Literature DB >> 28540423

Direct treatment of high-strength soft drink wastewater using a down-flow hanging sponge reactor: performance and microbial community dynamics.

Junhui Liao1, Curtis Fang1, Jimmy Yu2, Arun Sathyagal2, Eric Willman2, Wen-Tso Liu3.   

Abstract

A stand-alone down-flow hanging sponge (DHS) system with a two-stage configuration was operated for 700 days to treat synthetic soft drink wastewater at 3000 mg/L chemical oxygen demand (COD). Throughout the operation, >90% COD and total organic carbon (TOC) removal efficiency was obtained by the first stage, and a final effluent of COD <60 mg/L (TOC <20 mg/L) was consistently maintained with the second stage. Lower organic removal efficiency was observed to closely correlate with lower pH, higher volatile fatty acid (VFA) concentration, and higher suspended solid (SS) in the effluent. Occasionally, biomass sloughing was observed as a cause to unstable reactor performance in the first stage. The microbial community of the retained biomass on the sponges differed significantly based on spatial locations of sponges, sampling time points, and loading shocks. In general, Proteobacteria were found to be more abundant in the reactor at an organic removal efficiency >80% than that at <50%. Specifically, operational taxonomic units closely related to Tolumonas auensis and Rivicola pingtungensis were identified as important populations that were responsible for degrading the major substrate in the soft drink wastewater toward to the end of the reactor operation. In addition, high abundance of Bacteroidetes in the reactor was speculated to be responsible for the VFA accumulation in the effluent. This study demonstrated that stand-alone DHS reactor could be used in treating high-strength wastewater efficiently.

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Keywords:  16S rRNA gene; Down-flow hanging sponge (DHS); Microbial diversity; Soft drink wastewater

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Year:  2017        PMID: 28540423     DOI: 10.1007/s00253-017-8326-1

Source DB:  PubMed          Journal:  Appl Microbiol Biotechnol        ISSN: 0175-7598            Impact factor:   4.813


  1 in total

1.  Coupling growth kinetics modeling with machine learning reveals microbial immigration impacts and identifies key environmental parameters in a biological wastewater treatment process.

Authors:  Ran Mei; Jinha Kim; Fernanda P Wilson; Benjamin T W Bocher; Wen-Tso Liu
Journal:  Microbiome       Date:  2019-04-17       Impact factor: 14.650

  1 in total

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