4.7 Article

Identification of activated sludge properties affecting membrane fouling in submerged membrane bioreactors

期刊

SEPARATION AND PURIFICATION TECHNOLOGY
卷 51, 期 1, 页码 95-103

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ELSEVIER
DOI: 10.1016/j.seppur.2006.01.002

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membrane bioreactor; membrane fouling; activated sludge properties; sludge particle size distribution; extracellular polymeric substances; statistical analysis

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This paper examines the influence of activated sludge properties such as the mixed liquid of suspended solids (MLSS) concentration, sludge particle size distribution (PSD), extracellular polymeric substances (EPS), soluble microbial products (SMP), suspended solids in supernatant (SSs), dynamic viscosity (mu), relative hydrophobicity (RH), and zeta potential on membrane fouling. Activated sludge samples from different full-scale and lab-scale membrane bioreactor processes were used to study their impacts on membrane fouling. The influence of activated sludge properties on membrane permeation was identified with statistical methods. The results showed that MLSS concentration had an exponential relationship with membrane fouling resistance. The sludge particle size (r(p)=-0.730) was correlated inversely to the membrane fouling resistance significantly. The total EPS (r(p)=0.898) and protein (r(p)=0.810) had strong positive effect on membrane fouling resistance, but carbohydrate (r(p)=0.626) had a moderate correlation with membrane fouling resistance due to its low amounts. SMP (r(p)=0.757), SS, (r(p)=0.810), dynamic viscosity (r(p)=0.691), RH (r(p)=0.837), and zeta potential (r(p)=-0.881) also had significant influence on membrane permeability. However, protein (r(p)=0.936), SMP (r(p)=0.725), SS, (r(p)=0.783), dynamic viscosity (r(p)=0.633), RH (r(p)=0.877), and zeta potential (r(p)=-0.953) mainly resulted from the change of EPS concentration. These results confirm that MLSS concentration, PSD and EPS were the predominant factors affecting membrane fouling during membrane filtration of sludge suspension. The membrane fouling resistance can be predicted using a simple model based on MLSS concentration, PSD and EPS. (C) 2006 Elsevier B.V. All rights reserved.

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