Showing 3 results for Gholami
Elahe Allahyari, Abdollah Gholami, Morteza Arab-Zozani, Hosein Ameri, Negin Nasseh,
Volume 11, Issue 3 (9-2021)
Abstract
Introduction: These days, there is a consensus that emotional intelligence plays an important role in the success of individuals in different areas of life. Persons with higher emotional intelligence had lower stress in dealing with demands and pressures in the workplace. The purpose of this study was to use artificial neural network to predict job stress and to compare the performance of this model with the multivariate regression model.
Material and Methods: In order to do that, 892 participants were selected randomly in different job categories. Then, 15 dimensions of Bar-On questionnaire, 10 job categories, age and education were considered as input variables and 7 dimensions of health and safety executive HSE were determined as output variables in models.
Results: The results revealed that an artificial neural network with hyperbolic tangent and sigmoid transfer functions respectively in hidden and output layers with 375 hidden neurons had significantly better performance than multivariate regression. So that, correlation of predicted values and job stress were only between 0.192-0.364 in regression model, but neural network had at least correlation 0.527 in all dimensions of job stress.
Conclusion: In predicting job stress using emotional intelligence, artificial neural network method was much better than multivariate regression model.
Ehsan Ramezanifar, Kamran Gholamizadeh, Iraj Mohammadfam, Mostafa Mirzaei Aliabadi,
Volume 13, Issue 1 (3-2023)
Abstract
Introduction: Risk assessment is a scale for predicting reliability and can manage interactions between components and process variables. Moreover, the reliability of one component or barrier affects the overall risk of the system. Being one of the most critical safety barriers of the storage tank, the failures of Fixed Foam Systems (FFS) on demand can result in severe consequences. FFS, is of grave importance in decreasing the risks associated with fires and damages.
Material and Methods: This study aims to determine the probability of root causes related to FFS failure through Fuzzy Fault Tree Analysis (FFTA) to estimate system reliability. In conventional fault tree analysis, accurate data is usually used to assess the failure probability of basic events. Therefore, the introduced approaches were employed to quantify failure probabilities and uncertainty handling. Finally, system reliability was estimated according to the failure probability of the top event.
Results: The findings showed that 13 baseline events involved FFS performance. According to the results, failures of cable path and detection system (or resistance temperature detectors), set the activation switch (multi-position) incorrectly, and foam makers not continuously running are the three most critical basic events influencing the reliability of fixed foam systems. In addition, this paper estimated the system reliability at 0.8470.
Conclusion: The results showed that the FFTA could be used in matters such as reliability evaluation failure and risk assessment using experts’ judgment. This paper can also show the adaptation of the fuzzy approach to assess the failure probability of the basic event in the fault tree analysis (FTA).
Behzad Gholami, Mousa Jabbari, Davood Eskandari,
Volume 14, Issue 2 (6-2024)
Abstract
Introduction: One of the ways to produce electricity in power plants is to use gas turbines and generators. Due to the use of methane gas as the fuel of the burners and the high rotation speed, this equipment has a high DOW index level, therefore, if the hazardous conditions in the gas turbine are not controlled by the safety instrumented system and the process is not directed to a safe state, Catastrophic events will occur such as fire and explosion and damage to property and people as well as interruption of the power generation process will happen in the long term, so gas turbine safety instrumentation systems can be considered as “critical safety systems”. Therefore, the reliability and availability of their function should be evaluated. The purpose of this research is to determine and verify the safety integrity level (SIL) related to the safety instrumented function (SIF) of the gas turbine and generator in a combined cycle power plant.
Material and Methods: In this study, the safety integrity level was determined by using two methods, Calibrated Risk Graph (CRG) and Independent Protection Layer Analysis (LOPA), and to verify the safety integrity level, the requirements related to random hardware failure, hardware failure tolerance, and systematic capability are considered according to IEC 61511 and IEC 61508 standards.
Results: The results of a case study in gas turbine and generator showed that the LOPA method is more quantitative than CRG and provides more details of independent protective layers, so it is a more suitable method for determining SIL. The SIL verification results show the SIL2 level, closer to the LOPA results.
Conclusion: The obtained results show that the function of the studied gas turbine safety instrumentation system has a suitable level of reliability and availability and is well responsive to risky conditions and possible deviations. The present approach helps safety engineers and instrumentation engineers to calculate the reliability and availability of the Function of the safety instrumentation systems of their process equipment and ensure its acceptability or not.
