A fuzzy multi-objective optimisation model of risk-based gas detector placement methodology for explosion protection in oil and gas facilities

A flammable gas detection system is one of the critical control strategies of catastrophic events such as fire and explosion. While gas detector technology has improved significantly, adopting a methodology for optimal placement of gas detectors is still an issue, especially when integrated with a r...

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Veröffentlicht in:Process safety and environmental protection Jg. 161; S. 571 - 582
Hauptverfasser: Idris, Ahmad Muzammil, Rusli, Risza, Nasif, Mohammad Shakir, Ramli, Ahmad Fakrul, Lim, Jeng Shiun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Rugby Elsevier Ltd 01.05.2022
Elsevier Science Ltd
Schlagworte:
Catastrophic events
Computational fluid dynamics
Computer applications
Dynamic models
Explosion
Explosions
Flammability
Flammable gases
Fuzzy multi-objective
Gas detectors
Gas detectors optimisation
Greedy algorithms
Integer programming
Linear programming
Mathematical models
Methodology
Mixed integer
Multiple objective analysis
Natural gas
Optimization
Placement
Risk analysis
Risk-based
Sensitivity analysis
Gas detectors optimisation
Explosion
Risk-based
Fuzzy multi-objective
Computational fluid dynamics
ISSN:0957-5820, 1744-3598
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Zusammenfassung:A flammable gas detection system is one of the critical control strategies of catastrophic events such as fire and explosion. While gas detector technology has improved significantly, adopting a methodology for optimal placement of gas detectors is still an issue, especially when integrated with a risk-based approach. An enhancement of a risk-based approach is proposed to optimise the placement of flammable gas detectors by integrating a formulation of fuzzy multi-objective mixed-integer linear programming with the goal of minimising the residual risk and total number of detectors for effective explosion protection. The proposed methodology primarily begins with the identification of critical leak scenarios that require detection followed by the prediction of a targeted gas cloud and dispersion analysis using a computational fluid dynamic model. Risk analysis is conducted to identify high risk areas that need flammable gas detectors protection, which is the input for the mathematical model. The proposed risk-based model was tested using a case study involving a natural gas liquids (NGL) recovery unit, and the results were compared to a published greedy algorithm (GA) formulation. By using mixed-integer linear programming (MILP) formulation, the number of detectors needed are lower with higher risk reductions compared to the GA formulation. Additionally, a sensitivity analysis was performed to determine the proposed model’s response to parameter variations.
Bibliographie:ObjectType-Article-1
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ISSN:0957-5820
1744-3598
DOI:10.1016/j.psep.2022.03.001