Accepted Manuscript Title: Sulfonated-polysulfone membrane surface modification by employing methacrylic acid through UV-grafting: Optimization through response surface methodology approach Author: Ying Tao Chung Law Yong Ng Abdul Wahab Mohammad PII: DOI: Reference:
S1226-086X(13)00357-2 http://dx.doi.org/doi:10.1016/j.jiec.2013.07.046 JIEC 1487
To appear in: Received date: Revised date: Accepted date:
19-4-2013 2-7-2013 20-7-2013
Please cite this article as: Y.T. Chung, L.Y. Ng, A.W. Mohammad, Sulfonatedpolysulfone membrane surface modification by employing methacrylic acid through UV-grafting: Optimization through response surface methodology approach, Journal of Industrial and Engineering Chemistry (2013), http://dx.doi.org/10.1016/j.jiec.2013.07.046 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Sulfonated-polysulfone Membrane Surface Modification by Employing Methacrylic Acid through UV-grafting: Optimization through Response Surface Methodology
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Approach
Ying Tao Chung1, Law Yong Ng1 and Abdul Wahab Mohammad1,*
Department of Chemical and Process Engineering, Faculty of Engineering and Built
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1
Environment, UniversitiKebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia *
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Corresponding Author
*
E-mail:
[email protected]
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*
Telephone: +603-8921 6410 *
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Fax: +603-8921 6148
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Abstract
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Membrane surface modification through UV-grafting method was studied and optimized using Response Surface Methodology (RSM) approach. Sulfonated-polysulfone (SPS)
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membrane was modified through grafting process by employing methacrylic acid (MAA) monomer solution under the exposure of UV light. The parameters used were the concentration of MAA in the range of 0-6 wt% and UV activation time of 0-50 minutes. The optimized parameters from RSM were 2.61 wt% of MAA and 21.10 minutes of UV activation time. The optimized water permeability obtained was 8.75 L.m-2.hr-1.bar-1, while the rejection percentages for humic acid, NaCl and MgSO4 solution were 95.0%, 65.7% and 48.3%, respectively.
Keywords: Sulfonated-polysulfone membrane; Methacrylic acid; UV-grafting; Response Surface Methodology (RSM); Optimization
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1. Introduction Membrane fouling is one of the most critical issues in membrane separation technology as it contributes to higher production cost and energy consumption [1]. The membrane fouling is
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chiefly caused by the deposition or conglomeration of particles, colloids or macromolecules on the membrane surface or inside the membrane pores [2],[3],[4].In order to overcome the
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problem, membrane surface modification is an ideal approach to produce membrane with
better performances[5],[6],[7]. Theoretically, a good modified membrane must possess the
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following characteristics such as higher hydrophilicity, good mechanical strength, higher flux
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recovery and better solute rejection[8]. Polyethersulfone (PES) or polysulfone (PSf) membranes have been widely used in the separation technology due to their good mechanical
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strength, high thermal and chemical stability. SPS membrane, which has been produced by the sulfonation of the polysulfone, possesses similar properties like PES and PSf membranes
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and it is suitable to undergo modification process [9].
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There are several techniques to modify the membrane surface such as plasma treatment, application of hydrophilic surface macromolecule, blending with nanoparticles and
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grafting process[10]. The UV-grafting process by using hydrophilic monomer has been selected as the method for membrane surface modification in the current research work due to its effectiveness to alter the surface properties[11],[12]. This technique has been proven to successfully increase the membrane hydrophilicity, membrane permeability and fouling resistance[13].UV treatment is essential to activate the membrane surface so that the hydrophilic monomers such as N-2-vinyl-pyrrolidone (NVP), 2-hydroxyethyl methacrylate (HEMA) or acrylic acid (AA)can be grafted on the membrane surface easily[14]. For this research study, methacrylic acid (MAA) has been chosen as the monomer to undergo UV-grafting process. MAA can be readily polymerized and it improves the adhesion of surface coatings and adhesives. MAA is less reactive compared to acrylic acid and thus
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will not cause morphological changes to the membrane surface. Although there is very little research study on the membrane modification using MAA, MAA is a good hydrophilic monomer. The UV treatment also plays an important role as initiators are not needed with the
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presence of UV activation. Thus, the relationship between the MAA monomer and UV activation can be studied to improve the membrane surface modification process.
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Response surface methodology (RSM) is an optimization approach commonly used to improve the performance or quality of a process without significant cost consumption[15].
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RSM plays a very important role in the application of design and development of a process or
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product[16]. As for the optimization of modification process for nanofiltration membrane, a statistical experiment is designed to determine the minimum experiments needed for the
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optimization[17]. The aim of the experiment is to analyse the correlation between parameters and responses of the modification process. The quadratic model was established for each of
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the parameters and responses. Besides, the model will be verified through the analysis of
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variance (ANOVA). From the model, the most desired optimum point for a process can be well predicted [18].Many researchers have applied this statistical method to improve the
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membrane performances due to the ease of application[19],[20]. Besides, RSM also has been well practised in various fields other than membrane
industry [21]. There were several research works that studied the feasibility of the employment of RSM in membrane technology. RSM has been referred as an optimization technique used to determine the optimal operating conditions for the production of electro dialysis bipolar membrane (EDBM) [22]. Box-Behnken experimental design has been used as a measure of the process variables for the production costs. The experimental results demonstrate that the RSM as a production modelling tool can provide good prediction with high accuracy. The application of RSM in membrane technology can also be illustrated in the study of optimization of composite nanofiltration modified membranes. According to the
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study of Khayet et al.[18], the predictive models for simulation is developed and the optimization of modified nanofiltration membrane is done by building quadratic models between each parameter and response. The reliability and accuracy of the results were
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verified by ANOVA. Besides, RSM was also practised in optimizing the incorporation of silica nanoparticles in polysulfone membrane fabrication [19]. RSM has been considered as
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one of the best statistical technique used to analyse the interactions between all the factors. In this work, the experimental design was performed using the central composite design (CCD).
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To an extent, the effects of the monomer concentration and quantity of nanoparticles towards
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the responses were optimized using minimal experimental runs. From the literatures, there is a wide range of the applicability of RSM in membrane industry ranging from the fabrication,
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modification and to the application of membranes [23-25].
As for this research study, SPS membrane had been used as the substrate for
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membrane surface modification process through UV-grafting. The monomer solution used
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was methacrylic acid. With the aid of RSM design, the parameters of the experiment such as UV-activation time and concentrations of methacrylic acid were studied in order to identify
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their correlation with the responses like permeability and rejection. Through the RSM analysis, the permeability and rejection capability of the modified membrane can be optimized [18].
2. 2.1
Experimental
Materials and Apparatuses
The SPS membrane (ASP; Amfor Inc.) commercial nanofiltration membrane was used as substrate for surface modification. The methacrylic acid monomer solution (Aldrich; 99%) was used as soaking medium. Sodium chloride, NaCl (J. Kollin Chemicals) and magnesium sulphate, MgSO4 (R&M Chemicals) were used during the salt rejection tests. Humic acid was
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used as organic foulant during the membrane fouling test. The main apparatus, Sterlitech HP4750 Stirred Cell was used in permeability tests, salt rejection tests and also fouling tests.
2.2
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The LED UV lamp (UV-LED Module LC-L2, Hamamatsu, Japan) was used as UV source.
Surface Modification by UV-Grafting
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The SPS membrane was first rinsed with ultrapure water to remove any preservative agent.
The membrane was then activated by exposure to the UV source at 365 nm wavelength for 0
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to 50 minutes. Next, it would be dipped in methacrylic acid monomer solution at
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concentration of 0 to 6 wt%.
After the dipping process, the membrane would be exposed to UV light for another 10
Experiments
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2.3
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the non-reacted monomer solution.
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minutes. After the UV treatment, the membrane was washed with ultrapure water to remove
The permeability of membrane was determined by measuring the pure water fluxes using a
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stirred cell (Sterlitech HP4750). The membrane was tested at pressure of 6 to 9 bar. The pure water fluxwas calculated by using the following equation : J = V/s t
(1)
where J is the water flux (L.m-2.hr-1); V is the permeate volume (L); S is the effective membrane area (m2); t is the operation time (hr)[19]. Graph of water fluxes against pressures was plotted and the permeability was determined based on the gradient of the linear line. The rejection of 500ppm NaCl and MgSO4 solutions were conducted at 6 bar using the stirred cell. The concentration of the permeate solution was determined by using the conductivity meter. The salt rejection was determined by using the following equation: R = 1 – (Cp / Cf)
(2)
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where Cp is the permeate solution concentration and Cf is the feed solution concentration. Humic acid solution of 10 ppm was used during the fouling test. The permeate fluxes and rejections of humic acid can thus be determined. Besides, flux recovery was determined
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after the fouling test where the fouled membrane will be rinsed with ultrapure water for about 15 minutes. The flux recovery can be calculated using the following equation:
(3)
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Recovery percentage = (J2 / J1) x 100%
where J1 is the pure water flux before fouling test and J2 is the pure water flux after fouling
Membrane Characterization
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2.4.1 Membrane morphological study
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2.4
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test.
The surfaces and cross-sectional structures of the unmodified and modified membranes were
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obtained by using scanning electron microscope (SEM, Gemini SUPRA 55VP-ZEISS). The
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samples were coated with thin gold layer before sent for SEM scanning.
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2.4.2 Membrane surface hydrophilicity study The angle between water and membrane surface was measured with contact angle meter, Model Kruss GmbH Germany with Drop Shape Analysis software. Based on the differences of the contact angle values for each membrane, the membrane surface hydrophilicity was compared.
2.4.3 Membrane composition study with FTIR analysis FTIR-ATR analysis was done to detect the presence of functional groups in the modified membranes. FTIR Nicolet 6700 was used for this analysis. The spectra were obtained from 32 scans at 4 cm-1 resolution from 4000 to 400cm-1.
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2.5
Experimental Design
In response surface methodology (RSM), the central composite design (CCD) was used for optimization of the membrane modification process. The software used for the CCD design
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was Design Expert Software (Version 7.0.0, Stat-Ease Inc., MN, USA).The 2 variables studied in this design were methacrylic acid concentration (Factor A) and UV activation time
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(Factor B). There were 11 sets of experimental runs carried out in order to optimize the
Results and Discussion
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3.
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modification process based on the response data.
3.1.1 Effect of methacrylic acid concentration
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The effect of methacrylic acid is vital in the membrane modification process. This is mainly because there are functional groups disassociated from the acid and will be grafted on the
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membrane surface. However, the concentration of methacrylic acid must be in a proper
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amount since too high or low concentration will affect the performance of grafting process. Table 1 shows the effect of methacrylic acid concentration with constant UV activation time.
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The result showed that the concentration of 3 wt% gives the best result set with low permeability and high salt rejection capability after modification. This indicates that there is an optimum point between 0 wt. % to 6 wt. %. Insert Table 1.
3.1.2 Effect of UV exposure
UV exposure is an important element in membrane grafting process as it helps to alter the chemical bond of polymeric membrane [1, 11, 12, 26-28] and methacrylic acid. UV light acts as a polymer degrader which contributes to the initiation of membrane modification process. At constant concentration of methacrylic acid, the best membrane performance was observed
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for duration of 25 minutes. This is mainly due to the fact that long duration of the UV exposure will destroy the membrane surface which ends up with incomplete modification process. Table 2 shows the effect of UV exposure with constant acid concentration. From the
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result, the optimal UV exposure trend is between 0 to 50 minutes.
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Insert Table 2.
3.1.3 Response Surface Methodology (RSM)
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The main responses studied in the membrane modification process were membrane
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permeability, salt rejections and humic acid rejection. After knowing the trend of optimization based on the effects of methacrylic acid concentration and UV exposure time,
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response surface methodology (RSM) was applied to obtain the optimized set of factor for the optimum membrane modification process.
Insert Table 3.
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responses.
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Table 3 shows the central composite design with different value of the variables and the
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According to the experimental design, the quadratic models were established for all the responses. The model forms can be represented in terms of coded factors and actual factors. Both of the models can be expressed in equation 4 and 5: Coded factors = a0 + a1A + a2B + a11A2+ a12B2 + a22AB
(4)
Actual factors = b0 + b1MAA + b2UV + b11MAA2+ b12UV2 + b22MAA*UV
(5)
Table 4 shows the regression coefficients corresponding to the models in terms of coded factors. Insert Table 4.
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Table 5 shows the regression coefficients corresponding to the models in terms of actual factors.
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Insert Table 5.
Analysis of variance (ANOVA) has been used to verify the regression model. ANOVA is a
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summary of the sum of squares of residuals and regressions, degrees of freedom, F-value, Pvalue and ANOVA coefficients. In the other hand, F-value is a measure of variance of the
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data about the mean value. It can be assured that the input variables would have explained the variation in the mean of data if the F-value departs significantly from unity[29]. P-value is
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generated based on F-value and the degree of freedom. As for the R2 value, there was a slight
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adjustment on each of the responses in order to present a better model with higher accuracy. From the ANOVA analysis, regression models used to explain the responses of membrane
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permeability, NaCl and MgSO4 salt rejections exhibited only moderate correlations with the
inconsistent variables.
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experimental data. The possible explanation for this observation was the presence of several
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By referring to Table 6, the model F-value of 6.81 implies that the model is significant. From the statistical analysis, the value of Prob>F for the regression model is less than 0.05, which shows that the results can be fitted using the quadratic model. The "Lack of Fit F-value" of 0.35 implies the value is not significant relative to the pure error[30]. There is a large chance (79.70%) that a "Lack of Fit F-value" could occur due to the presence of noises. The nonsignificance of ‘Lack of Fit F-value’ shows that the model is well fitted and this further verified that the responses fitted the regression model well. The "Pred R-Squared" of 0.4968 is not as close to the "Adj R-Squared" of 0.7439 as normally expected. This may indicate a large block effect or a possible problem with the proposed model. Model reduction or
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response transformation are required to improve the model if there are many insignificant model terms. Insert Table 6.
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Insert Table 7. Insert Table 8.
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Insert Table 9.
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For the first response which was membrane permeability, it can be seen that the membrane
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permeability declined after the surface modification process. The decline of membrane permeability was mainly due to the membrane pore blockage. The contact between the
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monomer solutions and membrane surface would form a thin grafted layer on the membrane. To some extent, the grafted layer would block some of the inner pores of membrane. It is also
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possible that the penetration of monomer into the membrane pores causes partial
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blockages[31]. As a result, there was a higher hydraulic resistance during the flow of any solvent and thus the pure water flux is much lower after the modification. The high acid
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concentration also may spoilt the membrane structure by causing pore collapse of the membrane and thus affects the permeate flux of membrane. As for the NaCl and MgSO4 salt rejection, the highest rejection percentage was at 3 wt% of methacrylic acid with UV activation of 25 minutes. These parameters combination was found as the most optimized condition amongst all the experimental runs. The result indicated that the concentration of the monomer solution must be at an appropriate level so that it would not affect the membrane performance. This was mainly due to the fact that at high concentration the solution might deteriorate the membrane structure while at low concentration it was insufficient to undergo the grafting process. The UV activation time exhibited the same effects as the monomer concentration. Therefore, the UV light exposure must be within a
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proper range (0-50mins) to avoid the deterioration of the membrane performances. The third response was the humic acid rejection capability. The model obtained from RSM for this response was linear. There was neither maximum nor minimum curve exist. However, it was
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shown that the rejection percentage of humic acid decreased when the concentration of MAA solution and UV exposure time increased. Thus, the highest humic acid rejection percentage
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was recorded when 0.88 wt% of methacrylic acid used and exposed to UV activation for 7.32 minutes.
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The main objective of employing RSM is to obtain an optimized condition for the membrane modification process. The optimized condition for the membrane modification process
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produced membrane with a permeability value of about 9.25 L.m-2.hr-1 as predicted by the
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RSM approach. As for the rejection, it should yield high rejection percentages for all salt solutions and fouling solution. Thus, based on the numerical optimization analysis using
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Insert Table 10.
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in the Table 10.
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Design Expert Software, the predicted parameters for the optimized condition was as shown
Experiments were conducted according to the predicted parameters in order to verify its accuracy. Based on the result obtained experimentally, the permeability was 8.75 L.m-2.hr1
.bar-1, rejection for NaCl, MgSO4 and humic acid were 65.7%, 48.3%, and 95.0%,
respectively. The error percentage could be calculated using equation 6. Error percentage = |(Vexp – Vpredict) / Vexp| × 100%
(6)
where Vexp is experimental value and Vpredict is predicted value. After comparing with the predicted responses by the RSM, there was only a small percentage of error. This indicated that the predictions from the RSM were accurate and reliable. The error percentage for each response was shown in Table 11.
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Insert Table 11.
3.1.4 Response surface models
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Insert Fig. 1. Insert Fig. 2.
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Insert Fig. 3.
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Insert Fig. 4.
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Insert Fig. 5.
The above response surface models indicated the interaction between methacrylic acid
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concentration and UV activation time towards membrane performances. The response surface plotted were the analysis of membrane permeability, salt and humic acid rejection and also
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the predicted optimized response with respect of the mathematical function in analysis of
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variance (ANOVA) in Design Expert Software. Based on Figure 1, the least membrane permeability occurred in the middle of both of
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the parameters. This showed that the modified membrane produced lowest permeability at about 3wt% of methacrylic acid and UV activation duration of about 25 minutes. Therefore, the methacrylic acid concentration and UV activation time must be controlled in an appropriate range to achieve better membrane performances. According to the regression model based on the membrane permeability, the modified membrane permeability was highly correlated by the methacrylic acid concentration and UV activation time. The value of Prob>F for the regression model was less than 0.05 which indicates that all the experimental data fits satisfactorily into the quadratic model. For NaCl and MgSO4 salt rejection response models (Figure 2&3), no transformation of the data is required as all the data gives satisfactory result through the ANOVA analysis.
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According to both of the quadratic models, the optimum performances of salt rejections were shown in the middle positions of both variables (methacrylic acid concentration and UV activation time). Thus, an extreme increment or decrement of the main parameters will
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definitely affect the performance of salt rejection of membrane. However, from the surface models, it exhibited that the two main parameters are independent factors which did not
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correlate with each other.
According to Figure 4, the response surface model for humic acid rejection was a
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linear regression model. This model is less significant as the Prob>F value is greater than
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0.05. This indicates that the experimental data contributed by these two parameters did not precisely fit the regression model. Therefore, this response did not contribute much in the
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prediction of optimized response.
The predicted optimized response surface model was plotted after considering all the
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responses based on its significance in determining the appropriate set of parameters for the
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best performance of modified membrane. According to the ANOVA analysis, a quadratic model was plotted for the optimization process based on the combination of significant
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responses. Based on the predicted parameters in its optimized conditions, membrane with improved performances can be obtained.
3.2
Membrane Characterization
3.2.1 Permeability
As shown in the Figure 6, the optimized membrane exhibited the lowest permeability when compared with the unmodified membrane and membrane without the addition of monomer or exposure to the UV activation. This was a common phenomenon as reported in some of the literature studies [10, 11]. The main reason was that the modification process had altered the inner pore structure of the membrane which led to an increase in the hydraulic resistance of
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water. In addition, the deposition of MAA grafted onto the membrane surface might also penetrate deep into the pores of the membrane structure and thus caused blockages. As such, the pure water flux of the optimized membrane would decrease due to the alterations on the
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membrane porosity.
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Insert Fig. 6.
3.2.2 Membrane morphological study
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The SEM for the surface view of 3 types of membranes was shown in the Fig. 7 (a), (c) and
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(e). There was no special effect on the unmodified membrane while there were some spots on the modified membrane. For the fouled membrane, there was deposition of humic acid on its
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surface. For the cross-sectional view of the membranes, it was shown in Fig. 7 (b), (d) and (f). It could be seen that there was a great difference between the unmodified and modified
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membrane where there was a thin grafted layer on the modified membrane. The membrane
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structure formed after the UV-grafting process is a thin layer consisting of the attachment of MAA monomer chain on the membrane surface. The deposition of MAA monomer on the
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membrane surface may cause inner pore adsorption or blockage after the complete grafting process occurred. This showed that there was a structural modification and the modified membrane would yield a higher rejection capability. Insert Fig. 7.
3.2.3 Contact angle study
As for the contact angle study, the contact angles for the modified membranes were lower when compared with the unmodified membrane. This was due to the presence of methacrylic acid monomer which was more hydrophilic towards the membrane. From Figure 8, the optimized membrane which was modified completely had lower contact angle when
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compared with unmodified membrane. This indicated the hydrophilicity of membrane was increased after the modification process.
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Insert Fig. 8.
3.2.4 FTIR analysis
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The functional groups of the membrane were shown in the FTIR analysis. The comparison between unmodified, modified and fouled membrane were shown in Figure 9. The peak
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absorbance at wavenumber 1150 cm-1 showed that there was stretching vibration of O=S=O functional group in polysulfone membrane. The presence of –OH group could be detected at
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peak absorbance of 3400 cm-1 due to the overlapping of methacrylic acid and humic acid. The peak absorbance of 1580cm-1 showed the presence of C=C aromatic while C-O was detected
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at 1240 cm-1 and C-H at 3090 cm-1. The intensity of the peaks in unmodified membrane was
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higher than the modified membrane indicated that the methacrylic acid had adsorbed onto the
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Insert Fig. 9.
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SPS membrane surface.
3.2.5 Rejection and flux performance with Humic Acid All the four membranes were also tested with humic acid to ascertain the ability of the membranes against organic contaminants. In terms of rejection, the optimized membranes, as shown in Figure 10, displayed the highest rejection of about 95% followed by the unmodified membrane at 87% rejection, the no acid membrane at 83% and the no-UV membrane at 76%. This result showed that the effect of UV has improved the rejection of humic acid by 25% albeit with lower permeability.
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Interestingly, the flux decline profile showed behavior that is consistent with the degree of hydrophilicty of the membranes. Figure 11 showed the plot of normalized flux decline over time. The flux decline behavior showed that the flux decline behavior followed the sequence
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of no-acid membrane > no-UV membrane > blank membrane > optimized membrane. This sequence is partially correlated to the contact angle measurement (Figure 8) which showed
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that the no-acid membrane and no-UV membrane to be more hydrophilic compared to other membranes. Hydrophilicity has been shown to influence the degree of fouling to a certain
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hydrophilicity and thus not the flux decline behaviour.
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extent. The optimized membrane showed only a small improvement in terms of
Insert Fig. 10.
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Insert Fig. 11.
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Conclusion
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The modification of SPS membrane surface has been performed successfully by employing UV-grafting method. By employing RSM in the current study, variables such as methacrylic
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acid solution concentration and UV activation duration were optimized based on the responses studied in the current work (including membrane permeability, salt rejections and humic acid rejections). Based on the results obtained through RSM, the optimized parameters for the membrane modification process were identified as: 2.61wt% of methacrylic acid solution and UV activation time of 21.10 minutes. Experiments had been conducted according to the predicted parameters to verify the accuracy and reliability of the analysis of RSM. The error percentages were less than 8% when the predicted results were compared with the experimental results. Thus, it proved and verified the accuracy of the predicted value from RSM. Besides, membrane characterizations were done to study the membrane performances after the modification process which involved membrane surface hydrophilicity study,
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membrane morphological study (using SEM) and membrane functional group study (using FTIR analysis).
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[29] M. Khayet, C. Cojocaru, M. Essalhi, J. Membr. Sci., 368 (2011) 202-214. [30] N. Mandal, B. Doloi, B. Mondal, Int. J. Refract. Metals Hard Mater., 29 (2011) 273-280. [31] K. Mohammad Gheimasi, T. Mohammadi, O. Bakhtiari, J. Membr. Sci., 427 (2013) 399410.
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Figure Captions
Figures Captions
Fig. 1. Response surface model for membrane permeability. Fig. 2. Response surface model for NaCl rejection.
Fig. 4. Response surface model for humic acid rejection.
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Fig. 5. Response surface model for predicted optimized response.
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Fig. 3. Response surface model for MgSO4 rejection.
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Fig. 6. Graph of membrane permeability.
Fig. 7. SEM Micrograph for surface layer and cross section of membrane: (a) & (b)
Fig. 8. Contact angle of different membranes.
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blank membrane; (c) & (d) modified membrane; (e) & (f) fouled membrane.
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Fig. 9. FTIR analysis for 3 types of membrane. Fig. 10. Rejection percentage of humic acid.
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Fig. 11. Flux decline profile of humic acid.
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Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Figure 11
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Table 1
Table 1. Effects of methacrylic acid concentration. Factors (Actual)
Permeability (L.m-2.hr-1.bar-1)
Salt rejection (%)
wt%
minute
Before modification
After modification
NaCl
MgSO4
0.00 3.00 6.00
25.00 25.00 25.00
12.693 13.179 10.496 11.214
13.353 8.369 11.986
40.79 50.78 65.07 49.03
31.87 38.24 52.96 35.61
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B: UV activation time
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0 9 7 4
A: Methacrylic acid concentration
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Responses
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Table 2
Table 2. Effects of UV exposure. Factors (Actual)
Permeability (L.m-2.hr-1.bar-1)
Salt rejection (%)
wt%
minute
Before modification
After modification
NaCl
MgSO4
3.00 3.00 3.00
0.00 25.00 50.00
12.693 12.574 10.496 11.82
9.734 8.369 9.554
40.79 58.84 65.07 61.5
31.87 47.43 52.96 47.62
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B: UV activation time
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0 8 7 11
A: Methacrylic acid concentration
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Responses
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Table 3
Table 3. Experimental sets with actual factors in RSM and responses. Factors (Actual)
Permeability (L.m-2.hr-1.bar-1)
Salt rejection (%)
Humic acid rejection
wt%
minute
Before modification
After modification
NaCl
MgSO4
%
5.12 5.12 3.00 6.00 0.88 0.88 3.00 3.00 0.00 3.00 3.00
42.68 7.32 25.00 25.00 7.32 42.68 25.00 0.00 25.00 25.00 50.00
12.513 11.418 10.851 11.214 13.896 13.315 10.496 12.574 13.179 10.821 11.82
11.557 11.487 10.217 11.986 11.778 10.59 8.369 9.734 13.353 8.709 9.554
49.6 53.94 58.78 49.03 56.37 50.76 65.07 58.84 50.78 62.21 61.5
35.69 39.78 46.4 35.61 39.49 43.82 52.96 47.43 38.24 51.33 47.62
71.85 95.73 85.25 83.56 98.74 92.80 82.13 77.47 84.88 89.21 86.50
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B: UV activation time
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1 2 3 4 5 6 7 8 9 10 11
A: Methacrylic acid concentration
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Responses
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Table 4
Table 4. Regression coefficients corresponding to the models in terms of coded factors. a0 9.10 62.02 50.23 87.10
a1 -0.16 -0.76 -1.44 -5.00
a2 -0.17 -0.77 0.06 -2.13
a11 1.83 -6.65 -7.28 -
a12 0.32 -1.52 -1.99 -
a22 0.31 0.32 -2.11 -
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Coded Permeability NaCl MgSO4 Humic acid
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Table 5
Table 5. Regression coefficients corresponding to the models in terms of actual factors. b0 14.5053 48.4861 29.4335 97.1814
b1 -2.7299 8.2976 10.4355 -2.3522
b2 -0.0863 0.1736 0.4896 -0.1206
b11 0.4077 -1.4778 -1.6189 -
b12 1.03E-03 -4.86E-03 -6.35E-03 -
b22 8.39E-03 8.47E-03 -0.0561 -
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Actual Permeability NaCl MgSO4 Humic acid
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Table 6
20.13 0.20 0.24 0.40 19.01 0.59 1.02 0.8719 0.7439 0.4968 6.853
Prob> F
5 1 1 1 1 1 3
0.0276 0.5883 0.5557 0.4505 0.0024 0.3655 0.7970
6.81 0.33 0.40 0.67 32.15 0.99 0.35
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Model A-Methacrylic acid B-UV activation time AB A2 B2 Lack of Fit R-Squared Adj R-Squared Pred R-Squared Adeq Precision
DF F Value
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Sum of squares
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Table 6. ANOVA analysis for permeability response.
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Table 7
260.70 4.60 4.79 0.40 249.73 13.00 34.89 0.8265 0.6530 0.0719 5.882
F Value
Prob> F
5 1 1 1 1 1 3
4.7637 0.4201 0.4373 0.0368 22.8156 1.1881 1.1726
0.0559 0.5455 0.5377 0.8553 0.0050 0.3254 0.4909
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Model A-Methacrylic acid B-UV activation time AB A2 B2 Lack of Fit R-Squared Adj R-Squared Pred R-Squared Adeq Precision
DF
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Sum of squares
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Table 7. ANOVA analysis for response NaCl salt rejection.
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Table 8
Table 8. ANOVA analysis for response MgSO4 salt rejection. F Value
Prob> F
5 1 1 1 1 1 3
8.7387 2.1830 0.0042 2.3165 39.1700 2.9081 0.4264
0.0164 0.1996 0.9507 0.1885 0.0015 0.1489 0.7563
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DF
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Model A-Methacrylic acid B-UV activation time AB A2 B2 Lack of Fit R-Squared Adj R-Squared Pred R-Squared Adeq Precision
Sum of squares 334.31 16.70 0.032 17.72 299.70 22.25 14.92 0.8973 0.7946 0.5742 8.132
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Table 9
Table 9. ANOVA analysis for response humic acid rejection. Source
Sum of squares
DF F Value
Prob> F
Model A-Methacrylic acid B-UV activation time Lack of Fit R-Squared Adj R-Squared Pred R-Squared Adeq Precision
236.03 199.69 36.34 412.55 0.3503 0.1879 0.4428 3.690
2 1 1 6
0.1782 0.0884 0.4267 0.1628
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2.1568 3.9126 0.7120 5.4611
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Table 10
Table 10. Predicted parameters and responses from RSM for optimized condition. MAA
UV
Humic acid Desirability rejection rejection rejection 62.04 50.05 88.31 0.745 NaCl
(wt. %)
(min)
2.61
21.1
Permeability
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9.256
MgSO4
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Table 11
Table 11. Error percentages between the predicted and experimental responses. Experimental 8.75 65.7 48.3 95.0
Predicted 9.256 62.04 50.05 88.31
Error percentage (%) 5.74 5.54 3.56 7.02
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Responses Permeability NaCl rejection MgSO4 rejection Humic acid rejection
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