Prediction of effluent quality of an anaerobic treatment plant under unsteady state through ANFIS modeling with on-line input variables

Chemical Engineering Journal

Volumn 145, Issue 1, 2008, Pages 78-85

  Perendeci, Altunay ^a,c   Arslan, Sever ^a   Çelebi, Serdar S ^b   Tanyolaç, Abdurrahman ^b  

^a Turkish Sugar Factories Corporation   (Turkey)

^b HACETTEPE UNIVERSITY   (Turkey)

^c AKDENIZ UNIVERSITY   (Turkey)

Author keywords

Adaptive network based fuzzy inference system; Anaerobic wastewater treatment; Modeling; On line variables

Indexed keywords

EFFLUENT TREATMENT; EFFLUENTS; FUZZY SYSTEMS; NETWORK PROTOCOLS; SENSOR NETWORKS; WATER QUALITY;

ADAPTIVE NETWORK-BASED FUZZY INFERENCE SYSTEM; ANAEROBIC WASTEWATER TREATMENT; MODELING; ON-LINE VARIABLES;

FUZZY INFERENCE;

EID: 53849129539     PISSN: 13858947     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cej.2008.03.008     Document Type: Article

References (24)

1. Steyer J.P., Bouvier J.C., Conte T., Gras P., and Sousbie P. Evaluation of a four year experience with a fully instrumented anaerobic digestion. Water Sci. Technol. 45 4/5 (2002) 495-502
2. Premier G.C., Dinsdale R., Guwy A.J., Hawkes F.R., Hawkes D.L., and Wicox S.J. A comparison of the ability of black box and neural network models of arx structure to represent a fluidized bed anaerobic digestion process. Water Res. 33 4 (1997) 1027-1037
3. Müller A., Marsili-Libelli S., Aivasidis A., Lloyd T., Kroner S., and Wandrey C. Fuzzy control of disturbances in a wastewater treatment plant. Water Res. 31 12 (1997) 3157-3167
4. Flores J., Arcay B., and Arias J. An intelligent system for distributed control of an anaerobic wastewater treatment process. Eng. Appl. Artif. Intell. 13 (2000) 485-494
5. Harper S.R., and Suidan M.T. Anaerobic treatment kinetics: discussers' report. Water Sci. Technol. 24 8 (1991) 61-78
6. Tay J.H., and Zhang X. A fast predicting neural fuzzy model for high-rate anaerobic wastewater treatment systems. Water Res. 34 11 (2000) 2849-2860
7. Aubrun C., Theilliol D., Harmand J., and Steyer J.P. Software sensor design for COD estimation in an anaerobic fluidized bed reactor. Water Sci. Technol. 43 7 (2001) 115-120
8. Karama A., Bernard O., Genovesi A., Dochain D., Benhammou A., and Steyer J.P. Hybrid modeling of anerobic wastewater treatment processes. Water Sci. Technol. 43 1 (2001) 43-50
9. Belanche L.A., Valdes J.J., Comas J., Roda I.R., and Poch M. Towards a model of input-output behavior of wastewater treatment plants using soft computing techniques. Environ. Model. Softw. 14 (1999) 409-419
10. Jang R.J.S. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man and Cybern. 23 3 (1993) 665-685
11. Jang R.J.S., and Sun C.T. Functional equivalence between radial basis function networks and fuzzy inference system. IEEE Trans. Neural Netw. 4 1 (1993) 156-159
12. Jang R.J.S., and Sun C.T. Neuro-fuzzy modeling and control. Proc. IEEE 83 3 (1995) 378-405
13. Jang R.J.S., and Gulley N. Fuzzy Logic Toolbox User's Guide (1995), The Mathworks Inc., 24 Prime Park Way, Natick, MA 01760-1500
14. Tay J.H., and Zhang X. Neural fuzzy modeling of anaerobic biological wastewater treatment systems. J. Environ. Eng. 125 12 (1999) 1149-1159
15. El-Mansi E.M.T., and Bryce C.F.A. Fermentation, Microbiology and Biotechnology (1999), Taylor & Francis Inc., 325, Chestnut Street, 8th Floor, Philadelphia, PA, USA. 0-7484-0733-2
16. Weiland P., and Rozzi A. The startup, operation and monitoring of high rate anaerobic treatment systems: discusser's report. Water Sci. Technol. 24 8 (1991) 257-277
17. APHA, AWWA, and WEF. Standard Methods for the Examination of Water and Wastewater. 19th ed. (1995), APHA, Washington
18. Perendeci A., Arslan S., Tanyolaç A., and Çelebi S.S. Evaluation of input variables in adaptive-network based fuzzy inference system (anfis) modeling for an anaerobic wastewater treatment plant under unsteady state. J. Environ. Eng. 133 7 (2007) 765-771
19. Lee D.S., Jeon C.O., Park J.M., and Chang K.S. Hybrid neural network modeling of a full-scale industrial wastewater treatment process. Biotechnol. Bioeng. 78 6 (2002) 670-682
20. Tomida S., Hanai T., Honda H., and Kobayashi T. Construction of COD simulation model for activated sludge process by recursive fuzzy neural network. J. Chem. Eng. Jpn. 34 3 (2001) 369-375
21. Kim Y., Ko J., Boo K.M., Woo H.J., and Kim C. Software sensor using PNN model and knowledge base for sequencing batch reactor. In: Liong, Phoon, and Babovic (Eds). Proceeding of the 6th International Conference on Hydroinformatics. World Scientific Publishing Company (2004). 981-238-787-0
22. Grieu S., Traore A., Polit M., and Colprim J. Prediction of parameters characterizing the state of a pollution removal biologic process. Eng. Appl. Artif. Intell. 18 (2005) 559-573
23. 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 Res. 36 (2002) 2582-2588
24. Holubar P., Zani L., Hager M., Froschl W., Radak Z., and Braun R. Start up and recovery of a biogas reactor using a hierarchical neural network-based control tool. J. Chem. Technol. Biotechnol. 78 (2003) 847-854