Greywater treatment in SBR-SND reactor - optimization of hydraulic retention time, volumetric exchange ratio and sludge retention time

ABSTRACT

In this study, simultaneous nitrification and denitrification-sequencing batch reactor (SND-SBR) process was investigated to treat greywater. The effect of three process parameters, including hydraulic retention time (HRT), volumetric exchange ratio (VER) and sludge retention time (SRT), was optimised using a 2³ full factorial design. The statistic model was developed for two response variables, i.e. chemical oxygen demand (COD) and ammonia (NH₃-N) removal. The optimum conditions were 6.8 h HRT (anaerobic/aerobic/anoxic: 1.77 h/2.77 h/2.27 h), 0.7 VER and 7.94 d SRT, which resulted in 93.9% COD and 84.6% NH₃-N removal efficiency. SRT was the most significant factor, followed by HRT and VER for COD and NH₃-N removal. The interaction effect of VER and SRT was significant in COD removal. On the other hand, the interaction effects of HRT-VER and HRT-SRT were significant in NH₃-N removal. The removal efficiencies of 89.6 ± 1.1% and 83.7 ± 2.3% were observed for TKN and TN, respectively, in the optimised SND-SBR system. NH₃-N removal was obtained via nitrate pathway in the SND-SBR system. The PO₄^3--P removal of 74.2 ± 3.4% was obtained via aerobic phosphorus uptake and post anoxic denitrification at the optimal condition. To enhance PO₄^3--P removal, adsorption (using corn cob adsorbent) was integrated with SBR by adding the optimum adsorbent dose (0.5 g/L). The PO₄^3--P removal efficiency in the SBR-adsorption system was found to be 80 ± 1.5%. The biodegradation of emerging contaminants (ECs) was also carried out in the SND-SBR system, and the results showed removal rate of 58.9 ± 2.3% benzophenone-3 (BP) and 80.1 ± 2.2% anionic surfactant (AS).

GRAPHICAL ABSTRACT

KEYWORDS:

• Greywater
• full factorial design
• simultaneous nitrification and denitrification-sequencing batch reactor system
• nitrogen removal
• phosphate removal
• emerging contaminants removal

Acknowledgements

The authors acknowledge the financial support from the Department of Science and Technology (DST), India, under the Water Technology Initiative (WTI) (Project No: DST/TM/WTI/2K16/90).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Availability of data

All data, models and code generated or used during the study appear in the submitted article.

Additional information

Funding

This work was supported by “Water Technology Initiative” programme of Department of Science and Technology (DST) under Ministry of Science and Technology, Government of India: [Grant Number DST/TM/WTI/2K16/90].