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Prediction of Chemical Oxygen Demand (COD) Based on Wavelet Decomposition and Neural Networks

✍ Scribed by Davut Hanbay; Ibrahim Turkoglu; Yakup Demir

Publisher
John Wiley and Sons
Year
2007
Tongue
English
Weight
733 KB
Volume
35
Category
Article
ISSN
1863-0650

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✦ Synopsis

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.

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