Modeling of organic matter removal and nitrification in sewer systems - an approach to wastewater treatment

Baban A., Talinli I.

DESALINATION, vol.246, pp.640-647, 2009 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 246
  • Publication Date: 2009
  • Doi Number: 10.1016/j.desal.2008.07.018
  • Title of Journal : DESALINATION
  • Page Numbers: pp.640-647


Investigations were carried out to develop the best sustainable management system for domestic wastewaters. Sewer systems have a crucial significance within the current concept of wastewater management. It is suggested that sewer lines may be used as biological reactors to reduce pollution loads. The concept of emphasizing centralized treatment systems may cause transportation of wastewater for long distances. In this study organic matter removal and nitrification capability of sewer lines were investigated with a special emphasis on kinetic behavior. For this purpose, a sewage circulating reactor was set up and operated to assess the transformations of organic compounds and nutrients during the transport process and to determine the kinetic relations. Packing material was used in the circulating reactor to increase the biofilm area and to enhance removal efficiencies of pollutants. The model was operated by feeding with glucose based synthetic wastewater. Parameters related to organic content and nitrification were monitored. Kinetic constants were determined by using Monod and variable order model kinetics. Around 85-90% COD and TOC removal efficiencies were attained following 7 h circulation of the wastewater in the reactor. TN reduction of 55% was achieved at a 24 h circulation period. Equations representing the organic matter removal and nitrification rates were developed based on variable order and Monod concepts. The modeling results, obtained by using the developed equations at infinitesimal intervals, were compared to the experimental data. The Monod concept model, which takes into account the amount of biological growth, was shown to be slightly more accurate than the variable order model for this study.