The control of wastewater treatment process based on fuzzy neural network

Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

Volumn 2, Issue , 2006, Pages 9347-9351

  Chaobin, Liu ^a   Junfei, Qiao ^a   Fangfang, Zhang ^a  

^a BEIJING UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Dissolved oxygen; Fuzzy neural network; Intelligent control; Wastewater treatment

Indexed keywords

ACTIVATED SLUDGE PROCESS; DISSOLVED OXYGEN; FUZZY NEURAL NETWORKS; NONLINEAR SYSTEMS; TIME VARYING SYSTEMS; WASTEWATER TREATMENT; WATER QUALITY;

CONTAMINATION REMOVAL; CONTROL RULES; WASTEWATER TREATMENT PROCESS CONTROL;

INTELLIGENT CONTROL;

EID: 34047194862     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WCICA.2006.1713810     Document Type: Conference Paper

References (17)

1. Vincent J. Tunnel, David Williams and Karl O. Jones. Dissolved oxygen control strategy comparison for an activated sludge process. UKACC International Conference on Control'98: 1102-1105.
2. Dochain D., Design of adaptative controllers for non-linear stirred tank bioreactors: extension to the MIMO situation, Journal of Process Control, 1, 4148, 1991.
3. Nejjari F., Benhannou A., Dahhou B. and Row G. Nonlinear multivariable control of a biological wastewater treatment process, ECC 97, Brussels, Belguim, 1997.
4. Okubo, T., K. Kubo. M. Hosonmi A. Mwakami. A knowledge-based decision support system for selecting small-scale wastewater treatment processes. Water Science& Technology. 1994, 30(2): 175-184.
5. Serra, P., Sanchez, M., Lafuente J., Cortes, U. and Porch, M. ISCWAP: A knowledge-based system for supervising activated sludge process. Computer Chem. Eng. 1997, 21(2), 211-221.
6. Sanchez, M., Cortes. U., R.-Roda., L., Poch, M. and Lafuente, J. Learning and adaptation in WWTP through case-based reasoning. Micromputen in Civil Engineering. 1997,12(4), 251-266, (Special issue: Machine Learning).
7. Capodaglio, A.G., Jones, H.V., Novotny, V.and Feng, X. Sludge bulking analysis and forecasting: Application of system identification and artificial neural computing technologies. Water Research. 1991, 25(10), 1217-1224.
8. Cote, M., Grandjean, B.P.A., Lessard, P. and Thibault, J. Dynamic modeling of the activated sludge process: Improving predictive using neural networks. Water Research. 1995, 29(4), 995-1004.
9. Sancbea M., Cortes U., Laruente J., R.-Roda I., Poch M. DAI-DFPUR, a Distributed Architecture for Wastewater Treatment Plants Supervision. Artificial Intelligence in Engineering. 1996, 10(3), 275-285.
10. Zhao, H., O. J. Hao and T. J. McAvoy. Modeling nutrient dynamics in a sequencing batch reactor, Journal of Environmental Engineering 123(1997),311-319.
11. th IEEE International Symposium on Intelligent Control. 2000:169-173
12. Orhon D., and Artan N., Modeling of activated sludge system. Technomic Publisher Co., Lancaster, PA, USA. 1994.
13. Rong-Jong Wai. Robust fuzzy neural network control for nonlinear motor-toggle servomechanism. Fuzzy Sets and Systems 139(2003) 185-208.
14. Feipeng Da, Member, IEEE, and Wenzhong Song. Fuzzy neural networks for direct adaptive control. IEEE Transactions on Industrial Electronics. 2003, 50(3): 507-513.
15. CJ 3082-1999,China. Water Standard of Wastewater Allowed to Drain into The Urban Sewer.
16. Zijie Zhang. Theories and designs of wastewater treatment. China Architecture & Building Press, Beijing, China. 2003.
17. GB 18918-2002, China. Discharge Standard of Pollutants for Municipal Wastewater Treatment Plant.