A mathematical programming approach for the optimal placement of flame detectors in petrochemical facilities

Zhen, Todd; Klise, Katherine A.; Cunningham, Sean; Marszal, Edward; Laird, Carl D.

doi:10.1016/j.psep.2019.08.030

Title: A mathematical programming approach for the optimal placement of flame detectors in petrochemical facilities

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Abstract

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. In this study, 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.

Authors:

Zhen, Todd ^[1]; Klise, Katherine A. ^[2]; Cunningham, Sean ^[3]; Marszal, Edward ^[3]; Laird, Carl D. ^[4]

Purdue Univ., West Lafayette, IN (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Kenexis Consulting Corporation, Columbus, OH (United States)
Purdue Univ., West Lafayette, IN (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Publication Date:: Sun Dec 01 00:00:00 EST 2019

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

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

OSTI Identifier:: 1570261

Report Number(s):: SAND2019-11845J
Journal ID: ISSN 0957-5820; 679917

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

Resource Type:: Accepted Manuscript

Journal Name:: Process Safety and Environmental Protection

Additional Journal Information:: Journal Volume: 132; Journal Issue: C; Journal ID: ISSN 0957-5820

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 47 OTHER INSTRUMENTATION; optimization; flame detection; process safety

Citation Formats


                    Zhen, Todd, Klise, Katherine A., Cunningham, Sean, Marszal, Edward, and Laird, Carl D. A mathematical programming approach for the optimal placement of flame detectors in petrochemical facilities.  United States: N. p., 2019. 
Web.  doi:10.1016/j.psep.2019.08.030.

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                    Zhen, Todd, Klise, Katherine A., Cunningham, Sean, Marszal, Edward, & Laird, Carl D. A mathematical programming approach for the optimal placement of flame detectors in petrochemical facilities.  United States.  https://doi.org/10.1016/j.psep.2019.08.030

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                    Zhen, Todd, Klise, Katherine A., Cunningham, Sean, Marszal, Edward, and Laird, Carl D. Sun .  
"A mathematical programming approach for the optimal placement of flame detectors in petrochemical facilities".  United States.  https://doi.org/10.1016/j.psep.2019.08.030.  https://www.osti.gov/servlets/purl/1570261.

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@article{osti_1570261,

  title        = {A mathematical programming approach for the optimal placement of flame detectors in petrochemical facilities},

  author       = {Zhen, Todd and Klise, Katherine A. and Cunningham, Sean and Marszal, Edward and Laird, Carl D.},

  abstractNote = {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. In this study, 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.},

  doi          = {10.1016/j.psep.2019.08.030},

  journal      = {Process Safety and Environmental Protection},

  number       = C,

  volume       = 132,

  place        = {United States},

  year         = {Sun Dec 01 00:00:00 EST 2019},

  month        = {Sun Dec 01 00:00:00 EST 2019}

}

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