Effects of Hydraulic Conditions on Effluent Quality, Flux Behavior, and

Article pubs.acs.org/IECR

Effects of Hydraulic Conditions on Effluent Quality, Flux Behavior, and Energy Consumption in a Shear-Enhanced Membrane Filtration Using Box-Behnken Response Surface Methodology Wenxiang Zhang,† Zhenzhou Zhu,† Michel Y. Jaffrin,‡ and Luhui Ding*,† †

EA 4297 TIMR, University of Technology of Compiegne, 60205 Compiegne Cedex, France UMR 7338, Technological University of Compiegne, 60205 Compiegne Cedex, France

‡

S Supporting Information *

ABSTRACT: A systematic study has been carried out to get insight into the effect of hydraulic conditions (feed flow rate (Q), transmembrane pressure (TMP), and rotating speed (N)) on the treatment of dairy wastewater with shear-enhanced membrane flirtation using Box-Behnken response surface methodology (BBRSM). Performances of three types of ultrafiltration (UF) membrane (UH005P, UH030P, and PES050) were compared to choose the optimal membrane. A 1.5 h test was conducted on the analysis of kinetics of permeate flux decline under various hydraulic conditions. With the help of the BBRSM method, the influence and interactions of three hydraulic factors were investigated and regression models were built, while data on effluent quality, flux behavior, and energy consumption as the response values were collected. The results showed that TMP, rotating speed, and their interaction effects could improve significantly effluent quality and flux behavior, reduce several irreversible and total filtration resistancess and greatly decrease energy consumption. The optimal hydraulic conditions obtained by BBRSM were as follows: Q = 75.81 L/h, TMP = 7 bar, and N = 2250 rpm. Under these conditions, the best effluent quality, optimal flux behavior, and minimal energy consumption have been observed.

1. INTRODUCTION Shear-enhanced membrane filtration, which outweighs the conventional crossflow membrane filtration process in excellent effluent quality, stable permeate flux, low concentration polarization, and higher retention,1 has been successfully implemented in many field of research and engineering such as wastewater treatment,2,3 medical engineering,4 and food engineering5 as well as biotechnological separations.4 Shear-enhanced membrane filtration currently suffers from high equipment cost, membrane fouling, high energy consumption, etc.1 Membrane fouling remains a major problem that hinders large-scale applications due to flux decline, shortened membrane life, and increased operational cost.2 Various strategies have been proposed to reduce membrane fouling, including operation under threshold flux instead of limiting flux,2 membrane washing before cake fouling formation,5 the use of chemical cleaning process,6 and appropriate hydraulic conditions.7 As the most primary components of shear-enhanced membrane filtration, energy demand and membrane replacement are both markedly affected by membrane fouling.8,9 Hydraulics, which is a significant parameter of the design and operation of shear-enhanced membrane filtration, governs a large proportion of total energy requirement of shear-enhanced membrane filtration.1 Feed flow rate, TMP, and rotating speed are three main parameters of hydraulics. In shear-enhanced membrane filtration, shear stress is created by a rotating disk module, and appropriate shear rate can not only maintain the solids in suspension but also scour the membrane surface to reduce solutes concentration and particles deposition on the membrane surface, leading to a reduction of concentration © 2014 American Chemical Society

polarization and membrane fouling. The shear rate is an important parameter in shear-enhanced membrane filtration and has a direct incidence on permeate flux. Nevertheless, high shear rates could produce a high energy consumption and have a negative effect on the life of the equipment.1 Meanwhile, TMP as a driving force greatly affects the flux augmentation and membrane fouling. With decreasing TMP, membrane fouling would be alleviated and energy consumption would reduce, but permeate flux and filtration efficiency would drop. Moreover, the feed flow rate plays an important role in this filtration process.2 Selecting a suitable feed flow rate could improve permeate flux and control membrane fouling. Practically, detailed fundamental studies regarding the interactions among these hydraulics parameters of shear-enhanced membrane filtration are limited, although their interactions have important effects on the filtration process. Numerous experimental design approaches have been utilized. Factorial design and fractional factorial design were commonly used to optimize membrane processes.10,11 These methods are effective and can be easily implemented. However, an increase in the number of parameters can lead to a large number of tests. BBRSM is a collection of statistical and mathematical techniques that is useful for recognizing the performance of composite systems, analyzing interactions among factors, exploring the relationships between the independent variables and the response, and optimizing the Received: Revised: Accepted: Published: 7176

January 9, 2014 March 29, 2014 April 3, 2014 April 3, 2014 dx.doi.org/10.1021/ie500117u | Ind. Eng. Chem. Res. 2014, 53, 7176−7185

Industrial & Engineering Chemistry Research

Article

Figure 1. Schematic of the rotating disk module (RDM) and of the disk with vanes.

products or process in which multiple variables may affect the result.12,13 BBRSM is principally advantageous in estimating multiple parameters and their interactions with fewer experimental trials. BBRSM has been successfully used for wastewater treatment and other related fields, especially for researching interactions between process factors.14 To our knowledge, there is no available study of the effect of hydraulic parameters on effluent quality, flux behavior, and energy consumption in shear-enhanced membrane filtration with a combination of operating parameters using BBRSM. The current study attempts to carry out the three-factorial and three-level BBRSM experimental design to investigate the combined hydraulic effects of feed flow rate, TMP, and rotating speed on effluent quality, flux behavior, and energy consumption and to optimize the process parameters of shear-enhanced membrane filtration. The optimal hydraulic operating conditions resulting from batch experiments are expected to contribute to their potential application to continuous flux tests in the future.

Table 1. Properties of MICRODYN-NADIR Ultrafiltration Membranes Tested membrane

surface materiala

molecular weight cutoff (kD)

water permeability (L m−2 h−1 bar−1)b

UP005P UH030P PES50

PES PESH PES

5 30 50

15−16 40−50 >70

a

PES, Polyethersulphone; PESH, Permanently hydrophilic polyethersulphone. bOur own measurement at 25 °C and 0.1−0.8 MPa.

Table 2. Main Characteristics of Model Dairy Wastewater index −1

casein (g L ) whey protein (g L−1) lactose (g L−1) calcium (g L−1) sodium (g L−1) lipid (g L−1) COD (g O2 L−1) conductivity (μs cm−1) pH dry mass (g L−1)

2. MATERIALS AND METHODS 2.1. Experimental Apparatus and Membranes. The rotating disk module (RDM), which consists of one disk mounted on a single shaft and rotating near a fixed circular membranes, has been designed and built in our laboratory (Figure 1). A flat membrane, with an effective area of 176 cm2 (outer radius R = 7.72 cm, inner radius r = 1.88 cm), was fixed on the cover of the cylindrical housing in front of the disk. The disk equipped with 6-mm-high vanes can generate very high shear rates on the membrane, inducing high shear rates on the membrane. The shear rate could be adjusted by modifying the rotating speed of the disk. Three commercial UF membranes fabricated by MICRODYN-NADIR Membrane Corporation, Germany, were selected in this investigation owing to their strong antifouling performance and high retention permeability property, and their properties are summarized in Table 1. 2.2. Test Fluid. Model dairy wastewater was prepared from commercial UHT skim (Lait de Montagne, Carrefour, France), diluted 1:2 to one-third of normal concentration with deionized water (Aquadem E300, Veolia Water, France). The main compositions and characteristics of model effluents are described in Table 2. According to the previous studies,15 the effluent compositions and filtration behaviors for this model dairy effluent and the real dairy wastewater are very similar. 2.3. Experimental Procedure. A new membrane was used for each series of experiments to ensure the same initial

model diary wastewater 8.5 2.1 15.3 0.40 0.17 60 min, the permeate flux remains stable. Therefore, three stages can be distinguished. During the first stage (t ≤ 20 min), the permeate flux dropped markedly due to fouling. For the second stage, i.e., 20 ≤ t ≤ 60 min, the rotating speed produced a self-cleaning effect and the permeate flux stopped reducing, even rising slightly in run 15. In the last stage, for t > 60 min, the permeate flux reached a stable state corresponding to an equilibrium between TMP and rotating speed.

Figure 3. Permeate flux vs time for various runs with different hydraulic conditions. (a) TMP = 2 bar: run 1, 2, 9, and 11, (b) TMP = 7 bar: run 3, 4, 10, and 12, and (c) TMP = 4.5 bar: run 5, 6, 7, 8, 13, 14, 15.

3.3. Experimental Design and Response Surface Modeling. 3.3.1. Effluent Quality. The experimental results and the predicted values of effluent quality (COD, total protein, NTU, conductivity, and pH) under various conditions are presented in Table 4. The following five second-order polynomial equations in coded form were built to explain COD, total protein, NTU, conductivity, and pH, respectively. COD = 9.392 − 0.02725Q + 0.173TMP − 0.00057N − 0.00033Q × TMP + 0.000181Q 2 − 0.022TMP2 − 2.2 × 10−8N 2 7179

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dx.doi.org/10.1021/ie500117u | Ind. Eng. Chem. Res. 2014, 53, 7176−7185


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Table 4. Experimental and Predicted Values of COD, Total Protein, NTU, Conductivity, and pH effluent quality variable

COD (mg/L)

total protein (mg/L)

NTU

conductivity (μs/cm)

pH

run

Q (L/h)

TMP (bar)

N (rpm)

experimental

predicted

experimental

predicted

experimental

predicted

experimental

predicted

experimental

predicted

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

60 120 60 120 60 120 60 120 90 90 90 90 90 90 90

2 2 7 7 4.5 4.5 4.5 4.5 2 7 2 7 4.5 4.5 4.5

1500 1500 1500 1500 750 750 2250 2250 750 750 2250 2250 1500 1500 1500

7.7 8.0 7.5 7.7 8.2 8.4 7.3 7.5 8.2 7.9 7.2 6.9 7.6 7.7 7.8

7.7 8.0 7.5 7.7 8.2 8.4 7.3 7.5 8.2 7.9 7.2 6.9 7.7 7.7 7.7

2.72 2.95 2.02 2.28 3.06 3.31 1.29 1.41 3.14 2.94 1.85 1.04 2.31 2.36 2.32

2.69 2.89 2.08 2.31 2.99 3.27 1.34 1.49 3.25 2.96 1.84 0.94 2.33 2.33 2.33

2.3 2.5 2 2.4 2.5 2.6 1.7 1.8 2.6 2.4 1.9 1.6 2.2 2.2 2.3

2.4 2.5 2.0 2.3 2.4 2.6 1.7 1.9 2.6 2.4 1.9 1.6 2.2 2.2 2.2

1358 1364 1343 1342 1372 1377 1326 1330 1384 1365 1335 1310 1347 1360 1350

1358 1365 1342 1342 1374 1378 1325 1328 1382 1365 1336 1312 1352 1352 1352

6.48 6.44 6.55 6.59 6.71 6.68 6.67 6.61 6.70 6.66 6.59 6.8 6.58 6.59 6.60

6.50 6.44 6.56 6.57 6.68 6.67 6.68 6.64 6.71 6.68 6.57 6.79 6.59 6.59 6.59

could be explained by experimental factors and their interactions. R2 lies between 0 and 1. If R2 is close to 1, the experimental and predicted values have a good correlation.12 In this study, R2 of COD, total protein, NTU, conductivity, and pH were calculated as 0.9879, 0.9931, 0.9806, 0.9802, and 0.9658 (Supporting Information Table S3), respectively, suggesting that these fitted regression equations had a significant degree of fit of the model and the experimental values and predicted responses were well correlated. Besides, the adjusted determination coefficients (adjusted R2) are 0.9661, 0.9807, 0.9456, 0.9447, and 0.9042, indicating that only about 0.0339, 0.0193, 0.0544, 0.0553, and 0.0958 of the total variations cannot be explained by the fitted models. Thus, this guaranteed a satisfactory agreement of the model with experimental values. The analysis of variance (ANOVA) for response surface quadratic models was utilized to check the statistical significances of eqs 10−14, and the results are reported in Tables S4−S8 (Supporting Information), respectively. Sum of squares, mean square, F-value, and P-value were used for the ANOVA process.18 The mean squares are obtained by separating the sum of squares into the two sources of variation (the model and the error variance), according to the respective degrees of freedom. The F-value (= Sr2/Se2) is a statistically valid estimation which measures how well the factors depict the variation of the data about its average value, and it could be calculated by dividing the mean square, model variation, and error variance. The P-values were utilized to evaluate the significance of each coefficient and the interaction effects between independent variables. If a P-value was lower than 0.01, the coefficients could be considered to be significant. The larger the F-value and the smaller the P-value, the more significant is the corresponding coefficient.22 It was observed that all the models of COD, total protein, NTU, conductivity, and pH and the regressions of all the linear terms for these models were statistically significant. Three dimensional (3D) response surface plots as a function of two factors while fixing all the other factors for the measured responses could facilitate the understanding of the their interaction effects of these two factors, and they could be

Total protein = 3.996 − 0.01345Q − 0.105TMP + 0.000186N + 0.0001Q × TMP − 8.1 × 10−5N × TMP − 1.4 × 10−6Q × N + 0.000104Q 2 + 0.011TMP2 − 2.8 × 10−7N 2

(11)

NTU = 3.054167 − 0.00883Q − 0.115TMP + 0.000232 N + 0.000667Q × TMP − 1.3 × 10−5TMP × N + 5.09 × 10−5Q 2 + 0.003333TMP2 − 2.3 × 10−7N 2

(12)

Conductivity = 1393.892 − 0.03667Q + 1.65TMP − 0.01668N − 0.02333Q × TMP − 0.0008N × TMP − 1.1 × 10−5Q × N + 0.001204Q 2 − 0.26667TMP2 − 3.9 × 10−6N 2

(13)

pH = 6.8274 + 0.008425Q − 0.0149TMP − 0.0008N + 0.000267Q × TMP + 3.33 × 10−5N × TMP − 3.3 × 10−7Q × N − 5.3 × 10−5Q 2 − 0.0044 TMP2 + 2.22 × 10−7N 2

(14)

It can be observed from eqs 10−14 that the feed flow rate increased the values of COD, total protein, NTU, and conductivity, reducing the quality of permeate solution. With the growth of TMP, the conductivity of permeate solution rose, whereas the others decreased. Raising rotating speed increased slightly total protein and NTU, while reducing COD, conductivity, and pH. Relatively to feed flow rate and rotating speed, TMP played a more profound role in the effluent quality of the filtration process. The multiple regression coefficient (R2), which is defined as the ratio of explained variation to total variation, was applied to estimate the degree of fit for the observed response values and 7180



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high shear rate force, the diffusion penetration process could be inhibited.2 As for feed flow rate, it would increased the COD, due to strengthened diffusion penetration process through the membrane by the growth of feed flow rate. Besides, from Table S4 (Supporting Information), it could be seen that their independent and interaction effects on COD could be sequenced as rotating speed > TMP > feed flow rate > feed flow rate × TMP. There was no interaction effects between feed flow rate and rotating speed and TMP and rotating speed. The optimal COD of the variables could be studied by saddle point or by inspecting the maxima formed for the X, Y, and Z coordinates. The optimal conditions obtained at the saddle point produced best responses.12,23,24 Responses of feed flow rate ranged from 65 L/h to 75 L/h, that of TMP was 7 bar and those of rotating speed ranged from 2200 to 2250 rpm. Down to 6.9 mg/L of the COD was obtained under optimal hydraulic conditions. These points were situated within the experimental ranges; thus, the analytical techniques could be useful to identify the optimal conditions. Supporting Information Figure S1 clearly shows that, when TMP and rotating speed rose, total protein concentration in permeate solution increased. According to the study of Luo and Wan,25 the aim of a UF membrane is to retain most of the caseins and whey protein, but it could not retain lactose due to its small molecular weight. For a shear-enhanced membrane system, the growth of TMP and rotating speed improved the removal efficiency of total protein and TMP, for which rotating speed was the largest contributor. In addition, it is evident that the interaction effect between TMP and rotating speed on total protein was significant. Because a high TMP and high rotating speed coproduced a powerful shear-induced force, which swept solutes away from the membrane, elevating both values simultaneously would contribute to the low total protein concentration. Supporting Information Figure S2 shows 3D response surface plots for the relationship of hydraulic conditions on NTU. TMP and rotating speed both affected NTU more significantly than feed flow rate, and higher feed flow rate led to higher NTU. The lowest NTU (1.63) was obtained at the feed flow rate of 60 L/h, TMP of 4.5 bar, and the rotating speed of 750 rpm. It is clearly observed that the optimal effects on conductivity decreased in the order as follows: rotating speed > TMP > feed flow rate > feed flow rate × TMP > TMP × rotating speed (Figure S3 and Table S7 (Supporting Information)). All these effects except feed flow rate could reduce conductivity. Supporting Information Figure S4 describes the 3D response surface plot for the relationships between feed flow rate, TMP, and rotating speed on pH. The pH was prone to be mainly affected by TMP and the interaction effect between TMP and rotating speed. The pH decreased and then increased with increasing rotating speed, which could be attributed to the high rotating speed which affected profoundly the permeability of lactose, salt ions, and protein molecules, and the intercepted results decided the pH of permeate solution. 3.3.2. Flux Behavior. Hydraulic conditions play a critical role in flux behavior of membrane systems. Average flux (AF), flux decline (FD), and permeability loss index (PL) were selected to assess the flux behavior. Table 5 illustrates experimental and predicted values of AF, FD, and PL with various hydraulic conditions. The following three second-order polynomial equations were established accordingly:

obtained by eqs 10−14. For each 3D response surface plot, one variable was kept as a constant at the central level. Figure 4 illustrates a 3D response surface plot for the relations between feed flow rate, TMP, and rotating speed on

Figure 4. Three-dimensional response surface plot for the effect of feed flow rate, TMP, and rotating speed on COD in permeate solution. (a) Feed flow rate and rotating speed, (b) feed flow rate and TMP, and (c) rotating speed and TMP.

COD in permeate solution. With the increase of TMP and rotating speed, COD decreased profoundly, which can be explained as follows: at low rotating speed, a stronger concentration polarization occurred, where more solutes accumulated at the membrane surface or entered membrane pores, then diffused through the membrane. With the help of 7181



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Table 5. Experimental and Predicted Values of Average Flux, Flux Decline, and Permeability Index flux behavior average flux (L/(m2 h))

variable

flux decline

permeability index

run

Q (L/h)

TMP (bar)

N (rpm)

experimental

predicted

experimental

predicted

experimental

predicted

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

60 120 60 120 60 120 60 120 90 90 90 90 90 90 90

2 2 7 7 4.5 4.5 4.5 4.5 2 7 2 7 4.5 4.5 4.5

1500 1500 1500 1500 750 750 2250 2250 750 750 2250 2250 1500 1500 1500

41.32 45.81 125.11 136.36 79.43 94.60 96.70 101.95 37.04 108.34 57.02 128.18 89.64 90.09 90.25

44.72 50.38 120.54 132.96 78.12 92.12 99.19 103.27 34.96 114.23 51.14 130.27 89.99 89.99 89.99

12.09 15.24 15.26 16.79 19.86 20.59 6.41 7.43 19.84 21.75 4.02 6.81 14.14 14.18 14.26

12.46 14.88 15.62 16.42 20.01 21.48 5.53 7.28 19.32 21.23 4.54 7.33 14.19 14.19 14.19

11.67 11.98 13.74 15.40 20.47 21.67 5.41 5.67 18.61 22.01 3.67 6.33 12.46 13.50 13.51

11.66 11.85 13.88 15.41 20.35 21.68 5.40 5.79 18.74 21.99 3.69 6.20 13.16 13.16 13.16

had the utmost reduced effect upon average flux than the other two factors and reduced average flux slightly. Thus, increasing rotating speed and TMP and diminishing feed flow could improve flux behavior to a certain extent. The response surface plots for the effect of feed flow rate, TMP, and rotating speed on the flux decline and permeability loss index are shown in Supporting Information Figures S5 and S6, respectively. Flux decline and permeability loss index are two indicators that could reflect the total and irreversible membrane fouling, respectively. The most important influence in this section was attributed to rotating speed that obviously eliminated the membrane fouling phenomena. Therefore, the increment of rotating speed caused lower values of flux decline and permeability loss index. The main effect of TMP upon flux decline and permeability loss index could be divided into two situations by rotating speed. At low rotating speed, due to the weakness of shear-enhanced back transport, elevating TMP enhanced concentration polarization on the membrane; whereas, when rotating speed increased, the effect of shearenhanced back transport was too strong and the increment of TMP conducted to a reduction of membrane fouling. Besides, when feed flow rate grew, more retained particles would assemble together, and the fouling layer may be more compacted, leading to an augmentation of membrane fouling. The minimal flux decline and permeability loss index of 4.2 and 3.6 was obtained when the feed flow rate was 90 L/h, TMP was 2 bar, and rotating speed was 2250 rpm. 3.3.3. Energy Consumption. Energy consumption is another important factor that affects membrane operation. Table 6 illustrates experimental and predicted values of energy consumption under various hydraulic conditions. The fitting second-order polynomial equation was calculated as follows:

AF = −6.58253 − 0.55357Q + 23.4652TMP + 0.024286N + 0.022533Q × TMP − 1.9 × 10−5TMP × N − 0.00011Q × N + 0.004268Q 2 − 1.06947TMP2 − 1.2 × 10−6N 2 (15)

FD = 23.65802 − 0.06566Q + 0.6484TMP − 0.00408N − 0.0054Q × TMP + 0.000117TMP × N + 3.22 × 10−6Q × N + 0.000622Q 2 + 0.014733 TMP2 − 2.1 × 10−6N 2

(16)

PL = 25.77173 − 0.05938Q + 0.7591TMP − 0.00784N + 0.0045Q × TMP − 9.9 × 10−5TMP × N − 10−5Q × N + 0.000384Q 2 − 0.04873TMP2 − 3.5 × 10−7N 2

(17)

In order to check the statistical significances of eqs 15−17, ANOVA was used to analyze the degree of fit, and the results are summarized in Tables S9−12 (Supporting Information). The regressions of model and linear terms (Q, TMP, and N) were significant, because of the high F-value and low P-value. As seen from Supporting Information Table S3, the regression coefficients R2 were very high (>0.98), indicating the experimental data were well correlated with predicted values. As well, the adjusted R2 values (0.9674, 0.9794, and 0.9953) suggested that only 0.0326, 0.0206, and 0.0047 of the total variation cannot be explained by the model. This indicates that the accuracy and general ability of the quadratic model is good. 3D response surface plots for the effect of feed flow rate, TMP, and rotating speed on average flux are showed in Figure 5. As can be seen from eq 15 and Figure 5, the average flux increased almost linearly when the shear rate rose from 750 to 2250 rpm, which was due to two facts: first, higher shear rates can decrease solutes accumulation on membrane surface; second, osmotic pressure differences across the membrane were reduced. As expected, when TMP rose, the driving force enhanced, leading to higher average flux. As for feed flow rate, it

EC = 86.6858 − 1.77773Q + 18.6052TMP − 0.01157N + 0.0492Q × TMP + 0.006381TMP × N + 0.000123Q × N + 0.007925Q 2 − 1.8628TMP2 − 4.2 × 10−6N 2

(18)

It is also obvious in Supporting Information Table S3 that there is a good correlation between experimental and predicted values (R2 = 0.9974), and this model for energy consumption 7182



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Table 6. Experimental and Predicted Values of Energy Consumption energy consumption (kwh/m3)

variable run

Q (L/h)

TMP (bar)

N (rpm)

experimental

predicted

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

60 120 60 120 60 120 60 120 90 90 90 90 90 90 90

2 2 7 7 4.5 4.5 4.5 4.5 2 7 2 7 4.5 4.5 4.5

1500 1500 1500 1500 750 750 2250 2250 750 750 2250 2250 1500 1500 1500

602.61 543.55 199.02 182.60 177.81 149.30 453.96 430.58 381.31 130.36 769.87 342.47 277.78 276.39 275.90

588.89 535.73 206.84 196.32 175.35 140.95 462.31 433.04 397.49 124.99 775.24 326.29 276.69 276.69 276.69

Figure 5. Three-dimensional response surface plot for the effect of feed flow rate, TMP, and rotating speed on average flux. (a) Feed flow rate and rotating speed, (b) feed flow rate and TMP, and (c) rotating speed and TMP.

increased relationship with rotating speed and decreased evidently with TMP, which could be explained by their definitions (eqs 6−7). As well, the interaction effect of TMP and rotating speed also had a significant influence on energy consumption. For feed flow rate, energy consumption reduced slightly with the increment of feed flow rate. 3.4. Process Optimization. For the purpose of achieving the better effluent quality, optimal flux behavior, and lower energy consumption, the hydraulic conditions were optimized. Some constraints of the response optimization are presented in Supporting Information Table S13, in which the upper limit value of COD, total protein, NTU, conductivity, flux decline, permeability index, and energy consumption were the maximum acceptable values. We expected to minimize these parameters under the most optimal hydraulic conditions; therefore, the lower limits were not set. Similarly, average flux was targeted to be minimized; thus, its range was set as (37, 200). As for pH, they were set as in range without target, because they had changed little and had limited impact on effluent quality and membrane operation. In addition, according the significances of these constraints, importance values of each item were set variously. Table 7 shows the predicted and experimental values of the response at optimum hydraulic conditions. The results revealed the existence of a good agreement between the predicted values and experimental data, reflecting the validity of the model. As a consequence, RSM is an effective method to optimize hydraulic factors.

possesses high accuracy degree (adjusted R2 = 0.9928). Besides, this model has significance (P < 0.05) of model, linear term (Q, TMP, and N), interaction coefficient (TMP × N), and quadratic coefficients (TMP2 and N2). The effect of the hydraulic conditions upon the energy consumption behavior is given in Figure 6 and eq 18. In this study, the importance of parameters on energy consumption was generally TMP > rotating speed > TMP × rotating speed > feed flow rate. Energy consumption of the shear-enhanced membrane system depended on TMP and the rotating speed of the rotating disk. It can be seen that energy consumption showed a linearly

4. CONCLUSIONS The present study investigated the effect of hydraulic conditions on the effluent quality, flux behavior, and energy consumption in a shear-enhanced membrane filtration of dairy wastewater using BBRSM. The selected UF membranes enhanced the filtration in the following order: (1) UH030P, (2) UH005P, and (3) PES050. Then, the experimental study of permeate flux decline revealed that a high rotating speed could improve permeate flux behavior. Also, due to the production of turbulence, the disk rotation may have a self-cleaning effect, causing the reduction of membrane fouling. In terms of effluent quality, TMP and rotating speed were more significant than 7183



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feed flow rate, as the interaction effect of TMP and rotating speed was considerable. Concerning the average flux, it is the TMP which had the most effect, and feed flow rate affected it less significantly than rotating speed. As for flux decline and permeability index, rotating speed and its interaction with TMP played a profound role in reducing the total filtration resistance and the irreversible one. Moreover, TMP, rotating speed, and their significant interaction effect had significant influences on energy consumption. The optimal hydraulic conditions were established by means of BBRSM. These values were further validated by actually carrying out the experiment at the optimized values of these parameters. The experimental results of this study clearly confirm our choice of optimal hydraulic conditions in the shear-enhanced membrane system for dairy wastewater treatment.

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ASSOCIATED CONTENT

S Supporting Information *

Experimental results, ANOVA analysis, the response optimization, and three-dimensional response surface plot. This material is available free of charge via the Internet at http://pubs.acs. org/.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +33 3 4423 4634. Fax: +33 3 4423 7942. E-mail: luhui. [email protected] (L. Ding). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The financial support of China Scholarship Council (CSC) for W.Z. and Z. Z.’s thesis fellowship is acknowledged. NOMENCLATURE Q feed flow rate TMP transmembrane pressure N rotating speed DF degree of freedom a0, a1, a2, a3, and a4 the kinetic coefficients of permeate flux decline i and j subscripts (integer variables) J permeate flux of solution AF average permeate flux J(t) predicted average permeate flux by response surface methodology model t time FD permeate flux decline PL permeability loss index EC Energy consumption per m3 of permeate N the number of experimental runs for experimental design F value ratio of variances, computed value

Figure 6. Three-dimensional response surface plot for the effect of feed flow rate, TMP, and rotating speed on energy consumption. (a) Feed flow rate and rotating speed, (b) feed flow rate and TMP, and (c) rotating speed and TMP.

Table 7. Predicted and Experimental Values of the Response at Optimum Conditionsa hydraulic condition

a

flux behavior

effluent quality

Q (L/h)

TMP (bar)

N (rpm)

COD (mg/L)

total protein (mg/L)

NTU

conductivity (μs/cm)

pH

average flux (L/(m2 h))

flux decline

permeability index

energy consumption (kwh/m3)

75.81 75.81

7.00 7.00

2250.00 2250.00

6.93P 6.86E

0.92P 0.89E

1.54P 1.67E

1313P 1308E

6.78P 6.82E

129.36P 131.21E

7.23P 7.34E

6.03P 6.11E

328.42P 329.89E

E: Experimental value. P: Predicted value. 7184


Industrial & Engineering Chemistry Research P R2 R2adj SD Pd

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value statistical estimator coefficient of multiple determination adjusted statistic coefficient cumulative flux decline power of rotating disk motor

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