Enhanced dewatering characteristics of waste activated sludge with Fenton pretreatment: effectiveness and statistical optimization

Guangyin ZHEN, Xueqin LU, Baoying WANG, Youcai ZHAO, Xiaoli CHAI, Dongjie NIU, Tiantao ZHAO

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Front. Environ. Sci. Eng. ›› 2014, Vol. 8 ›› Issue (2) : 267-276. DOI: 10.1007/s11783-013-0530-3
RESEARCH ARTICLE
RESEARCH ARTICLE

Enhanced dewatering characteristics of waste activated sludge with Fenton pretreatment: effectiveness and statistical optimization

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Abstract

In this work, the enhanced dewaterabing characteristics of waste activated sludge using Fenton pretreatment was investigated in terms of effectiveness and statistical optimization. Response surface method (RSM) and central composite design (CCD) were applied to evaluate and optimize the effectiveness of important operational parameters, i.e., H2O2 concentrations, Fe2+ concentrations and initial pH values. A significant quadratic polynomial model was obtained (R2= 0.9189) with capillary suction time (CST) reduction efficiency as the response. Numerical optimization based on desirability function was carried out. The optimum values for H2O2, Fe2+, and initial pH were found to be 178 mg·g-1 VSS (volatile suspended solids), 211 mg·g-1 VSS and 3.8, respectively, at which CST reduction efficiency of 98.25% could be achieved. This complied well with those predicted by the established polynomial model. The results indicate that Fenton pretreatment is an effective technique for advanced waste activated sludge dewatering. The enhancement of sludge dewaterability by Fenton’s reagent lies in the migration of sludge bound water due to the disintegration of sludge flocs and microbial cells lysis.

Keywords

Fenton pretreatment / response surface methodology (RSM) / capillary suction time (CST) / dewaterabilty / molecular weight distribution

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Guangyin ZHEN, Xueqin LU, Baoying WANG, Youcai ZHAO, Xiaoli CHAI, Dongjie NIU, Tiantao ZHAO. Enhanced dewatering characteristics of waste activated sludge with Fenton pretreatment: effectiveness and statistical optimization. Front Envir Sci Eng, 2014, 8(2): 267‒276 https://doi.org/10.1007/s11783-013-0530-3

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Acknowledgements

This work was financially supported by Science and Technology Commission of Shanghai Municipality (Nos. 08 DZ 1202802, 09 DZ 1204105 and 09 DZ 2251700), the National Natural Science Foundation of China (Grant No. 51008322), China Scholarship Council (CSC) and Fundamental Research Funds for the Central Universities (No. 0400219152).

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
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