A quantitative assessment on the placement practices of gas detectors in the process industries

Gas detection is an important safety system with interfaces to several other safety safeguards. However, the generality of the regulations, standards and recommended practices in conjunction with the inherent challenges of the gas detector placement problem, has resulted in a widespread use of presc...

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Bibliographic Details
Published inJournal of loss prevention in the process industries Vol. 35; pp. 339 - 351
Main Authors Benavides-Serrano, A.J., Mannan, M.S., Laird, C.D.
Format Journal Article
LanguageEnglish
Published Kidlington Elsevier Ltd 01.05.2015
Elsevier Science Ltd
Subjects
Backup coverage
Detector placement
Dispersion
Gas detection
Gas detectors
Gas leaks
Mathematical programming
Mixed-integer linear programming
Optimization techniques
Process safety
Simulation
Stochastic models
Stochastic programming
Unavailability
Voting
Unavailability
Voting
Backup coverage
Gas detection
Detector placement
Process safety
Mixed-integer linear programming
Stochastic programming
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Summary:Gas detection is an important safety system with interfaces to several other safety safeguards. However, the generality of the regulations, standards and recommended practices in conjunction with the inherent challenges of the gas detector placement problem, has resulted in a widespread use of prescriptive and qualitative detector placement strategies. In order to take advantage of the quantitative information provided by dispersion simulations, a stochastic programming formulation (SP-UV) was previously proposed, developed and validated by the authors. This formulation identifies the gas detector layout that minimizes the expected value of an overall damage coefficient (i.e., the minimization of a risk metric) given a set of dispersion scenarios. Results demonstrated the potential and suitability of numerical optimization to solve the gas detector placement problem while rigorously considering its inherent uncertainties. In this work, four existing approaches for gas detector placement were implemented and compared with the previously proposed quantitative optimization-based approach using three different performance metrics in accordance to the objectives of gas detection systems. Results provide evidence on the effectiveness of the use of dispersion simulations, and mathematical programming, to supplement the gas detector placement problem.
ISSN:0950-4230
1873-3352
DOI:10.1016/j.jlp.2014.09.010