Prediction of the instantaneous fouling resistance of sodium alginate during water rinsing

https://doi.org/10.1016/j.cherd.2017.03.039Get rights and content

Highlights

  • Water rinsing mechanism of sodium alginate on the MF membrane was studied.

  • A model for first-order parallel-reversible rinsing process was developed.

  • The reversibility of SA fouling was well explained by XDLVO theory.

Abstract

Based on swelling and dissolution mechanism, a model describing first-order parallel-reversible process was developed to predict the fouling resistance of 0.1 μm polyacrylonitrile (PAN) membrane fouled with sodium alginate (SA) during water rinsing. The results showed that there were good agreements between model predictions and experimental values (R2 > 0.9). Especially in the situation when the impact of water rinsing condition on model parameter was considered. Moreover, the applicability of the proposed model was validated by a series of rinsing experiments with 0.1 μm polyvinylidene fluoride (PVDF) membrane or active sludge suspension. In addition, feed concentration had a great role in the fouling process while temperature impact on the cleaning process was also very appreciable. And the reversibility of SA fouling was well explained by the electrostatic and hydrophilic repulsions between PAN membrane and SA.

Introduction

Microfiltration has been widely applied in the wastewater treatment (He and Vidic, 2016, Lee et al., 2013), especially for membrane bioreactors (MBR) (Masao et al., 2016, Amarasiri et al., 2016). One of the major drawbacks to limit the widespread of MBR is membrane fouling, which makes a rapid decay of flux, increases operating cost and shortens the lifetime of the membrane. Extracellular polymeric substances (EPS), which are mainly composed of polysaccharide, protein, humic substances and uronic acid was proved to be as the main substance that causes membrane fouling in MBR (Chang et al., 2001, Cho et al., 2001, Cho et al., 2004, Ye et al., 2005). Sodium alginate (SA) has been frequently used, as one popular model substance of polysaccharide in EPS, to study its membrane fouling behavior (Nataraj et al., 2008, Van de Ven et al., 2008, Listiarini et al., 2009, Arndt et al., 2016).

Hydraulic cleaning is a popular approach for alleviating membrane fouling due to the environment-friendly character of no chemical reagents and the advantage of less membrane degradation/damage (Qi et al., 2016, Li et al., 2016, Chang et al., 2016). The fouling layer of forward osmosis membrane fouled with SA was easy to be removed by water rinsing (Mi and Elimelech, 2010). The flux recovery of polyvinylchloride UF membrane fouled with SA can achieve 92% for hydraulic flushing (Guo et al., 2009). Water rinsing could remove 80% protein from the UF inorganic membrane (Matzinos and Álvarez, 2002) and dissolve most of the deposits on the surface of PVDF MF membranes fouled with α-lactalbumin (Bansal et al., 2006). SA fouling on the UF membrane was highly reversible with backwashing (Katsoufidou et al., 2008). The flux recovery could reach 80% for UF membrane fouled by algae when backwashing followed by forward flushing were used (Liang et al., 2008). Moreover, the maximum cleaning efficiency of 90% was obtained for UF ceramic membrane fouled with whey protein when water rinsing was conducted under optimized conditions (Cabero et al., 1999).

Kinetic models provide basis and guide for industrial process control, process optimization, and automation (Fan et al., 2015). Some cleaning model has been developed. A sum model of two first-order models for water rinsing was developed to predict the fouling resistance decay for UF membrane fouled with whey proteins (Matzinos and Álvarez, 2002, Cabero et al., 1999). A first-order kinetic model was developed to describe the fouling resistance of commercial beer (Gan et al., 1999) and the cleaning ability of four cleaning agents in chemical cleaning was compared (Li et al., 2005). A first-order model of cleaning rates was also established to depict alkaline cleaning process (Xin et al., 2004). In addition, a first-order model containing three parameters (transmembrane pressure, pH, and turbidity) was used to predict the variation of the irreversible fouling state (Zondervan et al., 2007). A first-order kinetic model of cake resistance with a second-order swelled kinetics for in-pore fouling was proposed to predict the total resistance variation for alkali cleaning process (Popović et al., 2009a, Popović et al., 2009b). A second order model for cake resistance was combined with a second-order model for resistance due to in-pore swelling to describe the cleaning process of the inorganic MF membrane fouled with whey protein concentrate (Bird and Bartlett, 2002). A second order model for fouling resistance was developed for the NaOH cleaning process of UF membrane fouled with dairy (Alvarez et al., 2007). A cleaning model was hypothesized with 3 removal characteristics of protein during the rinsing and chemical cleaning process (Bartlett et al., 1995).

However, water rinsing of 0.1 μm PAN microfiltration membrane fouled with SA solution and the modeling of the fouling resistance of SA during water rinsing has not been reported. Therefore, the aim of this work is to establish a model considering swelled and solving mechanisms to predict the instantaneous fouling resistance of SA for 0.1 μm PAN MF membrane during water rinsing.

Section snippets

Chemicals and membranes

All chemicals of analytical grade including sodium alginate, sodium bicarbonate, glycerol, and diiodomethane were provided by Beijing Chemical Engineering Factory. DI-water was produced by a Milli-Q water system (Millipore, France). PAN and PVDF MF membranes with mean pore size of 0.1 μm were purchased from ANDE Membrane Separation Technology & Engineering, Beijing CO., Ltd. Before being used, the virgin membrane samples with an effective filtration area of 37.39 cm2 were soaked in the DI-water

The filterability of SA solution with 0.1 μm PAN membrane

A set of experiments were conducted to evaluate membrane filtration behaviors at different fouling conditions (SA concentration, stirring speed and TMP). Then the contributions of different operating conditions to the average fouling resistance (Fig. 1) was evaluated using a regression method (Wang et al., 2009) and the result was showed in Table 1.

It can be seen from Table 1 and Fig. 1, SA concentration, stirring speed and transmembrane pressure were all significant influence factors (all

Conclusions

Assuming the removal of foulant contains an irreversible process and a reversible dissolved process, a model including first-order parallel-reversible process was established to predict the instantaneous fouling resistance during the rinsing process. Then the model parameters were obtained by fitting the proposed model to experimental data. The following conclusions could be obtained:

  • (1)

    There were good agreements between model predictions and experimental data at different rinsing conditions for

Acknowledgement

The authors acknowledge the financial support from the National Natural Science Foundation of China (Project NOs. 21476006 and 21176006) for the financial support of this study.

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