Literature DB >> 20822790

Biological nitrogen removal with a real-time control strategy using moving slope changes of pH(mV)- and ORP-time profiles.

S G Won1, C S Ra.   

Abstract

A new real-time control strategy using moving slope changes of oxidation-reduction potential (ORP)- and pH(mV)-time profiles was designed. Its effectiveness was evaluated by operating a farm-scale sequencing batch reactor (SBR) process using the strategy. The working volume of the SBR was 18 m(3), and the volumetric loading rate of influent was 1 m(3) cycle(-1). The SBR process comprised six phases: feeding → anoxic → anaerobic → aerobic → settle → discharge. The anoxic and aerobic phases were controlled by the developed real-time control strategy. The nitrogen break point (NBP) in the pH(mV)-time profile and the nitrate knee point (NKP) in the ORP-time profile were designated as real-time control points for the aerobic and anoxic phases, respectively. Through successful real-time control, the duration of the aerobic and anoxic phases could be optimized and this resulted in very high N removal and a flexible hydraulic retention time. Despite the large variation in the loading rate (0.5-1.8 kg NH(4)-N m(-3) cycle(-1)) due to influent strength fluctuation, complete removal of NH(4)-N (100%) was always achieved. The removal efficiencies of soluble nitrogen (NH(4)-N plus NO(x)-N), soluble total organic carbon, and soluble chemical oxygen demand were 98%, 90%, and 82%, respectively. Monitoring the ORP and pH(mV) revealed that pH(mV) is a more reliable control parameter than ORP for the real-time control of the oxic phase. In some cases, a false NBP momentarily appeared in the ORP-time profile but was not observed in the pH(mV)-time profile. In contrast, ORP was more the reliable control parameter for NKP detection in the anoxic phase, since the appearance of NKP in the pH(mV)-time profile was sometimes vague.
Copyright © 2010 Elsevier Ltd. All rights reserved.

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Year:  2010        PMID: 20822790     DOI: 10.1016/j.watres.2010.08.030

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


  2 in total

1.  Improving nitrogen removal using a fuzzy neural network-based control system in the anoxic/oxic process.

Authors:  Mingzhi Huang; Yongwen Ma; Jinquan Wan; Yan Wang; Yangmei Chen; Changkyoo Yoo
Journal:  Environ Sci Pollut Res Int       Date:  2014-06-13       Impact factor: 4.223

2.  Simultaneous Removal of Pollutants and Recovery of Nutrients from High-Strength Swine Wastewater Using a Novel Integrated Treatment Process.

Authors:  Soomin Shim; Arif Reza; Seungsoo Kim; Naveed Ahmed; Seunggun Won; Changsix Ra
Journal:  Animals (Basel)       Date:  2020-05-12       Impact factor: 2.752

  2 in total

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