Prioritization of control measures in leakage scenario using Hendershot theory and FBWM-TOPSIS.

Currently, there is increasing concern about the safety and leakage of process industries. Therefore, the present study aims to prioritize control measures before and after the leakage scenario by using the Hendershot theory and MCDM techniques. In this study, two proactive and reactive layers were selected before and after leakage of tanks, respectively. Then, criteria and alternatives were selected to perform fuzzy TOPSIS (FTOPSIS) and find the best alternative based on the literature review and Hendershot approach. The linear model of the fuzzy Best-Worst method (FBWM) was constructed and resolved using Lingo 17 software. Subsequently, criteria were assigned weights based on thorough calculations of the inconsistency rate. The weight of study experts was equal to 0.25. The results of FBWM showed that the reliability index with a weight of 0.3727 was ranked first and the inconsistency rate ([Formula: see text]) was calculated to be equal to 0.040. Inherent Safety Design (ISD) (0.899) and passive safety (0.767) also ranked first before and after tank leaks, respectively. Using the FBWM method leads to fewer pairwise comparisons and at the same time more stability. Although ISD and passive strategies are more valid and strict, elements of all strategies are necessary for a comprehensive process safety management program.

Quantitative risk estimation of CNG station by using fuzzy bayesian networks and consequence modeling.

As one of the potential explosions and inflammation, compressed natural gas (CNG) stations in urban areas cause irreparable losses and casualties. Estimating risk assessment in gas stress based on coherent uses can reduce accidents in urban areas. The purpose of the present study was to estimate a small risk estimation at one of the CNG multipurpose stations, LPG, using combined models of the Fuzzy Bayesian Network, Bow-tie Diagram, and consequence modeling. This study was conducted based on the basic and 25 intermediate events. This study formed a seven-person safety team to identify the primary events and build the Bow-tie diagram. Then, because of the lack of a proper database, fuzzy theory was used to determine the probability of significant events. Bayesian networks were drawn based on the Bow-tie model using GeNLe software. Finally, the main events of the two Bow-tie, Bayesian network modeling, and risk estimation were performed with the help of PHAST/SAFETI (V8.22). The geographical information system software was used to zone the explosion effects. The Risk assessment result showed that the social risks and the Bayesian network model are more than Bow-tie, and the Bow-tie diagram is unacceptable. Therefore, using incompatible land uses in the vicinity of the CNG stations gives rise to the effects of accident scenarios in particular residential and administrative land uses, which decision-makers and city managers should consider. Based on the findings of this study, the obtained results can be utilized to implement effective control measures. These measures encompass devising a response plan tailored to address specific emergency conditions and conducting comprehensive training programs for the individuals and residents residing within the study area.

Analyzing the influential factors of process safety culture by hybrid hidden content analysis and fuzzy DEMATEL.

Due to the complex nature of safety culture and process industries, several factors influence process safety culture. This paper presents a novel framework that combines the hidden content analysis method with Decision Making Trial and Evaluation Laboratory (DEMATEL) and Fuzzy logic to achieve a comprehensive set of influential factors and their relationship. The proposed methodology consists of two primary stages. Firstly, combined methods of literature review and Delphi study were used to identifying influential factors of process safety culture. Secondly, the Fuzzy-DEMATEL approach is employed to quantify and determine the relationships between different influential factors. A diverse pool of experts' opinions is leveraged to assess the impact of each factor on others and process safety culture. In the first stage, 18 factors identified as influential factors on process safety. The findings of second stage revealed that eight variables were identified as causes, while ten variables were classified as effects. Also, the Organization management's commitment to safety factor had the greatest influence among all of the factors. As well as, the most significant interaction was associated with the risk assessment and management aspect. The integrated approach not only identified the influential factors, but also elucidates the cause-effect relationships among factors. By prioritizing factors and understanding their interconnections, organizations can implement targeted safety measures to improve process safety culture. Its effectiveness in quantifying qualitative data, identifying influential factors, and establishing cause-effect relationships make it a valuable tool for enhancing safety culture in process industries.

Dynamic risk assessment of storage tank using consequence modeling and fuzzy Bayesian network.

Accidents in process industries cause irreparable economic, human, financial and environmental losses annually. Accident assessment and analysis using modern risk assessment methods is a necessity for preventing these accidents. This study was conducted with the aim of Dynamic risk assessment of tank storage using modern methods and comparing them with traditional method. In this study, bow tie (BT) method was used to analyze the Leakage event and its consequences and model the cause of the outcome, and the Bayesian network method was used to update the probability rate of the consequences. Then, four release scenarios were used. Possible selection and release outcome were modeled using version 5.4 of ALOHA software. Finally, according to the degree of reproducibility of possible consequences and risk number modeling for the four scenarios were estimated. The results of modeling the cause and effect showed that 50 Basic events are effective in chemical leakage and Pool fire is the most probable consequence due to chemical leakage in both BT and Bayesian network (BN) models. Also, the modeling results showed that Leakage 50 mm diameter has the highest Emission rate (80 kg/min) and Leakage of 1 mm have the lowest emission rate. The results of risk assessment showed that the estimated risk number in both models is in the unacceptable range. In this study, an integrated approach including BT, Fuzzy Bayesian networks and consequence modeling was used to estimate the risk in tank storage. The use of these three approaches makes the results of risk assessment more objective than conventional methods. The results of outcome modeling can be used as a guide in adopting accident prevention and emergency preparedness approaches.