Use of sewer on-line total solids data in wastewater treatment plant modelling

Water Science and Technology

Volumn 62, Issue 4, 2010, Pages 743-750

  Poutiainen, H ^a   Niska, H ^a   Heinonen Tanski, H ^a   Kolehmainen, M ^a  

^a UNIVERSITY OF EASTERN FINLAND   (Finland)

Author keywords

Modelling; Neural networks; On line sensing; Optimization; Total solids; Wastewater treatment

Indexed keywords

DYNAMIC DATA; INPUT VARIABLES; MODEL INPUTS; MODELLING; MODELLING TOOLS; MULTI LAYER PERCEPTRON; MULTIPLE LINEAR REGRESSION MODELS; MUNICIPAL WASTEWATER TREATMENT PLANTS; NEURAL NETWORK MODEL; ON-LINE SENSING; PREDICTIVE PERFORMANCE; TOTAL SOLIDS; WASTEWATER TREATMENT PLANTS; WWTP EFFLUENT;

EFFLUENTS; LINEAR REGRESSION; MICROWAVE SENSORS; OPTIMIZATION; RECLAMATION; SEWAGE PUMPING PLANTS; SEWERS; WASTEWATER; WASTEWATER TREATMENT; WATER TREATMENT PLANTS;

NEURAL NETWORKS;

DATA SET; LINEARITY; MICROWAVE RADIATION; MUNICIPAL SOLID WASTE; NONLINEARITY; NUMERICAL MODEL; OPTIMIZATION; PERFORMANCE ASSESSMENT; REGRESSION ANALYSIS; SENSOR; SEWER NETWORK; WASTEWATER; WATER TREATMENT;

ARTICLE; ARTIFICIAL NEURAL NETWORK; PERFORMANCE; PREDICTION; SEWAGE EFFLUENT; SEWER; SOLID; WASTE WATER MANAGEMENT; WASTE WATER TREATMENT PLANT; FINLAND; METHODOLOGY; ONLINE SYSTEM; PREDICTION AND FORECASTING; REPRODUCIBILITY; SEWAGE; STATISTICAL MODEL; WASTE DISPOSAL;

FINLAND; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); ONLINE SYSTEMS; PREDICTIVE VALUE OF TESTS; REFUSE DISPOSAL; REPRODUCIBILITY OF RESULTS; SEWAGE; WASTE DISPOSAL, FLUID;

EID: 78349293443     PISSN: 02731223     EISSN: None     Source Type: Journal    
DOI: 10.2166/wst.2010.317     Document Type: Article

References (20)

1. Box, G. E. P., Jenkins, G. M. & Reinsel, G. C. 1976 Time Series Analysis: Forecasting and Control. Wiley, New York, p. 746.
2. Choi, D.-J. & Park, H. 2001 A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process. Water Res. 35, 3959-3967.
3. Copp, J. B. (ed.) 2002 The COST Simulation Benchmark-Description and Simulator Manual. Office for Official Publications of the European Communities, Luxembourg, ISBN 92-894-1658-0.
4. Dellana, S. A. & West, D. 2009 Predictive modeling for wastewater applications: linear and nonlinear approaches. Environ. Model. Softw. 24, 96-106.
5. Gruber, G., Bertrand-Krajewski, J. L., De Beneditis, J., Hochedlinger, M. & Lettl, W. 2006 Practical aspects, experiences and strategies by using UV/VIS sensors for long-term sewer monitoring. Water Pract. Technol. 1(1) (doi:10.2166/WPT.2006020).
6. Haykin, S. 1999 Neural Networks: A Comprehensive Foundation, 2nd edition. Prentice Hall, Upper Saddle River, NJ.
7. Henze, M., Grady, C. P. L. Jr., Gujer, W., Marais, G. v. R. & Matsuo, T. 1987 Activated Sludge Model No. 1. IAWQ Scientific and Technical Report No. 1, London, UK.
8. Ingildsen, P. & Olsson, G. 2002 Exploiting online in-situ ammonium, nitrate and phosphate sensors in full-scale wastewater plant operation. Water Sci. Technol. 46(4-5), 139-147.
9. Marsili-Libelli, S. &Müller, A. 1996 Adaptive fuzzy pattern in the anaerobic digestion process. Pattern Recognit. Lett. 17, 651-659.
10. Mjalli, F. S., Al-Asheh, S. & Alfadala, H. E. 2007 Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance. J. Environ. Manage. 83(3), 329-338.
11. Niska, H., Rantamäki, M., Hiltunen, T., Karppinen, A., Kukkonen, J., Ruuskanen, J. & Kolehmainen, M. 2005 Evaluation of an integrated modeling system containing a multi-layer perceptron model and the numerical weather prediction model HIRLAM for the forecasting of urban airborne pollutant concentrations. Atmos. Environ. 39, 6524-6536.
12. Poutiainen, H. & Heinonen-Tanski, H. 2010 Measurement and control practices in Finnish wastewater networks. Water Environ. Res. 82, 236-239.
13. Poutiainen, H., Laitinen, S., Juntunen, P. & Heinonen-Tanski, H. 2009 On-line microwave total solids sensoring in sewage characterization. Water Sci. Technol. 59(7), 1291-1297.
14. Revitt, M. D., Ellis, J. B. & Paterakis, N. 2006 Comparison of tracer techniques for monitoring sewer losses. J. Environ. Monit. 8, 564-571.
15. Rosen, C. & Yuan, Z. 2001 Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering. Water Sci. Technol. 43(7), 147-156.
16. Rosen, C., Larsson, M., Jeppsson, U. & Yuan, Z. 2002 A framework for extreme-event control in wastewater treatment. Water Sci. Technol. 45(4-5), 299-308.
17. Teppola, P., Mujunen, S.-P. & Minkkinen, P. 1997 Partial least squares modeling of an activated sludge plant: a case study. Chemom. Intell. Lab. Syst. 38, 197-208.
18. Vanrolleghem, P. A. & Lee, I.-B. 2003 On-line monitoring equipment for wastewater treatment processes: state of the art. Water Sci. Technol. 47(2), 1-34.
19. Yoo, C. K., Vanrolleghem, P. A. & Lee, I.-B. 2003 Nonlinear modeling and adaptive monitoring with fuzzy and multivariate statistical methods in biological wastewater treatment plants. J. Biotechnol. 105, 135-163.
20. Yoo, C. K., Lee, J.-M., Lee, I.-B. & Vanrolleghem, P. A. 2004 Dynamic monitoring system for full-scale wastewater treatment plants. Water Sci. Technol. 50(11), 163-171.