فهرست مطالب fereshteh nirumand

Resilience engineering (RE), as a new approach in the system safety domain, is intended to preserve the performance of socio-technical systems in various conditions; and accentuates the positive activities instead of the failure modes. The aim of this study was to develop a new framework for safety assessment on the basis of RE, using the Fuzzy Analytical Hierarchy Process (AHP) method.
Material and Current study is an analytical cross-sectional survey performed in a petrochemical industry. Initially, six RE indicators were selected, including top management commitment, just culture, learning culture, awareness, flexibility and emergency preparedness and accordingly an assessment framework was established. Then, the selected RE indicators were evaluated and validated by experts in a specialized panel. Following, an indicator was proposed named “resilience early warning indicator”. Finally, the RE indicator score of the total process was determined using the fuzzy evaluating vector.
Findings revealed that top management commitment and learning indicators have the most and the least effects on the RE level of the process, respectively. Besides, the flexibility (C3) indicator was located in orange early warning zone (OEWZ) while other indicators were positioned in the no early warning zone (NEWZ). Furthermore, the overall resilience level of the process was evaluated as level III (NEWZ).
Management commitment and emergency preparedness are two main indicators of RE and can carry out the most important effect for remaining the RE in the NEWZ level.

Risk assessment of hazardous processes is the priority of risk management. Layer of protection analysis (LOPA) is one of the most popular methods used for risk assessment. Due to the insufficient information or uncertainty in failure rates (PFD) of protective layers, risk assessment based on the conventional LOPA can result in error in calculations. In this study, we tried to use the fuzzy set theory to evaluate the ammonia receiving tank safety, using the LOPA method.
Initially, the fuzzy failure rate of protective layers were calculated using the subjective opinions of professionals. Then, by applying the fuzzy operators, fuzzy possibilities transformed to fuzzy probabilities and subsequently they were deffuzified to crisp failure rate. Afterwards, using the severity fuzzy logic, severity of the outcome event was calculated in the fuzzy form, and subsequently, fuzzy risk index was calculated using the fuzzy matrix.
In the ammonia release scenario, calculated severity, probability and risk levels were determined as P: Low, S: High, and R: TNA, and PF = -2.66, SF = 3.99, RF = 3.79 (0.2 TNA, 0.8 NA) for classic and fuzzy LOPA methods, respectively. In addition, after inserting additional layers of protection, the fuzzy risk index reduced from 3.79 (0.2 TNA, 0.8 NA) to 1.92 (0.1 A, 0.8 TA, 0.1 TNA).
In the condition of uncertainty and lack of information relating to probability and severity of risk scenarios, the experts’ opinions can be used in forms of linguistic variables and fuzzy relations to reduce calculation errors in risk assessment as much as possible.

Due to the features such as intuitive graphical appearance, ease of perception and straightforward applicability, risk matrix has become as one of the most used risk assessment tools. On the other hand, features such as the lack of precision in the classification of risk index, as well as subjective computational process, has limited its use. In order to solve this problem, in the current study we used fuzzy logic inference systems and mathematical operators (interval numbers and mapping operator).
In this study, first 10 risk scenarios in the excavation and piping process were selected, then the outcome of the risk assessment were studied using four types of matrix including traditional (ORM), displaced cells (RCM) , extended (ERM) and fuzzy (FRM) risk matrixes.
The results showed that the use of FRM and ERM matrix have prority, due to the high level of " Risk Tie Density" (RTD) and "Risk Level Density" (RLD) in the ORM and RCM matrix, as well as more accurate results presented in FRM and ERM, in risk assessment. While, FRM matrix provides more reliable results due to the application of fuzzy membership functions.
Using new mathematical issues such as fuzzy sets and arithmetic and mapping operators for risk assessment could improve the accuracy of risk matrix and increase the reliability of the risk assessment results, when the accurate data are not available, or its data are avaliable in a limit range.