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A response surface optimized nanofiltration-based system for efficient removal of selenium from drinking Water
Journal of Water Process Engineering 33 (2020) 101007
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A response surface optimized nanofiltration-based system for efficient removal of selenium from drinking Water
T
Meenakshi Malhotra, Madhubonti Pal, Parimal Pal* Environment& Membrane Technology Laboratory, Department of Chemical Engineering, National Institute of Technology Durgapur, 713209, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Selenium removal Nanocomposite membrane Cross flow module Optimized filtration
Selenium has been emerging as a serious health hazard due to presence in groundwater used as source of potable water in several regions of the world. Concentration of selenium in such contaminated water is sometimes as high as 400–700 μg/L. Removal of selenium from such water to the safe level of40 μg/l is a big challenge to the scientific community. Selenium was efficiently removed from live contaminated groundwater in this study, by flat sheet cross flow nanofiltration membrane modules under response surface optimized conditions. The governing parameters such as pH, feed dilution, cross flow rate and trans-membrane pressure were optimized. Through judicious selection of membrane, module and operating conditions, a low cost yet efficient nanofiltration-based process for selenium removal has been developed. The novel process uses commercial polyamide nanofiltration membrane in flat sheet and cross flow module that yields a sustainable pure water flux of around 140 L/(m2h) at only 14 bar pressure while removing above 98% of selenium from contaminated water under response surface optimized conditions. Thus potential of a novel nanofiltration membrane-based process and system is established which is scalable and can be quickly adopted in offering relief to the suffering milieu.
1. Introduction Treatment of selenium-contaminated ground water for potable purpose is a big challenge due to high solubility of selenium in water coupled with wide toxic concentration range in which selenium has been found in several regions. Triggered by geogenic and anthropogenic activities, contamination of groundwater from leached out minerals like arsenic, lead, chromium, iron and selenium is now widespread across the world [1–3]. Groundwater contamination has posed a major threat worldwide where many developing countries use groundwater as the major source of potable water particularly in the rural areas. Selenium contamination problem is of relatively recent detection. Among the major water contaminants, selenium (Se) has been reported [4] as an emerging one due to toxicological effects though it has been known as an essential micronutrient in small doses. The recommended dietary intake allowance of selenium ranges from 40 to 400 μg/day and permissible maximum concentration in drinking water is 40 μg/L. This indicates the double edge sword effects of selenium where both deficiency and excessive intake can cause mortal health hazards [5,6]. Many countries such as China, Japan, Jordan, USA, Ireland and India are affected by excessive selenium in groundwater [7]. In India, cases of selenium toxicity has been reported from north west areas ⁎
adjoining Shivalik ranges spread over the provinces of Haryana, Punjab and low lying areas of Himachal Pradesh. The crisis emanating from selenium contamination is further aggravated in the aquifers around the industrial belts of Punjab and Himachal Pradesh where the degree of contamination has been reported to be as high as 700 μg/l and 4475 μg/l respectively [8,9] far above the maximum contamination limit of 40 μg/l. The major sources of selenium comprise of both natural and anthropogenic activities such as volcanic eruption, erosion from seleniumrich sedimentary rocks, mining, agriculture, and combustion of selenium rich coal, use of insecticide, metal and oil refining [10,11]. Besides being an emerging contaminant in the environment, selenium has drawn much attention recently as it has wide application in the fields of molecular, genetic, biochemical, medicine and material science [12]. Selenium has physical properties in line with arsenic and chemical characteristics close to those of sulphur. Selenium can occur in inorganic, organic and amino acid compounds in the environment as well as in the biological systems. Inorganic selenium compounds such as selenate (SeO42−), selenite (SeO32−), selenide (Se2−) and elemental selenium (Se0) are mainly found in surface and groundwater. Organic species are generally prevalent in terrain and plants as methylselenol, methylselenides, trimethylselenoniumcation, dimethyldiselenide, dimethylseleniumsulfide and selenoamino acids [13]. Selenide is the
Corresponding author. E-mail addresses: [email protected] (M. Malhotra), [email protected] (M. Pal), [email protected], [email protected] (P. Pal).
https://doi.org/10.1016/j.jwpe.2019.101007 Received 24 September 2019; Accepted 14 October 2019 2214-7144/ © 2019 Elsevier Ltd. All rights reserved.
Journal of Water Process Engineering 33 (2020) 101007
M. Malhotra, et al.
Fig. 1. Design of the membrane module for the treatment of Selenium contaminated water.
by Donnan principle. As sustained flux and rejection is largely a function of operating conditions and membrane characteristics, the system is developed on optimizing the operating conditions by Response Surface Methodology (RSM) and by appropriately choosing a membrane.
most toxic among all the organic species but fortunately it briskly oxidizes to selenite (Se (IV) in presence of air. When selenium-contaminated water is used for irrigation, it results in seleniferous soil and selenium gets accumulated in the crops grown on such soil and directly enters the food chain. Exposure to excessive selenium may result in gastrointestinal disorders, hair brittleness and discoloration of nails, neurological disorders and birth deformities. High level of selenium poisoning can lead to pulmonary congestion, extensive fibrosis or liver cirrhosis and death [14]. Therefore, new technologies need to be developed for selenium removal specifically from aqueous systems. Recent studies show that adsorbents such as layered double hydroxide intercalated with zwitterionic glycine [15] cellulose acetate functionalized ZnO nanocomposite [16] have high selenium sorption capacities but frequent replacement and disposal of spent saturated adsorbent remains a major problem. Many studies on ion exchange have been reported but much of its effectiveness is lost in presence of the competing ions and cost is always another major concern [17]. Reverse Osmosis (RO) delivers high degree of selenium separation without any addition of third component and thermal energy but involves high operational costs [18,19].The current ecosystem sustainability scenarios demand urgent development of a simple, clean and cost effective selenium removal technology for purifying contaminated drinking water. Very few studies on NF/RO hollow fiber/spiral wound membrane modules have been reported on selenium removal [20–23]. Despite reasonably good rejection in some of these studies, flux still remains low. In some cases with synthetic membranes, flux decline is so rapid that within an hour, the system turns inoperable. This is despite conducting experiments with synthetic solution and under ideal conditions. Moreover, where a purification level ensured by nanofiltration (NF) is quite acceptable by drinking water standard [24], there is hardly any justification in deploying RO membrane-based system at much higher cost. Therefore, a sustainable technology needs to be developed based on NF membrane that permits operation at much lower operating pressure than what is required in RO-based separation. This study is conducted with real contaminated water from Punjab province of India where the problem is very acute. A membrane module is selected that is largely fouling-free. From a lot, an appropriate polyamide composite membrane is screened for selenium filtration which excludes selenium
2. Experimental 2.1. Chemicals and membranes Reagent grade chemicals were used in experiments. Sodium selenite, nickel nitrate hexahydrate, nitric acid and H2O2 were procured from E. Merck, Germany. Three different composites poly amide commercial nanofiltration membranes namely NF2, NF20 and NF1 (Sepromembranes Inc., USA) were used. Each membrane module had effective surface area of 100 cm2. 2.2. Set-up Contrary to the reported selenium removal studies by membrane using synthetic water [22], this study conducts experiments with selenium-contaminated live water from some affected areas of Punjab province of India. Major characteristics of such collected water samples have been tabulated in Table 1SM in the supplementary material. Experimental investigations on selenium removal were conducted in flat sheet cross flow module (FSCF) in continuous mode, using three varieties of nanofiltration membranes. The experimental set up comprises a continuously stirred feed tank, a diaphragm pump for pumping water at high pressure through two parallel cross flow modules. Pressure gauges and rotameter were used in monitoring pressure and cross flow velocity respectively during filtration as shown in Fig.1. Before carrying out selenium removal studies, each membrane was operated at a pressure of 15 kgf/cm2 for half an hour by using only deionised water. Thereafter, flux and rejection were determined for three different membranes over a pressure range of 6−15 kgf/cm2. 2.3. Analytics Measurement of concentration of selenium has beendone by atomic 2
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absorption spectroscopy - Graphite Furnace (AAS–GF), Thermo scientific iCE3000 series using selenium lamp at 196 nm wavelength. A selenium (IV) standard solution of 1000 ppm was prepared by dissolving Na2SeO3 in DI water. Nickel nitrate 5% and 1% solutions were prepared by dissolving appropriate amount of Ni(NO3)2. Standard solutions are prepared from stock solution by withdrawing appropriate aliquots, subsequently 1 ml of conc. HNO3, 2 ml of 30% H2O2 and 2 ml of 5% nickel nitrate solution were added and finally diluted with deionized distilled water to 100 ml solution. An unknown sample was first digested with HNO3 and 1 ml of 1 % Ni(NO3)2 solution was added with further dilution to make working volume of 10 ml. Rejection of percentage removal of selenium is computed in terms of concentration in feed(cf) and concentration in permeate stream(cp) as: % Removal of se (RSe) =
(1 − ) × 100 Cp
(1)
Cf
Fig. 2. Flux profile under varying trans-membrane pressure.
Where σ0 = constant regression coefficient, σi= linear regression coefficient, σij = interaction regression coefficient, σii = quadratic regression coefficient, xi and xj= coded input variables. Analysis of regression coefficient is done by using multiple regressions with least square model fitting approach.
3. Optimization of nanofiltration module by response surface methodology Response surface methodology (RSM) has been proved to be an efficient tool in optimizing process conditions. RSM designs the experiments to determine the optimum conditions of the governing factors [24]. A data-based model is then developed using various mathematical and statistical tools. This methodology involves lesser computer simulations and easier and more cost-effective than other methods based on finite element or computational fluid dynamics. RSM has its own limitations such as mathematical complexity due to independent responses implying that when outcomes of individual parameters implicate output variables necessitating assignment of different coefficient estimates to show these interactive effects. In this investigation, the best membrane out of a lot of three nanofiltration membranes has been finally used after screening to arrive at the optimized process parameters for treatment of selenium-contaminated groundwater. Determination of the linear and combined effects of the major process variables on membrane performance parameters has been done using central composite design of the Design Expert software of version 8.0.6. A total four independent input variables have been used as coded levels with actual units of measurement as presented in Table 1. The ‘−’ and ‘+’ signs stand for the minima and the maxima value of the variants, respectively. The total vital number of experimentations (N) has been calculated by using Eq. (1) which involves the number of centre, axial and factorial points as 6, 8 and 16 respectively as tabulated in Table 3SM in the supplementary material.
N=
(2P
+ 2P + 6) =
24
4. Results and discussion 4.1. Membrane performance A comprehensive study of three different nanofiltration membranes was conducted under different operating conditions in flat sheet membrane module so as to select the best membrane out of a given lot. 4.1.1Permeate flux behaviour under varying trans-membrane pressure Different operating pressures were selected in the range of 6–15 kgf/ cm2 and corresponding permeate fluxes as obtained for NF20, NF2 and NF1 membranes have been presented in Fig. 2. Though such linear dependency of the effluent flux with increase in trans-membrane pressure regardless of membrane nature have been observed in many cases [25,26] this illustration gives an indication of the relative fluxes of the investigated membranes at a given pressure. NF2 membrane in this study exhibits the maximum flux followed by NF20 and NF1. This directly specifies that NF1is the tightest among these membranes whereas NF2 is the loosest type. 4.1.1. Rejection trend under varying trans-membrane pressure Removal performance of selenium from groundwater has been studied on three different polyamide nanofiltration membranes with a constant initial concentration of 1600 ppb and a cross flow velocity of 700 LMH (litres per square meter per hour). Rejection of selenium increases linearly with trans-membrane pressure, regardless of the type of membrane used and is well illustrated in Fig. 3. Removal of selenium increases sharply with increase in operating pressure up to a level of 15 kg/cm2, however, further increase in pressure beyond this does not show any significant impact on rejection of selenium. Highest rejection is achieved by NF1 followed by NF20 and NF2 respectively at any applied pressure. In this investigation, removal of selenium by nanofiltration membranes can be explained using Donnan separation as well as the solution diffusion principle. The rejection behaviour is explained by solutiondiffusion mechanism in which solute and solvent fluxes are uncoupled and as solvent flux increases with trans-membrane pressure, the solute flux decreases implying greater rejection.
(2)
+ 2 × 4 + 6 = 30
Where‘P’ denotes number of independent variables. The operating parameters are adjusted as shown in Table 3SM and respective responses are determined as Y1 and Y2. Y1 represents rejection efficiency and Y2 denotes permeate water flux. The following polynomial equation describes the predicted values of the responses Y1 and Y2 as: n
Y1 or Y2 = σ0 +
n−1
n
n
∑ σi xi + ∑ σii xi2 + ∑ ∑ i=1
i=1
σij x i x j (3)
i = 1 j = iH
Table 1 Independent input variables range in terms of coded levels. Independent factors
Selenium(conc.) pH Operating Pressure Rate of fluid flow
Units
Symbols
Ppb
Co
Bar LPH
Po CFR
Coded levels -α
−1
0
+1
+α
400 2 6 100
800 4 9 300
1200 6 12 500
1600 8 15 700
2000 10 18 900
4.1.2. Effect of pH on flux and rejection of selenium Fig.4 shows that increase in pH from 2 to 12 results insignificant increase both in selenium rejection as well as insolvent flux. It is observed that Selenium rejection increases from 40% to 97.2% for NF1, 3
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Fig. 3. Trans-membrane pressure effect on rejection efficiency. Fig. 5. Variation in permeate flux and overall selenium rejection with cross flow.
membrane surface and results in increased permeate flux. Experimental data shows that rejection by all the nanofiltration membranes used has linear relationship with rate of fluid flow and increases a fluid rate increases from 300LPH to 750LPH at a fixed pressure of 12 kgf/cm2. High rate of fluid flow reduces fouling as the tangential flow over the membrane surface sweeps away the deposited layers of materials from membrane surface. This maximizes overall effective membrane surface area and both high permeate flux and high degree of separation can be achieved. Fig. 5 shows that enhancement in separation efficiency for NF2, NF20 and NF1 are by 9%, 6% and 4% respectively. This also indicates that the highest enhancement in separation efficiency following increase in rate of fluid flows is exhibited by the most loosely packed nanofiltration membrane.
Fig. 4. Flux and rejection behaviour under varying feed pH.
4.1.3. Effects of initial concentration of selenium in feed on permeate flux and removal The comparative flux and rejection of NF membranes as depicted in Fig.6 suggest that higher feed concentration leaves an adverse effect on selenium removal and permeate flux. The data on NF1 membrane shows that rejection decreases from 91.6% to 88.8% when initial selenium concentration is reduced from 400 ppb to 5000 ppb. It can be noticed that initially this effect was not so substantial but beyond a concentration of 1600 ppb, the rejection drops considerably. Since the effective charge on membrane surface is limited, increase in feed concentration results in partial neutralization of anionic surface of the membrane by counter ions and diminishes the electrostatic contacts among the ions and membrane. The experimental data also shows a negative effect on permeate flux for all the three membranes. Concentration polarization is the most accountable factor for this adverse effect. High selenium concentration results in forming a fouling film above the membrane surface and offers further resistance to the effective mass transfer. With increasing salt concentration, osmotic pressure also increases and accordingly the effective trans-membrane pressure decreases. From the comparative study of three membranes, it is elucidated that NF1 membranes give high rejection efficiency and permeate flux under a given set of operating conditions as compared to others two membranes. Therefore, NF1 membrane has been selected for further optimization of module for developing linear and combined correlation between independent individual variables and dependent parameters.
26% to 66% for NF2 and 32% to 78.5% for NF20 membrane. Pure water flux increases with increasing pH in similar fashion and reaches a maximum of 130.4, 166 and 281 for NF1, NF20 and NF2 respectively at a constant trans-membrane pressure, fluid flow rate and feed concentration. Such positive effect of pH on rejection of other anions such as arsenic and fluoride by nanofiltration membrane has been reported in earlier studies [27,28]. In aqueous system, selenite and selenate undergoes protonation and deprotonation depending on the pH of the system. With increase in pH selenite deprotonates as: H2SeO3- H+ HSeO3− HSeO3− - H+ SeO32This imparts more negative charge to the corresponding anion and enhances overall selenium rejection by negatively charged nanofiltration membrane. Superficial pore radii of polyamide nanofiltration membranes alter with pH of the solution [29] whereas a considerable increase in membrane charge concentration is being observed with increase in pH in earlier studies [30]. 4.1.4Effect of fluid flow on overall flux and selenium removal Fig. 5 shows a positive effect of fluid flow rate on permeate flux. This figure exhibits that as fluid flow rate increases from 300LPH to 750LPH at a fixed trans-membrane pressure of 12 kgf/cm2, the flux increases regardless of the type of membrane used. On comparing membranes performance, NF2 membrane flux increases up to 292LMH for a maximum rate of fluid flow of 750LPH followed by NF20 in which flux increases upto 182 LMH whereas NF1 shows a slight gain in flux from 102 LMH to 128 LMH. These trends are associated with the pore radius; high flux is obtained in NF2, the loosest membrane among these and lowest flux in NF1 membrane indicating its dense character. Increase in hourly fluid rate for a particular membrane reduces concentration polarization significantly by the tangential action on
4.2. Module performance The response function coefficients for rejection efficiency Y1 and permeate flux Y2 were computed by experimental data tabulated in Table 3SM where the ranges of input parameters are trans-membrane 4
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Fig. 6. Variation of selenium rejection & permeate flux with feed conc.
Permeatefluxoftreatedwater = + 126.12 + 24.39 × a + 0.44 × b − 0.14 × c − 0.12 × d − 0.087 × a × b − 0.037 × a × c + 0.0001 × a × d + 0.013 × b × c + 0.0001 × b × d − 0.026 × c × d − 11.47 × a2 − 1.34 × b2 (5) − 1.07 × c 2 − 1.09 × d 2 Where, a, b,c and d are operating pressure, hourly fluid rate, solution pH and concentration of the contaminant respectively. The experimental percentage selenium rejection (98.6%) is found to be quite close to the model predicted value (98.2%), Similar results are obtained in case of permeate flux of treated water, where experimental permeate flux (139 LMH) is almost equivalent to model predicted flux (138.5 LMH). It can be noticed that the desirability function is very much close to 1 i.e.0.966 for selenium removal which reflects the validation of model predicted data with experimental results. Fig. 7SM in the supplementary material illustrates the desirability factor of the model at varied working pressure and hourly fluid rate with a highest value of 0.966 for selenium removal and the process parameters (pressure of 13 bar and cross-flow rate of 700 LPH, pH value 8.0) are found to be optimized condition of this module. Selenium removal efficiency and permeate treated water flux are dependent on independent controlling parameters and is well demonstrated by response surface 3D plots. Fig. 8a & b represents the influence of operating pressure and rate of the fluid flow on selenium rejection and permeate flux respectively at a constant pH and initial Selenium concentration of 6.0 & 1200 ppb, respectively confirming a strong influence of operating pressure on flux and rejection rather than rate of fluid flow. The highest percentage selenium removal is found to be 98.6% with 138 LMH flux value at 15 bar working pressure and an hourly fluid rate of 700 L. Fig. 8c and d explain selenium rejection trend and water flux under varying pH and operating pressure while hourly fluid rate and feed concentration are fixed at a value of 500LPH and 1200 ppb respectively. Both the operational parameters were found to have profound effect on rejection efficiency. The membrane negative charge gets enhanced with increase in pH and rejects anions more efficiently at higher pH due to Donnan exclusion principle. High pH can also results in transforming selenium speciation in towards higher anionic forms further enhances rejection of selenium anions which is reflected in such high selenium rejection (98.6%) under 15 bar operating pressure and at a pH 8.00 in the RSM plot. However, from the plot 8d it can be concluded that water flux has a linear and quite strong dependency on pressure change rather than pH of the system. Initial concentration of selenium has also substantial influence on selenium rejection while maintaining pH at 6 and fluid flow rate at
Fig. 7. Flux profile of NF1 membrane module conditions: initial Se conc. 1600 ppb; pH 8; pressure 13.5 bar; CFR 700 l h−1 and temperature 303 K.
pressure (6−18 bar), fluid flow rate (100-900LPH), pH of feed (2–10) and initial feed concentration (400−2000 ppb of selenium). It has been observed that the model summary for this particular case of Selenium removal, agrees considerably with the experimental observations. The results show that low standard deviation values 2.64 and 5.17 and high correlation coefficient values, R2 of 0.9773 and 0.9781 for selenium rejection and pure water flux respectively. The model implication is reflected by high F- value 46.19 for selenium removal. The feed pH, trans-membrane pressure and fluid rate of fluid flow have a great impingement on both rejection efficiency and permeate flux and the same is strongly supported by values of p (< 1). Moreover, an adequate precision value i.e. 28.679 indicates that good capability of model for predicting system performance. The module experimental data is in line with the final regression model suggested by ANOVA and is represented in terms of coded variables (eq. 4,5)
SeleniumRejectionefficiency = + 94.80 + 11.92 × a + 0.33 × b + 0.021 × c + 0.062 × d − 0.14 × a × b − 6.250e − 003a × c − 6.250e − 003 × b × d + 0.019 × c × d − 6.27 × a2 (4) − 0.72 × b2 − 0.40 ×c 2 And,
5
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Fig. 8. Response surface 3D plot showing influence of operating pressure and hourly fluid flow rate on selenium removal. Table 2 Comparison of selenium removal studies and their performance. Water source
Membrane (Module used)
% Rejection
Maximum Flux obtained (LMH)
Reference
Contaminated Ground water
BW30 NF-90 (Hollow fiber module) FeO functionalized membrane (spiral wound module) POSS- polyamide TFN membrane (Dead end module) UiO-66 based TFN membrane (Dead end module) NF1 (FSCF module)
93.8 92.9 97.9% 97.4% 98.6% > 98 %
15.5 23.1 110 54 115 140
[20]
Scrubber waste water Synthetic water Synthetic water Contaminated Ground water
[21] [22] [23] Present work
initial selenium concentration. It can be noticed that up to the aforesaid concentration negative effect of concentration polarization are not visible. However, increasing concentration above a certain value may lead to concentration polarization since the interfacial membrane surface area for effective mass transfer is constant and this pile up the
500LPH as interpreted in Fig. 8e. The rejection percentage reaches a maximum of 98.9 under 15 bar operating pressure with an initial selenium concentration of 1600 ppb. Again, flux value does not vary much with the change in initial concentration value. The permeate flux reaches maximum of 138 LMH under aforesaid operating pressure and 6
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anions on the membrane surface causes increase in mass transfer resistance. Thus, an optimized set of operating parameters obtained through response surface methodology comprises of operating pressure around 13−14 bar, initial selenium concentration of 1600 ppb with an hourly fluid rate of 700 l and at a pH of 8. Using these set of optimized operating conditions selenium contaminated water can be successfully treated with an elevated flux of about 140 LMH and a rejection efficacy of > 98%. The flux obtained during this investigation seems to be quite higher when compared with the earlier reported selenium removal studies. The major problem associated with separation by using membrane technology is concentration polarization and decline in flux. Therefore, for determining the fouling propensity of NF1 membrane, the module was run continuously for around 240 h and it was observed that a total declination of only 6.06% flux has occurred as shown in Fig. 7. A comparison with literature as presented in Table 2 shows the membrane used in this work is one of the best performers in consideration of overall performance in terms of flux and rejection. Further, the membrane effectiveness can be revived by merely extensive washing with mild solutions of NaOH and HNO3acid. 200 ppm NaOCl solution can also be used to sterilize the membranes to reduce minor external fouling impact.
[14]
5. Conclusion
[15]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
For selenium removal from drinking water an efficient yet low cost system comprising flat sheet cross flow module of composite polyamide nanofiltration membrane under response surface optimized operating conditions has been developed. The investigated module under response surface optimized operating conditions ensured a high degree of separation of selenium (> 98%) while maintaining a sustained high flux of 140 LMH at 14 bar operating pressure. The membrane module is largely fouling-free as evident in very limited flux decline (5–6 %) despite prolonged operation of 250 h. Possibility of treating contaminated groundwater in a very simple design that involves little monitoring and operational cost leads to the conclusion that the proposed process can be scaled up easily and holds the potential of treatment of seleniumcontaminated water for potable purpose at a quite low cost.
[16]
[17]
[18]
[19]
[20]
Acknowledgement
[21]
The authors are thankful to the Department of Science and Technology for infrastructure and the Ministry of Human Resource Development Govt. of India for fellowship support.
[22]
Appendix A. Supplementary data [23]
Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jwpe.2019.101,007.
[24]
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He, Y. Tang, T.S. Chung, Concurrent removal of selenium and arsenic from water using POSS-polyamide thin-film nanocompositenanofiltration membranes, Ind. Eng. Chem. Res. 55 (2016) 12929–12938, https://doi.org/10.1021/acs.iecr. 6b04272. Y. He, J. Liu, G. Han, T.S. Chung, Novel thin-film composite nanofiltration membranes consisting of a zwitterionic co-polymer for selenium and arsenic removal, J. Memb. Sci. 555 (2018) 299–306, https://doi.org/10.1016/j.memsci.2018.03.055. V.V. Goncharuk, A.A. Kavitskaya, M.D. Skilskaya, Nanofiltration in drinking water supply, J. Water Chem. Technol. 33 (2011) 37, https://doi.org/10.3103/ S1063455X11010073. R.H. Myers, D.C. Montgomery, C.M. Anderson, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 3rd ed., Wiley, Hoboken, 2009. M. Tahaikt, R.E.I. Habbani, A. Aithaddou, I. Achary, Z. Amor, M. Taky, A. Alami, A. Boughriba, M. Hafsi, A. 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Drioli, Influence of operating parameters on the arsenic removal by nanofiltration, Water Res. 1 (44) (2010) 97–104, https://doi.org/10.1016/j.watres.2009.09.007.
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Journal of Water Process Engineering journal homepage: www.elsevier.com/locate/jwpe
A response surface optimized nanofiltration-based system for efficient removal of selenium from drinking Water
T
Meenakshi Malhotra, Madhubonti Pal, Parimal Pal* Environment& Membrane Technology Laboratory, Department of Chemical Engineering, National Institute of Technology Durgapur, 713209, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Selenium removal Nanocomposite membrane Cross flow module Optimized filtration
Selenium has been emerging as a serious health hazard due to presence in groundwater used as source of potable water in several regions of the world. Concentration of selenium in such contaminated water is sometimes as high as 400–700 μg/L. Removal of selenium from such water to the safe level of40 μg/l is a big challenge to the scientific community. Selenium was efficiently removed from live contaminated groundwater in this study, by flat sheet cross flow nanofiltration membrane modules under response surface optimized conditions. The governing parameters such as pH, feed dilution, cross flow rate and trans-membrane pressure were optimized. Through judicious selection of membrane, module and operating conditions, a low cost yet efficient nanofiltration-based process for selenium removal has been developed. The novel process uses commercial polyamide nanofiltration membrane in flat sheet and cross flow module that yields a sustainable pure water flux of around 140 L/(m2h) at only 14 bar pressure while removing above 98% of selenium from contaminated water under response surface optimized conditions. Thus potential of a novel nanofiltration membrane-based process and system is established which is scalable and can be quickly adopted in offering relief to the suffering milieu.
1. Introduction Treatment of selenium-contaminated ground water for potable purpose is a big challenge due to high solubility of selenium in water coupled with wide toxic concentration range in which selenium has been found in several regions. Triggered by geogenic and anthropogenic activities, contamination of groundwater from leached out minerals like arsenic, lead, chromium, iron and selenium is now widespread across the world [1–3]. Groundwater contamination has posed a major threat worldwide where many developing countries use groundwater as the major source of potable water particularly in the rural areas. Selenium contamination problem is of relatively recent detection. Among the major water contaminants, selenium (Se) has been reported [4] as an emerging one due to toxicological effects though it has been known as an essential micronutrient in small doses. The recommended dietary intake allowance of selenium ranges from 40 to 400 μg/day and permissible maximum concentration in drinking water is 40 μg/L. This indicates the double edge sword effects of selenium where both deficiency and excessive intake can cause mortal health hazards [5,6]. Many countries such as China, Japan, Jordan, USA, Ireland and India are affected by excessive selenium in groundwater [7]. In India, cases of selenium toxicity has been reported from north west areas ⁎
adjoining Shivalik ranges spread over the provinces of Haryana, Punjab and low lying areas of Himachal Pradesh. The crisis emanating from selenium contamination is further aggravated in the aquifers around the industrial belts of Punjab and Himachal Pradesh where the degree of contamination has been reported to be as high as 700 μg/l and 4475 μg/l respectively [8,9] far above the maximum contamination limit of 40 μg/l. The major sources of selenium comprise of both natural and anthropogenic activities such as volcanic eruption, erosion from seleniumrich sedimentary rocks, mining, agriculture, and combustion of selenium rich coal, use of insecticide, metal and oil refining [10,11]. Besides being an emerging contaminant in the environment, selenium has drawn much attention recently as it has wide application in the fields of molecular, genetic, biochemical, medicine and material science [12]. Selenium has physical properties in line with arsenic and chemical characteristics close to those of sulphur. Selenium can occur in inorganic, organic and amino acid compounds in the environment as well as in the biological systems. Inorganic selenium compounds such as selenate (SeO42−), selenite (SeO32−), selenide (Se2−) and elemental selenium (Se0) are mainly found in surface and groundwater. Organic species are generally prevalent in terrain and plants as methylselenol, methylselenides, trimethylselenoniumcation, dimethyldiselenide, dimethylseleniumsulfide and selenoamino acids [13]. Selenide is the
Corresponding author. E-mail addresses: [email protected] (M. Malhotra), [email protected] (M. Pal), [email protected], [email protected] (P. Pal).
https://doi.org/10.1016/j.jwpe.2019.101007 Received 24 September 2019; Accepted 14 October 2019 2214-7144/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Design of the membrane module for the treatment of Selenium contaminated water.
by Donnan principle. As sustained flux and rejection is largely a function of operating conditions and membrane characteristics, the system is developed on optimizing the operating conditions by Response Surface Methodology (RSM) and by appropriately choosing a membrane.
most toxic among all the organic species but fortunately it briskly oxidizes to selenite (Se (IV) in presence of air. When selenium-contaminated water is used for irrigation, it results in seleniferous soil and selenium gets accumulated in the crops grown on such soil and directly enters the food chain. Exposure to excessive selenium may result in gastrointestinal disorders, hair brittleness and discoloration of nails, neurological disorders and birth deformities. High level of selenium poisoning can lead to pulmonary congestion, extensive fibrosis or liver cirrhosis and death [14]. Therefore, new technologies need to be developed for selenium removal specifically from aqueous systems. Recent studies show that adsorbents such as layered double hydroxide intercalated with zwitterionic glycine [15] cellulose acetate functionalized ZnO nanocomposite [16] have high selenium sorption capacities but frequent replacement and disposal of spent saturated adsorbent remains a major problem. Many studies on ion exchange have been reported but much of its effectiveness is lost in presence of the competing ions and cost is always another major concern [17]. Reverse Osmosis (RO) delivers high degree of selenium separation without any addition of third component and thermal energy but involves high operational costs [18,19].The current ecosystem sustainability scenarios demand urgent development of a simple, clean and cost effective selenium removal technology for purifying contaminated drinking water. Very few studies on NF/RO hollow fiber/spiral wound membrane modules have been reported on selenium removal [20–23]. Despite reasonably good rejection in some of these studies, flux still remains low. In some cases with synthetic membranes, flux decline is so rapid that within an hour, the system turns inoperable. This is despite conducting experiments with synthetic solution and under ideal conditions. Moreover, where a purification level ensured by nanofiltration (NF) is quite acceptable by drinking water standard [24], there is hardly any justification in deploying RO membrane-based system at much higher cost. Therefore, a sustainable technology needs to be developed based on NF membrane that permits operation at much lower operating pressure than what is required in RO-based separation. This study is conducted with real contaminated water from Punjab province of India where the problem is very acute. A membrane module is selected that is largely fouling-free. From a lot, an appropriate polyamide composite membrane is screened for selenium filtration which excludes selenium
2. Experimental 2.1. Chemicals and membranes Reagent grade chemicals were used in experiments. Sodium selenite, nickel nitrate hexahydrate, nitric acid and H2O2 were procured from E. Merck, Germany. Three different composites poly amide commercial nanofiltration membranes namely NF2, NF20 and NF1 (Sepromembranes Inc., USA) were used. Each membrane module had effective surface area of 100 cm2. 2.2. Set-up Contrary to the reported selenium removal studies by membrane using synthetic water [22], this study conducts experiments with selenium-contaminated live water from some affected areas of Punjab province of India. Major characteristics of such collected water samples have been tabulated in Table 1SM in the supplementary material. Experimental investigations on selenium removal were conducted in flat sheet cross flow module (FSCF) in continuous mode, using three varieties of nanofiltration membranes. The experimental set up comprises a continuously stirred feed tank, a diaphragm pump for pumping water at high pressure through two parallel cross flow modules. Pressure gauges and rotameter were used in monitoring pressure and cross flow velocity respectively during filtration as shown in Fig.1. Before carrying out selenium removal studies, each membrane was operated at a pressure of 15 kgf/cm2 for half an hour by using only deionised water. Thereafter, flux and rejection were determined for three different membranes over a pressure range of 6−15 kgf/cm2. 2.3. Analytics Measurement of concentration of selenium has beendone by atomic 2
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absorption spectroscopy - Graphite Furnace (AAS–GF), Thermo scientific iCE3000 series using selenium lamp at 196 nm wavelength. A selenium (IV) standard solution of 1000 ppm was prepared by dissolving Na2SeO3 in DI water. Nickel nitrate 5% and 1% solutions were prepared by dissolving appropriate amount of Ni(NO3)2. Standard solutions are prepared from stock solution by withdrawing appropriate aliquots, subsequently 1 ml of conc. HNO3, 2 ml of 30% H2O2 and 2 ml of 5% nickel nitrate solution were added and finally diluted with deionized distilled water to 100 ml solution. An unknown sample was first digested with HNO3 and 1 ml of 1 % Ni(NO3)2 solution was added with further dilution to make working volume of 10 ml. Rejection of percentage removal of selenium is computed in terms of concentration in feed(cf) and concentration in permeate stream(cp) as: % Removal of se (RSe) =
(1 − ) × 100 Cp
(1)
Cf
Fig. 2. Flux profile under varying trans-membrane pressure.
Where σ0 = constant regression coefficient, σi= linear regression coefficient, σij = interaction regression coefficient, σii = quadratic regression coefficient, xi and xj= coded input variables. Analysis of regression coefficient is done by using multiple regressions with least square model fitting approach.
3. Optimization of nanofiltration module by response surface methodology Response surface methodology (RSM) has been proved to be an efficient tool in optimizing process conditions. RSM designs the experiments to determine the optimum conditions of the governing factors [24]. A data-based model is then developed using various mathematical and statistical tools. This methodology involves lesser computer simulations and easier and more cost-effective than other methods based on finite element or computational fluid dynamics. RSM has its own limitations such as mathematical complexity due to independent responses implying that when outcomes of individual parameters implicate output variables necessitating assignment of different coefficient estimates to show these interactive effects. In this investigation, the best membrane out of a lot of three nanofiltration membranes has been finally used after screening to arrive at the optimized process parameters for treatment of selenium-contaminated groundwater. Determination of the linear and combined effects of the major process variables on membrane performance parameters has been done using central composite design of the Design Expert software of version 8.0.6. A total four independent input variables have been used as coded levels with actual units of measurement as presented in Table 1. The ‘−’ and ‘+’ signs stand for the minima and the maxima value of the variants, respectively. The total vital number of experimentations (N) has been calculated by using Eq. (1) which involves the number of centre, axial and factorial points as 6, 8 and 16 respectively as tabulated in Table 3SM in the supplementary material.
N=
(2P
+ 2P + 6) =
24
4. Results and discussion 4.1. Membrane performance A comprehensive study of three different nanofiltration membranes was conducted under different operating conditions in flat sheet membrane module so as to select the best membrane out of a given lot. 4.1.1Permeate flux behaviour under varying trans-membrane pressure Different operating pressures were selected in the range of 6–15 kgf/ cm2 and corresponding permeate fluxes as obtained for NF20, NF2 and NF1 membranes have been presented in Fig. 2. Though such linear dependency of the effluent flux with increase in trans-membrane pressure regardless of membrane nature have been observed in many cases [25,26] this illustration gives an indication of the relative fluxes of the investigated membranes at a given pressure. NF2 membrane in this study exhibits the maximum flux followed by NF20 and NF1. This directly specifies that NF1is the tightest among these membranes whereas NF2 is the loosest type. 4.1.1. Rejection trend under varying trans-membrane pressure Removal performance of selenium from groundwater has been studied on three different polyamide nanofiltration membranes with a constant initial concentration of 1600 ppb and a cross flow velocity of 700 LMH (litres per square meter per hour). Rejection of selenium increases linearly with trans-membrane pressure, regardless of the type of membrane used and is well illustrated in Fig. 3. Removal of selenium increases sharply with increase in operating pressure up to a level of 15 kg/cm2, however, further increase in pressure beyond this does not show any significant impact on rejection of selenium. Highest rejection is achieved by NF1 followed by NF20 and NF2 respectively at any applied pressure. In this investigation, removal of selenium by nanofiltration membranes can be explained using Donnan separation as well as the solution diffusion principle. The rejection behaviour is explained by solutiondiffusion mechanism in which solute and solvent fluxes are uncoupled and as solvent flux increases with trans-membrane pressure, the solute flux decreases implying greater rejection.
(2)
+ 2 × 4 + 6 = 30
Where‘P’ denotes number of independent variables. The operating parameters are adjusted as shown in Table 3SM and respective responses are determined as Y1 and Y2. Y1 represents rejection efficiency and Y2 denotes permeate water flux. The following polynomial equation describes the predicted values of the responses Y1 and Y2 as: n
Y1 or Y2 = σ0 +
n−1
n
n
∑ σi xi + ∑ σii xi2 + ∑ ∑ i=1
i=1
σij x i x j (3)
i = 1 j = iH
Table 1 Independent input variables range in terms of coded levels. Independent factors
Selenium(conc.) pH Operating Pressure Rate of fluid flow
Units
Symbols
Ppb
Co
Bar LPH
Po CFR
Coded levels -α
−1
0
+1
+α
400 2 6 100
800 4 9 300
1200 6 12 500
1600 8 15 700
2000 10 18 900
4.1.2. Effect of pH on flux and rejection of selenium Fig.4 shows that increase in pH from 2 to 12 results insignificant increase both in selenium rejection as well as insolvent flux. It is observed that Selenium rejection increases from 40% to 97.2% for NF1, 3
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Fig. 3. Trans-membrane pressure effect on rejection efficiency. Fig. 5. Variation in permeate flux and overall selenium rejection with cross flow.
membrane surface and results in increased permeate flux. Experimental data shows that rejection by all the nanofiltration membranes used has linear relationship with rate of fluid flow and increases a fluid rate increases from 300LPH to 750LPH at a fixed pressure of 12 kgf/cm2. High rate of fluid flow reduces fouling as the tangential flow over the membrane surface sweeps away the deposited layers of materials from membrane surface. This maximizes overall effective membrane surface area and both high permeate flux and high degree of separation can be achieved. Fig. 5 shows that enhancement in separation efficiency for NF2, NF20 and NF1 are by 9%, 6% and 4% respectively. This also indicates that the highest enhancement in separation efficiency following increase in rate of fluid flows is exhibited by the most loosely packed nanofiltration membrane.
Fig. 4. Flux and rejection behaviour under varying feed pH.
4.1.3. Effects of initial concentration of selenium in feed on permeate flux and removal The comparative flux and rejection of NF membranes as depicted in Fig.6 suggest that higher feed concentration leaves an adverse effect on selenium removal and permeate flux. The data on NF1 membrane shows that rejection decreases from 91.6% to 88.8% when initial selenium concentration is reduced from 400 ppb to 5000 ppb. It can be noticed that initially this effect was not so substantial but beyond a concentration of 1600 ppb, the rejection drops considerably. Since the effective charge on membrane surface is limited, increase in feed concentration results in partial neutralization of anionic surface of the membrane by counter ions and diminishes the electrostatic contacts among the ions and membrane. The experimental data also shows a negative effect on permeate flux for all the three membranes. Concentration polarization is the most accountable factor for this adverse effect. High selenium concentration results in forming a fouling film above the membrane surface and offers further resistance to the effective mass transfer. With increasing salt concentration, osmotic pressure also increases and accordingly the effective trans-membrane pressure decreases. From the comparative study of three membranes, it is elucidated that NF1 membranes give high rejection efficiency and permeate flux under a given set of operating conditions as compared to others two membranes. Therefore, NF1 membrane has been selected for further optimization of module for developing linear and combined correlation between independent individual variables and dependent parameters.
26% to 66% for NF2 and 32% to 78.5% for NF20 membrane. Pure water flux increases with increasing pH in similar fashion and reaches a maximum of 130.4, 166 and 281 for NF1, NF20 and NF2 respectively at a constant trans-membrane pressure, fluid flow rate and feed concentration. Such positive effect of pH on rejection of other anions such as arsenic and fluoride by nanofiltration membrane has been reported in earlier studies [27,28]. In aqueous system, selenite and selenate undergoes protonation and deprotonation depending on the pH of the system. With increase in pH selenite deprotonates as: H2SeO3- H+ HSeO3− HSeO3− - H+ SeO32This imparts more negative charge to the corresponding anion and enhances overall selenium rejection by negatively charged nanofiltration membrane. Superficial pore radii of polyamide nanofiltration membranes alter with pH of the solution [29] whereas a considerable increase in membrane charge concentration is being observed with increase in pH in earlier studies [30]. 4.1.4Effect of fluid flow on overall flux and selenium removal Fig. 5 shows a positive effect of fluid flow rate on permeate flux. This figure exhibits that as fluid flow rate increases from 300LPH to 750LPH at a fixed trans-membrane pressure of 12 kgf/cm2, the flux increases regardless of the type of membrane used. On comparing membranes performance, NF2 membrane flux increases up to 292LMH for a maximum rate of fluid flow of 750LPH followed by NF20 in which flux increases upto 182 LMH whereas NF1 shows a slight gain in flux from 102 LMH to 128 LMH. These trends are associated with the pore radius; high flux is obtained in NF2, the loosest membrane among these and lowest flux in NF1 membrane indicating its dense character. Increase in hourly fluid rate for a particular membrane reduces concentration polarization significantly by the tangential action on
4.2. Module performance The response function coefficients for rejection efficiency Y1 and permeate flux Y2 were computed by experimental data tabulated in Table 3SM where the ranges of input parameters are trans-membrane 4
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Fig. 6. Variation of selenium rejection & permeate flux with feed conc.
Permeatefluxoftreatedwater = + 126.12 + 24.39 × a + 0.44 × b − 0.14 × c − 0.12 × d − 0.087 × a × b − 0.037 × a × c + 0.0001 × a × d + 0.013 × b × c + 0.0001 × b × d − 0.026 × c × d − 11.47 × a2 − 1.34 × b2 (5) − 1.07 × c 2 − 1.09 × d 2 Where, a, b,c and d are operating pressure, hourly fluid rate, solution pH and concentration of the contaminant respectively. The experimental percentage selenium rejection (98.6%) is found to be quite close to the model predicted value (98.2%), Similar results are obtained in case of permeate flux of treated water, where experimental permeate flux (139 LMH) is almost equivalent to model predicted flux (138.5 LMH). It can be noticed that the desirability function is very much close to 1 i.e.0.966 for selenium removal which reflects the validation of model predicted data with experimental results. Fig. 7SM in the supplementary material illustrates the desirability factor of the model at varied working pressure and hourly fluid rate with a highest value of 0.966 for selenium removal and the process parameters (pressure of 13 bar and cross-flow rate of 700 LPH, pH value 8.0) are found to be optimized condition of this module. Selenium removal efficiency and permeate treated water flux are dependent on independent controlling parameters and is well demonstrated by response surface 3D plots. Fig. 8a & b represents the influence of operating pressure and rate of the fluid flow on selenium rejection and permeate flux respectively at a constant pH and initial Selenium concentration of 6.0 & 1200 ppb, respectively confirming a strong influence of operating pressure on flux and rejection rather than rate of fluid flow. The highest percentage selenium removal is found to be 98.6% with 138 LMH flux value at 15 bar working pressure and an hourly fluid rate of 700 L. Fig. 8c and d explain selenium rejection trend and water flux under varying pH and operating pressure while hourly fluid rate and feed concentration are fixed at a value of 500LPH and 1200 ppb respectively. Both the operational parameters were found to have profound effect on rejection efficiency. The membrane negative charge gets enhanced with increase in pH and rejects anions more efficiently at higher pH due to Donnan exclusion principle. High pH can also results in transforming selenium speciation in towards higher anionic forms further enhances rejection of selenium anions which is reflected in such high selenium rejection (98.6%) under 15 bar operating pressure and at a pH 8.00 in the RSM plot. However, from the plot 8d it can be concluded that water flux has a linear and quite strong dependency on pressure change rather than pH of the system. Initial concentration of selenium has also substantial influence on selenium rejection while maintaining pH at 6 and fluid flow rate at
Fig. 7. Flux profile of NF1 membrane module conditions: initial Se conc. 1600 ppb; pH 8; pressure 13.5 bar; CFR 700 l h−1 and temperature 303 K.
pressure (6−18 bar), fluid flow rate (100-900LPH), pH of feed (2–10) and initial feed concentration (400−2000 ppb of selenium). It has been observed that the model summary for this particular case of Selenium removal, agrees considerably with the experimental observations. The results show that low standard deviation values 2.64 and 5.17 and high correlation coefficient values, R2 of 0.9773 and 0.9781 for selenium rejection and pure water flux respectively. The model implication is reflected by high F- value 46.19 for selenium removal. The feed pH, trans-membrane pressure and fluid rate of fluid flow have a great impingement on both rejection efficiency and permeate flux and the same is strongly supported by values of p (< 1). Moreover, an adequate precision value i.e. 28.679 indicates that good capability of model for predicting system performance. The module experimental data is in line with the final regression model suggested by ANOVA and is represented in terms of coded variables (eq. 4,5)
SeleniumRejectionefficiency = + 94.80 + 11.92 × a + 0.33 × b + 0.021 × c + 0.062 × d − 0.14 × a × b − 6.250e − 003a × c − 6.250e − 003 × b × d + 0.019 × c × d − 6.27 × a2 (4) − 0.72 × b2 − 0.40 ×c 2 And,
5
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Fig. 8. Response surface 3D plot showing influence of operating pressure and hourly fluid flow rate on selenium removal. Table 2 Comparison of selenium removal studies and their performance. Water source
Membrane (Module used)
% Rejection
Maximum Flux obtained (LMH)
Reference
Contaminated Ground water
BW30 NF-90 (Hollow fiber module) FeO functionalized membrane (spiral wound module) POSS- polyamide TFN membrane (Dead end module) UiO-66 based TFN membrane (Dead end module) NF1 (FSCF module)
93.8 92.9 97.9% 97.4% 98.6% > 98 %
15.5 23.1 110 54 115 140
[20]
Scrubber waste water Synthetic water Synthetic water Contaminated Ground water
[21] [22] [23] Present work
initial selenium concentration. It can be noticed that up to the aforesaid concentration negative effect of concentration polarization are not visible. However, increasing concentration above a certain value may lead to concentration polarization since the interfacial membrane surface area for effective mass transfer is constant and this pile up the
500LPH as interpreted in Fig. 8e. The rejection percentage reaches a maximum of 98.9 under 15 bar operating pressure with an initial selenium concentration of 1600 ppb. Again, flux value does not vary much with the change in initial concentration value. The permeate flux reaches maximum of 138 LMH under aforesaid operating pressure and 6
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anions on the membrane surface causes increase in mass transfer resistance. Thus, an optimized set of operating parameters obtained through response surface methodology comprises of operating pressure around 13−14 bar, initial selenium concentration of 1600 ppb with an hourly fluid rate of 700 l and at a pH of 8. Using these set of optimized operating conditions selenium contaminated water can be successfully treated with an elevated flux of about 140 LMH and a rejection efficacy of > 98%. The flux obtained during this investigation seems to be quite higher when compared with the earlier reported selenium removal studies. The major problem associated with separation by using membrane technology is concentration polarization and decline in flux. Therefore, for determining the fouling propensity of NF1 membrane, the module was run continuously for around 240 h and it was observed that a total declination of only 6.06% flux has occurred as shown in Fig. 7. A comparison with literature as presented in Table 2 shows the membrane used in this work is one of the best performers in consideration of overall performance in terms of flux and rejection. Further, the membrane effectiveness can be revived by merely extensive washing with mild solutions of NaOH and HNO3acid. 200 ppm NaOCl solution can also be used to sterilize the membranes to reduce minor external fouling impact.
[14]
5. Conclusion
[15]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
For selenium removal from drinking water an efficient yet low cost system comprising flat sheet cross flow module of composite polyamide nanofiltration membrane under response surface optimized operating conditions has been developed. The investigated module under response surface optimized operating conditions ensured a high degree of separation of selenium (> 98%) while maintaining a sustained high flux of 140 LMH at 14 bar operating pressure. The membrane module is largely fouling-free as evident in very limited flux decline (5–6 %) despite prolonged operation of 250 h. Possibility of treating contaminated groundwater in a very simple design that involves little monitoring and operational cost leads to the conclusion that the proposed process can be scaled up easily and holds the potential of treatment of seleniumcontaminated water for potable purpose at a quite low cost.
[16]
[17]
[18]
[19]
[20]
Acknowledgement
[21]
The authors are thankful to the Department of Science and Technology for infrastructure and the Ministry of Human Resource Development Govt. of India for fellowship support.
[22]
Appendix A. Supplementary data [23]
Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jwpe.2019.101,007.
[24]
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