Publications

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Flame detectors provide an important layer of protection for personnel in petrochemical plants, but effective placement can be challenging. A mixed-integer nonlinear programming formulation is proposed for optimal placement of flame detectors while considering non-uniform probabilities of detection failure. We show that this approach allows for the placement of fire detectors using a fixed sensor budget and outperforms models that do not account for imperfect detection. We develop a linear relaxation to the formulation and an efficient solution algorithm that achieves global optimality with reasonable computational effort. We integrate this problem formulation into the Python package, Chama, and demonstrate the effectiveness of this formulation on a small test case and on two real-world case studies using the fire and gas mapping software, Kenexis Effigy.

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Sampling of drinking water distribution systems is performed to ensure good water quality and protect public health. Sampling also satisfies regulatory requirements and is done to respond to customer complaints or emergency situations. Water distribution system modeling techniques can be used to plan and inform sampling strategies. However, a high degree of accuracy and confidence in the hydraulic and water quality models is required to support real-time response. One source of error in these models is related to uncertainty in model input parameters. Effective characterization of these uncertainties and their effect on contaminant transport during a contamination incident is critical for providing confidence estimates in model-based design and evaluation of different sampling strategies. In this paper, the effects of uncertainty in customer demand, isolation valve status, bulk reaction rate coefficient, contaminant injection location, start time, duration, and rate on the size and location of the contaminant plume are quantified for two example water distribution systems. Results show that the most important parameter was the injection location. The size of the plume was also affected by the reaction rate coefficient, injection rate, and injection duration, whereas the exact location of the plume was additionally affected by the isolation valve status. Uncertainty quantification provides a more complete picture of how contaminants move within a water distribution system and more information when using modeling results to select sampling locations.

Drinking water utilities use booster stations to maintain chlorine residuals throughout water distribution systems. Booster stations could also be used as part of an emergency response plan to minimize health risks in the event of an unintentional or malicious contamination incident. The benefit of booster stations for emergency response depends on several factors, including the reaction between chlorine and an unknown contaminant species, the fate and transport of the contaminant in the water distribution system, and the time delay between detection and initiation of boosted levels of chlorine. This paper takes these aspects into account and proposes a mixed-integer linear program formulation for optimizing the placement of booster stations for emergency response. A case study is used to explore the ability of optimally placed booster stations to reduce the impact of contamination in water distribution systems.

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This document summarizes research performed under the Laboratory Directed Research and Development (LDRD) project titled Developing Fugitive Emissions Sensor Networks: New Optimization Algorithms for Monitoring, Measurement and Verification. The purpose of this project is to develop methods and software to enhance detection programs through optimal design of the sensor network. This project includes both software development and field work. While this project is focused on methane emissions, the sensor placement optimization framework can be applied to a wide range of applications, including the placement of water quality sensors, surveillance cameras, fire and chemical detectors. This research has the potential to improve national security by improving the way sensors are deployed in the field.

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