239
Views
38
CrossRef citations to date
0
Altmetric
Research articles

A multi-objective approach for detecting and responding to accidental and intentional contamination events in water distribution systems

, &
Pages 115-135 | Received 09 Jan 2008, Accepted 21 Aug 2008, Published online: 08 May 2009
 

Abstract

The protection against contamination events in water distribution systems involves two distinct phases: detection of the presence of a contaminant and implementation of actions to isolate and/or expel it rapidly. The problem of detection is confronted by installing a series of monitoring stations, strategically placed across the distribution system and consisting of sensors to detect the presence of contaminants. The actions to be implemented may include operations on distribution system devices (valves and hydrants) or injection of reagents to eliminate the contaminant, or simply alert users. The procedure proposed here attempts to address the problems related to the two phases by means of two consecutive optimisation processes, both of them performed off-line and assuming a specific 24-hour water demand sequence in each network node, whereas the accidental/intentional injection of contaminant can occur in any node and at any hour of the day. With reference to this vast range of possible injection scenarios, the first multi-objective optimisation process defines the position of a pre-selected number ns of sensors across the distribution system in order to minimise the expected percentage of undetected contamination events and the expected volume of contaminated water consumed up to the beginning of the response operations following detection. A single configuration of stations is then selected from the Pareto front produced by this optimisation process (‘knee point’ of the Pareto front). At the end of this first optimisation process and with reference to the selected set of sensors, a potentially contaminated area in the network is associated to each sensor for each sub-period of the day. The second multi-objective optimisation process is then aimed to identify, with reference to each station and sub-period, and thus inside the corresponding potentially contaminated area, the hydrant-opening and valve-closing operations to be carried out in order to minimise both the number of operations and the expected volume of contaminated water consumed between the beginning of the response operations and the disappearance of the contaminant, assuming the availability of an unlimited number of response teams. Once these devices have been identified (‘knee point’ of the Pareto front relevant to the second optimisation process), an a posteriori analysis is performed to determine the sequence in which they should be activated based on the number of response teams actually available. In these optimisation processes, a hydraulic and quality simulator (EPANET) is linked to a multi-objective genetic algorithm (NSGA-II) in order to compute the value of the objective functions of the problem across different contamination scenarios. The results obtained applying the procedure to a real and complex water distribution system have shown it to be a robust and effective method for reducing the impact on the population.

Acknowledgements

This study was carried out within the framework of the project LARA-Ob.2 (LAboratorio a Rete Acque – Obiettivo 2). The authors would like to thank two anonymous reviewers and Dr. Francesco di Pierro for their constructive comments that greatly helped us to improve the quality of the manuscript.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 239.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.