e-NOSE response classification of sewage odors by neural networks and fuzzy clustering

  • Authors:

  • Güleda Önkal-Engin;Ibrahim Demir;Seref N. Engin

  • Affiliations:

  • Department of Environmental Engineering, Gebze Institute of Technology, Gebze, Kocaeli, Turkey;Environmental Informatics and Control Program, Warnell School of Forest Resources, University of Georgia, Athens, GA;Department of Electrical Engineering, Yildiz Technical University, Besiktas, Istanbul, Turkey

  • Venue:
  • ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
  • Year:
  • 2005

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Abstract

Each stage of the sewage treatment process emits odor causing compounds and these compounds may vary from one location in a sewage treatment works to another. In order to determine the boundaries of legal standards, reliable and efficient odor measurement methods need to be defined. An electronic NOSE equipped with 12 different polypyrrole sensors is used for the purpose of characterizing sewage odors. Samples collected at different locations of a WWTP were classified using a fuzzy clustering technique and a neural network trained with a back-propagation algorithm.