Safety assessment of natural gas storage tank using similarity aggregation method based fuzzy fault tree analysis (SAM-FFTA) approach

https://doi.org/10.1016/j.jlp.2020.104159Get rights and content

Highlights

  • A similarity aggregation method based fuzzy fault tree analysis approach (SAM-FFTA) has been proposed was proposed.

  • The proposed approach can deal with both subjective and objective data.

  • A natural gas storage tank located in an urban district was analyzed by SAM-FFTA.

Abstract

Fault tree analysis (FTA) is an important method to analyze the failure causes of engineering systems and evaluate their safety and reliability. In practical application, the probabilities of bottom events in FTA are usually estimated according to the opinions of experts or engineers because it is difficult to obtain sufficient probability data of bottom events in fault tree. However, in many cases, there are many experts with different opinions or different forms of opinions. How to reasonably aggregate expert opinions is a challenge for the engineering application of fault tree method. In this study, a fuzzy fault tree analysis approach based on similarity aggregation method (SAM-FFTA) has been proposed. This method combines SAM with fuzzy set theory and can handled comprehensively diverse forms of opinions of different experts to obtain the probabilities of bottom events in fault tree. Finally, for verifying the applicability and flexibility of the proposed method, a natural gas spherical storage tank with a volume of 10,000 m3 was analyzed, and the importance of each bottom event was determined. The results show that flame, lightning spark, electrostatic spark, impact spark, mechanical breakdown and deformation/breakage have the most significant influence on the explosion of the natural gas spherical storage tank.

Introduction

With the rapid increase of natural gas demand, gas facilities are being built continuously in cities. In order to meet the requirement of daily uninterrupted gas supply, natural gas storage plants are usually built in the urban district (Mao et al., 2005). So the safety of large and medium-sized natural gas storage tanks, which are the core facilities of the storage plants, has attracted more and more attention. Implementing safety assessment and risk management of storage tanks is an important measure to estimate their safety status and improve equipment safety management level.

Fault tree analysis (FTA) method, as a developed, efficient and logically tight safety assessment technique (Xing and Amari, 2008), can effectively identify and assess the reliability and risks of simple or complex engineering systems (Hu et al., 2003). FTA can not only graphically show the relationship between the final failure event (top event) of the system and all the causes (bottom and intermediate events) of this event, but also obtain the final failure probability of the system and the importance of each cause or process in the failure process (Shu et al., 2006; Yazdi and Zarei, 2018; Yazdi et al., 2019). However, in engineering practice, it is difficult to obtain the complete and precise failure data of the bottom events for calculating occurrence probability of the top event in FT (Yazdi and Zarei, 2018; Yazdi et al., 2019).

In view of the above problems, several investigators tried to introduce fuzzy set theory into FTA to estimate failure probability according to experts' professional knowledge and experience in case of insufficient failure data records. This extension of FTA (called fuzzy fault tree analysis, FFTA) has been applied in various engineering fields such as offshore drilling system (Ramzali et al., 2015), oil and gas pipelines (Shahriar et al., 2012; Badida et al., 2019), process equipment of petrochemical plant (Lavasani et al., 2015a), and storage tank (Wang et al., 2013; Yazdi et al., 2017a; Hu et al., 2019). Lin and Wang (1997) applied fuzzy set theory to fault tree reliability analysis and verified the feasibility in man-machine system. Lavasani et al. (2015b) provided a methodological development on using fuzzy set theory to convert expert opinion to probability and computing leakage probability of abandoned oil and natural-gas wells. Rajakarunakaran et al. (2015) proposed a reliability analysis method using FFTA combined with expert elicitation to evaluate the failure probability of LPG refueling facility. Purba et al. (2015) applied fuzzy fault tree analysis method to safety assessment and risk management of combustion engineering reactors. Cheliyan and Bhattacharyya (2018) applied fuzzy fault tree to analyze and evaluate oil and gas leakage in subsea production systems. Mohsendokht (2017) presented a model using fuzzy theory to obtain failure probability of each basic event as input to calculate the probability of uranium hexafluoride release from a uranium conversion facility. Yazdi (2017) applied FFTA and fuzzy AHP on deal with the uncertain knowledge of bottom events and subjectivity of expert weight assignment. Later, Yazdi et al. (2020) applied FFTA based on modified fuzzy AHP and fuzzy TOPSIS to analyze the fire and explosion in a spherical hydrocarbons storage tank.

In real-world application, more than one expert should be consulted so as to eliminate the subjective bias from individual expert. Many existing FFTA approaches are to request expert to choose a crisp value from a given interval, linguistic term or symmetrical triangular/trapezoidal fuzzy number in order to determine the probability of bottom events in FT. However, in many cases, experts are more inclined to give numerical intervals based on engineering experience and knowledge to express their opinions. In addition, different experts have different degrees of certainty about the probability of bottom events, which also affects the reliability of FTA analysis results. Therefore, how to aggregate the different types of expert opinions is a challenge. According to many studies, there are several methods available for aggregating the expert opinions such as voting, game theory, fuzzy Delphi method, max-min Delphi method and TOPSIS. However, these methods have been found to be not suitable for selecting the most appropriate opinion due to no stable and definite theoretical guidance. In order to effectively aggregate expert opinions, Hsu and Chen (1996) proposed a novel procedure named similarity aggregation method (SAM) to collect and give the reasonable opinion.

The main purpose of this study is to provide an alternative framework to reasonably deal with multi-source data including objective failure data and experts' opinions for safety assessment. The proposed method integrated fuzzy fault tree analysis (FFTA) with similarity aggregation method (SAM) to cope with the fusion of the expert group opinions on the failure probability of bottom events in FT. After collecting subjective data of experts with relatively fewer restrictions, this method can reasonably convert the opinions of multiple experts into a certain probability value through a fuzzy reasoning method, so as to solve the problems of difficulty in obtaining the probability of fault tree and handling expert opinions.

This paper is organized as follows: Section 2 briefly introduces conventional FTA and SAM and Section 3 describes the proposed SAM-FFTA method in detail. Section 4 demonstrates an engineering case to verify the applicability and flexibility of the presented method. Finally, some conclusions and suggestions are presented in Section 5.

Section snippets

Fault tree analysis (FTA) method

FTA uses a tree-like diagram to the logical relations between a critical accident (as a top event) and other events (as intermediate events and bottom events) that directly or indirectly cause the top event (Vesely et al., 1981). According to the different purpose, FTA can be divided into qualitative analysis and quantitative analysis.

FT qualitative analysis can find out possible failure paths resulting in the top event, and identify the weakest link in the system. In FTA, it is usually assumed

Framework of the proposed SAM-FFTA

This work proposed a similarity aggregation method based fuzzy fault tree analysis approach (SAM-FTA) for dealing with multi-source data including complex expert opinions. The framework of the proposed method is shown in Fig. 1. The detailed analysis process is as follows:

Step 1: Identify the evaluation system and draw the fault tree

The first step of FTA is to identify the system analyzed. And then according to the system features and relevant information, the fault tree needs to be designed.

Case information

The proposed method was used to a 104 m3 natural gas storage tank in a natural gas reserve plant in a city of western China (see Fig. 3). The specific design parameters of the storage tank are listed in Table 2.

Establishment of fault tree

According to the relevant literatures and experts' suggestions, the fault tree of the natural gas storage tank, as shown in Fig. 4, was established. The explosion of the tank is set as the top event T due to the flammable and explosive nature of the natural gas. 14 intermediate events

Conclusions

A fuzzy fault tree method based on similarity aggregation method (SAM-FFTA) was proposed to deal with multi-source data including the data from a large number of experts with different opinions. This method has the following characteristics compared with the traditional fuzzy fault tree method:

  • (1)

    Instead of asking experts to make a choice among the given language intervals in the traditional fuzzy fault tree method, SAM-FFTA adopts the more flexible graphics to facilitate experts to give their

CRediT authorship contribution statement

Hailong Yin: Methodology, Writing - original draft. Changhua Liu: Visualization, Formal analysis. Wei Wu: Conceptualization, Supervision, Funding acquisition. Ke Song: Investigation. Dongpeng Liu: Investigation. Yong Dan: Writing - review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2020JM-436) and National Natural Science Foundation of China (Grant no. 51605368).

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