valve control (Figure 4C) shows that the automatic valve in lane 2
closes to regulate the airflow. However, the valve in lane 1 has to be fixed at 100% open to prevent
the blowers working against closed valves. This would reduce blower efficiency and may lead to
blower surge. As a result of this in-depth analysis of the controller and actuator performance, we can
identify the two main system constraints: 1) the airflow distribution; 2) the minimum blower
turndown. Results also confirm the benefits of the ABAC reported in other studies (Amand et al.,
2013).
Figure 3: Performance evaluation of Scenario 1 over the base case. 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 1.
Oral ICA2017
8
Figure 4: ABAC performance evaluation in scenario 1. A: Input-output signals; B: oxygen profiles
of each lane; C: Valve position in % 0-1. DO in mg O2·L-1, Ammonia (NHx) in mg N·L-1.
Scenario 2: Aeration system upgrade. Scenario 2 upgrades the aeration system to overcome airflow
limitations in reactor AER3 by reducing the number of diffusers in the first two reactors and
increasing the number of diffusers in the two last reactors, and resizing the piping and valves, as
described in the scenario analysis. This allowed to operate the plant 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 into AER3 has improved, and the reactor can now reach higher
DO concentrations (Figure 5C). The airflow distribution changes were modelled and analysed as
shown in figure 6A.
Juan-García et al.
9
Figure 5: Performance evaluation of Scenario 2 over Scenario 1. A: Power consumption, B:
oxygen profiles across reactors in Lane 1 for scenario 2.
Figure 6: Airflow distribution. A: scenario 1 (ABAC) and scenario 2 (Aeration system upgrade); B:
scenario 2 (Aeration system upgrade) and scenario 3 (Blower downscale)
Improving the airflow distribution increases energy savings to 12%. This is also due to improved
denitrification; by increasing the nitrification capacity in AER3, there is more nitrite and nitrate being
recirculated to the anoxic reactors. The improvement in denitrification activity can be seen in Figure
9, which shows the ratio of kWh per kg NHx removed and Total Nitrogen removed. Both nitrification
and denitrification become more efficient with each scenario, which translates in less blower usage
(Figure 5A). The effluent concentration of total nitrogen in the effluent is reduced with each scenario.
Oral ICA2017
10
Nevertheless, the minimum blower turndown is still constraining the plant efficiency. Upgrading the
aeration system 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 more than the minimum air
demand. The turndown capacity of the blower is reached in low load periods, as seen in the airflow
measurements in Figure 6A-B, which cause the DO spikes in low peak periods in reactors AER3 and
AER4 (Figure 5B).
There are three main solutions to this problem. The first one was already implemented in the Base
case: fixing one valve to 70% open, and redirecting the airflow to the second lane. In our case, we
fixed the valve of the lane with less air demand to 100% open. The second solution is to implement
a blow off valve, and the third solution is to replace the main blower by a smaller one. Because of
space limitations, in this study we only show the third solution.
Scenario 3, Blower downscaling. The last scenario solves the minimum blower turndown limitation
by downscaling the main blower. The system now has a lower minimum airflow (Figure 6B), which
returns energy savings at low peak periods (Figure 7A) and improves the DO profile across reactors
(Figure 7B). The system dynamics are now smoother.
Figure 7: Performance evaluation of Scenario 3 over Scenario 2. A: Power consumption, B:
oxygen profiles across reactors in Lane 1 for scenario 3.
Influence of temperature
The calibration data used for the base case is from December, which had a recorded average
temperature of 16.5 ºC. As the temperature in the WRRF varies between 15ºC and 25ºC we evaluated
the effect of temperature on energy savings for the different optimisation options (Figure 8).
Juan-García et al.
11
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 consumption. B: Percent
savings, normalised to the savings of the base case at the given temperature.
Figure 9: kWh consumed per kg of ammonia/total nitrogen removed in each scenario
All scenarios are more energy-efficient as the temperature increases, both in terms of raw energy
savings (Figure 8A), and kWh per pollutant removed (Figure 9). This is mainly due to increased
bacterial activity, which allows the reactor to be run at a lower suspended solids concentration, and
lower DO setpoint. The base case at 25 ºC consumes 8% less energy than at 16.5 ºC. In Figure 8B,
we analyse the performance of each strategy compared to the base case at that temperature.
Results show that not every scenario behaves similarly with temperature changes. Upgrading the
aeration system (Scenario 2) shows less savings variation across temperatures, whereas the ABAC
(Scenario 1) is significantly more efficient at high temperature. On the other hand, 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 more efficient range, allowing maximum process modularity and improving energy
savings.
Oral ICA2017
12
Optioneering assessment
Table 3 summarizes the results for the tested scenarios, considering the effluent quality and system
response to ammonia-peaks. The return of investment (ROI) includes maintenance costs for the
ABAC controller during a 10 year period, and the price of the energy is considered to remain steady
for the same period. Scenarios 1 and 2 are recommended as valid optimisation options to save energy.
Replacing a blower is generally too costly to be considered an optimisation option; however, this
highlights the importance of good design, and the cost of over-scaling the 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 optimized.
The ammonia stress-test is generated in a low-load period, and thus the DO setpoint set by the
ammonia controller was at its lowest. This is the most disadvantageous moment for the ABAC system
to receive an ammonia peak. Still, all scenarios are able to contain the ammonia peak and maintain
effluent quality (Table 3-NHx peak).
Table 3: Summary of modelled cases and performance obtained. 1Scenarios are cumulative. Each
new scenario includes the optimisation options of the previous one. 2The range of savings
percentages for each scenario is calculated over the base case for each temperature.
Scenario
description1
Energy
savings (%)2
NHx daily
average
(g N/m3)
NHx peak
(g N/m3)
TN mean
(g N/m3)
Return of
investment
(years)
Base case
-
0.59
1.19
6,85
-
ABAC
6.8 – 16.3%
0.99
1.40
6.22
0.88
Aeration system
optimisation
11.6 – 19.4%
0.98
1.29
6.20
6.29
Blower
downscale
10.8 – 21.2%
1.02
1.33
6.12
15.64
CONCLUSIONS
A model-based energy and treatment performance audit has been carried out on the Girona WRRF
with a focus on the aeration system. Prior to the audit, the plant’s aeration system was already
controlled and considered to be optimized following a trial-and-error approach by operators.
However, using a detailed and fully dynamic aeration system model, it was possible to discover and
evaluate scenarios that further reduced the plant’s energy consumption by about 12-21%. The
modelled aeration control shows improvements beyond energy savings: the DO profile is the same
across reactors and follows the DO setpoint in all aerated tanks, and the aeration system can respond
faster to disturbances and draw more nitrification capacity.
Often the system constraints or limitations are hidden as they cannot or are not measured, such as the
positions of the manual valves, or the effect of pipes and valves sizing. The mechanistic dynamic air
supply model accurately represents the current system, showing airflow distribution and pressure
drops as they occur in the plant. This enables engineers to do an in-depth analysis of the aeration
together with the treatment performance, and to diagnose the main bottlenecks in the existing aeration
system: the piping size, diffuser distribution and blower minimum turndown. This study highlights
the importance of considering equipment constraints in energy assessments.
Juan-García et al.
13
ACKNOWLEDGMENTS
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 from Lluís Corominas (RYC-2013-14595). LEQUIA
and ICRA were recognized as consolidated research groups by the Catalan Government with codes
2014-SGR-1168 and 2014-SGR-291, respectively. 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., 2016 Comparison of Advanced Fine-Bubble Aeration Control Concepts with Respect to
Energy Efficiency and Robustness. In: Proceedings WEFTEC 2016, New Orleans, 136–147.
Amand, L., Olsson, G., and Carlsson, B. 2013 Aeration control – a review. Water Sci. Technol., 67(11), 2374-2398.
Amaral,A., Schraa,O., Rieger,L.,Gillot, S., Fayolle,Y.,Bellandi,G., Amerlinck, Y.,Mortier, S. T. F. C., Gori, R., Neves,
R.& Nopens, I. 2016 Towards advanced aeration modelling: from blower to bubbles to bulk. In: Proceedings of
the 5th IWA/WEF Wastewater Treatment Modelling seminar, Annecy, France.
Amerlinck, Y., Keyser, W. De, Urchegui, G., Nopens, I., 2016 A realistic dynamic blower energy consumption model
for wastewater applications. Water Sci. Technol., 74, 1561–1576. Thornton, A., Sunner, N., Haeck, M., 2010. 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
Ayesa, E, De la Sota, A, Grau, P, Sagarna J.M., Salterain, A, Suescun , J. 2006 Supervisory control strategies for the new
WWTP of Galindo-Bilbao: the long run from the conceptual design to the full-scale experimental validation. Water
Sci. Technol., 53 (4-5) 193-201
Barker, P. S. & Dold, P. L. 1997 General model for biological nutrient removal activated-sludge systems: model
presentation. Water Environ. Res., 69(5), 969–984.
Beltran, S. 2015 Optimización de la operación de estaciones depuradoras de aguas residuales mediante herramientas
avanzadas basadas en el modelado matemático. Aplicación a la EDAR de Galindo. PhD Thesis. Univ. Navarra.
Universidad de Navarra. doi:10.1017/CBO9781107415324.004
Enerwater, 2015 Deliverable 2.1. Study of published energy data. H2020-EE-2014-3-MarketUptake [ONLINE] Available
at http://www.enerwater.eu/download-documentation/ [Accessed 15 May 2017].
Gernaey, K.V., Jeppsson, U., Vanrolleghem, P.A., Copp, J.B. 2014 Benchmarking of Control Strategies for Wastewater
Treatment Plants. IWA Scientific and Technical Report No. 23, ISBN 9781843391463, IWA Publishing, London,
UK.
Ifak, 2017 [ONLINE] Available at https://simba.ifak.eu/ [Accessed 11 May 2017].
Olsson, G. 2012 Water and energy: threats and opportunities. IWA Publishing, London, UK.
Rieger, L., Takács, I. and Siegrist, H. 2012 Improving nutrient removal while reducing energy use at three Swiss WWTPs
using advanced control. Water Environ Res., 84(2), 171-189.
Rieger, L., Bott, C.B., Balzer, W.J. and Jones, R.M. 2012a Model-based aeration system design – Case study Nansemond
WWTP. In: Proceedings WEFTEC.12, New Orleans, Louisiana, USA.
Rieger, L., Gillot, S., Langergraber, G., Ohtsuki, T., Shaw, A., Takacs, I., Winkler, S. 2012b Guidelines for Using
Activated Sludge Models: IWA Task Group on Good Modelling Practice. Scientific and Technical Report No. 22.
Rieger, L., Jones, R.M., Dold, P.L., Bott, C.B. 2014 Ammonia-Based Feedforward and Feedback Aeration Control in
Activated Sludge Processes. Water Environ. Res., 86, 63–73. doi:10.2175/106143013X13596524516987
Rieger, L., Alex, J. and Schraa, O. 2016 Model-Supported Design, Testing, and Implementation of Process Control
Strategies. In Ingildsen, P. and Olsson, G. (ed.) Smart Water Utilities: Complexity Made Simple. IWA Publishing,
London, UK.
Rosso, D., Iranpour, R. and Stenstrom, M.K. 2005 Fifteen Years of Off-gas Transfer Efficiency Measurements on Fine-
Pore Aerators: Key Role of Sludge Age and Normalized Air Flux. Wat. Environ. Res., 77(3), 266-273.
Schraa, O., Rieger, L. and Alex, J. (2015). A Comprehensive Aeration System Model for WRRF Design and Control. In:
Proceedings WEFTEC.15, Chicago, Illinois, USA.
Schraa, O., Rieger, L., Alex, J., (2016). 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
