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

Clean - Soil, Air, Water

Volumn 35, Issue 3, 2007, Pages 250-254

  Hanbay, Davut ^a   Turkoglu, Ibrahim ^a   Demir, Yakup ^a  

^a FIRAT UNIVERSITY   (Turkey)

Author keywords

Chemical oxygen demand; Entropy; Modelling; Neural network; Wastewater; Wavelet decomposition

Indexed keywords

CHEMICAL OXYGEN DEMAND; MATHEMATICAL MODELS; NEURAL NETWORKS; WASTEWATER TREATMENT; WATER TREATMENT PLANTS; WAVELET DECOMPOSITION;

ARTIFICIAL NEURAL NETWORK; CHEMICAL OXYGEN DEMAND; ENTROPY; MODELING; WASTE TREATMENT; WASTEWATER; WAVELET ANALYSIS;

EURASIA; MALATYA [TURKEY]; TURKEY;

EID: 34447309364     PISSN: 18630650     EISSN: None     Source Type: Journal    
DOI: 10.1002/clen.200700039     Document Type: Article

References (19)

1. T. G. Müller, N. Noykova, M. Gyllenberg, J. Timmer, Parameter Identification in Dynamical Models of Anaerobic Waste Water Treatment, Math. Biosci. 2002, 177-178, 147-160.
2. M. M. Hamed, M. G. Khalafallah, E. A. Hassanien, Prediction of Wastewater Treatment Plant Performance Using Artificial Neural Networks, Environ. Modell. Softw. 2004, 19, 919-928.
3. J. C. Chen, N. B. Chang, W. K. Shieh, Assessing Wastewater Reclamation Potential by Neural Network Model, Eng. Appl. Artif. Intell. 2003, 16, 149-157.
4. L. Belanche, J. J. Valdés, J. Comas, I. R. Roda, M. Poch, Prediction of the Bulking Phenomenon in Wastewater Treatment Plants, Artif. Intell. Eng. 2000, 14-24, 307-317.
5. R. G. Shetty, S. Chellam, Predicting Membrane Fouling During Municipal Drinking Water Nanofiltration Using Artificial Neural Networks, J. Memb. Sci. 2003, 217-1-2, 69-86.
6. S. Göb, E. Oliveros, S. H. Bossmann, A. M. Braun, R. Guardani, C. A. O. Nascimento, Modeling the Kinetics of a Photochemical Water Treatment Process by Means of Artificial Neural Networks, Chem. Eng. Process. 1999, 38, 373-382.
7. C. Gagnon, B. P. A. Grandjean, J. Thibault, Modelling of Coagulant Dosage in a Water Treatment Plant, Artif. Intell. Eng. 1997, 11, 401-404.
8. R. M. Miller, K. Itoyama, A. Uda, H. Takada, N. Bhat, Modeling and Control of a Chemical Waste Water Treatment Plant, Comput. Chem. Eng. 1997, 21, 947-952.
9. W. Wan-liang, R. Min, Soft-Sensing Method for Wastewater Treatment Based on BP Neural Network, Proc. of the 4th World Congress on Intelligent Control and Automation, Shanghai, P. R. China 2002.
10. H. R. Maier, N. Morgan, C. W. K. Chow, Use of Artificial Neural Networks for Predicting Optimal Alum Doses and Treated Water Quality Parameters, Environ. Modell. Softw. 2004, 19, 485-494.
11. R. Q. Quiroga, O. A. Roso, E. Basar, Wavelet Entropy: A Measure of Order in Evoked Potentials, Evoked Potentials and Magnetic Fields, Elsevier, Amsterdam 1999, 298-302.
12. I. Turkoglu, A. Arslan, E. ve Ilkay, A Wavelet Neural Network for the Detection of the Heart Valve Diseases, Expert Syst.: Int. J. Knowl. Eng. Neural Networks 2003, 20, 1-7.
13. D. Hanbay, I. Turkoglu, Y. Demir, Prediction of Wastewater Treatment Plant Performance Based on Wavelet Packet Decomposition and Neural Networks, Exp. Syst. Appl. 2006, in press. DOI:10.1016/ j.eswa.2006.10.030.
14. S. R. Devasahayam, Signals and Systems in Biomedical Engineering, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2000.
15. C. S. Burrus, R. A. Gopinath, H. Guo, Introduction to Wavelet and Wavelet Transforms, Prentice Hall, New Jersey, USA, 1998.
16. P. I. J. Keeton, F. S. Schlindwein, Application of Wavelets in Doppler Ultrasound, Sensor Rev. 1997, 17, 38-45.
17. S. Haykin, Neural Networks, A Comprehensive Foundation, Macmillan College Publishing Company, Inc., New York 1994.
18. M. J. Zurada, Introduction to Artificial Neural Systems, West Publishing Company, Inc., New York 1992.
19. S. Chen, S. A. Billings, Neural Networks for Nonlinear Dynamic System Modeling and Identification, Int. J. Control 1992, 56, 319-346.