Reducing combined sewer overflows through model predictive control and capital investment

Andrea Zimmer, Arthur Schmidt, Avi Ostfeld, Barbara S Minsker

Research output: Contribution to journalArticlepeer-review

Abstract

Operational strategies to mitigate combined sewer overflows (CSOs) in older urban areas may be enhanced through real-time decision support provided to sewer operators. During severe rainfall events, real-time hydraulic simulations, coupled with control algorithms, can explore a large number of potential changes to control procedures at short time intervals to provide dynamic feedback and optimization. A model predictive control (MPC) genetic algorithm was developed in previous work and tested offline to explore the efficiency and effectiveness of alternative MPC approaches. This paper extends the MPC methodology to evaluate potential impacts of long-term capital investments on CSO frequency. An alternative strategy to mitigating CSOs in real time with sluice gates may involve replacing small-diameter pipes that cause high hydraulic grade lines throughout the system. CSO reductions may also be significantly enhanced through consideration of larger spatial scales. Replacing conduits is effective but expensive, and optimization over a larger spatial extent (without conduit replacement) has been shown to reduce CSOs by 14%. Optimization over the entire large-scale system is recommended for future work.

Original languageEnglish (US)
Article number04017091
JournalJournal of Water Resources Planning and Management
Volume144
Issue number2
DOIs
StatePublished - Feb 1 2018

Keywords

  • Capital investments
  • Combined sewer overflow
  • Costs
  • Decision support
  • Genetic algorithms
  • Model predictive control

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Geography, Planning and Development
  • Water Science and Technology
  • Management, Monitoring, Policy and Law

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