ADVANCED MODELING AND EFFICIENT OPTIMIZATION METHODS FOR REAL-TIME RESPONSE IN WATER NETWORKS

In response to a contamination incident in water distribution networks, effective mitigation procedures must be planned. Disinfectant booster stations can be used to neutralize a variety of contaminant and protect the public. In this thesis, two methods are proposed for the optimal placement of booster stations. Since the contaminant species is unknown a priori, these two methods differ in how they model the unknown reaction between the contaminant and the disinfectant. Both methods employ Mixed-Integer Linear Programming to minimize the expected impact over a large set of potential contamination scenarios that consider the uncertainty in the location and time of the incident. To make the optimal booster placement problem tractable for realistic large-scale networks, we exploit the symmetry in the problem structure to drastically reduce the problem size. The results highlight the effectiveness of booster stations in reducing the overall impact on the population, which is measured using two different metrics - mass of contaminant consumed, and population dosed above a cumulative mass threshold. Additionally, we also study the importance of various factors that influence the performance of disinfectant booster stations (e.g., sensor placement, contaminant reactivity and toxicity, etc.).

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Chemical Engineering

Year

2015

Author dc:creator

• Seth, Arpan

Contributors dc:contributor

• Carl D Laird
• Dulcy M Abraham
• Joseph F Pekny
• Zoltan K Nagy

• Large-scale
• Modeling
• Optimization
• Water Networks

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/1316

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-2532