New tool for evaluation of performance of wastewater treatment plant: Artificial neural network

Cinar O.

PROCESS BIOCHEMISTRY, vol.40, no.9, pp.2980-2984, 2005 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 40 Issue: 9
  • Publication Date: 2005
  • Doi Number: 10.1016/j.procbio.2005.01.012
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2980-2984
  • Keywords: Pelham wastewater treatment plant, artificial neural network, Kohonen self-organizing feature maps, ACTIVATED-SLUDGE PROCESS, COD SIMULATION-MODEL, CONSTRUCTION
  • Yıldız Technical University Affiliated: No


Kohonen self-organizing feature maps, a method of artificial intelligence method, was used to classify operational data of Pelham wastewater treatment plant and to determine the reasons for high effluent concentrations of biological oxygen demand (BOD), total suspended solids (TSS) and fecal coliform in this study. The reasons causing high effluent concentrations of these parameters were low pH in the biological reactor and high solid retention time (SRT). (c) 2005 Elsevier Ltd. All rights reserved.