Method for assessing the impact of rainfall depth on the stormwater volume in a sanitary sewage network

• Autorzy: Kozłowski Edward , Kowalski Dariusz , Kowalska Beata , Mazurkiewicz Dariusz

Identyfikatory

DOI

10.1007/s43452-021-00329-w

• Języki publikacji: EN

Abstrakty

EN

Sanitary sewage network is relatively rarely considered as the cause of urban floods. Its hydraulic overload can result not only in flooding, but also sanitary contamination of subcatchments. Stormwater is the main reason for this overload. In contrast to the stormwater or combined sewer system, these waters infiltrate into the network in an uncontrolled way, through ventilation holes of covers or structural faults and lack of tightness of manholes. Part of stormwater infiltrates into the soil, where it leaks into pipelines. This greatly hinders assessing the quantity of stormwater influent into the sanitary sewer system. Standard methods of finding correlation between rainfall and the intensity of stormwater flow are ineffective. This is confirmed, i.a. by the studies performed in an existing network, presented in this paper. Only when residuals analysis was performed using the ARIMA and ARIMAX methods, the authors were able to develop a mathematical model enabling to assess the influence of rainfall depth on the stormwater effluent from the sewage network. Owing to the possibility of using the rainfall depth forecasts, the developed mathematical model enables to prepare the local water and sewerage companies for the occurrence of urban floods as well as hydraulic overload of wastewater treatment plants.

Słowa kluczowe

EN

urban flooding; sanitary sewage system; stormwater influence; estimation method; prediction model

PL

powódź; kanalizacja; woda opadowa

• Wydawca: Springer ; Wrocław University of Science and Technology
• Czasopismo: Archives of Civil and Mechanical Engineering
• Rocznik: 2022
• Tom: Vol. 22, no. 1
• Strony: art. no. e8, 2022
• Opis fizyczny: Bibliogr. 35 poz., rys., wykr.

Twórcy

autor

Kozłowski Edward

• Faculty of Management, Department of Quantitative Methods in Management, Lublin University of Technology, Nadbystrzycka 38D, 20-618 Lublin, Poland

autor

Kowalski Dariusz

• Faculty of Environmental Engineering, Department of Water Supply and Wastewater Disposal, Lublin University of Technology, Nadbystrzycka 40B, 20-618 Lublin, Poland

autor

Kowalska Beata

• Faculty of Environmental Engineering, Department of Water Supply and Wastewater Disposal, Lublin University of Technology, Nadbystrzycka 40B, 20-618 Lublin, Poland

autor

Mazurkiewicz Dariusz

• Faculty of Mechanical Engineering, Department of Production Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-9887-8203

Bibliografia

• 1. Kozłowski E, Kowalska B, Kowalski D, Mazurkiewicz D. Water demand forecasting by trend and harmonic analysis. J ACME. 2018. https://doi.org/10.1016/j.acme.2017.05.006.
• 2. Flaga A, Bosak G, Pistol A, Flaga Ł. Wind tunnel model tests of snow precipitation and redistribution on rooftops, terraces and in the vicinity of high-rise buildings. J ACME. 2019. https://doi.org/10.1016/j.acme.2019.07.007.
• 3. Karimi HS, Natarajan B, Ramsey ChL, Henson J, Joshua L, Tedder JL, Kemper E. Comparison of learning-based wastewater flow prediction methodologies for smart sewer management. J Hydrol. 2019;577:123977–4012.
• 4. Garofalo G, Giordano A, Piro P, Spezzano G, Vinci A. A distributed real-time approach for mitigating CSO and flooding in urban drainage systems. J Netw Comp Appl. 2017;78:30–42.
• 5. Garofalo G, Giordano A, Piro P, Spezzano G, Vinci A. A distributed real-time approach for mitigating CSO and flooding in urban drainage systems. J Netw Comput Appl. 2017;78:30–42.
• 6. Piro P, Carbone M, Garofalo G. Distributed vs. Concentrated storage options for controlling CSO volumes and pollutant loads. J Water Pract Technol. 2010. https:// doi. org/ 10. 2166/wpt.2010.071.
• 7. Piro P, Carbone M. A modelling approach to assessing variations of total suspended solids (TSS) mass fluxes during storm events. Hydrol Process. 2014;28(4):2419–26.
• 8. Capodaglio AG. Evaluation ofmodelin g techniques for wastewater treatment plant automation. Water Sci Technol. 1994;30(2):149–56.
• 9. Chen J, Ganigué R, Liu Y, Yuan Z. Real-time multistep prediction of sewer flow for online chemical dosing control. J Environ Eng. 2014;140:04014037.
• 10. Liu Y, Ganigué R, Sharma K, Yuan Z. Event-driven model predictive control of sewage pumping stations for sulfide mitigation in sewer networks. Water Res. 2016;98:376–83.
• 11. Hernes RR, Gragne AS, Abdalla EMH, Braskerud BC, Alfredsen K, Muthanna TM. Assessing the effects of four SUDS scenarios on combined sewer overflows in Oslo, Norway: evaluating the low impact development module of the mike urban model. Hydrol Res. 2020 (in press).
• 12. Rossman LA, Supply W. Storm water management model, quality assurance report: dynamic wave flow routing. US Environmental Protection Agency, Office of Research and Development, National Research Management Research Laboratory; 2006.
• 13. Dirckx G, Schütze M, Kroll S, Thoeye C, De Gueldre G, Van De Steene B. RTC versus static solutions to mitigate csos impact. In: 12nd international conference on urban drainage, Porto Alegre, Brazil; 2011.
• 14. Achleitner S, Möderl M, Rauch W. City drain©—an open source approach for simulation of integrated urban drainage systems. Environ Model Softw. 2007;22(8):1184–95.
• 15. Pleau M, Colas H, Lavallee P, Pelletier G, Bonin R. Global optimal real-time control of the Quebec urban drainage system. Environ Model Softw. 2005;20:401–13.
• 16. Fu G, Butler D, Khu ST. Multiple objective optimal control of integrated urban wastewater systems. Environ Model Softw. 2008;23(2):225–34.
• 17. Schütze M, Campisano A, Colas H, Schilling W, Vanrolleghem PA. Real time control of urban wastewater systems: where do westand today? J Hydrol. 2004;299(3):335–48.
• 18. Beeneken T, Erbe V, Messmer A, Reder C, Rohlfing R, Scheer M, Schuetze M, Schumacher B, Weilandt M, Weyand M. Real time control (RTC) of urban drainage systems-a discussion of the additional efforts compared to conventionally operated systems. Urban Water J. 2013;10(5):293–9.
• 19. Darsono S, Labadie JW. Neural-optimal control algorithm for real-time regulation of in-line storage in combined sewer systems. Environ Model Softw. 2007;22:1349–61.
• 20. Vezzaro L, Grum M. A generalised dynamic overflow risk assessment (DORA) for real time control of urban drainage systems. J Hydrol. 2014;515:292–303.
• 21. Li J, Sharma K, Liu Y, Jiang G, Yuan Z. Real-time prediction of rain-impacted sewage flow for on-line control of chemical dosing in sewers. Water Res. 2019;149:311–21.
• 22. Zhang D, Martinez N, Lindholm G, Ratnaweera H. Manage sewer in-line storage control using hydraulic model and recurrent neural network. Water Resour Manag. 2018;32:2079–98.
• 23. Jean MÈ, Duchesne S, Pelletier G, Pleau M. Selection of rainfall information as input data for the design of combined sewer over-flow solutions. J Hydrol. 2018;565:559–69.
• 24. Carstensen J, Nielsen MK, Strandbaek H. Prediction of hydraulic load for urban storm control of a municipal WWT plant. Water Sci Technol. 1998;37(12):363–70.
• 25. El-Din AG, Smith DW. A neural network model to predict the wastewater inflow incorporating rainfall events. Water Res. 2002. https://doi.org/10.1016/s0043-1354(01)00287-1.
• 26. Wei X, Kusiak A, Sadat HR. Prediction of influent flow rate: data-mining approach. J Energy Eng. 2012;139:118–23.
• 27. Zhang D, Lindholm G, Ratnaweera H. Use long short-term memory to enhance internet of things for combined sewer overflow monitoring. J Hydrol. 2018;556:409–18.
• 28. Koronacki J, Mielniczuk J. Statistics for students of technical and natural sciences (Statystyka dla studentów kierunków technicznych i przyrodniczych), Wydawnictwa Naukowo-Techniczne (in Polish); 2009.
• 29. Hyndman RJ, Athanasopoulos G. Forecasting: principles and practice. OTexts; 2014. https://otexts.com/fpp2/.
• 30. Kuhn M, Johnson K. Applied predictive modeling. New York: Springer; 2018.
• 31. Shumway RH, Stoffer DS. Time series analysis and its applications: with r examples. Berlin: Springer; 2017.
• 32. Wayne HLG, Woodward A, Elliott AC. Applied time series analysis with r. Milton Park: Taylor & Francis Inc.; 2017.
• 33. Brown RG. Smoothing, forecasting and prediction of discrete time series. North Chelmsford: Courier Corporation; 2004.
• 34. Kosicka E, Kozłowski E, Mazurkiewicz D. The use of stationary tests for analysis of monitored residual processes. Eksploatacja i Niezawodnosc Maint Reliab. 2015;17(4):604–9. https://doi.org/10.17531/ein.2015.4.17.
• 35. Kozłowski E. Time series analysis and identification (Analiza i identyfikacja szeregów czasowych), Politechnika Lubelska (in Polish); 2015.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)