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Separation of inorganic and organic compounds by using a radial flow hollow-fiber reverse osmosis module
Desalination 196 (2006) 221–236
Separation of inorganic and organic compounds by using a radial flow hollow-fiber reverse osmosis module S. Senthilmurugan*, Sharad K. Gupta** Chemical Engineering Department, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India Tel. +91 (11) 2659 1023; Fax: +91 (11) 2658 1120; email: [email protected] Received 1 July 2005; accepted 20 February 2006
Abstract Phenol, 2,4-dinitrophenol (DNP), pentachlorophenol (PCP), NaCl, NaBr and KBr were separated from aqueous solutions under a variety of operating conditions by using a Permasep B9 hollow-fiber reverse osmosis module. The permeate characteristics for all compounds were analyzed by the combined film Spiegler–Kedem (CFSK) model available in the literature. The errors between experimental and theoretical predictions were less than 15% for both permeate flow rates as well as permeate concentrations. The model parameters such as the solute permeability and the reflection coefficient were constant for all phenolic compounds and KBr at constant temperature and pH. However, in the case of NaCl and NaBr, the solute permeability increased as the feed concentration increased to 15,000 ppm, after which the concentration of the solute permeabilities became independent of the feed concentration. On the other hand, the reflection coefficient for both NaCl and NaBr went through a minimum and then increased to a maximum before reaching a constant value as the feed concentration was increased. It is also shown that for both organic as well as inorganic compounds. the CFSK model provides much better theoretical predictions than the combined film solution diffusion model. Keywords: Reverse osmosis; Spiegler–Kedem model; Concentration polarization; Organic compound; Inorganic salt
1. Introduction *Present address: Reprocessing Research and Development Division, Reprocessing Group, IGCAR, Kalpakkam, Tamilnadu, India 603 102. **Corresponding author.
Reverse osmosis (RO) is one of the commercially viable processes available for water desalination for both domestic and industrial purposes. But recently RO is also being used for the separation of impurities from industrial effluent. Commonly three types of module con-
0011-9164/06/$– See front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.desal.2006.02.001
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figuration are available in the market. These are tubular, spiral-wound and hollow-fiber configurations. Among these the hollow-fiber module offers the highest surface area per unit volume of module. The separation of organic as well inorganic species has been extensively studied in the literature for RO and nanofiltration by using flatsheet membranes. Since these are needed for understanding the separation of organic and inorganic compounds in a hollow-fiber module presented in this work, these studies are briefly discussed below. The separation of organic compounds in a flatsheet membrane test cell was studied by several authors [1–3]. Most of these were carried out with cellulose acetate membranes while several others studied the separation by using non-cellulose membranes [2]. Anderson et al. [1] concluded that un-ionized and hydrophobic solutes are strongly adsorbed by the cellulose acetate membranes and such solutes exhibit poor rejection properties. Ionized and hydrophilic solutes, on the other hand, have low solubility in membranes and experience good rejection. Murthy et al. [2] analyzed the phenol data obtained in a membrane test cell by using both the solution diffusion as well as the Spiegler–Kedem model combined with concentration polarization and found that the rejection reached a maximum at some JV. The analytical solution for maximum rejection was also derived for both models. Finally, Ozaki et al. [3] carried out experiments with low molecular weight organic compounds using ultra-low pressure flat-sheet polyamide membranes. The pH of the feed solution was varied from 3 to 9. They concluded that the rejection of undissociated organic compounds was not affected significantly with respect to the feed pH. On the other hand, rejection increased with molecular weight as well as molecular width, but the rejection of dissociated organic compound increased with respect to feed pH.
Soltanieh et al. [4] reviewed the membrane transport models and their applications for the separation of inorganic compounds in flat-sheet RO membranes. They concluded that the nonlinear Spiegler–Kedem model is more appropriate for describing the solute flux for these compounds. Here, it was pointed out that the NaCl data of Lui [5] showed that the reflection coefficient (σ) was almost constant, and the rejection and solute permeability (Pm) increased as the feed concentration decreases. This is in contrast to the results of Push [6] who found that as the feed NaCl concentration decreases, σ increases, and the rejection and Pm decreases. Some other studies [7] have observed that the Pm and σ are constants with respect to feed NaCl concentration. Many researchers have studied the hydrodynamics of hollow-fiber RO (HFRO) modules [8–28]. In some studies analytical solutions [8, 19,21,23] were obtained while others tried numerical solutions [20,22,24–28] for determining the permeate flow rate and the permeate concentration for a given feed flow rate, feed concentration, and feed pressure. All the mathematical models for HFRO above can be differentiated based on the membrane transport model used for describing the membrane transport and the concentration polarization model used for mass transport across the film and estimation of pressure drop in both shell and bore side. The membrane transport model used is either the twoparameter membrane transport model such as the solution–diffusion model [8–23,25–27] or a three-parameter membrane transport model such as the Kedem–Katchalsky [24] or Spiegler– Kedem model [28]. In some cases concentration polarization is neglected [8–16,18,19,21,23, 24,27], while other models combine the concentration polarization with a suitable membrane transport model. Finally, the shell side pressure drop calculated by using Ergun’s equation shows a negligible pressure drop and therefore in most
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of the models the shell side pressure is assumed to be constant. On the other hand, the shell side pressure drop determined by solving the equations of continuity and motion shows that this may not be the case [24]. Very few experimental studies on hollow-fiber modules are available in the literature. Most of the models discussed above used the data of Ohya et al. [12] and Tweddle et al. [14] on a NaCl– water system for model validation purposes. Sekino [20] and Soltanieh et al. [16] also obtained some experimental data on a NaCl–water system on hollow-fiber modules. Recently, Chatterjee et al. [28] reported separation data for a phenol– water system in a radial flow hollow-fiber module. Furthermore, they also developed a mathematical model for this module where the three-parameter Spiegler–Kedem model was used for describing mass transport across the membrane. Concentration polarization was taken into account by using the film theory and the pressure drops in both permeate and bulk streams were also included. In addition, the analytical solution for pure water permeability was also derived. The mass transfer coefficient parameters and the membrane model parameters were determined by using an optimization technique — the simplex search. The model showed good agreement with the experimental results for both the NaCl–water and phenol–water systems. Finally, they also showed that the combined film Spiegler–Kedem model (CFSK) used by them provides better results than the combined film solution diffusion model of Sekino [20,22]. From the literature [12,14,16,20,28], it is clear that only the separation of NaCl and phenol have been studied in a HFRO module. For the case of experimental studies on flat-sheet membranes and inorganic salts, the membrane transport parameters are found to be changing for low feed concentrations, whereas a similar case may also be possible in a hollow-fiber module. But there are no experimental data available at a low con-
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centration of inorganic salts. In addition, only phenol separation has been studied experimentally. Thus, sufficient experimental data are not available in the literature to reach any definite conclusion about the validity of any mathematical models discussed above. The main objective of this work was to get the separation data of inorganic salts such as NaCl, NaBr and KBr. Furthermore, the separation of phenol, PCP and DNP from an aqueous solution was also studied in the same module. While the focus for phenolic compounds was to obtain data for different feed pH, the experimental data for inorganic compound was obtained at different feed concentrations. Finally, the experimental data for all six compounds were analyzed by using the CFSK model of Chatterjee et al. [28]. The membrane transport parameters appearing in this model are estimated, and the effect of feed pH for phenolic compounds and the effect of feed concentration for inorganic compounds on these parameters are also analyzed in detail. 2. Theory The HFRO membrane configuration was developed by DuPont in the late 1960s, followed by Dow Chemical and Toyobo (Japan). The construction of each manufacturer’s device is similar, but they differ in their fiber dimensions, fiber support and membrane materials. The DuPont HF membrane modules are made from asymmetric aramid fibers of 42 µm ID × 85 µm OD for B-9 and 42 µm ID X 95 µm OD for B-10 modules. The Dow modules, which are no longer available, were made from cellulose triacetate fibers of 250 µm OD. On the other hand, Toyobo Hollosep modules use cellulose triacetate fiber of dimensions 70 µm ID and 165 µm OD. All the experiments were carried out in a radial flow B-9 module. The module dimensions and internal structure are given in Table 1 and Fig. 1, respectively. The model equations for
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Fig. 1. Radial flow -9 HFRO module.
Table 1 Membrane specification for Du Pont B9 HFRO modules Membrane specification for:
Values
do, m di, m Do, m Di, m N L, m I s, m w Membrane area, m2
91×10!6 a 44 x 10-6 a 0.1016a 0.025a 383,820a 0.254a 0.02b 0 27.87a
a
As provided by Du Pont. b Assumed.
Jv =
vRT A ( Pb − Pp ) − σ ( Cm − C p ) (1) ρ Mw
R = 1−
Cp Cm
=
(1 − F )σ 1 − σF
(2)
Eq. (2) is combined with film theory for concentration polarization, and finally we obtain the following equation for the solute concentration in the permeate stream:
Cp =
Cb (1 − F )σ 1+ (1 − σ)φ
(3)
where hollow fiber module developed in previous work [28] are given below. The Spiegler–Kedem model is used for describing the membrane mass transport. According to this model the permeate flux, Jv, and the true rejection, R, are given by the following equations.
J (1 − σ) F = exp − v Pm and
(4)
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J v Cm − C p = k Cb − C p
φ = exp
(5)
Eqs. (1) and (3) along with the equations of conservation of mass for both solvent and solute are the starting point of the CFSK model. These equations may be solved numerically following the procedure previously outlined [28]. The same equations with σ equal to 1 are the equations of the CFSD model. The shell side mass transfer coefficient (k) used in Eq. (5) may be expressed as a function of Reynolds and Schmidt numbers:
Sh = a ( Re ) ( Sc ) b
1/ 3
(6)
The literature contains numerous correlations for shell side mass transfer coefficient in the form expressed in Eq. (6). Sekino [6,22] and Chatterjee et al. [28] have suggested the value of a = 0.048 and b = 0.6 for the Toyobo Hollosep and B-9 HFRO modules. Membrane transport parameters appearing in above model are A, Pm, σ, and mass transfer coefficient constants a and b in Eq. (6). 3. Experimental set-up and procedure The experimental set-up consists of a Permasep B9 HFRO module, a feed tank, and a reciprocating pump. The pressure gauges are placed before and after the module to measure the pressure at feed inlet, reject and the permeate streams. The schematic diagram of the set-up is shown in Fig. 2. A sump is provided to regulate the flow fluctuation due to the reciprocating action of the pump. The reject and permeate streams are sent back to the feed tank since the feed concentration is to be maintained at constant level. The experimental data for the separation of organic compounds were collected at different pH values in the range of 6.0±0.3 to 9.1±0.3, while the pH of 6.0±0.3 was maintained for the separa-
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tion of inorganic compounds. The experimental procedure consisted of a systematic variation of the four operating conditions namely, the feed pressure, flow rate, the concentration and pH of the feed stream. The permeate samples were collected by varying feed pressure with identical increments while maintaining the same feed flow rate. The same procedure as described above was followed for the new value of feed flow rate, pH and concentration. The temperature of the feed was maintained by using both a temperature controller and a cooling water unit. The experiments for separation of phenolic compounds were performed in winter (approximately 15–20EC ambient temperatures) at 25EC and for inorganic compounds were carried in summer (approximately 42–47EC ambient temperatures) at 30.5EC as the temperature in the summer could not be controlled at 25EC using the present cooling water unit. The feed solution was prepared by dissolving the requisite amount of salt in distilled water. Before conducting the next run with a new feed, the set-up was operated for 6 h to allow the residual phenolic compound or inorganic salt from previous runs in the module to be washed out. Intermittent stirring of the solution in the feed tank also helped in ensuring uniform concentration and pH throughout the feed solution. The feed flow rates were varied in steps of approximately 10 ml/s in the range of 50 ml/s to 100 ml/s. The solute concentration was varied from 25 to 100 ppm with steps of 25 ppm for organic components and 500, 1000, 1500, 5000, 10,000, 15,000, 18,000, and 20,000 ppm for inorganic salts. The feed pressure was varied from 80 psig (5.516 bar) to 400 psig (27.579 bar) in steps of 20 psig (1.379 bar). However, in the first phase the experiments were conducted for lower feed concentration by using the pump capacity of operating pressure from 80 (5.516 bar) to 200 psig (13.79 bar) and a feed flow rate of 50 to 100 ml/s. After analyzing lower feed concen-
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Fig. 2. Laboratory-scale membrane set-up.
tration data it was found that the membrane transport parameters of NaCl-water and NaBrwater system were changing with respect to feed concentration. To investigate this behavior further, the experiments for higher concentrations were also carried out but by using a different pump of capacity 80 psig (5.516 bar) to 400 psig (27.579 bar) and feed flow rates of 50 to 100 ml/s. PCP, phenol and 2, 4-DNP concentrations were measured using high-performance liquid chromatography (Merck) with a UV detector (wavelength of phenol = 273nm, PCP = 304 nm and DNP = 278 nm); a reverse phase (RP select B) column was also used. The eluents were water (60% v) and Acetonitrile (40% V) [29]. The concentrations of the inorganic salts were estimated by ion chromatography (792 Basic IC Metrohm) with the anion column (Metrosep A Supp 5), using sodium carbonate (3.2 mM) and sodium bicarbonate (1 mM) as eluents. 4. Results Figs. 3 to 8 show the typical behavior of the permeate stream in terms of observed rejection,
(1!CP/CF)*100 and recovery (the ratio between permeate and feed flow rate) with respect to feed pressure for different feed flow rates and feed concentrations of the inorganic salts: NaCl, NaBr and KBr. Fig. 3 shows the trend of the observed rejection and recovery of the NaCl–water system with respect to feed pressure at different feed flow rates varying from 50 to 70 ml/s and at a fixed feed concentration of 495 ppm. The observed rejection decreases while the recovery increases as the feed pressure is increased. At a local point in the module, the observed rejection goes from 0 to a max value and to 0 again as the feed pressure increases. In fact, Murthy [2,7] has mathematically shown that this maximum in the observed rejection occurs at some Jv given by the following equation:
J
v min
(1 − σ ) k ln 1 + pm = kpm (1 − σ ) k
(7)
Thus, depending on which side of this maximum we observe the data, the observed rejection may
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Fig. 3. Permeate characteristics of NaCl–water system at C [NaCl]F = 495 ppm.
Fig. 5. Permeate characteristics of NaBr–water system at C [NaBr]F = 632 ppm.
increase or decrease as the feed pressure is increased. The predicted rejection in Fig. 3 exhibits similar behavior. In the present case, there are two difficulties in carrying out experiments for both lower and higher feed pressures at a particular feed flow rate. 1. There is a minimum pressure required to maintain a particular feed flow rate, for example the minimum feed pressure required to maintain
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Fig. 4. Permeate characteristics of NaCl–water system at QF = 60 ml/s.
Fig. 6. Permeate characteristics of NaBr–water system at pH = 6.0 and QF = 60 ml/s.
the feed flow rate of 60 ml/s and 70 ml/s for 10,301 ppm of the NaCl–water system is equal to 6.9 bar and 8.3 bar, respectively, as shown in Table 2. 2. Considering the life of the module, the maximum recovery suggested by DuPont where the module should be operated is 40%. Thus, for the example cited above, this recovery can be achieved at a feed pressure of 23.4 bar for a feed flow rate of 60 ml/s, as can be seen from the same
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Table 2 Separation data of NaCl-water system at 30.5EC (parameters: S–K model, Pm = 18.74856×10!8 m/s, σ = 0.92947; S–D model, Pm = 3.627545×10!8 m/s PF bar
8.3 9.7 11.0 13.8 16.5 19.3 22.1 6.9 9.7 12.4 15.2 17.9 20.7 23.4
QF ml/s
70 70 70 70 70 70 70 60 60 60 60 60 60 60
CF ppm
10301 10301 10301 10301 10301 10301 10301 10301 10301 10301 10301 10301 10301 10301
QP ml/s
6.0 7.5 9.5 12.5 16.0 19.5 22.5 4.8 7.5 11.0 14.0 17.5 20.5 23.0
CP ppm
5460 4976 4663 4353 4203 4252 4367 5806 4701 4555 4613 4665 4717 5001
S–K model
S–D model
QP ml/s
CP ppm
Error % QP
CP
6.3 8.0 9.7 13.3 16.8 20.1 23.2 4.7 7.7 10.8 14.0 16.9 19.6 22.2
5432 4992 4663 4253 4067 4021 4066 6186 5311 4858 4670 4644 4717 4854
!5.4 !6.7 !2.5 !6.3 !4.9 !3.0 !3.0 2.5 !2.4 1.4 0.3 3.5 4.2 3.6
0.5 !0.3 0 2.3 3.2 5.4 6.9 !6.5 !13 !6.7 !1.2 0.5 0 2.9
table. Because of these two reasons, we could only obtain data for a certain range of feed pressures presented in this work. However, as can be seen from Fig. 4, we have data at higher feed concentrations (>5000 ppm) where the observed rejection first increases and then decreases as the feed pressure is increased. On the other hand, the recovery decreases and the observed rejection increase as the feed flow rate is increased. The reason for this is that at a higher feed flow rate, the mass transfer coefficient k becomes larger, and hence the concentration polarization is reduced. This results in a lower solute concentration at the membrane feed interface. As a result, the permeate flux increases, and at the same time the solute concentration in the permeate declines. Similar behavior of the observed rejection and recovery for KBr and NaBr systems are seen in Figs. 5 and 7. The behavior of the recovery with respect to the feed flow rate can be explained from Ergun’s
QP ml/s
CP ppm
6.0 8.1 10.2 14.2 17.9 21.2 24.1 3.8 7.5 11.1 14.3 17.2 19.7 21.9
1719 1411 1231 1053 991 982 1004 2426 1604 1336 1250 1240 1270 1322
Error % QP
CP
0.0 !8.1 !7.6 !14.0 !11.9 !8.7 !7.1 20.6 0.3 !0.6 !2.2 1.9 3.9 4.7
68.5 71.6 73.6 75.8 76.4 76.9 77.0 52.5 65.9 70.7 72.9 73.4 73.1 73.6
equation and concentration polarization model. The pressure drop across the fiber bundle increases as the feed flow rate is increased, and this causes a decrease in the transmembrane pressure across the membrane at all local points in the module. As a result, the permeate flow rate decreases as the feed flow rate is increased. On the other hand, concentration polarization decreases and the permeate flux should increase as the feed flow rate is increased. Because of these two opposite effects, the permeate flow rate may almost remain constant or increase or decrease for a given solute–water system. In present cases, the permeate flow rates almost remain constant but the recovery decreases as the feed flow rate is increased, as can be seen from Figs. 3, 5, 7, 9–11. Figs. 4, 6, and 8 show the behavior of the observed rejection and recovery for three inorganic salts with respect to feed pressure and feed concentration for a fixed feed flow rate. The observed rejections of both NaCl and NaBr are
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Fig. 7. Permeate characteristics of KBr–water system at C [KBr] F = 535 ppm.
Fig. 8. Permeate characteristics of KBr–water system at QF = 60 ml/s.
higher for smaller feed concentrations up to a certain feed pressure, but after this pressure the observed rejections become smaller for smaller feed concentrations. On the other hand, the observed rejection of KBr is always smaller for smaller feed concentrations. However, the theoretical prediction of observed rejection for all three compounds shows that the observed rejection is higher for smaller feed concentrations up to a certain feed pressure but after this
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pressure the observed rejections become smaller for smaller feed concentrations. This behavior of observed rejection for both NaCl and NaBr may be due to the dependence of the solute permeability with respect to the feed concentration and will be further explored once the solute permeabilities have been determined. The behavior of observed rejection with respect to increasing feed concentration is also discussed by Soltanieh et al. [4] in detail by comparing the experimental results of Push [6] and Lui [5]. As per the data of Push [6], the observed rejection decreases while the data of Lui [5] shows that the observed rejection increases as the feed concentration is increased. As far as the recoveries are concerned, these figures show that the recovery decreases as the feed concentration is increased for all salts. This is along the expected lines as the osmotic pressure increases as the feed concentration is increased. The permeate characteristics in terms of observed rejection and recovery for organic components such as DNP, PCP and phenol are shown in Figs. 9–11. It can be easily seen that the behavior of recovery with respect to change in feed pressures as well as feed flow rates is similar to those for the inorganic components. The observed rejections of DNP and PCP increase and seem to reach a maximum as the feed pressure is increased for feed flow rate of 50 ml/s. However, for higher flow rates, the observed rejection increases as the feed pressure is increased. Though we do not have the experimental data for these flow rates at higher feed pressures, our theoretical predictions show that the observed rejections actually decrease if the feed pressure is further increased. On the other hand, for phenol the observed rejections increase, reach a maximum and then decrease slightly as the feed pressure is increased. The same behavior may be observed from our theoretical predictions also as shown in the figures. Fig. 12 shows the observed rejection of PCP, DNP and phenol with respect to feed pressure for
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Fig. 9. Permeate characteristics of DNP–water system at C [DNP]F = 52.07 ppm, pH = 6.0.
Fig. 10. Permeate characteristics of PCP–eater system at C [PCP]F = 52.07 ppm, pH = 6.0.
Fig. 11. Permeate characteristics of phenol–water system C [phenol]F = 100 ppm, pH = 6.0.
Fig. 12. Rejection of PCP, DNP and phenol vs. PF at CF = 50 ppm, QF = 50 ml/s.
different pH ranging from 6.0 to 9.1. The observed rejection of all the three compounds increases as the feed pH increases. This behavior may be due to an increase in the value of the partition coefficient at the feed–membrane interface as the feed pH is increased [3]. Thus, the solute permeabilities may be lower at higher feed pH. The observed rejections for PCP and DNP have a maximum with respect to feed pressure for all feed pH values. However, for phenol the maximum in observed rejection can be seen for
feed pH of 6.0 and 7.5 only. For pH of 9.0, the maximum occurs at lower feed pressure as can be seen from the theoretical predictions. 5. Parameter estimation The parameter estimation algorithm developed by Chatterjee et al. [28] was used to obtain the membrane transport parameters of the CFSK (which uses the Spiegler–Kedem model for membrane transport) model as well as CFSD (which
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Table 3 Membrane transport parameters of organic and inorganic parameters at pH = 6.0±0.3 Component
Temperature, EC
Feed concentration, ppm
Pm×108, m/s
σ
PCP Phenol DNP KBr NaCl
25
50–100 25–100 50–100 546–1486 495 961 1,396 5,008 10,301 15,700 17,526 21,139 632 1,142 1,563 5,279 11,330 16,935 18,023 22,339
3.42145 45.73812 5.72340 0.05350 0.47186 1.36935 2.39338 11.25022 18.74856 28.54155 28.54155 28.54155 0.17097 0.51969 0.98374 4.71499 9.32945 18.98475 18.98475 18.98475
0.97306 0.43034 0.98796 0.92710 0.91538 0.89994 0.88811 0.88332 0.92947 0.96335 0.96335 0.96335 0.92360 0.90978 0.89333 0.84748 0.84118 0.90686 0.90686 0.90686
NaBr
30.5
uses the solution–diffusion model for membrane transport) models. The separation data shown in Figs. 3–10 of each salt for a given feed concentration and pH consist of five sets of feed flow rates (50, 60, 70, 80, and 90 ml/s). The data for a feed flow rate of 60 ml/s was used for estimation of the membrane transport parameters for a given feed concentration and pH, and thereafter these parameters were used to predict the permeate characteristics for the other set of feed flow rates. As per the model developed by Chatterjee et al. [28], there are five unknown parameters, namely membrane hydrodynamic permeability (A), solute permeability (Pm), reflection coefficient (σ) and two constants (a and b) used in the mass transfer coefficient correlation. However, the value of A can be calculated from the pure water permeability data given in Table 3 by using
the same parameter estimation algorithm where the feed concentration is assumed to be zero. It may be noted that the value of A increases with the feed water temperature, as shown in Table 4. The estimated values of A at 25EC and 30.5EC are 7.322×10!10 kg/m2.s.pa and 9.4015×10!10 kg/m2.s.pa, respectively. Using these A values, the errors between the experimental and predicted values of pure water permeate flow rates are less than 1.22%, as can be seen from Table 4. The constants used in the mass transfer coefficient correlation were estimated by Chatterjee et al. [28] for the HFRO module. Since the same module was also used in this work, these constants are known for the present case. The remaining two parameters are the solute permeability and the reflection coefficient and these may be easily estimated by their parameter
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Table 4 Pure water permeability data (Aat 30.5EC = 9.401543×10-10 kg m!2 s!1 Pa!1; Aat 25EC = 7.322×10!10 kg m!2 s!1 Pa!1 Temperature, EC
PF, bar
QF,. ml/s
QP exp, ml/s
QP the, ml/s
Error, %
30.5 30.5 30.5 30.5 25 25 25 25
6.9 8.3 9.7 11 8.3 9.7 11 12.4
70 70 70 70 70 70 70 70
14.5 18 21.5 25 14.2 17.0 19.8 22.5
14.68 18.12 21.56 25 14.32 17.04 19.75 22.47
!1.22 !0.65 !0.27 0 !0.52 !0.22 0 0.17
Table 5 Membrane transport parameter of organic compounds at different pH and 25EC Component
pH ±0.3
Pm×108, m/s σ
PCP CF = 50–100 ppm
6.0 7.5 9.1 6.0 7.5 9.1 6.0 7.5 9.1
3.421 2.740 1.792 5.723 3.802 2.150 45.738 29.24 9.690
DNP CF = 50–100 ppm Phenol CF = 25–100 ppm
0.973 0.979 0.980 0.988 0.992 0.999 0.430 0.484 0.538
estimation procedure. The results obtained for the CFSK model for pH of 6±0.3 for both phenolic and inorganic compounds are summarized in Table 3. It can be seen that the membrane parameters for phenolic compounds are independent of the feed concentration, while the membrane parameters for inorganic salts show a wide variation with the changes in the feed concentration. The estimated parameters for the phenolic compounds with respect to pH to feed are shown in Table 5. 6. Discussion The permeate characteristics of all the six components are predicted for different feed flow
rates by using the estimated parameters of CFSK model as given in Tables 3 and 5, and the results are shown in Figs. 3–10 where both the experimental as well as the theoretical values of recovery and observed rejection are plotted as a function of feed pressure for different feed flow rates, feed concentrations and feed pH. As can be seen from these figures, the agreement between the experimental and theoretical predictions is excellent. The data used for parameter estimation as well as predictions of the NaCl–water system are shown in Table 2. Similar tables were obtained for all the remaining five systems. The maximum error between the experimental and theoretical values was less than 15% for both permeate flow rate as well as the permeate concentration. To understand the importance of the third parameter in the S–K model, the reflection coefficient, the same data were also analyzed by assuming that the mass transport may be described by the S–D model. The procedure for this is same as before except that here we fix the value of the reflection coefficient as one. The only parameter in this case to be determined is the solute permeability. This way, the experimental results were also analyzed as before, and the results for the NaCl water system are also shown in Table 2. In the case of the CFSD model, the maximum error now is 77%. Similar results were obtained for all experimental data, and it may be concluded that
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the CFSK model is a better predictor than the CFSD model. Tables 3 and 5 show the effect of feed concentration and pH on CFSK model parameters such as the solute permeability and the reflection coefficient. For a fixed pH, these parameters are constant for all feed concentration for three phenolic compounds. Initially, the experimental data for the inorganic compound were collected at feed concentrations of 500, 1000, and 1500 ppm. When these data were analyzed, the solute permeability and the reflection coefficient for KBr were found to be constant as can be seen from Table 3. On the other hand, for NaCl and NaBr systems, these parameters were found to be a strong function of concentration. Therefore, it was decided to collect more data for these compounds by changing the feed concentrations. Parameter estimations for all the data for these systems show (Table 3) that the solute permeability increases as feed concentration increases till the concentration reaches a value of 15,000 ppm; after this feed concentration the solute permeability becomes a constant. The reflection coefficient, on the other hand, shows peculiar behavior for these two systems as it first shows a minimum, then a maximum and finally the reflection coefficient becomes constant as the feed concentration is increased from 500 to 15,000 ppm. As far as the solute permeability is concerned, the variation of it with respect to feed concentration for the S–D model has also been reported for some systems [4–6,30,31]. This may be explained by considering the physical meaning to the parameter Pm in the S–D model. Here, Pm is composed of diffusivity of the solute in the membrane phase, the partition coefficient which is defined as the ratio of concentration of solute in membrane phase to that in the bulk phase, and the effective thickness of the membrane. Miyoshi et al. [32] reported that the diffusivity of electrolytes in the membrane increases slightly with increases in the feed concentration for smaller
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feed concentrations less than 5000 ppm. In addition, some studies have reported that the partition coefficient also strongly depends on the feed concentration for electrolytes [6]. Thus, we may expect that the solute permeability for some electrolytes may show variations with respect to feed concentrations for smaller values of feed concentrations. The extent of variation depends on the membrane and the solute. For some cases, the variation may be negligible (NaBr) while for other systems the variations may be quite large (NaCl and NaBr). This argument is supported by studies [4–6,33] where it has been observed that the Pm for some systems shows variations with respect to feed concentrations up to 10,000 ppm; after this the Pm becomes constant. Finally, the variation of the reflection coefficient with respect to feed concentration may be explained to a certain extent by considering its interpretation in the pore flow models for RO and ultrafiltration [34]. In these models the reflection coefficient is defined as the product of partition coefficient and the correction factor for the diffusive mass transport. Since both may be functions of feed concentrations, the reflection coefficient may also be a function of concentration according to these pore flow models. However, the present peculiar behavior of the reflection coefficient for the NaCl and NaBr systems with respect to the feed concentration where it shows both maximum as well as minimum cannot be explained at present. For the organic solutes, the Pm decreases while the reflection coefficient increases as the feed pH is increased as shown in Table 5. Ozaki et al. [3] explained this by considering the degree of dissociation of the phenolic species with respect to the feed pH. They reported that the degree of dissociation varies as the feed pH is increased. Since the degree of dissociation is inversely proportional to the partition coefficient, the Pm decreases as the feed pH is increased.
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7. Conclusions The experimental data for the separation of phenol, DNP, PCP, NaCl, NaBr and KBr from aqueous solutions were obtained for a variety of operating conditions in a B9 HFRO module. The permeate characteristics for all compounds were analyzed by the CFSK model available in the literature. The errors between experimental and theoretical predictions are less than 15% for both permeate flow rates and permeate concentrations. The model parameters such as the solute permeability and the reflection coefficient are constant for all phenolic compounds as well as for KBr. In the case of NaCl and NaBr the solute permeability increases as the feed concentration increases up to 15,000 ppm and after this concentration the solute permeabilities become independent of the feed concentration. On the other hand, the reflection coefficient for both NaCl and NaBr goes through a minimum and then increases to a maximum before reaching a constant value as the feed concentration is increased. It is also shown that for both organic as well as inorganic compounds that the CFSK model provides much better predictions than the CFSD model. 8. Symbols a A b Cb CF Cm
— Constant used in correlation for mass transfer coefficient — Hydrodynamic permeability, kg.m!2 s!1 Pa!1 — Constant used in correlation for mass transfer coefficient — Concentration of solute at bulk/ reject stream (kg solute per m3 of solution) — Concentration of solute in feed stream (kg solute per m3 of solution) — Membrane–solution interface solute concentration at bulk side (kg solute per m3 of solution)
Cp di Di do Do F Jv k L ls Mw N PF Pm QF QP QPexp QPThe R RObs Re Sc Sh T W
— Concentration of solute in permeate (kg solute per m3 of solution) — Inside diameter of hollow fiber, m — Inside diameter of hollow-fiber module, m — Outside diameter of hollow fiber, m — Outside diameter of hollow-fiber module, m — Parameter used in Spiegler–Kedem model — Permeate flux, m3.m!2.s!1 — Mass transfer coefficient, m.s!1 — Length of hollow fiber involved in mass transfer, m — Thickness of epoxy seal, m — Molecular weight, kg.kmol!1 — Number of hollow fibers in module — Feed pressure, Pa — Solute permeability, m.s!1 — Feed flow rate, ml.s!1 — Permeate flow rate, ml.s!1 — Experimental permeate flow rate, ml.s!1 — Theoretically predicted permeate flow rate, ml.s!1 — True rejection of solute in Eq. (2); Universal gas constant in Eq. (1) (8314 J.kmol!1.EK!1) — Observed rejection = (1!CP/CF)*100 — Reynolds number — Schmidt number — Sherwood number — Temperature, EK — Number of wounds
Greek σ φ ν
— Reflection coefficient — Concentration polarization — Vant Hoff factor
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References [1] J.E. Anderson, S.J. Hoffman and C.R. Peters, Factors influencing reverse osmosis of organic solutes from aqueous solution, J. Phys. Chem., 76 (1972) 4006. [2] Z.V.P. Murthy and S.K. Gupta, Estimation of mass transfer coefficient using a combined nonlinear membrane transport and film theory model, Desalination, 109 (1997) 39. [3] H. Ozaki and H. Li, Rejection of organic compounds by ultra-low pressure reverse osmosis membrane, Water Res., 36 (2002) 123. [4] M. Soltanieh and W.N. Gill, Review of reverse osmosis membranes and transport models, Chem. Eng. Comm., 12 (1981) 279. [5] K.W. Lui, Experimental investigation of intramembrane transport model in reverse osmosis, M.S. Thesis, Chemical Engineering, State University of New York, Buffalo, 1978. [6] W. Push, Determination of transport parameters of synthetic membranes by hyperfiltration experiments. Part I: derivation of transport relationship from the linear relations of thermodynamics of irreversible process, Ber. Bunsenges. Physik. Chem., 81 (1977) 269. [7] Z.V.P. Murthy and S.K. Gupta, Thin film composite polyamide membrane parameters estimation for phenol–water system by reverse osmosis, Sep. Sci. Technol., 33(16) (1998) 2541. [8] J.J. Hermans, Hydrodynamics of hollow fiber reverse osmosis modules, Membr. Digest, 1 (1972) 45. [9] W.N. Gill and B. Bansal, Hollow fiber reverse osmosis systems analysis and design, AIChE J., 19(4) (1973) 823. [10] M.S. Dandavati, M.R. Doshi and W.N. Gill, Hollow fiber reverse osmosis: experiments and analysis of radial flow system, Chem. Eng. Sci., 30 (1975) 877. [11] M.R. Doshi, W.N. Gill and V.N. Kabadi, Optimal design of hollow fiber modules, AIChE J., 23(5) (1977) 765. [12] H. Ohya, H. Nakajima, K. Tagaki, S. Kagawa and Y. Negishi, An analysis of reverse osmotic characteristics of B-9 hollow fiber module, Desalination, 21 (1977) 257. [13] J.J. Hermans, Physical aspects governing the design of hollow fiber modules, Desalination, 26 (1978) 45. [14] T.A. Tweddle, W.L. Thayar, T. Matsuura, F. Hsieh and S. Sourirajan, Specification of commercial
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reverse osmosis modules and predictability of their performance for water treatment applications, Desalination, 32 (1980) 181. N.V. Churaev and V.M. Starov, The theory of reverses osmosis separation of solutions on hollow fiber membranes, J. Coll. Interf. Sci., 89 (1982) 77. M. Soltanieh.and W.N. Gill, An experimental study of the complete-mixing model for radial flow hollow fiber reverse osmosis systems, Desalination, 49 (1984) 57. S.K. Gupta, Design and analysis of radial flow hollow fiber reverse osmosis module, Ind. Eng. Chem. Res., 26 (1987) 2319. R. Rautenbach and W. Dahm, Design and optimization of spiral-wound and hollow fiber ROmodules, Desalination, 65 (1987) 259. W.N. Gill, M.R. Matsumoto, A.L. Gill and Y.T. Lee, Flow patterns in radial flow hollow fiber reverse osmosis systems, Desalination, 68 (1988) 29. M. Sekino, Precise analytical model of hollow fiber reverse osmosis modules, J. Membr. Sci., 85 (1993) 241. V.M. Starov, J. Smart and D.R. Lloyd, Performance optimization of hollow fiber reverse osmosis membranes. Part I. Development of theory, J. Membr. Sci., 103 (1995) 257. M. Sekino, Study of an analytical model for hollow fiber reverse osmosis module systems, Desalination, 100 (1995) 85. V.M. Starov, J. Smart and D.R. Lloyd, Performance optimization of hollow fiber reverse osmosis membranes. Part II. Comparative study of flow configurations, J. Membr. Sci., 119 (1996) 117. A.M. El-Halwagi, V. Manousiouthakis and M.M. ElHalwagi, Analysis and simulation of hollow-fiber reverse osmosis modules, Sep. Sci. Tech., 31 (1996) 2505. N.M. Al-Bastaki and A. Abbas, Modeling an industrial reverse osmosis unit, Desalination, 126 (1999) 33. N.M. Al-Bastaki and A. Abbas, Predicting the performance of RO membranes, Desalination, 132 (2000) 181. J. Marriott and E. Sorensen, A general approach to modelling membrane modules, Chem. Eng. Sci., 58 (2003) 4975. A. Chatterjee, A. Ahluwalia, S. Senthilmurugan and S.K. Gupta, Modeling of a radial flow hollow fiber
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module and estimation of model parameters using numerical techniques, J. Membr. Sci., 236 (2004) 1. [29] APHA, AWWA and WPCF, Method 4500-Cl- E: Automated ferricynide methods, Standard Methods for Examination of Water and Waste water, 18th ed., 1992. [30] D. Van Gauwbergen and J. Baeyens, Modeling reverse osmosis by irreversible thermodynamics, Sep. Purif. Tech., 13 (1998) 117. [31] R.I. Urama and B.J. Marinas, Mechanistic interpretation of solute permeation through a fully polyamide reverse osmosis membrane, J. Membr. Sci., 123 (1997) 267.
[32] H. Miyoshi, T. Fukumoto and T. Kataoka, An equation for estimating the diffusion coefficient of electrolytes in ion exchange membranes, Desalination, 48 (1983) 43. [33] F.J. Sapienza, W.N. Gill and M. Soltanieh, Separation of ternary salt/acid aqueous solutions using hollow fiber reverse osmosis, J. Membr. Sci., 54 (1990) 175. [34] W.S. Opong and A.L. Zydney, Diffusive and convective protein transport through asymmetric membranes, AIChE J., 37 (1991) 1497.
Separation of inorganic and organic compounds by using a radial flow hollow-fiber reverse osmosis module S. Senthilmurugan*, Sharad K. Gupta** Chemical Engineering Department, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India Tel. +91 (11) 2659 1023; Fax: +91 (11) 2658 1120; email: [email protected] Received 1 July 2005; accepted 20 February 2006
Abstract Phenol, 2,4-dinitrophenol (DNP), pentachlorophenol (PCP), NaCl, NaBr and KBr were separated from aqueous solutions under a variety of operating conditions by using a Permasep B9 hollow-fiber reverse osmosis module. The permeate characteristics for all compounds were analyzed by the combined film Spiegler–Kedem (CFSK) model available in the literature. The errors between experimental and theoretical predictions were less than 15% for both permeate flow rates as well as permeate concentrations. The model parameters such as the solute permeability and the reflection coefficient were constant for all phenolic compounds and KBr at constant temperature and pH. However, in the case of NaCl and NaBr, the solute permeability increased as the feed concentration increased to 15,000 ppm, after which the concentration of the solute permeabilities became independent of the feed concentration. On the other hand, the reflection coefficient for both NaCl and NaBr went through a minimum and then increased to a maximum before reaching a constant value as the feed concentration was increased. It is also shown that for both organic as well as inorganic compounds. the CFSK model provides much better theoretical predictions than the combined film solution diffusion model. Keywords: Reverse osmosis; Spiegler–Kedem model; Concentration polarization; Organic compound; Inorganic salt
1. Introduction *Present address: Reprocessing Research and Development Division, Reprocessing Group, IGCAR, Kalpakkam, Tamilnadu, India 603 102. **Corresponding author.
Reverse osmosis (RO) is one of the commercially viable processes available for water desalination for both domestic and industrial purposes. But recently RO is also being used for the separation of impurities from industrial effluent. Commonly three types of module con-
0011-9164/06/$– See front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.desal.2006.02.001
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figuration are available in the market. These are tubular, spiral-wound and hollow-fiber configurations. Among these the hollow-fiber module offers the highest surface area per unit volume of module. The separation of organic as well inorganic species has been extensively studied in the literature for RO and nanofiltration by using flatsheet membranes. Since these are needed for understanding the separation of organic and inorganic compounds in a hollow-fiber module presented in this work, these studies are briefly discussed below. The separation of organic compounds in a flatsheet membrane test cell was studied by several authors [1–3]. Most of these were carried out with cellulose acetate membranes while several others studied the separation by using non-cellulose membranes [2]. Anderson et al. [1] concluded that un-ionized and hydrophobic solutes are strongly adsorbed by the cellulose acetate membranes and such solutes exhibit poor rejection properties. Ionized and hydrophilic solutes, on the other hand, have low solubility in membranes and experience good rejection. Murthy et al. [2] analyzed the phenol data obtained in a membrane test cell by using both the solution diffusion as well as the Spiegler–Kedem model combined with concentration polarization and found that the rejection reached a maximum at some JV. The analytical solution for maximum rejection was also derived for both models. Finally, Ozaki et al. [3] carried out experiments with low molecular weight organic compounds using ultra-low pressure flat-sheet polyamide membranes. The pH of the feed solution was varied from 3 to 9. They concluded that the rejection of undissociated organic compounds was not affected significantly with respect to the feed pH. On the other hand, rejection increased with molecular weight as well as molecular width, but the rejection of dissociated organic compound increased with respect to feed pH.
Soltanieh et al. [4] reviewed the membrane transport models and their applications for the separation of inorganic compounds in flat-sheet RO membranes. They concluded that the nonlinear Spiegler–Kedem model is more appropriate for describing the solute flux for these compounds. Here, it was pointed out that the NaCl data of Lui [5] showed that the reflection coefficient (σ) was almost constant, and the rejection and solute permeability (Pm) increased as the feed concentration decreases. This is in contrast to the results of Push [6] who found that as the feed NaCl concentration decreases, σ increases, and the rejection and Pm decreases. Some other studies [7] have observed that the Pm and σ are constants with respect to feed NaCl concentration. Many researchers have studied the hydrodynamics of hollow-fiber RO (HFRO) modules [8–28]. In some studies analytical solutions [8, 19,21,23] were obtained while others tried numerical solutions [20,22,24–28] for determining the permeate flow rate and the permeate concentration for a given feed flow rate, feed concentration, and feed pressure. All the mathematical models for HFRO above can be differentiated based on the membrane transport model used for describing the membrane transport and the concentration polarization model used for mass transport across the film and estimation of pressure drop in both shell and bore side. The membrane transport model used is either the twoparameter membrane transport model such as the solution–diffusion model [8–23,25–27] or a three-parameter membrane transport model such as the Kedem–Katchalsky [24] or Spiegler– Kedem model [28]. In some cases concentration polarization is neglected [8–16,18,19,21,23, 24,27], while other models combine the concentration polarization with a suitable membrane transport model. Finally, the shell side pressure drop calculated by using Ergun’s equation shows a negligible pressure drop and therefore in most
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of the models the shell side pressure is assumed to be constant. On the other hand, the shell side pressure drop determined by solving the equations of continuity and motion shows that this may not be the case [24]. Very few experimental studies on hollow-fiber modules are available in the literature. Most of the models discussed above used the data of Ohya et al. [12] and Tweddle et al. [14] on a NaCl– water system for model validation purposes. Sekino [20] and Soltanieh et al. [16] also obtained some experimental data on a NaCl–water system on hollow-fiber modules. Recently, Chatterjee et al. [28] reported separation data for a phenol– water system in a radial flow hollow-fiber module. Furthermore, they also developed a mathematical model for this module where the three-parameter Spiegler–Kedem model was used for describing mass transport across the membrane. Concentration polarization was taken into account by using the film theory and the pressure drops in both permeate and bulk streams were also included. In addition, the analytical solution for pure water permeability was also derived. The mass transfer coefficient parameters and the membrane model parameters were determined by using an optimization technique — the simplex search. The model showed good agreement with the experimental results for both the NaCl–water and phenol–water systems. Finally, they also showed that the combined film Spiegler–Kedem model (CFSK) used by them provides better results than the combined film solution diffusion model of Sekino [20,22]. From the literature [12,14,16,20,28], it is clear that only the separation of NaCl and phenol have been studied in a HFRO module. For the case of experimental studies on flat-sheet membranes and inorganic salts, the membrane transport parameters are found to be changing for low feed concentrations, whereas a similar case may also be possible in a hollow-fiber module. But there are no experimental data available at a low con-
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centration of inorganic salts. In addition, only phenol separation has been studied experimentally. Thus, sufficient experimental data are not available in the literature to reach any definite conclusion about the validity of any mathematical models discussed above. The main objective of this work was to get the separation data of inorganic salts such as NaCl, NaBr and KBr. Furthermore, the separation of phenol, PCP and DNP from an aqueous solution was also studied in the same module. While the focus for phenolic compounds was to obtain data for different feed pH, the experimental data for inorganic compound was obtained at different feed concentrations. Finally, the experimental data for all six compounds were analyzed by using the CFSK model of Chatterjee et al. [28]. The membrane transport parameters appearing in this model are estimated, and the effect of feed pH for phenolic compounds and the effect of feed concentration for inorganic compounds on these parameters are also analyzed in detail. 2. Theory The HFRO membrane configuration was developed by DuPont in the late 1960s, followed by Dow Chemical and Toyobo (Japan). The construction of each manufacturer’s device is similar, but they differ in their fiber dimensions, fiber support and membrane materials. The DuPont HF membrane modules are made from asymmetric aramid fibers of 42 µm ID × 85 µm OD for B-9 and 42 µm ID X 95 µm OD for B-10 modules. The Dow modules, which are no longer available, were made from cellulose triacetate fibers of 250 µm OD. On the other hand, Toyobo Hollosep modules use cellulose triacetate fiber of dimensions 70 µm ID and 165 µm OD. All the experiments were carried out in a radial flow B-9 module. The module dimensions and internal structure are given in Table 1 and Fig. 1, respectively. The model equations for
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Fig. 1. Radial flow -9 HFRO module.
Table 1 Membrane specification for Du Pont B9 HFRO modules Membrane specification for:
Values
do, m di, m Do, m Di, m N L, m I s, m w Membrane area, m2
91×10!6 a 44 x 10-6 a 0.1016a 0.025a 383,820a 0.254a 0.02b 0 27.87a
a
As provided by Du Pont. b Assumed.
Jv =
vRT A ( Pb − Pp ) − σ ( Cm − C p ) (1) ρ Mw
R = 1−
Cp Cm
=
(1 − F )σ 1 − σF
(2)
Eq. (2) is combined with film theory for concentration polarization, and finally we obtain the following equation for the solute concentration in the permeate stream:
Cp =
Cb (1 − F )σ 1+ (1 − σ)φ
(3)
where hollow fiber module developed in previous work [28] are given below. The Spiegler–Kedem model is used for describing the membrane mass transport. According to this model the permeate flux, Jv, and the true rejection, R, are given by the following equations.
J (1 − σ) F = exp − v Pm and
(4)
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J v Cm − C p = k Cb − C p
φ = exp
(5)
Eqs. (1) and (3) along with the equations of conservation of mass for both solvent and solute are the starting point of the CFSK model. These equations may be solved numerically following the procedure previously outlined [28]. The same equations with σ equal to 1 are the equations of the CFSD model. The shell side mass transfer coefficient (k) used in Eq. (5) may be expressed as a function of Reynolds and Schmidt numbers:
Sh = a ( Re ) ( Sc ) b
1/ 3
(6)
The literature contains numerous correlations for shell side mass transfer coefficient in the form expressed in Eq. (6). Sekino [6,22] and Chatterjee et al. [28] have suggested the value of a = 0.048 and b = 0.6 for the Toyobo Hollosep and B-9 HFRO modules. Membrane transport parameters appearing in above model are A, Pm, σ, and mass transfer coefficient constants a and b in Eq. (6). 3. Experimental set-up and procedure The experimental set-up consists of a Permasep B9 HFRO module, a feed tank, and a reciprocating pump. The pressure gauges are placed before and after the module to measure the pressure at feed inlet, reject and the permeate streams. The schematic diagram of the set-up is shown in Fig. 2. A sump is provided to regulate the flow fluctuation due to the reciprocating action of the pump. The reject and permeate streams are sent back to the feed tank since the feed concentration is to be maintained at constant level. The experimental data for the separation of organic compounds were collected at different pH values in the range of 6.0±0.3 to 9.1±0.3, while the pH of 6.0±0.3 was maintained for the separa-
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tion of inorganic compounds. The experimental procedure consisted of a systematic variation of the four operating conditions namely, the feed pressure, flow rate, the concentration and pH of the feed stream. The permeate samples were collected by varying feed pressure with identical increments while maintaining the same feed flow rate. The same procedure as described above was followed for the new value of feed flow rate, pH and concentration. The temperature of the feed was maintained by using both a temperature controller and a cooling water unit. The experiments for separation of phenolic compounds were performed in winter (approximately 15–20EC ambient temperatures) at 25EC and for inorganic compounds were carried in summer (approximately 42–47EC ambient temperatures) at 30.5EC as the temperature in the summer could not be controlled at 25EC using the present cooling water unit. The feed solution was prepared by dissolving the requisite amount of salt in distilled water. Before conducting the next run with a new feed, the set-up was operated for 6 h to allow the residual phenolic compound or inorganic salt from previous runs in the module to be washed out. Intermittent stirring of the solution in the feed tank also helped in ensuring uniform concentration and pH throughout the feed solution. The feed flow rates were varied in steps of approximately 10 ml/s in the range of 50 ml/s to 100 ml/s. The solute concentration was varied from 25 to 100 ppm with steps of 25 ppm for organic components and 500, 1000, 1500, 5000, 10,000, 15,000, 18,000, and 20,000 ppm for inorganic salts. The feed pressure was varied from 80 psig (5.516 bar) to 400 psig (27.579 bar) in steps of 20 psig (1.379 bar). However, in the first phase the experiments were conducted for lower feed concentration by using the pump capacity of operating pressure from 80 (5.516 bar) to 200 psig (13.79 bar) and a feed flow rate of 50 to 100 ml/s. After analyzing lower feed concen-
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Fig. 2. Laboratory-scale membrane set-up.
tration data it was found that the membrane transport parameters of NaCl-water and NaBrwater system were changing with respect to feed concentration. To investigate this behavior further, the experiments for higher concentrations were also carried out but by using a different pump of capacity 80 psig (5.516 bar) to 400 psig (27.579 bar) and feed flow rates of 50 to 100 ml/s. PCP, phenol and 2, 4-DNP concentrations were measured using high-performance liquid chromatography (Merck) with a UV detector (wavelength of phenol = 273nm, PCP = 304 nm and DNP = 278 nm); a reverse phase (RP select B) column was also used. The eluents were water (60% v) and Acetonitrile (40% V) [29]. The concentrations of the inorganic salts were estimated by ion chromatography (792 Basic IC Metrohm) with the anion column (Metrosep A Supp 5), using sodium carbonate (3.2 mM) and sodium bicarbonate (1 mM) as eluents. 4. Results Figs. 3 to 8 show the typical behavior of the permeate stream in terms of observed rejection,
(1!CP/CF)*100 and recovery (the ratio between permeate and feed flow rate) with respect to feed pressure for different feed flow rates and feed concentrations of the inorganic salts: NaCl, NaBr and KBr. Fig. 3 shows the trend of the observed rejection and recovery of the NaCl–water system with respect to feed pressure at different feed flow rates varying from 50 to 70 ml/s and at a fixed feed concentration of 495 ppm. The observed rejection decreases while the recovery increases as the feed pressure is increased. At a local point in the module, the observed rejection goes from 0 to a max value and to 0 again as the feed pressure increases. In fact, Murthy [2,7] has mathematically shown that this maximum in the observed rejection occurs at some Jv given by the following equation:
J
v min
(1 − σ ) k ln 1 + pm = kpm (1 − σ ) k
(7)
Thus, depending on which side of this maximum we observe the data, the observed rejection may
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Fig. 3. Permeate characteristics of NaCl–water system at C [NaCl]F = 495 ppm.
Fig. 5. Permeate characteristics of NaBr–water system at C [NaBr]F = 632 ppm.
increase or decrease as the feed pressure is increased. The predicted rejection in Fig. 3 exhibits similar behavior. In the present case, there are two difficulties in carrying out experiments for both lower and higher feed pressures at a particular feed flow rate. 1. There is a minimum pressure required to maintain a particular feed flow rate, for example the minimum feed pressure required to maintain
227
Fig. 4. Permeate characteristics of NaCl–water system at QF = 60 ml/s.
Fig. 6. Permeate characteristics of NaBr–water system at pH = 6.0 and QF = 60 ml/s.
the feed flow rate of 60 ml/s and 70 ml/s for 10,301 ppm of the NaCl–water system is equal to 6.9 bar and 8.3 bar, respectively, as shown in Table 2. 2. Considering the life of the module, the maximum recovery suggested by DuPont where the module should be operated is 40%. Thus, for the example cited above, this recovery can be achieved at a feed pressure of 23.4 bar for a feed flow rate of 60 ml/s, as can be seen from the same
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Table 2 Separation data of NaCl-water system at 30.5EC (parameters: S–K model, Pm = 18.74856×10!8 m/s, σ = 0.92947; S–D model, Pm = 3.627545×10!8 m/s PF bar
8.3 9.7 11.0 13.8 16.5 19.3 22.1 6.9 9.7 12.4 15.2 17.9 20.7 23.4
QF ml/s
70 70 70 70 70 70 70 60 60 60 60 60 60 60
CF ppm
10301 10301 10301 10301 10301 10301 10301 10301 10301 10301 10301 10301 10301 10301
QP ml/s
6.0 7.5 9.5 12.5 16.0 19.5 22.5 4.8 7.5 11.0 14.0 17.5 20.5 23.0
CP ppm
5460 4976 4663 4353 4203 4252 4367 5806 4701 4555 4613 4665 4717 5001
S–K model
S–D model
QP ml/s
CP ppm
Error % QP
CP
6.3 8.0 9.7 13.3 16.8 20.1 23.2 4.7 7.7 10.8 14.0 16.9 19.6 22.2
5432 4992 4663 4253 4067 4021 4066 6186 5311 4858 4670 4644 4717 4854
!5.4 !6.7 !2.5 !6.3 !4.9 !3.0 !3.0 2.5 !2.4 1.4 0.3 3.5 4.2 3.6
0.5 !0.3 0 2.3 3.2 5.4 6.9 !6.5 !13 !6.7 !1.2 0.5 0 2.9
table. Because of these two reasons, we could only obtain data for a certain range of feed pressures presented in this work. However, as can be seen from Fig. 4, we have data at higher feed concentrations (>5000 ppm) where the observed rejection first increases and then decreases as the feed pressure is increased. On the other hand, the recovery decreases and the observed rejection increase as the feed flow rate is increased. The reason for this is that at a higher feed flow rate, the mass transfer coefficient k becomes larger, and hence the concentration polarization is reduced. This results in a lower solute concentration at the membrane feed interface. As a result, the permeate flux increases, and at the same time the solute concentration in the permeate declines. Similar behavior of the observed rejection and recovery for KBr and NaBr systems are seen in Figs. 5 and 7. The behavior of the recovery with respect to the feed flow rate can be explained from Ergun’s
QP ml/s
CP ppm
6.0 8.1 10.2 14.2 17.9 21.2 24.1 3.8 7.5 11.1 14.3 17.2 19.7 21.9
1719 1411 1231 1053 991 982 1004 2426 1604 1336 1250 1240 1270 1322
Error % QP
CP
0.0 !8.1 !7.6 !14.0 !11.9 !8.7 !7.1 20.6 0.3 !0.6 !2.2 1.9 3.9 4.7
68.5 71.6 73.6 75.8 76.4 76.9 77.0 52.5 65.9 70.7 72.9 73.4 73.1 73.6
equation and concentration polarization model. The pressure drop across the fiber bundle increases as the feed flow rate is increased, and this causes a decrease in the transmembrane pressure across the membrane at all local points in the module. As a result, the permeate flow rate decreases as the feed flow rate is increased. On the other hand, concentration polarization decreases and the permeate flux should increase as the feed flow rate is increased. Because of these two opposite effects, the permeate flow rate may almost remain constant or increase or decrease for a given solute–water system. In present cases, the permeate flow rates almost remain constant but the recovery decreases as the feed flow rate is increased, as can be seen from Figs. 3, 5, 7, 9–11. Figs. 4, 6, and 8 show the behavior of the observed rejection and recovery for three inorganic salts with respect to feed pressure and feed concentration for a fixed feed flow rate. The observed rejections of both NaCl and NaBr are
S. Senthilmurugan, S.K. Gupta / Desalination 196 (2006) 221–236
Fig. 7. Permeate characteristics of KBr–water system at C [KBr] F = 535 ppm.
Fig. 8. Permeate characteristics of KBr–water system at QF = 60 ml/s.
higher for smaller feed concentrations up to a certain feed pressure, but after this pressure the observed rejections become smaller for smaller feed concentrations. On the other hand, the observed rejection of KBr is always smaller for smaller feed concentrations. However, the theoretical prediction of observed rejection for all three compounds shows that the observed rejection is higher for smaller feed concentrations up to a certain feed pressure but after this
229
pressure the observed rejections become smaller for smaller feed concentrations. This behavior of observed rejection for both NaCl and NaBr may be due to the dependence of the solute permeability with respect to the feed concentration and will be further explored once the solute permeabilities have been determined. The behavior of observed rejection with respect to increasing feed concentration is also discussed by Soltanieh et al. [4] in detail by comparing the experimental results of Push [6] and Lui [5]. As per the data of Push [6], the observed rejection decreases while the data of Lui [5] shows that the observed rejection increases as the feed concentration is increased. As far as the recoveries are concerned, these figures show that the recovery decreases as the feed concentration is increased for all salts. This is along the expected lines as the osmotic pressure increases as the feed concentration is increased. The permeate characteristics in terms of observed rejection and recovery for organic components such as DNP, PCP and phenol are shown in Figs. 9–11. It can be easily seen that the behavior of recovery with respect to change in feed pressures as well as feed flow rates is similar to those for the inorganic components. The observed rejections of DNP and PCP increase and seem to reach a maximum as the feed pressure is increased for feed flow rate of 50 ml/s. However, for higher flow rates, the observed rejection increases as the feed pressure is increased. Though we do not have the experimental data for these flow rates at higher feed pressures, our theoretical predictions show that the observed rejections actually decrease if the feed pressure is further increased. On the other hand, for phenol the observed rejections increase, reach a maximum and then decrease slightly as the feed pressure is increased. The same behavior may be observed from our theoretical predictions also as shown in the figures. Fig. 12 shows the observed rejection of PCP, DNP and phenol with respect to feed pressure for
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Fig. 9. Permeate characteristics of DNP–water system at C [DNP]F = 52.07 ppm, pH = 6.0.
Fig. 10. Permeate characteristics of PCP–eater system at C [PCP]F = 52.07 ppm, pH = 6.0.
Fig. 11. Permeate characteristics of phenol–water system C [phenol]F = 100 ppm, pH = 6.0.
Fig. 12. Rejection of PCP, DNP and phenol vs. PF at CF = 50 ppm, QF = 50 ml/s.
different pH ranging from 6.0 to 9.1. The observed rejection of all the three compounds increases as the feed pH increases. This behavior may be due to an increase in the value of the partition coefficient at the feed–membrane interface as the feed pH is increased [3]. Thus, the solute permeabilities may be lower at higher feed pH. The observed rejections for PCP and DNP have a maximum with respect to feed pressure for all feed pH values. However, for phenol the maximum in observed rejection can be seen for
feed pH of 6.0 and 7.5 only. For pH of 9.0, the maximum occurs at lower feed pressure as can be seen from the theoretical predictions. 5. Parameter estimation The parameter estimation algorithm developed by Chatterjee et al. [28] was used to obtain the membrane transport parameters of the CFSK (which uses the Spiegler–Kedem model for membrane transport) model as well as CFSD (which
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Table 3 Membrane transport parameters of organic and inorganic parameters at pH = 6.0±0.3 Component
Temperature, EC
Feed concentration, ppm
Pm×108, m/s
σ
PCP Phenol DNP KBr NaCl
25
50–100 25–100 50–100 546–1486 495 961 1,396 5,008 10,301 15,700 17,526 21,139 632 1,142 1,563 5,279 11,330 16,935 18,023 22,339
3.42145 45.73812 5.72340 0.05350 0.47186 1.36935 2.39338 11.25022 18.74856 28.54155 28.54155 28.54155 0.17097 0.51969 0.98374 4.71499 9.32945 18.98475 18.98475 18.98475
0.97306 0.43034 0.98796 0.92710 0.91538 0.89994 0.88811 0.88332 0.92947 0.96335 0.96335 0.96335 0.92360 0.90978 0.89333 0.84748 0.84118 0.90686 0.90686 0.90686
NaBr
30.5
uses the solution–diffusion model for membrane transport) models. The separation data shown in Figs. 3–10 of each salt for a given feed concentration and pH consist of five sets of feed flow rates (50, 60, 70, 80, and 90 ml/s). The data for a feed flow rate of 60 ml/s was used for estimation of the membrane transport parameters for a given feed concentration and pH, and thereafter these parameters were used to predict the permeate characteristics for the other set of feed flow rates. As per the model developed by Chatterjee et al. [28], there are five unknown parameters, namely membrane hydrodynamic permeability (A), solute permeability (Pm), reflection coefficient (σ) and two constants (a and b) used in the mass transfer coefficient correlation. However, the value of A can be calculated from the pure water permeability data given in Table 3 by using
the same parameter estimation algorithm where the feed concentration is assumed to be zero. It may be noted that the value of A increases with the feed water temperature, as shown in Table 4. The estimated values of A at 25EC and 30.5EC are 7.322×10!10 kg/m2.s.pa and 9.4015×10!10 kg/m2.s.pa, respectively. Using these A values, the errors between the experimental and predicted values of pure water permeate flow rates are less than 1.22%, as can be seen from Table 4. The constants used in the mass transfer coefficient correlation were estimated by Chatterjee et al. [28] for the HFRO module. Since the same module was also used in this work, these constants are known for the present case. The remaining two parameters are the solute permeability and the reflection coefficient and these may be easily estimated by their parameter
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Table 4 Pure water permeability data (Aat 30.5EC = 9.401543×10-10 kg m!2 s!1 Pa!1; Aat 25EC = 7.322×10!10 kg m!2 s!1 Pa!1 Temperature, EC
PF, bar
QF,. ml/s
QP exp, ml/s
QP the, ml/s
Error, %
30.5 30.5 30.5 30.5 25 25 25 25
6.9 8.3 9.7 11 8.3 9.7 11 12.4
70 70 70 70 70 70 70 70
14.5 18 21.5 25 14.2 17.0 19.8 22.5
14.68 18.12 21.56 25 14.32 17.04 19.75 22.47
!1.22 !0.65 !0.27 0 !0.52 !0.22 0 0.17
Table 5 Membrane transport parameter of organic compounds at different pH and 25EC Component
pH ±0.3
Pm×108, m/s σ
PCP CF = 50–100 ppm
6.0 7.5 9.1 6.0 7.5 9.1 6.0 7.5 9.1
3.421 2.740 1.792 5.723 3.802 2.150 45.738 29.24 9.690
DNP CF = 50–100 ppm Phenol CF = 25–100 ppm
0.973 0.979 0.980 0.988 0.992 0.999 0.430 0.484 0.538
estimation procedure. The results obtained for the CFSK model for pH of 6±0.3 for both phenolic and inorganic compounds are summarized in Table 3. It can be seen that the membrane parameters for phenolic compounds are independent of the feed concentration, while the membrane parameters for inorganic salts show a wide variation with the changes in the feed concentration. The estimated parameters for the phenolic compounds with respect to pH to feed are shown in Table 5. 6. Discussion The permeate characteristics of all the six components are predicted for different feed flow
rates by using the estimated parameters of CFSK model as given in Tables 3 and 5, and the results are shown in Figs. 3–10 where both the experimental as well as the theoretical values of recovery and observed rejection are plotted as a function of feed pressure for different feed flow rates, feed concentrations and feed pH. As can be seen from these figures, the agreement between the experimental and theoretical predictions is excellent. The data used for parameter estimation as well as predictions of the NaCl–water system are shown in Table 2. Similar tables were obtained for all the remaining five systems. The maximum error between the experimental and theoretical values was less than 15% for both permeate flow rate as well as the permeate concentration. To understand the importance of the third parameter in the S–K model, the reflection coefficient, the same data were also analyzed by assuming that the mass transport may be described by the S–D model. The procedure for this is same as before except that here we fix the value of the reflection coefficient as one. The only parameter in this case to be determined is the solute permeability. This way, the experimental results were also analyzed as before, and the results for the NaCl water system are also shown in Table 2. In the case of the CFSD model, the maximum error now is 77%. Similar results were obtained for all experimental data, and it may be concluded that
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the CFSK model is a better predictor than the CFSD model. Tables 3 and 5 show the effect of feed concentration and pH on CFSK model parameters such as the solute permeability and the reflection coefficient. For a fixed pH, these parameters are constant for all feed concentration for three phenolic compounds. Initially, the experimental data for the inorganic compound were collected at feed concentrations of 500, 1000, and 1500 ppm. When these data were analyzed, the solute permeability and the reflection coefficient for KBr were found to be constant as can be seen from Table 3. On the other hand, for NaCl and NaBr systems, these parameters were found to be a strong function of concentration. Therefore, it was decided to collect more data for these compounds by changing the feed concentrations. Parameter estimations for all the data for these systems show (Table 3) that the solute permeability increases as feed concentration increases till the concentration reaches a value of 15,000 ppm; after this feed concentration the solute permeability becomes a constant. The reflection coefficient, on the other hand, shows peculiar behavior for these two systems as it first shows a minimum, then a maximum and finally the reflection coefficient becomes constant as the feed concentration is increased from 500 to 15,000 ppm. As far as the solute permeability is concerned, the variation of it with respect to feed concentration for the S–D model has also been reported for some systems [4–6,30,31]. This may be explained by considering the physical meaning to the parameter Pm in the S–D model. Here, Pm is composed of diffusivity of the solute in the membrane phase, the partition coefficient which is defined as the ratio of concentration of solute in membrane phase to that in the bulk phase, and the effective thickness of the membrane. Miyoshi et al. [32] reported that the diffusivity of electrolytes in the membrane increases slightly with increases in the feed concentration for smaller
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feed concentrations less than 5000 ppm. In addition, some studies have reported that the partition coefficient also strongly depends on the feed concentration for electrolytes [6]. Thus, we may expect that the solute permeability for some electrolytes may show variations with respect to feed concentrations for smaller values of feed concentrations. The extent of variation depends on the membrane and the solute. For some cases, the variation may be negligible (NaBr) while for other systems the variations may be quite large (NaCl and NaBr). This argument is supported by studies [4–6,33] where it has been observed that the Pm for some systems shows variations with respect to feed concentrations up to 10,000 ppm; after this the Pm becomes constant. Finally, the variation of the reflection coefficient with respect to feed concentration may be explained to a certain extent by considering its interpretation in the pore flow models for RO and ultrafiltration [34]. In these models the reflection coefficient is defined as the product of partition coefficient and the correction factor for the diffusive mass transport. Since both may be functions of feed concentrations, the reflection coefficient may also be a function of concentration according to these pore flow models. However, the present peculiar behavior of the reflection coefficient for the NaCl and NaBr systems with respect to the feed concentration where it shows both maximum as well as minimum cannot be explained at present. For the organic solutes, the Pm decreases while the reflection coefficient increases as the feed pH is increased as shown in Table 5. Ozaki et al. [3] explained this by considering the degree of dissociation of the phenolic species with respect to the feed pH. They reported that the degree of dissociation varies as the feed pH is increased. Since the degree of dissociation is inversely proportional to the partition coefficient, the Pm decreases as the feed pH is increased.
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7. Conclusions The experimental data for the separation of phenol, DNP, PCP, NaCl, NaBr and KBr from aqueous solutions were obtained for a variety of operating conditions in a B9 HFRO module. The permeate characteristics for all compounds were analyzed by the CFSK model available in the literature. The errors between experimental and theoretical predictions are less than 15% for both permeate flow rates and permeate concentrations. The model parameters such as the solute permeability and the reflection coefficient are constant for all phenolic compounds as well as for KBr. In the case of NaCl and NaBr the solute permeability increases as the feed concentration increases up to 15,000 ppm and after this concentration the solute permeabilities become independent of the feed concentration. On the other hand, the reflection coefficient for both NaCl and NaBr goes through a minimum and then increases to a maximum before reaching a constant value as the feed concentration is increased. It is also shown that for both organic as well as inorganic compounds that the CFSK model provides much better predictions than the CFSD model. 8. Symbols a A b Cb CF Cm
— Constant used in correlation for mass transfer coefficient — Hydrodynamic permeability, kg.m!2 s!1 Pa!1 — Constant used in correlation for mass transfer coefficient — Concentration of solute at bulk/ reject stream (kg solute per m3 of solution) — Concentration of solute in feed stream (kg solute per m3 of solution) — Membrane–solution interface solute concentration at bulk side (kg solute per m3 of solution)
Cp di Di do Do F Jv k L ls Mw N PF Pm QF QP QPexp QPThe R RObs Re Sc Sh T W
— Concentration of solute in permeate (kg solute per m3 of solution) — Inside diameter of hollow fiber, m — Inside diameter of hollow-fiber module, m — Outside diameter of hollow fiber, m — Outside diameter of hollow-fiber module, m — Parameter used in Spiegler–Kedem model — Permeate flux, m3.m!2.s!1 — Mass transfer coefficient, m.s!1 — Length of hollow fiber involved in mass transfer, m — Thickness of epoxy seal, m — Molecular weight, kg.kmol!1 — Number of hollow fibers in module — Feed pressure, Pa — Solute permeability, m.s!1 — Feed flow rate, ml.s!1 — Permeate flow rate, ml.s!1 — Experimental permeate flow rate, ml.s!1 — Theoretically predicted permeate flow rate, ml.s!1 — True rejection of solute in Eq. (2); Universal gas constant in Eq. (1) (8314 J.kmol!1.EK!1) — Observed rejection = (1!CP/CF)*100 — Reynolds number — Schmidt number — Sherwood number — Temperature, EK — Number of wounds
Greek σ φ ν
— Reflection coefficient — Concentration polarization — Vant Hoff factor
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