Abstract

AbstractSuccessful application of anammox has revolutionised the field of biological nitrogen removal and offers potential promise over conventional nitrification–denitrification processes owing to considerable saving in aeration costs, absence of external carbon sources and significantly low sludge production. For the purpose of study, anammox process was established in a hybrid reactor configuration, which integrates the concept of attached and suspended microbial growth for the treatment of nitrogen-laden effluents. To evolve the most economical design of bioreactor, HRT study was performed at different HRTs varying from 3.0 to 0.25 days. The study revealed best reactor performance at an optimal HRT of 1 day with a corresponding nitrogen removal efficiency (NRE) of 95.1%. Filter media (FM) in anammox hybrid reactor (AHR) benefitted the overall reactor performance in terms of additional 15.4% ammonium removal and enhanced biomass retention. The mass balance of nitrogen in AHR dictated major chunk (79.1%) of influent nitrogen conversion to N2 gas, in addition to 11.25% accounting for biomass synthesis. The novel anammox process offers immense opportunities for dealing with nitrogen-rich wastewater; however, the field-scale applications are restricted by organic matter (OM) presence in wastewater streams. To overcome this, a novel strategy of seeding anaerobic granular sludge was investigated to achieve simultaneous OM and nitrogen removal in AHR. The study deciphered outstanding performance of AHR at optimal COD/N ratio of 0.54 accounting for both OM and nitrogen removal of 94.8% and 96.8%, respectively. Haldane model was used to investigate the kinetics of substrate inhibition in AHR, which was attributed to the presence of OM, nitrite and free ammonia. The inoculation strategy of anaerobic granular sludge not only proved to be a boon for anammox sustenance in severely high organic loads but also expedited simultaneous removal of both the pollutants.KeywordsAnammoxHybrid reactorNitrogen removalOrganic matterInhibition modelling

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