COMPRESSIBILITY OF MEMBRANE BIOREACTOR SLUDGE: MODELLING CAKE BUILD-UP AND SPECIFIC CAKE RESISTANCE

T. V. Bugge1, M. L. Christensen1, A. D. Enevoldsen2, P. E. Jørgensen2, Jessica Bengtsson3, Nicolas Heinen4 and K. Keiding1

1 Dept. of Biotechnology, Chemistry and Environmental Eng., Aalborg University, Sohngaardsholmsvej 57, DK-9000, Aalborg, Denmark

2 DHI Water, Environment and Health, Agern Allé 5, DK-2970 Hørsholm, Denmark 3 Alfa Laval Copenhagen A/S, Maskinvej 5, DK-2860 Søborg, Denmark

4 Alfa Laval Nakskov A/S, Stavangervej 10, DK-4900 Nakskov, Denmark

ABSTRACT

Membrane bioreactors (MBRs) are applied for municipal wastewater treatment with advantages such as high effluent quality and low footprint. However, membrane fouling is inevitable even at low fluxes. This study aims to provide a deeper understanding of fouling mechanisms and filter cake characteristics based on modeling and results from both full, pilot and lab-scale MBRs.

It has been found that MBR sludge is highly compressible and applying this knowledge enabled modeling of the cake build-up and changes in specific cake resistance in MBR systems with both synthetic and real wastewater feed. Thus, the established model might enable optimization of operation parameters such as pressure level and relaxation frequency from lab-scale experiments with the same sludge and feed conditions.

KEYWORDS

Membrane Bioreactors MBR, Simulation, Sewage Sludge Filters, Microfiltration, Filtration Plants

1. Introduction

Membrane bioreactors (MBRs) are applied for municipal wastewater treatment with advantages such as high effluent quality and low footprint (Judd 2008, Meng et al. 2009, Drews 2010). The technology has been improved in the recent years e.g. in terms of energy efficiency and hence the market share of MBRs is increasing both with respect to new wastewater treatment plants as well as retrofits of old plants etc. (Judd 2008). Yet, the technology can still be improved and one of the most significant operational issues remains fouling of the membranes. This leads to increasing operational costs due to the need of membrane cleaning procedures (Le-Clech et al. 2006, Wang et al. 2007, Meng et al. 2009, Drews 2010).

Fouling may be characterized theoretically according to the mechanism behind; that is pore blocking, gel-formation, cake formation etc. A more practical approach relates

the fouling to the operation of the plant, i.e. whether it is reversible with respect to the specific fouling mitigation or cleaning procedure applied (cross flow, air scouring, relaxation, backflush, chemical cleaning) (Metzger et al. 2007, Meng et al. 2009).

From the practical point of view, fouling that is irreversible to the mechanical cleaning procedures typically gets most of the attention because it needs chemical cleaning, which is more expensive and time consuming. However, there will be some interplay between cake formation, which is typically reversible, and the formation of irreversible fouling; e.g. the cake can serve as a secondary membrane retaining colloidal material that potentially could foul the membrane.

Cake fouling can be studied by conducting pressure-step (or flux-step) experiments on a short term basis (Bacchin et al. 2006). Thereby, long term processes such a biofilm formation can be suppressed and insight to the reversible cake formation process may be obtained.

From studies of sludge from 7 different European full scale MBR Wastewater Treatment Plants (WWTPs), it has been found that MBR sludge is a highly compressible material (Keiding et al. 2012). Yet, compressibility of the fouling layer has not been subject to much attention in studies of fouling mechanisms in MBRs for municipal wastewater treatment. In full scale MBRs, the transmembrane pressure (TMP) is typically increased gradually to maintain the flux as the fouling resistance increases over time. Thus, the pressure dependent behavior (compressibility) of the fouling layer should be considered in order to fully understand changes in filterability during operation.

In this study, cake build-up and changes in specific fouling resistance during short term experiments were simulated applying a model with compressibility in terms of a pressure dependent cake resistance. The model was applied to simulate data from TMP step experiments in a labscale MBR as well as short term operational data from a pilot MBR from Alfa Laval A/S with Hollow Sheet Membranes operated at Lundtofte municipal WWTP.

2. Method

2.1 Labscale MBR Setup and Operation

The lab scale experiments were carried out with sludge from a 350 L submerged MBR system operated continuously. The membrane system consisted of 10 Hollow Sheet membrane modules (PVDF polymer membranes, 425 x 350mm, Alfa Laval A/S, Denmark) stacked with a spacing of 7 mm between membrane surfaces. The total effective membrane surface was 2 m2. Constant air scouring was supplied from an aerator module placed beneath the membrane units. TMP was kept at a constant level controlled by the water level difference between the bioreactor and the permeate buffer system (see Fig. 1). TMP can be adjusted to eight different levels by the valves on the permeate buffer tank. Permeate flow was logged from a magnetic flowmeter (Sitrans FM Magflo 1100, Siemens, Germany).

Fig. 1: Simplified sketch of the labscale MBR setup with 8 valves for TMP regulation.

The TMP step experiments were carried out in a similar setup but in a 175 L bioreactor and with an effective membrane surface of 0.8 m2 (4 modules). The air

scouring was 20 L/min⋅m2.

The labscale MBR was started up with activated sludge from Aalborg Municipal WWTP East, concentrated to the mixed liquor suspended solids (MLSS) set point of 10 g/L used for continuous operation. The MBR was operated at a constant feed to microorganism (F:M) ratio of 0.1 kg COD per kg MLSS per day. The “wastewater” feed was a mixture of dog feed and fish meal with approx. 50% proteins, 30% carbohydrates and 20% fats distribution of a total 85% organics. The system was operated continuously for several months prior to the experiments in this study.

2.2 TMP Step Experiments – Labscale MBR

The applied TMP step method was based on the method proposed by van der Marel et al. (2009). A step length of 5 minutes was arbitrarily chosen for all steps, and the pressure was increased from 5 to 40 kPa by steps of 5 kPa (8 steps in total). In between the filtration steps from 10 to 40 kPa, a 5 minute reversibility step at 5 kPa was carried out.

The TMP experiments were performed with sludge from the continuously operated MBR. Experiments were done with MLSS levels of 6, 10 and 14 g/L. 6 g/L was reached by diluting with permeate and 14 g/L was reached by settling. Three repetitions of the TMP experiments were carried out in one day for each of the MLSS levels.

The sludge was analyzed with respect to pH, conductivity, particle size distribution, dissolved EPS and MLSS to ensure that the sludge conditions were similar for all the experiments.

2.3 Pilot MBR setup

The pilot MBR comprised two identical lines operated in parallel. Each line consisted of an anoxic compartment and an aerobic compartment, each with an active volume of 1.1 m3, and with recycle of sludge from the aerobic to the anoxic compartment. Each of the aerobic compartments were equipped with a submerged Hollow Sheet membrane module (Alfa Laval A/S, Denmark) made out of 10 panels (1020 x 540 x 5 mm with membranes of PVDF) stacked with a spacing of 7 mm between membrane surfaces. The total effective membrane surface for each line was 10 m2. Constant air

scouring (8 L/min⋅m2) and process aeration was supplied from two separate air blowers. TMP was kept at a constant level controlled by the water level difference between the bioreactor and the permeate buffer system. Each line was equipped with online measurement of permeate flow as well as online measurement of DO-concentration and temperature in the aeration compartments.

The pilot was operated with an MLSS of 5-7 g/L and continuously fed with presettled wastewater from Lundtofte Municipal WWTP with an F:M ratio varying in the interval of 0.07-0.1 kg COD per kg MLSS per day.

Fig. 2: Schematic flow diagram for one of the two identical lines of the pilot MBR plant.

The applied pilot data are constant pressure filtration data after chemical cleaning; i.e. continuous operation with 2 minutes relaxation for each 10 minutes filtration. During the modeled filtration period, MLSS was 7 g/L. The applied pressure was 5 kPa.

3. Model

The simple relation between flux (J) and applied pressure (P) is given in Eq. 1, where µ denotes liquid viscosity, Rm the resistance of the membrane and Rf the resistance resulting from fouling of the membrane.

� =�

�∙(���) (1)

Rf may be expressed by the specific resistance to filtration (α) as Rf = α⋅ω, where ω is the mass of filter cake per area of filter. Different approaches can be found in literature, concerning the pressure dependence of the specific resistance of filter cakes. Sørensen and Sørensen (1997) have shown that the relation given in

equation 2 applies for highly compressible materials such as biological sludge.

� = � (1 +�

��) (2)

α0 refers to the specific cake resistance at zero pressure and Pa is the pressure needed to double the specific resistance.

Cake compression is a result of both deformation of the particles and structural rearrangements of particles within the cake (Christensen 2006). These processes are time dependent and therefore a time constant k (creep constant) is included in the model so that the specific resistance is dependent of both pressure and time.

Cake build-up is governed by the balance between the permeate flux (J) that transports material towards the membrane and back transport (Jb) which results from e.g. diffusive, surface interaction and hydrodynamic shear forces (Bacchin et al.

2006). Cake build-up (��

��) is typically modeled using a mass transport balance based

on the permeate flux (J), steady state flux (Jss) and bulk particle concentration (C).

��

��= (� − ��� ∙ �(ω)) ∙ � (3)

Here f(ω) is a function that gives the relation between back-transport and ω. Different approaches have been applied for this function. Tarabara et al. (2004) suggested

that back transport increases proportionally with cake thickness, that is f(ω) = �

��� and

this approach was applied in the model.

Based on the equations devised above, the model algorithm shown in fig. 3 was applied.

Fig. 3: Algorithm applied for the simulations of flux profiles in the different experiments.

4. Results

The measured and simulated flux data for the performed TMP step experiments with sludge of 6 g/L are plotted in fig. 4. As seen the model fits the data very well. In some of the last reversibility steps, there is a small discrepancy between data and model in the beginning of the steps. This occurs because it takes some seconds to empty the buffer tank to the desired level in the experimental setup when the pressure is changed. This gradual change in TMP was not included in the model.

Fig. 4: Measured and simulated flux from TMP step filtration of sludge (MLSS 6 g/L) from the labscale MBR.

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70

TM

P (

Pa)

Flu

x (

LM

H)

Time (min)

Data

Simulated

TMP

1 32 4 5 6 7 8

The flux profile at the filtration pressures (i.e. TMP>5 kPa) can be divided into two stages. Initially a sharp flux decline (a) followed by a slow flux decline (b), until the pressure is lowered to the reversibility pressure (see fig. 5). It is noted that the rapid flux decline in stage a becomes more pronounced at the higher filtration pressures.

As shown in fig. 5, the slow flux decline in stage b is virtually identical for each filtration step, independent of the pressure applied. A similar but opposite trend is found for the reversibility steps. Here, each step starts with a rapid flux increase (c), followed by an almost constant flux level (d). The rapid flux increase at the beginning of each cycle is more pronounced when the preceding pressure has been higher.

Fig. 5: Flux profiles of step 2 to 8 of the TMP step experiment with 6 g/L sludge. The profiles can roughly be described in 4 stages marked by the indexes a-d; the specific resistance changes rapidly due to increased pressure (a), the cake build-up stage (b), gradual decrease in pressure and resulting increase in specific resistance (c) and cake removal (d).

Similar data and simulations were obtained for the TMP step experiments with sludge concentrations of 10 and 14 g/L.

The experimental and simulated flux from the pilot plant MBR at Lundtofte WWTP is presented in fig. 6. The presented data are 80 minutes filtration after a prolonged relaxation period (5h). In the first filtration period where the cake has been swept off due to the prolonged relaxation period, cake build-up occurs with a resulting steady flux decline. In the following filtration periods, where cake remains from the previous steps, the profile can be divided into two sections comparable to the profiles from the labscale step experiments. That is, a rapid flux decline within seconds followed by a steady flux decline resulting from additional cake build-up. The overall flux level of the filtration periods slowly declines over time due to fouling which is not removed in the two minute relaxation periods.

0

10

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60

0 1 2 3 4 5 6 7 8 9 10

Flu

x (

LM

H)

Time (min)

Step 2

Step 3

Step 4

Step 5

Step 6

Step 7

Step 8

a b c d

The model fits the data well in these experiments as well. However, as seen in the last filtration period in fig. 6, there is a small deviation in the flux level. This effect becomes more pronounced over longer time and is probably caused by irreversible fouling such as pore blocking, which is not included in this cake build-up model.

Fig. 6: Experimental and simulated flux for 80 minutes filtration after chemical cleaning of the MBR pilot at Lundtofte municipal WWTP. Filtration was operated at constant pressure with 10 minutes filtration periods followed by 2 minutes relaxation.

Table 1 lists the values of experimental and fitting parameters that were applied for the simulations of the flux data. Especially α0 and Pa differ in the different

experiments, whereas there are smaller variations with respect to Jss, ωss and k.

Table 1: Parameter values applied for the simulations of data from the three TMP step experiments with varying MLSS and the data from the pilot MBR at Lundtofte are listed below. The determined MLSS of the sludge was applied as the concentration C.

Labscale Labscale Labscale Pilot

C (kg/m3) 6 10 14 7 Rm (m-1) 8.5 10-11 8.5 10-11 8.5 10-11 4.0 10-11

µ (kg s-1 m-1) 1 x 10-3 1 x 10-3 1 x 10-3 1 x 10-3

Jss (m/s) 3.5 x 10-6 4.2 x 10-6 4.0 x 10-6 4.3 x 10-6

ωss (kg/m2) 0.03 0.05 0.09 0.1

α0 (m/kg) 1.5 x 1013 1.0 x 1012 7.0 x 1011 5.0 x 1012

Pa (kPa) 13 0.8 0.9 10

k 0.05 0.01 0.015 0.05

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80

TM

P (

Pa)

Flu

x (

LM

H)

Time (min)

Data

Model

TMP

5. Discussion

The key feature of the model is the close fit to the slowly decreasing flux in the filtration steps and the almost constant level in the reversibility steps for the TMP step experiments and also to the flux profiles from the pilot MBR.

From the flux profiles of the individual steps in fig. 5 it is seen that for all the high-pressure steps, the flux reached the same level within 30 seconds independent of pressure. Hence, the change in specific resistance of the cake occurring within the first seconds was increasingly higher as higher pressures were applied. The initial flux is high since the existing cake has a low resistance due to the low pressure of the reversibility step. As the cake is compressed at the higher pressure, the resistance increases and the flux declines. This decline is most significant at the higher pressures since the initial flow is higher at these steps and the cake is more compressed due to the higher pressure. Hence, the flux ends up at the same level even at the highest pressure due to cake compression.

The model fits the flux profiles from the pilot MBR as well but over time the flux level of the profiles declines and this slow decline could not be fully simulated by the model. This slow decline is a result of irreversible fouling, which could include e.g. gel layer, biofilm formation and pore blocking. Hence, it might be needed to include an irreversible factor in the model to be able to simulate operational flux data from full scale plants. This will be subject to further work.

As seen in Table 1, the applied values of fitting parameters vary between the different experiments. Comparing the values from the three TMP step experiments with varying MLSS, it is seen that a higher MLSS implied a higher value of α0 and a lower value Pa. The values from fit to the pilot scale data are very comparable to the values found for the labscale experiments with 6 g/L. These experiments were carried out with a MLSS of 7 g/L, so this indicates that it might be possible to use the model and labscale experiments with same sludge conditions to optimize operational parameters for full scale plants.

6. Conclusion

MBR sludge is highly compressible and applying this knowledge in terms of a pressure dependent specific resistance enabled modeling of the cake build-up and changes in specific cake resistance in MBR systems with both synthetic and real wastewater feed. Comparing the fitting parameters from simulations of the experiments with varying concentrations indicated that higher sludge concentrations result in a more compressible cake layer with lower initial resistance. Thus, the developed model might enable optimization of operation parameters such as TMP and relaxation frequency from lab-scale experiments.

Acknowledgements

This study was funded through the Danish innovation consortium MEMBIO (Danish membrane bioreactor technology), which is an innovation consortium supported by the Danish Ministry of Science and Technology.

The authors would like to thank Lisbeth Wybrandt, Henrik Koch and Kim Mørkholt from Aalborg University and operating and laboratory staff at Lundtofte Municipal WWTP for technical assistance.

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