Rodriguez J S, Bynum M, Laird C, Hart D B, Klise K A, Burkhardt J, Haxton T
Ph.D. Candidate, Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47907.
SMTS, R&D S&E, Computer Science, Sandia National Laboratories, Eubank Blvd SE, Albuquerque, NM, 87123.
J Infrastruct Syst. 2021 Jun 28;27(3). doi: 10.1061/(asce)is.1943-555x.0000628.
Drinking water utilities rely on samples collected from the distribution system to provide assurance of water quality. If a water contamination incident is suspected, samples can be used to determine the source and extent of contamination. By determining the extent of contamination, the percentage of the population exposed to contamination, or areas of the system unaffected can be identified. Using water distribution system models for this purpose poses a challenge because significant uncertainty exists in the contamination scenarios (e.g., injection location, amount, duration, customer demands, contaminant characteristics). This article outlines an optimization framework to identify strategic sampling locations in water distribution systems. The framework seeks to identify the best sampling locations to quickly determine the extent of the contamination while considering uncertainty with respect to the contamination scenarios. The optimization formulations presented here solve for multiple optimal sampling locations simultaneously and efficiently, even for large systems with a large uncertainty space. These features are demonstrated in two case studies.
饮用水公用事业依赖于从配水系统采集的样本，以确保水质。如果怀疑发生水污染事件，样本可用于确定污染的来源和程度。通过确定污染程度，可以识别受污染的人口百分比或系统中未受影响的区域。为此使用配水系统模型面临挑战，因为污染情景中存在很大的不确定性（例如，注入位置、数量、持续时间、客户需求、污染物特性）。本文概述了一个优化框架，以确定配水系统中的战略采样位置。该框架旨在确定最佳采样位置，以便在考虑污染情景不确定性的同时快速确定污染程度。这里提出的优化公式能够同时有效地求解多个最优采样位置，即使对于具有很大不确定性空间的大型系统也是如此。在两个案例研究中展示了这些特点。
