Source identification of water distribution system contamination based on simulated annealing-particle swarm...

Area of Science:

• Environmental Engineering
• Water Resource Management
• Computational Intelligence

Background:

• Urban water safety relies on rapid detection and response to water quality anomalies in pipeline networks.
• Accurate contamination source identification (CSI) is crucial for public health and preventing widespread contamination.
• Existing particle swarm optimization (PSO) methods struggle with CSI due to local optima.

Purpose of the Study:

• To develop an improved algorithm for contamination source identification in water distribution systems (WDS).
• To overcome the limitations of the standard particle swarm optimization (PSO) algorithm in CSI.
• To enhance the speed and accuracy of identifying contamination sources in urban water supplies.

Main Methods:

• Integration of the Metropolis criterion from simulated annealing (SA) into the PSO algorithm, creating a novel SA-PSO algorithm.
• Conducting contamination localization experiments on the NET-3 pipeline network using simulated sudden contamination events.
• Developing a simulation-optimization inverse location model to fit pollutant concentrations at monitoring points over time.

Main Results:

• The SA-PSO algorithm demonstrated superior performance compared to the standard PSO in contamination source identification.
• SA-PSO achieved higher accuracy and speed in localizing contamination events within the WDS.
• The simulation-based inverse location model effectively utilized pollutant concentration data for source identification.

Conclusions:

• The developed SA-PSO algorithm offers an efficient and effective tool for contamination localization in urban water supply management.
• This research provides a significant advancement in addressing the challenges of CSI in water distribution systems.
• The findings support improved strategies for safeguarding urban water supplies against contamination incidents.