Analyzing intuitionistic fuzzy reliability of LNG-fuel gas supply system using simplified Lambda-tau techniques

Abstract

Liquefied natural gas-fuel gas supply system (LNG-FGSS) play a crucial role in the global energy sector, particularly in supplying natural gas (NG). Studying this system is pivotal for tackling energy needs, environmental issues, and energy infrastructure reliability. Thus, the objective of this paper is to introduce some new methodologies for assessing the intuitionistic fuzzy reliability (IFR) of LNG-FGSS that can incorporate the uncertainty by accumulating fuzziness and hesitation factors during the data processing. This paper presents two simplified IFR analysis techniques for repairable systems that include Min-Max operators based simplified intuitionistic fuzzy lambda-tau methodology (MBSIFLTM) and Min-Max operators and the weakest t-norm ( ) based simplified intuitionistic fuzzy lambda-tau methodology (MWBSIFLTM). In MBSIFLTM, we used Min-Max operators based simplified arithmetic operations for intuitionistic fuzzy set (IFS), Lambda-tau methodology, and fault tree analysis (FTA) for analyzing different reliability indices of the system. Whereas in MWBSIFLTM, various reliability parameters of the system are assessed by using the Min-Max operators along with the weakest t-norm based simplified arithmetic operations of IFS, Lambda-tau methodology, and FTA. Further to analyzed the credibility and precision of the suggested methodologies, an investigation has been carried out for a LNG-FGSS. Both the proposed techniques and four existing methods are employed to assess the IFR of LNG-FGSS. Consequently, to show the flexibility of the proposed models, a sensitivity analysis is also conducted. Moreover, the ranking of the system’s components has been carried out by utilizing the V-index method, aiming to optimize its overall performance. Comparing the outcomes of the suggested techniques with four other established methods leads to the conclusion that the suggested techniques offer simplicity, ease of application, and optimized IFR indices.