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Influence of operating conditions on cleaning efficiency in sequencing batch reactor (SBR) activated sludge process — water rinsing introduced membrane filtration process
Desalination 259 (2010) 235–242
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Desalination j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / d e s a l
Influence of operating conditions on cleaning efficiency in sequencing batch reactor (SBR) activated sludge process — water rinsing introduced membrane filtration process Zhan Wang ⁎, Shanshan Zhao, Feng Liu, Liying Yang, Yin Song, Xiuyan Wang, Xuejie Xi Department of Chemistry and Chemical Engineering, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing, 100124 China
a r t i c l e
i n f o
Article history: Received 8 September 2009 Received in revised form 23 March 2010 Accepted 25 March 2010 Available online 7 May 2010 Keywords: SBR Operating conditions Orthogonal table Multivariate linear regression methods Quantitative evaluation
a b s t r a c t In this paper, the influences of water rinsing conditions on the cumulative membrane permeate filtrate volume (CMPFV) were systematically investigated by a combination of orthogonal table and multivariate linear regression methods. The experiments were performed with a feed suspension from an SBR and deionized 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 filtration dominated the flux decline under the conditions studied. Water rinsing process can restore the declined flux nearly to its initial value, but its ability is gradually reducing with the increase of the cleaning cycle, which is associated 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%) N detergent temperature (21.6%) N washing times (19.3%) N agitation speed (11.2%) N detergent volume (9.6%). Here, except for the relative flux, the others factors, such as detergent temperature, washing times, agitation speed and detergent volume had a positive contribution to CMPFV. In addition, the relationship between water rinsing conditions and CMPFV was analyzed and defined quantitatively for 4 cycles respectively and it can give good predictive results. © 2010 Elsevier B.V. All rights reserved.
1. Introduction In recent years, the membrane bioreactor (MBR) process has been widely applied to treat various types of wastewater such as industrial wastewater, human excrement, and especially domestic wastewater [1–4] due to its small footprint, high quality effluent, low sludge production rate, highly retentive activated sludge concentration and easy management[5,6]. However, the biggest obstacle for membrane filtration in practical applications is membrane fouling, which decreases the life of membrane modules and increases costs [7]. Although many strategies, such as pretreatment of the feed suspension, optimization of operating conditions in the membrane module, and preparation of antifouling membranes [8], have been employed to improve membrane fouling, the membranes will become fouled eventually. Therefore, membrane cleaning is an inescapable and essential step in maintaining membrane filtration processes [9], and many physical and chemical cleaning methods have been employed to remove the deposited layers on the membrane surface and in the pores of membrane [10].
⁎ Corresponding author. Fax: + 86 10 6739 1983. E-mail addresses: [email protected], [email protected] (Z. Wang). 0011-9164/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.desal.2010.03.048
Water rinsing is a necessary step during the membrane cleaning process [11] and several authors have dealt with this matter. For example, Cabero et al. used water rinsing to reduce cleaning agent consumption and to restore the permeate flux after cleaning [12]. Matzinos et al. reported that one-third of the protein was removed during water rinsing [13]. Renner pointed out that up to 98% of the deposited layer can be removed during the water rinsing step [14]. Bansal et al. demonstrated that the water rinsing process can dissolve most of the deposits formed on the membrane surface, but it was not effective in removing the deposits formed inside the pores [15]. In fact, the water rinsing efficiency is governed by the membrane fouling mechanism, the pollutants species, the rinsing conditions (temperature, time, filtrate and permeate volume and transmembrane pressure) and so on [16,17]. So the optimization of the cleaning operating conditions plays a very important role in industrial membrane cleaning practices. For instance, Zondervan et al. optimized the chemical cleaning cycle and proposed a model to minimize the overall operating costs [18]. Chen et al. identified that higher membrane filtration capacity and efficiency can be achieved by using optimized conditions [19]. Farley et al. simulated and optimized the entire physical and chemical cleaning process [20]. Van Boxtel et al. reported that the optimization of cleaning operating conditions can save at least 10% of the operating cost [17]. Evgenia et al. developed a general model for simulating the rinsing and regeneration network
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(RRN), and the optimal time delay of 8% for the overall rinse water retention time was determined [21]. However, up to now, the procedure for the water rinsing processes was largely based on experience [11], the aspect of quantitatively defining the relationship between the operating conditions and the cleaning efficiency has received limited attention. Therefore, the present paper is an extensive study to determine the relationship between water rinsing operating conditions and rinsing efficiency to provide cost-effective cleaning and restoration procedures. 2. Experimental 2.1. System and methods The laboratory-scale experimental system (in Fig. 1) consisted of two parts. The first part is an intermittent mode bioreactor system with an effective volume of 25 l. The second part is a dead-end microfiltration cell. The feed solution (raw wastewater) was obtained from the storage tank of domestic sewage with qualities shown in Table 1. The operating parameters for the bioreactor system, such as the mixed liquor suspended solid (MLSS), temperature (T), dissolved oxygen (DO), pH and hydraulic retention time (HRT), are shown in Table 2. No sludge was discharged during the operation or test period. The dead-end microfiltration cell has an effective membrane area of 24.0 cm2. Before each experiment, the membrane (0.1 μm PES hydrophilic membranes purchased from Beijing Ande Membrane Separation Technology and Engineering) were soaked in deionized water for 12 h
Table 1 Quality 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
to remove glycerin that is present in the membrane to protect it for longterm storage. The main parameters of the feed suspension into the membrane 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 chemical oxygen demand(COD) (48.2–52.6 mg L−1). The mixed liquor suspended solids (MLSS) concentration was measured by weighing a dried sample and pH was measured with a pHS-3C acidity meter. The COD and NH3–N of the membrane influent and effluent, were measured by adopting the Chinese SEPA standard methods [22]. 2.2. Experimental procedure The feed solution was equilibrated to a constant COD, NH3–N concentration and MLSS. The experiment was conducted as follows: (1) the deionized water flux of the membrane was measured; (2) the membrane flux was measured with a feed solution until a predetermined drop in flux occurred; (3) the membrane was rinsed with deionized water for a different numbers of times with a certain temperature, agitation speed and soaking time; (4) the deionized water flux of the membrane was measured again. Each experiment was repeated four times as described above.
Fig. 1. Schematic diagram of the experimental system (a) intermittent bioreactor system and (b) dead-end filtration system.
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3.4. Determination of filtration resistances
Table 2 Operating 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
The resistance-in-series model is applied to investigate the fouling characteristics in terms of various filtration resistances
Rt = As mentioned above, seven main factors affecting the water rinsing efficiency 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 transmembrane pressure (TMP) were selected for this study. In order to optimize the water rinsing operating conditions and decrease the load of the experiment, the orthogonal experimental design utilized can be seen in Table 3. 3. Analytical methods
TMP = Rm + Rc + Rf μJ
ð3Þ
where Rt is the total membrane resistance, m−1; TMP is the transmembrane pressure, Pa; μ is liquid viscosity, Pa s; J is the permeate flux, m3 m−2 s−1; Rm is the intrinsic resistance of the membrane, m−1; Rc is the cake resistance, m−1; and Rf is the fouling resistance (pore plugging and adsorption), m−1. 4. Results and discussion 4.1. The intrinsic resistance of the membrane
3.1. The recovery of membrane permeability The recovery of membrane permeability (ri) that provides a measure of membrane irreversible fouling is calculated by:
ri =
237
Ji J0
When plot the permeate flux of deionized water vs. transmembrane pressure is in the range of 0.06–0.16 MPa, the intrinsic resistance of the membrane Rm can be calculated by the following formula (4) and it was calculated to be 6 × 108 m−1 for our PES membrane.
ð1Þ Rm =
TMP : μJ0
ð4Þ
where Ji is the initial suspension flux after each water rinsing cycle, m3 m−2 s−1; J0 is the initial suspension flux value of the virgin membrane, m3 m−2 s−1. 3.2. Analysis and approaches In order to quantify the influence of various cleaning conditions on the cleaning efficiency, the collected data were processed by orthogonal experimental design and multivariate linear regression methods which are described elsewhere in the literature [23–27]. 3.3. Fouling mechanism The Blocking Law was first proposed by Herman et al. [28] in 1935, and the common form is as follows [29]: n 2 d t dt = k dV dV 2
ð2Þ
where t is the filtration time, V is the total filtered volume and k is the proportionality coefficient. The exponent n characterizes the fouling mechanism, with n = 0 for cake filtration, n = 1 for intermediate blocking, n = 3 / 2 for pore constriction (also called standard blocking), and n = 2 for complete pore blocking [30].
Table 3 Factor-levels of orthogonal table. Factors
Levers
r (%) T (°C) V (ml) ω (min− 1) N t (s) TMP (MPa)
40 5 50 100 1 10 0.06
35 15 100 150 2 15 0.08
30 20 150 200 3 20 0.1
25 25 200 250 4 30 0.12
20 30 250 300 5 60 0.14
15 35 300 350 6 90 0.16
10 40 350 400 7 120 0.18
Fig. 2. Particle diameter distribution of feed suspension (a) and pore size distribution of membrane (b).
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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).
4.2. Membrane fouling mechanism In order to determine the membrane fouling mechanism, first both the particle size distribution of the feed solution and the pore size distribution of the PES membrane were measured. The experimental data were analyzed by using classical Herman's filtration laws. The MAF-5001 model Malvern laser particle diameter distribution instrument (Britain) was used to get the particle distribution of the feed solution, and the modified bubble point method was used to obtain the pore size distribution of the PES membrane (Fig. 2). It can be seen from Fig. 2 that the mean diameter of the activated sludge suspended particles is 1–10,000 μm, in contrast to the membrane pore size which is 0.06–0.5 μm. Obviously, the average surface pore size of a membrane (dm) is much smaller than one-tenth of the particle size (dp). The particles surrounded by macromolecules were compressed and rejected by the membrane surface. At this time, the particles will block the surface pores, and a cake will form on the membrane surface during the membrane filtration process [31]. Furthermore, the experimental data was analyzed by classical Herman's filtration laws and all analysis gave similar results. The analysis of one group of experimental data for 4 cycles is shown in
Fig. 3. It can be seen from Fig. 3 that the membrane fouling mechanism is cake filtration after 4 cycles, and this is also in agreement with reported results by Lim and Bai [30]. 4.3. Characteristics of the membrane filtration process 4.3.1. Dominant resistance Typical permeate flux declines during the membrane filtration process are shown in Fig. 4. The permeate flux deteriorated with time in each filtration cycle and this means that the total filtration resistance was increasing with time for each filtration cycle. As mentioned above, in the present process, the membrane fouling mechanisms are cake filtration in 4 cycles, so it is very important to know which resistance will dominate when water rinsing is introduced into the membrane filtration process. The plot of (Rt − Rm) / Rt vs. filtration time is shown in Fig. 5. The intrinsic membrane resistance can be ignored when the cake resistance is the dominant resistance in total resistance. 4.3.2. Water rinsing efficiency The relative flux (or recovery ratio) after water rinsing can be employed to characterize the water rinsing efficiency. As showed in Fig. 6, a very significant recovery of the membrane permeate flux is
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. 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).
Z. Wang et al. / Desalination 259 (2010) 235–242
experienced immediately after each water rinsing step. This indicates that water rinsing can remove membrane fouling efficiently [15]. However, we also can see from Fig. 6, the cleaning efficiency of water rinsing was gradually reduced with each consecutive filtration cycle. This means that the irreversible pollutants cannot be removed during the water rinsing process [15], increasing with each subsequent filtration cycle and making the membrane flux decline more severe. In order to give an intensive understanding of the water rinsing efficiency, the change of the total cake resistance and velocity of cake development vs. time were plotted in Fig. 7. In Fig. 7, a visible increase of the total cake resistance was observed as a result of the accumulation of cake on the membrane surface, and after water rinsing, the total resistance was reduced. The water rinsing process can restore the declined flux close to its initial value, but not completely because the accumulated irreversible foulants have been strongly embedded in the concavities of membrane surface as reported in literature [33] and the membrane fouling increased with each subsequent water rinsing cycle. So the effect of water rinsing, in other words, the ratio of irreversible fouling was gradually reduced with an increased number of filtration cycles, and the membrane resistance 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 as demonstrated in Fig. 7(a). That means the ratios of irreversible fouling were increasing gradually, and were 26.0%, 27.0%, 28.5% and 30.5% after each subsequent water rinsing. This may be attributed to a number of sensitive areas which were strongly and irreversibly fouled and these areas became smaller with each increasing fouling cycle. The additional foulant layer laid down during subsequent filtration cycles was easier to remove with the water rinsing process [34,35]. It is also noteworthy that, the velocity of cake development, dRt/dt is a very important factor in the water rinsing process. As showed in Fig. 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.
239
of each filtration process, so the membrane flux declined very fast in the initial stage too. Then the velocity of the cake development decreased with time and the corresponding membrane flux changes decreased also. It was also found that the velocity of cake development was decreased with increasing filtration cycle. This is probably due to the smaller pores getting blocked very quickly at the beginning [34]. These findings are also in line with those of Kawakatsu and Nakao who found that the pore blocking mechanism governed only the beginning of filtration cycle, which made the permeation flux decay rapid and significant [31]. However, in the later filtration stages, the process transitioned to a cake formation-limited process. The period of the pore blocking-limited process continued to be short, so the velocity of the cake development declined correspondingly [32]. 4.3.3. Membrane fouling driving force Assume the final total membrane fouling resistance is Rft, which is based on the constant relative flux of 0.8 in the present experiments and 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 any time t. As illustrated in Fig. 8, the membrane fouling driving force was very big at the beginning of the membrane filtration in each cycle, and then it gradually reduced with increasing filtration time for each cycle, until it became zero at the end of each cycle. The membrane fouling driving force can be restored by water rinsing, but this driving force was decreased with each subsequent filtration cycle. This is shown in Fig. 8, where the slope of the line is increasing with each subsequent filtration cycle and is related to the increasing of irreversible fouling as mentioned above. 4.4. The optimization of water rinsing conditions The experiments were conducted according to Table 3. The results of first water rinsing cycle are shown in Tables 4, 5 and Fig. 9. According to the value of R, the sequence of dominating factors on CMPFV for the first water rinsing cycle is relative flux (4387.25) N detergent temperature (846.44) N washing times (579.96) N agitation speed (527.98) N detergent volume (472.69) N soaking time (412.22) N TMP (300.18). Statistically, the cumulative filtrate volume of the membrane permeate was found to be strongly dependent on the relative flux rather than on the other rinsing conditions. The optimum water rinsing conditions for the first rinsing cycle can be determined by the combination of Table 4 and Fig. 10. The optimum water rinsing conditions for cumulative filtrate volume of the 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 the rest of the rinsing cycles can be determined by an analogous method and the comparison of the CMPFV in optimum water rinsing condition and 49 other conditions for up to four rinsing cycles is shown in Fig. 10. It can be seen from Fig. 10 that when the optimum water rinsing conditions were controlled, the value of CMPFV was higher than 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).
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Table 4 The CMPFV results of first cycle.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
TMP
r
T
V
t
ω
N
CMPFV− 1
0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.14 0.14 0.14 0.14 0.14 0.14 0.14
0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.1 0.15 0.2 0.25 0.3 0.35 0.4
5 15 20 25 30 35 40 15 20 25 30 35 40 5 20 25 30 35 40 5 15 25 30 35 40 5 15 20 30 35 40 5 15 20 25 35 40 5 15 20 25 30 40 5 15 20 25 30 35
50 100 150 200 250 300 350 150 200 250 300 350 50 100 250 300 350 50 100 150 200 350 50 100 150 200 250 300 100 150 200 250 300 350 50 200 250 300 350 50 100 150 300 350 50 100 150 200 250
10 15 20 30 60 90 120 30 60 90 120 10 15 20 120 10 15 20 30 60 90 20 30 60 90 120 10 15 90 120 10 15 20 30 60 15 20 30 60 90 120 10 60 90 120 10 15 20 30
100 150 200 250 300 350 400 300 350 400 100 150 200 250 150 200 250 300 350 400 100 350 400 100 150 200 250 300 200 250 300 350 400 100 150 400 100 150 200 250 300 350 250 300 350 400 100 150 200
1 2 3 4 5 6 7 6 7 1 2 3 4 5 4 5 6 7 1 2 3 2 3 4 5 6 7 1 7 1 2 3 4 5 6 5 6 7 1 2 3 4 3 4 5 6 7 1 2
886.54 496.35 386.79 306.32 250.51 175.38 117.50 580.47 408.98 249.34 232.16 209.34 184.97 121.78 1230.26 791.33 536.82 374.08 365.39 269.83 231.35 1184.01 899.21 573.66 404.14 250.29 341.03 288.72 935.77 565.47 364.75 336.30 252.47 182.47 216.51 918.03 572.81 458.28 315.11 285.86 266.65 199.08 569.69 370.73 377.97 284.41 247.03 172.97 243.73
4.5. The quantitative influence of operating conditions on CMPFV As discussed in the previous sections, different rinsing conditions are expected to affect the water rinsing cleaning efficiency. In order to confirm the quantitative contribution of water rinsing conditions on
CMPF, the multivariate linear regression method was used and the regression result of standardization data for the first rinsing cycle is shown in Table 6. It is clear from Table 6, that except for F6 and F7, all the absolute values of Fj exceed 1 in the first regression, which indicated that the
Table 5 Experimental results according to the orthogonal table (Table 4).
P
k1 P k2 P k3 P k4 P k5 P k6 P k7 R P
TMP
r
T
V
t
ω
N
2867.144 2868.346 2899.455 2743.658 2853.741 3015.810 2715.630 300.18
5755.351 4104.040 2895.053 2203.642 2012.847 1442.715 1368.105 4387.25
2542.982 2494.605 2915.470 3213.069 3275.409 2911.249 2428.969 846.44
2875.392 2944.080 2601.789 2701.828 3074.482 2717.642 2866.558 472.69
2727.657 3008.165 3014.229 2986.577 2652.578 2602.013 2790.534 412.22
2592.877 2977.205 2848.201 2967.096 3094.879 2566.896 2734.599 527.98
2576.811 2887.743 3156.771 2676.502 2745.882 2897.162 2840.882 579.96
Note: ki is average in every influence factor, in which i = 1, 2……7 is level. R is remainder of the maximum and the minimum.
Z. Wang et al. / Desalination 259 (2010) 235–242
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Table 7 The quantitative influence of operating conditions on CMPFV for 4 cycles. Washing cycle
r, %
T, %
N, %
ω,%
V, %
1 2 3 4 Average
38.1 38.3 38.6 38.9 38.3
20.4 21.4 22.2 22.6 21.6
20.0 19.2 18.6 18.3 19.3
11.1 11.3 11.3 11.2 11.2
10.4 9.8 9.3 9.0 9.6
four water rinsing cycles with respect to CMPFV and the water rinsing conditions, with a significant level of α = 0.05 are as follows: Fig. 9. Analysis of the weighted of influence factors.
first cycle CMPFV1 = −1187:24r + 8:07T + 0:65V + 0:86N + 33⋅93ω
ð5Þ 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
Fig. 10. Comparison of the CMPFV in optimum condition and 49 other conditions in Table 4.
soaking time and TMP had no effect on the CMPFV. Eliminating the soaking time and TMP, the rest of the factors were analyzed again with the regression method. According to the absolute values of Fj in the second regression, the sequence of influencing the degree of water rinsing conditions on CMPFV was relative flux N detergent temperature N washing times N agitation speed N detergent volume. In addition, the relative flux had a negative contribution to CMPFV according to the negative values of F2 and the regression coefficient b2, which indicated that an increase in the relative flux resulted in a decline in CMPFV. These conclusions were also consistent with the result of the orthogonal analysis in Section 4.4. According to the statistical analysis examined by linear regression, the regression equation for the first
Table 6 Values of regression coefficient, Fj and Dj for CMPFV. Regression times st
1 regression
j
bj
σj
Fj
Dj(%)
TMP r T V t N ω
350.30 − 1265.00 7.47 0.59 0.84 0.78 300.80 – − 1187.24 8.07 0.65 0.86 33.93
818.94 343.38 3.15 0.36 0.94 0.34 17.79 – 325.28 3.02 0.34 0.3 17.16
0.43 − 3.69 2.37 1.66 0.86 2.28 1.73 – − 3.65 2.67 1.90 2.65 1.98
– – –
2nd regression r T V N ω
– 38.08 20.38 10.35 20.00 11.18
As the above 4 formulas show, the quantitative relationship between CMPFV and the water rinsing conditions for the 4 cycles is similar. The quantitative contributions of water rinsing conditions to CMPFV in each cycle were calculated and the results are shown in the Table 7. The percentage contribution of a certain water rinsing condition in the 4 cycles are similar, and the 4 cycles average contribution to the relative flux, detergent temperature, washing times, agitation speed and detergent volume were 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 and results were compared with the formula (5) as shown in Table 8. It can be seen from Table 8 that the relative error between experimental value and predicted value by formula (5) is less than 8%, so the regression equations can be applied to predict the cumulative filtrate volume in MF membrane process with the four cleaning cycles. 5. Conclusion In this present paper, fouling and water rinsing experiments were performed with the feed suspension from an SBR in a laboratory-scale dead-end microfiltration test unit. The influence of the water rinsing conditions on the cumulative filtrate volume of the membrane permeate was studied by the combination of orthogonal table and multivariate linear regression methods. The experimental results showed that: (1) the membrane fouling mechanism was cake filtration, and the cake resistance was the dominant resistance in the membrane filtration process; (2) when the optimum water rinsing conditions were controlled, the value of the cumulative filtrate volume of membrane permeate was higher than that for any other conditions; (3) the sequence and the average percentage contribution of the water rinsing conditions on CMPFV of each rinsing cycle were relative flux (38.3%) N detergent temperature (21.6%) N washing times (19.3%) N agitation speed (11.2%) N detergent volume (9.6%). In addition, except for the relative flux, the other factors had a positive contribution to the cumulative filtrate volume collected during a specified time. (4) The regression equations for the first 4 cycles for the cumulative filtrate volume of the membrane permeate collected for a specified time and the operating conditions were obtained, and the results of the validation experiment showed that the relative error between the experimental value and the predicted value by these
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Table 8 Comparison of the experimental value and the model value. No.
r
T
V
ω
N
Experimental value
Predicted value by formula (4)
Relative error (%)
1 2 3 4 Average value
0.1 0.3 0.2 0.1
15 30 20 40
200 100 150 350
100 300 200 300
5 2 4 1
300.5 305.2 356.9 759.7
329.3 277.4 329.8 724.9
− 8.8 10.0 8.2 4.8 8.0
formulas was less than 8%. These regression equations can predict the cumulative filtrate volume of the membrane permeate in an industrial process. Nomenclature CMPFV the cumulative membrane permeate filtrate volume (ml) ri recovery of membrane permeability (%) Ji initial flux after each hydraulic cleaning cycle (m3 m−2 s−1) J0 initial flux value of virgin membrane (m3 m−2 s−1) J permeate flux (m3 m−2 s−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 times j factor number b regression coefficient of influence factor F regression coefficient of CMPFV D influence degree
Greek symbols μ liquid viscosity (Pa s) σ relative standard error α significant level
Acknowledgements The authors express appreciation to Dr. Caryn Heldt from Rensselaer Polytechnic Institute and Miss Xiaoli Li from the College of Foreign Languages of Beijing University of Technology for very extensive editing and English language assistance.
This project was supported by Beijing Municipal Natural Science Foundation (project no. 8052006) and National Natural Science Foundation of China (project no. 20276003) and the Beijing Municipal Commission of Education (project no. PHR200907105) for the financial support of this study.
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Desalination j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / d e s a l
Influence of operating conditions on cleaning efficiency in sequencing batch reactor (SBR) activated sludge process — water rinsing introduced membrane filtration process Zhan Wang ⁎, Shanshan Zhao, Feng Liu, Liying Yang, Yin Song, Xiuyan Wang, Xuejie Xi Department of Chemistry and Chemical Engineering, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing, 100124 China
a r t i c l e
i n f o
Article history: Received 8 September 2009 Received in revised form 23 March 2010 Accepted 25 March 2010 Available online 7 May 2010 Keywords: SBR Operating conditions Orthogonal table Multivariate linear regression methods Quantitative evaluation
a b s t r a c t In this paper, the influences of water rinsing conditions on the cumulative membrane permeate filtrate volume (CMPFV) were systematically investigated by a combination of orthogonal table and multivariate linear regression methods. The experiments were performed with a feed suspension from an SBR and deionized 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 filtration dominated the flux decline under the conditions studied. Water rinsing process can restore the declined flux nearly to its initial value, but its ability is gradually reducing with the increase of the cleaning cycle, which is associated 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%) N detergent temperature (21.6%) N washing times (19.3%) N agitation speed (11.2%) N detergent volume (9.6%). Here, except for the relative flux, the others factors, such as detergent temperature, washing times, agitation speed and detergent volume had a positive contribution to CMPFV. In addition, the relationship between water rinsing conditions and CMPFV was analyzed and defined quantitatively for 4 cycles respectively and it can give good predictive results. © 2010 Elsevier B.V. All rights reserved.
1. Introduction In recent years, the membrane bioreactor (MBR) process has been widely applied to treat various types of wastewater such as industrial wastewater, human excrement, and especially domestic wastewater [1–4] due to its small footprint, high quality effluent, low sludge production rate, highly retentive activated sludge concentration and easy management[5,6]. However, the biggest obstacle for membrane filtration in practical applications is membrane fouling, which decreases the life of membrane modules and increases costs [7]. Although many strategies, such as pretreatment of the feed suspension, optimization of operating conditions in the membrane module, and preparation of antifouling membranes [8], have been employed to improve membrane fouling, the membranes will become fouled eventually. Therefore, membrane cleaning is an inescapable and essential step in maintaining membrane filtration processes [9], and many physical and chemical cleaning methods have been employed to remove the deposited layers on the membrane surface and in the pores of membrane [10].
⁎ Corresponding author. Fax: + 86 10 6739 1983. E-mail addresses: [email protected], [email protected] (Z. Wang). 0011-9164/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.desal.2010.03.048
Water rinsing is a necessary step during the membrane cleaning process [11] and several authors have dealt with this matter. For example, Cabero et al. used water rinsing to reduce cleaning agent consumption and to restore the permeate flux after cleaning [12]. Matzinos et al. reported that one-third of the protein was removed during water rinsing [13]. Renner pointed out that up to 98% of the deposited layer can be removed during the water rinsing step [14]. Bansal et al. demonstrated that the water rinsing process can dissolve most of the deposits formed on the membrane surface, but it was not effective in removing the deposits formed inside the pores [15]. In fact, the water rinsing efficiency is governed by the membrane fouling mechanism, the pollutants species, the rinsing conditions (temperature, time, filtrate and permeate volume and transmembrane pressure) and so on [16,17]. So the optimization of the cleaning operating conditions plays a very important role in industrial membrane cleaning practices. For instance, Zondervan et al. optimized the chemical cleaning cycle and proposed a model to minimize the overall operating costs [18]. Chen et al. identified that higher membrane filtration capacity and efficiency can be achieved by using optimized conditions [19]. Farley et al. simulated and optimized the entire physical and chemical cleaning process [20]. Van Boxtel et al. reported that the optimization of cleaning operating conditions can save at least 10% of the operating cost [17]. Evgenia et al. developed a general model for simulating the rinsing and regeneration network
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(RRN), and the optimal time delay of 8% for the overall rinse water retention time was determined [21]. However, up to now, the procedure for the water rinsing processes was largely based on experience [11], the aspect of quantitatively defining the relationship between the operating conditions and the cleaning efficiency has received limited attention. Therefore, the present paper is an extensive study to determine the relationship between water rinsing operating conditions and rinsing efficiency to provide cost-effective cleaning and restoration procedures. 2. Experimental 2.1. System and methods The laboratory-scale experimental system (in Fig. 1) consisted of two parts. The first part is an intermittent mode bioreactor system with an effective volume of 25 l. The second part is a dead-end microfiltration cell. The feed solution (raw wastewater) was obtained from the storage tank of domestic sewage with qualities shown in Table 1. The operating parameters for the bioreactor system, such as the mixed liquor suspended solid (MLSS), temperature (T), dissolved oxygen (DO), pH and hydraulic retention time (HRT), are shown in Table 2. No sludge was discharged during the operation or test period. The dead-end microfiltration cell has an effective membrane area of 24.0 cm2. Before each experiment, the membrane (0.1 μm PES hydrophilic membranes purchased from Beijing Ande Membrane Separation Technology and Engineering) were soaked in deionized water for 12 h
Table 1 Quality 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
to remove glycerin that is present in the membrane to protect it for longterm storage. The main parameters of the feed suspension into the membrane 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 chemical oxygen demand(COD) (48.2–52.6 mg L−1). The mixed liquor suspended solids (MLSS) concentration was measured by weighing a dried sample and pH was measured with a pHS-3C acidity meter. The COD and NH3–N of the membrane influent and effluent, were measured by adopting the Chinese SEPA standard methods [22]. 2.2. Experimental procedure The feed solution was equilibrated to a constant COD, NH3–N concentration and MLSS. The experiment was conducted as follows: (1) the deionized water flux of the membrane was measured; (2) the membrane flux was measured with a feed solution until a predetermined drop in flux occurred; (3) the membrane was rinsed with deionized water for a different numbers of times with a certain temperature, agitation speed and soaking time; (4) the deionized water flux of the membrane was measured again. Each experiment was repeated four times as described above.
Fig. 1. Schematic diagram of the experimental system (a) intermittent bioreactor system and (b) dead-end filtration system.
Z. Wang et al. / Desalination 259 (2010) 235–242
3.4. Determination of filtration resistances
Table 2 Operating 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
The resistance-in-series model is applied to investigate the fouling characteristics in terms of various filtration resistances
Rt = As mentioned above, seven main factors affecting the water rinsing efficiency 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 transmembrane pressure (TMP) were selected for this study. In order to optimize the water rinsing operating conditions and decrease the load of the experiment, the orthogonal experimental design utilized can be seen in Table 3. 3. Analytical methods
TMP = Rm + Rc + Rf μJ
ð3Þ
where Rt is the total membrane resistance, m−1; TMP is the transmembrane pressure, Pa; μ is liquid viscosity, Pa s; J is the permeate flux, m3 m−2 s−1; Rm is the intrinsic resistance of the membrane, m−1; Rc is the cake resistance, m−1; and Rf is the fouling resistance (pore plugging and adsorption), m−1. 4. Results and discussion 4.1. The intrinsic resistance of the membrane
3.1. The recovery of membrane permeability The recovery of membrane permeability (ri) that provides a measure of membrane irreversible fouling is calculated by:
ri =
237
Ji J0
When plot the permeate flux of deionized water vs. transmembrane pressure is in the range of 0.06–0.16 MPa, the intrinsic resistance of the membrane Rm can be calculated by the following formula (4) and it was calculated to be 6 × 108 m−1 for our PES membrane.
ð1Þ Rm =
TMP : μJ0
ð4Þ
where Ji is the initial suspension flux after each water rinsing cycle, m3 m−2 s−1; J0 is the initial suspension flux value of the virgin membrane, m3 m−2 s−1. 3.2. Analysis and approaches In order to quantify the influence of various cleaning conditions on the cleaning efficiency, the collected data were processed by orthogonal experimental design and multivariate linear regression methods which are described elsewhere in the literature [23–27]. 3.3. Fouling mechanism The Blocking Law was first proposed by Herman et al. [28] in 1935, and the common form is as follows [29]: n 2 d t dt = k dV dV 2
ð2Þ
where t is the filtration time, V is the total filtered volume and k is the proportionality coefficient. The exponent n characterizes the fouling mechanism, with n = 0 for cake filtration, n = 1 for intermediate blocking, n = 3 / 2 for pore constriction (also called standard blocking), and n = 2 for complete pore blocking [30].
Table 3 Factor-levels of orthogonal table. Factors
Levers
r (%) T (°C) V (ml) ω (min− 1) N t (s) TMP (MPa)
40 5 50 100 1 10 0.06
35 15 100 150 2 15 0.08
30 20 150 200 3 20 0.1
25 25 200 250 4 30 0.12
20 30 250 300 5 60 0.14
15 35 300 350 6 90 0.16
10 40 350 400 7 120 0.18
Fig. 2. Particle diameter distribution of feed suspension (a) and pore size distribution of membrane (b).
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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).
4.2. Membrane fouling mechanism In order to determine the membrane fouling mechanism, first both the particle size distribution of the feed solution and the pore size distribution of the PES membrane were measured. The experimental data were analyzed by using classical Herman's filtration laws. The MAF-5001 model Malvern laser particle diameter distribution instrument (Britain) was used to get the particle distribution of the feed solution, and the modified bubble point method was used to obtain the pore size distribution of the PES membrane (Fig. 2). It can be seen from Fig. 2 that the mean diameter of the activated sludge suspended particles is 1–10,000 μm, in contrast to the membrane pore size which is 0.06–0.5 μm. Obviously, the average surface pore size of a membrane (dm) is much smaller than one-tenth of the particle size (dp). The particles surrounded by macromolecules were compressed and rejected by the membrane surface. At this time, the particles will block the surface pores, and a cake will form on the membrane surface during the membrane filtration process [31]. Furthermore, the experimental data was analyzed by classical Herman's filtration laws and all analysis gave similar results. The analysis of one group of experimental data for 4 cycles is shown in
Fig. 3. It can be seen from Fig. 3 that the membrane fouling mechanism is cake filtration after 4 cycles, and this is also in agreement with reported results by Lim and Bai [30]. 4.3. Characteristics of the membrane filtration process 4.3.1. Dominant resistance Typical permeate flux declines during the membrane filtration process are shown in Fig. 4. The permeate flux deteriorated with time in each filtration cycle and this means that the total filtration resistance was increasing with time for each filtration cycle. As mentioned above, in the present process, the membrane fouling mechanisms are cake filtration in 4 cycles, so it is very important to know which resistance will dominate when water rinsing is introduced into the membrane filtration process. The plot of (Rt − Rm) / Rt vs. filtration time is shown in Fig. 5. The intrinsic membrane resistance can be ignored when the cake resistance is the dominant resistance in total resistance. 4.3.2. Water rinsing efficiency The relative flux (or recovery ratio) after water rinsing can be employed to characterize the water rinsing efficiency. As showed in Fig. 6, a very significant recovery of the membrane permeate flux is
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. 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).
Z. Wang et al. / Desalination 259 (2010) 235–242
experienced immediately after each water rinsing step. This indicates that water rinsing can remove membrane fouling efficiently [15]. However, we also can see from Fig. 6, the cleaning efficiency of water rinsing was gradually reduced with each consecutive filtration cycle. This means that the irreversible pollutants cannot be removed during the water rinsing process [15], increasing with each subsequent filtration cycle and making the membrane flux decline more severe. In order to give an intensive understanding of the water rinsing efficiency, the change of the total cake resistance and velocity of cake development vs. time were plotted in Fig. 7. In Fig. 7, a visible increase of the total cake resistance was observed as a result of the accumulation of cake on the membrane surface, and after water rinsing, the total resistance was reduced. The water rinsing process can restore the declined flux close to its initial value, but not completely because the accumulated irreversible foulants have been strongly embedded in the concavities of membrane surface as reported in literature [33] and the membrane fouling increased with each subsequent water rinsing cycle. So the effect of water rinsing, in other words, the ratio of irreversible fouling was gradually reduced with an increased number of filtration cycles, and the membrane resistance 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 as demonstrated in Fig. 7(a). That means the ratios of irreversible fouling were increasing gradually, and were 26.0%, 27.0%, 28.5% and 30.5% after each subsequent water rinsing. This may be attributed to a number of sensitive areas which were strongly and irreversibly fouled and these areas became smaller with each increasing fouling cycle. The additional foulant layer laid down during subsequent filtration cycles was easier to remove with the water rinsing process [34,35]. It is also noteworthy that, the velocity of cake development, dRt/dt is a very important factor in the water rinsing process. As showed in Fig. 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.
239
of each filtration process, so the membrane flux declined very fast in the initial stage too. Then the velocity of the cake development decreased with time and the corresponding membrane flux changes decreased also. It was also found that the velocity of cake development was decreased with increasing filtration cycle. This is probably due to the smaller pores getting blocked very quickly at the beginning [34]. These findings are also in line with those of Kawakatsu and Nakao who found that the pore blocking mechanism governed only the beginning of filtration cycle, which made the permeation flux decay rapid and significant [31]. However, in the later filtration stages, the process transitioned to a cake formation-limited process. The period of the pore blocking-limited process continued to be short, so the velocity of the cake development declined correspondingly [32]. 4.3.3. Membrane fouling driving force Assume the final total membrane fouling resistance is Rft, which is based on the constant relative flux of 0.8 in the present experiments and 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 any time t. As illustrated in Fig. 8, the membrane fouling driving force was very big at the beginning of the membrane filtration in each cycle, and then it gradually reduced with increasing filtration time for each cycle, until it became zero at the end of each cycle. The membrane fouling driving force can be restored by water rinsing, but this driving force was decreased with each subsequent filtration cycle. This is shown in Fig. 8, where the slope of the line is increasing with each subsequent filtration cycle and is related to the increasing of irreversible fouling as mentioned above. 4.4. The optimization of water rinsing conditions The experiments were conducted according to Table 3. The results of first water rinsing cycle are shown in Tables 4, 5 and Fig. 9. According to the value of R, the sequence of dominating factors on CMPFV for the first water rinsing cycle is relative flux (4387.25) N detergent temperature (846.44) N washing times (579.96) N agitation speed (527.98) N detergent volume (472.69) N soaking time (412.22) N TMP (300.18). Statistically, the cumulative filtrate volume of the membrane permeate was found to be strongly dependent on the relative flux rather than on the other rinsing conditions. The optimum water rinsing conditions for the first rinsing cycle can be determined by the combination of Table 4 and Fig. 10. The optimum water rinsing conditions for cumulative filtrate volume of the 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 the rest of the rinsing cycles can be determined by an analogous method and the comparison of the CMPFV in optimum water rinsing condition and 49 other conditions for up to four rinsing cycles is shown in Fig. 10. It can be seen from Fig. 10 that when the optimum water rinsing conditions were controlled, the value of CMPFV was higher than 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).
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Table 4 The CMPFV results of first cycle.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
TMP
r
T
V
t
ω
N
CMPFV− 1
0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.14 0.14 0.14 0.14 0.14 0.14 0.14
0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.1 0.15 0.2 0.25 0.3 0.35 0.4
5 15 20 25 30 35 40 15 20 25 30 35 40 5 20 25 30 35 40 5 15 25 30 35 40 5 15 20 30 35 40 5 15 20 25 35 40 5 15 20 25 30 40 5 15 20 25 30 35
50 100 150 200 250 300 350 150 200 250 300 350 50 100 250 300 350 50 100 150 200 350 50 100 150 200 250 300 100 150 200 250 300 350 50 200 250 300 350 50 100 150 300 350 50 100 150 200 250
10 15 20 30 60 90 120 30 60 90 120 10 15 20 120 10 15 20 30 60 90 20 30 60 90 120 10 15 90 120 10 15 20 30 60 15 20 30 60 90 120 10 60 90 120 10 15 20 30
100 150 200 250 300 350 400 300 350 400 100 150 200 250 150 200 250 300 350 400 100 350 400 100 150 200 250 300 200 250 300 350 400 100 150 400 100 150 200 250 300 350 250 300 350 400 100 150 200
1 2 3 4 5 6 7 6 7 1 2 3 4 5 4 5 6 7 1 2 3 2 3 4 5 6 7 1 7 1 2 3 4 5 6 5 6 7 1 2 3 4 3 4 5 6 7 1 2
886.54 496.35 386.79 306.32 250.51 175.38 117.50 580.47 408.98 249.34 232.16 209.34 184.97 121.78 1230.26 791.33 536.82 374.08 365.39 269.83 231.35 1184.01 899.21 573.66 404.14 250.29 341.03 288.72 935.77 565.47 364.75 336.30 252.47 182.47 216.51 918.03 572.81 458.28 315.11 285.86 266.65 199.08 569.69 370.73 377.97 284.41 247.03 172.97 243.73
4.5. The quantitative influence of operating conditions on CMPFV As discussed in the previous sections, different rinsing conditions are expected to affect the water rinsing cleaning efficiency. In order to confirm the quantitative contribution of water rinsing conditions on
CMPF, the multivariate linear regression method was used and the regression result of standardization data for the first rinsing cycle is shown in Table 6. It is clear from Table 6, that except for F6 and F7, all the absolute values of Fj exceed 1 in the first regression, which indicated that the
Table 5 Experimental results according to the orthogonal table (Table 4).
P
k1 P k2 P k3 P k4 P k5 P k6 P k7 R P
TMP
r
T
V
t
ω
N
2867.144 2868.346 2899.455 2743.658 2853.741 3015.810 2715.630 300.18
5755.351 4104.040 2895.053 2203.642 2012.847 1442.715 1368.105 4387.25
2542.982 2494.605 2915.470 3213.069 3275.409 2911.249 2428.969 846.44
2875.392 2944.080 2601.789 2701.828 3074.482 2717.642 2866.558 472.69
2727.657 3008.165 3014.229 2986.577 2652.578 2602.013 2790.534 412.22
2592.877 2977.205 2848.201 2967.096 3094.879 2566.896 2734.599 527.98
2576.811 2887.743 3156.771 2676.502 2745.882 2897.162 2840.882 579.96
Note: ki is average in every influence factor, in which i = 1, 2……7 is level. R is remainder of the maximum and the minimum.
Z. Wang et al. / Desalination 259 (2010) 235–242
241
Table 7 The quantitative influence of operating conditions on CMPFV for 4 cycles. Washing cycle
r, %
T, %
N, %
ω,%
V, %
1 2 3 4 Average
38.1 38.3 38.6 38.9 38.3
20.4 21.4 22.2 22.6 21.6
20.0 19.2 18.6 18.3 19.3
11.1 11.3 11.3 11.2 11.2
10.4 9.8 9.3 9.0 9.6
four water rinsing cycles with respect to CMPFV and the water rinsing conditions, with a significant level of α = 0.05 are as follows: Fig. 9. Analysis of the weighted of influence factors.
first cycle CMPFV1 = −1187:24r + 8:07T + 0:65V + 0:86N + 33⋅93ω
ð5Þ 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
Fig. 10. Comparison of the CMPFV in optimum condition and 49 other conditions in Table 4.
soaking time and TMP had no effect on the CMPFV. Eliminating the soaking time and TMP, the rest of the factors were analyzed again with the regression method. According to the absolute values of Fj in the second regression, the sequence of influencing the degree of water rinsing conditions on CMPFV was relative flux N detergent temperature N washing times N agitation speed N detergent volume. In addition, the relative flux had a negative contribution to CMPFV according to the negative values of F2 and the regression coefficient b2, which indicated that an increase in the relative flux resulted in a decline in CMPFV. These conclusions were also consistent with the result of the orthogonal analysis in Section 4.4. According to the statistical analysis examined by linear regression, the regression equation for the first
Table 6 Values of regression coefficient, Fj and Dj for CMPFV. Regression times st
1 regression
j
bj
σj
Fj
Dj(%)
TMP r T V t N ω
350.30 − 1265.00 7.47 0.59 0.84 0.78 300.80 – − 1187.24 8.07 0.65 0.86 33.93
818.94 343.38 3.15 0.36 0.94 0.34 17.79 – 325.28 3.02 0.34 0.3 17.16
0.43 − 3.69 2.37 1.66 0.86 2.28 1.73 – − 3.65 2.67 1.90 2.65 1.98
– – –
2nd regression r T V N ω
– 38.08 20.38 10.35 20.00 11.18
As the above 4 formulas show, the quantitative relationship between CMPFV and the water rinsing conditions for the 4 cycles is similar. The quantitative contributions of water rinsing conditions to CMPFV in each cycle were calculated and the results are shown in the Table 7. The percentage contribution of a certain water rinsing condition in the 4 cycles are similar, and the 4 cycles average contribution to the relative flux, detergent temperature, washing times, agitation speed and detergent volume were 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 and results were compared with the formula (5) as shown in Table 8. It can be seen from Table 8 that the relative error between experimental value and predicted value by formula (5) is less than 8%, so the regression equations can be applied to predict the cumulative filtrate volume in MF membrane process with the four cleaning cycles. 5. Conclusion In this present paper, fouling and water rinsing experiments were performed with the feed suspension from an SBR in a laboratory-scale dead-end microfiltration test unit. The influence of the water rinsing conditions on the cumulative filtrate volume of the membrane permeate was studied by the combination of orthogonal table and multivariate linear regression methods. The experimental results showed that: (1) the membrane fouling mechanism was cake filtration, and the cake resistance was the dominant resistance in the membrane filtration process; (2) when the optimum water rinsing conditions were controlled, the value of the cumulative filtrate volume of membrane permeate was higher than that for any other conditions; (3) the sequence and the average percentage contribution of the water rinsing conditions on CMPFV of each rinsing cycle were relative flux (38.3%) N detergent temperature (21.6%) N washing times (19.3%) N agitation speed (11.2%) N detergent volume (9.6%). In addition, except for the relative flux, the other factors had a positive contribution to the cumulative filtrate volume collected during a specified time. (4) The regression equations for the first 4 cycles for the cumulative filtrate volume of the membrane permeate collected for a specified time and the operating conditions were obtained, and the results of the validation experiment showed that the relative error between the experimental value and the predicted value by these
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Table 8 Comparison of the experimental value and the model value. No.
r
T
V
ω
N
Experimental value
Predicted value by formula (4)
Relative error (%)
1 2 3 4 Average value
0.1 0.3 0.2 0.1
15 30 20 40
200 100 150 350
100 300 200 300
5 2 4 1
300.5 305.2 356.9 759.7
329.3 277.4 329.8 724.9
− 8.8 10.0 8.2 4.8 8.0
formulas was less than 8%. These regression equations can predict the cumulative filtrate volume of the membrane permeate in an industrial process. Nomenclature CMPFV the cumulative membrane permeate filtrate volume (ml) ri recovery of membrane permeability (%) Ji initial flux after each hydraulic cleaning cycle (m3 m−2 s−1) J0 initial flux value of virgin membrane (m3 m−2 s−1) J permeate flux (m3 m−2 s−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 times j factor number b regression coefficient of influence factor F regression coefficient of CMPFV D influence degree
Greek symbols μ liquid viscosity (Pa s) σ relative standard error α significant level
Acknowledgements The authors express appreciation to Dr. Caryn Heldt from Rensselaer Polytechnic Institute and Miss Xiaoli Li from the College of Foreign Languages of Beijing University of Technology for very extensive editing and English language assistance.
This project was supported by Beijing Municipal Natural Science Foundation (project no. 8052006) and National Natural Science Foundation of China (project no. 20276003) and the Beijing Municipal Commission of Education (project no. PHR200907105) for the financial support of this study.
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