Chemical Engineering Journal 193–194 (2012) 50–58

Contents lists available at SciVerse ScienceDirect

Chemical Engineering Journal

journal homepage: www.elsevier .com/locate /ce j

Insights into the effect of preparation variables on morphology and performanceof polyacrylonitrile membranes using Plackett–Burman design experiments

Pan Wang, Zhiwei Wang ⇑, Zhichao WuState Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water, Environment of Ministry of Education,School of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China

a r t i c l e i n f o a b s t r a c t

Article history:Received 20 October 2011Received in revised form 25 March 2012Accepted 7 April 2012Available online 16 April 2012

Keywords:Membrane preparationMembrane bioreactorPhase inversionPlackett–Burman designPolyacrylonitrile

1385-8947/$ - see front matter � 2012 Elsevier B.V. Ahttp://dx.doi.org/10.1016/j.cej.2012.04.017

⇑ Corresponding author. Tel./fax: +86 (21) 6598040E-mail address: zwwang@tongji.edu.cn (Z. Wang).

In this study, polyacrylonitrile (PAN) flat-sheet membranes were prepared using phase inversion methodfor the wastewater treatment by membrane bioreactor (MBR). Twelve processing factors in the mem-brane preparation were investigated by Plackett–Burman design (PBD) in order to prepare high-perfor-mance membranes. Test results showed that none of the twelve processing factors had a significanteffect on membrane pore size. The casting thickness, non-woven fabric type and casting speed werefound to have substantial negative effects on the pure water flux values, and were identified as the sig-nificant factors determining the membrane pure water flux. It was also found that the non-woven fabrictype, casting speed and casting thickness had significant positive effects on the fouling rate. Moreover,the model using membrane fouling rate as a response value was more credible and in good linear corre-lation within the tested range.

� 2012 Elsevier B.V. All rights reserved.

1. Introduction the ultimate membrane structure [6,7]. Research efforts have been

In recent years, membrane technology has emerged as a viableand cost-effective alternative for water and wastewater treatment.The membrane-assisted biological treatment, e.g., membrane bio-reactor (MBR), has been widely employed for municipal and indus-trial wastewater treatment. In MBRs, microfiltration (MF) orultrafiltration (UF) membrane is used as a solid–liquid separationdevice, which offers remarkable advantages over conventionalactivated sludge (CAS), including a higher biomass concentration,reduced footprint, and highly-improved effluent quality, etc. [1–3].

The most important part of MBRs is the membrane itself andpolymeric membranes are the widely used ones in MBR processes.Polyacrylonitrile (PAN) membrane is one of popular membranematerials due to its good solvent resistance, strong chemical stabil-ity, and high mechanical strength [4]. It also has been reported thatPAN membrane is relatively hydrophilic and has lower fouling ratein aqueous filtration compared with polysulfone (PSF) and polye-thersulfone (PES) [5].

There are several ways to prepare porous polymeric mem-branes, such as solution casting, sintering, stretching, track etchingand phase inversion. The phase inversion process induced byimmersion precipitation is a well-known technique to prepareasymmetric polymeric membranes and the complex phase separa-tion conditions have been recognized as major factors determining

ll rights reserved.

0.

dedicated to determine optimal preparation conditions for achiev-ing better membrane morphology and performance [8–10]. Salj-oughi and Mohammadi [11] identified three controllable factorsfor the asymmetric cellulose acetate (CA) membranes and deter-mined the main effects of each factor using Taguchi design ofexperiments. Idris et al. [12] also used the Taguchi design to opti-mize the membrane preparation and clarified the most importantfactors determining the performance of the CA membranes. Theorthogonal array method was adopted by Chau et al. [13] to studythe phase inversion factors influencing membrane and it wasfound that the PVP content and the temperature of the coagulationbath were the factors governing the molecular weight cut-off ofpolysulfone membranes. In addition, Bulut et al. [14] and Cawse[15] also used evolutionary optimization via genetic algorithmsto develop high-throughput and combinatorial membranes.Although the methods mentioned above are very helpful to screenthe important factors influencing membrane preparation and tooptimize membrane properties, it is insufficient to establish a gen-eral rule for manufacturing a certain kind of membrane. This is par-tially because there are enormous factors affecting membranepreparation. Another reason is due to the fact that some factorsare interacted. It is difficult to obtain the comprehensive effectsof some factors through a number of experiments. Plackett–Bur-man design (PBD) method which can examine up to N � 1 factors(f 6 N � 1) in N experiments, are commonly used in the optimiza-tion experiments of fermentation. It has been proven to be an effi-cient way of evaluating a large number of variables and identifyingthe significant ones [16,17].

P. Wang et al. / Chemical Engineering Journal 193–194 (2012) 50–58 51

Therefore, in this study, PBD method was taken into use tostudy the processing factors influencing flat-sheet membrane andto identify the significant ones in the fabrication of PAN membranefor wastewater treatment by phase inversion process. The effectsof twelve controllable processing factors on the membrane mor-phology and performance were studied. The twelve factors in-cluded the polymer dissolving temperature, polymer dissolvingtime, non-woven fabric type, casting speed, casting thickness,evaporation time, coagulation bath temperature, coagulation bathtime, post-treatment reagent concentration, post-treatment agenttime, heat-treatment temperature, and heat-treatment time. Mem-brane pore size, pure water flux and membrane fouling rate werechosen as the response values of the PBD experiments. The resultsobtained in this study are expected to provide a sound understand-ing of PAN membrane preparation using the phase inversionmethod.

2. Materials and methods

2.1. Materials

PAN polymer with molecular weight (MW) 70,000 Da was usedas the membrane-forming polymer which was supplied by the Sin-opec Shanghai Petrochemical Co. Ltd. (China). N,N-dimethylform-amide (DMF) of reagent grade was used as solvent withoutfurther purification. The non-woven fabrics used as supportingmaterials, TA3618 and PH554, were supplied by the Tianlue Ad-vanced Textile Co. Ltd. (China) and Teijin Co. Ltd. (Japan), respec-tively. The fabric TA3618 with 170 lm thickness which wassynthesized by thermal method was made of polyester filament.The fabric PH554 with 140 lm thickness which was synthesizedby wet method was made of polyester filament. Distilled waterwas used as the external coagulant. Glycerin was used as thepost-treatment reagent.

2.2. Membrane preparation

PAN flat membranes were prepared by phase inversion viaimmersion precipitation. PAN was dissolved in DMF to form20 wt.% of casting solution and was then stored at a certain temper-ature for a set of predetermined time. The polymer solution wassprinkled and cast into a film with 100 or 400 lm on the non-wo-ven fabric at a certain speed using a motorised film applicator(Elcometer 4340, Elcometer Instruments Ltd., England). The castfilm was exposed to air at 30% relative humidity for a series of pre-set evaporation time, and then, immersed into a coagulant bath ofdistilled water to form the porous membrane. In order to protectthe structure of the nascent membrane, the prepared porous mem-branes were post-treated by immersing into water/glycerin of agroup of ratios. At the final stage, membranes were heat-treatedat various preset temperature for different prefixed time.

2.3. Membrane characterization

2.3.1. Field emission scanning electron microscopy (FESEM) analysisFESEM (S-4800, Hitachi, Japan) was used to investigate the sur-

face morphologies of membranes. The membranes were freeze-dried in vacuum and gold sputtered for producing electric conduc-tivity. The micrographs were observed and the average pore sizeswere obtained from the images using Image-Pro Plus 6.0 software[18].

2.3.2. Pure water flux measurementThe membrane pure water flux was measured by a dead-end fil-

tration apparatus. The area of tested membrane was 12.56 cm2.

Each sample was soaked in distilled water for 24 h before testingand pre-compacted for 30 min at 0.08 MPa. Then, the trans-mem-brane pressure was lowered and the pure water flux (J) was ob-tained at 0.03 MPa by measuring filtrate volume within a certainperiod of time. To minimize the experimental error, each mem-brane was measured at three random locations and the average va-lue was reported. The pure water flux was calculated according tothe following equation:

J ¼ V=ðS � tÞ ð1Þ

where J is the pure water flux (L/(m2 h)), V volume of permeatedwater (L), S effective area of tested samples (S = 0.001256 m2), andt record time (h).

2.3.3. Membrane fouling rate determinationIn order to test the performance of membrane samples for

wastewater treatment, a short-term filtration experiment in amini-MBR (an MBR cell) was carried out to determine the foulingrate of each membrane. The prepared flat-sheet membranes werefirstly processed into modules with a filtration area of 0.01 m2.Then the modules were put into a mini-MBR with an effective vol-ume of 2.5 L in which the activated sludge was taken from a long-term stable pilot-scale bioreactor. Filtration performance of themini-membrane module was conducted with the constant flux of40 L/(m2 h), and the change of trans-membrane pressure (TMP)was monitored within 30 min. The aeration rate was controlledas 15 m3/(m2 h). The membrane fouling rate was evaluated bymembrane filtration resistance, which was calculated by the fol-lowing equation according to the literature [19,20]:

R ¼ ðTMP30 � TMP0Þ=l � J � Dt ð2Þ

where TMP30 and TMP0 are the TMP values of membrane samples atstarting time and after a 30-min filtration (Pa), respectively, J is themembrane permeate flux (m3/(m2 s)), l is the viscosity of filtrate(Pa s), and Dt is filtration time (h). In order to ensure the credibilityof the results, each membrane was examined for three times andafter each test the membranes were immerged in 0.5% (w/v) NaClOsolution to recover the permeability.

2.4. Experimental design

The purpose of PBD was to evaluate the effect of the processingfactors and identify the key ones influencing the membrane char-acteristics. Before designing the experiment, suitable values forthe twelve controllable factors were selected according to previousstudies [8–13] and our preliminary tests. PBD was developed usingthe Minitab software (Version 15.1.30, Minitab Inc., USA). Each fac-tor was investigated at two widely spaced levels: �1 for low leveland +1 for high level [21]. The 12 variables were evaluated by 20runs of experiments. Table 1 shows the factors under investigationas well as the levels of each factor used in the experimental design,whereas Table 2 lists the design matrix. Each row of Table 2 repre-sents a run, which is a specific set of factor levels to be applied.Each run was conducted twice, and 40 membrane samples werethus obtained. The response values used in this study includedthe membrane pore size, pure water flux and fouling rate. A com-monly used statistical method, analysis of variance (ANOVA), wasalso used to analyze the results of experiments. The fitted first-or-der model could be analyzed as follows:

Y ¼ b0 þ RbiXi ð3Þ

where Y is the predicted response, b0 and bi are constant coeffi-cients, and Xi is the coded independent factors.

Table 1The Plackett–Burman design for screening variables and their levels in the process ofmembrane preparation.

Factors Code Low level(�1)

High level(+1)

Polymer dissolving temperature (�C) A 20 70Polymer dissolving time (d) B 4 8Non-woven fabric type a C TA3618 PH554Casting thickness (lm) D 100 400Casting speed (m/min) E 0.6 3.6Evaporation time (s) F 10 180Coagulation bath temperature (�C) G 12 48Coagulation bath time (min) H 5 60Post-treatment reagent

concentration (%)I 10 40

Post-treatment reagent time (min) J 5 60Heat-treatment temperature (�C) K 50 130Heat-treatment time (min) L 5 60

a The non-woven fabric types included two kinds: TA3618 which was synthe-sized by thermal method and PH554 which was synthesized by wet method.

Table 2The Placket–Burman experiment design variables matrix (in coded levels).

Runs Variables

A B C D E F G H I J K L

1 1 �1 �1 �1 �1 1 �1 1 �1 1 1 12 �1 1 1 �1 1 1 �1 �1 �1 �1 1 �13 1 1 �1 1 1 �1 �1 �1 �1 1 �1 14 1 �1 1 1 �1 �1 �1 �1 1 �1 1 �15 �1 1 1 �1 �1 �1 �1 1 �1 1 �1 16 �1 1 �1 1 �1 1 1 1 1 �1 �1 17 1 1 1 1 �1 �1 1 1 �1 1 1 �18 1 1 �1 �1 1 1 �1 1 1 �1 �1 �19 �1 1 1 1 1 �1 �1 1 1 �1 1 1

10 1 �1 �1 1 1 �1 1 1 �1 �1 �1 �111 �1 1 �1 1 1 1 1 �1 �1 1 1 �112 �1 �1 1 �1 1 �1 1 1 1 1 �1 �113 1 �1 1 �1 1 1 1 1 �1 �1 1 114 1 1 �1 �1 �1 �1 1 �1 1 �1 1 115 �1 �1 �1 1 �1 1 �1 1 1 1 1 �116 �1 �1 1 1 �1 1 1 �1 �1 �1 �1 117 1 �1 1 1 1 1 �1 �1 1 1 �1 118 1 1 1 �1 �1 1 1 �1 1 1 �1 �119 �1 �1 �1 �1 1 �1 1 �1 1 1 1 120 �1 �1 �1 �1 �1 �1 �1 �1 �1 �1 �1 �1

52 P. Wang et al. / Chemical Engineering Journal 193–194 (2012) 50–58

3. Results and discussion

3.1. Surface morphology of prepared membranes

In the phase inversion process, the preparation conditions wererecognized as the major factor determining the ultimate mem-brane morphology under the same polymer compositions. Fig. 1shows the FESEM micrographs of the fabricated membranesaccording to the design. The magnification of each SEM imageswas 50,000�. It can be observed that the prepared membranesshowed distinct differences in pore morphologies, e.g., membranes8, 18, and 20 demonstrated large pore size morphologies while therest membranes showed relatively smaller pore size distribution.That is mainly ascribed to the different processing conditions. Inaddition, the membranes showed various pore sizes and surfaceporosity. The average pore sizes of various membranes were ob-tained from the images using Image-Pro Plus 6.0 software andthe results are listed in the second column of Table 3. Among them,under the Runs 8, 18 and 20, the fabricated membranes presenteda macroporous membrane surface and the mean pore size of thosemembranes was up to 2.26 lm. That is mainly because when thethree membranes (8, 18 and 20) were prepared, the casting

thickness, heat-treatment temperature and heat-treatment timein the process were set at their low levels (100 lm, 50 �C and5 min), respectively. Some researchers [22,23] have studied the ef-fect of membrane thickness on the surface morphology and foundthat the thinner films had a less compact top skin and larger porediameter in comparison with thicker ones as a result of differentconvection caused by the surface tension and density gradients.Besides, other researchers [24] also reported that skin layers ofmembranes could become denser due to the high heat-treatmenttemperature and long heat-treatment time, which in turn resultsin a small pore size distribution. However, the mean pore sizes ofthe other membranes were mostly around 0.08 lm, indicating thatthe effect of preparation conditions on the surface morphology wassignificant. The detailed analysis on the effects of each variable onmembrane pore size will be described in the following sections.

3.2. Effect of preparation factors on the membrane pore size

Standardized Pareto charts, representing the estimated effectsof parameters on responses, can allow us to check the statisticalsignificance of the PBD [17,25]. In order to determine the influenceof the most important parameters, the data in Table 3 was statisti-cally analyzed and the results are plotted in Fig. 2 using Minitab15.1.30. Fig. 2(a) is a standardized Pareto chart. It consists of barswith a length proportional to the absolute value of the estimatedeffects divided by the standard error, which is the t-value of theStudent’ s t-test. A tabulated student’s t-value (ttab) is a guide topredict the significance of each factor. A factor is considered tobe significant if the observed value of the t-test (tobs) is above ttab,and under tobs < ttab, the effect of the parameter can be negligible.The value ttab is dependent on the number of degrees of freedomand the applied significance level (a). ttab is calculated to be±1.895 at the usual 90% confidence interval (i.e., a = 0.1), which isshown in Fig. 2(a) by a vertical line. From Fig. 2(a), it can be ob-served that all of the 12 parameters had no significant effects onthe membrane pore size. However, the heat-treatment time (L),heat-treatment temperature (K) and casting thickness (D) had rel-atively stronger impacts on pore size than other factors. Fig. 2(b)shows the main effects of 12 parameters on the pore size. It canbe found that the aforementioned three factors (L, K and D) allhad a significant negative effect on the pore size. It means that asmall pore size can be obtained at long heat-treatment time, highheat-treatment temperature and increased casting thickness,which is in agreement with the previous report [24]. At longheat-treatment time and high heat-treatment temperature, themolecular movement of polymer will become more active andthe skin layers of the membranes can become denser, which inturn results in a small pore size distribution. As for the factor ofcasting thickness, it has been reported that the thinner films hada less compact top skin and larger pore diameter in comparisonwith thicker ones [22,23]. In this study, it was also found thatthe pore size was smaller at the high casting thickness comparedto low casting thickness using PBD experiments. It may be attrib-uted to the variations of surface tension and density gradients athigh casting thickness, which needs further investigating.

3.3. Effect of preparation factors on the membrane pure water flux

The pure water flux is usually taken as one of important eval-uation index to characterize the membrane performance. Table 3shows the average pure water flux values of the 20 kinds ofmembranes. According to the statistical analysis, the effects of12 processing factors on the pure water flux are presented inFig. 3. From Fig. 3(a), it can be observed that the casting thickness(D), non-woven fabric type (C) and casting speed (E), with tobs > t-tab, had significant influences on the pure water fluxes, while the

1 432

5 876

17 201918

13 161514

9 121110

Fig. 1. FESEM images of various membrane surfaces (50,000�).

Table 3Experimental results of pore size, pure water flux and fouling rate.

Run Mean pore size (lm) Pure water flux (L/(m2 h)) Membrane fouling rate (108 (m�1/s))

1 0.088 1088.8 0.832 0.074 287.3 2.583 0.085 158.8 3.774 0.098 242.0 2.295 0.084 502.8 1.776 0.078 718.3 0.507 0.089 192.8 2.008 0.970 809.0 0.689 0.087 151.2 2.95

10 0.085 68.0 2.8311 0.080 189.0 2.1012 0.078 264.6 1.8813 0.097 128.5 2.5114 0.075 1928.1 0.4315 0.089 185.2 0.7416 0.094 219.3 1.9217 0.078 151.2 2.5018 1.206 370.5 2.6119 0.079 257.1 0.4920 2.255 646.5 0.47

P. Wang et al. / Chemical Engineering Journal 193–194 (2012) 50–58 53

rest factors had relatively lower impacts. In addition, Fig. 3(b) showsthat the three key factors had significant negative correlations with

pure water fluxes, indicating that higher pure water fluxes could beobtained under lower values of casting speed and casting thickness.

Fig. 2. (a) Standardized Parreto chart of effects on membrane pore size; (b) Main effects of 12 processing factors on membrane pore size (a = 0.1).

54 P. Wang et al. / Chemical Engineering Journal 193–194 (2012) 50–58

The effects of the casting thickness and speed on the membranepure water flux have been studied by other researchers. Azariet al. [22] reported that membrane structure strongly dependson cast solution thickness. In addition, a higher shear rate in-duced by a higher casting speed would lead to a greater molecu-lar orientation [26] and stronger chain entanglement whichresulted in a decline of the membrane porosity and size. Thatfinally led to a lower pure water flux. Moreover, it is worth

mentioning that the non-woven fabric as the supporting layerof the flat-sheet membrane played a big role in governing thefinal membrane pure water flux in this study. Therefore, besidescasting speed and thickness, the non-woven fabric type shouldbe also paid attention to in order to achieve a high pure waterflux. It is worth noting that membranes 18 and 20 with a poresize of 1.206 and 2.255 lm, had lower flux value compared tothe membrane 1 with a pore size of 0.08 lm. That can be

Fig. 3. (a) Standardized Parreto chart of effects on pure water flux; (b) Main effects of 12 processing factors on pure water flux (a = 0.1).

P. Wang et al. / Chemical Engineering Journal 193–194 (2012) 50–58 55

explained as follows. First of all, the casting thickness, castingspeed and non-woven fabric type had significant negative corre-lations with pure water fluxes. In the preparation process ofmembrane 1, since the three key factors were set at their low lev-els, the higher pure water flux could be obtained. While the highlevel of non-woven type (PH554) was used in the preparationprocess of membrane 18, the membrane still presented a lowerpure water flux due to the higher supporting-layer resistancethough it had bigger surface pore size. Secondly, as for membrane

20, although the three key factors were all set at low levels, theheat-treatment time which had a relatively important positiveimpact on pure water flux was fixed at low level. However, theheat-treatment time was fixed at high level in the membrane 1preparation process, and thus membrane 1 showed a higher purewater flux. In addition, despite the pore size of membrane 20 waseven larger, the surface porosity was smaller compared withmembrane 1. That is why membrane 1 had a higher water fluxcompared to membrane 20.

Fig. 4. (a) Standardized Parreto chart of effects on membrane fouling rate; (b) Main effects of 12 processing factors on membrane fouling rate (a = 0.1).

56 P. Wang et al. / Chemical Engineering Journal 193–194 (2012) 50–58

3.4. Effect of preparation factors on membrane fouling rate

The membrane fouling rate is an index directly reflecting theperformance of the membranes, and lower fouling rate indicatesbetter performance of the membranes. The fouling rate of themembrane samples according to the PBD experiments are shownin Table 3. It can be found that the Runs 14, 20, 19 and 6 showedthe lowest fouling rate. Fig. 4 presents the effects of 12 factors on

the membrane fouling rate response using statistical analysis.From Fig. 4(a), it can be concluded that the non-woven fabric type(C), casting speed (E) and casting thickness (D), had substantial im-pacts on the membrane fouling rate (tobs > ttab, a = 0.1). Fig. 4(b)illustrates that the three factors had significant positive correla-tions with the fouling rate. Low fouling rate can be achieved usingTA3618 non-woven fabric under low values of casting speed andthickness. That is mainly ascribed to the higher membrane pure

Table 4Results of regression analysis for the Plackett–Burman design.

Factors Pore size Pure water flux Membrane fouling rate

Effect Coefficient P Effect Coefficient P Effect Coefficient P

A �0.0307 �0.0153 0.902 171.6 85.8 0.280 0.503 0.2514 0.149B �0.0394 �0.0197 0.874 205.6 102.8 0.204 0.293 0.1464 0.376C �0.2121 �0.1060 0.405 �353.8 �176.9 0.047* 1.018 0.5092 0.013*

D �0.3962 �0.1981 0.142 �400.8 �200.4 0.029* 0.738 0.3688 0.049*

E �0.2224 �0.1112 0.384 �363.0 �181.5 0.043* 0.872 0.4358 0.026*

F �0.0342 �0.0171 0.891 �26.4 �13.2 0.862 �0.19 �0.095 0.559G �0.2168 �0.1084 0.395 11.4 5.7 0.941 �0.13 �0.066 0.685H �0.2160 �0.1080 0.397 �34.0 �17.0 0.823 �0.25 �0.123 0.452I �0.0374 �0.0187 0.880 159.6 79.8 0.313 �0.57 �0.285 0.109J �0.2177 �0.1088 0.393 �183.8 �91.9 0.251 0.154 0.0769 0.635K �0.3976 �0.1988 0.141 74.0 37.0 0.629 �0.2 �0.101 0.536L �0.3999 �0.2000 0.139 205.0 102.5 0.205 �0.05 �0.025 0.877

Model aR2 = 64.10%, F = 1.04 0.501 R2 = 79.87%, F = 2.32 0.136 R2 = 82.58%, F = 2.77 0.092*

* p < 0.1 is considered as significant.a R2, coefficient of determination.

P. Wang et al. / Chemical Engineering Journal 193–194 (2012) 50–58 57

water flux under the same conditions according to the conclusionin the Section 3.3.

3.5. Results of variance (ANOVA) regression analysis

Table 4 shows the regression analysis by ANOVA of the experi-mental results using pore size, pure water flux and fouling rate asresponse values. It can be found that the factors C, D and E (non-woven fabric type, casting thickness, casting speed) had a signifi-cant effect on both membrane pure water flux and membrane foul-ing rate because of the little p-value.

As for the model using membrane fouling rate as the responsefactor, the p-value < 0.1 indicated that the regression equation cal-culated by PBD fitted the response well. Moreover, the coefficient(R2) of 0.8258, close to 1, indicated that the model was in good lin-ear correlation within the tested range. The model could be ex-pressed by the following Eq. (4). For the model of pore size andpure water flux responses, the p-values were higher than 0.1 andthe values of R2 were relatively small especially for the pore sizeresponse, indicating that the models did not fit the responses well:

Y fouling rate ¼ 1:7928þ 0:2514Aþ 0:1464Bþ 0:5092C

þ 0:3688Dþ 0:4358E� 0:095F � 0:066G

� 0:123H � 0:285I þ 0:0769J � 0:101K � 0:05L ð4Þ

To sum up, a comprehensive study using PBD was carried out toinvestigate the effects of 12 processing factors in the membranepreparation. It was found that the non-woven fabric type, castingthickness and casting speed had a major impact on the membraneperformance such as the membrane pure water flux and foulingrate. It is worth noting that the membranes presented a large purewater flux and a low fouling rate when the three factors were fixedat their low levels. In the current researches about flat-sheet mem-brane preparation, there has been little information on the effect ofnon-woven fabric type on the membrane performance. Test resultsof our study show that the non-woven fabric type is also one ofmajor factors influencing membrane performance.

Several factors such as heat-treatment temperature, coagula-tion bath temperature, and evaporation time, which have beeninvestigated by a group of researchers [8,11,27,28], were foundto have no significant impacts on membrane performance in thispolymer/solvent system. It indicates that under different polymercompositions, e.g., PAN and polyvinylidene fluoride (PVDF), themajor preparation factors influencing membrane morphology andperformance are different. Therefore, optimization of preparation

factors should be carried out in order to obtain high-performancemembranes for a certain kind of polymer membranes.

4. Conclusions

In this study, the statistical experimental design methodology,PBD, was applied to study twelve processing factors affectingmembrane performance and to screen the significant variables inthe preparation of PAN membranes for MBR using phase inversionmethod. The membrane pore size, pure water flux and fouling ratewere taken as response values. Based on the statistical analysis, thefollowing conclusions can be drawn.

(1) In the phase inversion process, none of the 12 processingfactors had significant effects on the membrane pore size.The heat-treatment time, heat-treatment temperatureand casting thickness were more influential than otherfactors on the pore size.

(2) The casting thickness, non-woven fabric type and castingspeed were found to have significant negative correla-tions with the pure water flux.

(3) The non-woven fabric type, casting speed and castingthickness also had significant positive effects on mem-brane fouling rate.

(4) The model using membrane fouling rate as the responsevalue was more credible and in good linear correlationwithin the tested range. The value of the fouling ratecould be reduced when the non-woven fabric type, cast-ing speed, casting thickness, polymer dissolving tempera-ture, dissolving time, post-treatment reagent time werefixed at the low level and the evaporation time, coagula-tion bath temperature, coagulation bath time, post-treat-ment reagent concentration, heat-treatment temperature,heat-treatment time were chosen at high level.

Acknowledgements

Financial support of this work by the International Science &Technology Cooperation Program of China (2011DFA90400), theNational Natural Science Foundation of China (Grant No.51008217) and by the Science & Technology Commission of Shang-hai Municipality Fund (10231201302) is gratefully acknowledged.

58 P. Wang et al. / Chemical Engineering Journal 193–194 (2012) 50–58

References

[1] K. Kimura, N. Yamato, H. Yamamura, Y. Watanabe, Membrane fouling in pilot-scale membrane bioreactors (MBRs) treating municipal wastewater, Environ.Sci. Technol. 39 (2005) 6293–6299.

[2] Y. Miura, Y. Watanabe, S. Okabe, Membrane biofouling in pilot-scalemembrane bioreactors (MBRs) treating municipal wastewater: impact ofbiofilm formation, Environ. Sci. Technol. 41 (2007) 632–638.

[3] T.I. Yoon, H.S. Lee, C.G. Kim, Comparison of pilot scale performances betweenmembrane bioreactor and hybrid conventional wastewater treatment systems,J. Membr. Sci. 242 (2004) 5–12.

[4] I.C. Kim, H.G. Yun, K.H. Lee, Preparation of asymmetric polyacrylonitrilemembrane with small pore size by phase inversion and post-treatmentprocess, J. Membr. Sci. 199 (2002) 75–84.

[5] E.R. Cornelissen, T. Boomgaard, H. Strathmann, Physicochemical aspects ofpolymer selection for ultrafiltration and microfiltration membranes, ColloidsSurf. A 138 (1998) 283–289.

[6] M.J. Han, S.T. Nam, Thermodynamic and rheological variation in polysulfonesolution by PVP and its effect in the preparation of phase inversion membrane,J. Membr. Sci. 202 (2002) 55–61.

[7] B. Jung, J.K. Yoon, B. Kima, H.W. Rheeb, Effect of molecular weight of polymericadditives on formation, permeation properties and hypochlorite treatment ofasymmetric polyacrylonitrile membranes, J. Membr. Sci. 243 (2004) 45–57.

[8] A. Rahimpour, S.S. Madaeni, M. Amirinejad, Y. Mansourpanah, S. Zereshki, Theeffect of heat treatment of PES and PVDF ultrafiltration membranes onmorphology and performance for milk filtration, J. Membr. Sci. 330 (2009)189–224.

[9] C. Feng, R. Wang, B.L. Shi, G.M. Li, Y.L. Wu, Factors affecting pore structure andperformance of poly(vinylidene fluoride-co-hexafluoro propylene) asymmetricporous membrane, J. Membr. Sci. 277 (2006) 55–64.

[10] X. Wang, X. Wang, L. Zhang, Q. An, H. Chen, Morphology and formationmechanism of poly(vinylidene fluoride) membranes prepared with immerseprecipitation: effect of dissolving temperature, J. Macromol. Sci.: Phys. 48(2009) 696–709.

[11] M.S.E. Saljoughi, T. Mohammadi, Effect of preparation variables onmorphology and pure water permeation flux through asymmetric celluloseacetate membranes, J. Membr. Sci. 326 (2009) 627–634.

[12] A. Idris, A.F. Ismail, M.Y. Noordin, S.J. Shilton, Optimization of cellulose acetatehollow fiber reverse osmosis membrane production using Taguchi method, J.Membr. Sci. 205 (2002) 223–237.

[13] J.L. Chau, S.S. Wang, C.L. Guo, H. Wei, T.C. Lien, Pilot production of polysulfonehollow fiber for ultrafiltration using orthogonal array experimentation, Ind.Chem. Res. 34 (1995) 813–819.

[14] M. Bulut, L.E.M. Gevers, J.S. Paul, I.F.J. Vankelecom, P.A. Jacobs, Directeddevelopment of high-performance membranes via high-throughput andcombinatorial strategies, J. Comb. Chem. 28 (2006) 168–173.

[15] J.N. Cawse, Experimental strategies for combinatorial and high-throughputmaterials development, Acc. Chem. Res. 34 (2001) 213–221.

[16] X. Li, J. Ouyang, Y. Xu, M. Chen, X.Y. Song, Q. Yong, S.Y. Yu, Optimization ofculture conditions for production of yeast biomass using bamboo wastewaterby response surface methodology, Bioresour. Technol. 100 (2009) 3613–3617.

[17] R.S. Liu, Y.J. Tang, Tuber melanosporum fermentation medium optimization byPlackett–Burman design coupled with Draper–Lin small composite design anddesirability function, Bioresour. Technol. 101 (2010) 3139–3146.

[18] A.L. Ahmada, S.C. Lowa, S.R. Abd Shukora, A. Ismail, Optimization of membraneperformance by thermal-mechanical stretching process using responsessurface methodology (RSM), Sep. Purif. Technol. 66 (2009) 177–186.

[19] Z.W. Wang, G.P. Yu, J.F. Liu, Z. Zhou, Relationship between sludgecharacteristics and membrane flux determination in submerged membranebioreactors, J. Membr. Sci. 284 (2006) 87–94.

[20] J.L. Wu, X. Huang, Effect of mixed liquor properties on fouling propensity inmembrane bioreactors, J. Membr. Sci. 342 (2009) 88–96.

[21] R.L. Plackett, J.P. Burman, The design of optimum multifactorial experiments,Biometrika 33 (1946) 305–325.

[22] S. Azari, M. Karimi, M.H. Kish, Structural properties of the poly(acrylonitrile)membrane prepared with different cast thicknesses, Ind. Eng. Chem. Res. 49(2010) 2442–2448.

[23] K.C. Khulbe, T. Matsuura, S.H. Noh, Effect of thickness of the PPO membraneson the surface morphology, J. Membr. Sci. 145 (1998) 243–251.

[24] H.A. Tsai, Y.S. Ciou, C.C. Hu, K.R. Lee, D.G. Yu, J.Y. Lai, Heat-treatment effect onthe morphology and pervaporation performances of asymmetric PAN hollowfiber membranes, J. Membr. Sci. 255 (2005) 33–47.

[25] S. Maryam, S.R. Rasoul, R. Mehdi, M. Mehrdad, A Plackett–Burman design inhydrothermal synthesis of TiO2-derived nanotubes, J. Porous Mater. 17 (2010)719–726.

[26] H.T. Dang, D. Rana, R.M. Narbaitz, T. Matsuura, Key factors affecting themanufacture of hydrophobic ultrafiltration membranes for surface watertreatment, J. Appl. Polym. Sci. 116 (2010) 2626–2637.

[27] X. Wang, L. Zhang, D. Sun, Q. An, H. Chen, Effect of coagulation bathtemperature on formation mechanism of poly(vinylidene fluoride)membrane, J. Appl. Polym. Sci. 110 (2008) 1656–1663.

[28] P. Vandezande, X.F. Li, L.E.M. Gevers, I.F.J. Vankelecom, High throughput studyof phase inversion parameters for polyimide-based SRNF membranes, J.Membr. Sci. 330 (2009) 307–318.