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Prediction of Chemical Oxygen Demand (COD) Based on Wavelet Decomposition and Neural Networks
Author(s) -
Hanbay Davut,
Turkoglu Ibrahim,
Demir Yakup
Publication year - 2007
Publication title -
clean – soil, air, water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 66
eISSN - 1863-0669
pISSN - 1863-0650
DOI - 10.1002/clen.200700039
Subject(s) - chemical oxygen demand , wavelet , artificial neural network , biochemical oxygen demand , wastewater , entropy (arrow of time) , wavelet transform , decomposition , effluent , mathematics , environmental science , computer science , artificial intelligence , environmental engineering , chemistry , thermodynamics , physics , organic chemistry
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.