License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.CP.2023.42
URN: urn:nbn:de:0030-drops-190795
Go to the corresponding LIPIcs Volume Portal

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

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

LIPIcs-CP-2023-42.pdf (0.7 MB)


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.

BibTeX - Entry

  author =	{Barbosa Vaz, Vincent and Bailey, James and Leckie, Christopher and J. Stuckey, Peter},
  title =	{{Predict-Then-Optimise Strategies for Water Flow Control}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{42:1--42:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-190795},
  doi =		{10.4230/LIPIcs.CP.2023.42},
  annote =	{Keywords: Water Flow Control, Optimization, Machine Learning}

Keywords: Water Flow Control, Optimization, Machine Learning
Collection: 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)
Issue Date: 2023
Date of publication: 22.09.2023

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI