Forecasting influent-effluent wastewater treatment plant using time series analysis and artificial neural network techniques

Chemical Product and Process Modeling

Volumn 2, Issue 3, 2007, Pages

  Al Asheh, Sameer ^a,c   Mjalli, Farouq Sabri ^b   Alfadala, Hassan E ^a  

^a QATAR UNIVERSITY   (Qatar)

^b UNIVERSITY OF MALAYA   (Malaysia)

^c Qatar University   (Qatar)

Author keywords

ADALINE; ARIMA; BOD; COD; Forecasting; MF FF; Predictions; TSS; WWTP

Indexed keywords

ALGORITHMS; BIOCHEMICAL OXYGEN DEMAND; MATHEMATICAL MODELS; NEURAL NETWORKS; TIME SERIES ANALYSIS; WASTEWATER TREATMENT;

ADAPTIVE LINEAR NEURON NETWORKS (ADALINE); EFFLUENT STREAM VARIABLES;

WATER TREATMENT PLANTS;

EID: 38849204948     PISSN: 19342659     EISSN: None     Source Type: Journal    
DOI: 10.2202/1934-2659.1063     Document Type: Article

References (27)

1. Adorf, H.-M., Interpolation of Irregularly Sampled Data Series: A Survey,. In: Astronomical Data Analysis Software and Systems IV, ASP Conference Series, Vol. 77, 1995, Book Editors: R. A. Shaw, H. E. Payne, and J. J. E. Hayes.
2. Anderson, J. S., Kevrekidis, I.G., and Rico-Martinez, R., A Comparison of Recurrent Training Algorithms for Time Series Analysis and System Identification, Computers chem. Engng Vol. 20, Suppl., pp. S751-S756, 1996.
3. Bhat, N.V., Minderman, P.A., Mcavoy, T., Wang., N.S., "Modeling Chemical Process Systems via Neural Computation. IEEE Control Systems Magazine, 10 (1990).
4. Bhattacherjee, A., Ramani, R.V., Natarajan, R. (1994) Time Series Analysis of Coal Mine Accident Experience. Journal of Safety Research 25: 229-234.
5. Box, G.E.P., and G.M. Jenkins. 1976. Time series analysis: forecasting and control, Revised edition, Holden-Day, San Francisco, CA. 575 p.
6. Cote, M., Grandijean, B.P., Lessard, P., Yhibault, J., "Dynamic Modeling of the Activated Sludge Process: Improving Prediction using Neural Networks. Water Research 29: 995-1004 (1995).
7. Demuth H., and Beale, M.,"Neural Networks Toolbox User's Guide", Fifth Printing, Version 3, Mathworks, MA (1998).
8. Eric A. Plummer, Time Series Forecasting with Feed-Forward Neural Networks: Guidelines and Limitations. M. Sc Thesis. The University of Wyoming. Laramie, Wyoming (2000).
9. Freivalds A., Johnson, A.B. (1990) Time-series analysis of industrial accident data. Journal of Occupational Accidents, 13: 179-193.
10. Gencay R. and Selcuk F, "Software reviews", International Journal of Forecasting 17 (2001) 305-322.
11. Gómez-Ramírez, E., Poznyak, A., González-Yunes A. and Avila-Alvarez M., Adaptive Architecture of Polynomial Artificial Neural Network to Forecast Nonlinear Time Series, Congress on Evolutionary Computation CEC 99. Washington D.C. July 6-9 1999.
12. Gutiérrez-Estrada, J.C., de Pedro-Sanz, E., López-Luque, R., Pulido-Calvo, I. Comparison between traditional methods and artificial neural networks for ammonia concentration forecasting in an eel (Anguilla anguilla L.) intensive rearing system. Aquacultural Engineering 31 (2004) 183-203.
13. Hamoda, M.F., Al-Gusain, I.A., Hassan, A.H., "Integrated Wastewater Treatment Plant Performance Evaluation Using Artificial Neural Network." Water Science and Technology 40: 55-69 (1999).
14. Hudson, J. L., Kubel. M., Adomaitis, R.A., Kevrekidis, I. G., Lapedes, A.S. Farber, R. M. Nonlinear Signal Processing and System Identification: Applications to Time Series From Electrochemical Reactions. Chemical Engineering Science 45: 2075-208.
15. Jagielska I. (1993), "The application of neural networks to business information systems", Proceedings of 4th Australian Conference on Information Systems, Brisbane, pp. 565-574.
16. Lapedes. A. and Farber. R. "Nonlinear signal processing using neural network prediction and system modeling", Technical Report LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos, NM. 1987.
17. Lee, D.S., Joen, C.O., Park, J.M., Change, K.S. (2002) Hybrid Neural Network Modeling of a Full Scale Industrial Wastewater Treatment Process. Biotech. Bioeng. 78: 670-682.
18. Madala H. R, Ivakhnenko A. G., Inductive Learning Algorithms for Complex Systems Modeling, Chapter 7, CRC Press, 1994.
19. Martin T. H., Demuth H, and Beale M.. Neural Network Design. PWS Publishing, 1996.
20. Minsky M. and S. Papert. "Perceptions", MIT Press. Cambridge, MA. 1969.
21. Plazl, I., Pipus, G., Drolka, M., Koloini, T., "Parametric Sensitivity and Evaluation of a Dynamic Model for Single-Stage Wastewater Treatment Plant." Acta Chim. Slov. 42: 289-300 (1999).
22. Seppalaa, C.T., Harrisb, T.J., Baconb, D. W. (2002) Time series methods for dynamic analysis of multiple controlled variables. Journal of Process Control 12: 257-276.
23. Tangang, F.T.; Tang, B.; Monahan, A.H.; Hsieh, W.W, 1998, Forecasting ENSO events: A neural network-extended EOF approach, Journal of Climate; VOL. 11; ISSUE: 1. pp. 29-41.
24. Widrow, B. and Hoff Jr., M. E. (1960). "Adaptive Switching Circuits," IRE Western Electric Show and Convention Record, Part 4, 96-104.
25. Windsor C. G. & Harker A. H. (1990), "Multi-variate financial index prediction a neural network study", Proceedings of International Neural Network Conference, Paris, France, pp. 357-360.
26. Zhang, G.P., Qi, M., Neural network forecasting for seasonal and trend time series, European Journal of operational Research, 160, 501-514(2005).
27. Zhao J. and Liang J., A novel neural network model for different information granulation processing, Granular Computing, 2006 IEEE International Conference, 49- 52, 10-12 May (2006).