Neural Fuzzy Modeling of Anaerobic Biological Wastewater Treatment Systems
Publication: Journal of Environmental Engineering
Volume 125, Issue 12
Abstract
Anaerobic biological wastewater treatment systems are difficult to model because their performance is complex and varies significantly with different reactor configurations, influent characteristics, and operational conditions. Instead of conventional kinetic modeling, advanced neural fuzzy technology was employed to develop a conceptual adaptive model for anaerobic treatment systems. The conceptual neural fuzzy model contains the robustness of fuzzy systems, the learning ability of neural networks, and can adapt to various situations. The conceptual model was used to simulate the daily performance of two high-rate anaerobic wastewater treatment systems with satisfactory results obtained.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
Aarts, R. J., and Suviranta, A. (1990). “Expert system in enzyme production control.” Food Biotechnology, 4(1), 301–315.
2.
Boscolo, A., Mangiavacchi, F., Drius, F., Rongione, F., Pavan, P., and Cecchi, F. (1993). “Fuzzy control of an anaerobic digester for the treatment of the organic fraction of municipal solid waste (MSW).” Water Sci. and Technol., 27(2), 57–68.
3.
Campos, C. M. M. ( 1990). “Physical aspects affecting granulation in UASB reactors,” PhD thesis, University of Newcastle upon Tyne, England.
4.
de Zeeuw, W. J. (1987). “Granular sludge in UASB reactors. Granular anaerobic sludge: Microbiology and technology.” Proc., of GASMAT Workshop, G. Lettinga, et al., eds., 132–145.
5.
Draaijer, H., Maas, J. A. W., Schaapman, J. E., and Khan, A. (1992). “Performance of the 5 MLD UASB reactor for sewage treatment at Kanpur, India.” Water Sci. and Technol., 25, 123–133.
6.
Emmanouilides, C., and Petyrou, L. (1997). “Identification and control of anaerobic digesters using adaptive, on-line trained neural networks.” Computers Chem. Engrg., 21(1), 113–143.
7.
Filev, D. P. (1984). Proc., 1st Eur. Workshop on Real Time Control of Large Scale Sys.
8.
Filev, D. P., Kishimoto, M., Sengupta, S., Yoshida, T., and Taguchi, H. (1985). “Application of the fuzzy theory to simulation of batch fermentation.” J. Ferment. Technol., 63(6), 545–553.
9.
Fu, C.-S., Wang, S.-Q., and Wang, J.-C. (1990). “Region fuzzy control for batch processes. Part 2. Feed timing prediction and control for an antibiotic fermentation production process.” Int. J. of Sys. Sci., 21(10), 1911–1921.
10.
Goh, T. C. (1995). “Modeling soil correlations using neural networks.”J. Comp. in Civ. Engrg., ASCE, 9(4), 275–278.
11.
Guwy, A. J., Hawjes, F. R., Wilcox, S. J., and Hawkes, D. L. (1997). “Neural network and on-off control of bicarbonate alkalinity in a fluidized-bed anaerobic digester.” Water Res., 31(8), 2019–2025.
12.
Harper, S. R., and Suidan, M. T. (1991). “Anaerobic treatment kinetics: Discussers' report.” Water Sci. and Technol., 24(8), 61–78.
13.
Hickey, R. F., Wu, W. M., Viega, M. C., and Jone, R. (1991). “Start-up, operation, monitoring and control of high-rate anaerobic treatment systems.” Water Sci. and Technol., 24(8), 207–256.
14.
Horikawa, S., Furuhashi, T., and Uchikawa, Y. (1992). “On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm.” IEEE Trans. Neural Networks, 3(5), 801–806.
15.
Hulshoff Pol, L. W., and Lettinga, G. (1986). “New technologies for anaerobic wastewater treatment.” Water Sci. and Technol., 18, 41–53.
16.
Jang, J.-S. R. (1993). “ANFIS: Adaptive-network-based fuzzy inference system.” IEEE Trans. Sys., Man and Cybernetics, 23(3), 665–685.
17.
Jang, J.-S. R., and Gulley, N. (1995). Fuzzy logic toolbox user's guide. The Mathworks, Inc.
18.
Jang, J.-S. R., and Sun, C.-T. (1995). “Neuro-fuzzy modeling and control.” Proc., IEEE, 83(3), 378–405.
19.
Jeris, J. S. (1983). “Industrial wastewater treatment using anaerobic fluidized bed reactors.” Water Sci. and Technol., 15, 1169–1176.
20.
Keller, J. M., Krishnapuram, R., and Rhee, F. C. H. (1992a). “Evidence aggregation networks for fuzzy logic interface.” IEEE Trans. Neural Networks, 3(5), 761–769.
21.
Keller, J. M., Yager, R. R., and Tahani, H. (1992b). “Neural network implementation of fuzzy logic rules with neural networks.” Int. J. Approximate Reasoning, 6, 221–240.
22.
Kishimoto, M., Kitta, Y., Takeuchi, S., Nakajima, M., and Yoshida, T. (1991). “Computer control of glutamic acid production based on fuzzy clusterization of culture phases.” J. of Fermentation and Bioengineering, 72(2), 110–114.
23.
Kosko, B. (1992). “Fuzzy systems as universal approximators,” Proc., IEEE Int. Conf. Fuzzy Sys., 1153–1162.
24.
Lettinga, G., and Hulshoff Pol, L. W. (1991). “UASB-process design for various types of wastewaters.” Water Sci. and Technol., 24(8), 87–107.
25.
Lettinga, G., van Velsen, A. F., Hobma, S. W., de Zeeuw, W., and Klapwyk, A. (1980). “Use of the upflow sludge blanket (USB) reactor concept for biological wastewater treatment especially for anaerobic treatment.” Biotech. Bioeng., 22, 699–734.
26.
Lin, C. T., and Lee, C. S. G. (1996). Neural fuzzy systems. Prentice-Hall, Englewood Cliffs, N.J.
27.
Lin, L. ( 1991). “An anaerobic treatment process model: Development and calibration,” PhD dissertation, Michigan Technological University, Houghton, Mich.
28.
Malina, J. F., and Pohland, F. G. ( 1992). “Design of anaerobic processes for the treatment of industrial and municipal wastes.” Water quality management library, Vol. 7, W. W. Eckenfelder, et al., eds., Technomic Publishing Co., Lancaster, Pa.
29.
Marsili-Libeli, S., and Muller, A. (1996). “Adaptive fuzzy pattern recognition in the anaerobic digestion process.” Pattern Recognition Letters, 17, 651–659.
30.
Shi, Z., and Shimizu, K. (1992). “Neural-fuzzy control of bioreactor systems with pattern recognition.” J. of Fermentation and Bioengineering, 74(1), 39–45.
31.
Speece, R. E. (1996). Anaerobic biotechnology for industrial wastewaters. Archae Press, Nashville, Tenn.
32.
Sugeno, M., and Kang, J. T. (1986). “Fuzzy modeling and control of multilayer incinerator.” Fuzzy Sets and Systems, 18, 329–346.
33.
Takagi, H., and Hayashi, I. (1991). “NN-driven fuzzy reasoning.” Int. J. Approximate Reasoning, 5(3), 191–212.
34.
Takagi, T., and Sugeno, M. (1983). “Derivation of fuzzy control rules from human operators actions.” Proc., IFAC Symp. on Fuzzy Information, 55–60.
35.
Takagi, T., and Sugeno, M. (1985). “Fuzzy identification systems and its application to modeling and control.” IEEE Trans. Sys., Man and Cybernetics, 15, 116–132.
36.
Works, G. A. (1989). “Neural network basics.” Proc., AUTOFACT'89, 29-1–29-9.
37.
Yager, R. R., and Filev, D. P. (1994). Essential of fuzzy modeling and control. Wiley (a Wiley-Interscience Publication), New York.
38.
Yamada, Y., Hanneda, K., Murayama, S., and Shiomi, S. (1991). “Application of fuzzy control system to coenzyme Q10.” J. of Chemical Engrg. of Japan, 24(1), 94–99.
39.
Zadeh, L. A. (1965). “Fuzzy sets.” Information and Control, 8, 338–353.
40.
Zadeh, L. A. (1973). “Outline of a new approach to the analysis of complex systems and decision processes.” IEEE Trans. Sys., Man, and Cybernetics, 3, 28–44.
Information & Authors
Information
Published In
History
Received: Aug 27, 1998
Published online: Dec 1, 1999
Published in print: Dec 1999
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.