Hindawi
Journal of Chemistry
Volume 2017, Article ID 8764510, 19 pages
https://doi.org/10.1155/2017/8764510
Research Article
Rheology and Microbiology of Sludge from a Thermophilic
Aerobic Membrane Reactor
Alessandro Abbà,1 Maria Cristina Collivignarelli,1 Sauro Manenti,1 Roberta Pedrazzani,2,3
Sara Todeschini,1 and Giorgio Bertanza3,4
1
Department of Civil and Architectural Engineering, University of Pavia, Via Ferrata 1, 27100 Pavia, Italy
Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy
3
University Research Center “Integrated Models for Prevention and Protection in Environmental and Occupational Health”
(MISTRAL), Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health,
University of Brescia, Viale Europa 11, 25123 Brescia, Italy
4
Department of Civil, Environmental, Architectural Engineering and Mathematics, University of Brescia,
Via Branze 43, 25123 Brescia, Italy
2
Correspondence should be addressed to Giorgio Bertanza; giorgio.bertanza@unibs.it
Received 13 April 2017; Revised 21 June 2017; Accepted 27 June 2017; Published 28 August 2017
Academic Editor: Nicolas Roche
Copyright © 2017 Alessandro Abbà et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
A thermophilic aerobic membrane reactor (TAMR) treating high-strength COD liquid wastes was submitted to an integrated
investigation, with the aim of characterizing the biomass and its rheological behaviour. These processes are still scarcely adopted,
also because the knowledge of their biology as well as of the physical-chemical properties of the sludge needs to be improved. In this
paper, samples of mixed liquor were taken from a TAMR and submitted to fluorescent in situ hybridization for the identification and
quantification of main bacterial groups. Measurements were also targeted at flocs features, filamentous bacteria, and microfauna, in
order to characterize the sludge. The studied rheological properties were selected as they influence significantly the performances
of membrane bioreactors (MBR) and, in particular, of the TAMR systems that operate under thermophilic conditions (i.e., around
50∘ C) with high MLSS concentrations (up to 200 gTS L−1 ). The proper description of the rheological behaviour of sludge represents
a useful and fundamental aspect that allows characterizing the hydrodynamics of sludge suspension devoted to the optimization
of the related processes. Therefore, in this study, the effects on the sludge rheology produced by the biomass concentration, pH,
temperature, and aeration were analysed.
1. Introduction
The monitoring of biological wastewater treatment processes,
whatever its final goal is (assessment of compliance with
effluent legal emission values, evaluation of specific stages
efficiency, calculation of the maximum treatment capacity
and comparison with the actual treated load, mass balance
for conventional and emerging pollutants, estimation of
energy consumption, etc.), is based on physical, chemical,
and biological analyses. Guidelines and technical documents
define the main parameters together with the proper analytical methods in case of conventional and/or nonspecific
pollutants, such as the COD (chemical oxygen demand), the
BOD (biological oxygen demand), nitrogen and phosphorus
compounds, surfactants, oils and greases, and suspended
solids. The biological process is based on the activity of
different populations of organisms set at growing levels of the
detritus food web. Therefore, any tools enabling us to identify
and measure the activity of these organisms provide valuable
information for improving and strengthening the operation
of wastewater treatment plants ([1–4] among others).
The removal capacity of specific pollutants can be
assessed by performing the measurement of metabolic activity, for example, by means of ammonia uptake rate (AUR) and
nitrate uptake rate (NUR) tests [5–9] and enzymatic activity
quantification [10, 11]. Besides, the microscopic observation
2
of floc characteristics and the identification and quantification of filamentous bacteria allow us to highlight disturbances
within their community and to foresee the occurrence of
specific dysfunctions, for example, sludge foaming, bulking,
and solids washout [4, 12–15]. Ecological features like specific
indexes (Sludge Biotic Index (SBI), designed by [1] and Sludge
Index (SI), proposed by Grupo de Bioindicación de Sevilla
(GBS, Spain) [16–19]) enable plant managers to tune the
operation parameters, such as sludge age, hydraulic retention
time, and dissolved oxygen concentration, in order to maintain optimum conditions for the sludge biotic components.
Scientific literature reports hundreds of applications of
conventional and innovative biological tools, as well as
examples of investigations based on ecological criteria [9, 20–
27]. Almost all the biological processes have been explored
in terms of biomass composition (bacteria, protozoa, metazoan, and fungi) and activity: conventional activated sludge,
attached biomass, membrane reactors, and treating either
municipal [27–34] or industrial wastewater [35–39]. Likewise, identification and quantification of microorganisms
operating under different conditions (aerobic, anoxic, and
anaerobic) have been reported [25, 40–46].
Rheological properties are crucial for activated sludge
applications in wastewater treatment plants, since they
severely impact the flow behaviour and many aspects that
interfere with process performance and energy consumption,
for example, sludge pumping, bioreactor hydrodynamics,
mass transfer efficiency of aeration systems, sludge-water
separation via settling, and filtration [47–54]. Due to the
observed significant impact of rheology on different processes, a good knowledge of the activated sludge rheological
behaviour is of great importance in both optimizing design
and operation of sewage treatment plants [55]. Appropriate
treatment of wastewater as a result of efficient design and
operation would help in environment protection and preservation [56]. This significance motivates the recent experimental investigations and mathematical modelling on activated
sludge rheology and on the impact of rheological properties
on operating parameters for conventional activated sludge
plants (e.g., [57–59]) and for membrane bioreactors MBRs
(e.g., [51, 60, 61]).
Basically, rheology of a sludge is defined by its viscous
characteristics, which can be determined by the relationship
between shear rate and shear stress, obtained through a
rheological measurement (which imposes either shear rate
or shear stress [62–64] and measures alternatively these
parameters). The observed relationship is strictly related
to the characteristics of the sludge, such as the relative
concentration of water and suspended matter due to the
nature of the wastewater and to the treatment process that is
subjected to [65].
At lower concentration of the suspended matter, sludge’s
behaviour can be reasonably approximated to a Newtonian
fluid characterized by a linear relation between shear stress
and rate of deformation, with the proportionality coefficient
being the fluid viscosity. The measured viscosity is rather
independent from shear rate, at given values of temperature
and pressure. The flow curve is therefore a straight line
through the origin of the coordinate axes.
Journal of Chemistry
As the particulate concentration increases, the sludge
deviates from Newtonian behaviour. For a non-Newtonian
liquid, the measured viscosity becomes dependent on the rate
of shear, and the so-called apparent viscosity is introduced
which is defined as the ratio between shear stress and shear
rate at a given point of the flow curve. The apparent viscosity
allows us to interpret the external and internal interaction
forces between the constituents of the sludge that influences
its rheological behaviour [51]. A highly concentrated sludge
exhibits yield stress that is the critical stress triggering plastic
deformation of the material continuum. Under the yield
stress, no significant flow can be detected. According to [49],
the yield stress can be related to reciprocal interactions among
the suspended solid particles opposing to the deformation.
There are two types of yield stress: static and dynamic. Static
yield stress can be measured in an undisturbed sample, while
dynamic yield stress refers to a perturbed sample and is
extrapolated from its equilibrium flow curve as shear rates
approach zero. It has been reported that the static yield
stress was not observed at a mixed liquor suspended solid
(MLSS) concentration equal to 16 g/L, probably because at
this concentration the static yield stress almost equals the
dynamic yield stress [51].
The rheological characterization of non-Newtonian fluids
leads to practical difficulties; different measurement protocols and devices (i.e., capillary rheometers as in [66] and
rotational rheometers as in [67]) have been adopted to study
the rheological properties and a wide variety of models have
been proposed. Widely used non-Newtonian relationships
between shear stress and shear rate for activated sludge are the
Ostwald de Vaele model, the Bingham model, the Herschel
and Bulkley model, and the Casson model [49, 68]. All these
models belong to the category of time-independent rheological models for which the relation between stress and applied
shear rate does not depend upon the duration of shearing.
The properties of activated sludge, such as MLSS concentration, particle size distribution and shape, interaction
among particles, flocculation ability, and surface physicochemical characteristics, all have effects on rheology and,
thus, on the kind of model most efficient in describing the
sludge rheological profile and on the values of the preferable
model parameters [49, 69].
A number of studies demonstrate that also temperature significantly affects the rheological characteristics:
sludge becomes progressively more fluid as the temperature
increases due to thermal [65, 70, 71]. These studies also prove
that temperature irreversibly modifies the sludge structure.
This paper reports the results of an integrated study performed on the sludge deriving from a real scale TAMR, supplied with pure oxygen (being the average dissolved oxygen
concentration of the mixed liquor equal to 2.3 mg L−1 , while
minimum and maximum values equal 0.2 and 15.0 mg L−1 ,
resp.) located at a facility for the treatment of solid and highstrength liquid wastes. The sludge has been characterized in
terms of microbiological and rheological features. Actually,
the authors found, previously, links between the amount of
filamentous bacteria and zooglea clusters and viscosity values
of mixed liquors from conventional activated plants treating
municipal wastewater, thus suggesting the use of rheological
Journal of Chemistry
3
Disposal
Chemical-physical
treatment
Aqueous
waste
NF concentrate
TAMR
Ultrafiltration
(UF) unit
Thermophilic
biological reactor
Nanofiltration
(NF) unit
UF concentrate
NF permeate
Sludge
treatment
Ammonia
stripping
Adsorption on
activated carbon
(if necessary)
Discharge
in public
sewer
Water line
Sludge line
Figure 1: Process scheme of the studied facility.
tools to control dysfunctions caused by the proliferation of
specific microorganism [72].
TAMRs operate at 45∘ C and are profitably applied for the
treatment of high-strength wastewater deriving from food
processing, pulp, and paper and pharmaceutical factories
[73–76]. Recently, their extension to sludge treatment has
been explored [77]. In effect, an aerobic thermophilic biomass
is characterized by a lower yield, hence a decrease in specific
sludge production (down to 0.02 kg SSV/kg CODremoved ).
Furthermore, other valuable advantages consist in faster
chemical reaction rates, increased organics solubility, high
process stability, that is, the prompt activity recovery after
operational changes, capability of degrading high salinity
and biorecalcitrant wastewater, and possibility of integrating
the thermophilic stage, as a pretreatment into a combined
scheme, thus improving the removal extent of organic substances [76, 78, 79]. Nevertheless, despite the aforementioned
advantages, thermophilic processes are still rarely adopted at
the real scale [80, 81].
This research was aimed, therefore, at exploring the
rheological and microbiological features of TAMR biomass,
which remain almost obscure, in spite of the increasing
knowledge gained about its excellent performances.
Before entering the biological tank, metals are removed by
means of a chemical-physical treatment which leads to their
precipitation as ammonium salts, hydroxides, and phosphates
(pH equal to 11 units). The TAMR surface is 267 m2 wide; net
volume is about 1000 m3 .
Pure oxygen is supplied through static mixers, yielding
oversaturation of the sewage, which is recirculated within
them. Ultrafiltration unit includes two parallel lines consisting in a feeding pump, a recirculation pump, and three
channels of ceramic membranes (each containing 99 tubular
membranes having 25 channels). Pores size allows retaining
molecules larger than 0.3 microns and with a molar mass
higher than 300 kDa. Minimum and maximum operation
pressure values are 3 and 5 bar, respectively.
The average concentrations of pollutants inlet to the
TAMR are 25,000 mg COD L−1 , 1200 mg TN L−1 , and
700 mg TP L−1 . The TAMR performances, in terms of
COD, TN, and TP removal yields, are 78%, 80%, and 90%,
respectively. The high removal yields for TP, as reported by
[79], are due to the chemical precipitation: this phenomenon
could be due to the dosage of lime (in the chemical-physical
treatment before the TAMR), but the most important aspect
is ascribed to the aeration of reactor that promotes the
phosphorus crystallization.
2. Materials and Methods
A synthetic description of the studied waste treatment facility,
together with sampling and analytical procedures, is presented.
2.1. The Waste Treatment Facility. The facility is located in
Northern Italy and treats 60,000 t/y of solid and liquid highstrength COD wastes (except mutagens and carcinogens);
since fifteen years ago, this technology has been investigated
in detail by [80].
Figure 1 depicts the scheme of the unit fed with liquid
wastes: the main phase consists in the TAMR (see [80] for
a complete description).
2.2. Characterization of Sludge. Samples taken from the
TAMR were submitted to investigations aimed to define their
microbial profiles and rheological behaviour.
2.2.1. Microbial Community Investigation. Mixed liquor samples were taken from TAMR and freighted immediately to
the laboratory at 4∘ C. After adding absolute ethanol (Sigma
Aldrich), (1 : 1) mixed liquor was pipetted onto a glass slide
and allowed to dry at 46∘ C for 15 minutes; afterwards,
samples were submitted to fluorescent in situ hybridization
(FISH), according to prescriptions of VIT (Vermicon AG,
Munich, Germany) gene probe technology, based on fluorescent labelled DNA probes. Only viable microorganisms
4
Journal of Chemistry
(a)
(b)
Figure 2: Experimental apparatus: coaxial cylinder CC25DIN (a) and rheometer RC20 (b).
are identified, quantified, and even visualized in their environment. The major advantage of this method is that the
analysis is based on the stable genetic material of the cells
and not dependent on phenotypic features which may be
quite variable among bacteria. Moreover, as the target of the
gene probes is the rRNA of the cells, only viable bacteria can
be detected. As fluorescent signal intensities are correlated
with the rRNA content within the cells, a further result
on the physiological activities of the detected cells can be
obtained. The detection limit is about 1000 cells mL−1 . A
process of image analysis enabled the quantification of single
populations, by comparing the signals of each group with the
fluorescence emitted by the all the viable microorganisms.
Besides, observation of floc features and identification
and abundance assessment of filamentous bacteria were
performed according to [12]. In particular, the analyses
were performed by using a Zeiss Axiostar Plus microscope
under phase contrast illumination. Raw samples were firstly
examined: afterwards, due to their conspicuous density, they
were diluted with the supernatant (1 : 10 and 1 : 100) in order
to enable the visualization of single flocs. Their morphology
was registered (according to [12]), by considering the shape
(irregular, round), the firmness, and the mean diameter.
For this purpose, 10 microscopy fields were considered as
replicates; flocs diameter was measured by employing a
graduated slide as a reference. This tool was used also for
measuring the length of filamentous bacteria. Likewise, the
effects of filaments on floc structure, their abundance (with
respect to each floc), and the presence of other important
factors, such as free cells in suspension, inorganic/organic
particles, and zoogleas, were recorded.
Mixed liquor samples were also submitted to the analysis
of the microfauna for the calculation of the Sludge Biotic
Index (SBI) according to [1] (microscopic observation under
direct illumination; magnification: 100x).
2.2.2. Rheological Tests. The rheological experiments were
carried out using rheometer RC20 (RheoTec) with a configuration CC25DIN of coaxial cylinders (Figure 2(a)): spindle
radius: 12.5 mm; internal radius of the measuring cylinder:
13.56 mm.
The working principle of the instrument is based on this
aspect: the sliding of the sludge in the cavity between the
coaxial cylinders, due to the spindle rotation with a fixed
share rate, while the external cylinder is held, requires a
torque that is measured by the instrument. Several rotational
tests with different controlled shear rate (CSR) were performed to obtain the rheological equation:
𝜏 = 𝑓 (𝛾)̇ ,
(1)
where 𝜏 is the shear stress [Pa] and 𝛾̇ is the share rate [s−1 ].
The temperature was controlled by the use of water in a
beaker placed on a heating magnetic stirrer (Figure 2(b)).
In Table 1, the details of rheological test are summarized
(the pH and temperature values represent the average data
recorded during the tests). The tests were carried out on two
samples of thermophilic biomass with different concentrations of total solids (TS). The pH values are between 6.6 and
9.1 (sulphuric acid and soda were used) and the temperature
is close to 45∘ C.
The biomass samples withdrawn from the TAMR were
delivered to the laboratory within 120 min after sampling.
They were stored (for 180 min) at 45∘ C under different
conditions: a first subsample was not submitted to aeration;
a second subsample was kept in aeration conditions by an
air compressor at lab scale (1 Lair min−1 ); only for the tests on
sample B, a third subsample was aerated with an air flow of
0.5 Lair min−1 .
Each rheological test was performed with fixed shear
rates that were maintained for 300 seconds; shear rate was
increased step by step as reported in Table 1. Shear stress and
apparent viscosity were recorded every 30 s (ten data sets for
each share rate).
Both the sludge samples presented in Table 1 were tested
starting from an imposed shear rate of 100 s−1 with a step
increment of 100 s−1 (or 50 s−1 in some cases). Anyway,
the adopted rheometer was unable to resolve the shear
Journal of Chemistry
5
Table 1: Rheological tests performed: operative conditions.
Average pH
Operative conditions
Average temperature [∘ C]
Aeration
Shear rate
[s−1 ]
Sample A
(150 gTS L−1 )
1
2
3
4
5
6
6.7
6.7
7.7
8.3
9.0
9.1
44.5
45.2
45.1
45.3
45.1
44.9
N
Y
N
Y
N
Y
700, 800, 900, 1000
700, 800, 900, 1000
700, 800, 900, 1000
750, 800, 900, 1000
750, 800, 850, 900, 1000
800, 850, 900, 1000
Sample B
(190 gTS L−1 )
7
8
9
10
11
12
13
14
15
7.0
6.7
6.6
7.8
8.3
8.4
8.9
9.0
9.1
45.4
45.3
45.0
44.9
45.2
45.3
45.0
45.3
45.0
N
1/2
Y
N
1/2
Y
N
1/2
Y
500, 600, 750, 900, 1000
500, 600, 750, 900, 1000
500, 600, 750, 900, 1000
500, 600, 750, 900, 1000
500, 600, 750, 900, 1000
500, 600, 750, 900, 1000
500, 600, 750, 900, 1000
500, 600, 750, 900, 1000
500, 600, 750, 900, 1000
Test #
N: no aeration; 1/2: limited aeration (0.5 Lair min−1 ); Y: full aeration (1 Lair min−1 ).
stress for values below 700 s−1 for the lower concentration
(150 gTS L−1 ) and, respectively, for values below 500 s−1 for
the higher concentration (190 gTS L−1 ).
It is worth noting that in the present study a limited
number of shear rates has been investigated (i.e., four values
for tests 1–4 and 6, and five in case of tests 7–15). This aspect,
in principle, may represent an inherent limitation of the work
that, however, has the merit to provide a first insight into the
rheological behaviour of a highly concentrated thermophilic
sludge. Furthermore, the preliminary findings (see Section 3.2) show that the trend of the flow curves exhibits a certain degree of reliability, as confirmed by the regression coefficient displayed in Table 3. Anyway, a more refined investigation by increasing the range of explored shear rates and the
amount of samples will be useful to clarify some issues concerning the effects of aeration on the rheological behaviour
and will be included in the next steps of the research.
3. Results and Discussion
This chapter gathers the information obtained from the
microbiological and the rheological analyses and sets them
in the scientific literature context.
3.1. Microbiological Features of Thermophilic Sludge. The
sludge appeared extremely thick, due to the great suspended solids concentration maintained in the TAMR
(150–200 g L−1 ). Figure 3 is a micrograph (100x magnification) of the mixed liquor, characterized by a huge amount of
inorganic particles (clusters of salts and hydroxides). Flocs are
almost absent and bacterial cells are free in the liquor.
Based on the characterization rules of [12], the mean floc
diameter was <150 𝜇m. Flocs could be classified as irregular
Figure 3: Micrograph of the raw sample (magnification: 100x).
and diffuse. Filamentous bacteria abundance belonged to
class I (few, which means that filaments are observed only in
an occasional floc), according to the distinction proposed by
[12], who identified six different cases, based on the average
number of filamentous bacteria located in a single floc.
Neither zoogleas nor spirochaetes were observed. Likewise,
Protozoa and Metazoa were completely absent (as noticed by
[78]): therefore, the Sludge Biotic Index (SBI) could not be
calculated.
These findings tally with the outcome reported in the scientific literature: a poor (or even lacking) flocculation is usually seen to occur under thermophilic conditions [78, 82–84].
About the amount of filamentous bacteria, however, [83]
observed filamentous abundances ascribable to class IV (very
common) and class V (abundant), while [85] reported two
cases, where filaments reach class IV, with their growth being
inversely proportional to the hydraulic retention time.
6
The paper [78] puts forward several hypotheses to explain
the lack of flocculation: firstly, the absence of floc forming microorganisms (such as zoogleas, not found also in
the present case, as previously mentioned); secondly, the
impossibility to reach the physiological state enabling the
flocculation; finally, the establishment of medium conditions interfering with flocculation and coagulation. Further
assumptions are made by authors in [84] who ascribed
the scarce flocculation to the high shear sensitivity of the
thermophilic floc, which becomes prone to erosion; they also
suggest a possible influence of decreased cell hydrophobicity.
Table 2 presents the results of FISH analyses, while Figure 4 highlights the percentages of detected taxa over the total
bacterial population. Only three major bacteria groups were
found with higher shares (Betaproteobacteria, Gammaproteobacteria, and Cytophaga-Flexibacter). Two further major
groups (Deltaproteobacteria and Chloroflexi) were quantified
with very low shares and the other groups were completely absent, indicating very limiting conditions for typical
wastewater treatment plant bacteria. The FISH analyses
confirmed the nearly total absence of filamentous bacteria.
Furthermore, physiological groups like nitrifying bacteria
were completely absent. Such a profile is quite unusual
for a mesophilic activated sludge and can be attributable
to temperature conditions and wastewater characteristics,
which trigger a very special composition of the population.
Actually, the above-mentioned factors, together with plant
running strategies, can be nowadays linked with the growth
of specific bacterial populations and the achievement of a
particular physiological state [86–88]; on the other hand,
this is even more true in the case study, where temperature
is constantly maintained at 45∘ C, and the influent sewage
consists in high-strength COD wastes.
Betaproteobacteria were the dominant group in the
sludge sample. They were detected with a remarkable high
share of 77% of the total viable bacteria. No filaments or
ammonium oxidizing bacteria belonging to this class were
observed, as found by [78]. Almost the complete population
was formed by members of the 𝛽1-group. All organisms of
this group showed bright fluorescence signals indicating a
good physiological status of the cells. Levels up to 25% of
this group are considered as normal in industrial wastewater
treatment plants. Gammaproteobacteria were measured with
an unusual high share of 15% in comparison to typical
wastewater sludge values. In effect, normally, this group does
not exceed a share of 10%. Here, they appeared mainly as thin
long rod-shaped bacteria with middle fluorescence signals
indicating a moderate physiological activity of the cells. No
characteristic filaments of this group like Thiothrix or Eikelboom type 021N were found. The authors in [88] postulated
that these cocci possess weak flocculation properties (at least
a subgroup of Gammaproteobacteria): also their abundance
might be related to the presence of small flocs.
The share of members of the Cytophaga-Flexibacter subphylum was 4% of the total viable flora, which corresponds
more or less to typical values in activated sludge, which can
reach a percentage of 10%. The thin rod-shaped cells showed
low fluorescence signals. Filaments like Haliscomenobacter
hydrossis were not detected.
Journal of Chemistry
Alphaproteobacteria
Deltaproteobacteria
Betaproteobacteria
Chloroflexi
Gammaproteobacteria
Cytophaga-Flexibacter
Figure 4: Pie chart of the detected taxa shares.
Alphaproteobacteria were detected with a share of only
1% and appeared completely as coccus-shaped uniform cells.
Shares of up to 20% of this group are quite normal for
industrial activated sludge.
Members of the Chloroflexi genus were measured with
a very low share of <1% in the sample. The single filaments
had a length of about 50 𝜇m. In wastewater treatment plants,
10–20% of this group are quite common. Chloroflexi bacteria
in WWTPs showed increased shares over the last years; they
cause bulking and foaming events if huge abundances are
reached.
Deltaproteobacteria, including most sulphate-reducing
bacteria, were represented as uniform single cells, with a share
of <1%. Values of up to 8% for this group can be found in
the mixed liquor of plants treating industrial wastewater. All
other main bacteria groups including filamentous bacteria
were not detected.
High values for total cell counts (dead and alive) were
determined, with 2.5 ⋅ 1010 mL−1 and total viable cell counts
with 5.0 ⋅ 109 mL−1 , which is larger than the total viable cell
counts of up to 1–3 ⋅ 109 mL−1 usually detected in industrial
wastewater activated sludge treatment plants.
Figures 5–7 clearly show the structure of the sludge,
consisting basically in clusters of salts and hydroxides and free
cells.
3.2. Rheological and Statistical Analysis of Activated Sludge.
Several analytical models have been proposed to mimic
the rheological behaviour of biological sludge, each with
different degree of complexity depending on the number of
the parameters contained [68]. Most of these models can be
mathematically described by a power-low curve including an
offset to account for yield stress.
In the paper [51], the rheological characteristics of an
activated sludge sampled in a pilot MBR system, with a MLSS
concentration varying between 2.74 g L−1 and 16 g L−1 , were
Journal of Chemistry
7
Table 2: Results of the application of the VIT gene probes.
Analysed target
microorganism(s)
Alphaproteobacteria
(amount of filamentous
bacteria)
Alysiosphaera
Betaproteobacteria
(amount of filamentous
bacteria)
𝛽1-group of the
Betaproteobacteria
(amount of filamentous
bacteria)
Gammaproteobacteria
(amount of filamentous
bacteria)
Acinetobacter
Thiothrix
Eikelboom type 021N
Deltaproteobacteria
(amount of filamentous
bacteria)
Chloroflexi
(amount of filamentous
bacteria)
Eikelboom type 1851
Herpetosiphon aurantiacus
Cytophaga-Flexibacter
subphylum
(amount of filamentous
bacteria)
Haliscomenobacter
hydrossis
Planctomycetes
(amount of filamentous
bacteria)
Candidatus Nostocoida
limicola type III
Physiological features
Group with high amount of heterotrophic organisms (e.g.,
genera Paracoccus, Sphingomonas, Rhizobium, Caulobacter, and
Rhodospirillum)
Group consisting of filamentous bacteria similar to Nostocoida
limicola II, with typical chain structures. Responsible for
bulking sludge in industrial wastewater treatment plants
Group consisting of the 𝛽1-group (many filamentous bacteria)
and of the 𝛽2-group (most ammonia oxidizing bacteria). In
municipal WWTPs, mostly the dominating group (e.g., genera
Burkholderia, Sphaerotilus, Alcaligenes, Thiobacillus, and
Nitrosomonas)
Group consisting of floc forming bacteria and S. natans related
filamentous bacteria
Group consisting of heterotrophic, mixotrophic, and
autotrophic microorganisms (e.g., genera Acinetobacter,
Aeromonas, Pseudomonas, Vibrio, and Thiothrix)
Filamentous and nonfilamentous bacteria. Common in
municipal and industrial WWTPs with a relatively high sludge
age. Favoured by scarce dissolved oxygen concentration
Filamentous sulphur bacterium, favoured by nutrient deficiency.
Adapted to low dissolved oxygen concentration
Filamentous sulphur bacterium common in municipal and
industrial WWTPs. favoured by nutrient deficiency. Adapted to
low dissolved oxygen concentration
Group including most sulphate reducing bacteria (e.g., families
Desulfobacteraceae, Desulfobulbaceae, and
Desulfovibrionaceae)
Group of filamentous organisms with increasing impact on
municipal and industrial WWTPs, due to their contribution to
foaming and bulking events, very stable against mechanical
stress (genus Herpetosiphon, type 1851, type 0803, and type 0092
and many other filaments still unknown)
Filamentous bacterium; contribution to sludge bulking.
Favoured by nutrient deficiency
Filamentous bacterium. Aerobic and anaerobic conditions
Share of each group in relation to the
overall bacteria population (%)
Species
Group
1
(n.d.)
n.d.
77
(n.d.)
76
(n.d.)
15
(n.d.)
n.d.
n.d.
n.d.
<1
(n.d.)
<1
(n.d.)
n.d.
n.d.
Group containing filamentous and floc forming bacteria. Mainly
in WWTPs with nutrients removal (e.g., genera Cytophaga,
Flavobacterium, and Flexibacter),
Filamentous bacterium; contribution to sludge bulking.
Favoured by high sludge age, low dissolved oxygen
concentration, and high ammonia concentration
Group, mainly in municipal WWTPs; members of this group
are involved in the ANAMMOX process. Adapted to low
dissolved oxygen concentration (e.g., genera Planctomyces,
Pirellula, Isosphaera, and Scalindua)
Filamentous bacterium; contribution to sludge bulking
4
(n.d.)
n.d.
n.d.
n.d.
8
Journal of Chemistry
Table 2: Continued.
Analysed target
microorganism(s)
Share of each group in relation to the
overall bacteria population (%)
Species
Group
Physiological features
Group of GRAM positive bacteria with a high DNA GC content.
Several problematic filamentous bacteria like Microthrix
parvicella and nocardioforms. In WWTPs with nutrients
removal (also biological phosphorous removal)
Filamentous bacterium; in WWTPs with nutrients removal.
Contribution to sludge bulking, floating, and foaming. Strong
hydrophobic characteristics
Group containing filamentous and floc forming bacteria.
Contribution to sludge bulking, floating, and foaming. Strong
hydrophobic characteristics
Typical chain structures. Contribution to sludge bulking. In
municipal WWTPs
Group of GRAM positive bacteria with a low DNA GC content.
Several fermentative bacteria like the genera Streptococcus,
Bacillus, and Lactobacillus
Actinobacteria
(amount of filamentous
bacteria)
Microthrix parvicella
Nocardioforms
Candidatus Nostocoida
limicola type II
Firmicutes
(amount of filamentous
bacteria)
Candidatus Nostocoida
limicola type I
n.d.
n.d.
n.d.
n.d.
Filamentous bacterium. Contribution to sludge bulking
Candidatus TM7
Group of ammonia
oxidizing bacteria (AOB)
Group of nitrite oxidizing
bacteria (NOB)
Phosphate accumulating
organisms (PAO)
Glycogen accumulating
organisms (GAO)
n.d.
Group recently detected, including filamentous bacteria like
Eikelboom type 0041
n.d.
For example, genera Nitrosomonas and Nitrosococcus
n.d.
For example, genera Nitrobacter and Nitrospira
n.d.
Responsible for the biological phosphorus removal (e.g.,
Candidatus Accumulibacter phosphatis)
n.d.
Candidatus Competibacter phosphatis
n.d.
examined. These authors showed that Herschel-Bulkley model
provides suitable fitting of experimental data for shear rate
above 25 s−1 .
Proper fitting of the experimental data analysed in this
work (MLSS concentrations in the range 150–190 g L−1 ) was
obtained with Herschel-Bulkley model which is recognized
to be more efficient in describing the overall rheological
behaviour of sludge with high solid concentration [65]:
𝜏 = 𝜏𝑦 + 𝐾𝛾𝑛̇ .
(2)
In (2), the two fitting parameters are the consistency coefficient 𝐾 and the flow behaviour index 𝑛. As explained in
the following, yield stress 𝜏𝑦 was not considered as a fitting
parameter of the flow curve.
Several statistical descriptors can be adopted to measure
the accuracy of prediction of rheological models and the
reliability of their estimated parameters [65]. In this work,
the coefficient of determination (or regression coefficient) 𝑅2
and the standard error of the estimate 𝜎est in (3) were adopted
according to [51]
𝜎est
n.d.
= [∑
[𝑖=1
𝑁
(𝑦𝑖 − 𝑦pre )
2
𝑁−2
]
]
1/2
.
(3)
In (3), 𝑁 denotes the number of samples included in the
population of the stochastic variable 𝑦 (i.e., 𝑁 is the number
of measures contained in the data set of each test carried out
at a given shear rate, temperature, pH, and aeration), while
𝑦pre is the corresponding predicted value obtained through
the following linear regression equation:
𝑦 = 𝑎𝑥 + 𝑏,
𝑦 = log (𝜏 − 𝜏𝑦 ) ,
𝑥 = log (𝛾)̇ ,
(4)
𝑎 = 𝑛,
𝑏 = log (𝐾) .
The linearized flow equation for the regression analysis,
denoted by the first equation of (4), contains three rheological
parameters of the sludge that are the consistency coefficient
𝐾, the flow behaviour index 𝑛, and the yield stress 𝜏𝑦 that may
be, in general, different from zero.
The method of ordinary least squares (OLS) was adopted
for linear regression for fitting the dependence of shear stress
versus shear rate.
As well known, only two of the three above-mentioned
model parameters can be reliably estimated through linear
Journal of Chemistry
9
(a)
(b)
(c)
Figure 5: Micrographs (magnification: 1000x) of the flocs under phase contrast and epifluorescence. Comparison of the same floc area.
Hybridization target: viable bacteria and Betaproteobacteria. (a) Phase contrast. Identical microscopic fields under fluorescence: (b) detection
of all viable bacteria and (c) analysis with a specific probe for Betaproteobacteria.
regression. Therefore, in the following statistical analysis, it
was assumed as fitting parameters the flow behaviour index 𝑛
and the consistency coefficient 𝐾 that are included in the two
parameters 𝑎 and 𝑏 of the linear equation (see (4)). However,
the proper value of yield stress should be estimated in order
to fit experimental data in all testing conditions.
In some works, the yield stress 𝜏𝑦 has been determined
through graphical extrapolation of the flow curve [51]. Owing
to the above-mentioned lack of experimental data below the
shear rate of 500 s−1 , in the present study, an alternative
procedure for the estimate of 𝜏𝑦 was adopted, as explained
below.
In each test, when carrying out the linear regression of the
experimental data with respect to the unknown parameters
𝐾 and 𝑛, the yield stress was considered as a deterministic
parameter ranging in the interval [0.0–3.9] Pa at steps of
0.05 Pa: for each of these values statistical estimates of both
𝑎 and 𝑏 were obtained along with the corresponding value
of the regression coefficient 𝑅2 . The value of the yield stress
that, for each test, maximizes the regression coefficient was
assumed as an estimate of 𝜏𝑦 .
The obtained results of the statistical analysis are summarized in Table 3 for both concentrations of suspended solid.
The results obtained for both MLSS concentrations show
that the flow behaviour index 𝑛 was greater than one; at the
lower MLSS concentration of 150 g L−1 , the sludge exhibited
shear-thickening (dilatant) behaviour, with the estimated
yield 𝜏𝑦 stress equal to zero.
For all the tests carried out, the apparent viscosity
increased, at any shear rate, with the solid content; moreover,
the differences in apparent viscosity measured at different
shear rates increased with solid content, in accordance with
[89]. This rheological behaviour was different from the
shear-thinning (pseudo-plastic) behaviour noticed by [51] for
MBR sludge samples with suspended solid concentration not
exceeding 16.0 g L−1 . In the present study, however, the MLSS
concentration was about one order of magnitude higher. The
interactions between the sludge particles were expected to be
even more intense and therefore one might have expected a
nonzero yield stress [89]. In fact, as the particle concentration
increases, they became progressively more close to each other,
thus leading to a rapid growth of the number of interactions
that opposes to deformation.
Anyway, such a dilatant behaviour may be related to
the fact that in this work the sludge samples were tested at
very high temperature of about 45∘ C, which is unusual if
one considers that generally the testing temperature does not
exceed 35∘ C [68].
In order to point out possible temperature effects on the
obtained results, the work by [65] can be considered which
investigated the rheology of mixed primary and secondary
sludge as a function two critical parameters affecting flow
10
Test #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Journal of Chemistry
Average 𝑇
[∘ C]
44.5
45.2
45.1
45.3
45.1
44.9
45.4
45.3
45.0
44.9
45.2
45.3
45.0
45.3
45.0
Table 3: Summary of rheological tests and results of linear regression analysis.
Average pH
[—]
6.7
6.7
7.7
8.3
9.0
9.1
7.0
6.7
6.6
7.8
8.3
8.4
8.9
9.0
9.1
Aeration
N
Y
N
Y
N
Y
N
1/2
Y
N
1/2
Y
N
1/2
Y
MLSS
[gTS L−1 ]
150
190
(a)
𝜏𝑦
[Pa]
0.0
0.0
0.0
0.0
0.0
0.0
2.35
1.95
2.05
1.90
1.65
1.70
1.40
1.60
1.25
𝑛
[—]
2.760
2.909
2.976
3.120
2.138
2.925
5.307
3.901
3.508
4.433
3.452
3.590
4.585
5.468
5.221
𝐾
[Pa sn ]
1.992𝑒 − 8
6.805𝑒 − 9
4.474𝑒 − 9
1.642𝑒 − 9
1.125𝑒 − 6
4.080𝑒 − 9
3.824𝑒 − 16
6.532𝑒 − 12
9.316𝑒 − 11
1.60𝑒 − 13
1.711𝑒 − 10
6.253𝑒 − 11
6.285𝑒 − 14
1.469𝑒 − 16
8.227𝑒 − 16
𝑅2
[—]
0.997
0.971
0.965
0.967
0.923
0.953
0.986
0.992
0.989
0.998
0.994
0.992
0.998
0.998
0.998
𝜎est
[—]
0.0296
0.0944
0.1066
0.0905
0.0711
0.0706
0.2107
0.1146
0.1199
0.0646
0.0875
0.1091
0.0651
0.0791
0.0852
(b)
(c)
Figure 6: Micrographs (magnification: 1000x) of the flocs under phase contrast and epifluorescence. Comparison of the same floc area.
Hybridization target: viable bacteria and 𝛽1, Betaproteobacteria. (a) Phase contrast. Identical microscopic fields under fluorescence: (b)
detection of all viable bacteria and (c) analysis with a specific probe for 𝛽1-group of Betaproteobacteria.
behaviour: temperature, ranging between 25∘ C to 55∘ C, and
solid concentration, varying between 4.3% and 9.8% that is
quite below the total solid content investigated in this work.
These authors have shown that (for a given solid concentration) yield stress depends exponentially on the inverse
of the temperature. Therefore, an increase of the testing
temperature seems consistent with a relevant reduction of the
yield stress.
It is worth noting that in all tests with suspended solid
concentration of MLSS = 150 g L−1 when imposing a shear
Journal of Chemistry
11
(a)
(b)
(c)
Figure 7: Micrographs (magnification: 1000x) of the flocs under phase contrast and epifluorescence. Comparison of the same floc area.
Hybridization target: viable bacteria and Gammaproteobacteria. (a) Phase contrast. Identical microscopic fields under fluorescence: (b)
detection of all viable bacteria and (c) analysis with a specific probe for Gammaproteobacteria.
rate below 100 s−1 the measured shear stress was under the
minimum value that can be resolved by the rheometer (see
Section 2.2.2). Indeed, the assumption of a zero yield stress
appears quite plausible.
At this stage of investigation, the flow properties of all
the sludge samples were tested at a shear rate greater than or
equal to 500 s−1 . This is due to technical reasons related to
the modelling of particular operating conditions of concern
in the treatment plant. From a theoretical point of view, the
experimental points below the shear rate value of 500 s−1 may
influence the extrapolation of the yield stress for the analysed
samples; for this reason, further studies will be carried out to
investigate this aspect.
In the work by [65], it has been noticed that (for a given
temperature) yield stress increases almost linearly with solid
concentration. This seems in accordance with the experimental results obtained for the higher solid concentration (MLSS)
of 190 g L−1 that showed significant shear stress values even at
the lower shear rate (see Figures 9(a) and 9(b) and Table 3).
In this case, proper fitting of the experimental points requires
nonzero yield stress to be considered in the mathematical
model of the flow curve in (2).
Suitable values were obtained for the regression coefficient in all tests. The best fitting of the adopted mathematical
model is obtained for the higher MLSS concentration of
190 g L−1 , as confirmed by the values of 𝑅2 for the tests from
7 to 15 in Table 3.
Concerning the lower MLSS concentration of 150 g L−1 ,
the minimum value of 𝑅2 = 0.923 was obtained for the
test number 5 and was however combined with a relatively
small value of the standard error of the estimate 𝜎est . In the
remaining tests at the same concentration, the regression
coefficient was significantly higher, and for the test case
number 1, the best fitting was obtained.
In the paper by [51], it has been shown that, by varying the
mathematical model to represent the rheological behaviour
of the sludge sample, the correlation coefficient may fall
below 0.9 and 𝜎est may rise to 0.9 at high concentrations
of suspended solids. Further investigations may thus be
carried out to assess if the result obtained for test number
5 might be influenced by the selected mathematical model.
Anyway, the interpolating curve obtained for test number 5
(see Figure 8) seems quite acceptable for technical purposes
in the engineering management of treatment plants where
several sources of uncertainty may commonly affect the
predictions.
Figures 8 and 9 show, for each rheological test, the
flow curves (blue) calculated from the linear regression; the
corresponding experimental data are plotted with red plus
markers along with symmetric error bars 2 ⋅ 𝜎𝜏 long, where
12
Journal of Chemistry
7
6
(Pa)
5
4
3
2
1
0
0
200
400
600
̇ (M−1 )
800
1000
Exper T = 44.5∘ C - J( = 6.7 - air N
Calc T = 44.5∘ C - J( = 6.7 - air N
Exper T = 45.2∘ C - J( = 6.7 - air Y
Calc T = 45.2∘ C - J( = 6.7 - air Y
7
6
(Pa)
5
4
3
2
1
0
0
200
400
600
800
1000
̇ (M−1 )
Exper T = 45.1∘ C - J( = 7.7 - air N
Calc T = 45.1∘ C - J( = 7.7 - air N
Exper T = 45.3∘ C - J( = 8.3 - air Y
Calc T = 45.3∘ C - J( = 8.3 - air Y
7
6
(Pa)
5
4
3
2
1
0
0
200
400
600
800
1000
̇ (M−1 )
Exper T = 45.1∘ C - J( = 9.0 - air N
Calc T = 45.1∘ C - J( = 9.0 - air N
Exper T = 44.9∘ C - J( = 9.1 - air Y
Calc T = 44.9∘ C - J( = 9.1 - air Y
Figure 8: Shear stress versus rate of deformation (MLSS 150 g L−1 ):
comparison between experimental and calculated values.
𝜎𝜏 is the standard deviation of the shear stress measurements
obtained with the rheometer at each shear rate value.
Figure 8 plots the results obtained for the MLSS concentration of 150 g L−1 . For a given pH value, the aeration
of the sludge sample (air Y) led, in general, to a reduction
of apparent viscosity (hence, of the shear stress) at any
shear rate with respect to the corresponding test on nonaerated samples (air N). This behaviour is clearly evident
in the case with pH around 9.0 (lower panel) where, in
the investigated shear rate range, the aeration of the sample
produced a significant reduction of the shear stress that
reaches 0.55 Pa at some shear rates between 700 s−1 and
1000 s−1 . At smaller pH values, the Y and N curves are very
close to each other and the above-mentioned shear stress
reduction may be caused by interpolation uncertainties connected to the low number of experimental points, rather than
being connected to the aeration. This aspect deserves further
investigation.
Obtained results seem however reasonable, since aeration
of the sludge may lead to bubble entrapment into the bioaggregate that increases the mean distance between suspended
solid particles and consequently weaken the interactions
between them; this lowers both apparent viscosity and shear
stress at any shear rate. Such an effect is clearly evident at the
highest investigated pH (lower panel).
Figures 9(a), 9(b), and 9(c) show the flow curves obtained
for the higher MLSS concentration of 190 g L−1 . In this
case, the effect of aeration on the rheology depends on pH
condition, as described below.
In those tests where pH ranged between 6.6 and 7.0
(Figure 9(a)), the effect of aeration (tests 8 and 9) led to a
reduction of shear stress and apparent viscosity when the rate
of deformation was below 600 s−1 or above 900 s−1 ; within
the range (700–900) s−1 the flow curves for tests 7 (air N)
and 8 (half aeration flow) were very close to each other
and below the flow curve estimated for test case number 9
corresponding to fully aerated condition (air Y). This shear
stress increment induced by aeration in the shear rate range
(700–900) s−1 is quite scarce and apparently in contrast with
the findings obtained for the 150 gTS L−1 sample showing that
the Y curves are generally below the corresponding N curves
at any shear rate (Figure 8).
Increasing the pH around 7.8–8.4 (Figure 9(b)), it could
be noticed that if the shear rate was below 540 s−1 the
aeration of sludge samples (tests 11 and 12) yielded reduction
of the shear stress and apparent viscosity with respect to
nonaerated sample (test 10); above this shear rate value
of 540 s−1 , an opposite effect was detected since the shear
stress and apparent viscosity became greater in the aerated
samples. In both cases, the shear stress differences between
these flow curves are quite small and may be related to
the interpolation procedure rather than reflecting the actual
rheological behaviour.
Considering the maximum value of the pH = 9.0 (Figure 9(c)), the shear stress and apparent viscosity for the
aerated sample at full flow rate (test 15, air Y) were smaller
than those obtained for the nonaerated sample (test 13, air N)
when the shear rate was below 970 s−1 . Above this shear rate
value, the shear stress was greater for the full aerated sample.
Journal of Chemistry
13
T = 45.4∘ C - J( = 7.0 - air N
6
6
5
5
4
4
3
T = 45.3∘ C - J( = 6.7 - air 1/2
7
(Pa)
(Pa)
7
3
2
2
1
1
0
0
0
200
400 600
̇ (M−1 )
0
800 1000
T = 45.0∘ C - J( = 6.6 - air Y
6
6
5
5
4
4
3
3
2
2
1
1
200
400 600
̇ (M−1 )
800 1000
Influence of aeration – pH 6.6–7.0 MLSS 190 A ,−1
7
(Pa)
(Pa)
7
0
400 600
̇ (M−1 )
Exper
Calc
Exper
Calc
0
200
0
800 1000
0
200
400 600
̇ (M−1 )
Test #7
Test #8
Exper
Calc
800 1000
Test #9
(a) Shear stress versus rate of deformation (MLSS 190 g L−1 ). Lower right-hand panel compares
calculated values for pH condition around 6.6–7.0
T = 44.9∘ C - J( = 7.8 - air N
6
6
5
5
4
4
3
T = 45.2∘ C - J( = 8.3 - air 1/2
7
(Pa)
(Pa)
7
3
2
2
1
1
0
0
0
200
400 600
̇ (M−1 )
800 1000
0
T = 45.3∘ C - J( = 8.4 - air Y
6
6
5
5
4
4
3
3
2
2
1
1
200
400 600
̇ (M−1 )
Exper
Calc
800 1000
Influence of aeration – pH 7.8–8.4 MLSS 190 A ,−1
7
(Pa)
(Pa)
7
0
400 600
̇ (M−1 )
Exper
Calc
Exper
Calc
0
200
800 1000
0
0
200
400 600
̇ (M−1 )
Test #10
Test #11
800 1000
Test #12
(b) Shear stress versus rate of deformation (MLSS 190 g L−1 ). Lower right-hand panel compares
calculated values for pH condition around 7.8–8.4
Figure 9: Continued.
14
Journal of Chemistry
T = 45.0∘ C - J( = 8.9 - air N
6
6
5
5
4
4
3
3
2
2
1
1
0
0
0
200
400 600
̇ (M−1 )
T = 45.3∘ C - J( = 9.0 - air 1/2
7
(Pa)
(Pa)
7
800 1000
0
400
600
800 1000
̇ (M−1 )
Exper
Calc
Exper
Calc
Influence of aeration – pH 8.9–9.1 MLSS 190 A ,−1
7
T = 45.0∘ C - J( = 9.1 - air Y
7
6
6
5
5
4
4
(Pa)
(Pa)
200
3
3
2
2
1
1
0
0
0
200
400
600
0
800 1000
200
400
600
800 1000
̇ (M−1 )
̇ (M−1 )
Test #13
Test #14
Exper
Calc
Test #15
(c) Shear stress versus rate of deformation (MLSS 190 g L−1 ). Lower right-hand panel compares
calculated values for pH condition around 8.9–9.1
Figure 9
On the contrary, the flow curve of the half aerated sample
(test 14, air 1/2) never fell below the flow curve obtained from
the nonaerated sample.
Based on the observation of the interpolated flow curves,
the influence of aeration on the rheological behaviour of the
samples appears to be not univocal: the reduction of the
apparent viscosity does not take place at any shear rate for
every investigated pH value. Furthermore, in those cases,
where the obtained shear stress differences are extremely
slight, such a discrepancy may be also ascribed to possible
uncertainties caused by the reduced number of experimental
samples available for interpolating the flow curves. Hence,
further investigations are required so as to assess the influence
of aeration with a higher degree of reliability.
Figure 10 shows how pH conditions of the nonaerated
samples influence the calculated flow curves for suspended
solid concentrations of 150 g L−1 (left side panel) and 190 g L−1
(right side panel).
In the case of MLSS = 150 g L−1 , it can be seen that
increasing the pH value from 6.7 (test 1) to about 7.7 (test 3)
caused a negligible variation of the shear stress and apparent
viscosity took place at any shear rate. Further increase of
the pH value to 9.0 (test 5) induced, at shear rates lower
than 730 s−1 , a negligible increment with respect to previous
curves of both shear stress and apparent viscosity, while above
730 s−1 the shear stress decreased progressively together with
the shear rate growth.
Considering the case MLSS = 190 g L−1 , the observed
behaviour was monotonic because, increasing pH values
from 7.0 (test 7) to 8.9 (test 13), the shear stress and apparent
viscosity diminished at any shear rate below 1000 s−1 .
Figure 11 shows the influence of pH on the rheological
behaviour of aerated sludge samples.
For the case MLSS = 150 g L−1 (left side panel), it can be
seen that passing from pH = 6.7 (test 2) to pH = 8.3 (test 4) a
negligible variation of both shear stress and apparent viscosity
took place. At pH = 9.1 (test 6), the corresponding flow curve
was always below the other two curves, resulting in a significant reduction of shear stress at higher shear rate values.
The analysis of the aerated samples with MLSS 190 g L−1
(right side panel) showed that, increasing the pH value
with respect to test 9, the reduction of the shear stress was
significant at shear rates below 800 s−1 , while above this value
the flow curves corresponding to pH = 8.4 (test 12) and pH
= 9.1 (test 15) tended to grow faster and overcome the curve
corresponding to pH = 6.6 (test 9).
Figure 12 shows the influence of pH on the rheological
behaviour of the denser sludge samples (MLSS 190 g L−1 ) for
half the aeration flow rate. The increases of pH value from 6.7
(test 8) to 8.3 (test 11) and 9.0 (test 14) led to a reduction of the
Journal of Chemistry
15
Influence of pH – nonaerated MLSS 150 A ,−1
7
6
6
5
5
4
4
(Pa)
(Pa)
7
3
3
2
2
1
1
0
0
200
400
600
800
Influence of pH – nonaerated MLSS 190 A ,−1
0
1000
0
200
400
̇ (M−1 )
600
̇ (M−1 )
800
1000
Test #7
Test #10
Test #13
Test #1
Test #3
Test #5
Influence of pH – aerated MLSS 150 A ,−1
7
7
Influence of pH – aerated MLSS 190 A ,−1
6
6
5
5
4
4
(Pa)
(Pa)
Figure 10: Shear stress versus rate of deformation: influence of pH for nonaerated samples.
3
3
2
2
1
1
0
0
0
200
400
600
800 1000
0
200
400
̇ (M−1 )
Test #2
Test #4
Test #6
600
800 1000
̇ (M−1 )
Test #9
Test #12
Test #15
Figure 11: Shear stress versus rate of deformation: influence of pH for aerated samples.
shear stress (and following apparent viscosity) if the shear rate
was below 700 s−1 and 980 s−1 , respectively.
The pH influence on rheological behaviour might be
ascribed to the bacteria aggregation extent: [90] showed that,
for the activated sludge, both the values of 𝜏𝑦 and viscosity
increase progressively with pH, until it reaches a value of
7, thus indicating that the strongest flocs cohesion occurs
under weakly acidic conditions (pH ranging from 6 to 7).
Paradoxically, however, changes of the zeta potential as a
function of pH highlight that it is strongly negative for values
from 6 to 7, with the isopotential conditions being quite far.
In a subsequent study [59], some tests on activated
sludge from both aeration tanks and laboratory scale pilot
plants were carried out using rotational rheometer. Although
the MLSS concentration was lower than 10 g L−1 , a similar
influence of pH on the mechanical properties of sludge
was found. In particular, the yield stress 𝜏𝑦 decreased after
chlorination, denoting a possible deflocculation.
The samples studied in [59, 90] have a MLSS concentration significantly lower than those tested in the present
research: further analyses will be necessary to explore the
behaviour of a highly concentrated sludge.
4. Conclusions
The population profile differed clearly from a typical profile
of municipal as well as industrial wastewater treatment
plants. Notable is the missing of several of the major
bacteria groups and the low diversity within the present
major bacteria groups. Beside the considerably dominating
Betaproteobacteria, only a few other main bacteria groups
were present showing only very limited diversity within the
groups. The remaining organisms seemed to be adapted to
the extreme conditions applied and the obtained population
profile was very different from typical profiles from other
WWTPs with “normal” temperature conditions. All in all, the
extreme temperature conditions seem to trigger the surviving
of the (fittest) “most adapted” organisms to the conditions
applied. Also, the almost complete absence of filamentous
bacteria is a typical feature of thermophilic aerobic sludge
treatment and can lead to sludge losses due to the inability
to form stable sludge flocs.
This early phase of the study was focused on a limited
number of shear rate values: nevertheless, the first results
provide a valuable insight into the rheological behaviour of
16
Journal of Chemistry
Influence of pH – 1/2 aerated MLSS 190 A ,−1
7
6
(Pa)
5
4
3
2
1
0
0
200
400
600
̇ (M−1 )
800
1000
Test #8
Test #11
Test #14
Figure 12: Shear stress versus rate of deformation: influence of pH
for 1/2 aerated samples.
a highly concentrated thermophilic sludge. Furthermore, the
trend of the flow curves shows a certain degree of reliability,
as corroborated by the regression coefficients.
The influence of some relevant operative parameters
(i.e., pH, temperature, biomass concentration, and aeration
within the biological reactor) on the rheological behaviour of
the activated sludge was confirmed, thus providing valuable
guidelines for an efficient management of the wastewater
treatment process. The effects of aeration were sometimes
not univocal and reveal an opposing behaviour depending
on the values of the other parameters. This aspect may be
related to possible uncertainties connected to the low number
of samples available for the interpolation of the flow curves,
thus resulting in small discrepancy between them. Therefore,
it will be better investigated within a future study.
The rheological behaviour affects significantly the treatment processes of TAMR systems because, with respect to
conventional processes involving activated sludge, the major
part of the energy demand is connected to the use of
membranes and is strongly affected by the suspended solid
concentration and the biomass viscosity.
The analyses carried out show that the rheological
behaviour of TAMR biomass is mainly affected by MLSS
concentration.
The sludge exhibited shear-thickening (dilatant) behaviour; at MLSS concentration of 150 g L−1 , the estimated yield
𝜏𝑦 stress was equal to zero, but when MLSS concentration
reached 190 g L−1 , the interactions between the sludge particles were expected to be even more intense and therefore
a nonzero yield stress was observed. As a consequence,
increasing the biomass concentration, up to 190 gTS L−1 ,
could provide important information, in terms of rheological
behaviour, for the optimal management of thermophilic
process.
Conflicts of Interest
The authors declare that there are no conflicts of interest
regarding the publication of this paper.
References
[1] P. Madoni, “A sludge biotic index (SBI) for the evaluation of the
biological performance of activated sludge plants based on the
microfauna analysis,” Water Research, vol. 28, no. 1, pp. 67–75,
1994.
[2] P. Madoni, “Protozoa in wastewater treatment processes: A
minireview,” Italian Journal of Zoology, vol. 78, no. 1, pp. 3–11,
2011.
[3] E. Castillo González, L. De Medina Salas, and A. Contreras Gutiérrez, “A practical procedure for the microbiological
monitoring of activated sludge plant functioning,” Water and
Environment Journal, vol. 30, no. 3-4, pp. 182–189, 2016.
[4] C. Leal, A. L. Amaral, and M. D. L. Costa, “Microbial-based
evaluation of foaming events in full-scale wastewater treatment
plants by microscopy survey and quantitative image analysis,”
Environmental Science and Pollution Research, vol. 23, no. 15, pp.
15638–15650, 2016.
[5] F. Fang, B.-J. Ni, X.-Y. Li, G.-P. Sheng, and H.-Q. Yu, “Kinetic
analysis on the two-step processes of AOB and NOB in aerobic
nitrifying granules,” Applied Microbiology and Biotechnology,
vol. 83, no. 6, pp. 1159–1169, 2009.
[6] R. Cui, W.-J. Chung, and D. Jahng, “A rapid and simple respirometric biosensor with immobilized cells of Nitrosomonas
europaea for detecting inhibitors of ammonia oxidation,”
Biosensors and Bioelectronics, vol. 20, no. 9, pp. 1788–1795, 2005.
[7] M. Saryoglu and F. Ciner, “Investigation of OUR and NUR
experiments for nitrogen and phosphorus removal with activated sludge: A lab-scale study,” International Journal of Environment and Pollution, vol. 19, no. 6, pp. 635–643, 2003.
[8] M. Hagman, J. L. Nielsen, P. H. Nielsen, and J. L. C. Jansen,
“Mixed carbon sources for nitrate reduction in activated sludgeidentification of bacteria and process activity studies,” Water
Research, vol. 42, no. 6-7, pp. 1539–1546, 2008.
[9] A. Mielcarek, J. Rodziewicz, W. Janczukowicz et al., “Citric
acid application for denitrification process support in biofilm
reactor,” Chemosphere, vol. 171, pp. 512–519, 2017.
[10] Q. Feng, Y. Xiao, X. Li et al., “Using the dehydrogenase activity
for alert of activated sludge system under different copper
concentrations,” Desalination and Water Treatment, vol. 57, no.
38, pp. 17836–17843, 2016.
[11] M. Ghribi, F. Meddeb-Mouelhi, and M. Beauregard, “Microbial
diversity in various types of paper mill sludge: identification
of enzyme activities with potential industrial applications,”
SpringerPlus, vol. 5, no. 1, article no. 1492, 2016.
[12] D. Jenkins, M. G. Richard, and G. T. Daigger, Manual on The
Causes and Control of Activated Sludge Bulking and Foaming,
Lewis Publishers, CRC Press LLC, 2004.
[13] D. Eikelboom, “Identification and Control of Filamentous
Micro-organisms in Industrial Wastewater Treatment Plants,”
in Multi-Media Training CD, IWA, International Water Association, 2006.
[14] B. Hu, R. Qi, W. An et al., “Dynamics of the microfauna
community in a full-scale municipal wastewater treatment plant
experiencing sludge bulking,” European Journal of Protistology,
vol. 49, no. 4, pp. 491–499, 2013.
[15] H. Salvadó, “Improvement of the intersection method for the
quantification of filamentous organisms: Basis and practice for
bulking and foaming bioindication purposes,” Water Science
and Technology, vol. 74, no. 6, pp. 1274–1282, 2016.
[16] L. Arregui, R. Liébana, E. Rodrı́guez et al., “Analysis of the
usefulness of biological parameters for the control of activated
Journal of Chemistry
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
sludge wastewater treatment plants in an interlaboratory study
context,” Journal of Environmental Monitoring, vol. 14, no. 5, pp.
1444–1452, 2012.
B. Pérez-Uz, L. Arregui, P. Calvo et al., “Assessment of plausible
bioindicators for plant performance in advanced wastewater
treatment systems,” Water Research, vol. 44, no. 17, pp. 5059–
5069, 2010.
G. Łagód, R. Babko, K. Jaromin-Gleń, T. Kuzmina, and A.
Bieganowski, “Biofilm Communities in Successive Stages of
Municipal Wastewater Treatment,” Environmental Engineering
Science, vol. 33, no. 5, pp. 306–316, 2016.
W. Foissner, “Protists as bioindicators in activated sludge:
Identification, ecology and future needs,” European Journal of
Protistology, vol. 55, pp. 75–94, 2016.
A. L. Amaral, M. Da Motta, M. N. Pons et al., “Survey of Protozoa and Metazoa populations in wastewater treatment plants by
image analysis and discriminant analysis,” Environmetrics, vol.
15, no. 4, pp. 381–390, 2004.
H. Lu, K. Chandran, and D. Stensel, “Microbial ecology of denitrification in biological wastewater treatment,” Water Research,
vol. 64, pp. 237–254, 2014.
M. Tian, F. Zhao, X. Shen et al., “The first metagenome of
activated sludge from full-scale anaerobic/anoxic/oxic (A2O)
nitrogen and phosphorus removal reactor using Illumina
sequencing,” Journal of Environmental Sciences, vol. 35, pp. 181–
190, 2015.
X.-R. Li, Y. Lv, H. Meng, J.-D. Gu, and Z.-X. Quan, “Analysis of
microbial diversity by pyrosequencing the small-subunit ribosomal RNA without PCR amplification,” Applied Microbiology
and Biotechnology, vol. 98, no. 8, pp. 3777–3789, 2014.
P. Asvapathanagul and B. H. Olson, “Improving qPCR methodology for detection of foaming bacteria by analysis of
broad-spectrum primers and a highly specific probe for quantification of Nocardia spp. in activated sludge,” Journal of applied
microbiology, vol. 122, no. 1, pp. 97–105, 2017.
R. Ferrentino, M. Langone, I. Gandolfi, V. Bertolini, A.
Franzetti, and G. Andreottola, “Shift in microbial community
structure of anaerobic side-stream reactor in response to
changes to anaerobic solid retention time and sludge interchange ratio,” Bioresource Technology, vol. 221, pp. 588–597,
2016.
I. Ferrera and O. Sánchez, “Insights into microbial diversity
in wastewater treatment systems: How far have we come?”
Biotechnology advances, vol. 34, no. 5, pp. 790–802, 2016.
L. Faust, M. Szendy, C. M. Plugge, P. F. H. van den Brink, H.
Temmink, and H. H. M. Rijnaarts, “Characterization of the
bacterial community involved in the bioflocculation process
of wastewater organic matter in high-loaded MBRs,” Applied
Microbiology and Biotechnology, vol. 99, no. 12, pp. 5327–5337,
2015.
D. Xu, S. Liu, Q. Chen, and J. Ni, “Microbial community compositions in different functional zones of Carrousel oxidation
ditch system for domestic wastewater treatment,” AMB Express,
vol. 7, no. 1, p. 40, 2017.
B. Hu, R. Qi, and M. Yang, “Systematic analysis of microfauna
indicator values for treatment performance in a full-scale
municipal wastewater treatment plant,” Journal of Environmental Sciences, vol. 25, no. 7, pp. 1379–1385, 2013.
A. Drzewicki and D. Kulikowska, “Limitation of Sludge Biotic
Index application for control of a wastewater treatment plant
working with shock organic and ammonium loadings,” European Journal of Protistology, vol. 47, no. 4, pp. 287–294, 2011.
17
[31] K. Zhou, M. Xu, J. Dai, and H. Cao, “The microfauna communities and operational monitoring of an activated sludge plant
in China,” European Journal of Protistology, vol. 42, no. 4, pp.
291–295, 2006.
[32] M.-K. H. Winkler, E. Kröber, W. W. Mohn, F. Koch, and D.
Frigon, “Comparison of microbial populations and foaming
dynamics in conventional versus membrane enhanced biological phosphorous removal systems,” Water and Environment
Journal, vol. 30, no. 1-2, pp. 102–112, 2016.
[33] Y. Deng, X. Zhang, Y. Miao, and B. Hu, “Exploration of rapid
start-up of the CANON process from activated sludge inoculum
in a sequencing biofilm batch reactor (SBBR),” Water Science
and Technology, vol. 73, no. 3, pp. 535–542, 2016.
[34] S. Rodriguez-Perez and F. G. Fermoso, “Influence of an oxic settling anoxic system on biomass yield, protozoa and filamentous
bacteria,” Bioresource Technology, vol. 200, pp. 170–177, 2016.
[35] R. Pedrazzani, L. Menoni, S. Nembrini, L. Manili, and G.
Bertanza, “Suitability of Sludge Biotic Index (SBI), Sludge Index
(SI) and filamentous bacteria analysis for assessing activated
sludge process performance: the case of piggery slaughterhouse
wastewater,” Journal of Industrial Microbiology and Biotechnology, vol. 43, no. 7, pp. 953–964, 2016.
[36] L. Araújo dos Santos, V. Ferreira, M. O. Pereira, and A. Nicolau,
“Relationship between protozoan and metazoan communities
and operation and performance parameters in a textile sewage
activated sludge system,” European Journal of Protistology, vol.
50, no. 4, pp. 319–328, 2014.
[37] G. Morales, S. Pesante, and G. Vidal, “Effects of black liquor
shocks on activated sludge treatment of bleached kraft pulp mill
wastewater,” Journal of Environmental Science and Health - Part
A, vol. 50, no. 6, pp. 639–645, 2015.
[38] A. L. Leal, M. S. Dalzochio, T. S. Flores, A. S. De Alves, J. C.
Macedo, and V. H. Valiati, “Implementation of the sludge biotic
index in a petrochemical WWTP in Brazil: Improving operational control with traditional methods,” Journal of Industrial
Microbiology and Biotechnology, vol. 40, no. 12, pp. 1415–1422,
2013.
[39] J. Jena, R. Kumar, M. Saifuddin, A. Dixit, and T. Das, “Anoxicaerobic SBR system for nitrate, phosphate and COD removal
from high-strength wastewater and diversity study of microbial
communities,” Biochemical Engineering Journal, vol. 105, pp. 80–
89, 2016.
[40] B. J. Thwaites, P. Reeve, N. Dinesh, M. D. Short, and B. van den
Akker, “Comparison of an anaerobic feed and split anaerobicaerobic feed on granular sludge development, performance and
ecology,” Chemosphere, vol. 172, pp. 408–417, 2017.
[41] M. Matsubayashi, Y. Shimada, Y. Y. Li, H. Harada, and K. Kubota, “Phylogenetic diversity and in situ detection of eukaryotes
in anaerobic sludge digesters,” PloS one, vol. 12, no. 3, Article ID
e0172888, 2017.
[42] G. Wang, X. Xu, Z. Gong, F. Gao, F. Yang, and H. Zhang,
“Study of simultaneous partial nitrification, ANAMMOX and
denitrification (SNAD) process in an intermittent aeration
membrane bioreactor,” Process Biochemistry, vol. 51, no. 5, pp.
632–641, 2016.
[43] S. Ge, S. Wang, X. Yang, S. Qiu, B. Li, and Y. Peng, “Detection of
nitrifiers and evaluation of partial nitrification for wastewater
treatment: A review,” Chemosphere, vol. 140, pp. 85–98, 2015.
[44] Y. Liu, Y. Yuan, X. Li, X. Kang, and M. Du, “Succession
of bacterial community in anaerobic–anoxic–aerobic (A2O)
bioreactor using sludge fermentation liquid as carbon source,”
18
[45]
[46]
[47]
[48]
[49]
[50]
[51]
[52]
[53]
[54]
[55]
[56]
[57]
[58]
[59]
[60]
[61]
Journal of Chemistry
Desalination and Water Treatment, vol. 54, no. 4-5, pp. 1061–
1069, 2015.
C. M. Fitzgerald, P. Camejo, J. Z. Oshlag, and D. R. Noguera,
“Ammonia-oxidizing microbial communities in reactors with
efficient nitrification at low-dissolved oxygen,” Water Research,
vol. 70, pp. 38–51, 2015.
P. Regmi, B. Holgate, D. Fredericks et al., “Optimization of
a mainstream nitritation-denitritation process and anammox
polishing,” Water Science and Technology, vol. 72, no. 4, pp. 632–
642, 2015.
P. Cornel, M. Wagner, and S. Krause, “Investigation of oxygen
transfer rates in full scale membrane bioreactors,” Water Science
and Technology, vol. 47, no. 11, Article ID 313e319, 2003.
B. De Clercq, Computational Fluid Dynamics of Settling Tanks:
Development of Experiments and Rheological, Settling and
Scraper Submodels [Ph.D. thesis], Ghent University, 2003.
I. Seyssiecq, J.-H. Ferrasse, and N. Roche, “State-of-the-art:
Rheological characterisation of wastewater treatment sludge,”
Biochemical Engineering Journal, vol. 16, no. 1, pp. 41–56, 2003.
G. Tchobanoglous, F. L. Burton, and H. D. Stensel, Wastewater
Engineering: Treatment and Reuse, McGraw-Hill, Boston, 2003.
F. Yang, A. Bick, S. Shandalov, A. Brenner, and G. Oron, “Yield
stress and rheological characteristics of activated sludge in an
airlift membrane bioreactor,” Journal of Membrane Science, vol.
334, no. 1-2, pp. 83–90, 2009.
M. Brannock, G. Leslie, Y. Wang, and S. Buetehorn, “Optimising
mixing and nutrient removal in membrane bioreactors: CFD
modelling and experimental validation,” Desalination, vol. 250,
no. 2, Article ID 815e818, 2010.
S. Todeschini, C. Ciaponi, and S. Papiri, “Laboratory Experiments and Numerical Modelling of the Scouring Effects of
Flushing Waves on Sediment Beds,” Engineering Applications of
Computational Fluid Mechanics, vol. 4, no. 3, pp. 365–373, 2010.
K. J. Craig, M. N. Nieuwoudt, and L. J. Niemand, “CFD
simulation of anaerobic digester with variable sewage sludge
rheology,” Water Research, vol. 47, no. 13, pp. 4485–4497, 2013.
R. Hreiz, M. A. Latifi, and N. Roche, “Optimal design and operation of activated sludge processes: State-of-the-art,” Chemical
Engineering Journal, vol. 281, pp. 900–920, 2015.
A. Asadi, A. A. L. Zinatizadeh, and M. Hasnain Isa, “Performance of intermittently aerated up-flow sludge bed reactor and
sequencing batch reactor treating industrial estate wastewater:
A comparative study,” Bioresource Technology, vol. 123, pp. 495–
506, 2012.
G. Moeller and L. G. Torres, “Rheological characterization of
primary and secondary sludges treated by both aerobic and
anaerobic digestion,” Bioresource Technology, vol. 61, no. 3, pp.
207–211, 1997.
N. Tixier, G. Guibaud, and M. Baudu, “Determination of some
rheological parameters for the characterization of activated
sludge,” Bioresource Technology, vol. 90, no. 2, pp. 215–220, 2003.
G. Guibaud, P. Dollet, N. Tixier, C. Dagot, and M. Baudu,
“Characterisation of the evolution of activated sludges using
rheological measurements,” Process Biochemistry, vol. 39, no. 11,
pp. 1803–1810, 2004.
H. Hasar, C. Kinaci, A. Ünlü, H. Toǧrul, and U. Ipek, “Rheological properties of activated sludge in a sMBR,” Biochemical
Engineering Journal, vol. 20, no. 1, pp. 1–6, 2004.
G. Laera, C. Giordano, A. Pollice, D. Saturno, and G. Mininni,
“Membrane bioreactor sludge rheology at different solid retention times,” Water Research, vol. 41, no. 18, pp. 4197–4203, 2007.
[62] S. Manenti, E. Pierobon, M. Gallati et al., “Vajont disaster:
Smoothed particle hydrodynamics modeling of the postevent
2D experiments,” Journal of Hydraulic Engineering, vol. 142, no.
4, 2015.
[63] R. Guandalini, G. Agate, S. Manenti, S. Sibilla, and M. Gallati,
“SPH based approach toward the simulation of non-cohesive
sediment removal by an innovative technique using a controlled
sequence of underwater micro-explosions,” Procedia IUTAM,
vol. 18, pp. 28–39, 2015.
[64] S. Manenti, S. Sibilla, M. Gallati, G. Agate, and R. Guandalini,
“SPH Simulation of Sediment Flushing Induced by a Rapid
Water Flow,” Journal of Hydraulic Engineering, vol. 138, no. 3,
pp. 272–284, 2012.
[65] S. Baroutian, N. Eshtiaghi, and D. J. Gapes, “Rheology of a
primary and secondary sewage sludge mixture: Dependency on
temperature and solid concentration,” Bioresource Technology,
vol. 140, pp. 227–233, 2013.
[66] P. T. Slatter, “The rheological characterisation of sludges,” Water
Science and Technology, vol. 36, no. 11, pp. 9–18, 1997.
[67] M. Mori, I. Seyssiecq, and N. Roche, “Rheological measurements of sewage sludge for various solids concentrations and
geometry,” Process Biochemistry, vol. 41, no. 7, pp. 1656–1662,
2006.
[68] N. Ratkovich, W. Horn, F. P. Helmus et al., “Activated sludge
rheology: A critical review on data collection and modelling,”
Water Research, vol. 47, no. 2, pp. 463–482, 2013.
[69] F. D. Sanin, “Effect of solution physical chemistry on the
rheological properties of activated sludge,” Water SA, vol. 28,
no. 2, pp. 207–211, 2002.
[70] O. Manoliadis and P. L. Bishop, “Temperature effect on rheology
of sludges,” Journal of Environmental Engineering, vol. 110, no. 1,
pp. 286–290, 1984.
[71] J. C. Baudez, P. Slatter, and N. Eshtiaghi, “The impact of
temperature on the rheological behaviour of anaerobic digested
sludge,” Chemical Engineering Journal, vol. 215-216, pp. 182–187,
2013.
[72] M. Papa, R. Pedrazzani, S. Nembrini, and G. Bertanza, “Should
rheological properties of activated sludge be measured?”
Applied Rheology, vol. 25, no. 2, Article ID 24590, 2015.
[73] S. J. Jahren, J. A. Rintala, and H. Ødegaard, “Aerobic moving bed
biofilm reactor treating thermomechanical pulping whitewater
under thermophilic conditions,” Water Research, vol. 36, no. 4,
pp. 1067–1075, 2002.
[74] A. Rozich and K. Bordacs, “Use of thermophilic biological
aerobic technology for industrial wastewater treatment,” Water
Science and Technology, vol. 46, no. 4-5, pp. 83–89, 2002.
[75] R. Kurian, C. Acharya, G. Nakhla, and A. Bassi, “Conventional
and thermophilic aerobic treatability of high strength oily pet
food wastewater using membrane-coupled bioreactors,” Water
Research, vol. 39, no. 18, pp. 4299–4308, 2005.
[76] M. C. Collivignarelli, A. Abbà, and G. Bertanza, “Treatment of
high strength pharmaceutical wastewaters in a Thermophilic
Aerobic Membrane Reactor (TAMR),” Water Research, vol. 63,
pp. 190–198, 2014.
[77] M. C. Collivignarelli, F. Castagnola, M. Sordi, and G. Bertanza,
“Treatment of sewage sludge in a thermophilic membrane
reactor (TMR) with alternate aeration cycles,” Journal of Environmental Management, vol. 162, pp. 132–138, 2015a.
[78] T. M. Lapara and J. E. Alleman, “Thermophilic aerobic biological wastewater treatment,” Water Research, vol. 33, no. 4, pp.
895–908, 1999.
Journal of Chemistry
[79] M. C. Collivignarelli, A. Abbà, and G. Bertanza, “Why use a
thermophilic aerobic membrane reactor for the treatment of
industrial wastewater/liquid waste?” Environmental Technology,
vol. 36, no. 16, pp. 2115–2124, 2015b.
[80] M. C. Collivignarelli, G. Bertanza, M. Sordi, and R. Pedrazzani,
“High-strength wastewater treatment in a pure oxygen thermophilic process: 11-year operation and monitoring of different
plant configurations,” Water Science and Technology, vol. 71, no.
4, pp. 588–596, 2015c.
[81] A. F. Rozich, R. J. Colvin, and C. D. Hahn, “Design and
Operation of a High Strength Organic Wastewater Treatment
System to Approach Zero Net Sludge Production at A Specialty
Chemical Plant,” in Proceedings of the Water Environment
Federation, vol. 2004, pp. 373–388, Biotec Company, West
Chester, Pennsylvania.
[82] J. C. T. Vogelaar, A. De Keizer, S. Spijker, and G. Lettinga,
“Bioflocculation of mesophilic and thermophilic activated
sludge,” Water Research, vol. 39, no. 1, pp. 37–46, 2005.
[83] B. Q. Liao, H. J. Lin, S. P. Langevin, W. J. Gao, and G. G.
Leppard, “Effects of temperature and dissolved oxygen on
sludge properties and their role in bioflocculation and settling,”
Water Research, vol. 45, no. 2, pp. 509–520, 2011.
[84] J. Suvilampi, A. Lehtomäki, and J. Rintala, “Comparative
study of laboratory-scale thermophilic and mesophilic activated
sludge processes,” Water Research, vol. 39, no. 5, pp. 741–750,
2005.
[85] J. Suvilampi, A. Lehtomäki, and J. Rintala, “Biomass characterization of laboratory-scale thermophilic-mesophilic wastewater
treatment processes,” Environmental Technology, vol. 27, no. 1,
pp. 41–51, 2006.
[86] F. Morgan-Sagastume and D. G. Allen, “Effects of temperature
transient conditions on aerobic biological treatment of wastewater,” Water Research, vol. 37, no. 15, pp. 3590–3601, 2003.
[87] B.-M. Wilén, B. Jin, and P. Lant, “Impacts of structural characteristics on activated sludge floc stability,” Water Research, vol.
37, no. 15, pp. 3632–3645, 2003.
[88] B.-M. Wilén, M. Onuki, M. Hermansson, D. Lumley, and T.
Mino, “Microbial community structure in activated sludge floc
analysed by fluorescence in situ hybridization and its relation
to floc stability,” Water Research, vol. 42, no. 8-9, pp. 2300–2308,
2008.
[89] L. H. Mikkelsen, “The shear sensitivity of activated sludge: Relations to filterability, rheology and surface chemistry,” Colloids
and Surfaces A: Physicochemical and Engineering Aspects, vol.
182, no. 1-3, pp. 1–14, 2001.
[90] P. Dollet, Application rhéologique à la caractérisation de l’état
de floculation des boues activées [Ph.D. thesis], Université dee
Limoges, 2000.
19
International Journal of
Medicinal Chemistry
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
International Journal of
Photoenergy
Organic Chemistry
International
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Hindawi Publishing Corporation
http://www.hindawi.com
Advances in
International Journal of
Analytical Chemistry
Physical Chemistry
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 201
Volume 2014
International Journal of
Carbohydrate
Chemistry
Journal of
Quantum Chemistry
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Submit your manuscripts at
https://www.hindawi.com
The Scientific
World Journal
Hindawi Publishing Corporation
http://www.hindawi.com
Journal of
International Journal of
International Journal of
Inorganic Chemistry
Volume 2014
Journal of
Theoretical Chemistry
Hindawi Publishing Corporation
http://www.hindawi.com
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Spectroscopy
Hindawi Publishing Corporation
http://www.hindawi.com
Journal of
Analytical Methods
in Chemistry
Volume 2014
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Chromatography
Research International
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Electrochemistry
Hindawi Publishing Corporation
http://www.hindawi.com
Applied Chemistry
Bioinorganic Chemistry
and Applications
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Journal of
Journal of
International Journal of
Catalysts
Chemistry
Spectroscopy
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Volume 2014
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Volume 2014