Simulation of a paper mill wastewater treatment using a fuzzy neural network

Expert Systems with Applications

Volumn 36, Issue 3 PART 1, 2009, Pages 5064-5070

  Mingzhi, Huang ^a   Ma, Yongwen ^a   Jinquan, Wan ^a   Yan, Wang ^a  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Fuzzy neural network; Predictive control; Simulation; Wastewater treatment

Indexed keywords

FUZZY INFERENCE; FUZZY NEURAL NETWORKS; OPTIMIZATION; PAPERMAKING MACHINERY; RECLAMATION; WASTEWATER TREATMENT;

ADAPTIVE FUZZY NEURAL NETWORK; FUZZY NEURAL NETWORK CONTROLLER; GRADIENT DESCENT ALGORITHMS; NON-LINEAR RELATIONSHIPS; OPTIMIZATION PROCEDURES; PERFORMANCE FUNCTIONS; PREDICTIVE CONTROL; SIMULATION;

FUZZY LOGIC;

COAGULATION; CONTROL SYSTEMS; EFFLUENT TREATMENT; FORECASTS; FUZZY LOGIC; NEURAL NETWORKS; NONLINEAR PROGRAMING; OPTIMIZATION; PAPER MAKING; PAPER MILLS; PULP MILLS; RECLAMATION; RECYCLING; WASTE WATERS;

EID: 58349085319     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.06.006     Document Type: Article

References (28)

1. Belanche L., Valdes J.J., Comas J., Roda I.R., et al. Prediction of the bulking phenomenon in wastewater treatment plants. Artificial Intelligence in Engineering 14 4 (2000) 307-317
2. Bongards M. Improving the efficiency of a wastewater treatment plant by fuzzy control and neural networks. Water Science and Technology 3 11 (2001) 189-196
3. Chen J.C., and Chang N.B. Mining the fuzzy control rules of aeration in a submerged biofilm wastewater treatment process. Engineering Applications of Artificial Intelligence (2007)
4. Chen J.C., Chang N.B., and Shieh W.K. Assessing wastewater reclamation potential by neural network model. Engineering Applications of Artificial Intelligence 16 (2003) 149-157
5. Ferrer J., Rodrigo M.A., Seco A., and Penya-roja J.M. Energy saving in the aeration process by fuzzy logic control. Water Science and Technology 38 3 (1998) 209-217
6. Fiter M., Guell D., Comas J., Colprim J., et al. Energy saving in a wastewater treatment process: An application of fuzzy logic control. Environmental Technology 26 11 (2005) 1263-1270
7. Fu C.S., and Poch M. Fuzzy model and decision of COD control for an activated sludge process. Fuzzy Sets Systems 41 (1998) 281-292
8. Haley P., and Soloway D. Generalized predictive control for active flutter suppression. IEEE Control Systems Magazine 17 4 (1997) 64-68
9. Holubar P., Zani L., Hager M., Froschl W., Radak Z., and Braun R. Advanced controlling of anaerobic digestion by means of hierarchical neural networks. Water Research 36 (2002) 2582-2588
10. Hornik K., Stinchcombe M., and White H. Multi-layer feedforward networks are universal approximators. Neural Networks 2 (1989) 359-366
11. Jang S.R. ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man & Cybernetics 23 3 (1993) 665-685
12. Kalker T.J.J., van Goor C.P., Roeleveld P.J., Ruland M.F., and Babuska R. Fuzzy control of aeration in an activated sludge wastewater treatment plant: Design, simulation and evaluation. Water Science and Technology 39 4 (1999) 71-77
13. Ma Y.W., Wu J., and Wan J.Q. Study on hydrolytic-acidification (HA) during the treatment of wastewater in recycled fibers base mills. China Pulp & Paper 25 10 (2006) 22-24
14. Montague G.A., Willis M.J., Tham M.T., and Morris A.J. Artificial neural network based control. International Conference on Control 1 (1991) 266-271
15. Murnleitner E., Becker T.M., and Delgado A. State detection and control of overloads in the anaerobic wastewater treatment using fuzzy logic. Water Research 36 1 (2002) 201-211
16. Rodrigo M.A., Seco A., Ferrer J., Penya-roja J.M., and Valverde J.L. Nonlinear control of an activated sludge aeration process: Use of fuzzy techniques for tuning PID controllers. ISA Transactions 38 (1997) 231-241
17. Serodes J.B., Rodriguez M.J., and Ponton A. Chlorcast: A methodology for developing decision-making tools for chlorine disinfection control. Environmental Modelling and Software 16 (2001) 53-62
18. Song J.J., and Park S. Neural model-predictive control for nonlinear chemical processes. Journal of Chemical Engineering of Japan 26 4 (1993) 347-354
19. Steyer J.P., Rolland D., Bouvier J.C., and Moletta R. Hybrid fuzzy neural network for diagnosis application to the anaerobic treatment of wine distillery wastewater in a fluidized bed reactor. Water Science and Technology 36 6-7 (1997) 209-217
20. Sugeno M., and Kang G.T. Structure identification of fuzzy model. Fuzzy Sets Systems 28 (1998) 15-33
21. Takagi, T., & Sugeno, M. (1985). Derivation of fuzzy control rules from human operator's control actions. In Proceedings of the IFAC symposium on fuzzy informations, knowledge representation and decision analysis, July (pp. 55-60).
22. Takagi T., and Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man & Cybernetics 15 (1985) 116-132
23. Takahashi Y. Adaptive predictive control of nonlinear time varying system using neural network. IEEE International Symposium on Neural Networks 3 (1993) 1464-1468
24. Tay J.H., and Zhang X. Neural fuzzy modeling of anaerobic biological wastewater treatment systems. Journal of Environmental Engineering, ASCE 125 12 (1999) 1149-1159
25. Tay J.H., and Zhang X. A fast predicting neural fuzzy model for high rate anaerobic wastewater treatment systems. Water Research 34 11 (2000) 2849-2860
26. Traore A., Grieu S., Thiery F., Polit M., and Colprim J. Control of sludge height in a secondary settler using fuzzy algorithms. Computers and Chemical Engineering 30 8 (2006) 1235-1242
27. Tsai Y.P., Ouyang C.F., Chiang W.L., and Wu M.Y. Construction of an on-line fuzzy controller for the dynamic activated sludge process. Water Research 28 4 (1994) 913-921
28. Zhang Q., and Stanley S.J. Real-time water treatment process control with neural network. Journal of Environmental Engineering, ASCE 125 2 (1999) 153-160