MBR activated sludge viscosity measurement using the Delft filtration characterization method

Journal of Water Process Engineering 5 (2015) 35–41

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MBR activated sludge viscosity measurement using the Delft filtration characterization method J. Lopez a,∗ , A. Moreau d , J.A. Gil e , J.H.J.M. van der Graaf c , J.B. van Lier b , N. Ratkovich a a

University of Los Andes, Department of Chemical Engineering, Product and Process Design Group (GDPP), Cra. 1 No. 18A-12, Bogota, Colombia Delft University of Technology (TUDelft), Faculty of Civil Engineering and Geosciences, P.O. Box 5048, 2600 GA Delft, The Netherlands Witteveen+Bos, Van Twickelostraat 2, 7400 AE Deventer, The Netherlands d Veolia Water Technologies, 12 Clematis Avenue Waltham, MA 02453, USA e GRUNDFOS BioBooster A/S, Randersvej 22a, 8870, Langå, Denmark b c

a r t i c l e

i n f o

Article history: Received 29 May 2014 Received in revised form 10 November 2014 Accepted 19 November 2014 Keywords: MBR Activated sludge Rheology Model

a b s t r a c t Viscosity measurements play an important role in activated sludge (AS) characterization, especially with respect to Membrane BioReactor (MBR) operation where low and high shear rates (velocity gradients) occur near the membrane surface and on the bioreactor tank due to the mixing, respectively. Moreover, viscosity plays a role in terms of energy consumption (e.g. pumping and mixing). Therefore, an accurate viscosity relationship as a function of total suspended solids (TSS) and temperature can help to model the behavior of the AS. A total number of 10 municipal and 11 industrial MBR plants throughout Europe were investigated during the period 2007–2009 using the Delft Filtration Characterization method (DFCm). Unlike other literature studies, AS rheology was measured on site, exploiting the resemblance of the DFCm to a tubular rheometer and compared against the results of a rotational rheometer. A new rheological model for the viscosity of AS was developed maintaining the same mathematical structure as previous rheological models made for MBRs. The model proposed in this study is valid for TSS and temperature ranges of 5–20 g L−1 and 10–25 ◦ C, respectively. This model proves that the DFCm unit can be used as a ‘cheap’ rheometer and produce the same results as a rotational rheometer for AS viscosity characterization. © 2014 Elsevier Ltd. All rights reserved.

1. Introduction Membrane b ioreactors (MBRs) combine biological treatment with membrane separation technology. MBRs are typically operated with elevated total suspended solids (TSS) in order to decrease the waste sludge, to increase the specific loading capacity, and thus decrease the plant footprint. However, these elevated TSS result in higher viscosity values compared to conventional activated sludge (CAS) systems. This elevated viscosity remains a challenge in terms of oxygen transfer, formation of dead zones and energy consumption of MBRs [1]. The viscosity (, Pa s) for Newtonian liquids is defined by the ratio between shear stress (, Pa) and shear rate (, ˙ s−1 ), defined in Eq. (1). =

 ˙

(1)

The viscosity of a liquid determines the hydraulic regime and transport phenomena near the membrane. Particulate suspensions, such as activated sludge (AS), exhibit complex flow behavior and are classified among the non-Newtonian liquids. Some models of power-law relationship for non-Newtonian fluids were proposed [2,3]. First, if the fluid behaves like a Bingham plastic the relationship is given by Eq. (2).  = o + k˙

If the fluid behaves as a shear-thickening fluid (dilatant) (n > 1) or shear-thinning fluid (pseudoplastic) (n < 1) the relationship is given by Eq. (3).  = k˙ n

http://dx.doi.org/10.1016/j.jwpe.2014.11.006 2214-7144/© 2014 Elsevier Ltd. All rights reserved.

(3)

And finally if it behaves according to Herschel – Buckley (0 < n < ∞) the relationship is given by Eq. (4).  = o + k˙ n

∗ Corresponding author. Tel.: +57 1339 4949x1776. E-mail address: [email protected] (J. Lopez).

(2)

(4)

where in Eqs. (2)–(4),  o (Pa) is the yield stress, k is the consistency index (Pa sn ), and n is the flow behavior index.

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J. Lopez et al. / Journal of Water Process Engineering 5 (2015) 35–41

The rheological properties of non-Newtonian liquids are complex, and they have a substantial importance in industrial applications related to handling, transportation and processing. They are generally taken into account in the equipment design, e.g. mixing, dispersing and pumping. Its characterization is based on the measurement of the apparent viscosity, which depends on the deformation (shear) rate. MBR AS is considered a pseudo-plastic liquid [4] (Eq. (2)). It is composed of flocs which tend to get disrupted under high shear rate conditions, resulting in a decrease in the apparent viscosity. AS rheology was also found to be dependent on Solids Retention Time (SRT) within the range of 40–80 days [5]. Apparent viscosity for a large number of small scale MBRs, with a TSS concentration in the range 5–40 g L−1 was also reported [2]. Typically, municipal full-scale MBR systems are operated around 10–12 g L−1 and SRT of around 20 days. Thus far, no uniform model of apparent viscosity that can be used for design and optimization of equipment has been created. Models developed in the literature [2,5–8] do not seem valid for different AS mixtures (high heterogeneity among different AS samples) and they are not valid in the total wide range of TSS content, level of turbulence, etc. The DFCm (Delft Filtration Characterization method) was originally built to measure AS filterability characteristics. However, in the way the DFCm unit is operated, it can actually be used as a tubular rheometer [9]. Nevertheless, it is important to highlight that a capillary rheometer typically has a diameter of 1 mm, whereas the DFCm unit has a diameter of 8 mm. However, the same principle can be used, although attention should be paid to the occurrence of concentration gradients, which will give rise to a viscosity gradient between the wall and the bulk region. The applicability of the DFCm to serve as a tubular rheometer will be investigated in Section 2.3. It is important to highlight that when an AS sample is under continuous shear stress in a rotational or tubular rheometer, it is expected that de-flocculation, or floc breakage, may occur. Nevertheless, this was not considered in this study. In this work, 10 municipal and 11 industrial MBR plants were investigated during 2007–2011, and data needed to calculate the apparent viscosity of AS for each plant was gathered. These data were compared to 6 municipal and 3 industrial MBR plants, where the viscosity was measured with a rotational rheometer. The objective of this study is to investigate the rheological properties of AS under different conditions and to determine the most suitable mathematical model that expresses the viscosity of the AS.

2.3. Viscosity A temperature dependent relation for the viscosity of water is given by an exponential Arrhenius type relation defined by Eq. (7) [11,13]. w = 1.2182 · 10−6 e16440.4488/Rgas T

where Rgas is the universal gas constant (=8.3145 J K−1 mol−1 ) and the previous relation is valid for the range 0–100 ◦ C (273–373 K). As it was mentioned before, AS exhibits non-Newtonian behavior. Therefore, some modifications are needed regarding the viscosity. The apparent viscosity (app ) of a liquid flowing in a pipe can be calculated using Eq. (8) [14]. app = k

 3n + 1 n  8u n−1 4n

d

2.4. Viscous flow in pipes The total pressure drop (Fig. 1) along the DFCm unit (Ptotal ) can be obtained from the pressure measured (Pmea ) from the pressure

10 municipal and 11 industrial MBRs were investigated in this study. A detailed description of the MBR configuration can be found in Moreau et al. [9] and Gil et al. [10] and they are briefly summarized in Table 1. 2.1. Thermo-physical properties of liquids The thermo-physical properties of liquids, such as density and viscosity, play an important role in the viscous flow in pipes. 2.2. Density The density of water (w , kg m−3 ) is a function of temperature and is defined by Eq. (5) [11]. (5)

where T is the absolute temperature (K) and the previous relation is valid for the range 0–100 ◦ C (273–373 K). The density of AS (AS ) is estimated using Eq. (6) [12]. AS = w + 0.2 TSS

(6)

(8)

where u is the velocity (m s−1 ) and d is the internal pipe diameter (m). For the case of a Newtonian liquid (n = 1), the apparent viscosity becomes the viscosity of the liquid. Eq. (8) shows the dependence of viscosity with flow velocity and the diameter of the pipe, but does not consider the fluid physicochemical parameters, for this reason the viscosity calculated with this method is considered ‘apparent’. The parameters k and n found in the equation are those used to find both TSS and the temperature dependence.

2. Materials and methods

w = 1855.8979 − 1.5664T − 116862.3T −1

(7)

Fig. 1. Pressure drop along the DFCm unit.

J. Lopez et al. / Journal of Water Process Engineering 5 (2015) 35–41

37

Table 1 Characteristics of the MBR plants used in this study. Reference

Location

[9]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

[10]

Country

GB BE GE CH GE GE GE NL NL IT NL NL BE NL NL NL GE NL BE NL NL

Wastewater

Municipal Municipal Municipal Municipal Municipal Municipal Municipal Municipal Municipal Municipal Industrial Industrial Industrial Industrial Industrial Industrial Industrial Industrial Industrial Industrial Industrial

Scale

Pilot Full Full Pilot Pilot Full Full Full Full Pilot Full Full Full Full Full Full Full Full Full Full Full

Membrane

Rheometer

HF/FS/MT HF HF HF/HS FS FS HF MT HF HF FS HF HF MT MT MT HF HF HF HF HF

DFCm DFCm DFCm DFCm DFCm DFCm DFCm DFCm DFCm DFCm DFCm DFCm DFCm DFCm DFCm DFCm DFCm DFCm DFCm DFCm DFCm

TSS

Temperature

g L−1

◦

5.5 6.9 9.0 3.5 5.6 6.4 14.0 9.0 9.8 8.0 12.0 6.7 5.8 32.0 11.0 3.1 2.8 10.0 15.0 5.7 13.0

11.5 13.9 9.5 13.6 10.4 16.4 14.9 9.3 11.0 8.5 15.0 9.0 9.7 61.0 12.0 9.0 3.8 11.0 17.0 10.0 24.0

C

9.7 12.7 9.7 16.0 17.0 11.9 17.9 17.6 21.1 27.9 23.0 22.0 19.0 27.0 20.0 25.0 24.0 23.0 19.0 23.0 18.0

14.1 17.8 12.4 19.6 27.4 24.7 21.0 18.9 22.2 25.0 24.0 27.0 24.0 29.0 22.0 26.0 28.0 28.0 24.0 27.0 25.0

HF: hollow fiber, FS: flat sheet, MT: multi tube.

sensors, and can also be obtained by Eq. (9). Pmea = Ptotal = Ph + Pin + Pout + Pmem + PS,con + PS,exp

(9)

where Ph is the pressure drop (Pa) due to the head loss in the liquid column, Pin and Pout are the pressures at the inlet and outlet sections respectively, Pmem is the pressure drop at the membrane section and PS,con and PS,exp are the pressure drops due to singularities, i.e. sudden contractions and expansions of the fluid due to system design, respectively [15]. To compare the prediction of Eq. (9) with the measured values of the DFCm unit under different conditions and recover the viscosity model parameters from it, it is necessary to find out the remaining terms in Eq. (9). If the total pressure drop measure by the sensors (Pmea ) is equal to the pressure drop calculated by Eq. (9) (terms in the equation depend on the proposed viscosity model), the viscosity model will be validated. The pressure drop due to the column height is defined by Eq. (10) [15].

AS ui di 

ReMR =

2 Lin AS uin 2 din

Lout AS u2out 2 dout

(16)

AS ui di app

(17)

The friction factor for Newtonian and non-Newtonian liquids depends on the flow regime i.e. laminar or turbulent, and they are defined by Eqs. (18) and (19), respectively [14,16]. Laminar ReMR < ReMR,c

fi =

16 ReMR



(10)

where g is the gravity acceleration (=9.81 m s−1 ) and h is the liquid column height (m). The pressure at the inlet, outlet, membrane, contraction and expansion are defined by Eqs. (11), (12), (13), (14) and (15), respectively [15].

Pout = fout

Re =

Turbulent

Ph = AS gh

Pin = fin

inlet, mem: membrane and out: outlet). This last term is a function of the local Reynolds number (Re, –) (Eq. (16)) or the Reynolds number of Metzner and Reed (ReMR , –) (Eq. (17)) [14] for Newtonian and non-Newtonian liquids, respectively.

ReMR ≥ ReMR,c

fi =

(18)

2n+4 77n

 4n 3n2 1 1/(3n+1) 3n + 1

ReMR (19)

The critical Reynolds number (ReMR,c , –) is defined by Eq. (20) [14,16]. 6464n(2 + n)(2+n)/(1+n)

(11)

ReMR,c =

(12)

In the case of a Newtonian liquid (n = 1), the friction factor for the turbulent regime becomes the Blasius expression, and the critical Reynolds number becomes 2100 for the transition from laminar to turbulent for Newtonian liquids. The pressure drop due to singularities on pipe flow depends on the contraction [17–19] and expansion [16] coefficients for nonNewtonian liquids defined by Eqs. (21) and (22), respectively.

Pmem = fmem

Lmem AS u2mem 2 dmem

(13)

PS,con = Kcon

AS u2mem 2

(14)

PS,exp = Kexp

AS u2mem 2

(15)

where Li is the pipe section length (m), Kcon is the contraction coefficient (–), Kexp is the expansion coefficient (–) and fi is the Moody friction factor (–) for each section (i) of the tube (i.e. in:

Kcon = KH + Kexp =

(20)

(3n + 1)2

KC ReMR

(21)

 3n + 1   (n + 3) 2n + 1

2(5n + 3)

ˇ2 − ˇ +

3(3n + 1) 2(5n + 3)

 (22)

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J. Lopez et al. / Journal of Water Process Engineering 5 (2015) 35–41

Table 2 Dimensional and operational parameters of the DFCm unit.

3. Results and discussion

Parameter

Unit

Value

Lin = Lout Lmem din = dout dmem ˇ uin = uout umem

m m m m – m s−1 m s−1

0.0925 1 0.015 0.008 0.284 0.284 1

Table 3 Parameters for Eqs. (27) and (28). Parameters

[2]

a1 a2 a3 a4 a5

0.001 2.000 0.410 0.230 0.370

(Pa s) (–) (–) (–) (–)

where KH is the Hagenbach correction factor (–) (Eq. (23)) [19], KC is the Couette correction factor (–) (Eq. (24)) and ˇ is the diameter ratio (–) (Eq. (25)).

The results of the TSS vs k (flow behavior index) and the TSS vs k (flow consistency index) are presented in Fig. 2, respectively, for all the examined municipal and industrial MBRs. As shown in Fig. 2, despite having treatment plants of various types (municipal and industrial), a similar trend is shown in the consistency index (n) and the behavior index (k) for each location. In the case of consistency index (n), 2 cases do not follow the general trend; this may be due to errors in the experimentation process or a different type of treated water. Despite this, these two cases are within the limits of TSS obtained in the studied cases. A clear trend is observed with the flow behavior and flow consistency index, respectively, being functions of the TSS as reported in literature [2,5–8]. Rosenberger et al. [2] did not include a correction for temperature as their experiments were conducted at constant temperature (21 ◦ C). However, in this study, the temperature in each measurement was different. Therefore, the exponential Arrhenius parameter is included in this analysis for k and an extra term including the temperature for n as described by Carezzato et al. [20] are used. The new models for k and n as a function of the TSS and the temperature are presented by Eqs. (29) and (30), respectively. a

KH = 2.3387[n(1 − ˇ)]

0.3506

(23)

 n 1.74

KC = 591 ˇ=

n = 1 − a4 TSS e (24)

2

mem

(29)

a5 (a6 T )

1−ˇ

d

k = a1 ea2 TSS 3 eEa /(Rgas T )

(25)

din

Finally, for the DFCm unit, the inlet and outlet diameter are the same (din = dout ); therefore the velocities at the inlet and outlet are the same as presented by Eq. (26). uin = uout = umem ˇ

(26)

The dimension and operational parameters of the DFCm unit are presented in Table 2. The determination of the apparent viscosity of AS using the data obtained from the DFCm unit, is an iterative process (Eqs. (5)–(26)) in order to find k and n to calculate the friction factor. The iteration was performed with the Solver tool (Microsoft Excel 2010). Rosenberger et al. [2] proposed models for k and n as a function of the TSS presented by Eqs. (27) and (28) respectively. a3

k = a1 ea2 TSS

(27)

n = 1 − a4 TSSa5

(28)

where a1 –a5 are fitting parameters. Reported values are summarized in Table 3. It is important to highlight that the model of Rosenberger et al. [2] is independent of the temperature.

(30)

To determine the different parameters of Eqs. (27)–(30), the statistical analysis software SPSS v19 (SPSS Corporation) was used. Four non-linear regressions were performed, two without considering the temperature (Eqs. (27) and (28)) and the other two including the temperature (Eqs. (29) and (30)) for the DFCm data. Therefore Rosenberger et al. [2] data are not included in this analysis. Table 4 summarizes the parameters for Eqs. (27)–(30) with the standard deviation and confidence interval, and Table 5 shows the R2 , residual square error and degrees of freedom. Table 5 shows no significant difference for R2 with or without including the temperature in the model. However, the sum of residual squared errors (RSE) indicates that the more complex model (Eqs. (29) and (30)) fits slightly better than Eqs. (27) and (28). Therefore, an F-test has been applied to determine whether or not the difference between the two models is significant, Eq. (31) and (32) for k and n, respectively. F-test = F-test =

(RSEEq. (29) − RSEEq. (31) )/(DFEq. (29) − DFEq. (31) ) RSEEq. (31) /DFEq. (31) (RSEEq. (30) − RSEEq. (32) )/(DFEq. (30) − DFEq. (32) ) RSEEq. (32) /DFEq. (32)

(31)

(32)

where DF are the degrees of freedom (86 data points minus the number of variables). Evaluating Eqs. (31) and (32) with the values from Table 4, the value of the F-test are 232.253 and 0.379

Table 4 Parameters for Eqs. (27)–(30), standard deviation and 95% confidence interval. Parameter

a1 a2 a3 a4 a5 a6 Ea

(Pa s) (–) (–) (–) (–) (–) (J mol−1 )

Without temperature correction Eqs. (27) and (28) Estimate

Std. error

3.64E−03 1.258 0.496 0.189 0.422 – –

0.006 1.1 0.194 0.013 0.029 – –

With temperature correction Eqs. (29) and (30) 95% Confidence interval Lower bound

Upper bound

−0.008 −0.931 0.109 0.164 0.365 – –

0.016 3.448 0.883 0.214 0.479 – –

Estimate

Std. error

3.30E−06 1.03 0.549 0.133 0.42 0.001 17,693.648

2.73E−06 0.426 0.096 0.075 0.029 0.002 1140.636

95% Confidence interval Lower bound

Upper bound

−2.14E−06 0.182 0.357 −0.017 0.363 −0.003 15,422.816

8.74E−06 1.877 0.74 0.283 0.478 0.005 19,964.481

J. Lopez et al. / Journal of Water Process Engineering 5 (2015) 35–41

39

Fig. 2. (a) TSS vs k and (b) TSS vs n for all the examined municipal and industrial MBRs presented in Table 1.

Fig. 3. (a) TSS vs k and (b) TSS vs n including DFCm experimental data, Rosenberger et al. [2] model (Eq. (28) with parameters of Table 3) and the model proposed in this work (Eq. (29) with parameters of Table 4 at 20 ◦ C).

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J. Lopez et al. / Journal of Water Process Engineering 5 (2015) 35–41

Table 5 R2, residual square error and degrees of freedom. Parameters

R2 RSE DF

k

n

Eq. (27)

Eq. (29)

Eq. (28)

Eq. (30)

0.949 0.086 79

0.987 0.022 78

0.727 0.138 80

0.729 0.137 79

for k and n, respectively. Comparing this value to the tabulated F-value for a 95% confidence limit, being 6.8, the conclusion is that there is no statistically significant improvement of adding an additional parameter (temperature correction) to improve the correlation performance of n. However, the temperature correction should be included for k. Based on these results, the suggested correlation for k and n are Eqs. (29) and (28), respectively. Therefore, temperature is found to be significant for the variations in AS viscosity under normal ranges of operation (15–25 ◦ C). Fig. 3 shows the comparison between the experimental data with Eqs. (29) and (28) respectively. As seen in Fig. 3, the model proposed by Rosenberger et al. [2] and the model proposed in this work (Eq. (29)) are relatively similar. The main difference between these two models is the inclusion of temperature. In Fig. 3, the results gathered with the DFCm, and the model proposed by Rosenberger et al. [2], match with just an error of 6 and 3% for k and n respectively. Adding the temperature does not affect the overall behavior of the AS viscosity. Therefore, the model proposed by Rosenberger et al. [2] (Eqs. (27) and (28)) correctly describe the rheological properties of bulk liquid in MBRs for design but the addition of a temperature parameter increases the robustness and flexibility of the model to different data sets. The model proposed in this study is valid for TSS and temperature ranges of 5–20 g L−1 and 15–25 ◦ C, respectively. However, it is important to note the significant spread in the apparent viscosity data at fixed TSS concentration, which indicates that other important factors and/or processes are missing in the model e.g. floc structure and floc size distributions [21–24]. 4. Conclusions The DFCm unit has been used as an on-site rheometer to calculate the AS apparent viscosity from 10 municipal and 11 industrial MBR plants. A new rheological model for MBR AS is presented, based on the data collected from the 21 MBR plants. The new model was compared with a previous model. All models presented the same mathematical structure. The main factor influencing AS apparent viscosities was the TSS content, confirming the findings in the literature. Temperature was found to only affect the flow consistency value (k). Good agreement was found between the model proposed by Rosenberger et al. [2] and the MBR plants monitored in this study, and since no significant contribution to the apparent viscosity by temperature was found, it is concluded that the model proposed by Rosenberger et al. [2] is completely valid for the calculation of viscous properties of AS. The temperature range at which experimental measurements were made is consistent with typical temperature values reported in the literature, but the proposed model cannot be properly adjusted outside this range. Finally, concerning viscosity measurement in activated sludge, the DFCm has shown advantages compared to other methods i.e. simplicity and the possibility to measure in situ. Nomenclature

a1 –a5 A

fitting parameters cross-sectional area of pipe (m2 )

d epump Ea Epump fi g h Kcon KC Kexp KH k Li n Ph Pin Pmea Pmem Pout PS,con PS,exp Q Re ReMR ReMR,c Rgas T u

pipe diameter (m). pump efficiency (–) activation energy (J mol−1 ) pump power (W) Moody friction factor (–) gravity acceleration (=9.81 m s−1 ) liquid column height (m) contraction coefficient (–) Couette correction factor (–) expansion coefficient (–) Hagenbach correction factor (–) flow consistency index (Pa sn ) pipe section length (m) flow behavior index (–) hydrostatic pressure drop (Pa) pressure drop at the inlet (Pa) pressure drop measured by the DFCm unit (Pa) pressure drop along the membrane (Pa) pressure drop at the outlet (Pa) pressure drop due to a contraction (Pa) pressure drop due to an expansion (Pa) volume flow rate (m3 s−1 ) Reynolds number (–) Reynolds number of Metzner and Reed (–) critical Reynolds number (–) universal gas constant (=8.3145 J K−1 mol−1 ) absolute temperature (K) velocity (m s−1 )

Greek symbols diameter ratio (–) ˇ ˙ shear rate (s−1 )  dynamic viscosity (Pa s) app apparent viscosity (Pa s) density of AS (kg m−3 ) AS w density of water (kg m−3 )  shear stress (Pa) o yield stress (Pa) Subscripts in inlet mem membrane out outlet References [1] S. Judd, The MBR Book, Elsevier, Amsterdam, 2006. [2] R. Rosenberger, K. Kubin, M. Kraume, Rheology of activated sludge in membrane bioreactors, Eng. Life Sci. 2 (9) (2002) 269–275. [3] De Clercq B., Computational Fluid Dynamics of Settling Tanks: Development of Experiments and Rheological, Settling and Scraper Submodels, Ghent University, Ghent, Belgium, 2003. [4] I. Seyssiecq, J.H. Ferrasse, N. Roche, State-of-the-art: rheological characterisation of wastewater treatment sludge, Biochem. Eng. J. 16 (1) (2003) 41–56. [5] G. Laera, C. Giordano, A. Pollice, D. Saturno, G. Mininni, Membrane bioreactor sludge rheology at different solid retention times, Water Res. 41 (18) (2007) 4197–4203. [6] A. Pollice, C. Giordano, G. Laera, D. Saturno, G. Mininni, Rheology of sludge in a complete retention membrane bioreactor, Environ. Technol. 27 (7) (2006) 723–732. [7] A. Pollice, C. Giordano, G. Laera, D. Saturno, G. Mininni, Physical characteristics of the sludge in a complete retention membrane bioreactor, Water Res. 41 (8) (2007) 1832–1840. [8] A.H.K. Garakani, N. Mostoufi, F. Sadeghi, M. Hosseinzadeh, H. Fatourechi, M.H. Sarrafzadeh, M.R. Mehrnia, Comparison between different models for rheological characterization of activated sludge, Iran. J. Environ. Health Sci. Eng. 8 (3) (2011) 255–264. [9] A.A. Moreau, N. Ratkovich, I. Nopens, J.H.J.M. van der Graaf, The (in)significance of apparent viscosity in full-scale municipal membrane bioreactors, J. Membr. Sci. 340 (1–2) (2009) 249–256. [10] J.A. Gil, P. Krzeminski, J.B. van Lier, J.H.J.M. van der Graaf, T. Wijffels, D. Prats, Analysis of the filterability in industrial MBRs, Influence of activated sludge

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