Environ. Sci. Technol. 2006, 40, 6830-6836
Secondary Wastewater Polishing with Ultrafiltration Membranes for Unrestricted Reuse: Fouling and Flushing Modeling LEONID GILLERMAN,† AMOS BICK,‡ NISAN BURIAKOVSKY,† AND G I D E O N O R O N * ,†,§,|, Environment Water Resources, J. Blaustein Institutes for Desert Research, Ben-Gurion University of The Negev, Kiryat Sde-Boker 84990, Israel, The Department of Industrial Engineering and Management, Jerusalem College of Technology, Jerusale´n 91160, Israel, The Department of Industrial Engineering and Management and The Biotechnology & Environmental Engineering Department, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel, and The Grand Water Research Institute, Technion, Haifa 32000, Israel
The effects of operating parameters such as transmembrane pressure, retentate, and recirculation volumetric flow rates on the productivity of an ultrafiltration membrane were studied using field data and development of a management model. Correlation equations for predicting the volumetric permeate flow rates were derived from general membrane blocking laws and experimental data. The experimental data were obtained from a pilot study carried out in the Arad wastewater treatment system (a pilot plant operating in feed and bleed operation mode) located several kilometers west of the City of Arad, Israel. Correlation predictions were confirmed with the independent experimental results. The results enabled us to develop a mathematical expression accurately describing the decline in flux due to fouling.
Introduction Wastewater Reuse and Membrane Technology. The growing demand for water and increasing environmental awareness has led to intensive efforts in the treatment and reuse of wastewater. During the last two decades, the reuse of treated domestic wastewater for agricultural irrigation has expanded, especially in arid and semiarid regions, mitigating water scarcity and improving means for local food production (1, 2). The increased salinity of wastewater from household and local small industry wastes, combined with the open-surface storage associated with effluent reuse from waste stabilization ponds (WSP), raises agricultural sustainability problems. Increased demands for land and related high costs have pro* Corresponding author e-mail:
[email protected]. Corresponding author address: The Department of Chemical Engineering The Environmental Engineering Program, Yale University, New Haven, CT 06520-8286. † J. Blaustein Institutes for Desert Research, Ben-Gurion University of The Negev. ‡ Jerusalem College of Technology. § The Department of Industrial Engineering and Management and The Biotechnology & Environmental Engineering Department, BenGurion University of the Negev. | The Grand Water Research Institute. 6830
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voked the search for more efficient water treatment technologies. Improved technology for removing particles, turbidity, bacteria, viruses, nematodes, and cysts with minimal use of disinfectants is based primarily on the use of membranes, mainly microfiltration (MF) and ultrafiltration (UF) membranes (2, 3). The development of membrane technology, including a reduction in membrane costs and energy consumption, has resulted in their increased application in many areas, including wastewater reclamation (4-6). There are several advantages associated with the implementation of membrane technology for wastewater treatment and polishing: (i) physical separation of a broad range of contaminants; (ii) simplicity of operation and adaptation to existing treatment facilities; (iii) continuous processing which can be automated; (iv) low chemical requirements; and (vi) no generation of disinfection byproducts. There are also several drawbacks to the membrane processes which include concentration-polarization and membrane fouling. Fouling during wastewater treatment by membrane technology is primarily due to the variability of organic matter content (7). Frequently, options for brine disposal generate economic and environmental burdens. The concentration-polarization phenomenon is frequently manifested in a local increase in concentration of the rejected solutes at the membrane surface and is commonly associated with flux decline. Membrane fouling generally occurs as a result of the deposition and accumulation of nanoparticles on the membrane surface and within the pores of the membrane itself (8-12). This contributes to permeate flow rate decline and decreased solute rejection. Successful use of a UF system requires deep understanding of the fouling phenomena in order to determine optimum operating conditions. For UF systems treating naturally low quality waters or wastewater, fouling was found to be the most important factor influencing permeate flow rate and productivity as well as operational costs and membrane replacement needs (13, 14). Fouling effects are characterized by an irreversible decline in flux with time of operation, when all operating parameters, such as transmembrane pressure, flow rate, temperature, and feed concentration, are kept constant (15-17). Membrane fouling can be considered as a combined reversible and irreversible processes, subject to deposit characteristics (temporary or permanent), and the possibilities of restoring initial flux by backwashing or chemical cleaning (13, 16, 18). The dominant factors that influence fouling and its control are related to (i) the composition of feedwater; (ii) membrane nature and surface properties; (iii) hydrodynamic behavior within the membrane; and (iv) operating process conditions (5, 6). Fouling - Data Analysis and Modeling. Fouling is due to gradual and continuous blocking processes of the membrane pores. According to the blocking model, the general expression referring to the filtrate volume V and the duration of membrane operation ti of one membrane for a constant inlet pressure is given by (19):
d2ti dV
2
) kf
( ) dti dV
nf
(1)
where kf (time-1) is a filtration constant, and nf is a filtration exponent (dimensionless), both of which define the physical filtration processes. There are four main blocking situations (19): (i) nf ) 0 - cake filtration, which assumes that the resistance is increasing due to particle accumulation in the form of a cake on the filter media; (ii) nf ) 1 - intermediate 10.1021/es061235z CCC: $33.50
2006 American Chemical Society Published on Web 10/30/2006
FIGURE 1. A schematic description of the membrane secondary wastewater polishing treatment system. blocking (long term adsorption), that assumes particle buildup is sealing the membrane pores; (iii) nf ) 1.5 - standard blocking, which assumes that the pore volume decreases proportionally with filtrate volume by internal deposition; and; (iv) nf ) 2 - complete blocking, assuming that the particles arriving at the membrane will seal the pores. Commonly, due to the complexity of the conditions, it is quite difficult to identify the real blocking model, namely the most probable value of nf. According to previous findings, operational expenses and membrane replacement are affected primarily by fouling, due to the gradual decrease of UF product flow rate with time, even if the major operating parameters (pressure, flow rate, feed concentration, temperature, etc.) are kept constant. Backwashing and chemical cleaning as well as a number of pretreatment techniques which are required for fouling prevention will significantly increase the costs of using a UF system. Moreover, the prediction of membrane permeate flow rate and its decline with time is very important for assessing the overall productivity, optimization, automation and for up-scaling of the system. Earlier findings also indicate that by increasing the crossflow velocity fouling can be reduced (14, 17, 18). However, there is an optimum cross-flow velocity range above which the increase in flux is coupled with high organic concentration and blocking phenomena. Similar processes take place during changes of the retentate flux. The increase in retentate volumetric flow rates may reduce the initial (at time t ) 0 after flush cleaning) permeate volumetric flow rate; however, at the same time this will reduce the fouling course. Consequently, in order to determine optimum design and operation conditions for the UF systems (maximization of accumulated permeate volume under constraints of time, temperature, and feed influent quality) a management model was developed. The model is based on finding the linkage between permeate volumetric flow rate, time, and intrinsic membrane hydraulic properties. The functional dependence includes also the pressure, retentate, and recirculation flowrate within the spiral wound (SW) UF membranes (in case of feed-and bleed operation system). The purpose of this paper is to present a management approach that allows us to determine the optimal operational conditions of a UF SW membrane system, subject to a series of constraints such as membrane characteristics, retentate flows (amounts) and recirculation, energy requirements and flux decline in order to obtain maximal permeate discharges at minimum expense. Regression analyses allowed us to use the field data for identifying the best functional dependence between main operational control parameters [e.g. flows,
transmembrane pressure (TMP)]. Furthermore, the proposed model allows users to develop functional relationships for a given system operating under a feed-and-bleed (F&B) mode.
Materials and Methods The Arad Pilot Treatment System. An advanced large scale experimental membrane pilot system (around 150 m3/d) is in operation adjacent to the wastewater treatment plant of the city of Arad, Israel. Secondary wastewater was upgraded using the two stages membrane pilot unit in order to minimize health and environmental risks (3). Secondary effluent from the waste stabilization pond system (WSPS) forms the feed of the pilot membrane system (20). The feed at the first stage (after mechanical screening) is treated by UF membranes. The permeate from the UF system is used for irrigation as well as for feed for the subsequent reverse osmosis (RO) second membrane stage (Figure 1). The Ultrafiltration Component in the Pilot System. The membrane reclamation pilot plant near Arad consists of a disk filter (for removal of relatively large suspended particles), a circulation pump, a UF stage, a flushing system, and a chemical cleaning unit (a container with a pump). The UF component consists of three modules with four UF 8” spiral wound (SW) elements, type NIROSOFT RM10-8, 8040 SW. The molecular weight cutoff (MWCO) of the membrane was 20K Dalton. The defined removal rate was in the range of 88-93% at 3 bar, 20 °C, in a stirring cell. The UF system was operated under a feed and bleed mode (Figure 1). The UF membrane system is operated at a pH range of 7-8, the feed pressure varies between 4 and 6 bar, and the operating temperature is around 30 °C. Permeate flow rates were measured for different values of transmembrane pressures as well as for varying retentate and recirculation flows within the UF membrane unit. Several complementary experimental series were conducted (Table 1). A series of manual valves allows us to maintain different combinations of transmembrane pressures, permeate, retentate, and recirculation flow rates. The first experimental sets (1a and 1b) were conducted with clean tap water in order to assess several typical intrinsic characteristics of the membrane. The validity of the predicted parameters of the calibration expressions was tested under another series of experiments, still with tap water. During those first sets of experiments no membrane cleaning was conducted. All consecutive sets of experiments (Table 1: 2-4 sets) were conducted with secondary effluent, including intermittent chemical cleaning [with caustic soda 0.1%, pH 11.5, at a temperature of around 30 °C for 1 h, according to hydraVOL. 40, NO. 21, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 1. Experimental Programa
a
TMP - trans membrane pressure; Qr - recirculated flow rate; Qb - retentate flow rate; nf - the exponent characterizing the blocking process.
nautics guidelines (21)] between the experiment runs (Table 1). Filtration duration was commonly less than 2 h, subject to the constraint of water supply for irrigation and maintaining a flux decline of less than 50%. The validity of the various parameters was also examined under an extra set of membrane runs. The final results enabled us to determine the type of membrane blocking model [actually the value of nf - eq 1]. Further analysis allowed us to derive an analytical expression for the reduced flux “teeth saw” performance, typical for the fouling phenomena of UF membranes. The experimental data of the first series were used to predict calibration curves, applying high quality potable water as feed for the UF membrane and obtaining analytical expressions for the permeate flow rates. The calibrated functions were subsequently used to assess the effluent permeate flow rates depending on a series of effluent qualities under dif6832
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ferent operational conditions. Comparing the predicted previously attained expressions (first sets) with the independent experimental results obtained in the second stage enabled examination of the goodness of fit of the different parameters. Model Testing - Goodness of Fit. The goodness of fit between the measured and predicted values was estimated by using the root-mean-square error (RMSE) parameter, which presents the average deviation between the calculated and measured values:
x
n
∑(C - M )
RMSE )
i
2
i
i)1
n
(2)
and the dimensionless Willmont index of agreement Iw (22)
[ ] n
∑(C - M )
2
i
Iw ) 1 -
i
i)1
n
(3)
∑(|C′| - |M′|)
2
i
i
i)1
where n is the number of samples, Ci and Mi are the ith calculated and measured values, respectively, and
C′i ) Ci - Mm
(4)
M′i ) Mi - Mm
(5)
where Mm is the mean of the observed values. The common correlation coefficient R2 is primarily used to assess best line fit for two clusters of data. The RMSE parameter is used to assess system performance based on prediction-observation pairings; however, it is subject to large errors (due to squaring). The Iw refers to the entire data range including trends since it is based simultaneously on predicted and measured deviations from Mm (mean) matched, both in magnitude and sign and varies between 0.0 and 1.0, where 1.0 represents perfect agreement (22). Final experimental phase was conducted with secondary wastewater as feed [BOD5 (biological oxygen demand) ) 77 ( 5 mg/L; fecal coliforms ) 2.5 × 105 ( 0.7 × 105 CFU/100 mL; EC (electrical conductivity) ) 2.1 ( 0.1 dS/m; turbidity ) 213 ( 46 NTU], implementing standard methods for quality analysis (23).
Results and Discussion Effect of Intrinsic Membrane Hydraulic Properties on Permeate Flow Rate - Clean Tap Water. One of the most important parameters, also characterizing the energy requirements, is the transmembrane pressure (TMP) given by (24):
TMP ) (Pf + Pb)/2 -Pp
(6)
where Pf and Pb are the feed and retentate pressures, respectively, and Pp is the permeate pressure. During the experiments the values of TMP were changed from 0.2 to 4.2 bar, and the values of the retentate and recirculation discharges were changed from 1 m3/h to 3 m3/h and from 0 to 15 m3/h, respectively. The permeate flow rate was measured for different values of TMP and the retentate (Qb) and recirculation discharges (Qr). According to regression analysis TMP had the major effect on the permeate flow rate (P-value , 0.0001). Accordingly, the effect of intrinsic membrane hydraulic properties on volumetric permeate flow rate was characterized by developing a functional relationship between permeate flow rate Qc (using clean tap water for the feed) and TMP. Clean tap water was used for reference, and the obtained functional relationship was as depicted in Figure 2 and eq 7:
Qc ) 1.91 TMP0.71, n ) 112, R2 ) 0.96
(7)
where Qc is the flow rate of the clean tap water. It can be assumed that values of the obtained coefficients (1.91 and 0.71) depend on the intrinsic membrane resistance. Consequently, the values of Qc can be used to assess the flow rates and as a reference parameter characterizing current intrinsic membrane resistance combined with the TMP. Equation 7 was used to compare between calculatedpredicted and measured values of Qc obtained in a second series of experiments. As shown (Figure 3), the linear agreement between measured and predicted values is very good. The larger number of monitored/predicted data points
FIGURE 2. The relationship between permeate volumetric flow and transmembrane pressure for the clean tap water experiment (first set, first series, 1a).
FIGURE 3. Comparison between measured and predicted [eq 7] values of Qc. The line represents perfect agreement (first set, second series, clean tap water, 1b). with high flow rates is due to the need to supply enough effluent for irrigation (Figure 3). The values of RMSE and Willmont index of agreement Iw for the clean tap water were 0.20 m3/h and 0.99, respectively. Effect of UF Operation Conditions on Initial Permeate Flow Rate With Secondary Wastewater. Further efforts were carried out in order to obtain a functional relationship between the initial (at time t ) 0) permeate flow rate and typical system operation parameters when treating secondary wastewater. The evaluated intrinsic membrane resistance as expressed by the TMP and the permeate flow Qc facilitated defining an operational index that depends on the retentate Qb and the recirculation flow Qr. The dimensionless operating index Φop expresses the ratio between the sum of flows Qb and Qr vs Qc [eq 8] and is used for further correlation:
Φop ) (Qb + Qr)/Qc
(8)
The index Φop presents the effects of cross-flow velocities on the membrane surface (as expressed by Qb and Qr and Qc) combined with current TMP and the membrane resistance. It should be noted that Qr characterizes the bleed operation mode and also affects the combined feed which enters into the membranes (Figure 1). The data obtained from the wastewater experiments (second set, first series, 2a) were used to evaluate the relationship between the initial permeate flow rate Qo and the operating index Φop [eq 9 (based on the best R2) and Figure 4]:
Qo ) 4.17 exp(-0.06 Φop), n ) 51, R2 ) 0.84
(9)
Equation 9 was used to match up the predicted and the calculated values for the initial flow Qo obtained in the second set of experiments (second series, 2b) with secondary wastewater. A total of 24 data points were used to assess the linkage between the initial measures and initial predicted VOL. 40, NO. 21, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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flow Qo, yielding a correlation coefficient of R2 ) 0.72. The results for the fitness parameters as expressed by the values of RMSE and Willmont index of agreement Iw were 0.22 m3/h and 0.86, respectively, indicating high correlation. Fouling. As stated membrane fouling is the main phenomenon affecting the economic operation of a membrane treatment system. The fouling process was analyzed in two stages: (i) fouling behavior during filtration and (ii) developing functional relationships according to the most appropriate fouling law obtained during the first stage. (a) Data Analysis for Secondary Effluent Upgrading. The general membrane filtration law describing the membrane pore blocking during one run (between two successive chemical flushings) is given by eq 1. The field data collected in the pilot plant and other related theories [Figure 5 and (10)] enable us to identify the type of filtration mechanism and consequently to determine the value of nf [eq 1] and subsequently the corresponding equations for the accumulated filtration volumes and flow rates. Accordingly, the following functional relationships were depicted (Figure 5): (a) a linear filtration volume V vs exp(-t) that indicates complete blocking; (b) a linear filtration ratio given by t/V vs time t which verifies standard blocking conditions; (c) a linear change in the derivative dt/dV vs the time t that is valid for intermediate blocking conditions; and (d) a linear filtration expression as given by t/V vs the volume that is typical for cake filtration conditions. According to the results obtained (Figure 5) the best correlation (R2 ) 0.98) was obtained for the UF membrane treating secondary effluent for a linear dependence between V and the time expressed by exp(-t). Consequently, complete blocking was obtained, and it is reasonable to assume that for this situation nf ) 2 (12). Subsequently, the following expressions were adopted (due to best fit by correlation) to describe the time-dependent relationships for the volumetric permeate flow rate Q(ti) and the accumulated permeate volume V(ti) for one membrane run:
Q(ti) ) Qo exp(-kfti)
(10)
V(ti) ) Qo[1-exp(-kfti)]/kf
(11)
Actually eqs 10 and 11 describe the membrane fouling rate between two successive cleanings. Chemical cleaning allows us to make two main assumptions: (i) almost initial membrane properties were reached after each chemical flush (there is always the accumulated effect of permanent blocking) and (ii) a sequence of runs of one membrane resembles a series of membranes under similar cleaning regimes. Under these conditions and assuming that all membranes in a series have the same initial flow Qo, eqs 10 and 11 can be rearranged as follows:
FIGURE 4. Relationship between initial permeate volumetric flow rate Qo and operating index Φop for secondary wastewater (second set, first series, wastewater, 2a). (b) Fouling Model for Wastewater Filtration. According to the results obtained (permeate flow rate vs time), the value of operating index Φop reliably expresses the time effect on permeate decline (P-value
