Siting Bio‐Samplers in Buildings

Sohn, Michael D.; Lorenzetti, David M.

doi:10.1111/j.1539-6924.2007.00929.x

Cited by 15 publications

(9 citation statements)

References 8 publications

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“…Many other approaches have been taken (2,3,4) that advance the state of this research, but none account for the relative likelihoods of uncertain conditions. This paper extends the work in (1) by: 1) developing a more complete analysis framework, 2) adding a new metric for evaluating network performance, and 3) applying the resulting algorithms to a real building.…”

Section: Introductionmentioning

confidence: 90%

“…The Probabilistic Approach to Sampler Siting (PASS) (1) finds the expected network performance by aggregating the outcomes of many deterministic model runs, each drawing its input parameters from distributions of likely values.…”

Section: Expected Performancementioning

confidence: 99%

“…The algorithm reported in (1) maximized the expected probability of detecting a release. This goal implicitly acknowledged the fact that first-generation samplers required many hours to collect and analyze 4 samples before returning results.…”

Section: Performance Metricsmentioning

confidence: 99%

“…This contradicts (1) , in which maximizing the probability of detection, across networks of different sizes, tended to place more-sensitive samplers in bathrooms (to take advantage of exhaust airflows), and less-sensitive samplers in ventilation system return ducts (which effectively sample air from throughout the building). We attribute the lack of favored sampler locations in the convention center to the large airflows between zones.…”

Section: Optimal Locationsmentioning

confidence: 99%

“…To address these questions, Sohn and Lorenzetti (1) proposed a probabilistic approach to network design, and demonstrated its application in a synthetic building. Many other approaches have been taken (2,3,4) that advance the state of this research, but none account for the relative likelihoods of uncertain conditions.…”

Section: Introductionmentioning

confidence: 99%

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Siting Samplers to Minimize Expected Time to Detection

Walter¹,

Lorenzetti²,

Sohn³

2012

Risk Analysis

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We present a probabilistic approach to designing an indoor sampler network for detecting an accidental or intentional chemical or biological release, and demonstrate it for a real building. In an earlier article, Sohn and Lorenzetti developed a proof of concept algorithm that assumed samplers could return measurements only slowly (on the order of hours). This led to optimal “detect to treat” architectures that maximize the probability of detecting a release. This article develops a more general approach and applies it to samplers that can return measurements relatively quickly (in minutes). This leads to optimal “detect to warn” architectures that minimize the expected time to detection. Using a model of a real, large, commercial building, we demonstrate the approach by optimizing networks against uncertain release locations, source terms, and sampler characteristics. Finally, we speculate on rules of thumb for general sampler placement.

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Section: Introductionmentioning

confidence: 90%

Section: Expected Performancementioning

confidence: 99%

Section: Performance Metricsmentioning

confidence: 99%

Section: Optimal Locationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Siting Samplers to Minimize Expected Time to Detection

Walter¹,

Lorenzetti²,

Sohn³

2012

Risk Analysis

Self Cite

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Multi-objective optimization for sensor placement against suddenly released contaminant in air duct system

et al. 2017

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When a chemical or biological agent is suddenly released into a ventilation system, its dispersion needs to be promptly and accurately detected. In this work, an optimization method for sensors layout in air ductwork was presented. Three optimal objectives were defined, i.e. the minimum detection time, minimum contaminant exposure, and minimum probability of undetected pollution events. Genetic algorithm (GA) method was used to obtain the non-dominated solutions of multiobjectives optimization problem and the global optimal solution was selected among all of the non-dominated solutions by ordering solutions method. Since the biochemical attack occurred in a ventilation system was a random process, two releasing scenarios were proposed, i.e. the uniform and the air volume-based probability distribution. It was found that such a probability distribution affected the results of optimal sensors layout and also resulted in different detect time and different probability of undetected events. It was discussed how the objective functions are being compatible and competitive with each other, and how sensor quantity affect the optimal results and computational load. The impact of changes on other parameters was given, i.e. the deposition coefficient, the air volume distribution and the manual releasing. This work presents an angle of air ductwork design for indoor environment protection and expects to help in realizing the optimized sensor system design for sudden contaminant releasing within ventilation systems.

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State-space methods for calculating concentration dynamics in multizone buildings

Parker

Bowman

2011

Building and Environment

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Siting Bio‐Samplers in Buildings

Cited by 15 publications

References 8 publications

Siting Samplers to Minimize Expected Time to Detection

Siting Samplers to Minimize Expected Time to Detection

Multi-objective optimization for sensor placement against suddenly released contaminant in air duct system

State-space methods for calculating concentration dynamics in multizone buildings

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