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Contribution of fouling and gel polarization during ultrafiltration of raw apple juice at industrial scale
Desalination 258 (2010) 194–200
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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 e v i e r. c o m / l o c a t e / d e s a l
Contribution of fouling and gel polarization during ultrafiltration of raw apple juice at industrial scale Mehdi Yazdanshenas a, Seyyed Ali Reza Tabatabaee-Nezhad a, Mohammad Soltanieh b,⁎, Reza Roostaazad b, Ali Baradar Khoshfetrat a a b
Department of Chemical Engineering, Sahand University of Technology, Tabriz, Iran Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran
a r t i c l e
i n f o
Article history: Received 6 November 2009 Received in revised form 3 March 2010 Accepted 5 March 2010 Available online 9 April 2010 Keywords: Raw apple juice Clarification Ultrafiltration Concentration polarization Fouling
a b s t r a c t The flux behavior during the industrial cross-flow ultrafiltration of apple juice in a batch process was modeled using a combination of the fouling and concentration polarization models. It was observed that the major flux reduction was at the beginning and at the end of operation due to fouling and increasing solute concentration in the feed tank, respectively. The fouling phenomenon was analyzed by classical and empirical models and it was shown that the empirical one has the best correlation within less than 0.3% error for each experiment. The most significant advantage of this model is its ability to predict a steady flux, while other models predict zero flux at infinite time, which is contrary to the observation. With continued permeation, the solute concentration in the feed becomes important in which the back diffusion of solute from the membrane surface to the bulk or concentration polarization becomes controlling. Consequently about 4:53 h after starting the operation, corresponding to increase of solute concentration to about 151% of its initial value, the dominant mechanism changes from fouling to concentration polarization. It was confirmed that at every moment of ultrafiltration actual experimental flux follows that model (fouling or concentration polarization) which predicts lower flux. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Fruit juice technology is a very important sector of the overall processed fruit industry and the consumption of juices has increased significantly during recent years. Apples are amongst the most widely grown and consumed fruit of temperate crops [1,2]. A major amount of apple juice is consumed as a brightly clear product which is traditionally achieved by addition of filter aids and filtration of the suspended particles, improving juice's stability and appearance and thus being more acceptable with the consumers [2,3]. For over 30 years, ultrafiltration (UF) has been used commercially for the clarification of apple juice due to its less labor requirements, higher efficiency, shorter process time and therefore considerably lower operational costs than those of the classic processes [2,4–7]. Furthermore, the UF clarified juices are of better quality than those clarified via classic processes [8–11]. The UF system can be set up as a single pass batch configuration or as a feed-and-bleed system [2]. Large-scale continuous units normally operate in a stage-in-series mode using several individual feed-andbleed stages [12]. Wide ranges of pore size are being used today in the industry, from 18,000 molecular weight cut-off (MWCO) to 0.2 µm.
⁎ Corresponding author. Tel.: +98 21 6616 5417; fax: +98 21 6602 2853. E-mail address: [email protected] (M. Soltanieh). 0011-9164/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.desal.2010.03.014
However, if the membrane pore size is bigger than 25,000 MWCO, tannins may pass into the clarified juice, resulting in a brownish color and sharp flavor [4]. Although tubular polymeric membranes were the first to be used in juice clarification, hollow fiber membranes are also currently used [2]. In recent years, several fruit juice installations have incorporated ceramic membranes, which its higher cost has been justified by the higher flux, much longer life and its resistance to aggressive processing and cleaning conditions. The ability to backflush to unblock feed channels and back-pulsing during operation are other advantages [4,5]. Despite the enormous benefits of ultrafiltration in apple juice clarification, the performance of this operation is influenced by the declining permeate flux with time which is a prevalent phenomenon in such processes [4,13,14]. Previous studies show that the flux decline is caused by mechanical plugging of pores and adsorption of feed components within the membrane structure (membrane fouling), followed by a more gradual decline in flux due to the accumulation of solute on the membrane surface (concentration polarization and gel layer) which can be explained by the concentration polarization model [4,12,14–16]. In the apple juice clarification, membrane fouling may be caused by pectin, tannins, proteins, starch, hemicelluloses and cellulose [6,17,18]. The formation of fouling can be subdivided into three major types: (i) the cake layer consisting of a compact deposit of particles, debris and coagulated materials; (ii) discrete particles and solute blocking
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the pores; (iii) soluble macromolecules adsorbed onto the membrane surface and pore walls [19]. The formulations of these phenomena lead to the classical fouling models of cake filtration model, complete pore blocking model and standard blocking model, respectively [4,20–24]. Other than the initial flux decline which is due to the solute– membrane interaction and starts at the onset of the feed stream to the clean membrane, the analysis of permeate flux is mainly done using either of the concentration polarization model [25–27], osmotic pressure model [28] or resistance-in-series model [29]. The concentration polarization model is used to describe the case that the transmembrane pressure (TMP) is large enough to form a gel layer on the membrane surface and the membrane permeation rate is limited by back diffusion of the solute from the membrane surface to the bulk of the feed. In the osmotic pressure model, during the ultrafiltration of macromolecules, the osmotic pressure is constantly rising due to an increasing surface concentration. At constant TMP, this increase in osmotic pressure as a function of surface concentration leads to lower fluxes. Falling permeate flux in the resistance-in-series model is owing to the resistances caused by fouling or solute adsorption and concentration polarization. In this work, a full scale industrial cross-flow ultrafiltration plant was utilized for apple juice clarification and its flux behavior was investigated in the whole duration of the operating time. Identification of effective flux decline mechanism in this operation would be essential in the system design and implementing appropriate flux improvement techniques. Two distinct fouling and polarization controlled zones have been identified during ultrafiltration. For explanation of these regions empirical fouling model together with concentration polarization model have been validated in agreement with filtration time and solute concentration respectively. The objective of this paper is to evaluate the dominance of fouling and concentration polarization mechanisms as possible controlling mechanisms in industrial cross-flow ultrafiltration of apple juice. 2. Materials and methods 2.1. Apple juice The apple juice used in this study was from “Golden Delicious” apple type. The apples were obtained from the Western Azerbaijan province orchards produced in October and November. After preliminary stages and enzyme treatment, the raw apple juice was filled into the feed tank. The physicochemical analysis of the feed, retentate and permeate in three experiments are presented in Table 1. The retention of pectin in the ultrafiltration of apple juice can be considered 100% because of the big difference between the membrane cut-off (18 kDa) and the pectin molecular weight which is typically 53 kDa [30]. 2.2. Experimental set-up and ultrafiltration unit A full scale industrial cross-flow ultrafiltration unit (BUCHER®, Germany) was utilized for the experiments, which is schematically
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presented in Fig. 1. Experimental set-up was composed of 5 major parts: a mixing feed tank, a centrifugal feed pump, a plate heat exchanger, the filtration unit and the permeate tank. On-line measurements were carried out for the flow rate of filtrate and retentate (Fischer-Porter® Model 10b 14650) with the accuracy of ±0.05 m3/h, inlet temperature (with the accuracy of ±0.5 °C), inlet and outlet relative pressure (with the accuracy of ±0.1 bar). The system was controlled by a PLC controller (Simatic S5 PLC, SIEMENS®). Due to the temperature difference between the process and ambient and to compensate the heat loss, the feed temperature was regulated before passing through the filtration module (to 50 ± 1.0 °C) by a small plate type heat exchanger as illustrated in Fig. 1 and the retentate stream was recycled to the feed tank. The filtration system includes 48 three-meter modules (Super-Cor® from Koch Membrane Systems, Inc., USA) arranged in 3 parallel groups of 16 serial units. Each module has 19 tubes with a diameter of about 12.3 mm. Membranes (water permeability = 3.13 × 10−5 ms−1 bar−1, Φ = 12.3 mm, L = 3.0 m, filtration area/membrane = 0.116 m2) are made of polyvinylidene fluoride (PVDF) with a molecular weight cutoff (MWCO) of 18 kDa. This type of ultrafiltration modules is under industrial utilization before 1986 [4] and is currently used to process a major portion of worldwide production of manufactured apple juice [31]. Ultrafiltration unit had an overall cross section equal to 6.77 × 10−3 m2 and filtration area of 3 × 35.2 m2. 2.3. Operating condition and procedure The filtration unit was operated in a batch mode up to a volume reduction factor of about 25 to 32. In each run, 100 m3 of the raw apple juice was loaded in the feed tank and maintained at around 50 °C under a constant stirring with the speed of 80 rpm (ROSSI®, Italy). In filtration unit, temperature and TMP were controlled at 50 °C (±1.0 °C) and 2.5 bar (±0.1 bar) respectively using a PLC system. Experiments were repeated three times and the data were collected in three different days during October and November. Retentate flow of the filtration system was set initially at 23.3 m3/h (standard deviation during experiment of 0.05), resulting in an average fluid velocity of 1.21 m/s. The Reynolds number of 20410 assured establishment of a fully turbulent regime in filtration module. At final steps of operation, the filtration was stopped automatically according to the PLC settings and the feed stream displacement with water, cleaning and rinsing followed sequentially. The cleaning of the membranes was performed in 2 phases. First sodium hydroxide (NaOH) with the pH = 10.5 at 50 °C was circulated for 20–30 min and after draining the solution in the system was rinsed with soft water. The second phase of cleaning was the same as the first, except the addition of about 150 ppm sodium hypochlorite (NaClO) to the cleaning solution. After the final system rinsing the effectiveness of the cleaning was checked by the measurement of water flux to reach to Jwater = 7.8 × 10−5 m3/m2s at the temperature of 50 °C and transmembrane pressure of 2.5 bar, respectively. Samples were taken from permeate and retentate streams and their viscosity and Brix were measured offline in the laboratory. The
Table 1 The physicochemical analysis of the feed, permeate and retentate. Exp. 1
Dry solids (%) Brix pH Density at 50 °C (kg/m3) Kinematic viscosity at 50 °C (m2/s × 109) Pectin (mg/L)
Exp. 2
Exp. 3
Feed
Retentate
Permeate
Feed
Retentate
Permeate
Feed
Retentate
Permeate
12.33 11.5 3.50 1034 748 202
11.45 10 3.52 1028 863 3441
11.2 11.1 3.48 1032 732 0
11.26 11.2 3.54 1033 736 141
16.90 12.9 3.56 1040 919 3150
12.04 11.6 3.54 1034 742 0
12.12 11.3 3.55 1033 739 144
13.96 12.5 3.59 1038 872 2438
11.33 11.0 3.55 1032 730 0
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Fig. 1. Schematic diagram of the apple juice ultrafiltration set-up.
interval time of sampling was every 2 h initially, where there were no considerable changes in concentration, and at final stages of ultrafiltration, where the feed tank was concentrated, the samples was taken approximately every 15 min. Each sample was divided into three sub-samples that were analyzed separately for quality assurance in measurements. The average of the three experimental results was reported. Viscosity measurements were based on ASTM D2515 using an Ubbelohd viscometer (SCHOT-GERATE, Germany) in a constant temperature water bath. Brix measurements were performed by a refractometer with a temperature regulator (ATAGO, Japan). The solute concentration is calculated based on the existing correlations, obtained from previous works [14] which allow this calculation as a function of Brix and viscosity. According to this literature the viscosity of a raw apple juice is exponentially increased by solute concentration:
matically to prevent membrane damage. Fig. 2 shows this behavior for the third experimental run (Exp. 3, Table 1) for which the initial flux data was available. During the ultrafiltration process, other than the temperature, trans-membrane pressure and the cross-flow velocity which were adjusted and controlled by PLC, the only parameter which was altered during the process was the concentrations of retentate and of solute. It started from the initial concentration of feed tanks with a volume of about 100 m3 and increased gradually by the permeating clarified apple juice and reducing the feed volume to about 3 to 4 m3 finally. At the end of operation, the change of volume became substantial and therefore the solute concentration increased drastically. Fig. 3 shows the Brix and kinematic viscosity of the feed and Fig. 4 shows the variation of solute concentration in the feed tank during the clarification for the three experiments described in Table 1.
−5 ln ðv = vs Þ = 5:648 × 10 CB
3.1. Fouling mechanism
ð1Þ
where CB (mg/L) is solute concentration, ν(m2/s) kinematic viscosity of sample which can be measured by a viscometer and νs (m2/s) kinematic viscosity of pectin-free apple juice. νs is a function of sugar content of the sample (Brix) and its temperature by the following equation: ln ðvs = vw Þ =
1 729:14 × Bx T 100−0:769 × Bx
As presented in Fig. 4, the solute concentration at the beginning of operation is almost constant like other operational parameters. The sharp decline in flux immediately after starting the ultrafiltration through the cleaned and washed membranes can be attributed to the interaction of the feed and the membrane which causes fouling [4,32].
ð2Þ
where νw (m2/s) is kinematic viscosity of water, Bx apple juice Brix and T (K) temperature [14]. The F-test analysis of variance was also performed to analyze the data statistically at a 95% confidence interval. Estimation of the parameters and the analysis of variance was carried out using the software DATAFIT (version 8.0.32). 3. Results and discussion In the full scale ultrafiltration of apple juice, flux through the cleaned membrane declined drastically at the beginning of clarification process and after 1 h it became steady. This quasi-steady flux did not continue more than 5 h and it reduced again until the end of operation when the unit was programmed to stop the process auto-
Fig. 2. Permeate flux during ultrafiltration of apple juice.
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Fig. 3. Brix and kinematic viscosity of the feed tank during ultrafiltration of apple juice.
Several models have been developed based on the pore blocking mechanisms for prediction of the membrane fouling [4,20,22–24]. In one case, it is assumed that only a fraction of the pores is completely blocked which is proportional to the amount of permeate. This model eventually takes the following form [20,21]:
Field et al. have considered that cross-flow leads to a constant rate of particle removal from the pore entrance which controls the rate of increment in fouled area of the membrane and consequently modified the pore blocking mechanism (Eq. 3) as [34]:
Jt = J0 exp ð−J0 ⋅σ⋅t Þ
Jt = Jss + ð J0 −Jss Þ⋅e
ð3Þ
where t (s) is the filtration time, Jt (m3/m2 s) the flux at time t (s), J0 (m3/m2 s) the initial flux and σ (m−1) blocking parameter characteristic of the nature of the suspension and its fouling potential [20,21]. The cake filtration version of this model assumes that the entire surface is covered by a layer of foulants and its resistance is proportional to the cumulative permeate volume, [20,24]i.e.: 2⋅J0 ⋅α⋅t −0:5 Jt = J 0 1 + RM
ð4Þ
where RM (m−1) is the intrinsic membrane resistance and α (m−2), is a fouling characteristic constant (like σ) [20]. The internal pore plugging model assumes that the pore of the membrane get plugged up due to deposition or adsorption of micro-solutes on the internal pores' walls. The final form of this model is [24,33]: J ⋅β⋅t −2 J t = J0 1 + 0 ε⋅Δx
ð5Þ
According to all of the above models, the flux will be zero at infinite time, which may not occur in cross-flow membrane filtration.
Fig. 4. The alteration of solute concentration in the feed tank during ultrafiltration of apple juice.
−b⋅t
ð6Þ
where Jss (m3/m2 s) stands for the quasi-steady state flux and b is a constant parameter characterizing the fouling potential of the solution and membrane. Jss in this model is related to the particle removal rate from the pores mouths, surface porosity of the membrane and pore blocking parameter (σ). This model has been referred as the empirical fouling model in some references[4,20]. Field et al. have also modified the cake filtration model (Eq. 4) considering an erosion factor due to the cross-flow velocity which controls the cake resistance. This leads to a new model including the steady state flux [34]:
t=
1 J J0 −Jss 1 1 −Jss ⋅ − ln ⋅ 2 J0 J−Jss J J0 G⋅J0
ð7Þ
where G (s/m2) is the model parameter. According to the last two models the flux of permeation in cross-flow membrane filtration does not approach zero due to fouling which is contrary to the prediction of classical models [4,20,34]. According to the internal pore blocking mechanism the pore volume decreases due to particle deposition within the pores. This form of fouling will not be mediated by back diffusion from the membrane surface and the steady state flux cannot be described by this mechanism [34]. The data gathered from two consecutive days in this work was correlated with the above fouling models and the data fitting parameters and model agreement for each mechanism is presented in Table 2. As shown in Table 2, the empirical model (Eq. 6) predicted the data reasonably better than the other ones. It is interesting that this model correlates with each of the experiment with less than 0.3% error. This is due to the fact that the flux decline by fouling will lead to a quasisteady state and not to zero at infinitive time and the flux behavior agrees that the controlling mechanism of fouling is the blocking of the pores rather than the formation of the cake layer. Fig. 5 shows the fouling data and the prediction of the above five mechanistic models. Fig. 8 shows the same for Exp. 3 (Table 1) which its initial data was available. Application of the empirical model for analysis of the fouling phenomenon in clarification of apple and other fruit juices has also been performed in other researches with good results [33,35,36].
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Table 2 The correlation of the ultrafiltration flux data with fouling models. Models
Fitting parameters R2
Pore blocking
Eq. (3) 0.4807
Cake filtration
Eq. (4) 0.7925
F-ratio
Model parameters P-value J0 = 4.027 × 10−5 m3/m2s σ = 0.3295 m−1 0.00014 J0 = 4.069 × 10−5 m3/m2s α = 2.444x1012m–2 0.00011 J0 = 4.038 × 10−5 m3/m2s β/ε Δx = 0.1753 m−1 0.00000 Jss = 3.425 × 10−5 m3/m2s J0 = 4.215 × 10−5 m3/m2s b = 2.718 × 10−4 s−1
7.478 0.034 42.02
Internal pore Eq. (5) 0.7572 34.31 plugging Pore blocking Eq. (6) 0.9531 101.55 modified for steady state flux (empirical model) Cake filtration Eq. (7) 0.9067 48.61 modified for steady state flux
0.00001 Jss = 3.369 × 10−5 m3/m2s J0 = 4.192 × 10−5 m3/m2s G = 1.567 × 105 s/m2
3.2. Concentration polarization mechanism The changes in solute concentration in the feed at final stages of operation which results in the prevention of the back diffusion of solute to the bulk of the feed, is described by the concentration polarization model [14,25,28]. This flux decline in the termination of the three different experiments is presented in Fig. 6. The concentration polarization model has been validated previously for the prediction of flux with concentration of the feed bulk [14]. In the concentration polarization model, it is assumed that solutes that are rejected by the membrane accumulate on the membrane surface and form a concentration polarization layer. At steady state, a mass balance for the solute leads to the following equation for the flux: Jt = k⋅ ln
CG CB
ð8Þ
Where Jt (m3/m2 s) is the permeate flux, k (kg/m2 s) the mass transfer coefficient, CG (kg/m3) the solute concentration at membrane wall or gel concentration and CB (kg/m3) the average solute concentration in bulk. Application of this model for each of the experimental runs and the correlation presented above are shown in Fig. 7 and Table 3. According to Fig. 7 and Table 3, gel concentration (CG) in all data is almost the same (deviation less than 5%) and all the lines converge to
Fig. 5. Initial permeation flux data and the correlation of fouling models for two sequential runs.
Fig. 6. Flux reduction in the final steps of raw apple juice ultrafiltration plant.
one point which is in agreement with the definition of CG. A shift in mass transfer coefficient is noticeable amongst the 3 days data. The coefficient increased with almost the same rate from one set to the next within these three sets of experiments, which have been carried out sequentially every 8 days. This variation can be attributed to the change in physiochemical and compositional properties of the apple due to ripening. 3.3. Numerical investigation It was shown above that the analysis of the overall duration of the process is possible with consideration of both fouling and concentration polarization models. However, the latter one is concentration explicit and the governing differential equations need to be solved for prediction of the flux as a function of process time at final stages of ultrafiltration. Contribution of these models has been investigated for the third experiment (Exp. 3) for which detailed flux data over the entire time of operation is available. The flux at starting point of ultrafiltration was J0 = 4.235 × 10−5 m3/m2s which is needed for both models. Correlation of the empirical fouling model with the initial data before the fast final decline confirms a reasonable agreement (R2 = 0.9973) and parameters have been calculated as Jss = 3.156 × 10−5 m3/m2s and b = 7.78 × 10−4 s−1 (Fig. 8). Assuming the complete removal of pectin, a mass balance over the system provides: V0: CB0 = V:CB
ð9Þ
Fig. 7. Permeate flux vs. solute concentration and their correlation with the concentration polarization model.
M. Yazdanshenas et al. / Desalination 258 (2010) 194–200 Table 3 Concentration polarization model parameters for the experimental runs. Model parameters
CG (mg/L)
k (10−6 m/s)
F-ratio
P-value
R2
Exp. 1 Exp. 2 Exp. 3 All
31622 31246 31014 32383
8.9352 8.4998 7.9718 8.3572
146.7 47.3 284.0 140.0
0.0024 0.0063 0.0001 0.0000
0.9861 0.9403 0.9684 0.9091
Where V0 (m3) and V (m3) are initial and instant volume of feed, respectively, and CB0 (kg/m3) is the initial solute concentration of feed. The pectin-free permeate flux can be calculated as: Jt = −
1 dV A dt
ð10Þ
Using Eqs. (8)–(10) and rearrangement, the following differential equation can be obtained: dJ k⋅A⋅CG −J J exp =− V0 CB0 dt k
ð11Þ
Using finite difference discretization scheme with time intervals of 100 s, knowing the flux at starting of the operation (J0 = 4.235 × 10−5 m3/m2s), this equation can be numerically solved by two point forward difference technique assuming Jss = 3.156 × 10−5 m3/m2s, k = 7.97 × 10−6, A = 105.6 m2, CG = 31014 mg/L, b = 7.780 × 10−4 s-1, V0 = 100 m3 and CB0 = 144.2 mg/L (Fig. 8). As it is seen in Fig. 8, throughout the ultrafiltration two distinct regions can be identified in which the two models are applicable. In the fouling controlled region the concentration polarization model over-predicts the experimental data, whereas in the concentration polarization region the fouling model over-predicts the data. It means that the fouling is the controlling mechanism initially, however, after reaching to a steady state flux due to the permeation of the clarified juice and retention of the solute in the feed tank, the solute concentration in the feed stream increases and the polarization effects become controlling. The transition time from fouling to concentration polarization model has been evaluated to be about 4:53 h after starting which corresponds to about 151% increase of solute concentration from its initial value. The calculation of transition time can be a subject for the future researches. 4. Conclusions The permeation flux of a full scale apple juice ultrafiltration unit was studied throughout the duration of the process. The unit was
Fig. 8. Prediction of flux by fouling and concentration polarization mechanisms over the entire ultrafiltration time.
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operated as the single pass batch configuration by returning the retentate to the feed tank. All the operational parameters were controlled by a PLC device other than the solute concentration. Flux exhibited different behaviors as it reduced at the beginning and at the end of filtration and remained almost constant in between. The initial flux reduction has been attributed to the solute–membrane interaction and correlated with fouling models. The obtained results and their statistical analysis showed that the classical fouling models could not correlate the data adequately, however the empirical model (Eq. 6) describes the first flux decline reasonably well. The classical fouling model predicts zero flux at the final stages of ultrafiltration process, whereas in the actual cross-flow ultrafiltration the behavior is different and we observe a quasi-steady state flux. This quasi-steady state flux can be described by the empirical fouling model. The increase in solute concentration in the feed stream due to the recirculation of retentate to the feed tank is the reason of flux decline toward the end of the process where concentration polarization is the controlling mechanism. The concentration polarization model shows a reasonable agreement between the flux and the feed solute concentration. For evaluation of the flux with time of operation during concentration polarization controlled region, the governing differential equation has been solved numerically by finite difference method. Finally, it has been concluded that during ultrafiltration each of the fouling and concentration polarization mechanisms, which predict a lower flux, is controlling the permeation. The permeation flux during the first 5 h of operation was controlled by fouling and afterward the concentration polarization predicted the flux. The contribution of these two models presented reasonable agreement in predicting the flux over the entire duration of the apple juice ultrafiltration. Acknowledgement This work is supported by Shahde Salmas Co. and the authors would like to thank the management for its supports, Dr. A. R. Bakhtiari for his professional advices, Mr. Shadan for technical assistance and the operators for performing the needed tests on the full scale ultrafiltration plant. References [1] S. Álvarez, F.A. Riera, R. Álvarez, J. Coca, F.P. Cuperus, S. Th Bouwer, G. Boswinkel, R.W. van Gemert, J.W. Veldsink, L. Giorno, L. Donato, S. Todisco, E. Drioli, J. Olsson, G. Trägårdh, S.N. Gaeta, L. Panyor, A new integrated membrane process for producing clarified apple juice and apple juice aroma concentrate, Journal of Food Engineering 46 (2000) 109–125. [2] M.R. McLellan, O.I. Padilla-Zakour, Juice processing, in: D.M. Barrett, L. Somogyi, H. Ramaswamy (Eds.), Processing Fruits: Science and Technology, CRC Press, Florida, 2005. [3] B. Veleirinho, J.A. Lopes-da-Silva, Application of electrospun poly(ethylene terephthalate) nanofiber mat to apple juice clarification, Process Biochemistry 44 (2009) 353–356. [4] M. Cheryan, Ultrafiltration and Microfiltration Handbook, Lancaster, Technomic, 1998. [5] P.-Z. O, M.R. McLELLAN, Optimization and modeling of apple juice cross-flow microfiltration with a ceramic membrane, Journal of Food Science 58 (1993) 369–374. [6] V. Alvarez, L.J. Andres, F.A. Riera, R. Alvarez, Microfiltration of apple juice using inorganic membranes: process optimization and juice stability, The Canadian Journal of Chemical Engineering 74 (1996) 156–162. [7] A.-S. Jönsson, G. Trägårdh, Ultrafiltration applications, Desalination 77 (1990) 135–179. [8] B. Dutré, G. Trägårdh, Macrosolute-microsolute separation by ultrafiltration: a review of diafiltration processes and applications, Desalination 95 (1994) 227–267. [9] A. Cassano, F. Tasselli, C. Conidi, E. Drioli, Ultrafiltration of Clementine mandarin juice by hollow fibre membranes, Desalination 241 (2009) 302–308. [10] B. Koroknai, Z. Csanádi, L. Gubicza, K. Bélafi-Bakó, Preservation of antioxidant capacity and flux enhancement in concentration of red fruit juices by membrane processes, Desalination 228 (2008) 295–301. [11] S. Gunko, S. Verbych, M. Bryk, N. Hilal, Concentration of apple juice using direct contact membrane distillation, Desalination 190 (2006) 117–124. [12] M. Mondor, B. Girard, C. Moresoli, Modeling flux behavior for membrane filtration of apple juice, Food Research International 33 (2000) 539–548. [13] M. Araya-Farias, M. Mondor, F. Lamarche, S. Tajchakavit, J. Makhlouf, Clarification of apple juice by electroflotation, Innovative Food Science & Emerging Technologies 9 (2008) 320–327.
200
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[14] M. Yazdanshenas, A.R. Tabatabaeenezhad, R. Roostaazad, A.B. Khoshfetrat, Full scale analysis of apple juice ultrafiltration and optimization of diafiltration, Separation and Purification Technology 47 (2005) 52–57. [15] M.A. R.A.O., T.E. A., H.J. C., R.W. E., Clarification of apple juice by hollow fiber ultrafiltration: fluxes and retention of odor-active volatiles, Journal of Food Science 52 (1987) 375–377. [16] K. Riedl, B. Girard, R.W. Lencki, Influence of membrane structure on fouling layer morphology during apple juice clarification, Journal of Membrane Science 139 (1998) 155–166. [17] K. Riedl, B. Girard, R.W. Lencki, Interactions responsible for fouling layer formation during apple juice microfiltration, Journal of Agricultural and Food Chemistry 46 (1998) 2458–2464. [18] Y. He, Z. Ji, S. Li, Effective clarification of apple juice using membrane filtration without enzyme and pasteurization pretreatment, Separation and Purification Technology 57 (2007) 366–373. [19] L. Fillaudeau, H. Carrère, Yeast cells, beer composition and mean pore diameter impacts on fouling and retention during cross-flow filtration of beer with ceramic membranes, Journal of Membrane Science 196 (2002) 39–57. [20] C.-W. Jung, H.-J. Son, L.-S. Kang, Effects of membrane material and pretreatment coagulation on membrane fouling: fouling mechanism and NOM removal, Desalination 197 (2006) 154–164. [21] M.-C. Vincent-Vela, S. Álvarez-Blanco, J. Lora-García, E. Bergantiños-Rodríguez, Application of a dynamic model that combines pore blocking and cake formation in crossflow ultrafiltration, Desalination 200 (2006) 138–139. [22] J. de Bruijn, R. Bórquez, Analysis of the fouling mechanisms during cross-flow ultrafiltration of apple juice, LWT - Food Science and Technology 39 (2006) 861–871. [23] R. van Reis, A. Zydney, Bioprocess membrane technology, Journal of Membrane Science 297 (2007) 16–50. [24] J. Hermia, Constant pressure blocking filtration laws—application to power-law non-Newtonian fluids, Trans IchemE 60 (1982) 183–187.
[25] S.S. Sablani, M.F.A. Goosen, R. Al-Belushi, M. Wilf, Concentration polarization in ultrafiltration and reverse osmosis: a critical review, Desalination 141 (2001) 269–289. [26] N. Shin-Ichi, N. Tsuyoshi, K. Shoji, Characteristics of macromolecular gel layer formed on ultrafiltration tubular membrane, AIChE Journal 25 (1979) 615–622. [27] H.M. Yeh, T.W. Cheng, J.W. Tsai, Modified concentration polarization model for hollow-fiber membrane ultrafiltration, Separation and Purification Technology 16 (1999) 189–192. [28] G.B. van den Berg, C.A. Smolders, Flux decline in ultrafiltration processes, Desalination 77 (1990) 101–133. [29] B. Tansel, W.Y. Bao, I.N. Tansel, Characterization of fouling kinetics in ultrafiltration systems by resistances in series model, Desalination 129 (2000) 7–14. [30] M. Gulfi, E. Arrigoni, R. Amadò, The chemical characteristics of apple pectin influence its fermentability in vitro, LWT - Food Science and Technology 39 (2006) 1001–1004. [31] KMS launches RO technology and exhibits at food expo, Membrane Technology, 2009 (2009) 2–3. [32] J. de Bruijn, A. Venegas, R. Borquez, Influence of crossflow ultrafiltration on membrane fouling and apple juice quality, Desalination 148 (2002) 131–136. [33] A. Cassano, M. Marchio, E. Drioli, Clarification of blood orange juice by ultrafiltration: analyses of operating parameters, membrane fouling and juice quality, Desalination 212 (2007) 15–27. [34] R.W. Field, D. Wu, J.A. Howell, B.B. Gupta, Critical flux concept for microfiltration fouling, Journal of Membrane Science 100 (1995) 259–272. [35] L. Giorno, L. Donato, S. Todisco, E. Drioli, Study of fouling phenomena in apple juice clarification by enzyme membrane reactor, Separation Science and Technology 33 (1998) 739–756. [36] S.T.D. de Barros, C.M.G. Andrade, E.S. Mendes, L. Peres, Study of fouling mechanism in pineapple juice clarification by ultrafiltration, Journal of Membrane Science 215 (2003) 213–224.
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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 e v i e r. c o m / l o c a t e / d e s a l
Contribution of fouling and gel polarization during ultrafiltration of raw apple juice at industrial scale Mehdi Yazdanshenas a, Seyyed Ali Reza Tabatabaee-Nezhad a, Mohammad Soltanieh b,⁎, Reza Roostaazad b, Ali Baradar Khoshfetrat a a b
Department of Chemical Engineering, Sahand University of Technology, Tabriz, Iran Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran
a r t i c l e
i n f o
Article history: Received 6 November 2009 Received in revised form 3 March 2010 Accepted 5 March 2010 Available online 9 April 2010 Keywords: Raw apple juice Clarification Ultrafiltration Concentration polarization Fouling
a b s t r a c t The flux behavior during the industrial cross-flow ultrafiltration of apple juice in a batch process was modeled using a combination of the fouling and concentration polarization models. It was observed that the major flux reduction was at the beginning and at the end of operation due to fouling and increasing solute concentration in the feed tank, respectively. The fouling phenomenon was analyzed by classical and empirical models and it was shown that the empirical one has the best correlation within less than 0.3% error for each experiment. The most significant advantage of this model is its ability to predict a steady flux, while other models predict zero flux at infinite time, which is contrary to the observation. With continued permeation, the solute concentration in the feed becomes important in which the back diffusion of solute from the membrane surface to the bulk or concentration polarization becomes controlling. Consequently about 4:53 h after starting the operation, corresponding to increase of solute concentration to about 151% of its initial value, the dominant mechanism changes from fouling to concentration polarization. It was confirmed that at every moment of ultrafiltration actual experimental flux follows that model (fouling or concentration polarization) which predicts lower flux. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Fruit juice technology is a very important sector of the overall processed fruit industry and the consumption of juices has increased significantly during recent years. Apples are amongst the most widely grown and consumed fruit of temperate crops [1,2]. A major amount of apple juice is consumed as a brightly clear product which is traditionally achieved by addition of filter aids and filtration of the suspended particles, improving juice's stability and appearance and thus being more acceptable with the consumers [2,3]. For over 30 years, ultrafiltration (UF) has been used commercially for the clarification of apple juice due to its less labor requirements, higher efficiency, shorter process time and therefore considerably lower operational costs than those of the classic processes [2,4–7]. Furthermore, the UF clarified juices are of better quality than those clarified via classic processes [8–11]. The UF system can be set up as a single pass batch configuration or as a feed-and-bleed system [2]. Large-scale continuous units normally operate in a stage-in-series mode using several individual feed-andbleed stages [12]. Wide ranges of pore size are being used today in the industry, from 18,000 molecular weight cut-off (MWCO) to 0.2 µm.
⁎ Corresponding author. Tel.: +98 21 6616 5417; fax: +98 21 6602 2853. E-mail address: [email protected] (M. Soltanieh). 0011-9164/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.desal.2010.03.014
However, if the membrane pore size is bigger than 25,000 MWCO, tannins may pass into the clarified juice, resulting in a brownish color and sharp flavor [4]. Although tubular polymeric membranes were the first to be used in juice clarification, hollow fiber membranes are also currently used [2]. In recent years, several fruit juice installations have incorporated ceramic membranes, which its higher cost has been justified by the higher flux, much longer life and its resistance to aggressive processing and cleaning conditions. The ability to backflush to unblock feed channels and back-pulsing during operation are other advantages [4,5]. Despite the enormous benefits of ultrafiltration in apple juice clarification, the performance of this operation is influenced by the declining permeate flux with time which is a prevalent phenomenon in such processes [4,13,14]. Previous studies show that the flux decline is caused by mechanical plugging of pores and adsorption of feed components within the membrane structure (membrane fouling), followed by a more gradual decline in flux due to the accumulation of solute on the membrane surface (concentration polarization and gel layer) which can be explained by the concentration polarization model [4,12,14–16]. In the apple juice clarification, membrane fouling may be caused by pectin, tannins, proteins, starch, hemicelluloses and cellulose [6,17,18]. The formation of fouling can be subdivided into three major types: (i) the cake layer consisting of a compact deposit of particles, debris and coagulated materials; (ii) discrete particles and solute blocking
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the pores; (iii) soluble macromolecules adsorbed onto the membrane surface and pore walls [19]. The formulations of these phenomena lead to the classical fouling models of cake filtration model, complete pore blocking model and standard blocking model, respectively [4,20–24]. Other than the initial flux decline which is due to the solute– membrane interaction and starts at the onset of the feed stream to the clean membrane, the analysis of permeate flux is mainly done using either of the concentration polarization model [25–27], osmotic pressure model [28] or resistance-in-series model [29]. The concentration polarization model is used to describe the case that the transmembrane pressure (TMP) is large enough to form a gel layer on the membrane surface and the membrane permeation rate is limited by back diffusion of the solute from the membrane surface to the bulk of the feed. In the osmotic pressure model, during the ultrafiltration of macromolecules, the osmotic pressure is constantly rising due to an increasing surface concentration. At constant TMP, this increase in osmotic pressure as a function of surface concentration leads to lower fluxes. Falling permeate flux in the resistance-in-series model is owing to the resistances caused by fouling or solute adsorption and concentration polarization. In this work, a full scale industrial cross-flow ultrafiltration plant was utilized for apple juice clarification and its flux behavior was investigated in the whole duration of the operating time. Identification of effective flux decline mechanism in this operation would be essential in the system design and implementing appropriate flux improvement techniques. Two distinct fouling and polarization controlled zones have been identified during ultrafiltration. For explanation of these regions empirical fouling model together with concentration polarization model have been validated in agreement with filtration time and solute concentration respectively. The objective of this paper is to evaluate the dominance of fouling and concentration polarization mechanisms as possible controlling mechanisms in industrial cross-flow ultrafiltration of apple juice. 2. Materials and methods 2.1. Apple juice The apple juice used in this study was from “Golden Delicious” apple type. The apples were obtained from the Western Azerbaijan province orchards produced in October and November. After preliminary stages and enzyme treatment, the raw apple juice was filled into the feed tank. The physicochemical analysis of the feed, retentate and permeate in three experiments are presented in Table 1. The retention of pectin in the ultrafiltration of apple juice can be considered 100% because of the big difference between the membrane cut-off (18 kDa) and the pectin molecular weight which is typically 53 kDa [30]. 2.2. Experimental set-up and ultrafiltration unit A full scale industrial cross-flow ultrafiltration unit (BUCHER®, Germany) was utilized for the experiments, which is schematically
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presented in Fig. 1. Experimental set-up was composed of 5 major parts: a mixing feed tank, a centrifugal feed pump, a plate heat exchanger, the filtration unit and the permeate tank. On-line measurements were carried out for the flow rate of filtrate and retentate (Fischer-Porter® Model 10b 14650) with the accuracy of ±0.05 m3/h, inlet temperature (with the accuracy of ±0.5 °C), inlet and outlet relative pressure (with the accuracy of ±0.1 bar). The system was controlled by a PLC controller (Simatic S5 PLC, SIEMENS®). Due to the temperature difference between the process and ambient and to compensate the heat loss, the feed temperature was regulated before passing through the filtration module (to 50 ± 1.0 °C) by a small plate type heat exchanger as illustrated in Fig. 1 and the retentate stream was recycled to the feed tank. The filtration system includes 48 three-meter modules (Super-Cor® from Koch Membrane Systems, Inc., USA) arranged in 3 parallel groups of 16 serial units. Each module has 19 tubes with a diameter of about 12.3 mm. Membranes (water permeability = 3.13 × 10−5 ms−1 bar−1, Φ = 12.3 mm, L = 3.0 m, filtration area/membrane = 0.116 m2) are made of polyvinylidene fluoride (PVDF) with a molecular weight cutoff (MWCO) of 18 kDa. This type of ultrafiltration modules is under industrial utilization before 1986 [4] and is currently used to process a major portion of worldwide production of manufactured apple juice [31]. Ultrafiltration unit had an overall cross section equal to 6.77 × 10−3 m2 and filtration area of 3 × 35.2 m2. 2.3. Operating condition and procedure The filtration unit was operated in a batch mode up to a volume reduction factor of about 25 to 32. In each run, 100 m3 of the raw apple juice was loaded in the feed tank and maintained at around 50 °C under a constant stirring with the speed of 80 rpm (ROSSI®, Italy). In filtration unit, temperature and TMP were controlled at 50 °C (±1.0 °C) and 2.5 bar (±0.1 bar) respectively using a PLC system. Experiments were repeated three times and the data were collected in three different days during October and November. Retentate flow of the filtration system was set initially at 23.3 m3/h (standard deviation during experiment of 0.05), resulting in an average fluid velocity of 1.21 m/s. The Reynolds number of 20410 assured establishment of a fully turbulent regime in filtration module. At final steps of operation, the filtration was stopped automatically according to the PLC settings and the feed stream displacement with water, cleaning and rinsing followed sequentially. The cleaning of the membranes was performed in 2 phases. First sodium hydroxide (NaOH) with the pH = 10.5 at 50 °C was circulated for 20–30 min and after draining the solution in the system was rinsed with soft water. The second phase of cleaning was the same as the first, except the addition of about 150 ppm sodium hypochlorite (NaClO) to the cleaning solution. After the final system rinsing the effectiveness of the cleaning was checked by the measurement of water flux to reach to Jwater = 7.8 × 10−5 m3/m2s at the temperature of 50 °C and transmembrane pressure of 2.5 bar, respectively. Samples were taken from permeate and retentate streams and their viscosity and Brix were measured offline in the laboratory. The
Table 1 The physicochemical analysis of the feed, permeate and retentate. Exp. 1
Dry solids (%) Brix pH Density at 50 °C (kg/m3) Kinematic viscosity at 50 °C (m2/s × 109) Pectin (mg/L)
Exp. 2
Exp. 3
Feed
Retentate
Permeate
Feed
Retentate
Permeate
Feed
Retentate
Permeate
12.33 11.5 3.50 1034 748 202
11.45 10 3.52 1028 863 3441
11.2 11.1 3.48 1032 732 0
11.26 11.2 3.54 1033 736 141
16.90 12.9 3.56 1040 919 3150
12.04 11.6 3.54 1034 742 0
12.12 11.3 3.55 1033 739 144
13.96 12.5 3.59 1038 872 2438
11.33 11.0 3.55 1032 730 0
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Fig. 1. Schematic diagram of the apple juice ultrafiltration set-up.
interval time of sampling was every 2 h initially, where there were no considerable changes in concentration, and at final stages of ultrafiltration, where the feed tank was concentrated, the samples was taken approximately every 15 min. Each sample was divided into three sub-samples that were analyzed separately for quality assurance in measurements. The average of the three experimental results was reported. Viscosity measurements were based on ASTM D2515 using an Ubbelohd viscometer (SCHOT-GERATE, Germany) in a constant temperature water bath. Brix measurements were performed by a refractometer with a temperature regulator (ATAGO, Japan). The solute concentration is calculated based on the existing correlations, obtained from previous works [14] which allow this calculation as a function of Brix and viscosity. According to this literature the viscosity of a raw apple juice is exponentially increased by solute concentration:
matically to prevent membrane damage. Fig. 2 shows this behavior for the third experimental run (Exp. 3, Table 1) for which the initial flux data was available. During the ultrafiltration process, other than the temperature, trans-membrane pressure and the cross-flow velocity which were adjusted and controlled by PLC, the only parameter which was altered during the process was the concentrations of retentate and of solute. It started from the initial concentration of feed tanks with a volume of about 100 m3 and increased gradually by the permeating clarified apple juice and reducing the feed volume to about 3 to 4 m3 finally. At the end of operation, the change of volume became substantial and therefore the solute concentration increased drastically. Fig. 3 shows the Brix and kinematic viscosity of the feed and Fig. 4 shows the variation of solute concentration in the feed tank during the clarification for the three experiments described in Table 1.
−5 ln ðv = vs Þ = 5:648 × 10 CB
3.1. Fouling mechanism
ð1Þ
where CB (mg/L) is solute concentration, ν(m2/s) kinematic viscosity of sample which can be measured by a viscometer and νs (m2/s) kinematic viscosity of pectin-free apple juice. νs is a function of sugar content of the sample (Brix) and its temperature by the following equation: ln ðvs = vw Þ =
1 729:14 × Bx T 100−0:769 × Bx
As presented in Fig. 4, the solute concentration at the beginning of operation is almost constant like other operational parameters. The sharp decline in flux immediately after starting the ultrafiltration through the cleaned and washed membranes can be attributed to the interaction of the feed and the membrane which causes fouling [4,32].
ð2Þ
where νw (m2/s) is kinematic viscosity of water, Bx apple juice Brix and T (K) temperature [14]. The F-test analysis of variance was also performed to analyze the data statistically at a 95% confidence interval. Estimation of the parameters and the analysis of variance was carried out using the software DATAFIT (version 8.0.32). 3. Results and discussion In the full scale ultrafiltration of apple juice, flux through the cleaned membrane declined drastically at the beginning of clarification process and after 1 h it became steady. This quasi-steady flux did not continue more than 5 h and it reduced again until the end of operation when the unit was programmed to stop the process auto-
Fig. 2. Permeate flux during ultrafiltration of apple juice.
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Fig. 3. Brix and kinematic viscosity of the feed tank during ultrafiltration of apple juice.
Several models have been developed based on the pore blocking mechanisms for prediction of the membrane fouling [4,20,22–24]. In one case, it is assumed that only a fraction of the pores is completely blocked which is proportional to the amount of permeate. This model eventually takes the following form [20,21]:
Field et al. have considered that cross-flow leads to a constant rate of particle removal from the pore entrance which controls the rate of increment in fouled area of the membrane and consequently modified the pore blocking mechanism (Eq. 3) as [34]:
Jt = J0 exp ð−J0 ⋅σ⋅t Þ
Jt = Jss + ð J0 −Jss Þ⋅e
ð3Þ
where t (s) is the filtration time, Jt (m3/m2 s) the flux at time t (s), J0 (m3/m2 s) the initial flux and σ (m−1) blocking parameter characteristic of the nature of the suspension and its fouling potential [20,21]. The cake filtration version of this model assumes that the entire surface is covered by a layer of foulants and its resistance is proportional to the cumulative permeate volume, [20,24]i.e.: 2⋅J0 ⋅α⋅t −0:5 Jt = J 0 1 + RM
ð4Þ
where RM (m−1) is the intrinsic membrane resistance and α (m−2), is a fouling characteristic constant (like σ) [20]. The internal pore plugging model assumes that the pore of the membrane get plugged up due to deposition or adsorption of micro-solutes on the internal pores' walls. The final form of this model is [24,33]: J ⋅β⋅t −2 J t = J0 1 + 0 ε⋅Δx
ð5Þ
According to all of the above models, the flux will be zero at infinite time, which may not occur in cross-flow membrane filtration.
Fig. 4. The alteration of solute concentration in the feed tank during ultrafiltration of apple juice.
−b⋅t
ð6Þ
where Jss (m3/m2 s) stands for the quasi-steady state flux and b is a constant parameter characterizing the fouling potential of the solution and membrane. Jss in this model is related to the particle removal rate from the pores mouths, surface porosity of the membrane and pore blocking parameter (σ). This model has been referred as the empirical fouling model in some references[4,20]. Field et al. have also modified the cake filtration model (Eq. 4) considering an erosion factor due to the cross-flow velocity which controls the cake resistance. This leads to a new model including the steady state flux [34]:
t=
1 J J0 −Jss 1 1 −Jss ⋅ − ln ⋅ 2 J0 J−Jss J J0 G⋅J0
ð7Þ
where G (s/m2) is the model parameter. According to the last two models the flux of permeation in cross-flow membrane filtration does not approach zero due to fouling which is contrary to the prediction of classical models [4,20,34]. According to the internal pore blocking mechanism the pore volume decreases due to particle deposition within the pores. This form of fouling will not be mediated by back diffusion from the membrane surface and the steady state flux cannot be described by this mechanism [34]. The data gathered from two consecutive days in this work was correlated with the above fouling models and the data fitting parameters and model agreement for each mechanism is presented in Table 2. As shown in Table 2, the empirical model (Eq. 6) predicted the data reasonably better than the other ones. It is interesting that this model correlates with each of the experiment with less than 0.3% error. This is due to the fact that the flux decline by fouling will lead to a quasisteady state and not to zero at infinitive time and the flux behavior agrees that the controlling mechanism of fouling is the blocking of the pores rather than the formation of the cake layer. Fig. 5 shows the fouling data and the prediction of the above five mechanistic models. Fig. 8 shows the same for Exp. 3 (Table 1) which its initial data was available. Application of the empirical model for analysis of the fouling phenomenon in clarification of apple and other fruit juices has also been performed in other researches with good results [33,35,36].
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Table 2 The correlation of the ultrafiltration flux data with fouling models. Models
Fitting parameters R2
Pore blocking
Eq. (3) 0.4807
Cake filtration
Eq. (4) 0.7925
F-ratio
Model parameters P-value J0 = 4.027 × 10−5 m3/m2s σ = 0.3295 m−1 0.00014 J0 = 4.069 × 10−5 m3/m2s α = 2.444x1012m–2 0.00011 J0 = 4.038 × 10−5 m3/m2s β/ε Δx = 0.1753 m−1 0.00000 Jss = 3.425 × 10−5 m3/m2s J0 = 4.215 × 10−5 m3/m2s b = 2.718 × 10−4 s−1
7.478 0.034 42.02
Internal pore Eq. (5) 0.7572 34.31 plugging Pore blocking Eq. (6) 0.9531 101.55 modified for steady state flux (empirical model) Cake filtration Eq. (7) 0.9067 48.61 modified for steady state flux
0.00001 Jss = 3.369 × 10−5 m3/m2s J0 = 4.192 × 10−5 m3/m2s G = 1.567 × 105 s/m2
3.2. Concentration polarization mechanism The changes in solute concentration in the feed at final stages of operation which results in the prevention of the back diffusion of solute to the bulk of the feed, is described by the concentration polarization model [14,25,28]. This flux decline in the termination of the three different experiments is presented in Fig. 6. The concentration polarization model has been validated previously for the prediction of flux with concentration of the feed bulk [14]. In the concentration polarization model, it is assumed that solutes that are rejected by the membrane accumulate on the membrane surface and form a concentration polarization layer. At steady state, a mass balance for the solute leads to the following equation for the flux: Jt = k⋅ ln
CG CB
ð8Þ
Where Jt (m3/m2 s) is the permeate flux, k (kg/m2 s) the mass transfer coefficient, CG (kg/m3) the solute concentration at membrane wall or gel concentration and CB (kg/m3) the average solute concentration in bulk. Application of this model for each of the experimental runs and the correlation presented above are shown in Fig. 7 and Table 3. According to Fig. 7 and Table 3, gel concentration (CG) in all data is almost the same (deviation less than 5%) and all the lines converge to
Fig. 5. Initial permeation flux data and the correlation of fouling models for two sequential runs.
Fig. 6. Flux reduction in the final steps of raw apple juice ultrafiltration plant.
one point which is in agreement with the definition of CG. A shift in mass transfer coefficient is noticeable amongst the 3 days data. The coefficient increased with almost the same rate from one set to the next within these three sets of experiments, which have been carried out sequentially every 8 days. This variation can be attributed to the change in physiochemical and compositional properties of the apple due to ripening. 3.3. Numerical investigation It was shown above that the analysis of the overall duration of the process is possible with consideration of both fouling and concentration polarization models. However, the latter one is concentration explicit and the governing differential equations need to be solved for prediction of the flux as a function of process time at final stages of ultrafiltration. Contribution of these models has been investigated for the third experiment (Exp. 3) for which detailed flux data over the entire time of operation is available. The flux at starting point of ultrafiltration was J0 = 4.235 × 10−5 m3/m2s which is needed for both models. Correlation of the empirical fouling model with the initial data before the fast final decline confirms a reasonable agreement (R2 = 0.9973) and parameters have been calculated as Jss = 3.156 × 10−5 m3/m2s and b = 7.78 × 10−4 s−1 (Fig. 8). Assuming the complete removal of pectin, a mass balance over the system provides: V0: CB0 = V:CB
ð9Þ
Fig. 7. Permeate flux vs. solute concentration and their correlation with the concentration polarization model.
M. Yazdanshenas et al. / Desalination 258 (2010) 194–200 Table 3 Concentration polarization model parameters for the experimental runs. Model parameters
CG (mg/L)
k (10−6 m/s)
F-ratio
P-value
R2
Exp. 1 Exp. 2 Exp. 3 All
31622 31246 31014 32383
8.9352 8.4998 7.9718 8.3572
146.7 47.3 284.0 140.0
0.0024 0.0063 0.0001 0.0000
0.9861 0.9403 0.9684 0.9091
Where V0 (m3) and V (m3) are initial and instant volume of feed, respectively, and CB0 (kg/m3) is the initial solute concentration of feed. The pectin-free permeate flux can be calculated as: Jt = −
1 dV A dt
ð10Þ
Using Eqs. (8)–(10) and rearrangement, the following differential equation can be obtained: dJ k⋅A⋅CG −J J exp =− V0 CB0 dt k
ð11Þ
Using finite difference discretization scheme with time intervals of 100 s, knowing the flux at starting of the operation (J0 = 4.235 × 10−5 m3/m2s), this equation can be numerically solved by two point forward difference technique assuming Jss = 3.156 × 10−5 m3/m2s, k = 7.97 × 10−6, A = 105.6 m2, CG = 31014 mg/L, b = 7.780 × 10−4 s-1, V0 = 100 m3 and CB0 = 144.2 mg/L (Fig. 8). As it is seen in Fig. 8, throughout the ultrafiltration two distinct regions can be identified in which the two models are applicable. In the fouling controlled region the concentration polarization model over-predicts the experimental data, whereas in the concentration polarization region the fouling model over-predicts the data. It means that the fouling is the controlling mechanism initially, however, after reaching to a steady state flux due to the permeation of the clarified juice and retention of the solute in the feed tank, the solute concentration in the feed stream increases and the polarization effects become controlling. The transition time from fouling to concentration polarization model has been evaluated to be about 4:53 h after starting which corresponds to about 151% increase of solute concentration from its initial value. The calculation of transition time can be a subject for the future researches. 4. Conclusions The permeation flux of a full scale apple juice ultrafiltration unit was studied throughout the duration of the process. The unit was
Fig. 8. Prediction of flux by fouling and concentration polarization mechanisms over the entire ultrafiltration time.
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operated as the single pass batch configuration by returning the retentate to the feed tank. All the operational parameters were controlled by a PLC device other than the solute concentration. Flux exhibited different behaviors as it reduced at the beginning and at the end of filtration and remained almost constant in between. The initial flux reduction has been attributed to the solute–membrane interaction and correlated with fouling models. The obtained results and their statistical analysis showed that the classical fouling models could not correlate the data adequately, however the empirical model (Eq. 6) describes the first flux decline reasonably well. The classical fouling model predicts zero flux at the final stages of ultrafiltration process, whereas in the actual cross-flow ultrafiltration the behavior is different and we observe a quasi-steady state flux. This quasi-steady state flux can be described by the empirical fouling model. The increase in solute concentration in the feed stream due to the recirculation of retentate to the feed tank is the reason of flux decline toward the end of the process where concentration polarization is the controlling mechanism. The concentration polarization model shows a reasonable agreement between the flux and the feed solute concentration. For evaluation of the flux with time of operation during concentration polarization controlled region, the governing differential equation has been solved numerically by finite difference method. Finally, it has been concluded that during ultrafiltration each of the fouling and concentration polarization mechanisms, which predict a lower flux, is controlling the permeation. The permeation flux during the first 5 h of operation was controlled by fouling and afterward the concentration polarization predicted the flux. The contribution of these two models presented reasonable agreement in predicting the flux over the entire duration of the apple juice ultrafiltration. Acknowledgement This work is supported by Shahde Salmas Co. and the authors would like to thank the management for its supports, Dr. A. R. Bakhtiari for his professional advices, Mr. Shadan for technical assistance and the operators for performing the needed tests on the full scale ultrafiltration plant. References [1] S. Álvarez, F.A. Riera, R. Álvarez, J. Coca, F.P. Cuperus, S. Th Bouwer, G. Boswinkel, R.W. van Gemert, J.W. Veldsink, L. Giorno, L. Donato, S. Todisco, E. Drioli, J. Olsson, G. Trägårdh, S.N. Gaeta, L. Panyor, A new integrated membrane process for producing clarified apple juice and apple juice aroma concentrate, Journal of Food Engineering 46 (2000) 109–125. [2] M.R. McLellan, O.I. Padilla-Zakour, Juice processing, in: D.M. Barrett, L. Somogyi, H. Ramaswamy (Eds.), Processing Fruits: Science and Technology, CRC Press, Florida, 2005. [3] B. Veleirinho, J.A. Lopes-da-Silva, Application of electrospun poly(ethylene terephthalate) nanofiber mat to apple juice clarification, Process Biochemistry 44 (2009) 353–356. [4] M. Cheryan, Ultrafiltration and Microfiltration Handbook, Lancaster, Technomic, 1998. [5] P.-Z. O, M.R. McLELLAN, Optimization and modeling of apple juice cross-flow microfiltration with a ceramic membrane, Journal of Food Science 58 (1993) 369–374. [6] V. Alvarez, L.J. Andres, F.A. Riera, R. Alvarez, Microfiltration of apple juice using inorganic membranes: process optimization and juice stability, The Canadian Journal of Chemical Engineering 74 (1996) 156–162. [7] A.-S. Jönsson, G. Trägårdh, Ultrafiltration applications, Desalination 77 (1990) 135–179. [8] B. Dutré, G. Trägårdh, Macrosolute-microsolute separation by ultrafiltration: a review of diafiltration processes and applications, Desalination 95 (1994) 227–267. [9] A. Cassano, F. Tasselli, C. Conidi, E. Drioli, Ultrafiltration of Clementine mandarin juice by hollow fibre membranes, Desalination 241 (2009) 302–308. [10] B. Koroknai, Z. Csanádi, L. Gubicza, K. Bélafi-Bakó, Preservation of antioxidant capacity and flux enhancement in concentration of red fruit juices by membrane processes, Desalination 228 (2008) 295–301. [11] S. Gunko, S. Verbych, M. Bryk, N. Hilal, Concentration of apple juice using direct contact membrane distillation, Desalination 190 (2006) 117–124. [12] M. Mondor, B. Girard, C. Moresoli, Modeling flux behavior for membrane filtration of apple juice, Food Research International 33 (2000) 539–548. [13] M. Araya-Farias, M. Mondor, F. Lamarche, S. Tajchakavit, J. Makhlouf, Clarification of apple juice by electroflotation, Innovative Food Science & Emerging Technologies 9 (2008) 320–327.
200
M. Yazdanshenas et al. / Desalination 258 (2010) 194–200
[14] M. Yazdanshenas, A.R. Tabatabaeenezhad, R. Roostaazad, A.B. Khoshfetrat, Full scale analysis of apple juice ultrafiltration and optimization of diafiltration, Separation and Purification Technology 47 (2005) 52–57. [15] M.A. R.A.O., T.E. A., H.J. C., R.W. E., Clarification of apple juice by hollow fiber ultrafiltration: fluxes and retention of odor-active volatiles, Journal of Food Science 52 (1987) 375–377. [16] K. Riedl, B. Girard, R.W. Lencki, Influence of membrane structure on fouling layer morphology during apple juice clarification, Journal of Membrane Science 139 (1998) 155–166. [17] K. Riedl, B. Girard, R.W. Lencki, Interactions responsible for fouling layer formation during apple juice microfiltration, Journal of Agricultural and Food Chemistry 46 (1998) 2458–2464. [18] Y. He, Z. Ji, S. Li, Effective clarification of apple juice using membrane filtration without enzyme and pasteurization pretreatment, Separation and Purification Technology 57 (2007) 366–373. [19] L. Fillaudeau, H. Carrère, Yeast cells, beer composition and mean pore diameter impacts on fouling and retention during cross-flow filtration of beer with ceramic membranes, Journal of Membrane Science 196 (2002) 39–57. [20] C.-W. Jung, H.-J. Son, L.-S. Kang, Effects of membrane material and pretreatment coagulation on membrane fouling: fouling mechanism and NOM removal, Desalination 197 (2006) 154–164. [21] M.-C. Vincent-Vela, S. Álvarez-Blanco, J. Lora-García, E. Bergantiños-Rodríguez, Application of a dynamic model that combines pore blocking and cake formation in crossflow ultrafiltration, Desalination 200 (2006) 138–139. [22] J. de Bruijn, R. Bórquez, Analysis of the fouling mechanisms during cross-flow ultrafiltration of apple juice, LWT - Food Science and Technology 39 (2006) 861–871. [23] R. van Reis, A. Zydney, Bioprocess membrane technology, Journal of Membrane Science 297 (2007) 16–50. [24] J. Hermia, Constant pressure blocking filtration laws—application to power-law non-Newtonian fluids, Trans IchemE 60 (1982) 183–187.
[25] S.S. Sablani, M.F.A. Goosen, R. Al-Belushi, M. Wilf, Concentration polarization in ultrafiltration and reverse osmosis: a critical review, Desalination 141 (2001) 269–289. [26] N. Shin-Ichi, N. Tsuyoshi, K. Shoji, Characteristics of macromolecular gel layer formed on ultrafiltration tubular membrane, AIChE Journal 25 (1979) 615–622. [27] H.M. Yeh, T.W. Cheng, J.W. Tsai, Modified concentration polarization model for hollow-fiber membrane ultrafiltration, Separation and Purification Technology 16 (1999) 189–192. [28] G.B. van den Berg, C.A. Smolders, Flux decline in ultrafiltration processes, Desalination 77 (1990) 101–133. [29] B. Tansel, W.Y. Bao, I.N. Tansel, Characterization of fouling kinetics in ultrafiltration systems by resistances in series model, Desalination 129 (2000) 7–14. [30] M. Gulfi, E. Arrigoni, R. Amadò, The chemical characteristics of apple pectin influence its fermentability in vitro, LWT - Food Science and Technology 39 (2006) 1001–1004. [31] KMS launches RO technology and exhibits at food expo, Membrane Technology, 2009 (2009) 2–3. [32] J. de Bruijn, A. Venegas, R. Borquez, Influence of crossflow ultrafiltration on membrane fouling and apple juice quality, Desalination 148 (2002) 131–136. [33] A. Cassano, M. Marchio, E. Drioli, Clarification of blood orange juice by ultrafiltration: analyses of operating parameters, membrane fouling and juice quality, Desalination 212 (2007) 15–27. [34] R.W. Field, D. Wu, J.A. Howell, B.B. Gupta, Critical flux concept for microfiltration fouling, Journal of Membrane Science 100 (1995) 259–272. [35] L. Giorno, L. Donato, S. Todisco, E. Drioli, Study of fouling phenomena in apple juice clarification by enzyme membrane reactor, Separation Science and Technology 33 (1998) 739–756. [36] S.T.D. de Barros, C.M.G. Andrade, E.S. Mendes, L. Peres, Study of fouling mechanism in pineapple juice clarification by ultrafiltration, Journal of Membrane Science 215 (2003) 213–224.