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Water Resources Management

Erschienen in:

17.08.2022

Comparison of Real-time Control Methods for CSO Reduction with Two Evaluation Indices: Computing Load Rate and Double Baseline Normalized Distance

verfasst von: Zhenliang Liao, Zhiyu Zhang, Wenchong Tian, Xianyong Gu, Jiaqiang Xie

Erschienen in: Water Resources Management | Ausgabe 12/2022

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Abstract

Real-time control (RTC) methods, which utilize real-time information to control the existing infrastructures in combined sewer systems, are effective in reducing combined sewer overflows (CSOs). However, it is difficult to compare the performance of RTC systems due to their diverse frameworks and application scenarios. This study provides a comparison of different RTC strategies through two proposed evaluation indices: computing load rate (CLR) and double-baseline normalized distance (DBND). CLR represents the computing load as a percentage of the control step interval, while DBND indicates the control effect normalized by the lower and upper bounds of the control process. Three different RTC methods, heuristic control (HC), model predictive control (MPC), and reinforcement learning control (RLC), were compared and studied through the two indices. In this study, RLC trains an artificial intelligence agent to regulate sewage pumps during rainfall events for CSO reduction. A combined sewer system in eastern China is taken as a case study. According to the simulation results and indices: (i) CLR is an effective index for the computing cost and efficiency evaluation of diverse RTC systems. (ii) DBND enables the comparison of control effects between RTC systems that differ in rainfall events and the capacities of combined sewer systems. (iii) HC, MPC, and RLC vary in computing time costs and control effects, and among them, RLC is the most cost-effective control method.

Vorheriger Artikel Reliability-based Operation of Reservoirs Using Combined Monte Carlo Simulation Model and a Novel Nature-inspired Algorithm

Nächster Artikel A Hierarchical Index System for Analysis of Water Supply-Demand Situation

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Bowes BD, Tavakoli A, Wang C et al (2020) Flood mitigation in coastal urban catchments using real-time stormwater infrastructure control and reinforcement learning. J Hydroinformatics. https://doi.org/10.2166/hydro.2020.080CrossRef

Butler D, Schutze M (2005) Integrating simulation models with a view to optimal control of urban wastewater systems. Environ Modell Softw 20:415–426. https://doi.org/10.1016/j.envsoft.2004.02.003CrossRef

Fu G, Butler D, Khu S-T (2008) Multiple objective optimal control of integrated urban wastewater systems. Environ Modell Softw 23:225–234. https://doi.org/10.1016/j.envsoft.2007.06.003CrossRef

Garcia L, Barreiro-Gomez J, Escobar E et al (2015) Modeling and real-time control of urban drainage systems: A review. Adv Water Resour 85:120–132. https://doi.org/10.1016/j.advwatres.2015.08.007CrossRef

Giraldo JM, Leirens S, Díaz-Granados M et al (2010) Nonlinear optimization for improving the operation of sewer systems: the Bogota Case Study. In International Congress on Environmental Modelling and Software, Ottawa, Ontario, Canada, pp 2229–2236

Gu X, Liao Z, Zhang G et al (2017) Modelling the effects of water diversion and combined sewer overflow on urban inland river quality. Environ Sci Pollut Res 24:21038–21049. https://doi.org/10.1007/s11356-017-9686-xCrossRef

Jafari F, Mousavi SJ, Yazdi J et al (2018) Real-time operation of pumping systems for urban flood mitigation: single-period vs multi-period optimization. Water Resour Manag 32:4643–4660. https://doi.org/10.1007/s11269-018-2076-4CrossRef

Joseph-Duran B, Jung MN, Ocampo-Martinez C et al (2014) Minimization of sewage network overflow. Water Resour Manag 28:41–63. https://doi.org/10.1007/s11269-013-0468-zCrossRef

Joseph-Duran B, Ocampo-Martinez C, Cembrano G (2015) Output-feedback control of combined sewer networks through receding horizon control with moving horizon estimation. Water Resour Res 51:8129–8145. https://doi.org/10.1002/2014wr016696CrossRef

Lacour C, Schutze M (2011) Real-time control of sewer systems using turbidity measurements. Water Sci Technol 63:2628–2632. https://doi.org/10.2166/wst.2011.159CrossRef

Liao Z, Gu X, Xie J et al (2019) An integrated assessment of drainage system reconstruction based on a drainage network model. Environ Sci Pollut Res 26:26563–26576. https://doi.org/10.1007/s11356-019-05280-1CrossRef

Lund NSV, Borup M, Madsen H et al (2020) CSO reduction by integrated model predictive control of stormwater inflows: A simulated proof of concept using linear surrogate models. Water Resour Res. https://doi.org/10.1029/2019wr026272CrossRef

Lund NSV, Falk AKV, Borup M et al (2018) Model predictive control of urban drainage systems: A review and perspective towards smart real-time water management. Crit Rev Env Sci Tec 48:279–339. https://doi.org/10.1080/10643389.2018.1455484CrossRef

Mailhot A, Talbot G, Lavallee B (2015) Relationships between rainfall and Combined Sewer Overflow (CSO) occurrences. J Hydrol 523:602–609. https://doi.org/10.1016/j.jhydrol.2015.01.063CrossRef

Mahmoodian M, Carbajal JP, Bellos V et al (2018) A hybrid surrogate modelling strategy for simplification of detailed urban drainage simulators. Water Resour Manag 32:5241–5256. https://doi.org/10.1007/s11269-018-2157-4CrossRef

Meneses EJ, Gaussens M, Jakobsen C et al (2018) Coordinating rule-based and system-wide model predictive control strategies to reduce storage expansion of combined urban drainage systems: The case study of lundtofte. Denmark Water. https://doi.org/10.3390/w10010076CrossRef

Mollerup AL, Mikkelsen PS, Sin G (2016) A methodological approach to the design of optimising control strategies for sewer systems. Environ Modell Softw 83:103–115. https://doi.org/10.1016/j.envsoft.2016.05.004CrossRef

Mullapudi A, Lewis MJ, Gruden CL et al (2020) Deep reinforcement learning for the real time control of stormwater systems. Adv Water Resour 140:103600. https://doi.org/10.1016/j.advwatres.2020.103600CrossRef

Ocampo-Martinez C, Ingimundarson A, Puig V et al (2008) Objective prioritization sing lexicographic minimizers for MPC of sewer networks. IEEE Trans Control Syst Technol 16:113–121. https://doi.org/10.1109/tcst.2007.899741CrossRef

Ocampo-Martinez C, Puig V (2010) Piece-wise linear functions-based model predictive control of large-scale sewage systems. IET Contr Theory Appl 4:1581–1593. https://doi.org/10.1049/iet-cta.2009.0206CrossRef

Saliba SM, Bowes BD, Adams S et al (2020) Deep reinforcement learning with uncertain data for real-time stormwater system control and flood mitigation. Water 12:3222. https://doi.org/10.3390/w12113222CrossRef

Schütze M, Campisano A, Colas H et al (2004) Real Time Control of urban wastewater systems—where do we stand today? J Hydrol 299:335–348. https://doi.org/10.1016/j.jhydrol.2004.08.010CrossRef

Singh A, Sarma AK, Hack J (2020) Cost-effective optimization of nature-based solutions for reducing urban floods considering limited space availability. Environ Process 7:297–319. https://doi.org/10.1007/s40710-019-00420-8CrossRef

Soriano L, Rubio J (2019) Impacts of Combined Sewer Overflows on surface water bodies. The case study of the Ebro River in Zaragoza city. J Clean Prod 226:1–5. https://doi.org/10.1016/j.jclepro.2019.04.033CrossRef

Sun C, Puig V, Cembrano G (2020a) Real-time control of urban water cycle under cyber-physical systems framework. Water. https://doi.org/10.3390/w12020406CrossRef

Sun C, Svensen JL, Borup M et al (2020b) An MPC-enabled SWMM implementation of the astlingen RTC benchmarking network. Water. https://doi.org/10.3390/w12041034CrossRef

Sutton RS, Barto AG (2018) Reinforcement learning: An introduction. 2nd edn. MIT Press

van der Werf J, Kapelan Z, Langeveld J (2021) Quantifying the true potential of Real Time Control in urban drainage systems. Urban Water J 18(10):873–884. https://doi.org/10.1080/1573062X.2021.1943460CrossRef

Vezzaro L, Grum M (2014) A generalised Dynamic Overflow Risk Assessment (DORA) for Real Time Control of urban drainage systems. J Hydrol 515:292–303. https://doi.org/10.1016/j.jhydrol.2014.05.019CrossRef

Wang X, Tian W, Liao Z (2021) Offline optimization of sluice control rules in the urban water system for flooding mitigation. Water Resour Manag 35:949–962. https://doi.org/10.1007/s11269-020-02760-9CrossRef

Wolfs V, Willems P (2017) Modular conceptual modelling approach and software for sewer hydraulic computations. Water Resour Manag 31:283–298. https://doi.org/10.1007/s11269-016-1524-2CrossRef

Zhi G, Liao Z, Tian W et al (2020) Urban flood risk assessment and analysis with a 3D visualization method coupling the PP-PSO algorithm and building data. J Environ Manage 268:110521. https://doi.org/10.1016/j.jenvman.2020.110521CrossRef

Titel: Comparison of Real-time Control Methods for CSO Reduction with Two Evaluation Indices: Computing Load Rate and Double Baseline Normalized Distance
verfasst von: Zhenliang Liao
Zhiyu Zhang
Wenchong Tian
Xianyong Gu
Jiaqiang Xie
Publikationsdatum: 17.08.2022
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 12/2022
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-022-03221-1

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