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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
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    References listed on IDEAS

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    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.
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