Modelling of Activated Sludge Process Using Artificial Neuro-Fuzzy Inference System

IJEP 43(8): 686-696 : Vol. 43 Issue. 8 (August 2023)

Saurabh Sahadev1,2, G. Madhu2* and Roy M. Thomas2

1. Government Engineering College, Department of Chemical Engineering, Thrissur, Kerala – 680 009, India
2. Cochin University of Science and Technology, School of Engineering, Kochi, Kerala – 682 022, India


Different approaches for modelling activated sludge process (ASP) have been employed to improve the understanding of the process and control the quality of effluent discharged. But as process is non-linear and complex, modelling the process has been a challenge. In this study, artificial neuro-fuzzy inference system (ANFIS) was applied to predict water quality parameters in treated effluent from sewage treatment plants employing activated sludge process. The study area selected was in central district of southern state of Kerala, India. The parameters investigated were biochemical oxygen demand (BOD), suspended solids (SS) and pH. Statistical parameters of correlation coefficient (R) and root mean square error (RMSE) were used for model evaluation. Fuzzy logic toolbox of MATLAB 2015b was used for modelling and simulation study. It has been found that effluent biological oxygen demand was predicted with correlation coefficient of 0.9396 and root mean square error of 0.043; effluent suspended solids was predicted with correlation coefficient of 0.932 and root mean square value of 0.0862 and effluent pH was predicted with correlation coefficient of 0.75 and root mean square error of 0.1367.


Activated sludge process, ANFIS modelling, Biochemical oxygen demand, Suspended solids, pH, MATLAB


  1. Singh, K. P., et al. 2010. Modelling the performance of bup-flow anaerobic sludge-blanket reactor-based wastewater treatment plant using linear and nonlinear approaches- A case study. Anal. Chim. Acta 658: 1-11.
  2. Dupuit, E., et al. 2007. Decision support methodology using rule-based reasoning coupled to non-parametric measurement for industrial wastewater network management. Env. Model. Softw., 22:1153-1163.
  3. Araromi, D.O., J.A. Sonibare and J.O. Emu-oyibofarhe. 2014. Fuzzy identification of reactive distillation for acetic acid recovery from wastewater. J. Env. Chem., 2: 1394-1403.
  4. Nadiri, A.A., et al. 2018. Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model. J. Clean Prod., 180:539-549.
  5. Hamed, M.M., M.G. Khalafallah and E.A. Hassanien. 2004. Prediction of wastewater treatment plant performance using artificial neural networks. Env. Model. Softw.,19: 919-928.
  6. IS 3025-1. 1987. Methods of sampling and test (physical and chemical) for water and wastewater. Part 1: Sampling. Bureau of Indian Standards, New Delhi.
  7. Jang, J.S.R. 1993. ANFIS-adaptive network-based fuzzy inference system. IEEE Transactions Systems Man Cybernetics. 23: 665-685.
  8. Firat, M. and M. Gungor. 2007. River flow estimation using adaptive neuro-fuzzy inference system. Math Comput. Simul., 75: 87-96.
  9. Guillaume, S. 2001. Designing fuzzy inference systems from data: an interpretability-oriented review. IEEE Transactions Fuzzy Systems. 9: 426-443
  10. Karray, F. O. and C.W. De Silva. 2004. Soft computing and intelligent systems design: theory, tools and applications. Pearson Education Limited, Essex, England.
  11. Mingzhi, H., et al. 2009. Control rules of aeration in a submerged bio-film wastewater treatment process using fuzzy neural networks. Expert Syst. Appl., 36:10428-10437.
  12. Keskin, M.E., O. Terzi and D. Taylan. 2004. Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey. Hydrol. Sci. J., 49(6): 1001-1010.
  13. Mjalli, F.S., S. Al-Ashehand and H.E. Alfadala. 2007. Use of artificial neural network black-box modelling for the prediction of wastewater treatment plants performance. J. Env. Manage., 83: 329-338.
  14. Gayaa, M.S., et al. 2014. ANFIS modelling of carbon and nitrogen removal in domestic wastewater treatment plant. Appl. Mech. Mater., 372:597-601.
  15. Vernieuwe, H., et al. 2005. Comparison of data-driven Takagi-Sugeno models of rainfall- discharge dynamics. J. Hydrol., 302(1-4):173-186.