THE DIGITAL TWIN OF THE AEROTANK IN THE TASKS OF INTELLIGENT MANAGEMENT OF SEWAGE TREATMENT PLANTS

In the context of water scarcity and tightening environmental requirements, improving the energy efficiency of biological wastewater treatment processes has become particularly important. The aeration tank is one of the most energy-intensive and dynamically complex components units, strongly affected by the variability in influent flow and composition. Conventional PID control, do not provide predictive disturbance compensation and often result in excessive aeration and increased energy consumption. The study proposes an intelligent control approach based on a digital twin, neural network-based influent flow forecasting, and model predictive control (MPC). The digital twin represents a dynamic model of the biological process incorporating key state variables, including substrate, activated sludge, and dissolved oxygen concentrations. A neural network model is used to predict the diurnal variability coefficient of influent flow based on long-term statistical observations. The predicted values are incorporated into the MPC algorithm as measured disturbances, enabling anticipatory aeration system. Simulation results show stabilisation of dissolved oxygen under variable inflow conditions and reduces energy consumption by preventing over-aeration. The proposed architecture is suitable for implementation within existing industrial PLC-SCADA systems in advisory MPC mode and improves energy efficiency, robustness, and environmental performance.

1. UNESCO World Water Development Report. – Paris: UNESCO Publishing, 2023.

2. Drechsel P. The WHO Guidelines for Safe Wastewater Use in Agriculture: A Review of Implementation Challenges and Possible Solutions in the Global South / P. Drechsel, M. Qadir, D. Galibourg // Water. – 2022. – Vol. 14, № 6. – P. 864. https://doi.org/10.3390/w14060864.

3. Rosso D. Energy efficiency in wastewater treatment plants / D. Rosso, M.K. Stenstrom // Water Research. – 2006. – Vol. 40. № 4. – P. 737-746.

4. Energy efficiency in wastewater treatment plants / J. Ahn et al // Renewable and Sustainable Energy Reviews. – 2017. – Vol. 75. – P. 1136-1143.

5. Biological Wastewater Treatment: Principles, Modelling and Design / M. Henze et al. – London: IWA Publishing, 2008. – 511 p.

6. Activated Sludge Models ASM1, ASM2, ASM2d and ASM3 / M. Henze et al. – London: IWA Publishing, 2000. – 130 p. https://doi.org/10.2166/9781780402369.

7. Åström K.J. PID Controllers: Theory, Design, and Tuning / K.J. Åström, T. Hägglund. – 2nd ed. – Research Triangle Park: Instrument Society of America, 1995. – 360 p.

8. Åmand L. Aeration control – a review / L. Åmand, G. Olsson, B. Carlsson // Water Science and Technology. – 2013. – Vol. 67, № 11. – P. 2374-2398. https://doi.org/10.2166/wst.2013.139.

9. Takagi T. Fuzzy identification of systems and its applications to modeling and control / T. Takagi, M. Sugeno // IEEE Transactions on Systems, Man, and Cybernetics. – 1985. – Vol. SMC-15. – № 1. – P. 116-132.

10. Meyer U. Fuzzy-control for improved nitrogen removal and energy saving in WWTPs with predenitrification / U. Meyer, H.J. Pöpel // Water Science and Technology. – 2003. – Vol. 47, № 11. – P. 69-76.

11. Qin S.J. A survey of industrial model predictive control technology / S.J. Qin, T.A. Badgwell // Control Engineering Practice. – 2003. – Vol. 11, № 7. – P. 733-764.

12. Benchmark for evaluating control strategies in wastewater treatment plants / J. Alex et al // Proceedings of the European Control Conference (ECC 1999). – Karlsruhe, Germany, 1999. – P. 3746-3751.

13. Olsson G. ICA and me – A subjective review / G. Olsson // Water Research. – 2012. – Vol. 46, № 6. – P. 1585-1624. https://doi.org/10.1016/j.watres.2011.12.054.

14. Kim H.-S. Fuzzy model-based predictive control of dissolved oxygen in activated sludge processes / H.-S. Kim, J.-H. Lee // Neurocomputing. – 2014. – Vol. 136. – P. 88-95. https://doi.org/10.1016/j.neucom.2014.01.025.

15. Åmand L. Aeration control – a review / L. Åmand, G. Olsson, B. Carlsson Aeration // Water Science and Technology. – 2013. – Vol. 67, № 11. – P. 2374-2398. https://doi.org/10.2166/wst.2013.139.

16. Benchmark Simulation Model No. 1 (BSM1): Technical Report TEIE-7229 / J. Alex et al. – Lund: Department of Industrial Electrical Engineering and Automation, Lund University, 2008. – 62 p.

17. Çengel Y.A. Fluid Mechanics: Fundamentals and Applications / Y.A. Çengel, J.M. Cimbala. – 5th ed. – New York: McGraw-Hill Education, 2024. – 944 p.

18. Takács I. A dynamic model of the clarification thickening process / I. Takács, G.G. Patry, D. Nolasco // Water Research. – 1991. – Vol. 25, № 10. – P. 1263-1271. https://doi.org/10.1016/0043-1354(91)90066-Y.

19. Olsson G. Wastewater Treatment Systems: Modelling, Diagnosis and Control / G. Olsson, B. Newell. – London: IWA Publishing, 1999. – 742 p.

20. Activated Sludge Model № 1 / M. Henze et al. – London: IWA Publishing, 1986. – 33 p.

21. Wastewater Engineering: Treatment and Resource Recovery / G. Tchobanoglous et al. – New York: McGraw-Hill Education, 2014. – 2000 p.

22. Spravochnik po nailuchshim dostupnym tekhnikam «Ochistka stochnykh vod tsentralizovannykh sistem vodootvedeniya naselennykh punktov»: utv. Postanovleniem Pravitel'stva Respubliki Kazakhstan ot 17 maya 2025 g. № 348. – Astana, 2025. (In Russian).

23. Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems // F.-J. Kahlen et al. – Cham: Springer International Publishing, 2017. – P. 85-113. https://doi.org/10.1007/978-3-319-38756-7_4.

24. Wastewater Engineering: Treatment and Resource Recovery / G. Tchobanoglous et al. – New York: McGraw-Hill Education, 2014. – 2049 p.

25. Montgomery J.M. Process Control of Activated Sludge Systems / J.M. Montgomery. – Lancaster: Technomic Publishing Company, 1985. – 349 p.

26. Jeppsson U. Benchmarking of Control Strategies for Wastewater Treatment Plants / U. Jeppsson, J. Alex. – London: IWA Publishing, 2003. – 141 p.

27. Ochistka stochnykh vod. Biologicheskie i khimicheskie protsessy / per. s angl. pod red. S. V. Kalyuzhnogo. – M.: Mir, 2004. – 480 s. (In Russian).