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Multiobjective optimization of strategies for operation and testing of low-demand safety instrumented systems using a genetic algorithm and fault trees

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  • Longhi, Antonio Eduardo Bier
  • Pessoa, Artur Alves
  • Garcia, Pauli Adriano de Almada

Abstract

Since low-demand safety instrumented systems (SISs) do not operate continuously, their failures are often only detected when the system is demanded or tested. The conduction of tests, besides adding costs, can raise risks of failure on demand during their execution and also increase the frequency of spurious activation. Additionally, it is often necessary to interrupt production to carry out tests. In light of this scenario, this paper presents a model to optimize strategies for operation and testing of these systems, applying modeling by fault trees associated with optimization by a genetic algorithm. Its main differences are: (i) ability to represent four modes of operation and test them for each SIS subsystem; (ii) ability to represent a SIS that executes more than one safety instrumented function; (iii) ability to keep track of the down-time generated in the production system; and (iv) alteration of a genetic selection mechanism that permits identification of more efficient solutions with smaller influence on the optimization parameters. These aspects are presented by applying this model in three case studies. The results obtained show the applicability of the proposed approach and its potential to help make more informed decisions.

Suggested Citation

  • Longhi, Antonio Eduardo Bier & Pessoa, Artur Alves & Garcia, Pauli Adriano de Almada, 2015. "Multiobjective optimization of strategies for operation and testing of low-demand safety instrumented systems using a genetic algorithm and fault trees," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 525-538.
    • Handle: RePEc:eee:reensy:v:142:y:2015:i:c:p:525-538
    DOI: 10.1016/j.ress.2015.06.010
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    References listed on IDEAS

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    1. Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2012. "Multi-objective optimization of design and testing of safety instrumented systems with MooN voting architectures using a genetic algorithm," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 45-60.
    2. Lundteigen, Mary Ann & Rausand, Marvin, 2008. "Spurious activation of safety instrumented systems in the oil and gas industry: Basic concepts and formulas," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1208-1217.
    3. Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2011. "Modeling safety instrumented systems with MooN voting architectures addressing system reconfiguration for testing," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 545-563.
    4. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    5. Innal, Fares & Dutuit, Yves & Chebila, Mourad, 2015. "Safety and operational integrity evaluation and design optimization of safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 32-50.
    6. Marseguerra, M. & Zio, E. & Martorell, S., 2006. "Basics of genetic algorithms optimization for RAMS applications," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 977-991.
    7. Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2009. "Modelling and optimization of proof testing policies for safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 838-854.
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    Cited by:

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    7. Meng, Huixing & Kloul, Leïla & Rauzy, Antoine, 2018. "Modeling patterns for reliability assessment of safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 111-123.

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