Modeling of Activated Sludge Process Using Sequential Adaptive Neuro-fuzzy Inference System

Document Type : Technical Note



In this study, an adaptive neuro-fuzzy inference system (ANFIS) has been applied to model activated sludge wastewater treatment process of Mobin petrochemical company. The correlation coefficients between the input variables and the output variable were calculated to determine the input with the highest influence on the output (the quality of the outlet flow) in order to compare three neuro-fuzzy structures with different number of parameters. The predictions of the neuro-fuzzy models were compared with those of multilayer artificial neural network models with similar structure. The comparison indicated that both methods resulted in flexible, robust and effective models for the activated sludge system. Moreover, the root mean square of the error for neuro-fuzzy and neural network models were 5.14 and 6.59, respectively, which means the former is the superior method.


1. Fallahpour, M., and Fazeli M. (2008). “The use of novel methods to treat municipal wastewater, aiming at the removal of nitrogen and phosphate.” 14th Civil Student Conference, Semnan. (In Persian)
2. Saeedi, M., and Khalvati Fahiliani, A. (2010). “Application of electro-coalescence to reduce COD of the southern Pars gas refinery wastewater.” J. of Water and Wastewater, 73, 40-48. (In Persian)
3. Nabi Beedhendi, Gh., Jafari, B., Vosoughi, A., Baghvand, A., and Daryabeigi Zand, A. (2012). “Upgrading performance of activated sludge to treat petrochemical wastewater, using low temperature biofilm.” J. of Water and Wastewater, 84, 22-28. (In Persian)
4. Andrews, J. F. (1992). “Modeling and simulation of wastewater treatment systems.” Wat.Sci. Tech., 28, 141-150.
5. Henze, C. P. L., Grady, Jr., Gujer, W., Marais, G. V. R., and Matsuo, T. (1987). Activated sludge model No. 1, IAWQ Scientific and Technical Report No.1, London.
6. Henze, M., Gujer, W., Mino, T., Matsuo, T., Wentzel, M. C. M., and Marais, G. V. R. (1999). Activated sludge model No. 2, IWA Scientific and Technical Report No. 3, London.
7. Gujer, W., Henze, M., Mino, T., and van Loosdrecht, M. C. M. (1999). “Activated sludge model No. 3.” Water Sci. Technol., 39(1), 183-193.
8. Karamooz, M., Tabesh, M., Nazeef, S., and Moridi, A. (2005). “Applications of artificial neural network and neuro-fuzzy inference system to predict pressure of water pipe networks.” J. of Water and Wastewater, 53, 3-14. (In Persian)
9. Cinar, O. (2005).” New tool for evaluation of performance of wastewater treatment plant: Artificial neural network.” Process  Biochemistry, 40, 2980-2984.
10. Hck, M. (1996).” Estimation of wastewater process parameters using neural networks.” Water Sci. Technol., 33(1), 101.
11. Hamed, M., Khalafallah, M., and Hassanien, E. (2004).” Prediction of wastewater treatment plant performance using artificial neural networks.” Environment Modelling and Software, 19, 919-928.
12. Aguado, D., Ribes, J., Montoya, T., Ferrenr, J., and Seco, A. (2009).”A methodology for sequencing batch reactor identification with artificial neural networks: A case study.” Computers and Chemical Engineering, 33, 465-472.
13. Mjalli, F., Alasheh, S., and Alfadala, H. E. (2007).”Use of artificial neural network black- box  modeling  for the prediction of wastewater treatment plants.” Performance Journal of Environment Management, 83, 329-338.
14. Jang, J. S. R. (1993). “ANFIS: Adaptive-network-based fuzzy inference system.” IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.
15. Steyer, J. P., Rolland, D., and Bouvier, J. C. (1997). “Hybrid fuzzy neural network for diagnosis application to the anaerobic treatment of wine distillery wastewater in a fluidized bed reactor.” Water Science and Technology, 6(6-7), 209-217.
16. Tay, J. H., and Zhang, X. (1999). “Neural fuzzy modeling of anaerobic biological wastewater treatment systems.” J. of Environmental Engineering, 125(12),1149-1159.
17. Tay, J. H., and Zhang, X. (2000). “A fast predicting neural fuzzy model for high rate anaerobic wastewater treatment systems.” Water Research, 34(11), 2849-2860.
18. Yordanova, S., Noikova, N., Petrova, R., and Tzvetkov, P. (2005).” Neuro-fuzzy modeling on experimental data in anaerobic digestion of organic waste in waters.” IEEE Technology and Applications, Sofia, 5-7.
19. Civelekoglu, G., Perendeci, A., Yigit, N. O., and Kitis, M. (2007).”Modeling carbon and nitrogen removal in an industrial wastewater treatment plant using an adaptive network based fuzzy inference system.” Clean, 35(6), 617-625.
20. Mingzhi, H., Yongwen, M., Jinquan, W., and Yan, W. (2009). “Simulation of a paper mill wastewater treatment using a fuzzy neural network.” Expert Systems with Applications, 36, 5064-5070.
21. Pai, T. Y., Wan, T. J., Hsu, S. T., Chang, T. C., Tsai, Y. P., Lin, C. Y., Su, H. C., and Yu, L. F. (2009). “Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent.” Computers and Chemical Engineering, 33,1272-1278.