Advertisement

Model of Aeration System at Biological Wastewater Treatment Plant for Control Design Purposes

Conference paper
  • 390 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1196)

Abstract

The wastewater treatment plant (WWTP) is a dynamic, very complex system, in which the most important control parameter is the dissolved oxygen (DO) concentration. The air is supplied to biological WWTP by the aeration system. Aeration is an important and expensive activity in WWTP. The aeration of sewage fulfils a twofold role. Firstly, oxygen is provided as the main component for biological processes. Secondly, it supports mixing the sludge with the delivered sewage, which helps to treat the sewage. The paper proposes a model of the aeration system for biological WWTP located in Northeast Poland. This aeration system consists of the blowers, the main collector pipeline, three lines of the aeration with different diameters and lengths and diffusers. This system is a nonlinear dynamic system with faster dynamics compared to the internal dynamics of the DO at the biological WWTP. Control of the aeration system is also difficult in terms of control of the DO. A practical approach to model identification and validation is proposed. Simulation tests for aeration system at Matowskie Pastwiska WWTP are presented.

Keywords

Aeration system Modelling Nonlinear system Wastewater treatment plant 

Notes

Acknowledgements

The authors would like to thank the staff of the Matowskie Pastwiska WWTP for their help with access to the plant, information and data.

References

  1. 1.
    Wilderer, P.A., Irvine, R.L., Goronszy, M.: Sequencing Batch Reactor Technology. Scientific and Technical Report No. 10, IWA Publishing, London (2001)Google Scholar
  2. 2.
    Jenkins, T.E.: Aeration Control System Design. A Practical Guide to Energy and Process Optimization. Wiley, New Jersey (2013)CrossRefGoogle Scholar
  3. 3.
    Belchior, C.A.C., Araújo, R.A.M., Landeck, J.A.C.: Dissolved oxygen control of the activated sludge wastewater treatment process using stable adaptive fuzzy control. Comput. Chem. Eng. 37(10), 152–162 (2012)CrossRefGoogle Scholar
  4. 4.
    Vrečko, D., Hvala, N., Stražar, M.: The application of model predictive control of ammonia nitrogen in an activated sludge process. Water Sci. Technol. 64(5), 1115–1121 (2011)CrossRefGoogle Scholar
  5. 5.
    Åmand, L., Carlsson, B.: Optimal aeration control in a nitrifying activated sludge process. Water Res. 46(7), 2101–2110 (2012)CrossRefGoogle Scholar
  6. 6.
    Błaszkiewicz, K., Piotrowski, R., Duzinkiewicz, K.: A model-based improved control of dissolved oxygen concentration in sequencing wastewater batch reactor. Stud. Inf. Control 23(4), 323–332 (2014)Google Scholar
  7. 7.
    Yang, T., Qiu, W., Ma, Y., Chadli, M., Zhang, L.: Fuzzy model-based predictive control of dissolved oxygen in activated sludge processes. Neurocomputing 136, 88–95 (2014)CrossRefGoogle Scholar
  8. 8.
    Piotrowski, R.: Two-Level multivariable control system of dissolved oxygen tracking and aeration system for activated sludge processes. Water Environ. Res. 87(1), 3–13 (2015)CrossRefGoogle Scholar
  9. 9.
    Piotrowski, R., Skiba, A.: Nonlinear fuzzy control system for dissolved oxygen with aeration system in sequencing batch reactor. Inf. Technol. Control 44(2), 182–195 (2015)Google Scholar
  10. 10.
    Santín, I., Pedret, C., Vilanova, R.: Applying variable dissolved oxygen set point in a two level hierarchical control structure to a wastewater treatment process. J. Process Control 28, 40–55 (2015)CrossRefGoogle Scholar
  11. 11.
    Piotrowski, R., Błaszkiewicz, K., Duzinkiewicz, K.: Analysis the parameters of the adaptive controller for quality control of dissolved oxygen concentration. Inf. Technol. Control 45(1), 42–51 (2016)Google Scholar
  12. 12.
    Ruan, J., Zhang, C., Li, Y., Li, P., Yang, Z., Cheng, X., Huang, M., Zhang, T.: Improving the efficiency of dissolved oxygen control using an on-line control system based on a genetic algorithm evolving FWNN software sensor. J. Environ. Manag. 187, 550–559 (2017)CrossRefGoogle Scholar
  13. 13.
    Du, X., Wang, J., Jegatheesan, V., Shi, G.: Dissolved oxygen control in activated sludge process using a neural network-based adaptive pid algorithm. Appl. Sci. 8, 261 (2018)CrossRefGoogle Scholar
  14. 14.
    Piotrowski, R., Brdyś, M.A., Konarczak, K., Duzinkiewicz, K., Chotkowski, W.: Hierarchical dissolved oxygen control for activated sludge processes. Control Eng. Pract. 16(1), 114–131 (2008)CrossRefGoogle Scholar
  15. 15.
    Krawczyk, W., Piotrowski, R., Brdyś, M.A., Chotkowski, W.: Modelling and identification of aeration systems for model predictive control of dissolved oxygen – Swarzewo wastewater treatment plant case study. In: Conference: Proceedings of the 10th IFAC Symposium on Computer Applications in Biotechnology, Cancun, Mexico, 4–6 June 2007 (2007)Google Scholar
  16. 16.
    Renouard, M.P.: Nouvelles règles à calcul pour la détermination des pertes de charge dans les conduites de gaz. Journal des Usines à Gaz, 337–339 (1952)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Electrical and Control EngineeringGdańsk University of TechnologyGdanskPoland

Personalised recommendations