Quantitative analysis of the irreversible membrane fouling of forward osmosis during wastewater reclamation: Correlation with the modified fouling index

Journal of Membrane Science 597 (2020) 117757

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Quantitative analysis of the irreversible membrane fouling of forward osmosis during wastewater reclamation: Correlation with the modified fouling index Min Zhan a, Gimun Gwak b, David Inhyuk Kim a, c, Kiho Park a, Seungkwan Hong a, * a

School of Civil, Environmental & Architectural Engineering, Korea University, 145, Anam-Ro, Seongbuk-Gu, Seoul, 02841, Republic of Korea Department of Research Planning, Seoul Institute of Technology, 37 Maebongsan-ro, Mapo-gu, Seoul, 03909, Republic of Korea Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney (UTS), 15 Broadway, NSW, 2007, Australia b c

A R T I C L E I N F O

A B S T R A C T

Keywords: Forward osmosis (FO) Irreversible fouling Quantitative analysis Fouling reversibility (FR) Modified fouling index (MFI)

In this study, the fouling behavior in the long-term performance of the forward osmosis (FO)-based osmotic dilution process was systematically explored by assessing the impact of irreversibility. The effects of physical cleaning, scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR) analyses, and foulant distributions were systematically investigated to control irreversible organic foulants on the membrane surface. It was observed that specific foulants in the secondary wastewater effluent impeded the long-term performance of FO fouling reversibility and final flux. A strong correlation between irreversible foulant con­ centrations, especially that of protein-like biopolymer, and physical fouling reversibility was observed, which caused continual irreversible fouling in the FO process. Multiple modified fouling indexes (MFIs) were measured to evaluate the fouling potential of the feed water with size fractionation. It was confirmed that the MFI-UF employing a 300 kDa ultrafiltration (UF) membrane is an appropriate water quality index for studying irre­ versible membrane fouling. It is expected that the application of MFI in predicting the fouling potential of FO intake water will significantly contribute toward the development of operational guidelines for FO plants.

1. Introduction Forward osmosis (FO) is an osmotically driven membrane process that can be used as an alternative to conventional pressure-driven membrane processes for desalting unconventional saline water re­ sources with enhanced sustainability [1]. Recently, the FO process in­ tegrated with reverse osmosis (RO) as an osmotic dilution process was proposed to attain two goals simultaneously: wastewater reclamation and seawater desalination. In this system, wastewater is primarily filtered through FO utilizing seawater as the draw solution, and thus, the osmotically diluted seawater is processed through the subsequent RO [2–7]. Consequently, the loads of contaminants in the wastewater, which jeopardize the sustainable operation of the RO membrane, can be reduced as the FO membrane performs the role of pretreatment of the wastewater. Furthermore, highly purified wastewater and seawater can be obtained with lower energy consumption as low hydraulic pressure is required in the RO stage to produce clean water from diluted seawater.

In the applications of FO, most previous lab-scale studies of organic fouling on the FO membrane surface caused by wastewater have investigated the fouling behavior in terms of fouling reversibility (FR). The fouling caused by the organic impurities during FO was shown to be readily mitigated through the introduction of various operational methods such as higher cross-flow velocity, flow pulsation, and intro­ duction of air bubbles. A relatively good FR as high as 60–100% was observed with the fouling mitigation of FO system through physical flushing at high cross-flow velocities when various model foulants were loaded in synthetic wastewaters: single fouling (i.e., alginate, humic acid, bovine serum albumin (BSA), colloidal silica, gypsum scaling, etc.) and combined fouling (i.e., alginate þ colloidal silica, alginate þ BSA, etc.) [3,8–14]. The lab-scale works on FO revealed that the osmotic membrane is more durable to membrane fouling and the cake/gel layer loosely formed on the membrane surface can be effectively removed [3, 9,15]. However, notably, the implementation of this FO-RO dilution process is still considered to be hampered by the potential irreversible

* Corresponding author. E-mail address: [email protected] (S. Hong). https://doi.org/10.1016/j.memsci.2019.117757 Received 20 September 2019; Received in revised form 21 November 2019; Accepted 15 December 2019 Available online 19 December 2019 0376-7388/© 2019 Elsevier B.V. All rights reserved.

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membrane fouling over a long period, as the organic pollutants con­ tained in real wastewater are complex in nature [16,17]. The dissolved organic matters in secondary wastewater effluent, commonly known as effluent organic matter (EfOM), mainly comprise proteins, carbohydrates, and natural organic matter (NOM) [1,7]. Earlier studies have suggested hydrophilic, dissolved or colloidal organic matters, namely proteins and carbohydrates, to be the primary cause of irreversible fouling during membrane-based wastewater treat­ ment processes [18–22]. In recent studies, Choi et al. reported that hy­ drophilic organic macromolecules, referred to as biopolymers, in impaired water strongly adhered to the FO membrane surface and remained, thus forming an irreversible fouling layer on the membrane surface during the FO-RO hybrid process [4]. Notably, the irreversible fouling caused by adsorption and entrapment of those dissolved organic matters into the membrane pores has an adverse influence on the long-term operation of FO-RO. Hence, characterization of organic con­ stituents in wastewater effluent should be analyzed not only qualita­ tively but also quantitatively, to understand their contribution to membrane fouling. Thus, in this study, a quantitative correlation anal­ ysis of the importance of organic components is performed by incor­ porating the concept of FR for simulating the FO irreversible fouling and long-term performance. Furthermore, to ensure the sustainable operation of FO-RO, it is essential to measure the fouling potential of organic matters in real wastewaters for predicting irreversible membrane fouling prior to the application of FO membrane. Over the past few decades, as an industrial standard, fouling indexes (i.e., silt density index (SDI) and modified fouling index (MFI)) have been utilized as a water-quality sensor to determine colloidal or organic fouling propensity and predict the fouling potential of RO systems [23,24]. However, several limitations of on the SDI, especially related to its measuring method, which does not accu­ rately represent the actual RO operating condition, have been noted, and recent studies reported that the MFI measurements employing ultrafil­ tration (UF) membranes with smaller pore size were developed to determine the fouling potential of smaller colloids such as NOM, in addition to the microfiltration (MF) membrane used in SDI measure­ ments [25–28]. In contrast to the standardized fouling index proposed for the RO process, studies on the quantification of the FO fouling po­ tential through a standard fouling index have been rarely performed. Therefore, an assessment to verify whether the MFI-based fouling index could be related to the actual FO fouling process is necessary to develop the design and operational guidance for FO. In this study, real secondary wastewater effluent collected from a domestic wastewater treatment plant located in Guri-si, Gyeonggi-do, Korea was used to examine the fouling propensity during the osmotic dilution process over a long period. Physical cleaning methods and membrane surface morphology analyses were systematically performed to assess the impact of irreversible foulants. In addition, bench-scale MF/UF stepwise filtration experiments that can identify the particle size distribution of irreversible organic foulants were carried out to control the contaminants causing irreversible membrane fouling. Sub­ sequently, quantitative analysis was performed based on the relation­ ship between the concentrations of different foulants and the FR from the results of FO tests. Finally, multiple MFIs were measured to correlate the fouling potential of the feed wastewater with size fractionation. Further implications of MFI fouling indices were delineated through this study, and the observations were utilized to provide new insights into real-time monitoring of the FO system performance for its sustainable operation.

Korea (37� 00 1100 N, 126� 270 1200 E)). Secondary wastewater effluent (SE) was obtained from a domestic wastewater treatment plant (using the Modified Ludzack Ettinger (MLE) biological process combined with coagulation in Guri-si, Gyeonggi-do, Korea) and used as the feed solu­ tion. The water qualities of the SEa and SW are listed in Table 1. The collected samples were stored at 4 � C and used in the fouling experi­ ments within 2 weeks. 2.2. Lab-scale FO experiments All FO experiments were conducted in lab-scale as described else­ where [12,29–31]. The commercially available flat-sheet FO membrane employed in the fouling runs was purchased from Porifera, Inc. (PFO-100, Hayward, CA, USA). The membrane coupon with an effective area of 20.02 cm2 was rinsed with deionized (DI) water several times and inserted in the FO cell (rectangular channels: 7.7 cm in length, 2.6 cm in width and 0.3 cm in depth). The temperature of both the feed and draw solutions was maintained at 20 � 1.0 � C with a cross-flow velocity of 8.54 cm/s. Variable-speed gear pumps (Cole-Parmer, IL) were used for the recirculation of 2 L of both the feed and draw solutions in FO. 2.2.1. Continuous FO fouling test As the FO system exhibits superior fouling controllability, a longterm continuous lab-scale FO test was performed beforehand to di­ agnose its ability to utilize wastewater. The FO tests were carried out by repeating every five operation cycles in two experimental protocols, 1) no cleaning protocol: after finishing every cycle, the FO experiment was resumed with new feed and draw solutions without any cleaning pro­ tocol; a 4 mM single NaCl solution simulating the same total dissolved solids (TDS) concentration of the secondary wastewater effluent (SEa) was prepared for the baseline experiment to rule out the influence of dilution of the draw solution; 2) physical cleaning protocol: hydraulic flushing (HF) was performed with DI water to control membrane fouling by increasing the cross-flow velocity to generate the shear force; the HF cleaning method was performed after every FO fouling cycle for 30 min each by increasing the cross-flow velocity to 25.62 cm/s, which was three times the velocity of normal FO operations. After cleaning, FO was resumed with new feed and draw solutions, the cross-flow velocity was reduced to its initial level (8.54 cm/s), it took about 30 min to stabilize the recovered water flux, which was determined to assess the fouling reversibility. The water recovery rate during all the FO tests was fixed at 50%, and the operation time for the entire five cycles was 260 � 5 h.

Table 1 Main characteristics of secondary wastewater effluent (SEa) (domestic waste­ water treatment plant using BNR system, Guri-si, Korea.) and raw seawater (SW) (from Yellow Sea near Samgilpo Port, Seosan-si, Korea.). Water quality pH TDS Suspended solids (SS) COD T-N T-P TOC SO24 Cl NO3 Mg K Na Ca Si Al

2. Methods and materials 2.1. Source water The FO experiments were carried out with a seawater (SW) draw solution collected from the Yellow Sea (near Samgilpo Port, Seosan-si,

a

2

N.A.: Not available, assuming ‘0’.

Concentration (mg/L) SEa

SW

7.08 251.9 7.2 18.5 13.1 0.73 8.7 44.9 88 9.7 7.29 13.5 55.3 39.6 11.1 3.69

7.8 30600 N.A.a 2.0 0.2 0.03 3.4 2480 20200 1.4 1270 370 9550 394 0.3 0.04

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Journal of Membrane Science 597 (2020) 117757

2.2.2. FR quantification test In this experimental protocol, the continuous FO test was repeated every four operation cycles without any cleaning protocol, each cycle was successively repeated when the water recovery reached 50%, and the time taken by the four operation cycles was approximately 208 � 3 h. After completing the fourth operation cycle, HF was performed with DI water for 30 min by increasing the cross-flow velocity to 25.62 cm/s. Subsequently, a new feed solution (i.e., the same pre-filtered permeate water) and draw solution were added to the feed and draw reservoirs, respectively, to measure the flux recovered by physical cleaning.

USA). All the filtration experiments were conducted under dead-end filtration mode, and the membrane flux was recorded every 5 s by using an electronic balance connected to a personal computer via HyperTerminal Software [33]. The concentration of dissolved organic groups (i.e., mixture of proteins, carbohydrates, and humic substances) in raw SEa and permeate in the cell were measured at the end of the filtration experiment. The proteins and humics were determined ac­ cording to the modified Lowry method [34] by using BSA (Sigma Inc., USA) and humic acids (Sigma Inc., USA) as standards, respectively. The carbohydrate content was measured using the phenol method with glucose as the standard [35].

2.3. Analytical methods

2.5. Modified fouling index (MFI) analysis

2.3.1. Water quality analysis Typical wastewater characteristics, including pH, TDS, suspended solid, chemical oxygen demand (COD), total nitrogen (T-N), total phosphorus (T-P), sulfate (SO24 ) and nitrate (NO3 ) were measured by a water analysis institution (Chung-Myung Environmental CO. LTD, Korea). The concentration of total organic carbon (TOC) was determined using a TOC analyzer (TOC-V, Shimadzu, Japan). Cation analysis was conducted using inductively coupled plasma optical emission spectros­ copy (ICP-OES) (iCAP 6300, Thermo Scientific, USA).

Instead of monitoring various water quality parameters, we devel­ oped a method to verify the applicability of MFI to evaluate the mem­ brane fouling potential of SE feed water. All the MFI experiments under constant pressure mode were conducted using the lab-scale multiple membrane array system (MMAS) described in our previous reports [25, 36]. Two MF membranes (0.45 μm and 0.1 μm, Millipore Corp., USA) and three UF membranes (300 kDa, 100 kDa and 10 kDa, Millipore Corp., USA) were selected, and membranes of diameter 47 mm (3.8 � 10 4 m2) were designed for dead-end filtration during the MFI tests [24, 27]. The concepts of both MFI-MF and MFI-UF are derived based on the cake filtration theory without cake compression (see Supporting infor­ mation for illustration of the MFI measurement principles under con­ stant pressure, Fig. S1) [37]. Under constant pressure, both MFI-MF and MFI-UF are defined as the slope of the linear portion of the t/V and V filtration curve, where t refers to the filtration time (s) and V is the filtrate volume (L). The MFI can be calculated using Eqs. (2) and (3) at dead-end measurement:

2.3.2. Membrane fouling characterization To elucidate the membrane fouling mechanisms, the fouled FO membrane from each fouling test was removed from the membrane cell and the surfaces of the membranes were observed and analyzed using scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX, Hitachi S-4800, Japan). The functional groups of the fou­ lants attached on the FO membrane surfaces were detected using com­ bined Raman Fourier-transform infrared spectroscopy (FTIR) spectrometer (LabRam Aramis IR2, Horiba Jobin Yvon, USA). Liquid chromatography-organic carbon detection (LC-OCD, DOC-LABOR, Ger­ many) analysis of the organics obtained from the FO membrane surface was also performed after physical cleaning for identifying the substances primarily responsible for the irreversible membrane fouling. More detailed information on the LC-OCD system can be found elsewhere [2, 3,32].

Jxf � 100 Jxf

μI 2ΔPA2

(3)

where ΔP is the applied transmembrane pressure (Pa), μ is the solution viscosity (Pa∙s), I is the resistivity (m 2), Rm is the membrane resistance (m 1), and A is the membrane surface area (m2).

2.4.1. Fouling reversibility During FO, interactions between the membrane and the inorganics or organics in feed water cause membrane fouling and consequently a decrease in the membrane flux. We define reversible fouling as fouling that can be removed with physical cleaning; therefore, FR is defined as the ratio (%) of water flux recovered by physical cleaning to the initial flux. Irreversible membrane fouling occurs when physical cleaning does not restore the original flux value and is caused by more or less per­ manent deposition of particles on the surface of the membrane, which is characterized by a longer-term decline in the flux [3,5]. The change in FR is calculated using Eq. (1). Jxr Jxi

(2)

MFI ¼

2.4. Quantitative analysis of the irreversible membrane fouling of FO

Fouling reversibility ðFRÞð%Þ ¼

t μR m μI ¼ V þ V ΔPA 2ΔPA2

2.6. Statistical analysis Each variable analyzed in this study was subjected to a one-way analysis of variance (ANOVA) performed with the MIXED procedure of SAS software (SAS Statistical System Software, v9.4, SAS Institute Inc., Cary, NC, USA). Statistically significant differences were consid­ ered at p-values <0.05. To examine the relationships between the three variables of TOC, foulants concentration in the SE feed (proteins, car­ bohydrates, and humic substances) and FR values, we used regression and correlation analyses, using a p-value of 0.05 to determine significance.

(1)

3. Results and discussion 3.1. Fouling behavior on the FO membrane during wastewater reclamation

where Jxi is the initial water flux before fouling (virgin membrane) (LMH), Jxf is defined as the final fluxes of each operational cycle (LMH), and Jxr indicates the flux recovered by physical cleaning (LMH).

After a successive operation for five cycles, the fouling behavior in the FO-RO osmotic dilution process was examined by performing labscale fouling runs. Fig. 1(a) presents the normalized initial and final water fluxes in the long-term FO tests using a SEa feed solution. The initial and final fluxes in the baseline experiments were also provided to observe the influence of dilution of the draw solution. After 50% of the feed water volume was extracted by the draw solution in each cycle, a

2.4.2. Irreversible foulants distributions To identify the particle size distribution of irreversible foulants causing membrane fouling futher, SEa was filtrated sequentially through a series of porous MF membranes (pore size: 8 μm, 5 μm, 1.2 μm, 0.45 μm and 0.1 μm, Millipore Corp., USA) and UF membranes (molecular weight cut-off (MWCO): 300 kDa, 100 kDa and 10 kDa, Millipore Corp., 3

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Journal of Membrane Science 597 (2020) 117757

4000–750 cm 1, in Fig. 3(d), showed that new and strong absorption bands appeared on the fouled FO membrane at approximately 1637 cm 1, which were ascribed to the stretching vibration of saturated C–N and N–H bond, referred to the most common bonds in protein-like substances such as amide I and II groups [39]. The observed bands at approximately 1390 and 1720 cm 1 corresponding to the bending vi­ – C and C– – O bond demonstrated the existence of bration of saturated C– benzene rings in carbohydrate-like substances, and the most significant peak at 1044 cm 1 belonged to the C–O stretching of humic-like units. The LC-OCD results of dissolved organic matter (DOM) on FO membrane after physical cleaning in Table S1 again confirmed that biopolymers and humic substances exhibited the greatest contribution to the form of irreversible fouling. Therefore, we speculated that certain organic sub­ stances, such as protein-, carbohydrate- and humic-like organic matters, were the major cause of irreversible membrane fouling in the long-term FO process during osmotic dilution. The organic matters causing irreversible membrane fouling, identi­ fied in this work, raise concerns regarding the deterioration of the FO performance over the long term. Although FO membrane fouling was reported to be easier to control, irreversible fouling is expected to remain a critical issue for practical use of the FO-based osmotic dilution process.

gradual reduction in the water flux owing to the adsorption of con­ taminants in the SEa on the membrane surface was observed. A signifi­ cant fouling after 260 h of operation led to a significant drop in the water flux, as shown in results of the fifth cycle of operation (33% and 68% reduction in the initial and final water fluxes, respectively). The module images of the virgin and fouling tests membranes surfaces in Fig. S2 confirmed that a uniform fouling layer was built up on the osmotic membrane surface after the long FO run. To assess the FR, HF cleaning was performed after each FO fouling cycle by increasing the cross-flow velocity. As shown in Fig. 1(b), the FRs in the first two cycles were higher than 99%, in contrast to those experiments in the third to fifth cycles where the FRs sharply decreased. This indicates that the fouling layer formed on the active layer surface was not fully reversible, and it became harder to remove it through by physical cleaning methods. We concluded that the majority of the fou­ lants were loosely adhered to the membrane and were readily removed by inducing a high shear force in the first two cycles. However, a small amount of foulants likely strongly adhered to the membrane, remaining on the surface even after HF was employed. The observation supports our hypothesis, as schematically illustrated in Fig. 2, we speculate that FO fouling in the early stage was subject more to hydrodynamic interactions between the membrane surface and the contaminants than intermolecular adhesion forces among the con­ taminants. Therefore, the fouling layer structure in FO is likely to be looser, could be reversible by simple physical cleaning induced by hy­ drodynamic shear [10]. In the meantime, some foulants with stronger intermolecular adhesion forces in the SE remained on the membrane and were not effectively removed through HF. Consequently, once a foulant layer was built up on the membrane surface, the foulant deposition tended to be easier owing to rougher foulant surface and the fou­ lant–foulant interactions. Fouling became much less sensitive to changes in hydrodynamic conditions, and fouling layers become much denser, thicker and much less reversible. In the last stage, as a rapid flux decline was observed, but further changes in the hydrodynamic conditions were hardly efficient in flushing out the fouling layer, which resulted in almost irreversible membrane fouling [4,8,9,38]. SEM-EDX and FTIR measurements were obtained to identify the composition of the foulants adsorbed onto the surface. Compared with the surface of the virgin membrane (Fig. 3(a)), the SEM images in Fig. 3 (b) and (c) confirmed a uniform fouling layer covering the osmotic membrane surface, and revealed a major presence of inorganic or organic crystals. The EDX spectra in Fig. 3(b) and (c) identified typical P, S, Si, and Al signals on the fouled and physically cleaned membranes, demonstrating that such fouling matters were irreversible by the phys­ ical cleaning and still remained on top of the FO membrane. The FTIR spectra of the SE-fouled membranes in the spectral region

3.2. Investigation of irreversible membrane fouling: quantitative analysis Previous studies have shown that proteins and carbohydrates are difficult to dislodge from the membrane through physical cleaning owing to their stickiness [18,19,40–42]. Therefore, membrane fouling has to be quantitatively correlated to the organic substances that are responsible for irreversible flux decline in FO to predict the degree of fouling and establish optimized fouling control strategies. 3.2.1. Irreversible membrane fouling control A stepwise pre-filtration of SEa with different pore sizes of MF and UF membranes was conducted to investigate the effect of the different-sized organic substances on irreversible fouling. During the pre-filtration stages, there were marginal differences in water flux decline when using filters sized 0.45–8 μm MF (see Supporting information for the normalized water flux decline during different pre-filtration methods, Fig. S3). A larger gap in flux decline was observed between the use of 0.45 μm MF and 100 kDa UF. However, much severer membrane fouling was observed when the pore size of the pre-filtrating membrane was further reduced to 10 kDa UF with a very limited permeate water flux. The FO fouling tests were also conducted using differently prefiltered SEa to investigate quantitatively the effect of the size of organic matters on the irreversible fouling (Fig. 4). After the long-term

Fig. 1. (a) Normalized initial and final water fluxes using a SEa feed solution after each cycle with no cleaning. The initial water fluxes of the single baseline solution and SEa feed solution were 15.7 � 0.1 LMH and 15.1 � 0.2 LMH, respectively. (b) FR using the HF cleaning method (n ¼ 3). 4

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Fig. 2. Schematic illustration of the different stages of the FO membrane fouling process.

FO test using raw SEa as the feed, severe physically irreversible fouling was observed on the membrane surface, and physical cleaning was less effective with only 74 � 2.4% of FR. However, the final and recovered fluxes in each cycle were gradually improved when the SEa was prefiltrated by using MF and UF membranes with pore sizes of 8 μm–10 kDa. The FR was 95 � 0.9% when the SEa was pre-filtrated by the 10 kDa UF membrane. Interestingly, the FR was significantly improved by employing a filter sized smaller than 0.45 μm, its value increased from 80 � 0.8% using the 0.45 μm membrane to 87 � 1.3% by pre-filtrating the SEa with the 0.1 μm filter. These results suggest that the organic substances with a particle size distribution smaller than 0.45 μm could be the main cause for the irreversible fouling. For the analysis, all the pre-filtered permeate water samples were carefully collected after the fouling runs and concentration distributions of those dissolved EfOM groups, such as proteins, carbohydrates, and humic substances, were measured. From Fig. 5(a), the concentration of carbohydrates (1.1 � 0.5 mg/L) of dissolved organic groups in the raw SEa was lower than those of proteins (5.1 � 0.4 mg/L) and humic sub­ stances (2.8 � 0.3 mg/L) which accounted for a large proportion of TOC (8.7 � 0.7 mg/L). The carbohydrates and humic substances were hardly removed by treating the SEa with smaller-pore-sized filters indicating that those organic substances have a molecular weight smaller than 10 kDa. However, a distinct decrease in the concentrations of proteins was observed when the 0.1 μm membrane was used for pre-filtration, and the protein-like substances were almost removed by using smaller-poresized UF membranes. LC-OCD analysis of the organics of the FO fouling layer caused by the differently pre-filtered SEa was performed after physical cleaning in the last cycle to identify the substances that were irreversibly attached on the membrane surface. As shown in Fig. 5 (b), all five kinds of dissolved EfOMs in raw SEa (i.e., biopolymers, humics, building blocks, low-MW neutrals, and low-MW acids) were attached to the membrane surface. Among these components, biopolymers and humics exhibited the greatest contribution to the formation of irreversible fouling. A signifi­ cant decrease in the concentration of biopolymers was observed on the membrane surface whereas that of the humics was slightly changed when the pore size of the pre-filtrating membrane was sequentially decreased from 8 μm to 10 kDa. The results present that the main cause of irreversible fouling in FO by the raw SEa was attributed to the bio­ polymers, whereas the smaller-sized humic substances were responsible for irreversible fouling even when the wastewater was tightly pre-

filtered by the 10 kDa UF membrane. Considering that biopolymers (i.e., proteins and carbohydrates, molecular weight cut-off (MWCO) of 20–2000 kDa) are relatively larger macromolecules, their high removal could be achieved through the UF membranes [43]. Both Figs. 4 and 5 showed that MF/UF pre-filtrating methods with a pore size between 0.45 μm and 100 kDa could effec­ tively remove the EfOM-foulants, especially the large-sized biopolymers such as proteins. Nonetheless, the carbohydrates and humic substances remained in the pre-filtered SEa owing to their smaller molecular weight (MWCO of several kilodalton to a few hundred kilodalton). In general, fouling in the treatment of wastewater by membrane technology usually comprises particles, colloids and organic molecules [6]. These different contaminants can interact through various mecha­ nisms such as charge transfer effects, hydrophobic effects and electro­ static interactions, etc. It has been reported that macromolecular compounds and/or colloidal organic matter in the hydrophilic NOM fraction may be a foulant in low-pressure membranes [21,22]. Those large-sized biopolymers, especially protein-like matters, display an ad­ hesive nature towards membrane surfaces, making their removal diffi­ cult by physical cleaning alone [3,4]. 3.2.2. Correlation between irreversible membrane fouling and foulant distribution in SE The fouling in FO during wastewater reclamation was quantitatively correlated with the particle size distribution of the organic components in SE. Correlation coefficients between TOC, foulants concentration in the SEa feed (proteins, carbohydrates, and humic substances) and FR values were calculated, and relationships were assessed by Pearson’s correlation coefficient (Table 2). Extremely significant correlations were evidently shown among the water quality parameters of TOC and pro­ teins, suggesting that the two components may be good indicators of the FR. In addition, the analysis also showed significant correlation between carbohydrates and FR, whereas the humics did not. These results suggest that the irreversible membrane fouling was mostly caused through a combined effect of the initial two components of TOC and proteins. However, as the two components were both observed to have extremely significant correlations with FR as shown in Table 2, it was difficult to determine their individual degree of influence on fouling. Thus, it was suggested that an alternate analysis method needs to be applied for a more detailed investigation of the effects of the various components and the quantitative regression analysis method was conducted. 5

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Fig. 3. A. Imaging and characterization of FO membranes using SEM-EDX analysis. (a) Original membrane, (b) fouled membranes affected by SEa feed water, (c) fouled membranes after the 5th HF cleaning; B. FTIR spectra (d) of the original and fouled FO membranes by using physical cleaning.

Based on the results of Pearson’s correlation analysis and Fig. 5, and the assumption that no organic foulants are present in the feed water at the point of FR ¼ 100%, two-parameter regression with single linear regression model, single exponential regression and logarithmic model, and three-parameter regression with single exponential regression and quadratic model were performed to verify the correlation between the concentration of irreversible EfOM-foulants in each pre-treated SEa and FR data in Table 3 (see Supporting information for several quantitative regression analysis models, Table S2). In order to clarify main contri­ bution of the irreversible flux decline, the correlation coefficient R2 value was calculated. In a single linear regression, as shown in Table 3, a stronger corre­ lation was observed with the concentration of TOC (R2 ¼ 0.82) and protein-like substances (R2 ¼ 0.88), whereas the correlations with carbohydrate-like (R2 ¼ 0.63) and humic substances (R2 ¼ 0.42) were poor. In the case of other exponential regression, logarithmic regression and quadratic model analysis, similar results were also observed with a negative correlation between FR and TOC content. In addition, as the main component of TOC, the concentration of protein-like substances also showed a good correlation with the FR values (R2 � 0.70). On the other hand, the concentration of carbohydrate-like and humic sub­ stances showed poorer R2 than single linear regression model. This

Fig. 4. Effect of the organic size on FR using pre-filtered SEa as feed water. The water recovery rate during all the FO tests was fixed at 50%, and HF cleaning was performed after the 4th cycle of every FO fouling test by increasing the cross-flow velocity to 25.62 cm/s. 6

Journal of Membrane Science 597 (2020) 117757

M. Zhan et al.

Fig. 5. (a) Concentration distributions of dissolved organic groups in the pre-filtered permeate water; and (b) the main foulants concentration distribution maps on the fouled FO membrane surface during the stepwise filtration process with different pore sizes.

has to be noted that biochemical assay methods need considerable time and chemical costs for the analytical determination of the protein con­ tent in water samples. Therefore, a more simplified and intuitive method for monitoring and predicting the fouling behavior must be developed, which would lead to a more practical and sustainable application of FO in the wastewater treatment field.

Table 2 Pearson’s correlation coefficients between foulants concentration in the SEa feed and the FR. Component

TOC

Proteins

Carbohydrates

Humics

TOC Proteins Carbohydrates Humics FR

1

0.963** 1

0.739* 0.744* 1

0.600 0.579 0.526 1

FR

1

0.912** 0.941** 0.794* 0.655

3.3. Applicability of the MFI 3.3.1. Irreversible membrane fouling potential prediction Several works focused on developing fouling potential indicators to better quantify the propensity of fouling caused by organic and colloidal impurities [23,44]. A common parameter practically used for estimating the fouling potential of RO and nanofiltration (NF) feed waters is the MFI. The MFI has been investigated the linear relationship with particle concentration and model flux decline or pressure increase in real RO plants [26,36]. Typically, MFI-UF using UF membranes with different MWCOs showed a high correlation in determining the fouling potential of a small-sized targeted organic foulant [25]. Therefore, it could be speculated that perhaps the MFI may be utilized to predict the FR in the FO process due to the high correlation between the concentration of the small-sized irreversible EfOM-foulants and FR values. In this study, we introduced the MFI to evaluate the membrane

* indicates a significant correlation between parameters (p < 0.05), ** indicates an extremely significant correlation between parameters (p < 0.01).

revealed that the fouling reversibility of the protein-like substances can be modelled by quantitative regression, but the carbohydrate-like and humic substances cannot. In membrane processes, the particles, colloids, and organic molecules account for membrane fouling during wastewater treatment [6]. In the FO process for wastewater reclamation, as dis­ cussed in this paper, the biopolymers, especially protein-like matters, majorly contributed to the formation of the irreversible fouling layer in the long-term operation. Therefore, quantification of the protein-like biopolymers can be a good indicator for predicting the membrane fouling and its reversibility for sustainable FO operation. However, it

Table 3 Quantitative regression analysis results of foulants concentration in the SEa feed and the FR, in the modeling equation, y represents FR and x represents foulants concentration. Equations

R2

y ¼ b þ ax

y¼ y¼ y¼ y¼

100–3.68x 100–4.04x 100–28.5x 100–15.4x

0.82 0.88 0.63 0.42

TOC Proteins Carbohydrates Humics

y ¼ aebx

y¼ y¼ y¼ y¼

100e 100e 100e 100e

0.29x

0.81 0.72 0.57 0.29

TOC Proteins Carbohydrates Humics

y ¼ c þ aebx

y¼ y¼ y¼ y¼

100 þ 100 þ 100 þ 100 þ

Logarithmic model (2 parameter)

TOC Proteins Carbohydrates Humics

y ¼ b þ alnx

y¼ y¼ y¼ y¼

100-17.7lnx 100-4.8lnx 100-18.9lnx 100-34.8lnx

0.73 0.87 0 0.35

Quadratic model (3 parameter)

TOC Proteins Carbohydrates Humics

y ¼ c þ bx þ ax2

y¼ y¼ y¼ y¼

100–6.51xþ0.26 � 2 100–6.02xþ0.4 � 2 100–29.4xþ0.59 � 2 100–13.1x-0.49 � 2

0.83 0.70 0.62 0.43

Regression analysis

Foulants

Fit model

Linear regression (2 parameter)

TOC Proteins Carbohydrates Humics

Exponential regression (2 parameter)

Exponential regression (3 parameter)

Diagrams

7

0.49x 1.8x 1.56x

1.73e 1.73e 1.73e 1.73e

0.16x 0.49x 0.92x 0.36x

0.77 0.85 0.56 0.42

M. Zhan et al.

Journal of Membrane Science 597 (2020) 117757

Table 4 Water quality analysis results of wastewater secondary effluentsa from 3 wastewater treatment plants. WWTP

Location

Process

SEa SEb SEc

Guri-si, Gyeonggi-do Seongdong-gu, Seoul Gangnam-gu, Seoul

MLEbþcoagulation A2Ocþcoagulation MLE þ coagulation

a b c

Concentration (mg/L) TDS

SS

TOC

Proteins

Carbohydrates

Humics

252 � 6 366 � 8 372 � 9

7.2 � 0.2 12.4 � 0.5 8.8 � 0.6

8.7 � 0.5 10.9 � 1.1 9.0 � 0.4

5.0 � 0.4 7.2 � 0.8 4.9 � 0.5

1.1 � 0.3 1.5 � 0.0 1.3 � 0.1

2.9 � 0.2 3.8 � 0.5 3.2 � 0.3

Effluent was sampled for 5 successive days. MLE: Modified Ludzack Ettinger process. A2O: Anaerobic-anoxic-aerobic process.

Fig. 6. Correlations analysis between the concentration of TOC, proteins and multiple MFI indexes through linear regression; MFI tests with two MF membranes (0.45 μm and 0.1 μm, MFI-MF0.45 and MFI-MF0.1) and three UF membranes (300 kDa, 100 kDa and 10 kDa, MFI-UF300, MFI-UF100, and MFI-UF10) were conducted to determine the fouling propensity of different SE waters. 8

M. Zhan et al.

Journal of Membrane Science 597 (2020) 117757

0.004–0.1 μm of pore size) [47] were observed to be the main cause of the irreversible fouling layer in long-term operation as it decreases process performance and increases operational costs. Therefore, we suggest that MFI-UF300 can be used to evaluate the fouling potential of SE feed waters, which could provide valuable information to real FO practices including the major causes of fouling as well as the most optimal pre-filter size for the removal of potential foulants contained in the wastewater. 3.3.2. Correlations between FR and MFI Additional FO experiments were conducted with different feed so­ lutions to investigate the consistency of the correlation between the FR and MFI. Fig. 7 shows the results of the FR variations and MFI values of all the samples measured for 5 successive days using the wastewater effluent sampled daily. The R2 value of MFI-UF300 and FR was found to be within the range of 0.61–0.83, which signifies that the strong cor­ relation between the FR and MFI-UF300 is a common characteristic. These results confirmed that MFI-UF300 well predicts the FR values in FO fouling, suggesting that EfOM-foulants (MWCO � 300 kDa) in waste­ water effluent are the primary source of FO irreversible fouling. Therefore, based on the consistency of the observed relationships, it can be concluded that MFI-UF300 well predicted the reversibility of fouling in FO, and thus, it has potential to be utilized as a general index for simulating the FO performance by predicting the FR. In addition, this study proposed the MFI index as a water quality measuring method to simulate the FO fouling and guide the FO system operation, more rigorous and in-depth verifications should be further carried out to complete the MFI, such as pretreatment strategy, various filter materials of index, error analysis and correction of more than one type of fouling in FO influent, etc.

Fig. 7. Correlations analysis between the FR and MFI values in the FO process through linear regression. MFI-UF300 were measured to determine the fouling propensity of different water samples every day.

fouling potential of the SE feed water, focusing on TOC and the proteinlike biopolymers. The MFI tests were performed using different poresized membranes—two MF membranes (0.45 μm and 0.1 μm, MFIMF0.45 and MFI-MF0.1) and three UF membranes (300 kDa, 100 kDa and 10 kDa, MFI-UF300, MFI-UF100, and MFI-UF10) —to correlate foulants concentration in the SE feed and MFI values of the differently prefiltered SE waters. To validate the applicability of MFI in predicting water quality parameter, three SE samples (SEa, SEb, and SEc) were collected from different WWTPs in Korea as feed solutions for the cyclical FO tests, to obtain the values of MFI and FR. All the SE samples were obtained from the treatment plants for 5 successive days, and the quality of each sample was analyzed and listed in Table 4. The TOC and SS values of SEb, and SEc effluents were more than 9.0 � 0.4 mg/L and 8.8 � 0.6 mg/L, respectively, showing high fouling potentials. Fig. 6 shows the results of the statistical analysis between the con­ centration of TOC, proteins and MFI values of the three SE samples. Based on the findings from the linear regression, and the assumption that no organic foulants are present in the feed water at the point of MFI ¼ 0, the R2 values indicating the correlations between TOC and MFIMF0.45, MFI-MF0.1 and MFI-UF300, were 0.71, 0.68 and 0.80, respec­ tively, whereas those for MFI-UF100 and MFI-UF10, were 0.20 and 0.43, respectively. Interestingly, we found that MFI was also positively correlated with foulants concentration in the SE samples, and MFI-UF300 was shown to most precisely predict the concentration of proteins (R2 ¼ 0.87) compared to carbohydrate-like and humic substances (see Sup­ porting information for the correlations analysis between the concen­ tration of carbohydrates, humics and multiple MFI indexes, Fig. S4). According to previous studies, the fouling potential of colloids smaller than the MF membrane pore size (0.45 μm) cannot be accurately predicted by SDI and MFI-MF (particulate-MFI) [26,36], which is in the case of SWRO where the SDI and MFI indices have been commonly employed as indicators of membrane fouling. However, since the basic physicochemical properties of RO and FO are identical, the MFI can also be applicable for the FO process. Besides, the MFI is established on the cake filtration theory and is dependent on particle size through the Carman–Kozeny equation for specific cake resistance [37,45]. The sus­ pended solids in the three wastewater secondary effluents (average particle size over 2 μm) include silt and clay particles, plankton, algae, fine organic debris, and other particulate matter, can contribute to the particulate fouling and affect the accuracy of actual irreversible fouling potential [46]. Though the MFI-MF has limitations as mentioned above, the MFI-UF can accurately analyze the fouling potentials of smaller colloids and likewise, protein-like biopolymers (20–2000 kDa, approximately

4. Conclusions In this study, we systematically investigated the irreversibility of the fouling layer on an osmotic membrane during wastewater reclamation. Real secondary wastewater was used during the fouling runs and the major cause contributing to the irreversibility was clarified. We also correlated the FR with the concentration of irreversible foulants in a quantitative manner. The correlation between multiple MFIs and FR was also assessed to determine the applicability of MFI as a simple fouling indicator for the FO processes. The main findings of this study are summarized as follows: � The corresponding loss of FR or final flux after the long-term fouling test was attributed to specific foulants in the SE. SEM and FTIR an­ alyses also indicated the uniform fouling layer on the membrane surface. � Multiple MF/UF stepwise filtration methods were investigated to control the physically irreversible membrane fouling by removing EfOM-foulants in raw SE. Biopolymers and humics exhibited the greatest contribution to irreversible fouling. � By investigating the relationships between the EfOM-foulants con­ centration and FR, it was revealed that a strong correlation between the concentration of irreversible foulants (especially proteins, R2 ¼ 0.88 of linear regression) and FR was observed. � Multiple MFIs (MFI-MF0.45 and MFI-MF0.1, MFI-UF300, MFI-UF100, and MFI-UF10) were measured to evaluate the fouling potential of feed water with size fractionation. MFI-UF300 showed the most dominant influence to assess the impact of protein-like biopolymer matters during the FO validation test. Our study proposes MFI-UF300 as a possible fouling index for pre­ dicting FR variations based on our analysis demonstrated by the longterm FO operation. We expect that the accumulation of field data in FO using different real wastewaters could support to establish new criteria for a more accurate prediction of fouling potential, and thus, a 9

M. Zhan et al.

Journal of Membrane Science 597 (2020) 117757

more effective fouling control strategy can be delicately designed for the sustainable operation of FO.

[18]

Declaration of competing interest

[19]

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

[20]

CRediT authorship contribution statement

[21]

Min Zhan: Writing - original draft, Writing - review & editing, Conceptualization, Data curation, Methodology. Gimun Gwak: Writing - review & editing. David Inhyuk Kim: Visualization, Validation. Kiho Park: Software, Formal analysis. Seungkwan Hong: Supervision, Validation.

[22] [23] [24]

Acknowledgement

[25]

This work was supported by Korea Environment Industry & Tech­ nology Institute (KEITI) through Industrial Facilities & Infrastructure Research Program, funded by Korea Ministry of Environment (MOE) (1485016282).

[26] [27]

Appendix A. Supplementary data

[28] [29]

Supplementary data to this article can be found online at https://doi. org/10.1016/j.memsci.2019.117757.

[30]

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[31]

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