PI and Fuzzy Control for P-removal in Wastewater Treatment Plant

  • Hongyang Xu
  • Ramon Vilanova


Due to the complex and non linear character, wastewater treatment process is difficult to be controlled. The demand for removing the pollutant, especially for nitrogen (N) and phosphorus (P), as well as reducing the cost of wastewater treatment plant is an important research theme recently. Thus, in this paper, the benchmark proposed default control strategy and 10 additional control strategies are applied on the combined biological P and N removal Benchmark Simulation Model No.1 (BSM1-P). In addition, according to the results of applying PI controllers, as usual, we also chose the group with the better performance, as well as the default control strategy, to replace the PI controllers with fuzzy controllers. In this way, it can be seen that in all cases the quality of effluent of the controlled process could be improved in some degree; and the fuzzy controllers get a better phosphorus removal.


[1] Ingildsen, P. (2002); Realising full-scale control in wastewater treatment systems using in situ nutrient sensors, Ph.D. Thesis, Department of Industrial Electrical Engineering and Automation, Lund University, Sweden.

[2] Vrecko, D., Hvala, N., Carlsson, B. (2003); Feedforward-feedback control of an activated sludge process: a simulation study, Water Sci. Technol., 47 (12): 19 - 26.

[3] Lindberg, C.F. (1997): Control and estimation strategies applied to the activated sludge process, Ph.D. Thesis, Department of Systems and Control, Uppsala University, Sweden.

[4] Yuan, Z., Oehmen, A., Ingildsen, P. (2002); Control of nitrate recirculation flow in preden- itrification systems, Water Sci. Technol., 45 (4-5): 29-36.

[5] Jeppsson, U., Alex, J., Batstone, D., Benedetti, L., Comas, J., Copp, J., Corominas, L., Flores-Alsina, X., Gernaey, K., and Nopens, I. (2011); Quo vadis benchmark simulation models, 8th IWA symposium on systems analysis and integrated assessment, 493-506.

[6] Copp, J. (2002); The COST simulation benchmark: Description and simulator manual, Office for official publications of the European Community, Luxembourg.

[7] Gernaey V., Jorgensen B. (2004); Benchmarking combined biological phosphorus and nitro- gen removal wastewater treatment process, Control Engineering Practice, 12: 357-373.

[8] L.A. Zadeh (1965); Fuzzy sets, Information and Control, 8 (3): 338-353.

[9] E.H. Mamdani (1974); Applications of fuzzy algorithms for control of a simple dynamic plant, Proceedings of the IEE 121, 12: 1585-1588.

[10] E.H. Mamdani, S. Assilian (1975); An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, 7 (1):1-13.

[11] U.-C. Moon, K.Y. Lee (2003); Hybrid algorithm with fuzzy system and conventional PI control for the temperature control of TV glass furnace, IEEE Transactions on Control Systems Technology, 11(4): 548-554.

[12] M. Ramirez, R. Haber, V. Pena, I. Rodriguez (2004); Fuzzy control of a multiple hearth urnace, Computers in Industry, 54(1): 105-113.

[13] M. Onat, M. Dogruel (2004); Fuzzy plus integral control of the effluent turbidity in direct filtration, IEEE Transactions on Control Systems Technology, 12(1): 65-74.

[14] M. Eftekhari, L. Marjanovic, P. Angelov (2003); Design and performance of a rule-based controller in a naturally ventilated room, Computers in Industry, 52(3); 299-326.

[15] J.N. Lygouras, V.S. Kodogiannis, T. Pachidis, K.N. Tarchanidis, C.S. Koukourlis (2008);

Variable structure TITO fuzzy-logic controller implementation for a solar airconditionint system, Applied Energy, 85(4): 190-203.

[16] A. Maidi, M. Diaf, J.-P. Corriou (2008), Optimal linear PI fuzzy controller design of a heat exchanger, Chemical Engineering and Processing: Process Intensification, 47(5): 938-945.

[17] Y.-H. Lee, R. Kopp (2001); Application of fuzzy control for a hydraulic forging machine, Fuzzy Sets and Systems, 118(1): 99-108.

[18] Ernst, M., Thomas, M.B., Antonio D. (2002); State detection and control of overloads in the anaerobic wastewater treatment using fuzzy logic, Water research, 36: 201-211.

[19] Radu-Emil Precup, Hans Hellendoorn (2011); A survey of industrial applications of fuzzy control, Computers in Industrial, 62: 213-226

[20] Wentzel, M. C., Comeau, Y., Ekama, G. A., van Loosdrecht, M.C.M., Brdjanovic, D. (2008);

Enhanced Biological Phosphorus Removal, in: Henze, M., van Loosdrecht, M.C. M., Ekama, G. A., Brdjanovic, D. (Eds.), Biological Wastewater Treatment: Principles, Modelling and Design, IWA Publishing, London, UK, 154-220.

[21] Vanrolleghem, P. A., & Gillot, S. (2002); Robustness and economic measures as control benchmark performance criteria, Water Science and Technology, 45(4-5): 117-126.
How to Cite
XU, Hongyang; VILANOVA, Ramon. PI and Fuzzy Control for P-removal in Wastewater Treatment Plant. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 10, n. 6, p. 176-191, oct. 2015. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2081>. Date accessed: 11 aug. 2020. doi: https://doi.org/10.15837/ijccc.2015.6.2081.


wastewater treatment plant, PI controllers, fuzzy control, P removal