IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i12d10.1007_s11269-022-03221-1.html
   My bibliography  Save this article

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

Author

Listed:
  • Zhenliang Liao

    (Xinjiang University
    Tongji University
    Key Laboratory of Cities’ Mitigation and Adaptation to Climate Change in Shanghai, China Meteorological Administration (CMACC)
    Shanghai Institute of Pollution Control and Ecological Security)

  • Zhiyu Zhang

    (Tongji University
    Key Laboratory of Cities’ Mitigation and Adaptation to Climate Change in Shanghai, China Meteorological Administration (CMACC)
    Shanghai Institute of Pollution Control and Ecological Security)

  • Wenchong Tian

    (Tongji University
    Key Laboratory of Cities’ Mitigation and Adaptation to Climate Change in Shanghai, China Meteorological Administration (CMACC)
    Shanghai Institute of Pollution Control and Ecological Security)

  • Xianyong Gu

    (Tongji University
    Key Laboratory of Cities’ Mitigation and Adaptation to Climate Change in Shanghai, China Meteorological Administration (CMACC)
    Shanghai Institute of Pollution Control and Ecological Security)

  • Jiaqiang Xie

    (Tongji University
    Key Laboratory of Cities’ Mitigation and Adaptation to Climate Change in Shanghai, China Meteorological Administration (CMACC)
    Shanghai Institute of Pollution Control and Ecological Security)

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.

Suggested Citation

  • Zhenliang Liao & Zhiyu Zhang & Wenchong Tian & Xianyong Gu & Jiaqiang Xie, 2022. "Comparison of Real-time Control Methods for CSO Reduction with Two Evaluation Indices: Computing Load Rate and Double Baseline Normalized Distance," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4469-4484, September.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:12:d:10.1007_s11269-022-03221-1
    DOI: 10.1007/s11269-022-03221-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03221-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-022-03221-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fatemeh Jafari & S. Jamshid Mousavi & Jafar Yazdi & Joong Hoon Kim, 2018. "Real-Time Operation of Pumping Systems for Urban Flood Mitigation: Single-Period vs. Multi-Period Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4643-4660, November.
    2. Mahmood Mahmoodian & Juan Pablo Carbajal & Vasilis Bellos & Ulrich Leopold & Georges Schutz & Francois Clemens, 2018. "A Hybrid Surrogate Modelling Strategy for Simplification of Detailed Urban Drainage Simulators," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 5241-5256, December.
    3. Xuan Wang & Wenchong Tian & Zhenliang Liao, 2021. "Offline Optimization of Sluice Control Rules in the Urban Water System for Flooding Mitigation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 949-962, February.
    4. Vincent Wolfs & Patrick Willems, 2017. "Modular Conceptual Modelling Approach and Software for Sewer Hydraulic Computations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 283-298, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kun Xie & Jong-Suk Kim & Linjuan Hu & Hua Chen & Chong-Yu Xu & Jung Hwan Lee & Jie Chen & Sun-Kwon Yoon & Di Zhu & Shaobo Zhang & Yang Liu, 2023. "Intelligent Scheduling of Urban Drainage Systems: Effective Local Adaptation Strategies for Increased Climate Variability," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 91-111, January.
    2. Vassilios A. Tsihrintzis & Harris Vangelis, 2018. "Water Resources and Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 4813-4817, December.
    3. Shahin Zandmoghaddam & Ali Nazemi & Elmira Hassanzadeh & Shadi Hatami, 2019. "Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(10), pages 3579-3594, August.
    4. Vasilis Bellos & Ino Papageorgaki & Ioannis Kourtis & Harris Vangelis & Ioannis Kalogiros & George Tsakiris, 2020. "Reconstruction of a flash flood event using a 2D hydrodynamic model under spatial and temporal variability of storm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 101(3), pages 711-726, April.
    5. María Bermúdez & Victor Ntegeka & Vincent Wolfs & Patrick Willems, 2018. "Development and Comparison of Two Fast Surrogate Models for Urban Pluvial Flood Simulations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2801-2815, June.
    6. J. Yazdi, 2019. "Optimal Operation of Urban Storm Detention Ponds for Flood Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 2109-2121, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:36:y:2022:i:12:d:10.1007_s11269-022-03221-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.