Experimental Study and Computational Fluid Dynamics Simulation of

Jun 24, 2013 - Experimental Study and Computational Fluid Dynamics Simulation of a Full-Scale Membrane Bioreactor for Municipal Wastewater Treatment ...
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Experimental Study and Computational Fluid Dynamics Simulation of a Full-Scale Membrane Bioreactor for Municipal Wastewater Treatment Application Ershad Amini,† Mohammad Reza Mehrnia,*,† Seyyed Mohammad Mousavi,‡ and Navid Mostoufi† †

School of Chemical Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran Biotechnology Group, Chemical Engineering Department, Tarbiat Modares University, P.O. Box 14115-143, Tehran, Iran

‡

ABSTRACT: Membrane bioreactors (MBRs) are becoming more suitable alternatives for conventional wastewater treatment devices. The performance of a pressure-driven MBR is dominantly affected by the hydrodynamic conditions of the system. This study was conducted to investigate various hydrodynamic characteristics including shear stress, cross-flow velocity, and membrane fouling resistance, using computational fluid dynamics (CFD). Simulation of two- and three-phase flow for a flat-sheet submerged membrane module was carried out, and the results were compared with the experimental data. The CFD simulation was implemented to analyze the fluid-flow pattern, shear stress on membrane surfaces, and cross-flow velocity between membranes at various mixed liquor suspended solid concentrations in the bioreactor. It was shown that the cross-flow velocity plays an important role in the membrane fouling and determination of the critical particle diameter. To achieve an optimal operating condition, the critical particle diameter was calculated at different air flow rates and permeate fluxes. The CFD results showed that the outermost membranes are more prone to fouling because of the lower shear stress on their surface as well as the lower cross-flow velocity between them and the module wall. Moreover, the effect of the air bubble diameter on the air and liquid shear stress was investigated to determine an optimal bubble size.

1. INTRODUCTION Membrane bioreactors (MBRs) are a combination of biological treatment with physical separation using membranes.1 MBR technology has many advantages over conventional activated sludge (CAS) technology including the use of a high mixed liquor suspended solid (MLSS) concentration, increasing organic loading, the high quality of the effluent (permeate), and a reduction in foaming and settling problems. The implementation of membrane filtration leads to the elimination of a secondary clarifier.2−4 Although driving forces like strict legislation for sewage treatment and environment, local water scarcity, reduced investment costs, and limitation for using ground have encouraged the application of MBRs, their high operating cost compared to that of CAS technology is an impediment to the widespread application of MBR technology. This high operating cost is due to a high energy consumption for fouling reduction by air scouring.3 Therefore, it is important to optimize the parameters that can affect the efficiency of the filtration system. These parameters include hydrodynamic and mixing characteristics, air-bubble-size distribution, fluid flow, mass transfer, module of the MBR configuration, activated sludge, aeration for scouring and cleaning the membrane surface and providing oxygen for biological activities, and shear stress on the membrane surface.5−11 Monitoring these parameters in the MBR system is a formidable task or sometimes impossible because high cost of experiments, inaccessibility to all locations in the system, turbulent multiphase flow, and murky fluid. Therefore, computational fluid dynamics (CFD) simulation, which is a cost-effective tool, can shed light on the investigation and prediction of these characteristics.11−13 CFD is a theoretical method for simulation © 2013 American Chemical Society

that can provide an optimization tool and has the ability to predict the effect of reactor design features on hydrodynamics and the performance of the process. For instance, optimizing mixing characteristics can affect the energy input for aeration, which is a big proportion of energy consumption. For a tubular membrane, Ratkovich et al.14 studied the shear stress and velocity distribution around the gas slugs using CFD. They have shown that for high liquid and low gas flow rates the CFD result was in good agreement with the experimental data. Buetehorn et al.12 investigated a novel geometry in a hollowfiber submerged MBR (sMBR) using CFD. The fibers were moving with irregular arrangement. Because the energy input consumption of the aeration is high in sMBRs, the effect of irregular fiber arrangement can be important in the aeration efficiency and flow pattern. The fluid cross-flow velocity, reactor structure, and MLSS concentration play integral roles in the membrane fouling rate.1 The hydrodynamics near the membrane surface can have a drastic role in fouling reduction. Khalili-Garakani et al.15,16 investigated the shear stress on the membrane surface using CFD and membrane resistances by several experiments at various inlet air flow rates, liquid levels, and reactor configurations, and it was found that shear stresses have a high correlation with the resistance data. In another study, Brannock et al.17 investigated the mixing in two different membrane configurations. They concluded that the plug-flow condition is beneficial for pollutant removal, and for becoming close to the plug-flow condition, the aeration should be Received: Revised: Accepted: Published: 9930

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Figure 1. Microdyn-NADIR module configuration (left), structure of a membrane sheet (center), and membrane module arrangement (right).

cleaning, which is a difficult task compared to the outside configuration. The manufacturer has also recommended the assembly in a separate filter tank for large applications. The BIO-CEL module was submerged directly into the activated sludge. The permeate was drawn out by a vacuum. Filtration was performed from outside to inside. Treated wastewater was discharged from the permeate pipes to the permeate tank or simply discharged. During the filtration process, the activated sludge was dewatered and the sludge was removed from the module. For this reason, the BIO-CEL module was equipped with an additional aeration system (five air sapargers) located below the membranes. These spargers induced an upflow alongside the membranes and thus created a cross-flow. For installation on the floor of the tank, the module was equipped with spacers (bases), which guarantee a distance of about 15 cm from the floor. The full-scale MBR was located in the Ekbatan wastewater treatment plant, Tehran, Iran. A disadvantage of flat-sheet MBRs against hollow-fiber MBRs is that there is no backflushing,3 but Microdyn-NADIR GmbH has combined the advantages of hollow-fiber and flat-sheet MBRs so that the BIO-CEL module is backflushable. The filtration cycle was set to 8.5 min for filtration (drawing out the permeate flow) followed by 30 s for first relaxation, 30 s for backflush, and 30 s for a second relaxation time. After the membranes were arranged in the filtration tank, an adequate liquid return to the aeration tank was set based on drainage of the sludge. A recirculation ratio (recirculation volumetric flow to permeate volumetric flow) of about 4:5 was recommended by the manufacturer. The trans-membrane pressure (TMP) was monitored by a pressure gauge. Three different sludge concentrations with [MLSS] = 2.3, 4.9, and 7.6 g/L at a fixed permeate flux and a constant air flow rate (4.5 m3/h) were used in the experiments. In conventional filtration, it is common to fix the value of permeate flux and then measure the TMP.3 To validate the simulation, the local liquid velocity was experimentally measured in the downcomer at four points (161.2, 160.3, 158.9, and 157.5 cm distances from the bottom of the bioreactor) using a tracer. The measurements were carried out in a transparent solution (water) so that the tracer can be visually observed. Lapis powder in water was used as the tracer for its intense blue color. The tracer was injected in the downcomer at the interface of water and air. A Canon digital camera was used to record 10 s movies of the tracer movement. Each movie was broken down into frames of 0.04 s based on which local velocity in different areas was determined. Moreover, the average velocity of these points was determined by plotting the distance versus elapsed time between each point

lowered. Hence, CFD can provide a technique to optimize energy usage by estimating the flow conditions in the reactor. CFD provides a more precise solution compared with simple calculation (using equations or correlations).18 The main objective of this study was to simulate and predict various hydrodynamic characteristics in a flat-sheet sMBR using CFD for different sludge concentrations with experimental validation. This is the first study that describes the two- and three-phase CFD simulation in a full-scale flat-sheet sMBR applying different sludge concentrations combined with experimental findings.

2. MATERIALS AND METHODS 2.1. Experiments. The experiments were carried out in a flat-sheet sMBR. The setup was supplied by Microdyn-NADIR GmbH. The membrane module was an ultrafiltration unit, and the commercial module type was BIO-CEL-BC 10-10. The module contained 20 flat-sheet membranes with five air spargers made of flexible rubber membrane, 1 in. × 10 in. The distance between membranes was 8 mm. Figure 1 shows the module configuration with its dimension (left), structure of a membrane sheet (center), and membrane module arrangement (right). The operation and material data are listed in Table 1. The operating temperature and pH of the current MBR were 7 and 30 °C, respectively. Table 1. Operation and Material Data membrane material membrane area pore size air flow rate pH range operating temperature permeate flux backwash flux

poly(ether sulfone) 10 m2 0.04 μm 3−6 m3/h 2−11 5−55 °C 16 L/(m2·h) 30 L/(m2·h)

Figure 2 shows the full-scale MBR configuration. In this kind of configuration, the module was submerged in a separate filtration tank (outside configuration), which is advantageous over the submerged module directly into the aeration tank (inside configuration). The outside configuration provides easier cleaning of the membrane because of its accessibility. However, using an extra pump increases the energy demand.17 In the case of an inside configuration, the membrane module is immersed in the aeration tank. Thus, it is necessary to remove the membrane module from the aeration tank for membrane 9931

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Figure 2. Integration of the BIO-CEL module inside the filtration tank.

where f and τj are the drag function and particulate relaxation time, respectively:

(0.04 s). The slope of the line was the average velocity in this area (31 cm/s). 2.2. Mathematical Modeling. 2.2.1. Governing Equations. A Euler−Euler multiphase approach with two-phase (air and mixed liquor)9,10,15−17,19,20 and three-phase (air, mixed liquor, and biomass as the solid phase)11 flow was implemented for three-dimensional (3D) simulation to investigate the hydrodynamic characteristics of the MBR. A single pressure was used for all phases, and momentum and continuity equations were solved for each phase. In each simulation, a uniform bubble size was used.4 The continuity equation and the conservation of momentum for phase i are ∂ (αiρi ) + ∇·(αiρi vi⃗) = 0 ∂t

f=

τj =

n

(2)

where τ i is the ith phase stress−strain tensor, defined as ⎛ 2 ⎞ τi = αiμi (∇vi⃗ + vi⃗ ) + αi⎜λi − μi ⎟∇·vi⃗I ̅ ⎝ 3 ⎠

(3)

(4)

The interphase force R⃗ ji = −R⃗ ij depends on the friction, pressure, cohesion, and other effects, defined as

i=1

∑ αiρi i=1

(5)

(12)

n

vm⃗ =

∑i = 1 αiρi vi⃗ n

∑i = 1 αiρi

μt,m = ρm Cμ

αiαjρj f τj

(11)

n

ρm =

where Kji = Kij is the interphase momentum exchange coefficient. Each secondary phase j is assumed to form a bubble or droplets. The exchange coefficient for this bubbly type can be written as follows:11

Kji =

(9)

The following mixture properties were used:

n

∑ Kji(vi⃗ − vj⃗)

μi

∂ (ρ ε) + ∇·(ρm vm⃗ ε) ∂t m ⎛ μt,m ⎞ = ∇·⎜ ∇ε⎟ + C1εGk ,m − C2ερm ε ⎝ σε ⎠

1/2

j=1

ρi |vj⃗ − vi⃗|dj

(10)

The solids bulk viscosity was calculated from the equation of Lun et al.:21 ⎛Θ ⎞ 4 αiρi g0, ii(1 + eii)⎜ i ⎟ ⎝π⎠ 3

(8)

⎛ μt,m ⎞ ∂ (ρm k) + ∇·(ρm vm⃗ k) = ∇·⎜ ∇k ⎟ + Gk ,m − ρm ε ∂t ⎝ σk ⎠

T

∑ R⃗ ji =

18μi

For modeling the turbulence, the standard k−ε model was used.4,15,16,19,20 The standard k−ε model is a semiempirical model based on a model transport equation for turbulent kinetic energy k and its dissipation rate ε and is valid for fully turbulent flows. This model contains two equations for the mixture phase as follows:15

∑ R⃗ ji + αiρi (Fi ⃗) j=1

n

ρj dj 2

(1)

= −αi∇p + ∇ . τi + αiρi g ̅ +

(7)

where CD is the drag coefficient and Re is the Reynolds number:11 Re =

∂ (αiρi vi⃗) + ∇·(αiρi vv i⃗ i⃗) ∂t

λi =

C DRe 24

k2 ε

(13)

(14)

in which Cμ = 0.09, C1ε = 1.44, and C2ε = 1.92.22,23

(6) 9932

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Table 2. Grid Independence Data case number 1 2 3 4 5 6

grid size

grid type

air volume fraction (%)

average air velocity (m/s)

average liquid velocity (m/s)

time (s)

× × × × × ×

unstructured unstructured unstructured unstructured structured structured

4.43 4.53 4.60 4.26 4.76 4.79

0.35 0.36 0.36 0.34 0.37 0.37

0.37 0.40 0.40 0.36 0.43 0.43

17 17 17 17 10 10

2.3 1.4 9 6 1.5 2

106 106 105 105 106 106

Figure 3. Mesh diagram of the MBR geometry (symmetry view): (a) 3D view; (b) side view with magnification.

recommended by Buetehorn et al.12 In the most previous MBR studies, the air bubble size was considered to be in the range from 2 to 5 mm.4,9,24 Therefore, four constant air bubble diameters of 2−5 mm were used in the simulation of this work. In each simulation, a constant bubble size was used. The mixed liquor was presumed to be Newtonian and independent of the shear dynamic viscosity to simplify the overall model.12 When the MLSS concentration was less than 9.122 g/L (in this study, all three MLSS concentrations for experimental data were less than this value), the mixed liquor viscosity was calculated from eq 15:25

The equations for shear-stress analysis on the membrane surfaces are presented elsewhere.15 All of the simulations were carried out on four computers equipped with a 24-core processor and 16 GB RAM. 2.2.2. Numerical Implementations. The rate of fluid (mostly liquid) pumping from the aeration tank to the filtration tank was 0.6 m3/h, which is small compared to the sparger aeration intensity in the filtration tank (4.5 m3/h). Therefore, only the filtration tank was simulated, and pumping from the aeration tank to the filtration tank was neglected. The same approach has been adopted for CFD simulation of industrial or commercial MBRs.9,12 The geometry in the simulations was identical with that in the filtration tank in Figure 2. Because the flow between membranes and the shear stress on the membrane surfaces are important parameters, a fine mesh was generated between the membranes. However, to decrease the number of computational cells, only one quarter of the setup was considered as the simulation domain because of symmetry from both sides. The side and front faces of the tank, module, and membranes were assumed as symmetry planes. The projected area of the air spargers at the bottom of the system was considered as the air velocity inlet boundary condition. The boundary condition at the top of the filtration tank was set to open to atmosphere in order to let the air exit to the atmosphere. Because permeate extraction is negligible against the total flow in the system, the membranes were assumed to be rigid and the wall boundary condition was set for them.4,9−12,15,16 The membranes in flat-sheet modules are fixed. This fact eliminates the complexity of assuming membrane movement in the simulation. However, in hollow-fiber modules, it is better to consider the membrane movement as

μL = 1.05μ W e 0.08C

(15)

where μW and C are the water viscosity and MLSS concentration, respectively. The values of the viscosity for [MLSS] = 2.3, 4.9, and 7.6 g/L were 1.266, 1.559, and 1.934 mPa·s, respectively. The water and air viscosities were assumed to be 1.003 and 0.0179 mPa·s, respectively. The density of mixed liquor was evaluated from26 ⎛ MLSS ⎞ ⎟⎟ ρ = MLSS + 1000⎜⎜1 − ρDS ⎠ ⎝

(16)

where ρDS is the specific density of dry solids (biomass), which is equal to 1250 kg/m3.27 Three-phase simulation with [MLSS] = 7.6 g/L was carried out to determine in which parts of the bioreactor sludge particles accumulate. For the three-phase simulation, the mean size of 10 μm was chosen for the biomass particles. In this work, the commercial software FLUENT 6.3.26 was used for the simulations. The phase-coupled simple (PC9933

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SIMPLE) with pressure based solver was applied for the Eulerian multiphase simulations.20 The velocities were solved coupled by phases, but in a discrete method. This method solves the momentum- and pressure-based continuity equations simultaneously; thus, the rate of convergence improves compared to the segregated method, which solves the governing equations sequentially. The time step was 0.01 s, and the maximum iteration per time step was set to 100 to make sure that it was enough for convergence in each time step. The grid independence was checked to achieve the optimized number of elements. Six grid sizes of different types were used in the simulations. The results are shown in Table 2. It was found that, in the case of the structured grid, based on the number of iterations, the system reaches its convergence condition sooner than that in the unstructured grid. The air volume fraction of case 4 in Table 2 is considerably different from that in other cases, which means that the grid size in this case is not appropriate; thus, this grid was ruled out. Cases 3 and 5 were less computationally demanding. The mesh diagram (case 5) is shown in Figure 3. The geometry was meshed with a growth rate at the entrance and exit of the module as well as between membranes. More cells were chosen near the membrane surfaces because the flow pattern in these areas is of great importance.

Figure 4. CFD results of the gas holdup in the bioreactor for an air velocity of 1.5 m/s and constant MLSS.

presence of membranes and the resistance of membranes. In contrast to the results of this work, Prieske et al.9 showed that the gas holdup in the riser decreases with increasing bubble size. This difference may be related to the absence of internals (membranes) in their work. The presence of membranes in this work resulted in the entrapment of larger bubbles. The air volume fraction in the downcomer decreases with increasing bubble size. The larger bubbles that exited from the riser had a higher rise velocity compared with the smaller bubbles. Therefore, the majority of the exiting bubbles cannot recirculate to downcomer and exit to the atmosphere. 3.3. Effect of the Bubble Diameter and MLSS Concentration on the Shear Stress in Two-Phase Simulation. Effect of the bubble size and MLSS concentration on the air volume fraction in the riser and the average shear stress produced by air are illustrated in Figures 5 and 6,

3. RESULTS AND DISCUSSION 3.1. Validation of the Model. The results of the tracer study were compared with the simulation results for two mesh types, and calculated errors are shown in Table 3. According to Table 3. Validation Results of the Experimental Velocity against Simulation range of distance from the bottom of the bioreactor (cm) liquid velocity (cm/s) (experiment) liquid velocity (cm/s) (simulation)

error (%)

structured mesh unstructured mesh structured mesh unstructured mesh

157.5− 161.2 31 34 26 9 19

this table, a good agreement between the simulation and experimental data can be observed for a structured-type grid. In addition, for an air velocity of 1.5 m/s, the total gas holdup (4%) was measured experimentally by dividing the aerated liquid (water) height by the unaerated liquid height. The average of total gas holdup obtained by the CFD simulations for water and four bubble diameters (2−5 mm) in 1.5 m/s air velocity was calculated as 4.7%; thus, a reasonable agreement between the gas holdup from experiment and simulation was obtained. 3.2. Effect of the Bubble Diameter on the Volume Fraction in Two-Phase Simulation. Four different bubble diameters (2, 3, 4, and 5 mm) at identical air velocity (1.5 m/s) were used in the simulation. Figure 4 represents the effect of the bubble size on the gas holdup. As expected, a larger bubble size results in a lower air volume fraction in the whole bioreactor because the bubble rise velocity increases with increasing bubble size. However, in the riser, a larger bubble size results in a greater air volume fraction because of the

Figure 5. Gas holdup in the riser for different bubble sizes and MLSS concentrations.

respectively. According to Figure 5, increasing the MLSS concentration and a subsequent increase in the liquid viscosity lead to more bubble entrapment in the riser, and greater gas holdup within the riser is observed. This increase in the gas holdup of the riser causes an increase in the shear stress produced by air, as shown in Figure 6. It is known that the lift force acting on the bubble correlates positively with the third power of the bubble diameter.28 Larger bubbles receive a greater lift force, which moves them to the center of a space between two contiguous membranes, whereas smaller bubbles lie near membrane surfaces. Accordingly, larger bubbles produce lower shear stress compared to smaller ones (Figure 6). Increasing bubble size is associated with higher gas holdup in the riser and a greater lift force. The former increases while 9934

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Table 4. TMP, Fouling Resistance, and Critical Particle Diameter for Three MLSS Concentrations at a Constant Flux MLSS (g/L)

J (L/m2·h)

TMP (mbar)

R (×10−11 1/m)

dp,critical (μm)

2.3 4.9 7.6

16 16 16

42.5 55.0 73.5

9.53 12.34 16.49

0.44 0.52 0.63

Figure 6. Average shear stress on membranes produced by air for different bubble sizes and MLSS concentrations.

the latter decreases the shear stress. However, the net effect is a decrease in the shear stress, supporting the significance of the effect of lift forces. The effect of the bubble size and MLSS concentration on the average shear stress produced by liquid is shown in Figure 7. In Figure 8. Average cross-flow velocity between membranes for different MLSS concentrations.

reducing fouling by exerting lift and drag forces on sludge particles. These forces have a strong dependence on the fluid velocity, which causes removal of the sludge particles from the membrane surface.29,30 The critical diameter of the sludge particles can be obtained by solving the following equations:28 Fdrag,PF = 3πμd pJ Flift = 0.761 ×

the flat-sheet submerged membranes, there is an optimal bubble size that produces maximum liquid shear stress on the surface of the membrane, resulting in a more efficacious cleaning.4 It was shown in this study that this optimal size is 3 mm irrespective of the MLSS concentration (Figure 7). A comparison between Figures 6 and 7 reveals that the shear stress produced by air is negligible compared to that by liquid. 3.4. TMP, Critical Particle Diameter, and Liquid Shear Stress in Two- and Three-Phase Simulation. The TMP was measured experimentally, and its average for a 10-day period was used to calculate the fouling resistance (R):3 TMP μJ

τw1.5d p3ρ0.5 μ

(19)

where Fdrag,PF is the drag force exerted by the permeate flux. Particle deposition on the membranes occurs when Fdrag,PF > Flift. The wall shear stress (τw) in eq 19 was obtained from simulation results. The results of the critical diameter of sludge particles (CFD results of 3 mm bubble diameter and 1.5 m/s air velocity) are listed in Table 4. At the optimal bubble diameter (3 mm), the lowest (optimal) critical diameter was achieved. According to Table 4, the critical diameter increases with increasing MLSS concentration as well as lowering crossflow velocity. The particle-size distribution of the sludge was obtained using a video microscope (BMZ-04-DZ; Behin Pajouhesh Eng. Co., Iran), and then the digital photographs were processed. Figure 9 illustrates the particle-size distribution for the sludge with [MLSS] = 7.6 g/L. Hydrodynamic conditions such as the aeration intensity have a considerable impact on the particle-size distribution. Because the operating conditions such as the air flow rate for these three MLSS concentrations are the same (4.5 m3/h), the particle-size distribution is approximately the same for other MLSS concentrations used in this study. The sludge used in the MBR has two peaks in the particle-size distribution. The smaller one is related to colloids, bacterial cells, or cell fragments. This range is usually between 0.1 and 1 μm.26 Therefore, it is important to calculate the critical particle diameter at different operating conditions of the MBR system to reach an optimal condition. For this system, which has farreaching application in industry, the critical particle diameter using a three-phase simulation was calculated at different air

Figure 7. Average shear stress on membranes produced by liquid for different bubble sizes and MLSS concentrations.

R=

(18)

(17)

where J is the permeate flux. The results are shown in Table 4. It can be seen that increasing the MLSS concentration in a fixed aeration caused more fouling on the membrane. However, according to Figure 7, the higher the MLSS concentration, therefore, the higher the fouling associated with higher liquid shear stress. Figure 8 illustrates the average cross-flow velocities between membranes based on the CFD results in two-phase simulation. This figure demonstrates that the average cross-flow velocity decreases at higher mixed liquor concentration. Therefore, the cross-flow velocity plays a dominant role in 9935

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Figure 9. Particle-size distribution for the sludge with [MLSS] = 7.6 g/L.

velocities and permeate fluxes for an identical MLSS concentration (7.6 g/L). The results of a three-phase simulation are shown in Figure 10. Results of this work can

Figure 11. Flow velocity vectors at selected parts of the bioreactor. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

Figure 10. Critical particle diameter at different air flow rates and permeate fluxes.

be used to determine the appropriate air flow rate and permeate flux according to critical particle diameters by measuring the particle-size distribution. Figure 10 shows that critical particle diameters exceed the range of colloidal size for fluxes of more than 24 L/(m2·h) and 1 m/s air flow rate. The results of this study showed that the permeate flux (16 L/m2·h) is far enough below the critical permeate flux (30 L/m2·h). 3.5. Velocity Vectors and Shear-Stress Profile in TwoPhase Simulation. Figure 11 shows the flow velocity vectors at the bottom of the bioreactor. The fluid moves downward in the downcomer and recirculates in the riser. This alteration in the direction of the fluid flow is associated with displacement of a considerable amount of fluid toward the middle of the membrane module. This flow pattern leads to producing the strongest shear stress on the middle membranes (membrane no. 10 or 11), as can be seen in Figure 12a, and the weakest shear stress on the outer membranes (membrane nos. 1 or 20), as shown in Figure 12b. Furthermore, the highest and lowest velocities in the system occur at the center of the bioreactor (Figure 13a) and near the module wall (Figure 13b), respectively. 3.6. Volume Fraction Profile for Three-Phase Simulation. Figure 14 represents the profile of the volume fraction for the three phases including air, liquid, and biomass. Although the spargers were distributed uniformly below the module, a higher air volume fraction was observed at the center of the module. Upflow and downflow circulations in the riser and

Figure 12. Shear-stress profile produced by liquid flow on the (a) middle membrane and (b) last membrane (symmetry view).

downcomer, respectively, and a subsequent change in the direction of the flow at the bottom of the bioreactor lead to displacement of air toward the middle of the riser and explain the higher air volume fraction and lower liquid volume fraction at the center of the system, as illustrated in parts a and b of Figure 14, respectively. Figure 14c shows the biomass volume fraction, which tends to accumulate at the bottom of the system, in the downcomer, and near the module wall because of lower air sparging. Figure 15 shows the biomass volume fraction at various cross sections, attesting to higher accumulation of the biomass phase at the outer membranes as well as the lower parts of the system. 9936

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Figure 13. Liquid velocity profile (m/s) between (a) membrane nos. 10 and 11 and (b) the first membrane and module (symmetry view).

Figure 15. Volume fraction profiles for the biomass at different cross sections (symmetry view).

4. CONCLUSION Two- and three-phase CFD simulations were carried out to investigate the effect of different characteristics of full-scale MBR, such as the bubble diameter, MLSS concentration, aeration rate, and presence of the biomass phase, on the hydrodynamics and performance of the MBR. Simulation results were validated against the experimental data. It was shown that the cross-flow velocity between membranes at various MLSS concentrations is an imperative factor in reducing membrane fouling. Increasing the MLSS concentration causes higher fouling because of a lower cross-flow velocity, which can effectively prevent sludge deposition on the membrane. Thus, an additional aeration is needed at higher MLSS concentration to provide higher cross-flow velocity. It was also revealed that there is an optimal bubble diameter at which the maximum liquid shear stress is achieved regardless of the MLSS concentration. To have an optimum air flow rate and permeate flux, the critical particle diameter at which smaller particles than this value would be transported to the membrane was reported. Because the flow tends to move to the middle of the module, the outermost membranes feel the lowest shear stress. In addition, the space between the outermost membranes and module wall is higher compared to the space

between each membrane (24 vs 8 mm, respectively). In the three-phase simulation, it was found that the biomass accumulates near the module wall, predisposing the outermost membranes to fouling. Therefore, inserting a baffle between the outermost membranes and module wall will reduce the space between them. This recommendation may be considered as a solution to mitigate fouling by increasing the cross-flow velocity and shear stress.

■

AUTHOR INFORMATION

Corresponding Author

*Tel.: +98 21 6111 2184. Fax: +98 21 6695 4041. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

■

NOMENCLATURE

Symbols

C C1ε C2ε

MLSS concentration (g/L) constant constant

Figure 14. Volume fraction profiles for different phases: (a) air; (b) mixed liquor; (c) biomass (symmetry view). 9937

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Industrial & Engineering Chemistry Research CD dj eii Fdrag,PF F⃗i Flift f Gk,m g̅ g0,ii J k Kji p R R⃗ ji Re TMP vi⃗ vm⃗

Article

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drag coefficient bubble diameter of phase j (m) restitution coefficient drag force exerted by permeate flow (N) external body force (N) lift force exerted on the bubble or sludge particle (N) drag function generation of turbulent kinetic energy gravitation acceleration (9.81 m/s2) radial distribution function permeate flux (L/m2·h) turbulent kinetic energy (m2/s2) interphase momentum exchange coefficient (kg/s) pressure (Pa) membrane fouling resistance (1/m) interaction force between phases (N) relative Reynolds number trans-membrane pressure (Pa) velocity of phase i (m/s) mixture velocity (m/s)

Greek Letters

αi ε λi μi μL μt,m μW ρDS ρi ρm σk σε τi τj τw Θi

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volume fraction of phase i turbulent dissipation rate (m2/s3) bulk viscosity of phase i (Pa·s) viscosity of phase i (Pa·s) mixed liquor viscosity (Pa·s) turbulent viscosity (Pa·s) water viscosity (Pa·s) density of dry solids (kg/m3) density of phase i (kg/m3) mixture density (kg/m3) constant constant stress−strain tensor of phase i (Pa) particulate relaxation time (s) wall shear stress (Pa) granular temperature (K)

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Industrial & Engineering Chemistry Research

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dx.doi.org/10.1021/ie400632y | Ind. Eng. Chem. Res. 2013, 52, 9930−9939