## 2 Literature Background
Other computational tools and methods have been developed to facilitate the reliability calculation of a complex system, as is the case of the UltraSAN software of the University of Illinois (1995), which uses the Monte Carlo technique and has been reported by [PERSON] and [PERSON] [26] as a highly sophisticated simulation modeling tool. New trends in data processing have also begun to be used in the analysis of LNG facilities. The article by [PERSON], [PERSON], et al. [25] develops a data-driven methodology for reliability analysis of natural gas compressor units. The study focuses mainly on historical failure data (catastrophic and degradation) as well as performance data. The techniques used are regression models and support vector machines. Based on this, the reliability of the compressor unit is assessed and forecasted by combining the reliability functions of both catastrophic and degradation failures. Moreover, the formulated approach is tested on a real compressor unit to validate its practicality, and the unit's reliability is then estimated. The evaluation results highlight the influence of operating conditions on the precise prediction of performance operational parameters. The economic dimension is not less important in such analyses [27]. In this respect, it is strongly related to the aspect of reliability and quality assurance in LNG plants. In [PERSON] and [PERSON] [28], the application of a risk-assessment methodology is presented which provides information on the use of risk indicators such as PML (Probable Maximum Loss) and MFL (Maximum Foreseeable Loss). Based on both technical literature and operational data extracted from a typical large natural gas liquefaction facility, the common sources of risk are considered. The result of that work constitutes a good approximation of the economic loss derived from the risk in two hypothetical natural gas liquefaction plants. Additionally, the life-cycle costs analysis of LNG facilities and assets allows the characterization of the behavior of costs over the useful life including aspects such as asset's health, reliability, and systemic availability ([PERSON] et al. [29]). A recent trend in the gas industry is the use of artificial intelligence tools integrating cloud, big data, internet of things, computer simulation, and information security, which will become a reliable technical support to the process of operation, optimization, and continuous improvement in the plants for obtaining and processing gas [30]. Thus, AI tools have been incorporated into the decision-making process in LNG plants, such as the work of [PERSON], [PERSON], et al. [31], which strives to formulate a tool to assist decision-makers in risk-oriented maintenance scheduling within an LNG sweetening facility. The tool considers two competing objectives: total maintenance cost and reliability. The model is developed by integrating a genetic algorithm and simulation-based optimization to find the optimal maintenance schedules. In the article by [PERSON], [PERSON] [32], a technique based on semantic fault networks (FSN) is used. Through the use of previously mentionedtechniques, a forecasting algorithm to pinpoint and estimate safety measures for each operation step and process was developed. Then, the proposed solution was validated using a process-state approach regarding events that may stop operation in LNG plants. As can be seen, computational tools are emerging to support decision-making processes in LNG plants and their maintenance [33].