A Critical Review of the Factors
Affecting Modeling Oxygen
Transfer by Fine-Pore Diffusers in
Activated Sludge
Gustavo Andrés Baquero-Rodrı́guez1*, Jaime Andrés Lara-Borrero2, Daniel Nolasco4,5†,
Diego Rosso3,5*†
ABSTRACT: In this review, the factors affecting the transfer of
oxygen in activated sludge processes using fine-pore diffusers for
water resource recovery are critically discussed. In water
resource recovery facilities, the energy required for aeration
constitutes 50% to 80% of the total energy consumed by the
plant. This critical review highlights the use of fine-pore diffuser
aeration and emphasizes the significance of accounting for the
following factors: diffuser aging and fouling, diffuser layout,
diffuser type, selector benefits, local environmental conditions
(temperature and atmospheric pressure), influent wastewater
variability, dissolved oxygen control systems, and airflow rates.
In our review, we were unable to find mathematical models that
could be used to develop dynamic -factor predictions and
diffuser fouling predictions. Although the development of a
model that considers all the factors that affect oxygen transfer
efficiency (OTE) in activated sludge systems would be extremely
valuable, the creation of such a model is outside the scope of this
review. Water Environ. Res., 90, 431 (2018).
KEYWORDS: aeration; oxygen transfer efficiency; activated
sludge; fine-pore diffusers; wastewater; energy.
1
Facultad de Ingenierı́a, Universidad Militar Nueva Granada, Sede
Campus Nueva Granada, Km 2, vı́a Cajicá - Zipaquirá, Colombia.
GREST - Group of Research on Environment, Science & Technology.
2
Ciencia e Ingenierı́a del Agua y el Ambiente, Departamento de
Ingenierı́a Civil, Pontificia Universidad Javeriana, Carrera 7 No 4062, Bogotá, Colombia.
3
Department of Civil and Environmental Engineering, University of
California, Irvine, CA 92697-2175, U.S.A.
4
Nolasco y Asociados, S.A., Congreso 1908, Piso 2D, Buenos Aires,
C1428BVB, Argentina.
5
Water-Energy Nexus Center, University of California, Irvine, CA
92697-2175, U.S.A.; e-mail: bidui@uci.edu
*
Facultad de Ingenierı́a, Universidad Militar Nueva Granada, Sede
Campus Nueva Granada, Km 2, vı́a Cajicá - Zipaquirá, Colombia.
GREST - Group of Research on Environment, Science & Technology;
e-mail: gustavo.baquero@unimilitar.edu.co
†
WEF member
WATER ENVIRONMENT RESEARCH
May 2018
doi:10.2175/106143017X15131012152988
Introduction
In the past century, water resource recovery technology has
transitioned from being innovative to established. Throughout
the development of its numerous configurations, the need for
oxygen to support aerobic biokinetics has included the operation
of aeration systems as an integral part of water resource
recovery. In the activated sludge process, aeration systems serve
two functions: (1) to meet the process oxygen demand, and (2)
to provide sufficient mixing to ensure that solids remain in
suspension.
The concept previously known as Wastewater Treatment
Plant (WWTP) has evolved, and recent developments in
wastewater treatment have led to the development of the term
Water Resource Recovery Facility (WRRF). Activated sludge
aeration is a primary contributor to the energy demands and
energy costs of WRRF. In fact, among the contributors to the
operating costs of a WRRF, the energy required for the
electromechanical equipment used in aeration represents 50%
to 80% of the total energy costs of WRRFs (Reardon, 1995;
Rosso and Stenstrom, 2005; Water Environment Federation,
2009).
The importance of specifying accurate and realistic efficiency
values for aeration systems, in which numerous factors must be
considered, cannot be overstated. Hence, understanding the
factors affecting oxygen transfer provides useful information for
designing fine-pore aeration systems, and for the decisionmaking aimed at reducing the operating costs, energy footprint,
and carbon emissions associated with power generation. The
objective of this paper is to review the factors affecting oxygen
transfer, as well as the optimal conditions needed to minimize
aeration energy demands in WRRFs. This review also considers
modeling oxygen transfer in activated sludge and research needs
for practitioners.
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Baquero-Rodrı́guez et al.
Fundamentals of Oxygen Transfer
Basic Model for Oxygen Transfer. The basic model for
oxygen transfer in a dispersed gas-liquid system is expressed
using eq. 1, which is related to the OTR and KLa. KL
corresponds to the velocity of the gas-liquid oxygen transfer and
‘‘a’’ corresponds to the specific surface area of the bubbles (i.e.,
the surface area per unit volume). Because measuring the
interfacial area is often impractical, the KLa coefficient generally
becomes a reference variable for characterizing aeration
(Eckenfelder, 1959). The driving force for oxygen transfer in the
basic model is the concentration gradient between the C‘ and
dissolved oxygen:
ð1Þ
OTR ¼ kL a c‘ DO V
where OTR: oxygen transfer rate (mass O2, time1), KLa: liquidside volumetric oxygen transfer coefficient (time1), C‘ : oxygen
saturation concentration (mass O2, length3), DO: dissolved
oxygen concentration (mass O2, length3), and V: Water volume
(length3).
In its differential form, the oxygen transfer mass balance is:
V
dDO
¼ OTR v OUR
dt
ð2Þ
where the left term is the oxygen accumulation and the right
term is the net between the oxygen transfer rate and the oxygen
uptake rate (OUR). When the dissolved oxygen is not varying,
eq. 2 can be rearranged to produce a calculation of OUR:
OUR ¼
OTR
V
ð3Þ
Henry’s Law. Henry’s Law establishes that the amount of a
determined gas, dissolved in a certain type and volume of liquid
at constant temperature, is directly proportional to the partial
pressure of the gas in equilibrium with the liquid and is
expressed as follows:
p ¼ KH DO
ð4Þ
where p: partial pressure of the gaseous solution (Pa), KH:
Henry’s constant (Pa m3/kg), and DO: dissolved gas concentration (kg/m3). Henry’s Law Constant (KH) depends on the
solute, solvent, and temperature.
Bubble Formation. In fine-pore diffused aeration, bubble
formation is created by means of continuously pushing
compressed air through diffusers with small orifices or porous
materials. Bubble size depends on several factors such as flow
rate, inlet pressure, and the contact angle with the rubber
membrane. Among these factors, the flow rate has the largest
effect on the bubble size, followed by membrane material and
contact angle. The punch size has a moderate effect on the
bubble size, whereas the punch length and punch direction have
a slight effect on bubble size. Finer bubbles are preferable to the
coarse bubble diffusers. Fine pore diffusers provide larger
surface area and longer residency time, which will improve the
standard oxygen transfer. In addition to the above factors,
wastewater characteristics and submergence must be taken into
account under particular conditions; the liquid depth will
432
modify the bubble column residence time (Alkhalidi and
Amano, 2015). A detailed description of bubble formation in
submerged orifices, and a review of the theoretical models
encompassed by this process, are provided by Clift et al. (2005).
Factors to consider in bubble formation may vary according
to flow characteristics of the aerobic reactor of the activated
sludge process. Important differences are found when comparing
bubble formation near to quiescent conditions, under no
horizontal flow regime with bubble formation under liquid
cross-flow conditions (Fayolle et al., 2010). In the latter case, the
dynamics of bubble formation are more complex (velocity
gradient, bubble inclination, and distortion). The aforementioned flow regimens are characteristics, in some variation, of
the activated sludge process, as is the case of the sequencing
batch reactors (SBR) as well as the oxidation ditch, respectively.
Under no horizontal flow regime, the detached bubbles have
significantly smaller sizes and higher frequencies when compared to bubble formation under quiescent liquid conditions.
The bubble detachment is controlled by the drag force as a result
of the liquid motion and not the buoyancy force (Loubière et al.,
2004).
Parameter Estimation for Oxygen Transfer
Efficiency
Oxygen Transfer Measurements. To better understand
and evaluate aeration equipment performance under actual
process conditions, many procedures and test methods have
been used. Measuring the rate of oxygen transfer in clean water
is a process that is described by the standard ASCE/EWRI 2-06
(American Society of Civil Engineers, 2007). In process water
(mixed liquor), OTR measurement is performed according to
the ASCE 18-96 standard (American Society of Civil Engineers,
1997). German standards provide the steps that should be
followed when conducting clean water and process water tests in
the form of ‘‘ATV M 209E’’ (DWA, 1996). The use of either
standard should be consistent between clean and process water
to guarantee consistency in dissolved oxygen correction with
depth.
Various parameters can be used to represent the relationship
between oxygen transfer rate and power input. To test clean
water, the dissolved oxygen in the water must be removed. The
parameters representing the aeration system are estimated from
data gathered during reoxygenation, ranging from the complete
absence of dissolved oxygen to saturation. As reoxygenation
occurs, the dissolved oxygen is monitored, and parameters such
as the KLa and C‘ can be estimated using a mass transfer model
(American Society of Civil Engineers, 2007). The oxygen can be
scavenged either chemically (i.e., using sodium sulfite and cobalt
chloride) or physically via nitrogen stripping (Hwang and
Stenstrom, 1985).
Process water requires its own testing procedures. Applicable
techniques include: (I) recording dissolved oxygen after perturbation of the stable condition by adding H2O2 or changing the
blower power (Non-Steady State Method), (II) using gaseous
tracers with radioactive isotopes (Tracer Measurement of
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Baquero-Rodrı́guez et al.
Oxygen Transfer; American Society of Civil Engineers, 1997), or
(III) by considering gas-phase mass balance by directly
measuring the oxygen transfer efficiency (OTE) (off-gas
method). Turning our attention to the third method, the off-gas
method employs one or more fixed, or floating, collection hoods
to capture and convey off-gases to an analyzer that measures gas
flow rate and gas-phase oxygen content (American Society of
Civil Engineers, 2007). Thus, this method is the easiest to
recommend because it frees users from dealing with aeration
tank power levels, does not rely on constant oxygen uptake, and
avoids hazardous chemical use. Moreover, the off-gas method is
a low-cost WWTP/WRRF monitoring option that provides
results with low variability at approximately 10% (Capela et al.,
2004; Mueller and Boyle, 1988; Zhou et al., 2013).
The Obscure a-Factor. The reduction in the OTR caused by
contaminants found in wastewater is represented by the afactor. The a-factor is calculated as follows:
a¼
kL a Process Water
a SOTE
¼
SOTE
kL a Clean Water
ð5Þ
The a-factor varies depending on the aeration process, tank
geometry, water characteristics, and other factors. The significant component in the wastewater affecting the a-factor is
surfactants. For fine-bubble diffusers, a-factor values ranging
from 0.3 for fine bubble diffusion up to 0.85 are commonly
reported in the literature. Becuase the wastewater composition
cannot be known, the a-factor results unknown a priori and may
only be calculated a posteriori using eq. 5.
Dissolved Oxygen Probe Response Time. Philichi and
Stenstrom (1989) outline a procedure for establishing the
response time of a sensor and its effect on estimating KLa. The
typical response time of polarographic sensors with highly
sensitive membranes is approximately 8 s, and for optic sensors
the response time approaches 40 s (YSI Incorporated, 2009). The
development of standard procedures for estimating oxygen
transfer parameters began in the 1980s (Brenner, 1983). At that
time, optical dissolved oxygen probes were not commercially
available. The first references discussing the applications of
optical dissolved oxygen probes for water resource recovery
were published in 2000 (Santos and Farahi, 2014). This
technology should be considered for oxygen transfer parameter
estimation tests because of its recent development and its wide
commercial availability. According to the literature review, the
effects of measuring response time on the optical sensors, when
they are applied for estimating the KLa, have not yet been
considered. Nevertheless, recent findings contradict previous
studies which claim that lag time in polarographic dissolved
oxygen probes response can bias estimates of KLa. On the basis
of the results obtained for the KLa, estimated using different
types of dissolved oxygen probes (optic and polarographic),
Baquero-Rodriguez and Lara-Borrero (2016) concluded that
there is no significant influence of the probe lag (time constant)
or probe characteristics on the parameters used to assess oxygen
transfer efficiency in clean water.
Each type of dissolved oxygen sensor (optic and polarographic) has advantages and disadvantages. From a WWTP/
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WRRF operator viewpoint, optical sensors require less maintenance than polarographic sensors. From a practical viewpoint,
lower maintenance is an attractive alternative when selecting
dissolved oxygen sensors. Additional details regarding optical
sensor specifications are widely available on their corresponding
manufacturer websites.
Factors Affecting Oxygen Transfer
Efficiency
The factors that must be considered when studying oxygen
transfer in fine-pore aeration systems vary widely. For example,
diffuser-related issues such as type, installation depth, distribution, time in operation and air flow rate, and aerobic reactor
features such as depth, volume, and type of reactor, as well as
operating conditions such as Solids Retention Time (SRT),
nutrient removal processes, temperature, turbulence, and
wastewater composition must be considered (Eckenfelder, 1959;
Groves et al., 1992; Henkel, 2010; Schierholz et al., 2006). In the
following sections, we present state-of-the-art advances regarding these factors, and their effects on oxygen transfer in
activated sludge processes used for water resource recovery. In
some cases, the interactions among several factors have not been
fully described.
Environmental Factors Affecting Oxygen Transfer
Efficiency. Environmental factors are not under the control of
the operator and affect OTE in clean and process water.
Temperature. Changes in water temperature induce changes in
the C‘ in water. The C‘ in water decreases with increasing
temperature, and the decreased solubility of oxygen at higher
temperature is an important factor in aeration system design.
Increases in the mixed liquor temperature generally coincide
with increases in ambient air temperature, and decrease the
capacities of the blowers in diffused aeration systems (Jenkins,
2013).
Barometric Pressure. Despite remarkable progress in aeration,
the effects of barometric pressure on OTE have not been
studied. Regarding aeration energy demands, the blower
performance is inversely related to the altitude at which the
blower is installed, because of the effects of altitude on
atmospheric pressure and air density. Thus, atmospheric
pressure has been considered in this review.
Three factors decrease the air density: increases in air
temperature, decreases in atmospheric pressure, and increases in
relative humidity. From a mechanical efficiency standpoint, a
decrease in air density results in a greater demand for volumetric
airflow production by the blower, to guarantee an equivalent
airflow mass with respect to standard conditions. The
evaluation of energy requirements depends on the operating
conditions, control techniques, and blower type (Water
Environment Federation, 2009).
A comparison of energy consumption at sea level and at
higher altitudes is obtained by dividing the energy consumption
under standard conditions by the correction factor of the air
density under the desired conditions. For cities with elevations
above sea level ranging from 1600 to 3600 masl, the
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Baquero-Rodrı́guez et al.
Table 1—a-Factor prediction based on SRT.
Reference
Conditions for Validity*
Rosso et al. (2005)
SRT , 20 d
Gillot and Héduit (2008)
SRT ~ 15 d (Z . 5.5m)
SRT ~ 15 d (Z , 5.5m)
SRT . 25 d
1 d , SRT , 30 d
(1d , MLVSS ,12 g/L)
Henkel et al. (2011)
Equation
(6)
a ¼ 0:172 log v 0:131
v ¼ SRT
QN
AFR
QN ¼ aN
DZ
0:44 < a < 0:67
0:53 < a < 0:78
0:73 < a < 0:98
a ¼ 0:51 0:062 MLVSS þ 0:019 SRT60:114
(7)
(8)
* With information made available in the publication.
Where: AFR ¼ Airflow rate (m3/s); a ¼ Diffuser area (m2); ND ¼ Total number of diffusers; Z ¼ Diffuser submergence (m); SRT (d); MLVSS (mixed liquor
volatile suspended solids g/L); QN: Normalized air flux (1/s)
corresponding altitude correction factor is in the range between
0.81 and 0.69 (Ludwig, 2001). Such is the case in cities like La
Paz, Bolivia (3640 masl), Quito, Ecuador (2850 masl), Bogotá,
Colombia (2625 masl), Mexico City, Mexico (2240 masl), and
Denver, United States (1600 masl). Most of the above-mentioned
locations are considered developing countries with poor
sanitation coverage, and since, at the present moment, the
development of their WRRFs is largely underway, we highlight
here the shortage of published knowledge on aerated processes
in high-elevation regions.
Saturation Concentration of Oxygen in Water. The C‘ in
water is important for aeration and represents the maximum
amount of oxygen that can be dissolved in water (Jenkins, 2013).
The C‘ in water is a function of barometric pressure, water
pressure, salinity, and temperature (Benson and Krause, 1980;
Jenkins, 2013), and decreases noticeably when the atmospheric
pressure is not 1 atm, and/or the salinity (based on the Practical
Salinity Scale) or temperature are high. The ranges associated
with small variations from standard conditions include the
following: atmospheric pressure, 1.1 , P , 0.9 atm; salinity, S
, 40 Practical Salinity Unit; and temperature, 0 , t , 30 8C.
The procedures for estimating C‘ under these ‘‘standard’’
conditions can be found in Benson and Krause (1984). As stated
before, the driving force for oxygen transfer is the concentration
gradient between the C‘ and dissolved oxygen.
Process Conditions Affecting Oxygen Transfer Efficiency. Process conditions may not be under the direct control of the
process designer or operator, which may affect the OTE in
process water. In practice, the most common approach is to
operate a process in the same layout for which it was initially
designed; however, during reconfiguration or upgrading, the
process may be substantially altered to significantly improve the
effluent water quality. We describe two conditions here: the
process conditions that affect oxygen transfer as a result of inprocess actions taken by designers or operators, and the process
conditions related to external factors beyond the control of the
designers or operators.
Surface Active Agents (Surfactants). The process of bubble
aeration is challenged by surface active agents, known as
surfactants (e.g., fatty acids, oils, soaps, and detergents).
Surfactants are typically present in wastewater. The presence of
surfactants affects the bubble generation phenomenon, and thus
434
the bubble surface area and the different mass transfer
parameters, such as the kLa. This situation stems from a
decrease in the average bubble size as the surfactant concentrations increase, with bubbles ranging from 4 mm in clean
water to 1 mm in diameter in water with surfactants
(Eckenfelder and Barnhart, 1961; Henze et al., 2008; Jimenez et
al., 2014; Painmanakul et al., 2005; Rosso et al, 2005). High flow
rates help counteract the contamination brought about by
surfactants. High flow rates result in high gas-liquid interface
renewal rates and slow the negative effects of the surfactants
(Rosso et al, 2005).
Under typical conditions, the velocities at which bubbles rise
in fine-pore and coarse-bubble (non-porous) aeration systems
are 0.2 m/s and 1.5 m/s, respectively. The residence times for
fine bubbles are longer, which generates more surfactant
accumulation; consequently, surfactants play a prominent role
in fine-pore aeration systems because the reduced ascending
velocity results in smaller renewal rates (Rosso et al, 2005; Rosso
and Stenstrom, 2006). Removing the readily biodegradable
substrate (surfactant) before the aerobic reactor can increase
transfer efficiency and reduce the operating costs associated with
aeration (Rosso et al., 2008). However, selectors are generally
used for this process (for more information on selectors, see
Section ‘‘Process Layout and Selectors’’).
Solids Retention Time. The SRT, also referred to as ‘sludge
age’ or mean cell retention time (MCRT) is directly related to
the length of time the biomass remains in the reactor and
dictates the treatment grade, biomass concentration, and oxygen
requirements. High SRT values increase oxygen requirements,
and are also related to the removal of surfactants that negatively
affect oxygen transfer (contained in the readily biodegradable
COD, rbCOD), which accumulate at bubble surfaces and
severely reduce oxygen. As a consequence, the removal of
surfactants increases aeration efficiency and the a-factor (Gillot
and Héduit, 2008; Henkel et al., 2011; Rosso et al., 2005, 2008;
U.S. EPA, 1989). Table 1 summarizes the main efforts, thus far,
to correlate the a-factor and SRT.
Mixed Liquor Concentrations. In the absence of respiration,
the physical presence of solids has a detrimental effect on the
OTR because the layer of solids that accumulates on the bubbles
has low permeability and blocks oxygen transfer (Sundararajan
and Ju, 1995). The oxygen transfer has, for a long time, been
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Baquero-Rodrı́guez et al.
Table 2—a-Factor prediction based on MLSS concentration.
Reference
MLSS Range
Krampe and Krauth (2003)
Henkel et al. (2011)
This paper (Figure 1)
Equation
0.08788 MLSS
0–30 g/L
1–12 g/L
0–30 g/L
a¼e
a ¼ 0.062 3 MLVSS þ 0.972 6 0.070
Y ¼ (u/(u-v))*(exp(-v*X)-exp(-u*X))
(9)
(10)
(11)
Where: Y ¼ a-factor, X ¼ MLSS (g/L), u ¼ 0.507248767, v ¼ 0.1043568988
considered inversely related to the MLSS concentrations in the
reactor (Boogerd et al., 1990; Freitas and Teixeira, 2001; Henkel
et al., 2011; Ju and Sundararajan, 1994; Krampe and Krauth,
2003; Muller et al, 1995; Ozdemir and Yenigun, 2013). To
represent this phenomenon, different authors have proposed
relations between MLSS concentration and the a-factor. For
example, Krampe and Krauth (2003) proposed an inverse
exponential relation and Henkel et al. (2011) theorized an
inverse linear relation (see Table 2). However, Rosso et al.
(2005) had shown the relationship between the a-factor and
MCRT (hence, MLSS) in activated sludge for fine-pore aeration.
To conciliate the different positions, we plotted in Figure 1 all
the data from Rosso et al. (2005) and compared it with other
data from fine-pore aeration of highly concentrated MLSS
reactors. The data sets plotted in Figure 1 are from both pilot
and full-scale tests. The fitting line is a double exponential curve
with descriptive statistics reported in the plot (See eq. 11 and
Figure 1). Note the gap between 4 and 6 g/L, where membrane
bio-reactors are uneconomical to operate and activated sludge
clarifiers are solid limited. In summary, these relationships allow
us to obtain similar results for concentrations less than 10 g/L
MLSS. However, for greater concentrations, variations emerge
because of the type of relationship proposed, and the conditions
for validity in each case, as shown in Figure 1.
As the sludge concentration increases, the bubble coalescence
must increase because of the shear-thinning nature of the sludge.
In fact, coalesced bubbles (associated with more interfacial
shear) can thin the fluid and experience less resistance to rise.
Such coalesce bubbles, having a substantially lower ‘‘a’’ in their
kLa, do indeed exhibit lower a factors.
Figure 1—Dependence of the a-factor on the MLSS concentration. http://wst.iwaponline.com/content/
47/11/313
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Baquero-Rodrı́guez et al.
Correlations between the aeration parameters and the
apparent viscosity of the activated sludge have been reported.
For stirred tank reactors, an exponential correlation exists
between the activated sludge KLa and the Reynolds number
(Krampe and Krauth, 2003; Nittami et al., 2013). It can be
assumed that a rise in apparent viscosity may lead to the
production of larger bubbles at the formation stage. Larger
bubbles will lead to a reduction of the specific surface area
(Durán et al., 2016).
Extracellular Polymeric Substances. Extracellular polymeric
substances (EPS) represent a significant component when
discussing microbial aggregates because they hold the aggregates
together in a three-dimensional matrix (Sheng et al., 2010). The
EPSc increase the porosity of the flocs and, therefore, their
diffusivity, which is beneficial for oxygen transfer as it is
facilitated by the formation of large flocs (Germain et al., 2007).
Germain et al. (2007) describe a statistical analysis method for
identifying the relative effects of various biomass characteristics
on oxygen transfer. These authors found that the value of KLa is
influenced by the following factors in decreasing order: MLSS .
airflow rate . EPSc . SMPCOD. For the a-factor, the
influencing factors decreased as follows: MLSS . SMPCOD .
EPSc . airflow rate. Moreover, the authors showed that the KLa
and a-factor mirrored aeration, and the EPSc (i.e., both
parameters increased as aeration and the EPSc increased) were
inversely related to the MLSS and SMPCOD (the two parameters
decreased with increasing levels of MLSS and SMPCOD).
Overall, the MLSS parameter had the most control over oxygen
transfer in the aforementioned investigation.
Process Layout and Selectors. A selector is a tank or
compartment in which influent wastewater is mixed with
return activated sludge. Selectors are located before the aeration
tank, and their primary function is to encourage the growth of
floc-forming bacteria and impede the proliferation of filamentous bacteria. In addition to improving settling and promoting
nutrient removal, on account of their influences on the former
groups of bacteria, selectors positively influence OTE because
they use rbCOD as well as surface active agents (Grady et al.,
2011; Metcalf and Eddy, 2013). Activated sludge configurations
using anaerobic or anoxic bioselector designs can remove readily
biodegradable substrate before the aeration tank.
Rosso et al. (2008) compare the a-factors of aerobic reactors
from different activated sludge process configurations. The
average a-factor increases from 0.37 to 0.48 to 0.59 for
conventional, nitrifying, and nitrification/denitrification systems, respectively. Based on the aforementioned data, activated
sludge processes using selectors have a greater a-factor than
conventional activated sludge processes.
Salinity. Salinity affects the solubility of oxygen in water and
is usually expressed as TDS. Increasing salinity corresponds with
lower C‘ (Jenkins, 2013). The b factor represents the roles of
salinity and alkalinity, with values generally between 0.9 and
0.99. The b values for wastewater with TDS , 1500 mg/L, and
industrial wastewater with a TDS contents of approximately 10
000 mg/L, are 0.99 and 0.94, respectively. The estimated value of
the b factor is based on a comparison of the C‘ of process water
436
and the C‘ of clean water. Alternatively, the b factor can be
calculated from the TDS concentrations (b ¼ 1-5.7 3 10^-6 3
TDS mg/L) (Mueller et al., 2002; Water Environment Federation, 2009).
Diffuser-Related Issues. The following sections summarize
several aspects regarding diffuser characteristics, operating
conditions, and their influences on oxygen transfer efficiency.
Fine-Pore Diffusers. Fine-pore diffusers consist of a thin
flexible membrane made from either a thermoplastic material or
an elastomer. Diffusers have fine orifices, through which gas is
forced to generate bubbles that travel up through the water
column and pop when they reach the surface, and as the bubbles
ascend, molecule interchange occurs from the gas phase to the
liquid phase (Benjamin, 2013; U.S. EPA, 1989).
Fine-pore diffusers produce bubbles with a diameter of
approximately 2 to 5 mm. The bubble size produced by finepore devices is affected by airflow, and becomes somewhat
larger as airflow increases (U.S. EPA, 1989). For fine-pore
diffusers, bubble size depends on airflow rate. The effect of a
reduction in orifice size is a reduction in bubble size, which
increases the KLa and the standard oxygen transfer rate via
increased surface area per unit volume and increased contact
time. The airflow rate also affects bubble shape, bubble rise
velocity, and system turbulence (Mueller et al., 2002).
Airflow Rate. In fine-pore diffusers, bubble size is linked to
airflow rate, with more flow per diffuser resulting in larger
bubble diameters. Consequently, these larger bubbles result in a
smaller interfacial area. Therefore, as the airflow rate per
diffuser increases, the OTE decreases (U.S. EPA, 1989).
Furthermore, increased aeration rates generally result in
increased OTRs, potentially because of the effects of turbulence
caused by aeration. Greater airflow rates and mixing turbulences can reduce the depth of the boundary layer, improving the
oxygen transfer coefficient KL and OTR (Ji and Zhou, 2006;
Vogelaar et al., 2000).
Diffuser Density. Typically, an increase in diffuser density,
defined as the area covered by the diffusers in relation to the
total area of the tank floor, is expected to result in a higher
OTE. However, a maximum value exists for the density of
diffusers in which the SOTE increase is minimal (diminishing
returns), which is determined by diffuser size, airflow rate, and
the space between diffusers (U.S. EPA, 1989).
Flow Regime. According to the flow regime, aerobic reactors
can be designed and constructed to operate in terms of plug flow
or complete-mix. Each of these mixing regimes is accompanied
by a particular set of conditions that affect the OTE. In the plug
flow regime, loads are variable throughout the aerobic reactor
and generally decrease from the point of entry of the reactor, to
the exit. Thus, the a-factor is low at the entrance of the aerobic
reactor and high at the exit. The a-factor is higher for
nitrification/denitrification processes than for conventional
processes because of the uptake of low molecular-weight
surfactants resulting from denitrification in the anoxic selector
(Rosso and Stenstrom, 2006).
For a complete-mix aerobic reactor, higher concentrations of
pollutants may occur at the point of entry for wastewater and
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Baquero-Rodrı́guez et al.
return activated sludge in the tank, and lower concentrations
may occur at the effluent weir; these variations are generally not
significant. On one hand, complete-mix reactors offer a uniform
distribution but lower a-factors when compared with those of
plug flow reactors. On the other hand, these systems typical ly
exhibit higher average a-factors than the influent end of a plug
flow reactor. A commonly encountered variation in plug flow
reactors is tapered aeration. In this configuration, the diffuser
density is greater at the influent end of the tank than at the
effluent end of the tank. Because the influent side of the reactor
exhibits a higher load, the point of entry exhibits greater aerobic
oxygen demand. Up to half (50%) of the total air demand can be
traced to the first fifth (20%) of the aerobic reactor (Jenkins,
2013).
Regarding stirred reactors; the effect of rotational speed, gas
flow rate, and impeller type have been studied. In general terms,
for all operating conditions including the stirred reactor, KLa
values increase with increasing total power consumption. For
the same total power consumption, the KLa values obtained with
bubble columns are higher than those provided by the stirred
gas–liquid reactor. This difference is explained by the higher
values of interfacial area obtained in bubble columns (Bouaifi et
al., 2001; Bouaifi and Roustan, 2001).
Depth of the Aerobic Reactor. The OTE of the aerobic
reactor increases with increasing depth because of the greater
residence time of the bubbles, and the greater partial pressure of
oxygen at the moment of bubble formation. As the partial
pressure increases with diffuser depth, the operating pressure for
the blower also increases. Nonetheless, the Standard Aeration
Efficiency (SAE) remains constant because increases in depth
reflect increases in energy consumption (U.S. EPA, 1989; Water
Environment Federation, 2009).
Fouling, Scaling, and Aging of Fine-Pore Diffusers. Fouling is
caused by the formation of biological slime on the external
surface of the diffuser, and scaling is caused by inorganic
precipitates such as silica, calcium carbonate, and gypsum,
among others (Henze et al., 2008; Metcalf and Eddy, 2013). The
diffuser efficiency decreases as organic and mineral coatings
accumulate on the diffuser surface and membrane materials lose
their elasticity. Porous diffusers have been used in activated
sludge processes since 1916. With the operation of these
elements, it has become evident that pore obstruction is a
problem (Boyle and Redmon, 1983). The factor F for diffused
aeration is usually applied in the design process to account for
these variations (Jenkins, 2013). Among the previously cited
disadvantages of fine-pore diffusers, the need for periodic
cleaning, and the negative effects of fouling on oxygen transfer,
are significant (Rosso and Stenstrom, 2006).
There is a greater decrease in a-factor from diffuser organic
and/or inorganic fouling with increased time (U.S. EPA, 1989).
One method for evaluating these two issues is to periodically
monitor the DWP, which is defined as the pressure differential
(head loss) across the diffuser element, when operating under
submerged conditions, and is expressed as the water depth at a
specific airflow rate. Smaller bubble sizes correspond with
higher DWPs. Occasionally, the DWP required by fouled
WATER ENVIRONMENT RESEARCH
May 2018
diffusers may be too high, which prevents the diffusers from
releasing air. In other cases, blowers may be used to counteract
the DWP generated by fouled diffusers if they are employed
outside of their optimum efficiency range (i.e., essentially
overworking the blower), resulting in greater power and
maintenance costs (Henze et al., 2008; Mueller et al., 2002).
Overall, the DWP increases and the OTE decreases over time.
The progression of biofouling favors the coalescence of bubbles
and decreases the OTE, and biofouling is evidenced and
therefore measured by an increase in the DWP (Boyle and
Redmon, 1983; Rosso and Stenstrom, 2006; U.S. EPA, 1989).
Another important aspect is the frequency of diffuser
cleaning, which plays a role in water quality, treatment
processes, diffuser characteristics, diffuser maintenance, and
overall cleanliness. Cleaning restores OTEs and allows for the
inspection and replacement (if necessary) of deteriorated diffuser
elements (U.S. EPA, 1989). Regular diffuser cleaning can reduce
average power costs by up to 18% (Leu et al., 2009). Despite the
demands made by cleaning, it is a worthwhile activity as it
counteracts several undesirable conditions. For example, the
OTE decreases with material age because the orifices become
dilated and produce larger bubbles that are unfavorable for the
transfer process. Consequently, changes in the polymer properties occur and the membranes become harder, which allow
fouling and scaling to plague the orifice openings. Changes in
polymer properties have been linked to decreases in SOTE and
higher levels of DWP (Rosso et al., 2008). When considering
diffuser fouling in plug flow reactors, the diffuser fouling at the
entry side of the reactor mainly occurs as a result of the high
organic loads found in this section of the reactor (Mueller et al.,
2002; U.S. EPA, 1989). Diffusers operated in processes or areas
within a reactor with low a-factors also usually display higher
biofouling rates than diffusers operating with high a-factors
(U.S. EPA, 1989).
Diffusers are subject to fouling and scaling, resulting in a loss
of transfer efficiency as biofilms form and change material
properties (a combination of biofouling, scaling, and material
ageing) producing larger bubbles, thus hindering mass transfer
and contributing to increased facility energy costs (Kim and
Boyle, 1993). The connection between biofilm growth and
fouling has been implicit in discussions of diffuser fouling over
many years. Recent research has measured a quantitative
connection between the extent of biofouling and reduced
diffuser efficiency. Although each diffuser type presents its own
performance and specific fouling response, there is a high
correlation between biofouling phenomena, measured as biofilm
DNA, and diffuser performance, expressed by the decrease in
oxygen transfer efficiency (Garrido-Baserba et al., 2016, 2017).
New diffuser materials (e.g., silicone, polyurethane, EPDM with
PTFE coating, EPDM impregnated with PTFE) are being
introduced on the market, to reduce fouling propensity and
extend the periods between cleaning events. Future research
should focus on independent evaluations of these.
Proper Selection of Diffusers. Commercially, several diffuser
varieties are available. Although each model has its own
advantages and disadvantages, proper selection depends on the
437
Baquero-Rodrı́guez et al.
characteristics of the process for which it will be used. The most
relevant characteristics for selecting a diffuser generally include
oxygen transfer efficiency, maintenance requirements, and
energy efficiency (Jenkins, 2013).
Results for oxygen transfer tests in clean water are relevant,
not only because they allow us to identify the parameters that
characterize an aeration system, but also because they can be
used to compare and select diffusers. Improper selection can
result in extra operating costs, more required maintenance, and
earlier diffuser replacement (Ashley et al., 1991). Initial diffuser
selection is one of many crucial factors that affect the efficiency
of an aeration system. Other aspects, such as the OTE of the
aeration system and upgrading the aeration system components,
deserve more attention.
Rosso et al. (2012) proposed a procedure for designing
aeration systems and providing specifications that relied on onsite column testing. Generally, this procedure uses extended
fouling testing of the aerobic reactors at a recovery facility to
collect site-specific aeration performance data. The aforementioned procedure can also be developed while a new plant is
being designed and constructed, or while an aerobic reactor at
an existing plant is being updated.
Bridging the Gap Between Field
Measurements and Oxygen Transfer
Modeling
Measuring Oxygen Transfer in Full-Scale Activated
Sludge Processes. Ample evidence is available that demonstrates how off-gas testing is the best technique for determining
the in situ performance of diffused aeration systems. The basic
principles of off-gas testing involve studying the oxygen mass
balance of the volume or system being aerated. Off-gas is the gas
emitted from the surface of the liquid volume being aerated. By
measuring the oxygen concentration in off-gas, and determining
the oxygen concentrations or molar percentages (e.g., 20.9% for
air, 100% for pure oxygen) of the gas being diffused into the
system, the OTE can be calculated from any location within the
aerobic reactor (Jeung et al., 2013).
The first reference to a technique similar to what is currently
referred to as off-gas analysis for water resource recovery was
presented by Sawyer and Nichols (1939). When this technique
was first used, the equipment was called an ‘‘Oxy-Utilometer,’’
and a volumetric method was used to measure the amount of
oxygen consumed by the activated sludge during water resource
recovery. Decades later, starting in the 1960s, discussions of the
OTE in aeration systems for water resource recovery that used
techniques similar to off-gas analysis appeared in the scientific
literature. These analyses were conducted to define aeration
system performance, with each analysis opting for a different
focus, including considering the volume of air used per mass of
biological oxygen demand (BOD) removed, determining the
diffuser placement pattern that would provide the best oxygentransfer performance, or comparing different diffuser configurations (Barker et al., 1961; Leary et al., 1968; Technical Practice
438
Committee-Subcommittee on Aeration in Wastewater Treatment, 1969; West and Paulson, 1969).
In the 1980s, Boyle and Redmon (1983) presented generalities
for the functions of off-gas analyzers (as they are known today),
and guidelines for their use in field studies. In the 1980s, off-gas
analyzers were composed of the following four principal
components: (1) a floating hood to capture the gas; (2) a hose
connecting the hood to the analytical circuit; (3) an analytical
circuit for monitoring the off-gas composition, temperature,
pressure, and gas flow rate; and (4) a vacuum source for drawing
gas from the hood through the analytical circuit.
Regarding recent and cutting-edge trends connected to realtime off-gas monitoring, the work performed by Jeung et al
(2013) is notable. These authors reported experiences and results
related to the construction and operation of fully automated offgas analyzers coupled with floating collection hoods. These
innovative off-gas analyzers directly measure the OTE and
airflow rate through a flow pipe that collects data at hourly
intervals. Data from four WRRFs in different southern
California cities were gathered over periods spanning 3 to 12
months. The results of their study indicated that real-time offgas monitoring can be applied to continuously monitor the
efficiencies of wastewater aeration processes over extended
periods.
A configuration of the off-gas analyzer, similar to that
described by Boyle and Redmon (1983) is presented in the ASCE
18-96 standard (American Society of Civil Engineers, 1997). This
document also contains a graphic representation of the analyzer
that is most commonly employed today. Details regarding the
required materials (including costs), drawbacks, instrument and
hood assembly for permanent installation, operation and
maintenance of off-gas analyzers, data processing information,
and reference analysis are available in Jeung et al. (2013).
Overall, off-gas monitoring can be used to determine the best
options for designing and expanding aeration systems. Examples
of analyses that can be conducted with off-gas monitoring while
considering cost-benefit ratios include the following: (1)
evaluation of several diffuser types in side-by-side tests under
process conditions; (2) evaluation of diffuser fouling problems
and diffuser cleaning procedures in terms of effectiveness; (3)
optimization of cleaning schedules by comparing the energy
required and cleaning costs; (4) comparison of fixed and variable
flow blowers, and the evaluation of the benefits of using an
equalization basin; (5) evaluation of aeration system control
procedures; and (6) real-time analysis of OTE signals which can
have dynamic operational implications, such as feed-forward
off-gas control and energy minimization when connected to
facility data systems (Boyle and Redmon, 1983, Jeung et al.,
2013).
Modeling Oxygen Transfer in Activated Sludge Processes. Multiple modeling applications for water resource
recovery systems can be successfully implemented with the aid of
commercial simulators. Commercial simulators used for activated sludge incorporate models for oxygen transfer to represent
aeration processes; however, the functionalities of commercial
simulators vary. Generally, biological-process simulators for
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May 2018
Baquero-Rodrı́guez et al.
water resource recovery rely on control systems to maintain
specific dissolved oxygen within an aerobic reactor, which
allows researchers to estimate air demand for a specific
configuration of the activated sludge process. In some cases,
aeration models are equipped with the option to retrocalculate
KLa, which allows users to illustrate the variability of a
parameter throughout the aerobic reactor, in addition to
estimating air demand. This more robust option allows for the
identification of spatial variability of air demand throughout the
aerobic reactor. Other simulators use significant typical initial
suppositions regarding the parameters associated with the
aeration process, for example, the employed a-factor, F, and b
values are defined by the user as constant values and do not
change during the simulation.
These aeration models must account for the roles of several
factors, including temperature, wastewater (a-factor, b), atmospheric pressure, fouling, depth, and the diffuser type or density.
Aeration models do not individually consider specific factors
involved in oxygen transfer by fine-pore diffusers in activated
sludge, such as surfactants, wastewater dynamic composition,
diffuser aging, and treatment system operating conditions (e.g.,
SRT and solids concentration in the reactor). Examples of oxygen
transfer models and their application to modeling activated sludge
can be found in Makinia (2010) and Rieger et al (2013). The
definition of the aforementioned individual factors would improve
our understanding of the factors associated with oxygen transfer
in biological water resource recovery processes and their respective
effects on the energy efficiency of such recovery processes.
Significant advances have been made regarding the development of mathematical models that depict oxygen transfer in the
activated sludge process under varying operational conditions. A
recent study conducted by Pittoors et al. (2014) is a prime
example of these significant advances. The authors ran tests in
cylindrical batch reactors and used the resulting data to create a
nine-variable model (i.e., it proposes an empirical correlation
between KLa and nine variables). When considering a diffused
aerated cylindrical batch reactor (volume 3–9 L), the authors
proposed relations between the KLa and the tank volume,
height, diameter, surface area, airflow rate, diffuser surface area
and depth, bubble size, and the dynamic viscosity (the Reynolds
and Froude numbers are also used).
Research Needs on Aeration Optimization Modeling.
Considering this review of the factors affecting oxygen transfer
when using fine-pore diffusers, and the current needs for
designing, modeling, and controlling activated sludge processes
for water resource recovery, we present some current research
needs in this section. Use of the dynamic a-factor: currently,
guidelines in aeration systems rely on using a single constant
value to represent the a-factor. The current practice of using a
single value must be changed to the practice of using a dynamic
a-factor; a dynamic model should be used to describe aeration
energy demand, both in 24-hour periods with load variations
and a-factor changes, and over months or years as diffusers
become more fouled and aged. However, this approach (Jiang,
Garrido-Baserba et al., 2017) should soon be validated with
commercial simulators.
WATER ENVIRONMENT RESEARCH
May 2018
Use of the dynamic F value: the deterioration of the OTE as a
result of fouled diffusers is a widely known issue. As in the case
of the a-factor, a current need exists for a definition of the
dynamic representation of F. One initial approach for obtaining
this definition is to consider the time since the last diffuser
cleaning. The time scale, conditions, and variables to be taken
into account should be proposed and revised.
Conclusions
In this literature review, the authors recognize the need for a
coupled mathematical model that considers all of the factors
affecting OTE, individually, in activated sludge systems. The
challenges facing research on this process include the following
activated sludge conditions: the quality and variability of the
influent wastewater, the operating conditions of the WRRF,
dissolved oxygen control systems, the local environmental
conditions (especially temperature and atmospheric pressure),
and the phenomena associated with diffuser aging and fouling.
Future investigations should be aimed at developing a
dynamic mathematical model that accounts for multiple
parameters and allows WRRF designers and operators to assess
different scenarios for operating and maintaining aeration
systems. A model of this nature would be invaluable for
optimizing aeration systems of activated sludge processes.
Although the effects of different factors on oxygen transfer can
be represented by parameters (e.g., a-factor, b, and F), the
ability to represent parameters, individually and dynamically,
would undoubtedly enhance our understanding of the roles that
different factors play in oxygen transfer, paving the way for
aeration system optimization strategies.
Notation
wastewater correction factor, ratio of process water to
clean water KLa
B
correction factor for salinity and dissolved solids, ratio
of C‘ in wastewater to tap water
oxygen saturation concentration (mg/L)
C‘
DWP
dynamic wet pressure (cm)
EPS
extracellular polymeric substances
EPSc
carbohydrate fraction of the extracellular EPS
F
fouling factor, ratio of the KLa of a fouled diffuser to
that of a new diffuser
Henry’s constant (Pa m3)
KH
volumetric mass transfer coefficient (1/t)
KLa
MLVSS mixed liquor volatile suspended solids (mg/L or g/L)
MLSS
mixed liquor suspended solids (mg/L or g/L)
OTE
oxygen transfer efficiency under process conditions (%)
OTR
oxygen transfer rate under process conditions (kg O2/h)
P
partial pressure of the gaseous solution (Pa)
SAE
standard aeration efficiency (kg/kWh)
SRT
solids retention time (d)
SMPCOD COD fraction of the soluble microbial products
SOTE standard oxygen transfer efficiency (%)
TDS
total dissolved solids concentration (mg/L)
WWTP wastewater treatment plant
a
439
Baquero-Rodrı́guez et al.
WRRF
water resource recovery facility
Acknowledgments and Roles
The authors thank COLCIENCIAS (Colombian Administrative Department of Science, Technology and Innovation) for
financial support. Special thanks go to Jörg Krampe of TU-Wien
for his feedback. At the time of submittal: G.A. BaqueroRodriguez was Assistant Professor at the Facultad de Ingenierı́a
of the Universidad Militar Nueva Granada in Zipaquirá,
Colombia; J.A. Lara-Borrero was Associate Professor of Ciencia
e Ingenierı́a del Agua y el Ambiente at the Pontificia Universidad
Javeriana in Bogotá, Colombia; D. Nolasco was President of
Nolasco y Asociados in Buenos Aires, Argentina and Fellow of
the Water-Energy Nexus Center at the University of California,
Irvine; D. Rosso was Associate Professor of Civil & Environmental Engineering and Director of the Water-Energy Nexus
Center at the University of California, Irvine.
Submitted for publication September 3, 2016; accepted for
publication August 15, 2017.
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