A global stochastic programming approach for the optimal placement of gas detectors with nonuniform unavailabilities

Liu, Jianfeng; Laird, Carl Damon

doi:10.1016/j.jlp.2017.09.007

Title: A global stochastic programming approach for the optimal placement of gas detectors with nonuniform unavailabilities

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Abstract

Optimal design of a gas detection systems is challenging because of the numerous sources of uncertainty, including weather and environmental conditions, leak location and characteristics, and process conditions. Rigorous CFD simulations of dispersion scenarios combined with stochastic programming techniques have been successfully applied to the problem of optimal gas detector placement; however, rigorous treatment of sensor failure and nonuniform unavailability has received less attention. To improve reliability of the design, this paper proposes a problem formulation that explicitly considers nonuniform unavailabilities and all backup detection levels. The resulting sensor placement problem is a large-scale mixed-integer nonlinear programming (MINLP) problem that requires a tailored solution approach for efficient solution. We have developed a multitree method which depends on iteratively solving a sequence of upper-bounding master problems and lower-bounding subproblems. The tailored global solution strategy is tested on a real data problem and the encouraging numerical results indicate that our solution framework is promising in solving sensor placement problems. This study was selected for the special issue in JLPPI from the 2016 International Symposium of the MKO Process Safety Center.

Authors:

Liu, Jianfeng ^[1]; Laird, Carl Damon ^[1]; Sandia National Lab. ^[2]

Purdue Univ., West Lafayette, IN (United States)
(SNL-NM), Albuquerque, NM (United States)

Publication Date:: Fri Sep 22 00:00:00 EDT 2017

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1444083

Alternate Identifier(s):: OSTI ID: 1549310

Report Number(s):: SAND-2018-4847J
Journal ID: ISSN 0950-4230; 663810

Grant/Contract Number:: AC04-94AL85000; NA0003525

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Loss Prevention in the Process Industries

Additional Journal Information:: Journal Volume: 51; Journal Issue: C; Journal ID: ISSN 0950-4230

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 47 OTHER INSTRUMENTATION

Citation Formats


                    Liu, Jianfeng, Laird, Carl Damon, and Sandia National Lab. A global stochastic programming approach for the optimal placement of gas detectors with nonuniform unavailabilities.  United States: N. p., 2017. 
Web.  doi:10.1016/j.jlp.2017.09.007.

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                    Liu, Jianfeng, Laird, Carl Damon, & Sandia National Lab. A global stochastic programming approach for the optimal placement of gas detectors with nonuniform unavailabilities.  United States.  https://doi.org/10.1016/j.jlp.2017.09.007

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                    Liu, Jianfeng, Laird, Carl Damon, and Sandia National Lab. Fri .  
"A global stochastic programming approach for the optimal placement of gas detectors with nonuniform unavailabilities".  United States.  https://doi.org/10.1016/j.jlp.2017.09.007.  https://www.osti.gov/servlets/purl/1444083.

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@article{osti_1444083,

  title        = {A global stochastic programming approach for the optimal placement of gas detectors with nonuniform unavailabilities},

  author       = {Liu, Jianfeng and Laird, Carl Damon and Sandia National Lab.},

  abstractNote = {Optimal design of a gas detection systems is challenging because of the numerous sources of uncertainty, including weather and environmental conditions, leak location and characteristics, and process conditions. Rigorous CFD simulations of dispersion scenarios combined with stochastic programming techniques have been successfully applied to the problem of optimal gas detector placement; however, rigorous treatment of sensor failure and nonuniform unavailability has received less attention. To improve reliability of the design, this paper proposes a problem formulation that explicitly considers nonuniform unavailabilities and all backup detection levels. The resulting sensor placement problem is a large-scale mixed-integer nonlinear programming (MINLP) problem that requires a tailored solution approach for efficient solution. We have developed a multitree method which depends on iteratively solving a sequence of upper-bounding master problems and lower-bounding subproblems. The tailored global solution strategy is tested on a real data problem and the encouraging numerical results indicate that our solution framework is promising in solving sensor placement problems. This study was selected for the special issue in JLPPI from the 2016 International Symposium of the MKO Process Safety Center.},

  doi          = {10.1016/j.jlp.2017.09.007},

  journal      = {Journal of Loss Prevention in the Process Industries},

  number       = C,

  volume       = 51,

  place        = {United States},

  year         = {Fri Sep 22 00:00:00 EDT 2017},

  month        = {Fri Sep 22 00:00:00 EDT 2017}

}

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