Desalination 259 (2010) 235–242

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Desalination

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Influence of operating conditions on cleaning efficiency in sequencing batchreactor (SBR) activated sludge process — water rinsing introduced membranefiltration process

Zhan Wang ⁎, Shanshan Zhao, Feng Liu, Liying Yang, Yin Song, Xiuyan Wang, Xuejie XiDepartment of Chemistry and Chemical Engineering, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing, 100124 China

⁎ Corresponding author. Fax: +86 10 6739 1983.E-mail addresses: zhanwang3401@yahoo.com.cn,

zhaoshanshan0098@emails.bjut.edu.cn (Z. Wang).

0011-9164/$ – see front matter © 2010 Elsevier B.V. Aldoi:10.1016/j.desal.2010.03.048

a b s t r a c t

a r t i c l e i n f o

Article history:Received 8 September 2009Received in revised form 23 March 2010Accepted 25 March 2010Available online 7 May 2010

Keywords:SBROperating conditionsOrthogonal tableMultivariate linear regression methodsQuantitative evaluation

In this paper, the influences of water rinsing conditions on the cumulative membrane permeate filtratevolume (CMPFV) were systematically investigated by a combination of orthogonal table and multivariatelinear regression methods. The experiments were performed with a feed suspension from an SBR anddeionized water in a laboratory-scale dead-end microfiltration test unit with a 0.1 μm polyethersulfone (PES)microfiltration membrane respectively. The results showed that the resistance due to cake filtrationdominated the flux decline under the conditions studied. Water rinsing process can restore the declined fluxnearly to its initial value, but its ability is gradually reducing with the increase of the cleaning cycle, which isassociated with the increasing accumulation of irreversible pollutants onto and into the membrane pores.The average contribution of water rinsing conditions on CMPFV were relative flux (38.3%)Ndetergenttemperature (21.6%)Nwashing times (19.3%)Nagitation speed (11.2%)Ndetergent volume (9.6%). Here,except for the relative flux, the others factors, such as detergent temperature, washing times, agitation speedand detergent volume had a positive contribution to CMPFV. In addition, the relationship between waterrinsing conditions and CMPFV was analyzed and defined quantitatively for 4 cycles respectively and it cangive good predictive results.

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© 2010 Elsevier B.V. All rights reserved.

1. Introduction

In recent years, the membrane bioreactor (MBR) process has beenwidely applied to treat various types of wastewater such as industrialwastewater, human excrement, and especially domestic wastewater[1–4] due to its small footprint, high quality effluent, low sludgeproduction rate, highly retentive activated sludge concentration andeasy management[5,6]. However, the biggest obstacle for membranefiltration in practical applications is membrane fouling, whichdecreases the life of membrane modules and increases costs [7].Although many strategies, such as pretreatment of the feed suspen-sion, optimization of operating conditions in the membrane module,and preparation of antifoulingmembranes [8], have been employed toimprove membrane fouling, the membranes will become fouledeventually. Therefore, membrane cleaning is an inescapable andessential step in maintaining membrane filtration processes [9], andmany physical and chemical cleaningmethods have been employed toremove the deposited layers on themembrane surface and in the poresof membrane [10].

Water rinsing is a necessary step during the membrane cleaningprocess [11] and several authors have dealt with this matter. Forexample, Cabero et al. used water rinsing to reduce cleaning agentconsumption and to restore the permeate flux after cleaning [12].Matzinos et al. reported that one-third of the protein was removedduring water rinsing [13]. Renner pointed out that up to 98% of thedeposited layer can be removed during the water rinsing step [14].Bansal et al. demonstrated that the water rinsing process can dissolvemost of the deposits formed on the membrane surface, but it was noteffective in removing the deposits formed inside the pores [15].

In fact, the water rinsing efficiency is governed by the membranefouling mechanism, the pollutants species, the rinsing conditions(temperature, time,filtrate and permeate volume and transmembranepressure) and so on [16,17]. So the optimization of the cleaningoperating conditions plays a very important role in industrialmembrane cleaning practices. For instance, Zondervan et al. optimizedthe chemical cleaning cycle and proposed a model to minimize theoverall operating costs [18]. Chen et al. identified that highermembrane filtration capacity and efficiency can be achieved by usingoptimized conditions [19]. Farley et al. simulated and optimized theentire physical and chemical cleaning process [20]. Van Boxtel et al.reported that the optimization of cleaning operating conditions cansave at least 10% of the operating cost [17]. Evgenia et al. developed ageneral model for simulating the rinsing and regeneration network

Table 1Quality of raw wastewater used in the experiment.

COD (mg L−1) NH3-N (mg L−1) TOC (mg L−1) pH Turbidity/NTU

180.6–225.8 45.9–73.6 86.5–115.5 7.5–8.0 20–26

236 Z. Wang et al. / Desalination 259 (2010) 235–242

(RRN), and the optimal time delay of 8% for the overall rinse waterretention time was determined [21]. However, up to now, theprocedure for the water rinsing processes was largely based onexperience [11], the aspect of quantitatively defining the relationshipbetween the operating conditions and the cleaning efficiency hasreceived limited attention. Therefore, the present paper is an extensivestudy to determine the relationship between water rinsing operatingconditions and rinsing efficiency to provide cost-effective cleaning andrestoration procedures.

2. Experimental

2.1. System and methods

The laboratory-scale experimental system (in Fig. 1) consisted oftwo parts. The first part is an intermittent mode bioreactor systemwith an effective volume of 25 l. The second part is a dead-endmicrofiltration cell. The feed solution (raw wastewater) was obtainedfrom the storage tank of domestic sewage with qualities shown inTable 1.

The operating parameters for the bioreactor system, such as themixed liquor suspended solid (MLSS), temperature (T), dissolvedoxygen (DO), pH and hydraulic retention time (HRT), are shown inTable 2. No sludge was discharged during the operation or test period.

The dead-end microfiltration cell has an effective membrane area of24.0 cm2. Before each experiment, the membrane (0.1 μm PES hydro-philic membranes purchased from Beijing Ande Membrane SeparationTechnology and Engineering) were soaked in deionized water for 12 h

Fig. 1. Schematic diagram of the experimental system (a) interm

to removeglycerin that is present in themembrane toprotect it for long-term storage. The main parameters of the feed suspension into themembrane cell used in the experiments were total oxygen content(TOC) (33.5–38.4 mg L−1), NH3–N (9.7–10.75 mg L−1) and chemicaloxygen demand(COD) (48.2–52.6 mg L−1).

The mixed liquor suspended solids (MLSS) concentration wasmeasured by weighing a dried sample and pH was measured with apHS-3C acidity meter. The COD and NH3–N of the membrane influentand effluent, were measured by adopting the Chinese SEPA standardmethods [22].

2.2. Experimental procedure

The feed solution was equilibrated to a constant COD, NH3–Nconcentration and MLSS. The experiment was conducted as follows:(1) the deionized water flux of the membrane was measured; (2) themembrane flux was measured with a feed solution until a prede-termined drop in flux occurred; (3) the membrane was rinsed withdeionized water for a different numbers of times with a certaintemperature, agitation speed and soaking time; (4) the deionizedwater flux of the membrane was measured again. Each experimentwas repeated four times as described above.

ittent bioreactor system and (b) dead-end filtration system.

Table 2Operating parameters of bioreactor.

MLSS (g L−1) T (°C) DO (mg L−1) pH HRT (h)

2.7 20 4.0 7.5–8.0 18

237Z. Wang et al. / Desalination 259 (2010) 235–242

As mentioned above, seven main factors affecting the water rinsingefficiency such as the relative flux (r), detergent temperature (T),detergent volume (V), agitation speed of magnetic stirrer (ω),membrane soaking time (t), washing times (N) and transmembranepressure (TMP) were selected for this study. In order to optimize thewater rinsing operating conditions and decrease the load of theexperiment, the orthogonal experimental design utilized can be seenin Table 3.

3. Analytical methods

3.1. The recovery of membrane permeability

The recovery of membrane permeability (ri) that provides ameasure of membrane irreversible fouling is calculated by:

ri =JiJ0

ð1Þ

where Ji is the initial suspension flux after each water rinsing cycle,m3m−2s−1; J0 is the initial suspension flux value of the virginmembrane, m3m−2s−1.

3.2. Analysis and approaches

In order to quantify the influence of various cleaning conditions onthe cleaning efficiency, the collected data were processed byorthogonal experimental design and multivariate linear regressionmethods which are described elsewhere in the literature [23–27].

3.3. Fouling mechanism

The Blocking Lawwas first proposed by Herman et al. [28] in 1935,and the common form is as follows [29]:

d2tdV2 = k

dtdV

� �nð2Þ

where t is the filtration time, V is the total filtered volume and k is theproportionality coefficient. The exponent n characterizes the foulingmechanism, with n=0 for cake filtration, n=1 for intermediateblocking, n=3 /2 for pore constriction (also called standard blocking),and n=2 for complete pore blocking [30].

Table 3Factor-levels of orthogonal table.

Factors Levers

r (%) 40 35 30 25 20 15 10T (°C) 5 15 20 25 30 35 40V (ml) 50 100 150 200 250 300 350ω (min−1) 100 150 200 250 300 350 400N 1 2 3 4 5 6 7t (s) 10 15 20 30 60 90 120TMP (MPa) 0.06 0.08 0.1 0.12 0.14 0.16 0.18

3.4. Determination of filtration resistances

The resistance-in-series model is applied to investigate the foulingcharacteristics in terms of various filtration resistances

Rt =TMPμJ

= Rm + Rc + Rf ð3Þ

where Rt is the total membrane resistance, m−1; TMP is thetransmembrane pressure, Pa; μ is liquid viscosity, Pa s; J is thepermeate flux, m3m−2s−1; Rm is the intrinsic resistance of themembrane, m−1; Rc is the cake resistance, m−1; and Rf is the foulingresistance (pore plugging and adsorption), m−1.

4. Results and discussion

4.1. The intrinsic resistance of the membrane

When plot the permeate flux of deionized water vs. transmem-brane pressure is in the range of 0.06–0.16 MPa, the intrinsicresistance of the membrane Rm can be calculated by the followingformula (4) and it was calculated to be 6×108 m−1 for our PESmembrane.

Rm =TMPμJ0

: ð4Þ

Fig. 2. Particle diameter distribution of feed suspension (a) and pore size distribution ofmembrane (b).

Fig. 3. n value of different membrane filtration cycles (TMP=0.1 MPa, r=0.2, T=40 °C, V=200 ml, t=10 s).

238 Z. Wang et al. / Desalination 259 (2010) 235–242

4.2. Membrane fouling mechanism

In order to determine themembrane foulingmechanism, first boththe particle size distribution of the feed solution and the pore sizedistribution of the PES membrane were measured. The experimentaldata were analyzed by using classical Herman's filtration laws.

The MAF-5001 model Malvern laser particle diameter distributioninstrument (Britain) was used to get the particle distribution of thefeed solution, and the modified bubble point method was used toobtain the pore size distribution of the PES membrane (Fig. 2). It canbe seen from Fig. 2 that the mean diameter of the activated sludgesuspended particles is 1–10,000 μm, in contrast to themembrane poresize which is 0.06–0.5 μm.

Obviously, the average surface pore size of a membrane (dm) ismuch smaller than one-tenth of the particle size (dp). The particlessurrounded by macromolecules were compressed and rejected by themembrane surface. At this time, the particles will block the surfacepores, and a cake will form on the membrane surface during themembrane filtration process [31].

Furthermore, the experimental data was analyzed by classicalHerman's filtration laws and all analysis gave similar results. Theanalysis of one group of experimental data for 4 cycles is shown in

Fig. 4. J vs. time (TMP=0.1 MPa, r=0.2, T=40 °C, V=200 ml, t=10 s).

Fig. 5. (Rt−Rm)/Rt vs. filtration time (TMP=0.1 MPa, r=0.2, T=40 °C, V=200 ml,t=10 s).

Fig. 3. It can be seen from Fig. 3 that themembrane foulingmechanismis cake filtration after 4 cycles, and this is also in agreement withreported results by Lim and Bai [30].

4.3. Characteristics of the membrane filtration process

4.3.1. Dominant resistanceTypical permeate flux declines during the membrane filtration

process are shown in Fig. 4. The permeate flux deteriorated with timein each filtration cycle and this means that the total filtrationresistance was increasing with time for each filtration cycle.

As mentioned above, in the present process, themembrane foulingmechanisms are cake filtration in 4 cycles, so it is very important toknow which resistance will dominate when water rinsing isintroduced into the membrane filtration process.

The plot of (Rt−Rm)/Rt vs. filtration time is shown in Fig. 5. Theintrinsic membrane resistance can be ignored when the cakeresistance is the dominant resistance in total resistance.

4.3.2. Water rinsing efficiencyThe relative flux (or recovery ratio) after water rinsing can be

employed to characterize the water rinsing efficiency. As showed inFig. 6, a very significant recovery of the membrane permeate flux is

Fig. 6. Relative flux or recovery ratio vs. filtration time (TMP=0.1 MPa, r=0.2,T=40 °C, V=200 ml, t=10 s).

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experienced immediately after each water rinsing step. This indicatesthat water rinsing can remove membrane fouling efficiently [15].However, we also can see from Fig. 6, the cleaning efficiency of waterrinsing was gradually reduced with each consecutive filtration cycle.This means that the irreversible pollutants cannot be removed duringthe water rinsing process [15], increasing with each subsequentfiltration cycle and making the membrane flux decline more severe.

In order to give an intensive understanding of the water rinsingefficiency, the change of the total cake resistance and velocity of cakedevelopment vs. time were plotted in Fig. 7. In Fig. 7, a visible increaseof the total cake resistance was observed as a result of theaccumulation of cake on the membrane surface, and after waterrinsing, the total resistance was reduced. The water rinsing processcan restore the declined flux close to its initial value, but notcompletely because the accumulated irreversible foulants have beenstrongly embedded in the concavities of membrane surface asreported in literature [33] and the membrane fouling increased witheach subsequent water rinsing cycle. So the effect of water rinsing, inother words, the ratio of irreversible fouling was gradually reducedwith an increased number of filtration cycles, and the membraneresistance after each water rinsing was correspondingly increasing.The resistances of irreversible fouling were 0.2×1011, 0.23×1011,0.28×1011 and 0.32×1011 m−1 as compared with respectively and asdemonstrated in Fig. 7(a). That means the ratios of irreversible foulingwere increasing gradually, and were 26.0%, 27.0%, 28.5% and 30.5%after each subsequent water rinsing. This may be attributed to anumber of sensitive areas which were strongly and irreversibly fouledand these areas became smaller with each increasing fouling cycle.The additional foulant layer laid down during subsequent filtrationcycles was easier to remove with the water rinsing process [34,35].

It is also noteworthy that, the velocity of cake development, dRt/dtis a very important factor in the water rinsing process. As showed inFig. 7(b), the velocity of cake development was fast in the initial stage

Fig. 7. Rt and dRt/dt vs. filtration time (TMP=0.1 MPa, r=0.2, T=40 °C, V=200 ml,t=10 s). (a) Rt vs. time and (b) dRt/dt vs. time.

of each filtration process, so the membrane flux declined very fast inthe initial stage too. Then the velocity of the cake developmentdecreased with time and the corresponding membrane flux changesdecreased also. It was also found that the velocity of cakedevelopment was decreased with increasing filtration cycle. This isprobably due to the smaller pores getting blocked very quickly at thebeginning [34]. These findings are also in line with those of Kawakatsuand Nakao who found that the pore blocking mechanism governedonly the beginning of filtration cycle, whichmade the permeation fluxdecay rapid and significant [31]. However, in the later filtration stages,the process transitioned to a cake formation-limited process. Theperiod of the pore blocking-limited process continued to be short, sothe velocity of the cake development declined correspondingly [32].

4.3.3. Membrane fouling driving forceAssume the final total membrane fouling resistance is Rft, which is

based on the constant relative flux of 0.8 in the present experimentsand was unchanged in each cycle. In such a way, the value of (Rft–Rt) /Rft can be used to assess the membrane fouling driving force at anytime t. As illustrated in Fig. 8, the membrane fouling driving force wasvery big at the beginning of the membrane filtration in each cycle, andthen it gradually reducedwith increasing filtration time for each cycle,until it became zero at the end of each cycle. The membrane foulingdriving force can be restored by water rinsing, but this driving forcewas decreased with each subsequent filtration cycle. This is shown inFig. 8, where the slope of the line is increasing with each subsequentfiltration cycle and is related to the increasing of irreversible fouling asmentioned above.

4.4. The optimization of water rinsing conditions

The experiments were conducted according to Table 3. The resultsof first water rinsing cycle are shown in Tables 4, 5 and Fig. 9.

According to the value of R, the sequence of dominating factors onCMPFV for the first water rinsing cycle is relative flux (4387.25)Ndetergent temperature (846.44)Nwashing times (579.96)Nagitationspeed (527.98)Ndetergent volume (472.69)Nsoaking time (412.22)NTMP (300.18). Statistically, the cumulative filtrate volume of themembrane permeate was found to be strongly dependent on therelative flux rather than on the other rinsing conditions.

The optimum water rinsing conditions for the first rinsing cyclecan be determined by the combination of Table 4 and Fig. 10. Theoptimum water rinsing conditions for cumulative filtrate volume ofthe membrane permeate filtration volume are as follows:TMP=0.12 MPa, r=0.1, T=30 °C, V=250 ml, ω=200 min−1,N=5 and t=20 s. The optimum water rinsing conditions for therest of the rinsing cycles can be determined by an analogous methodand the comparison of the CMPFV in optimumwater rinsing conditionand 49 other conditions for up to four rinsing cycles is shown inFig. 10. It can be seen from Fig. 10 that when the optimum waterrinsing conditions were controlled, the value of CMPFV was higherthan that in any other conditions.

Fig. 8. (Rft–Rt) /Rft vs. filtration time (TMP=0.1 MPa, r=0.2, T=40 °C, V=200 ml,t=10 s).

Table 4The CMPFV results of first cycle.

TMP r T V t ω N CMPFV−1

1 0.16 0.1 5 50 10 100 1 886.542 0.16 0.15 15 100 15 150 2 496.353 0.16 0.2 20 150 20 200 3 386.794 0.16 0.25 25 200 30 250 4 306.325 0.16 0.3 30 250 60 300 5 250.516 0.16 0.35 35 300 90 350 6 175.387 0.16 0.4 40 350 120 400 7 117.508 0.18 0.1 15 150 30 300 6 580.479 0.18 0.15 20 200 60 350 7 408.9810 0.18 0.2 25 250 90 400 1 249.3411 0.18 0.25 30 300 120 100 2 232.1612 0.18 0.3 35 350 10 150 3 209.3413 0.18 0.35 40 50 15 200 4 184.9714 0.18 0.4 5 100 20 250 5 121.7815 0.06 0.1 20 250 120 150 4 1230.2616 0.06 0.15 25 300 10 200 5 791.3317 0.06 0.2 30 350 15 250 6 536.8218 0.06 0.25 35 50 20 300 7 374.0819 0.06 0.3 40 100 30 350 1 365.3920 0.06 0.35 5 150 60 400 2 269.8321 0.06 0.4 15 200 90 100 3 231.3522 0.08 0.1 25 350 20 350 2 1184.0123 0.08 0.15 30 50 30 400 3 899.2124 0.08 0.2 35 100 60 100 4 573.6625 0.08 0.25 40 150 90 150 5 404.1426 0.08 0.3 5 200 120 200 6 250.2927 0.08 0.35 15 250 10 250 7 341.0328 0.08 0.4 20 300 15 300 1 288.7229 0.1 0.1 30 100 90 200 7 935.7730 0.1 0.15 35 150 120 250 1 565.4731 0.1 0.2 40 200 10 300 2 364.7532 0.1 0.25 5 250 15 350 3 336.3033 0.1 0.3 15 300 20 400 4 252.4734 0.1 0.35 20 350 30 100 5 182.4735 0.1 0.4 25 50 60 150 6 216.5136 0.12 0.1 35 200 15 400 5 918.0337 0.12 0.15 40 250 20 100 6 572.8138 0.12 0.2 5 300 30 150 7 458.2839 0.12 0.25 15 350 60 200 1 315.1140 0.12 0.3 20 50 90 250 2 285.8641 0.12 0.35 25 100 120 300 3 266.6542 0.12 0.4 30 150 10 350 4 199.0843 0.14 0.1 40 300 60 250 3 569.6944 0.14 0.15 5 350 90 300 4 370.7345 0.14 0.2 15 50 120 350 5 377.9746 0.14 0.25 20 100 10 400 6 284.4147 0.14 0.3 25 150 15 100 7 247.0348 0.14 0.35 30 200 20 150 1 172.9749 0.14 0.4 35 250 30 200 2 243.73

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4.5. The quantitative influence of operating conditions on CMPFV

As discussed in the previous sections, different rinsing conditionsare expected to affect the water rinsing cleaning efficiency. In order toconfirm the quantitative contribution of water rinsing conditions on

Table 5Experimental results according to the orthogonal table (Table 4).

TMP r TPk1 2867.144 5755.351 2542.982Pk2 2868.346 4104.040 2494.605Pk3 2899.455 2895.053 2915.470Pk4 2743.658 2203.642 3213.069Pk5 2853.741 2012.847 3275.409Pk6 3015.810 1442.715 2911.249Pk7 2715.630 1368.105 2428.969R 300.18 4387.25 846.44

Note:Pki is average in every influence factor, in which i=1, 2……7 is level.

R is remainder of the maximum and the minimum.

CMPF, the multivariate linear regression method was used and theregression result of standardization data for the first rinsing cycle isshown in Table 6.

It is clear from Table 6, that except for F6 and F7, all the absolutevalues of Fj exceed 1 in the first regression, which indicated that the

V t ω N

2875.392 2727.657 2592.877 2576.8112944.080 3008.165 2977.205 2887.7432601.789 3014.229 2848.201 3156.7712701.828 2986.577 2967.096 2676.5023074.482 2652.578 3094.879 2745.8822717.642 2602.013 2566.896 2897.1622866.558 2790.534 2734.599 2840.882472.69 412.22 527.98 579.96

Fig. 9. Analysis of the weighted of influence factors.

Fig. 10. Comparison of the CMPFV in optimum condition and 49 other conditions inTable 4.

Table 7The quantitative influence of operating conditions on CMPFV for 4 cycles.

Washing cycle r, % T, % N, % ω,% V, %

1 38.1 20.4 20.0 11.1 10.42 38.3 21.4 19.2 11.3 9.83 38.6 22.2 18.6 11.3 9.34 38.9 22.6 18.3 11.2 9.0Average 38.3 21.6 19.3 11.2 9.6

241Z. Wang et al. / Desalination 259 (2010) 235–242

soaking time and TMP had no effect on the CMPFV. Eliminating thesoaking time and TMP, the rest of the factors were analyzed againwiththe regression method. According to the absolute values of Fj in thesecond regression, the sequence of influencing the degree of waterrinsing conditions on CMPFV was relative fluxNdetergent tempera-tureNwashing timesNagitation speedNdetergent volume. In addition,the relative flux had a negative contribution to CMPFV according to thenegative values of F2 and the regression coefficient b2, which indicatedthat an increase in the relative flux resulted in a decline in CMPFV.These conclusions were also consistent with the result of theorthogonal analysis in Section 4.4. According to the statistical analysisexamined by linear regression, the regression equation for the first

Table 6Values of regression coefficient, Fj and Dj for CMPFV.

Regression times j bj σj Fj Dj(%)

1st regression TMP 350.30 818.94 0.43 –

r −1265.00 343.38 −3.69 –

T 7.47 3.15 2.37 –

V 0.59 0.36 1.66t 0.84 0.94 0.86N 0.78 0.34 2.28ω 300.80 17.79 1.73

2nd regression – – – –

r −1187.24 325.28 −3.65 38.08T 8.07 3.02 2.67 20.38V 0.65 0.34 1.90 10.35N 0.86 0.3 2.65 20.00ω 33.93 17.16 1.98 11.18

four water rinsing cycles with respect to CMPFV and the water rinsingconditions, with a significant level of α=0.05 are as follows:

first cycle CMPFV1 = −1187:24r + 8:07T + 0:65V + 0:86N + 33⋅93ωð5Þ

second cycle CMPFV2 = −1774:71r + 12:34T + 0:94V + 1:26N + 50:90ω

ð6Þ

third cycle CMPFV3 = −2375:42r + 16:77T + 1:22V + 1:65N + 68:40ω

ð7Þ

fourth cycle CMPFV4 = −2968:36r + 21:03T + 1:50V + 2:03N + 84:35ω

ð8Þ

As the above 4 formulas show, the quantitative relationshipbetween CMPFV and the water rinsing conditions for the 4 cycles issimilar. The quantitative contributions of water rinsing conditions toCMPFV in each cycle were calculated and the results are shown in theTable 7. The percentage contribution of a certain water rinsingcondition in the 4 cycles are similar, and the 4 cycles averagecontribution to the relative flux, detergent temperature, washingtimes, agitation speed and detergent volumewere 38.3%, 21.6%, 19.3%,11.2% and 9.6%, respectively.

In order to validate the accuracy of the four regression equations,some additional experiments were conducted for the first cycle andresults were compared with the formula (5) as shown in Table 8. Itcan be seen from Table 8 that the relative error between experimentalvalue and predicted value by formula (5) is less than 8%, so theregression equations can be applied to predict the cumulative filtratevolume in MF membrane process with the four cleaning cycles.

5. Conclusion

In this present paper, fouling and water rinsing experiments wereperformed with the feed suspension from an SBR in a laboratory-scaledead-end microfiltration test unit. The influence of the water rinsingconditions on the cumulative filtrate volume of the membranepermeate was studied by the combination of orthogonal table andmultivariate linear regression methods. The experimental resultsshowed that: (1) the membrane fouling mechanism was cakefiltration, and the cake resistance was the dominant resistance inthe membrane filtration process; (2) when the optimum waterrinsing conditions were controlled, the value of the cumulative filtratevolume of membrane permeate was higher than that for any otherconditions; (3) the sequence and the average percentage contributionof the water rinsing conditions on CMPFV of each rinsing cycle wererelative flux (38.3%)Ndetergent temperature (21.6%)Nwashing times(19.3%)Nagitation speed (11.2%)Ndetergent volume (9.6%). In addi-tion, except for the relative flux, the other factors had a positivecontribution to the cumulative filtrate volume collected during aspecified time. (4) The regression equations for the first 4 cycles forthe cumulative filtrate volume of the membrane permeate collectedfor a specified time and the operating conditions were obtained, andthe results of the validation experiment showed that the relative errorbetween the experimental value and the predicted value by these

Table 8Comparison of the experimental value and the model value.

No. r T V ω N Experimental value Predicted value by formula (4) Relative error (%)

1 0.1 15 200 100 5 300.5 329.3 −8.82 0.3 30 100 300 2 305.2 277.4 10.03 0.2 20 150 200 4 356.9 329.8 8.24 0.1 40 350 300 1 759.7 724.9 4.8Average value 8.0

242 Z. Wang et al. / Desalination 259 (2010) 235–242

formulas was less than 8%. These regression equations can predict thecumulative filtrate volume of themembrane permeate in an industrialprocess.

NomenclatureCMPFV the cumulative membrane permeate filtrate volume (ml)ri recovery of membrane permeability (%)Ji initial flux after each hydraulic cleaning cycle (m3m−2s−1)J0 initial flux value of virgin membrane (m3m−2s−1)J permeate flux (m3m−2s−1)Rt total membrane resistance (m−1)Rm intrinsic resistance of the membrane (m−1)Rc cake resistance (m−1)Rf fouling resistance (pore plugging and adsorption) (m−1)TMP transmembrane pressure (Pa)r relative flux (%)T detergent temperature (°C)V detergent volume (ml)ω agitation speed of magnetic stirrer (min−1)t soaked time (s)N washing timesj factor numberb regression coefficient of influence factorF regression coefficient of CMPFVD influence degree

Greek symbolsμ liquid viscosity (Pa s)σ relative standard errorα significant level

Acknowledgements

The authors express appreciation to Dr. Caryn Heldt fromRensselaer Polytechnic Institute and Miss Xiaoli Li from the Collegeof Foreign Languages of Beijing University of Technology for veryextensive editing and English language assistance.

This project was supported by Beijing Municipal Natural ScienceFoundation (project no. 8052006) and National Natural ScienceFoundation of China (project no. 20276003) and the BeijingMunicipalCommission of Education (project no. PHR200907105) for thefinancial support of this study.

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