Quantitative HAZOP Risk Analysis for Oil Tanks Using the Fuzzy Set Theory

2012 ◽  
Author(s):  
Tao Cao ◽  
Huabing Zhang ◽  
Honglong Zheng ◽  
Yufeng Yang ◽  
Xin Wang
Keyword(s):  
Quantitative Analysis ◽  
Risk Analysis ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Fuzzy Numbers ◽  
Storage Tanks ◽  
Worst Case ◽  
Quantitative Calculation ◽  
Oil Storage

Oil storage tanks are identified as the major hazard installations because of the hazard of huge fire & explosion. Evaluating the risk and controlling the danger is the advancement of accident prevention and the compulsory requirement of laws. HAZOP (Hazard and Operability Analysis), is a simple but intensive, systematic, qualitative risk analytical method, is an important technique for the identification of hidden hazards in operation of tank facility. It can only qualitatively account for potential risks, but cannot quantify their possibility and severity. Thus, research involving HAZOP quantitative analysis has become an area of focus. Due to uncertainty of deviation probability in the traditional HAZOP analysis, this paper discusses a new approach of HAZOP quantitative analysis, combining HAZOP with fuzzy set theory to calculate the possibilities of deviation and consequence quantitatively. The evaluation process and technique route of risk analysis is also brought forward in this paper. Based on the traditional HAZOP analysis, the typical outputs of a HAZOP analysis can be achieved, including identification of possible deviation states, identification of the possible causes for deviations and probable worst case consequence. Next, according to fuzzy evaluation of some HAZOP experts on deviation by means of their knowledge, the triangular fuzzy-number is introduced in this paper to conduct quantitative calculation. Based on the fuzzy set theory, combining Delphi method and judging matrix method, the linguistic values are transformed into triangular fuzzy numbers. The probability of deviation is evaluated by fuzzy cut and raking method of fuzzy numbers. Finally, the probability of consequence caused by deviation is evaluated. A complete quantitative risk analysis example for oil tank is conducted, the probabilities of deviations and incident consequence are computed, the quantitative risk is determined and reasonable mitigations of the storage tanks are given. It is shown that the proposed approach is advanced and practicable to make a quantitative assessment on the risk of existing deviation, which is helpful for risk managers to bring forward the suggested measure and regulate the hidden hazards.

Evaluation of Quantified Statements Using Gradual Numbers

2011 ◽  
pp. 246-269 ◽  
Author(s):  
Ludovic Liétard ◽  
Daniel Rocacher
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Relational Databases ◽  
Fuzzy Numbers ◽  
Theoretical Background ◽  
Definition Of ◽  
Flexible Querying

This chapter is devoted to the evaluation of quantified statements which can be found in many applications as decision making, expert systems, or flexible querying of relational databases using fuzzy set theory. Its contribution is to introduce the main techniques to evaluate such statements and to propose a new theoretical background for the evaluation of quantified statements of type “Q X are A” and “Q B X are A.” In this context, quantified statements are interpreted using an arithmetic on gradual numbers from Nf, Zf, and Qf. It is shown that the context of fuzzy numbers provides a framework to unify previous approaches and can be the base for the definition of new approaches.

scholarly journals Fuzzy Insurance

1990 ◽  
Vol 20 (1) ◽  
pp. 33-55 ◽  
Author(s):  
Jean Lemaire
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Fuzzy Set ◽  
Life Insurance ◽  
Fuzzy Set Theory ◽  
Fuzzy Numbers ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Basic Concepts ◽  
Definition Of

AbstractFuzzy set theory is a recently developed field of mathematics, that introduces sets of objects whose boundaries are not sharply defined. Whereas in ordinary Boolean algebra an element is either contained or not contained in a given set, in fuzzy set theory the transition between membership and non-membership is gradual. The theory aims at modelizing situations described in vague or imprecise terms, or situations that are too complex or ill-defined to be analysed by conventional methods. This paper aims at presenting the basic concepts of the theory in an insurance framework. First the basic definitions of fuzzy logic are presented, and applied to provide a flexible definition of a “preferred policyholder” in life insurance. Next, fuzzy decision-making procedures are illustrated by a reinsurance application, and the theory of fuzzy numbers is extended to define fuzzy insurance premiums.

scholarly journals Reliability Analysis in Planning of Traction Substation Reconstruction Based on Fuzzy Set Theory

2020 ◽  
pp. 52-62
Author(s):  
Vladislav G. Belov ◽  
Vladimir A. Tremyasov
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Fuzzy Numbers ◽  
Probabilistic Method ◽  
Reliability Assessment ◽  
Electrical Equipment ◽  
Triangular Fuzzy Numbers ◽  
Traction Substation ◽  
Reliability Indicators

The study proposes a probabilistic method using triangular fuzzy numbers to analyze the reliability of the traction substation. With this approach, the reliability assessment of the traction substation can be performed considering changes in the values of reliability indicators of electrical equipment, determined on the basis of the fuzzy set theory

Risk Analysis of Propulsion System based on Similarity Measure and Weighted Fuzzy Risk Priority Number in FMEA

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Hang Zhou ◽  
Yuan-Jian Yang ◽  
Hong-Zhong Huang ◽  
Yan-Feng Li ◽  
Jinhua Mi
Keyword(s):  
Risk Analysis ◽  
Set Theory ◽  
Similarity Measure ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Failure Modes ◽  
Propulsion System ◽  
Second Phase ◽  
Risk Priority Number ◽  
Weight Analysis

Abstract Due to the epistemic uncertainty, it is difficult for the experts to give precise parameter values in Risk Priority Number (RPN) evaluations. To overcome this drawback, a hybrid method is proposed by integrating the concepts of fuzzy set theory, weight analysis and similarity value measure of fuzzy numbers. The analysis process is divided into two phases to identify the hazard source. The first phase uses fuzzy Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA), then the main potential failure modes can be determined. The importance analysis of basic events can be calculated using fuzzy set theory and weight analysis. In the second phase, the multiple failure modes and component correlations are modelled using the Fuzzy Risk Priority Number (FRPN) evaluation and the Similarity Measure Value Method (SMVM). The proposed method has been applied to the risk analysis of a satellite propulsion system to show the effectiveness and applicability.

Interval Methods and Fuzzy Optimization

1997 ◽  
Vol 05 (03) ◽  
pp. 239-249 ◽  
Author(s):  
Weldon A. Lodwick ◽  
K. David Jamison
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Interval Analysis ◽  
Fuzzy Set Theory ◽  
Fuzzy Numbers ◽  
Optimization Problems ◽  
Fuzzy Optimization ◽  
Interval Methods

In this paper, we describe interval-based methods for solving constrained fuzzy optimization problems. The class of fuzzy functions we consider for the optimization problems is the set of real-valued functions where one or more parameters/coefficients are fuzzy numbers. The focus of this research is to explore some relationships between fuzzy set theory and interval analysis as it relates to optimization problems.

scholarly journals FUZZY SET THEORY---------- SOME USEFUL DISCUSSIONS AND INVESTIGATIONS

2021 ◽  
Vol 9 (8) ◽  
pp. 125-149
Author(s):  
Surajit Bhattacharyya
Keyword(s):  
Fuzzy Sets ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Fuzzy Numbers

In this paper I have discussed some basic but very important theories of fuzzy set theory with numerous examples. I have investigated α-sets, operations of fuzzy numbers, on interval fuzzy sets and also on fuzzy mappings. I have introduced S.Bs. class of fuzzy complements with its increasing and decreasing generators .

Sign in / Sign up

Export Citation Format

Share Document