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Showing 4 results for Chemical Industry

Golmohamadi, Mohammadfam, Shafie Motlagh, Faradmal,
Volume 3, Issue 3 (12-2013)
Abstract

Introduction: Every year many people around the world lose their lives or suffer from injuries and serious damages in industrial fire. This study aims at evaluating fire risk using an suitable method and determining endangered humane, financial and environmental capitals in various parts of a chemical industries.

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Material and Method: In this analytical study the developed Frank and Morgan method was used to evaluate the risk of fire in all units of a chemical company. Improved checklists validity was confirmed by experts and then, its reliability was determined by test-retest analyzing method. Human, financial and environmental probable losses were calculated in the case of fire. A risk factor was determined for each unit and all of them were prioritized accordingly.

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Result: The study of developed checklists’ validity showed that there was a high conformity (homogeneity) between results of two measured loads (ICC=0.87 %95CI: 0.699-0.952). Mean value of risk in units was 115.45 and research and development (R&D) and sparse part store units have the highest and lowest risk values, respectively. Endangered humane, financial and environmental capitals had the highest to lowest score, respectively.

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Conclusion: Results showed that the developed Frank and Morgan method can be a suitable tool for evaluating industrial fire risk and prioritizing units in general level of an industrial complex especially chemicals company. According to the findings in this study, the investigation of likely damages to environment in the case of fire has high importance.


G. A. Shirali , T. Hosseinzadeh, D. Afshari, M. S. Moradi,
Volume 5, Issue 2 (7-2015)
Abstract

Introduction: Safety signs provide information,related to hazards or dangers in the industry,in form of instructions. These signs are effective as long as they are designed in accordance with principles of ergonomics and design cognitive features. The purpose of this present research was to study the relationship between cognitive features of signs and ability to guess, and to develop the relevant regression model.

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Materials and methods: This descriptive cross-sectional study was carried out on 100 employees in a petrochemical industry complex. A three part questionnaire was used to collect required data while first part of the questionnaire dealt with demographic information, second part included cognitive features of signs designand the third part proceeded on testing the ability to guess. Then, a regression model was developed to determine the relationship between cognitive features, and the ability to guess.

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Results: Mean and standard deviation obtained for the ability to guess the total study signs were 63.73 and 4.36, respectively. The feature of “familiarity” obtained the lowest possible score (49.15). The “semantic closeness” (β=0/390) and “meaningfulness” (β=0/369) had the highest correlation with the ability to guess safety signs.

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Conclusion: According to results of this study, use of principles of ergonomic design of signs and training are necessary to promote the ability to guess the safety signs to the minimum available standards. Therefore, it is possible to balance cognitive features especially “familiarity”, with the lowest score, and “meaningfulness” and “semantic closeness”, with the highest influential relationship with the ability to guess of signs. The developed regression model for this industry can be used to predict the ability to guess of safety signs in future studies


Safoura Karimie, Iraj Mohammadfam, Mostafa Mirzaei Aliabadi,
Volume 9, Issue 2 (6-2019)
Abstract

Introduction: Nowadays, human error is one of the main causes of incidents in the industry. One of the vital characteristics of modern industries is that the precise control of key parts of the process is performed by operators from central control rooms, so an error by the control room staff can be disastrous. The present study is aimed at identifying and evaluating human errors in the control room of the petrochemical industry.  
Material and Methods: This is a descriptive-analytic case study that was conducted in a control room of the petrochemical industry. In this research, firstly by using hierarchical task analysis (HTA), the tasks in the control room were identified and analyzed. Then, using the extended CREAM method, possible human errors were identified, their cognitive category was determined, and their probabilities were calculated using a new approach based on BN.
Results: The results of the study showed that the most prevalent control modes for the Boardman and the senior board man were strategic and scrambled modes with error probabilities of 0.136 and 0.171, respectively.
Conclusion: According to the results obtained in the modeling section, BN can be proposed as an approach with high processing accuracy and also high accuracy in modeling human errors and problems with high input parameters affecting the output parameter.
Ahmad Soltanzadeh, Hamidreza Heidari, Heidar Mohammad, Abolfazl Mohammadbeigi, Vali Sarsangi, Milad Darakhshan Jazari,
Volume 9, Issue 4 (12-2019)
Abstract

Introduction: The causal analysis of occupational accidents’ severity in the chemical industries may improve safety design programs in these industries. This comprehensive study was implemented to analyze the factors affecting occupational accidents’ severity in the chemical industries.
Methods and Materials: An analytical study was conducted in 22 chemical industries during 2016-2017. The study data included 41 independent factors and 872 accidents in a ten-year period (2006-2015) as a dependent variable. Feature selection algorithm and multiplied linear regression techniques were used to analyze this study.
Results: Accident severity rate mean was calculated 214.63 ± 145.12. The results of feature selection showed that 30 factors had high impacts on the severity of accidents. In addition, based on regression analysis, the severity of accidents in the chemical industries was affected by 22 individuals, organizational, HSE training, risk management, unsafe conditions and unsafe acts, as well as accident types (p<0.05).
Conclusion: The findings of this study confirmed that accidents’ severity in the chemical industry followed the multi-factorial theory. In addition, the main finding of this study indicated that the combination of features selection algorithm and multiple linear regression methods can be useful and applicable for comprehensive analysis of accidents and other HSE data.


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