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

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:
 [1];  [2]
  1. Purdue Univ., West Lafayette, IN (United States)
  2. Purdue Univ., West Lafayette, IN (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1444083
Report Number(s):
SAND-2018-4847J
Journal ID: ISSN 0950-4230; 663810
Grant/Contract Number:
AC04-94AL85000
Resource Type:
Journal Article: 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, and Laird, Carl Damon. 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.
Liu, Jianfeng, & Laird, Carl Damon. A global stochastic programming approach for the optimal placement of gas detectors with nonuniform unavailabilities. United States. doi:10.1016/j.jlp.2017.09.007.
Liu, Jianfeng, and Laird, Carl Damon. Fri . "A global stochastic programming approach for the optimal placement of gas detectors with nonuniform unavailabilities". United States. doi:10.1016/j.jlp.2017.09.007.
@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},
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}
}

Journal Article:
Free Publicly Available Full Text
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