Abstract
This study aims to propose a multi-point integrated real-time control method based on discrete dynamic water level variations, which can be realized only based on the programmable logic controller (PLC) system without using a complex mathematical model. A discretized water level control model was developed to conduct the real-time control based on data-automation. It combines the upstream pumping stations and the downstream influent pumping systems of wastewater treatment plant (WWTP). The discretized water level control method can regulate dynamic wastewater pumping flow of pumps following the dynamic water level variation in the sewer system. This control method has been successfully applied in practical integrated operations of sewer-WWTP following the sensitive flow disturbances of the sewer system. The operational results showed that the control method could provide a more stabilized regulate pumping flow for treatment process; it can also reduce the occurrence risk of combined sewer overflow (CSO) during heavy rainfall events by increasing transport capacity of pumping station and influent flow in WWTP, which takes full advantage of storage space in the sewer system.
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References
Berggren K, Olofsson M, Viklander M, Svensson G, Gustafsson A M (2012). Hydraulic impacts on urban drainage systems due to changes in rainfall caused by climatic change. Journal of Hydrologic Engineering, 17(1): 92–98
Buerge I J, Poiger T, Müller M D, Buser H R (2006). Combined sewer overflows to surface waters detected by the anthropogenic marker caffeine. Environmental Science & Technology, 40(13): 4096–4102
Campisano A, Modica C (2002). Pid and plc units for the real-time control of sewer systems. Water Science and Technology, 45(7): 95–104
Cembrano G, Quevedo J, Salamero M, Puig V, Figueras J, Martí J (2004). Optimal control of urban drainage systems: A case study. Control Engineering Practice, 12(1): 1–9
Chiang Y M, Chang L C, Tsai M J, Wang Y F, Chang F J C (2011). Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks. Hydrology and Earth System Sciences, 15(1): 185–196
Dong X, Huang S, Zeng S (2017). Design and evaluation of control strategies in urban drainage systems in Kunming city. Frontiers of Environmental Science & Engineering, 11(4): 13
Fatemeh J, Jamshid M S, Jafar Y, Hoon K J (2018). Real-time operation of pumping systems for urban flood mitigation: Single-period vs. multi-period optimization. Water Resources Management, 32(14): 4643–4660
García L, Barreiro-Gomez J, Escobar E, Téllez D, Quijano N, Ocampo-Martinez C (2015). Modeling and real-time control of urban drainage systems: A review. Advances in Water Resources, 85: 120–132
Heeringen K J V, Gooijer J, Schwanenberg D (2013). Practical application of drainage system control by using MPC in Noorderzijlvest. Proceedings of the EGU General Assembly Conference, 2013, Vienna (Austria), 15: 11965–11974
Klepiszewski K, Schmitt T G (2002). Comparison of conventional rule based flow control with control processes based on fuzzy logic in a combined sewer system. Water Science and Technology, 46(6–7): 77–84
Lemos J, Pinto L (2012). Distributed linear-quadratic control of serially chained systems: Application to a water delivery canal. Control Systems IEEE, 32(6): 26–38
Mailhot A, Talbot G, Lavallée B (2015). Relationships between rainfall and Combined Sewer Overflow (CSO) occurrences. Journal of Hydrology (Amsterdam), 523: 602–609
Marchionni V, Lopes N, Mamouros L, Covas D (2014). Modelling sewer systems costs with multiple linear regression. Water Resources Management, 28(13): 4415–4431
Mollerup A L, Mikkelsen P S, Thornberg D, Sin G (2017). controlling sewer systems: A critical review based on systems in three EU cities. Urban Water Journal, 14(4): 435–442
Ning X, Liu Y, Chen J, Dong X, Li W, Liang B (2013). Sustainability of urban drainage management: A perspective on infrastructure resilience and thresholds. Frontiers of Environmental Science & Engineering, 7(5): 658–668
Ocampomartinez C, Vicenç P, Cembrano G, Quevedo J (2013). Application of predictive control strategies to the management of complex networks in the urban water cycle. Control Systems IEEE, 33(1): 15–41
Olsson J, Amaguchi H, Alsterhag E, Dåverhög M, Adrian P E, Kawamura A (2013). Adaptation to climate change impacts on urban storm water: A case study in Arvika, Sweden. Climatic Change, 116(2): 231–247
Péter B, Benedetti L, Dirckx G, Keyser W D, Vanrolleghem P A (2008). Modelling real-time control options on virtual sewer systems. Journal of Environmental Engineering and Science, 7(4): 395–410
Schutze M, Campisano A, Colas H, Schilling W, Vanrolleghem P (2004). Real time control of urban wastewater systems—Where do we stand today? Journal of Hydrology (Amsterdam), 299(3–4): 335–348
Seggelke K, LoWe R, Beeneken T, Fuchs L (2013). Implementation of an integrated real-time control system of sewer system and waste water treatment plant in the city of Wilhelmshaven. Urban Water Journal, 10(5): 330–341
Toro O R (2011). Smart tuning of predictive controllers for drinking water networked systems. IFAC Proceedings Volumes, 44(1): 14507–14512
Vanrolleghem P A, Mannina G, Cosenza A, Neumann M B (2015). Global sensitivity analysis for urban water quality modelling: Terminology, convergence and comparison of different methods. Journal of Hydrology (Amsterdam), 522: 339–352
Wang M, Zhang M, Shi H, Huang X, Liu Y (2019). Uncertainty analysis of a pollutant-hydrograph model in assessing inflow and infiltration of sanitary sewer systems. Journal of Hydrology (Amsterdam), 574: 64–74
Yazdanfar Z, Sharma A (2015). Urban drainage system planning and design — challenges with climate change and urbanization: A review. Water Science and Technology, 72(2): 165–179
Zhang D, Lindholm G, Ratnaweera H (2018a). Use long short-term memory to enhance internet of things for combined sewer overflow monitoring. Journal of Hydrology (Amsterdam), 556: 409–418
Zhang M, Jing H, Liu Y, Shi H (2017). Estimation and optimization operation in dealing with inflow and infiltration of a hybrid sewerage system in limited infrastructure facility data. Frontiers of Environmental Science & Engineering, 11(2): 1–10
Zhang M, Liu Y, Cheng X, Zhu D Z, Yuan Z (2018b). Quantifying rainfall-derived inflow and infiltration in sanitary sewer systems based on conductivity monitoring. Journal of Hydrology (Amsterdam), 558: 174–183
Zimmer A, Schmidt A, Ostfeld A, Minsker B (2015). Evolutionary algorithm enhancement for model predictive control and real-time decision support. Environmental Modelling & Software, 69: 330–341
Acknowledgements
This research was supported by the Major Science and Technology Program for Water Pollution Control and Treatment of China (Nos. 2017ZX07103-007 and 2018ZX07111-006), the Tsinghua University Initiative Scientific Research Program (No. 2018Z02ALB01).
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Highlights
• A model-free sewer-WWTP integrated control was proposed.
• A dynamic discrete control based on the water level was developed.
• The approach could improve the sewer operation against flow fluctuation.
• The approach could increase transport capacity and enhance pump efficiency.
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Lu, Z., Wang, M., Zhang, M. et al. Water-level based discrete integrated dynamic control to regulate the flow for sewer-WWTP operation. Front. Environ. Sci. Eng. 14, 45 (2020). https://doi.org/10.1007/s11783-020-1222-4
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DOI: https://doi.org/10.1007/s11783-020-1222-4