Enhancement of Biological Nutrient Removal in an Alternating Anaerobic-Aerobic Sequencing Batch Reactor: Optimization of Anaerobic and Aerobic Hydraulic Retention Times

Biological phosphorus removal (BPR), possesses the significant advantages of low operational costs and little environmental impacts, is an economical and sustainable process to remove P by recycling the activated sludge through anaerobic and aerobic processes. For the BPR system, the hydraulic reten...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2019-09, Vol.326 (1), p.12009
Main Authors: Pang, J W, Yang, S S, Yang, X Y, Wu, Z Y, Ren, N Q
Format: Article
Language:English
Subjects:
Activated sludge
Aerobic processes
Anaerobic processes
Batch reactors
Design optimization
Effluents
Environmental impact
Nutrient removal
Phosphorus
Phosphorus removal
Reactors
Regression coefficients
Response surface methodology
Retention
Sequencing batch reactor
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Summary:Biological phosphorus removal (BPR), possesses the significant advantages of low operational costs and little environmental impacts, is an economical and sustainable process to remove P by recycling the activated sludge through anaerobic and aerobic processes. For the BPR system, the hydraulic retention times (HRT) in aerobic and anaerobic processes are the most significant controlling parameters which can directly affect the effluent quality, especially the effluent phosphorus removal efficiencies. In this study, six lab-scale sequencing batch reactors (SBRs) were operated to conduct the single-factor experiments. 13 experimental runs designed by a 2-factor and 5-level response surface methodology (RSM) using Central composite design (CCD) were used to optimize the relationship between anaerobic HRT (X1, h) and aerobic HRT (X2, h) and two most important responses, COD removal efficiency (CRE, Y1, %) and P removal efficiency (PRE, Y2, %). High squared regression coefficients R2 (> 0.99) and adjusted R2 (> 0.99) indicated a high degree of correlation between the predicted and actual responses, which means that the model could fit the response well. Experimental validation by operating under the optimal combination of the two operational HRTs were conducted. Good correlation between the predicted and experiments values provides confidence in the obtained models.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/326/1/012009