Variation and prediction of membrane fouling index under various feed water characteristics

Transcription

1 Journal of Membrane Science 284 (2006) Variation and prediction of membrane fouling index under various feed water characteristics Chanhyuk Park a, Hana Kim b, Seungkwan Hong b,, Suing-Il Choi c a Water Environment and Remediation Research Center, Korea Institute of Science and Technology, P.O. Box 131, Cheongryang, Seoul , South Korea b Department of Civil and Environmental Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul , South Korea c Department of Environmental Engineering, Korea University, Seochang-dong, Jochiwon-up, Yeongi-gun, Choongnam , South Korea Received 5 December 2005; received in revised form 25 July 2006; accepted 27 July 2006 Available online 4 August 2006 Abstract Membrane fouling index such as silt density index (SDI) and modified fouling index (MFI) is an important parameter in design of the integrated RO (reverse osmosis) and NF (nanofiltration) membrane processes for drinking water treatment. In this study, the effect of various foulant characteristics on membrane fouling index was investigated systematically. As expected, the fouling index (both SDI and MFI) increased with increasing particle concentration. When organic matter was the primary cause of membrane fouling, the MFI based on cake filtration theory was not accurately measured due to internal fouling such as pore adsorption. The fouling index was determined mainly by particle characteristics when both particulate and organic foulants coexisted in the feed water. This observation was attributed to lessening of organic pore adsorption by particle cake layer formed on the membrane surface. Prediction of MFI by using Happel cell model for the hydraulic resistance of the particle cake layer was also performed. The effect of primary model parameters including particle size (a p ) and particle concentration (C 0 ), were accurately assessed without any fitting parameters, and the MFI values predicted by the model exhibited very good agreement with the experimental results Elsevier B.V. All rights reserved. Keywords: Membrane fouling index; Silt density index (SDI); Modified fouling index (MFI); Particle fouling; Happel cell model 1. Introduction The demand for high-quality water is continuously increasing throughout the world. As the availability of adequate drinking water resources is becoming scarce, membrane filtration has received considerable attention as an advanced water treatment technology to augment water supplies [1,2]. A critical issue in the successful application of membrane systems for drinking water treatment is the fouling which arises from specific interactions between membrane and foulants in the raw water [3 7]. Membrane fouling, an inevitable phenomenon in membrane processes, makes systems less efficient and reduces the economic viability of membrane processes [8,9]. Deposition and accumulation of foulants such as particles and organics on the membrane surface not only cause permeate flux decline with time, but also Corresponding author. Tel.: ; fax: address: skhong21@korea.ac.kr (S. Hong). deteriorates the permeate quality in many situations [10,11]. Although membrane fouling is affected by the operating conditions such as flux, recovery, and crossflow rate [12 15], the more fundamental cause for membrane fouling is properties of the feed water, i.e. the fouling potential of the feed water [16 18]. There is a pressing need for a reliable method to measure and predict the fouling potential of the feed water to membrane filtration systems [19,20]. Such a method can be used at the design stage to assess the pretreatment required and later to monitor the effectiveness and performance of a pretreatment system during plant operation. In RO/NF applications, the silt density index (SDI) and the modified fouling index (MFI) are the most widely applied method to evaluate the particulate fouling potential of the feed water. SDI and MFI are determined from simple membrane experiments. The required index values prior to conventional reverse osmosis (RO) and nanofiltration (NF) membrane treatment are given in Table 1 [21]. The utilization of fouling indices enables the engineers to determine the pretreatment requirements without conducting a pilot study which /$ see front matter 2006 Elsevier B.V. All rights reserved. doi: /j.memsci

2 C. Park et al. / Journal of Membrane Science 284 (2006) Table 1 General fouling index approximations for RO/NF processes Fouling index Range Application SDI 0 2 Reverse osmosis (RO) 0 3 Nanofiltration (NF) MFI 0 2 s/l 2 Reverse osmosis (RO) 0 10 s/l 2 Nanofiltration (NF) needs considerable time and expenses. Although these indices are the standardized parameters and widely used in engineering practices, they are considered to be unsatisfactory indicators that often fail to reflect the true fouling strength of the feed water [18,20]. More specifically, the membrane fouling indices using a membrane with 0.45 m pores are not reflecting various fouling mechanisms of RO/NF membranes. Thus, recent studies devoted their effort to improve the predictability of fouling indices by modifying fouling index experiments, particularly utilizing UF and NF membranes which have pores much smaller than 0.45 m [20,22,23]. However, prior to developing new fouling indices, it is very critical to fundamentally understand the variation of existing fouling indices under various feed water characteristics. The main objective of this paper is to systematically investigate the influence of colloidal particles, natural organic matter (NOM), and their combination on membrane fouling indices. This investigation is further developed to predict MFI using Happel cell model for the hydraulic resistance of the accumulated particles in the cake layer. The results from this study are expected to provide valuable information for the correct utilization of existing fouling indices and for the development of new improved fouling indices in the future. 2. Materials and methods 2.1. Colloidal foulants Commercially available silica (SiO 2 ) particles, SILNOS-3M and SILNOS-20M (ABCNanoTech, Seoul, South Korea), were used as model foulants for fouling index experiments. The sizes of model particles are 3 and 20 m. Size and shape of the particles were also verified by scanning electron microscope (Vega TS5130MM, TESCAN, Czech) imaging. The surface area of the colloidal silica, as measured by surface area/porosimetry analyzer (ASAP 2010 Analyzer, Micromeritics Corp., USA), was found to be 110 and 130 m 2 /g, respectively. Gravimetric analysis revealed the density of the particles to be 2.30 g/cm 3. The zeta potential of these particles ranged approximately from 20 mv at ph 3 to 40 mv at ph 10. The feed colloidal suspensions of different concentrations were prepared by dilution of the particle powder with deionized (DI) water (D , Easy Pure RO system, LabScience, South Korea) and tap water. The quality of tap water were measured to be ph 7.0 ± 0.2 (ph), 0.15 NTU turbidity, and 102 mg/l TDS concentration. For the feed colloidal suspensions, the ionic strength and hardness concentrations were adjusted by the addition of NaCl and CaCl 2, respectively. The temperature of feed suspensions during all experiments was kept at 20 C NOM foulants Commercial Aldrich humic acid (AHA) was chosen as a model NOM. AHA was purchased from Aldrich Chemicals (St. Louis, MO) and was received in a powder form. Stock solution (0.1 g/l) was prepared by dissolving humic acid in DI water and ph was adjusted using H 2 SO 4 and NaOH. All fouling index experiments were performed without any further purification. The organic concentrations of feed and permeate samples were measured using UV absorbance at 254 nm (DR5000, HACH, CO). The primary characteristics of AHA are summarized in Table 2. To determine the molecular weight distribution of organic samples, the high performance-size exclusion chromatography (HP-SEC) method was utilized [24,25]. Polystyrene sulfonate (PSS, Polysciences Inc., USA), with various MW values: 210, 1800, 4600, 8000, and 18,000 Da, was used to construct a calibration curve between column passing time and molecular weight. XAD-8 and XAD-4 resins (Supelite DAX-8, Amberlite XAD-4, Bellefonte, PA) were used for fractionating NOM into hydrophobic (XAD-8 adsorbable), transphilic (XAD- 4 adsorbable), and hydrophilic (neither XAD-8 nor XAD-4 adsorbable) components. The carboxylic acidity of the humic substances was measured using potentiometric titration with an autotitrator (702 SM Titrino, Metrohm, Switzerland). The humic solution was titrated with 0.05N NaOH using an automatic titrator. During the titration, N 2 gas was purged continuously into the reaction vessel to maintain a CO 2 -free environment. The carboxylic acidity was estimated from the net titration curve (i.e. humic titration blank titration) at ph 8.0. Phenolic acidity was titrated to ph 12.0 with a 0.05N NaOH solution using the above-mentioned automatic titrator SDI and MFI tests In order to evaluate feed fouling potential, the silt density index (SDI) measurement was performed by a standard method instructed in ASTM-D4189. Specifically, dead-end filtration at 207 kpa (30 psi), through a 47 mm diameter ( m 2 ) MF membrane (Millipore Corp., Bedford, MA) with an average Table 2 Aldrich humic acid (AHA) characteristics in terms of size, structure, and functionality Molecular weight (g/mol) 4170 Structure Hydrophobic (%) 39.1 Transphilic (%) 12.3 Hydrophilic (%) 48.6 Acidity Carboxylic (meq/g) 20.3 Phenolic (meq/g) 26.6

3 250 C. Park et al. / Journal of Membrane Science 284 (2006) pore size of 0.45 m was conducted on the feed. The MF membrane was fixed by silicone O-ring within in-line filter holder (Millipore Corp., Bedford, MA). The feed water was pumped to membrane within in-line filter holder at a constant pressure using a mini pressure regulator valve (14R113FC, Parker Hannifim Corp., MI). During each fouling index experiment, feed and permeate samples were collected and analyzed for UV 254 (DR5000, HACH, CO), ph (Orion 520A +, Thermo Electron Corp., MA), and turbidity (2100P turbidimeter, HACH, CO). The first two time intervals are the times to collect an initial 500 ml (t i ) and final 500 ml (t f ). The third time interval (t)is5, 10, or 15 min and is the time between the collection of the initial and final sample: SDI = [1 (t i/t f )] 100 (1) t The modified fouling index (MFI) is determined using the same equipment and procedure used for the SDI, except that the volume is recorded every 30 s over a 15 min filtration period [26]. The development of the MFI is consistent with Darcy s law. The MFI is derived in Eq. (2) and is defined as the slope of an inverse flow rate (1/Q) versus cumulative volume (V) curve: 1 = a + MFI V (2) Q 3. Results and discussion 3.1. Influence of feed water characteristics Effect of particle foulants Fouling index experiments were conducted using 3 and 20 m silica particles. Each of the fouling index experiments was repeated more than twice and the results were very reproducible. Particle concentrations were varied in the range of mg/l. Fig. 1(a) and (b) illustrates the variations of fouling index (both SDI and MFI) for particle size and concentration. The results showed that higher fouling index values were observed when filtering suspensions with smaller size and higher feed particle concentration. In colloidal suspensions of smaller particle size, fouling index increased more drastically when increasing particle concentration than those of larger particle size. The number of particles in the cake layer, for the same feed concentration, increases markedly with decreasing particle size. Thus, smaller particles provide larger hydraulic resistance to permeate flow than larger particles, resulting in larger fouling index Effect of NOM foulants Fouling index experiments were also conducted with humic acid solutions (i.e. 0 2 mg/l of AHA). The fouling index measurements as a function of organic concentration are presented in Fig. 2. As shown, SDI values increased slowly with increasing NOM concentration upto 1.5 mg/l, and then increased drastically. At high organic concentrations, membrane pores are rapidly blocked by pore adsorption, causing more organics captured and accumulated on the membrane during fouling index experiments. Fig. 1. Fouling index variations for 3 and 20 m silica particles: (a) SDI and (b) MFI. Another point to notice is that the MFI of organic-rich feed water was not accurately assessed because of the non-linearity of 1/Q versus V plot, which determines the MFI values. Typical plots showing this trend are presented in Fig. 3 for various organic concentrations. This observation is ascribed to complex mechanisms of organic fouling, which involve organic adsorption to membrane pores, and formation of a gel layer on the membrane surface Effect of NOM foulants at the presence of particles The influence of NOM foulants was further investigated at the presence of particle foulants. Fig. 4 presents the SDI values when filtering feed water containing both particle and NOM foulants. As shown, increasing organic concentration had no impact on fouling indices when particles were present in the feed water. As summarized in Table 3, SDI values of particle and organic combined were always lower than those calculated Fig. 2. SDI measurements under various feed organic (AHA) concentration.

4 C. Park et al. / Journal of Membrane Science 284 (2006) Fig. 3. Evaluation of MFI for water containing organic matter. Fig. 4. Effect of organic matter on SDI measurements at the presence of particles (3 m). from fouling index experiments of particle and organics only. This can be explained by organic adsorption on particle surface as schematically presented in Fig. 5. NOM adsorbs onto the surface of particles accumulated on the membrane surface, and as a result, NOM adsorption on membrane surface and/or within membrane pores was significantly reduced Prediction of MFI by using Happel cell model Theoretical model development In pressure-driven membrane filtration, suspended particles are transported to the membrane surface by the permeate flow. As particle accumulation continues, particle concentration near Table 3 Silt density index (SDI) measurements for particle, organic, and both combined Foulant SDI Particle (mg/l) Organic (mg/l) Particle (1) Organic (2) Combined a (1) + (2) b a Combined: SDI of particle + organic. b (1) + (2): SDI of particle only + SDI of organic only. Fig. 5. Schematic description of organic adsorption onto particles accumulated on the membrane surface.

5 252 C. Park et al. / Journal of Membrane Science 284 (2006) the membrane surface reaches its maximum value and a particle cake layer forms on the membrane surface. Particle accumulation in the cake layer provides an additional resistance to permeate flow and, hence, reduces permeate flux. Resulting pressure drops in the membrane filtration system can be expressed as: P = P m + P c (3) The applied pressure drop P is equal to the sum of the pressure drops across the membrane ( P m ) and the cake layer ( P c ). The pressure drop in the cake layer is associated with the frictional drag resulting from the flow of filtrate through the dense layer of accumulated particles: P c = kt D A S(θ)v w M c (4) Here, kt/d (=6πµa p ) is the frictional drag coefficient, k the Boltzmann constant, T the absolute temperature, D the particle diffusion coefficient, µ the solvent viscosity, a p the particle radius, v w the permeate flux, and M c is the total number of particles (per unit area) accumulated in the cake layer. The A S (θ) is a correction function accounting for the effect of neighboring retained particles and can be evaluated from Happel cell model [27]: A S = 1 + (2/3)θ 5 1 (3/2)θ + (3/2)θ 5 θ 6 (5) where θ =(1 ε) 1/3 is a porosity dependent variable, with ε being the porosity of the cake layer of accumulated particles. The total number of accumulated particles, M c, is related to the cake layer thickness δ c by: θ3 max M c = (4/3)πap 3, δ c = 3C c 4πap 3 δ c (6) where C c (= θmax 3 ) is the particle volume fraction in the cake layer and θ max is the particle volume fraction of the cake corresponding to maximum random packing (i.e. ε = 0.36). By combining with convective-diffusion equation [28], the flux decline observed during the membrane filtration of colloidal suspensions can be estimated by: ( v w = 1 + t ) 1/2v0 2πap 3, τ = DR2 m (7) τ 3kTA S (θ max )C 0 P Here R m is the membrane resistance and C 0 is the bulk particle volume fraction. Eq. (7) can be further simplified when t τ: ( v w = 1 3kTA S(θ max )C 0 P 4πap 3 t DR2 m ) v 0 (8) Fig. 6. Variation of fouling indices under various ionic strength (NaCl). Fouling index experimental conditions: particle size, 3 m; feed particle concentration, 50 mg/l Comparison between measured MFI and predicted MFI The model developed suggests that physical parameters, such as cake layer structure, particle and membrane characteristics, have a marked influence on the dynamic behavior of permeate flux decline. More specifically, the prediction of MFI using Eq. (9) is affected by A S (θ) correlated to porosity, particle size (a p ), and membrane resistance (R m ). In order to verify the effect of these parameters on MFI, membrane fouling index experiments were performed systematically, and experimental results were compared with theoretical values predicted by the model. A series of fouling index experiments using particles of 3 m in diameter were conducted first at three different concentrations of indifferent monovalent ( M NaCl) and divalent cation ( M CaCl 2 ). The results are presented in Figs. 6 and 7. As shown, feed solution chemistry had no impact on fouling index under identical feed particle concentration, which is generally expected for particles larger than 1 m. This observation led to the assumption that colloidal interactions play no significant role in particle accumulation on the membranes surface. Thus, the porosity of the cake layer formed is assumed to be a constant value, 0.36 (v/v), based on maximum random packing density of spheres (i.e. ε = 0.36 or θ max = 0.86, A S (θ max ) = ). After obtaining v w based on Eq. (8), the MFI can be determined by calculating the slope of 1/Q versus V graph as follows: 1 tf t=0 (v w A) = a + MFI t f t=0 (v w A t) (9) Fig. 7. Variation of fouling indices under various hardness concentrations (CaCl 2 ). Fouling index experimental conditions: particle size, 3 m; feed particle concentration, 50 mg/l.

6 C. Park et al. / Journal of Membrane Science 284 (2006) which prevented organics from adsorbing onto membrane surfaces and/or within pores. The MFI prediction model developed using Happel cell demonstrated that cake layer structure, particle characteristics, and membrane properties are the primary factors affecting the determination of MFI. The theoretical prediction without any fitting parameters showed a very good agreement with experimental observations, indicating the applicability of the model for particle suspensions larger than 0.45 m. Acknowledgement This study is supported by Saehan Inc., RO/NF membrane manufacturer in Korea. Fig. 8. Comparison between measured MFI and predicted MFI under various experimental conditions: particle size, 3 and 20 m; feed particle concentration, 0 50 mg/l; feed organic concentration, 0 2 mg/l. As shown in Fig. 8, the MFI predictions for both 3 and 20 m particles are in very good agreement with experimental observations, which demonstrated that the MFI of particle suspensions could be accurately assessed by the model developed without any fitting parameters. However, it should be mentioned that the application of this model is limited only to the cases in which cake formation is the primary fouling mechanism. The model cannot predict the MFI of feed water containing particles and organics that are small enough to enter 0.45 m pores of MF membrane used for fouling index experiments. For example, the model failed to estimate the MFI of feed water with organics as shown in Fig. 8. In order to measure the fouling strength of smaller particles and/or organics, recent studies [20,23] attempted to utilize the tighter UF and NF membranes in their fouling index experiments. 4. Conclusions Primary inferences from this research are summarized as follows: Current fouling indices including both SDI and MFI showed the impact of various particle characteristics on their measurements. Specifically, fouling indices significantly increased as particle size decreased under identical particle loading, which were similarly observed in many studies of RO/NF processes in the literature. When organic matter was the principal cause of membrane fouling, the MFI values were not accurately measured due to the nonlinear slope of 1/Q versus V plot, suggesting that the MFI assessment based on cake filtration theory failed to determine fouling strength of organic matter smaller than pores of the membrane used in fouling index experiments. At the presence of particles, the organic fouling potential may be underestimated by current fouling indices (both SDI and MFI) because of organic adsorption onto particle surfaces, References [1] E.M. Vrijenhoek, S. Hong, M. Elimelech, Influence of membrane surface properties on initial rate of colloidal fouling of reverse osmosis and nanofiltration membranes, J. Membr. Sci. 188 (2001) [2] X. Zhu, M. Elimelech, Colloidal fouling of reverse osmosis membranes: measurements and fouling mechanisms, Environ. Sci. Technol. 31 (1997) [3] V.T. Lahoussine, M.R. Wiesner, J.Y. Bottero, Fouling in tangential flow filtration: the effect of colloidal size and coagulation treatment, J. Membr. Sci. 52 (1990) [4] S. Lee, J. Cho, M. Elimelech, Combined influence of natural organic matter (NOM) and colloidal particles on nanofiltration membrane fouling, J. Membr. Sci. 262 (2005) [5] R.M. McDonogh, A.G. Fane, C.J.D. Fell, H.-D. Flemming, The influence of polydispersity on the hydraulic behavior of colloidal fouling layers on membranes: perturbations on the behavior of the ideal colloidal layer, Colloids Surf. A: Physicochem. Eng. Aspects 138 (1998) [6] E.M.V. Hoek, A.S. Kim, M. Elimelech, Influence of crossflow membrane filter geometry and shear rate on colloidal fouling in reverse osmosis and nanofiltration separations, Environ. Eng. Sci. 19 (2002) [7] S. Hong, M. Elimelech, Chemical and physical aspects of natural organic matter (NOM) fouling of nanofiltration membranes, J. Membr. Sci. 132 (1997) [8] A. Braghetta, F.A. DiGiano, W.P. Ball, Nanofiltration of natural organic matter: ph and ionic strength effects, J. Environ. Eng. ASCE 123 (7) (1997) [9] J. Cho, G. Amy, J. Pellegrino, Membrane filtration of natural organic matter: factors and mechanisms affecting rejection and flux decline with charged ultrafiltration (UF) membrane, J. Membr. Sci. 164 (2000) [10] E.M.V. Hoek, M. Elimelech, Cake-enhanced concentration polarization: a new fouling mechanism for salt-rejecting membranes, Environ. Sci. Technol. 37 (2003) [11] H.Y. Ng, M. Elimelech, Influence of colloidal fouling on rejection of trace organic contaminants by reverse osmosis, J. Membr. Sci. 244 (2004) [12] W.R. Bowen, A. Mongruel, P.M. Williams, Prediction of the rate of crossflow membrane ultrafiltration: a colloidal interaction approach, Chem. Eng. Sci. 51 (1996) [13] K.L. Chen, L. Song, S.L. Ong, W.J. Ng, The development of membrane fouling in full-scale RO processes, J. Membr. Sci. 232 (2004) [14] R.W. Field, D. Wu, J.A. Howell, B.B. Gupta, Critical flux concept for microfiltration fouling, J. Membr. Sci. 100 (1995) [15] L. Song, Flux decline in crossflow microfiltration and ultrafiltration: mechanisms and modeling of membrane fouling, J. Membr. Sci. 139 (1998) [16] C. Jucker, M.M. Clark, Adsorption of aquatic humic substances on hydrophobic ultrafiltration membranes, J. Membr. Sci. 97 (1994)

7 254 C. Park et al. / Journal of Membrane Science 284 (2006) [17] S.S. Kremen, M. Tanner, Silt density indices (SDI), percent plugging factor (%PF): their relation to actual foulant deposition, Desalination 119 (1998) [18] J.S. Vrouwenvelder, J.W.N.M. Kappelhof, S.G.J. Heijman, J.C. Schippers, D. Kooij, Tools for fouling diagnosis of NF and RO membranes and assessment of the fouling potential of feed water, Desalination 157 (1 3) (2003) [19] L. Song, G. Singh, Influence of various monovalent cations and calcium ion on the colloidal fouling potential, J. Colloids Interf. Sci. 289 (2005) [20] S.F.E. Boerlage, M.D. Kennedy, M.P. Aniye, J.C. Schippers, Applications of the MFI-UF to measure and predict particulate fouling in RO systems, J. Membr. Sci. 220 (2003) [21] L. Eaux, Water Treatment Membrane Processes, McGraw-Hill, New York, 1996, pp [22] S.F.E. Boerlage, M.D. Kennedy, M.R. Dickson, D.E.Y. El-Hodali, J.C. Schippers, The modified fouling index using ultrafiltration membranes (MFI-UF): characterization, filtration mechanisms and proposed reference membrane, J. Membr. Sci. 197 (2002) [23] S. Khirani, R.B. Aim, M.-H. Manero, Improving the measurement of the modified fouling index using nanofiltration membranes (NF MFI), Desalination 191 (1 3) (2006) 1 7. [24] J.C. Schippers, J. Verdouw, The modified fouling index, a method of determining the fouling characteristics of water, Desalination 32 (1980) [25] N. Her, G. Amy, H.-R. Park, M. Song, Characterizing algogenic organic matter (AOM) and evaluating associated NF membrane fouling, Water Res. 38 (2004) [26] N. Park, Y. Yoon, S. Moon, J. Cho, Evaluation of the performance of tight- UF membranes with respect to NOM removal using effective MWCO, molecular weight, and apparent diffusivity of NOM, Desalination 164 (1) (2004) [27] L. Song, M. Elimelech, Theory of concentration polarization in crossflow filtration, J. Chem. Soc., Faraday Trans. 91 (1995) [28] S. Hong, R.S. Faibish, M. Elimelech, Kinetics of permeate flux decline in crossflow membrane filtration of colloidal suspensions, J. Colloids Interf. Sci. 196 (1997)