A modified fouling index (MFI40) and fouling predicting approach for ultrafiltration of secondary effluents

Fouling indices for evaluating fouling propensity of secondary effluents (SEF) as feed of ultrafiltration (UF) systems are important parameters for the design and operation of the UF process. However, limited fouling indices have been developed and applied for UF feedwater. This study (i) established a modified UF fouling index (MFI40) by raising operating pressure from 30 psi in a traditional MFI test to 40 psi. Standard deviation of MFI40 tests was lower than that of traditional MFI by 68.6%, indicating better stability and repeatability of MFI40. It (ii) investigated the combined effects of UF feedwater characteristics on MFI40. Biopolymers and turbidity played a dominant and secondary positive role in the MFI40, respectively. The effect of conductivity on MFI40 changed from positive to negative with a turbidity increase. It also (iii) validated the MFI40 in both laboratoryand pilot-scale UF membrane units, and UF fouling rates were linearly correlated to the MFI40 of their feeds, and (iv) explored the practical use of the MFI40. It was applied to determine the maximum allowable UF feedwater quality (MFI40max), which could be used to select an appropriate pre-treatment process. A fouling predicting model was established based on the feedwater MFI40 and the operating flux, with an average predicting error of 26.8%. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). doi: 10.2166/wrd.2018.020 s://iwaponline.com/jwrd/article-pdf/9/1/67/523018/jwrd0090067.pdf B. J. Cai (corresponding author) SUEZ NWS, 5F, Tian’an Center, No. 338 Nanjing Road West, Shanghai 200003, China E-mail: cbjsusan661031@hotmail.com I. Baudin SUEZ, CIRSEE, 38, rue du Président-Wilson, 78230 Le Pecq, France H. Y. Ng Centre for Water Research, Department of Civil and Environmental Engineering, Faculty of Engineering, National University of Singapore, Singapore 117576


INTRODUCTION
In order to reuse wastewater, ultrafiltration (UF), as one of the low-pressure membrane processes, is widely used to pre-treat secondary effluent (SEF) to improve the feedwater quality of the subsequent reverse osmosis (RO) membrane process. The UF filtration process is a reliable and costeffective technology due to its high and guaranteed removal of almost all suspended particles and large dissolved organic matter. Compared with conventional pre-treatment processes for RO, UF membrane technology offers the advantages of a water product with higher quality, a smaller footprint and relatively lower cost (Mulder ). However, membrane fouling is a major obstacle for the UF membrane process because it would decrease water productivity, increase operational energy consumption and increase maintenance costs (Huang et  In order to establish such a fouling index, modification to traditional fouling tests could be explored. During the traditional fouling indices tests, applied pressure provides the driving force to overcome heavy clogging of foulants. In this case, higher applied pressure could cause higher flux and bring higher fouling load to the filter paper (Alhadidi et al.  For the compressible cake layer, α changes with pressure as follows (Almy & Lewis ): where ω is the compressibility factor of the cake and α 0 is a constant. For incompressible cakes, ω is zero and α is a constant. The larger the ω, the more compressible the cake layer is.  Table 1.

Laboratory-scale UF unit
SEFs were fed to a hollow fiber UF membrane system ( Figure 1) in dead-end filtration mode at a flux of either 35

Pilot-scale UF unit
A UF pilot unit with hollow-fibre membrane modules (Zee- This design, which is a modified fractional factorial design,

RESULTS AND DISCUSSION
The The average MFI 40 value was lower than MFI 30 . A low fouling index at higher operating pressure was also observed by Sim et al. (), where the explanation of cake compression effect was provided. By combining Equations (1) and (2), the MFI was reformatted and represented as follows: Equation (3)   MFI 30 values were rather similar when corrected to the same pressure. (1,156 s/L 2 ) was lower than that of the MFI 30 (3,676 s/L 2 ) by 68.6% over the 10 repeating tests, which indicates that MFI 40 were more reproducible and stable. Alhadidi et al.

Improvement of repeatability of fouling index test
() suggested that during the fouling index test, dominated by the cake formation process, a more stable filtration process may occur due to a more stable cake layer. Under higher pressure, a more stable structure of cake was formed when more foulants were brought to the surface of the filter paper by a larger driving force, leading to the formation of a denser cake layer and an increase in the specific cake resistance (Sim et al. ). By combining Equations (1) and (2), the cake resistivity could also be expressed as Equation (4): As previously discussed, ω is between 0 and 1 for SEFs in this study. Thus, it could be deduced that I would increase with operating pressure according to Equation (4).

Influence of feed water characteristics on MFI 40
In order to investigate the effect of feed water characteristics Independence of fouling-relevant water quality parameters.
In order to avoid the co-correlation of selected water quality parameters and select the independent parameters, the dependency between these parameters was firstly investigated by calculating the correlation coefficient ρ for every pair of parameters, with equation of ρ ¼ Cov(y 1 , y 2 ) σ 1 σ 2 (Box et al. ). Table 3 shows the result of the correlation analysis for the eight tested parameters. According to statistical explanation for the matrix, two parameters are significantly correlated when the correlation coefficient is larger than 0.6. The larger the coefficient is, the more significantly these two parameters interact. A positive value of the coefficient indicates a positive correlation between two parameters, while a negative value indicates a negative correlation.
For the tested organic parameters, it can be observed from () also found that a high Pearson's coefficient r, over 0.9, was found for both fluorophores humic-and fulvic-like substance against total organic carbon (TOC) parameters.
For the inorganic parameters, correlation coefficients for calcium/turbidity, calcium/biopolymers, magnesium/turbidity, magnesium/biopolymers, conductivity/turbidity and conductivity/biopolymers were found to be lower than 0.6.
This indicates that calcium, magnesium, and conductivity were relatively independent on turbidity and biopolymers.
Thus, turbidity, biopolymers, calcium, magnesium and conductivity were relatively independent of each other, and were selected to be studied.
Importance of the independent water quality parameters.
The significance of these five independent parameters was compared to determine the most important ones relevant to MFI 40 . This was performed by utilizing a multi-level factorial analysis, which was used to analyze the impact of multi-level factors on response by Ng & Ng (). The importance of these five independent water quality parameters is shown in   þ 38613:0(B) þ 6:6(C) À 4881:4 (7) where T, B, and C stand for turbidity, biopolymers and conductivity, respectively.  . When the turbidity was low, the particle amount was so small that its size effect on the MFI 40 was insignificant; thus, the interaction between the foulants and membrane filter dominated the fouling process. In this case, the effect of compressing the electrostatic double layer might overwhelm the effect of aggregating particles, which led to a positive response of MFI 40 to conductivity. In contrast, the negative response of MFI 40 to conductivity could be caused by the predominant effect of particle aggregation when the turbidity was high. In this case, size screening dominated the filtration process, and therefore the particle and colloidal status influenced the MFI 40 more significantly than interactions between the foulants and the filter paper. It leads to the effect of particle aggregation subduing the effect of electrostatic double layer compression. Abdelrasoul et al. () also found that aggregation of particles mitigated membrane fouling due to reduction of pore blocking and irreversible fouling.
The single and mutual effects of biopolymers and conductivity on the MFI 40 illustrated by Equation (7)  It is also interesting to note that the gradient of the fouling rate versus the MFI 40 curve was steeper at higher flux, indicating that a rise in MFI 40 caused a more obvious increase in fouling rate. It reveals that the positive response of MFI 40 to fouling rate was more significant under higher flux. This phenomenon was explained by the increased resistance of the compressed fouling layer and subsequently amplified TMP increase caused by the same feedwater (Roorda & van der Graaf ).

Practical use of MFI 40
Determining maximum allowable UF feedwater quality One possible application of the MFI 40 is to determine the maximum allowable UF feedwater quality by calculating MFI 40max . In order to calculate MFI 40max , the following information was collected first: (i) highest TMP limit for the membrane (TMP foul ) recommended by membrane supplier; (ii) initial TMP of UF membrane process (TMP initial ); (iii) designed filtration duration between two cleanings (T ); and (iv) the correlation equation of fouling rate versus feedwater MFI 40 obtained from filtration runs, which could be written as Fouling rate ¼ a × MFI40 þ b, as shown in Figures 8 and 9. MFI 40max value was then calculated by substituting TMP foul, TMP initial, and T into the correlation equation as follows: where a and b are the coefficients in the linear correlation between the fouling rate and MFI 40max .
Using the above approach, MFI 40max. values for laboratory-scale (ZW1) and pilot-scale (ZW500d) systems were calculated and are shown in Table 4. It was found that MFI 40max. values for the both systems at lower flux were higher than those at higher flux, indicating that the UF system operated at lower flux could accept a feedwater with a higher fouling potential. In addition, MFI 40max value for the pilot-scale system were close to that for the laboratory- Analysis of variance (ANOVA) for this regression reveals that R 2 value was as high as 0.95 and the F value  A possible application of the MFI 40 was to determine the maximum allowable UF feedwater quality (MFI 40max ), which could be used to evaluate UF pre-treatment efficiency and to select an appropriate pre-treatment process.
Additionally, a regression fouling predicting model was established based on the feedwater MFI 40 and the operating flux, with an average predicting error of 26.8%. Such an easily adopted model could be a promising fouling predicting approach for practical design and operation.