Environ. Sci. Technol. 1998, 32, 75-81
Modeling and Experimental Verification of Pilot-Scale Hollow Fiber, Direct Flow Microfiltration with Periodic Backwashing S H A N K A R A R A M A N C H E L L A M , * ,† JOSEPH G. JACANGELO,† AND THOMAS P. BONACQUISTI‡ Montgomery Watson, 560 Herndon Parkway #300, Herndon, Virginia 20170, and Fairfax County Water Authority, Merrifield, Virginia 22116
A mechanistic model was derived for direct flow microfiltration (MF), one which lends more insight into the factors that control MF performance compared to a statistical curve-fitting procedure that is often used to estimate chemical cleaning intervals at pilot-scale. This theoretical approach to MF fouling was based on fundamental feed water, membrane, and cake characteristics and was shown to predict well pilot-scale experimental specific flux profiles when backwash effectiveness is used as a fitting parameter. Long-term experimental specific flux profiles obtained during the constant flux filtration of natural colloidal materials are reported under conditions typical of water treatment applications. Cakes formed during the filtration of untreated surface waters were found to be highly resistant and compressible. Longer chemical cleaning intervals (and higher values of the backwash effectiveness parameter) were obtained during the constant flux filtration of natural colloidal materials by a decrease in flux, feed water turbidity, and/or backwash interval.
Introduction In recent years, there has been an increasing interest in the use of pressure-driven membrane technology for environmental applications. In water treatment, microfiltration (MF) has been shown to be effective for the removal of particles, turbidity, coliform bacteria, Giardia, and Cryptosporidium (1). It also has the potential to be employed as a pretreatment technique for higher pressure membrane processes such as nanofiltration and reverse osmosis (2). In wastewater treatment, MF is being investigated as the solids separation step to improve the performance of activated sludge and upflow anaerobic sludge bed reactors (3, 4) and for the removal of immiscible organics from oil-water emulsions (5). One of the most significant barriers to the increased use of MF and ultrafiltration (UF) has been membrane fouling (6-8). A method of decreasing the rate of membrane fouling is to periodically reverse the direction of flow through the membrane. As a result of this procedure, called backwashing, a fraction of the flux lost during the filtration cycle is recovered by removing materials deposited near the membrane. Short* Corresponding author. fax: (703) 478-3375; email:
[email protected]. † Montgomery Watson. ‡ Fairfax County Water Authority. S0013-936X(96)01004-8 CCC: $14.00 Published on Web 01/01/1998
1997 American Chemical Society
term data on flux enhancement in MF and UF using backwashing are available from model systems of interest in beverage and biotechnology applications. To the authors’ knowledge, there is no systematic water treatment study available in the peer-reviewed literature on fouling control using backwashing for extended operational periods. Liquid and air backwashing procedures have been found to improve transmembrane fluxes during the crossflow filtration of yeast suspensions at the bench-scale (9, 10). UF membrane permeability was shown to be maintained at a maximum value with a backwash interval of approximately 5 min (11). Shorter backwash intervals resulted in a decrease in the rejection of model organic solutes. High-frequency backwashing (0.25-5 Hz) was found to improve permeate flux during the laminar ultrafiltration of 1% albumin at pH 7.4 (12). Flux enhancement was found to be primarily dependent on backwashing frequency. The magnitude of the backwash pressure was not found to have an effect on flux. Liquid backpulsing at short intervals (1-40 s) and at transmembrane pressures of 34 and 69 kPa were also found to improve permeate fluxes during cross-flow microfiltration of yeast suspensions (13). In the studies reported above, membrane fouling was evaluated over a short duration (order of minutes or hours) with the system operating in a cross-flow mode using model suspensions that formed relatively nonadhesive cakes. Because filtration was conducted under constant pressure, effects of cake compressibility on fouling were not evaluated. In contrast, many MF and UF systems for water and wastewater treatment are operated in constant flux, direct flow (dead-end) mode (2, 14). Economic (and environmental) considerations related to concentrate/waste disposal and membrane cost necessitates direct-flow operation for many MF and UF systems thereby ensuring a high feed water recovery. This is in contrast to higher pressure systems such as nanofiltration and reverse osmosis where cross-flow is the preferred mode of operation to reduce fouling. Also, particles and macromolecules in feed waters encountered in these applications span a wide range of size, composition, and physical, chemical, and biological characteristics. These materials may adhere to membrane materials and form compressible cakes. Hence, long-term specific flux (defined as the transmembrane flux divided by the applied pressure) data using feed suspensions typical of water and wastewater treatment are necessary to evaluate the feasibility of MF for these applications. MF design parameters in water treatment are often determined by conducting pilot studies. Specific flux profiles observed in pilot studies have been found to differ depending on feed water quality and operational parameters. Linear and/or exponential curve fits are often used to empirically model the kinetics of specific flux decline. The slopes and/ or the exponents of these empirical curve fits are interpreted as fouling rates and are used to estimate chemical cleaning intervals (15). The lack of a theoretical model for MF fouling for water treatment applications necessitates long and costly pilot studies to determine operational parameters appropriate for the particular raw water. A more fundamental model that provides insight into mechanisms of membrane fouling compared to an empirical curve-fitting procedure is warranted. Outside-in, hollow fiber MF technology has been used in numerous water treatment applications and has an installed capacity in excess of 200 million liters per day in the United States. Hence, this was chosen as the model system for this study. VOL. 32, NO. 1, 1998 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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In this paper, direct-flow MF using outside-in, hollow fiber membranes with periodic backwashing is analyzed to develop a model for the prediction of specific flux profiles. The theoretical framework is based on fundamental membrane, water, and cake characteristics. Long-term experimental specific flux profiles obtained at pilot-scale during the constant flux filtration of an untreated surface water are reported and compared to theoretical predictions. Values of a backwash effectiveness parameter are derived at different feed water turbidities, transmembrane fluxes, and backwash intervals.
Theoretical Analysis Cake Filtration. The permeate flux J (L T-1) of a fluid with absolute viscosity M (ML-1 T-1) during filtration under a transmembrane pressure ∆P (ML-1 T-2) is given by Darcy’s law as
J)
∆P 1 dV ) Am dt µ(Rm + R′c)
(1)
where Am (L2) is the effective membrane area, V(L3) is the cumulative filtrate volume, t(T) is the time of filtration, Rm(L-1) is the membrane resistance, and R′c (L-1) is the resistance of the cake formed by the particles rejected on the membrane surface. Cake Specific Resistance and Compressibility. The cake resistance is related to the specific cake resistance R (LM-1), the membrane area and the mass of cake deposited M (M) by
R′c )
(2)
RM Am
Therefore, R is the cake resistance normalized to the mass of materials deposited per unit membrane surface area. Under conditions of perfect rejection, when back-transport is negligible, the mass of deposited particles at any given instant is given by the product of the bulk volume fraction φo, the particle density ρ (ML-3), and the cumulative filtrate volume V (L3) produced up to that time. If the imposed transmembrane pressure is constant with time, a closed form solution to eqs 1 and 2 can be derived as
µRm µRρφoV t ) + V Am∆P 2A2 ∆P
(3)
m
Therefore, the slope of linear fits for data from constant pressure direct filtration experiments plotted as t/V versus V give the specific cake resistance if other physical parameters are known (7). The specific resistance of compressible cakes is modeled using a power law as follows:
R ) Ro∆Pn
(4)
where Ro is the specific resistance at unit pressure, and n is the compressibility index. Thus, the slope and intercept of linear fits to logarithmic plots of R at various pressures given n and Ro, respectively. For cakes formed on the outside surface of hollow fiber membranes, the resistance can be described by
Cake Growth and Membrane Fouling during Direct Filtration. During constant flux, deadend operation of hollow fiber MF membranes when filtrate flow is from the outside to the inside, the change in δc over time can be derived from a mass balance on the surface of a growing cake layer:
dδc Ro φo J ) dt (φc - φc) (Ro + δc) o
(6)
where Jo (L T-1) is the flux maintained at a constant value. Equation 6 accounts for changes in normal area with radial position for cakes formed on cylindrical surfaces and is valid for membranes that completely reject the particles forming the cake (6). The approach developed here is not exact because eq 6 does not account for possible decreases in the cake layer thickness (and associated increases in cake solidosity) due to compression with increasing pressure during the filtration cycle. However, membranes were backwashed periodically and pressure increases during each filtration cycle were small. Therefore, this error is not expected to be significant. For constant flux filtration, eq 6 can be integrated to obtain an explicit solution as given in eq 7.
δc )
x
R2o +
2φoRoJot - Ro φc - φo
(7)
Rejection of materials by the membrane results in an increase in the applied pressure necessary to maintain a constant flux. The increase in the applied pressure ∆P(t) to maintain the flux at Jo is obtained by combining eqs 1, 4, and 5:
[
(
Joµ Rm + Ro[∆P(t)]nρφcRo ln
)]
Ro + δc Ro
) ∆P(t)
(8)
Backwash Effectiveness. Because natural colloidal materials span a large size range (e.g., refs 17 and 18), a fraction of these colloids may penetrate membrane pores. Also, selected adsorptive interactions between natural organic matter and hydrophobic membranes are likely to be irreversible (19). Other particle-membrane interactions arising from hydrophobicity, van der Waals and electrostatic forces along with attachment and colonization of bacteria on membranes (20) can also result in inefficient backwashes. Hence, the efficacy of the backwashing procedure is likely to be less than 100% during the filtration of natural waters. Therefore, a backwash effectiveness term η can be defined based on the initial (Pi) and final pressures (Pf) during the filtration cycle to determine the applied pressure subsequent to backwashing (Pnew). η is defined as the ratio of the pressure recovered following a backwash to the pressure increase during the filtration period (eq 9).
Pf - Pnew η ) 100 Pf - Pi
(9)
(LM-1)
(
R′c ) RFφcRo ln
)
Ro + δc Ro
(5)
where φc is the volume fraction of solids forming the cake, Ro (L) is the outside radius of the hollow fiber, and δc (L) is the cake thickness (16). 76
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Materials and Methods Solution of Governing Equations. For each time increment, eq 7 was used to calculate the cake thickness (with φc ) 0.8). The root of eq 8 was then obtained using the bisection method to calculate the pressure. This procedure was repeated for discrete time increments for the duration of a filtration cycle (the time between two backwash events Tbw). The pressure following a backwash Pnew was calculated using the value assigned to η using eq 9 and was set equal to Pi for the next cycle. At the conclusion of every backwash, the membrane resistance was adjusted to account for the increase in pressure to maintain the flux Jo. This procedure was repeated for the
TABLE 1. Summary of Important Feed Water Quality Parameters Measured during the Pilot Study parameter turbidity TOC concentration heterotrophic plate count bacteria concentration total coliform concentration alkalinity pH bulk volume fraction × 106 temperature
range
number of observations
NTU mg/L mL-1
units
17 5.1 3950
2-170 3.3-6.6 800-22000
279 32 20
100 mL-1 mg/L as CaCO3
230 32 6.6 3.5 9
1-7286 12-56 6.2-7.0 1.1-9.8 1.3-26
17 22 201 17 135
°C
entire duration of MF operation. Results from these simulations are reported as variations in the instantaneous specific flux Js [tJo/∆P(t)] normalized to the initial specific flux Jso [tJo/∆P(0)]. Model predictions of the temporal variation in the specific flux were compared to experimental observations by obtaining least-squares fits using η as a fitting parameter. An optimization search procedure using the golden search technique was used to adjust η so as to minimize the sum of the squares of the residuals between experimental data and theoretical predictions of the normalized specific flux (21). Determination of optimal η that best predicts experimental specific fluxes may be more meaningful when numerical predictions are also compared with direct observations of specific flux and pressure behavior during the filtration cycle. Because pressure increases during each filtration cycle were small, this comparison was not possible at pilot-scale.
median
TABLE 2. MF Membrane and System Specifications parameter
value
material geometry flow direction operating configuration nominal bubble point nominal pore size fiber outside radius effective outside membrane area per module
polypropylene (hydrophobic) hollow fiber transverse flow (outside-in) direct flow (dead-end) 207 kPa 0.2 µm 275 µm 33.5 m2
Source Water. Water from a reservoir in northern Virginia was used without any chemical pretreatment. Table 1 lists some of the important raw water quality parameters measured during the study. This water is slightly acidic with moderate TOC and alkalinity concentrations. The median concentrations of heterotrophic and total coliform bacteria were 3950 mL and 230/100 mL, respectively. In dry periods, the turbidity was less than 10 NTU. Following high volume runoff events, the turbidity and TOC was found to increase, whereas the alkalinity decreased. Particles in the raw water in the size range 1-300 µm were counted using a bench top light blocking instrument (Met One, Model 9000 with LB1010 sensor, Grant Pass, OR). Number-based particle size distributions (PSDs) in the raw water were seen to be bimodal with peaks near 5 and 2 µm. Before a precipitation event, the fraction of small particles were high and the number length mean diameter was near 1.7 µm. As the turbidity increased following precipitation, PSDs shifted toward larger particles and the number length mean diameter was approximately 6.6 µm. Data from deadend filtration experiments using 0.1 µm disc membranes were plotted according to eq 3 to determine the resistance of cakes formed by natural colloidal materials. This was used in numerical simulations as an approximation for the actual cake resistance even though MF operating conditions have been shown to affect the resistance of cakes formed on membranes (22). These experiments were conducted at pressures in the range 69-207 kPa to determine cake compressibility indices, which were found to vary in the range 0.38-1.12. Thus, cakes formed during the constant flux operation of the microfilter offered greater resistance as the pressure was increased to maintain the flux. Membrane Pilot Plant and Operation. A skid-mounted pilot plant (Model 3M10C′, MEMCOR, Timonium, MD) was located at the site of Fairfax County Water Authority’s water treatment plant (WTP) in Lorton, VA. The characteristics of the membrane system are summarized in Table 2. Additional
FIGURE 1. Pressure profile during backwashing, s, Feed pressure, - - -, filtrate pressure, and - - -, transmembrane pressure (TMP). information on this MF system has been presented elsewhere (2, 14). A self priming centrifugal pump delivered raw water using a service line attached to the WTP’s main intake line. All experiments were conducted in the constant flux mode. A valve in the filtrate side was operated manually to change the pressure to maintain a constant flux. A centrifugal pump delivered water to the shell side of the pressure vessel from both ends. Because of the pressure differential, water was forced to the inside of the hollow fiber membranes (outsidein flow). The transmembrane flux was corrected to 20 °C by accounting for changes in water viscosity with temperature (23) using
J ) Qpe-0.0240(T-20)/Am
(10)
where Qp is the filtrate flow and T is the temperature in degrees Celcius. Backwashing Procedure. Figure 1 depicts the pressure profile during various steps in a typical backwash cycle. The entire sequence of events was automated using a programmable logic controller (2, 14). The backwash was started by closing the feed valve (step 1) and draining the water from the inside of the hollow-fibers by using compressed air (step 2). The whole system was then pressurized to 620 kPa (step VOL. 32, NO. 1, 1998 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 3. Summary of Pilot-Scale Experimental Conditions and Model Parameters run
start date
1 2 3 4
13-Sep-95 5-Oct-95 6-Nov-95 17-Jan-96
end date
run J0 at average time 20 °C Tbw T recovery turbidity O0 × Rm × 10-10 r0 × 10-13 (h) (L/m2/h) (min) (°C) (%) (NTU) 106 (cm-1) (cm/g)
n
η (%) 97.3 97.7 98.1 97.9
29-Sep-95 446 31-Oct-95 616 5-Jan-96 1305 21-Jan-96 100
33 39 37 50
30 20 20 20
25 18 7 3
94 91 88 90
5 18 17 70
3.64 6.82 4.09 5.45
2.90 3.20 3.41 3.54
8.22 4.38 7.31 3.65
0.65 0.65 0.65 0.65
5 22-Jan-96 24-Jan-96 43 6 24-Jan-96 18-Mar-96 1230
53 37
20 20
2 4
90 88
128 31
9.82 3.09
3.16 3.27
0.46 6.77
1.10 34.0 0.65 82.9
262 114 19 89 40 526
49 44 48 40 40 39
20 20 20 20 20 20
8 9 9 9 10 13
91 90 91 90 90 90
13 23 23 24 24 12
3.09 3.64 3.64 3.64 3.64 3.64
2.01 2.98 2.94 3.09 3.65 2.61
7.09 8.22 8.22 8.22 8.22 8.22
0.65 0.65 0.65 0.65 0.65 0.65
557
51
14.5 17
91
12
4.55
2.15
6.58
0.65 98.6
7 8 9 10 11 12
18-Mar-96 29-Mar-96 3-Apr-96 4-Apr-96 8-Apr-96 10-Apr-96
29-Mar-96 3-Apr-96 4-Apr-96 8-Apr-96 10-Apr-96 6-May-96
13 6-May-96 4-Jun-96
3). The backwash valves were then opened rapidly resulting in a large negative transmembrane pressure (step 4). Under these conditions, materials deposited near the membrane surface were dislodged. Feed water was then introduced from the bottom of the membrane module while compressed air supply was maintained for backwashing (step 5). In this step, particulate matter dislogdged during step 4 were scoured away from near the membrane. The compressed air supply was then turned off while maintaining a cross-flow of feed water (step 6). The remaining air in the shell and lumens was then purged by pumping in water (steps 7 and 8). Because membranes were hydrophobic, a high pressure was applied to wet the membranes prior to placing the membranes in normal filtration mode. In this rewet step, both feed and filtrate valves were closed and the system was pressurized to 620 kPa allowing the air bubbles trapped in membrane pores to dissolve in the water (step 9). Note that unlike step 4, where air was forced from the inside to the outside of the hollow fibers, the transmembrane pressure was not maintained at a negative value during this step. Finally, the feed and filtrate valves were opened, and the air purged from the system by pumping in water (step 10) before placing the unit back in filtration mode. Membrane Resistance and Cleaning. The resistance of the clean membrane to the filtration of water was evaluated using water that had been treated with MF and nanofiltration. The transmembrane flux was found to increase linearly in the range 0-69 kPa. Higher pressures could not be evaluated because of hydraulic limitations of the system. The membrane resistance Rm, was calculated to be equal to 3 × 1010 cm-1. The linearity of the pressure-flux profile indicated that the effects of membrane compaction were negligible in the range of conditions tested. Membranes were cleaned when the transmembrane pressure increased to approximately 124 kPa using a 1% NaOH solution mixed with surfactants heated to 40 °C. The membrane resistance measured at the start of each experiment was used in numerical simulations. Direct Filtration Experiments. Experiments were conducted over a 9 month period at fluxes in the range 33 e J0 e 53 L/m2/h and three different backwash intervals (Tbw ) 14.5, 20, and 30 min). A total of 13 experiments spanning a MF run time of approximately 5,350 h were conducted. Experimental conditions and model parameters measured during these runs are given in Table 3. Data from a total of seven experiments during which raw water quality did not change appreciably have been used formodel validation. Changes in feed water quality during other experiments 78
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95.0 93.0 70.6 92.8 96.4 89.0
comments included in analysis included in analysis included in analysis jump in turbidity from 20 to 170 NTU at 54 h included in analysis turbidity range:12-108 NTU; interruptions in water supply to pilot plant after first 50 h included in analysis included in analysis defective valve defective valve defective valve change in turbidity at first 55 h; turbidity range: 4-24 NTU included in analysis
resulted in changes in membrane fouling patterns and are not reported in this paper (see Table 3). Experiments 1-7 were conducted using two membrane modules. An additional module was employed for experiments 8-13. All model parameters except the backwash effectiveness and compressibility index were measured during each experiment. The median value of the compressibility index (0.65) was used for model fits to experiments during which n was not measured. Silt Density Index. Silt density index (SDI) tests were conducted using 0.1 and 0.45 µm membranes (Durapore, Millipore Corp., Malborough, MA) for a duration of 15 min at 207 kPa (24).
Results and Discussion Comparison of Theoretical Predictions with Experimental Observations. Table 3 summarizes experimental conditions along with values of the backwash effectiveness parameter that best describe specific flux data. Results of the first two experiments (runs 1 and 2) are shown along with model predictions in Figure 2, panels a and b, respectively. Run 1 corresponds to the longest backwash interval (30 min), lowest transmembrane flux (33 L/m2/h), and feed water turbidity (5 NTU) studied. Run 2 corresponds to a higher flux (39 L/m2/h), shorter backwash interval (20 min), and a higher feed water turbidity (18 NTU). The theoretical model fits experimental data well with high backwashing effectiveness (≈97.3% for run 1 and 97.9% for run 2). Cleaning intervals of approximately 450 and 600 h were observed under these conditions. Specific flux profiles measured at different feed water turbidities are shown in Figure 3. As the average feed water turbidity increased from 13 NTU (φo ) 3.10 × 10-6) to 128 NTU (φo ) 9.82 × 10-6), membranes fouled faster and the run length decreased from approximately 260 to 50 h. Under these conditions, the backwash effectiveness was calculated to decrease from approximately 95 to 34% with a 10-fold increase in feed water turbidity. The experiment at high turbidity (128 NTU) was conducted during a period corresponding to a high volume run-off event. During this time, the PSD of the feed water was observed to shift toward larger sizes. During such events, an increase in the total organic carbon of the water was also observed. The compressibility index was also observed to increase to 1.1. These observations suggest that η may decrease with deteriorating raw water quality.
FIGURE 4. Effect of backwash interval on specific flux. Experimental and model parameters are given in Table 3.
FIGURE 2. (a) Comparison of experimental and theoretical specific flux profiles (Jo ) 33 L/m2/h, Tbw ) 30 min). (b) Comparison of experimental and theoretical specific flux profiles (Jo ) 39 L/m2/h, Tbw ) 20 min).
FIGURE 5. Effect of transmembrane flux on specific flux. Experimental and model parameters are given in Table 3. a decrease in the feed water recovery will determine “optimal” MF operating conditions.
FIGURE 3. Effect of feed water turbidity on specific flux. Experimental and model parameters are given in Table 3. The effect of backwash interval on specific flux profiles is depicted in Figure 4. The time between chemical cleaning intervals was calculated to increase from 260 h to approximately 600 h as the backwash interval was decreased from 20 to 14.5 min. The decrease in the backwash interval corresponds to an increase in η from 95 to 98.6%. These data suggest that increased run lengths could be obtained by decreasing the time between two backwashes. Because the volume of water used for each backwash was a constant (approximately 40 L per module), the feed water recovery of the system will decrease as the backwash frequency is increased while maintaining the flux. Thus, a balance between an increase in the chemical cleaning interval and
Results of experiments conducted to determine the effect of transmembrane flux on membrane fouling when the feed water quality was relatively stable are shown in Figure 5. At a flux of 37 L/m2/h, approximately 50 × 103 L was filtered per unit membrane area before chemical cleaning (corresponding to a run length of 1305 h). The volume filtered per unit membrane area decreased to approximately 13 × 103 L/m2 as the flux was increased to 49 L/m2/h corresponding to a run length of 260 h. Again, for a given backwash interval, the system operated under a higher feed water recovery as the flux was increased. Thus, the feed water recovery increased from 88% for run 3 to 91% for run 7. Because shorter run lengths and lower filtrate volumes were achieved at higher fluxes, the “optimal” operating condition may be one of lower flux and feed water recovery (with a long chemical cleaning interval). It is important to note that experimental run lengths were of the order of weeks for many of these experiments. Thus, experimental data were collected for much longer durations compared to many previously reported studies on flux profiles with periodic membrane backwashing (9-13). Factors Affecting Backwash Effectiveness. More insight into the mechanism of colloidal fouling of microfilters was obtained by filtering conventionally treated water using 0.45 and 0.1 µm membranes. The SDI values calculated from these experiments are depicted in Figure 6 in the form of a box plot with the number of observations for each test under each box. The median value of the SDI measured using 0.45 µm membranes (5.1) was approximately 4 times higher than that measured using 0.1 µm membranes (1.2), suggesting VOL. 32, NO. 1, 1998 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 6. Effect of nominal membrane pore size on SDI. that the 0.45 µm membranes were fouled to a greater extent than the membranes rated at 0.1 µm. A two sample t-test 2 assuming unequal population variances (σ0.1µm ) 0.127; 2 σ0.45 µm) 1.898) was run to compare the SDI values using 0.45 and 0.1 µm membranes with the null hypothesis set as “no difference between the two population means”. The onetailed p value was determined to be 2.91 × 10-6 (critical t statistic ) 2.72 at 99% confidence). This low p value indicates that the SDI measured using the 0.1 µm membranes were significantly lower than those measured using 0.45 µm membranes. These membranes were examined under a scanning electron microscope (SEM) to determine the location of filtered materials as shown in Figure 7. Figure 7a shows an SEM image of a 0.1 µm membrane following a SDI test. It was observed that surface deposition (cake formation) was the important fouling mechanism for this membrane. No cake formation was observed in the 0.45 µm membrane following the SDI test (see Figure 7b). Thus, fouling in the 0.45 µm membranes may have been dominated by pore penetration. These observations suggest larger fouling potential (and lower backwash effectiveness) if particles in the feed water are able to penetrate membrane pores compared to those that form a cake on the membrane surface. Combinations of pretreatment processes such as flocculation, coagulation, and sedimentation or dissolved air flotation designed to reduce particle concentration are therefore expected to reduce membrane fouling only if the backwash effectiveness is not decreased. Values of η observed in pilot-scale MF experiments will depend on the interplay between inorganic, organic, and microbiological contaminants and the membrane surface. Pore penetration, adsorptive, and biological fouling may all contribute to observed effectiveness of backwashes. Future research needs to be oriented toward quantifying the factors affecting backwashing effectiveness. The analysis developed in paper may be particularly useful in assessing feasibility of MF based on feed water characteristics, in better designing pilot scale experiments, in better predicting chemical cleaning intervals when “calibrated” to specific test conditions, in evaluating MF pretreatment strategies and in developing more accurate treatment cost estimates. On being able to predict η, the flux and backwash parameters such as interval, pressure and duration can be adjusted to obtain longest possible run lengths. This approach will result in lower operational and maintenance costs and the design of a more efficient MF system.
Acknowledgments We thank Chris Lutz, Mark Marinacci, Charles Reiss, and Barbara Schauer for their help in conducting some experiments. Drs. Srinivas Veerapaneni and Mark Wiesner helped 80
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FIGURE 7. (a) SEM image of the surface of a 0.1 µm membrane following a SDI test. Note that a dense cake has been formed. (b) SEM image of the surface of a 0.45 µm membrane following a SDI test. The darker sections are the membrane pores and lighter sections are the deposited materials. Note that no cake was formed in this case. in obtaining SEM images. Dr. Issam Najm, Dr. Rhodes Trussell, and four anonymous reviewers provided comments on an earlier version of the manuscript. We gratefully acknowledge the assistance provided by Brett Alexander and Bob McCarthy during the pilot study.
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(20) Ridgeway, H. F.; Flemming, H.-C. In Water treatment membrane processes; Mallevialle, J.; Odendaal, P. E.; Wiesner, M. R., Eds.; McGraw Hill: New York, 1996; pp 6.1-6.62. (21) Press, W. H.; Teukolsky, S. A.; Vetterling, W. T.; Flannery, B. P. Numerical recipes: the art of scientific computing, 2nd ed.; 1992; p 963. (22) Chellam, S.; Wiesner, M. R. Environ. Sci. Technol. 1996, 31 (3), 819-824. (23) Weast, R. C., Ed. Handbook of Chemistry and Physics, 63rd ed.; CRC Press Inc.: Boca Raton, FL, 1983. (24) Brunelle, M. T. Desalination 1980, 32, 127-135.
Received for review December 4, 1996. Revised manuscript received July 22, 1997. Accepted October 3, 1997.X ES9610040 X
Abstract published in Advance ACS Abstracts, November 15, 1997.
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