Literature DB >> 20421124

Monitoring off-gas O2/CO2 to predict nitrification performance in activated sludge processes.

Shao-Yuan Leu1, Judy A Libra, Michael K Stenstrom.   

Abstract

Nitrification/denitrification (NDN) processes are the most widely used technique to remove nitrogenous pollutants from municipal wastewater. The performance of nitrogen removal in the NDN process depends on the metabolism of nitrifying bacteria, and is dependent on adequate oxygen supply. Off-gas testing is a convenient and popular method for measuring oxygen transfer efficiency (OTE) under process conditions and can be performed in real-time. Since carbon dioxide is produced by carbonaceous oxidizing organism and not by nitrifiers, it should be possible to use the off-gas carbon dioxide mole fraction to estimate nitrification performance independently of the oxygen uptake rate (OUR) or OTE. This paper used off-gas data with a dynamic model to estimate nitrifying efficiency for various activated sludge process conditions. The relationship among nitrification, oxygen transfer, carbon dioxide production, and pH change was investigated. Experimental results of an online off-gas monitoring for a full-scale treatment plant were used to validate the model. The results showed measurable differences in OUR and carbon dioxide transfer rate (CTR) and the simulations successfully predicted the effluent ammonia by using the measured CO(2) and O(2) contents in off-gas as input signal. Carbon dioxide in the off-gas could be a useful technique to control aeration and to monitor nitrification rate. Copyright 2010 Elsevier Ltd. All rights reserved.

Entities:  

Mesh:

Substances:

Year:  2010        PMID: 20421124     DOI: 10.1016/j.watres.2010.03.022

Source DB:  PubMed          Journal:  Water Res        ISSN: 0043-1354            Impact factor:   11.236


  1 in total

1.  Dynamic simulation of continuous mixed sugar fermentation with increasing cell retention time for lactic acid production using Enterococcus mundtii QU 25.

Authors:  Ying Wang; Ka-Lai Chan; Mohamed Ali Abdel-Rahman; Kenji Sonomoto; Shao-Yuan Leu
Journal:  Biotechnol Biofuels       Date:  2020-06-26       Impact factor: 6.040

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.