Prediction of chemical oxygen demand (COD) based on wavelet decomposition and neural networks

Hanbay D., Turkoglu I., Demir Y.

CLEAN-SOIL AIR WATER, vol.35, no.3, pp.250-254, 2007 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 35 Issue: 3
  • Publication Date: 2007
  • Doi Number: 10.1002/clen.200700039
  • Journal Name: CLEAN-SOIL AIR WATER
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.250-254
  • Keywords: chemical oxygen demand, entropy, modelling, neural network, wavelet decomposition, wastewater, WASTE-WATER TREATMENT, TREATMENT-PLANT, IDENTIFICATION
  • Inonu University Affiliated: No

Abstract

The chemical oxygen demand (COD) parameter of a wastewater treatment plant is predicted based on wavelet decomposition, entropy, and neural networks (NN) for rapid COD analysis. This paper also describes the usage of wavelet and NNs for parameter prediction. Data from a wastewater treatment plant in Malatya, Turkey, were used. This dataset consists of daily values of influents and effluents for a year. To reduce the dimension of input parameters and to decrease the NN training time, wavelet decomposition and entropy were used. Test results were presented graphically. The test results of the trained model were found to be closer to the measured COD values.