Predict-Then-Optimise Strategies for Water Flow Control (Short Paper)

Authors Vincent Barbosa Vaz , James Bailey , Christopher Leckie , Peter J. Stuckey



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Author Details

Vincent Barbosa Vaz
  • The University of Melbourne, Australia
  • the Australian Research Council OPTIMA ITTC, Melbourne, Australia
James Bailey
  • The University of Melbourne, Australia
Christopher Leckie
  • The University of Melbourne, Australia
Peter J. Stuckey
  • Monash University, Australia
  • the Australian Research Council OPTIMA ITTC, Melbourne, Australia

Cite AsGet BibTex

Vincent Barbosa Vaz, James Bailey, Christopher Leckie, and Peter J. Stuckey. Predict-Then-Optimise Strategies for Water Flow Control (Short Paper). In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 42:1-42:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.CP.2023.42

Abstract

A pressure sewer system is a network of pump stations used to collect and manage sewage from individual properties that cannot be directly connected to the gravity driven sewer network due to the topography of the terrain. We consider a common scenario for a pressure sewer system, where individual sites collect sewage in a local tank, and then pump it into the gravity fed sewage network. Standard control systems simply wait until the local tank reaches (near) capacity and begin pumping out. Unfortunately such simple control usually leads to peaks in sewage flow in the morning and evening, corresponding to peak water usage in the properties. High peak flows require equalization basins or overflow systems, or larger capacity sewage treatment plants. In this paper we investigate combining prediction and optimisation to better manage peak sewage flows. We use simple prediction methods to generate realistic possible future scenarios, and then develop optimisation models to generate pumping plans that try to smooth out flows into the network. The solutions of these models create a policy for pumping out that is specialized to individual properties and which overall is able to substantially reduce peak flows.

Subject Classification

ACM Subject Classification
  • Applied computing → Operations research
  • Mathematics of computing → Mixed discrete-continuous optimization
Keywords
  • Water Flow Control
  • Optimization
  • Machine Learning

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