Journal of Health and Safety at Work

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Introduction: Process industries, often work with hazardous and operational chemical units with high temperature and pressure conditions, such as reactors and storage tanks. Thus, probabilities of incidence such as explosions, and fire are extremely high, The purpose of this study was to present a comprehensive and efficient method for the quantitative risk assessment of fire and explosion in the process units.

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Material and Method: The proposed method in this study is known as the QRA and includes seven steps. After determination of study objectives and perfect identification of study process, first, qualitative methods are used to screen and identify hazard points and the possible scenarios appropriate are identified and prioritized. Then, estimation of frequency rate are done using past records and statistics or Fault Tree Analysis along whit Event Tree. PAHST professional software and probit equations are used in order to consequence modeling and consequence evaluation, respectively. In the last step by combination of consequence and frequency of each scenario, individual and social risk and overall risk of process or under study unit was calculated.

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Result: Applying the proposed method showed that the jet fire, flash fire and explosion are most dangerous consequence of hydrogen generation unit. Results showed that social risk of the both fire and explosion caused by full bore rupture in Desulphrizing reactor (Scenari3), Reformer (scenario 9) and Hydrogen purification absorbers are unacceptable. All of the hydrogen generation unit fall in ARARP zone of fire individual risk (FIR) and FIR up to 160 m of boundary limit unit is unacceptable. This distance is not only beyond of hydrogen generation unit boundary limit, but also beyond of complex boundary limit. Desulphurization Reactor (75%) and Reformer (34%) had the highest role in explosion individual risk in the control room and their risks are unacceptable.

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Conclusion: Since the proposed method is applicable in all phases of process or system design, and estimates the risk of fire and explosion by a quantitative, comprehensive and mathematical-based equations approach. It can be used as an alternative method instead of qualitative and semi quantitative methods.

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Introduction: Occupational exposure to crystalline silica increases the risk of lung cancer and restrictive lung disease with extensive fibrosis. Silica dust is a major health hazard in foundry factories. The aim of this study was to determine core making workers’ exposure to respirable crystalline silica dust in a foundry factory.

Material and Method: This cross-sectional study was conducted in core-making unit of a foundry factory in 2015. Occupational exposure of 55 workers to respirable crystalline silica aerosols was evaluated by using the improved NIOSH7602 method in core-making unit. Risk assessments for silicosis and excess lifetime risk of mortality from lung cancer were done according to Manettej and Rice models, respectively. Data was analyzed with Spss19 software.

Result: The mean of respirable crystalline silica dust was 0.246 ± 0.351 (mg/m3). All  workers’ exposure to respirable crystalline silica was higher than recommended occupational exposure limits. Silicosis mortality risk and excess lifetime risk of mortality from lung cancer were estimated in the range of 6-63 and 65 per thousand people, respectively.

Conclusion: The mean of workers’ exposure to respirable crystalline silica was higher than recommended occupational exposure standards in core making unit. The risk assessment of silicosis mortality and excess lifetime risk of mortality from lung cancer were higher than acceptable levels of risk.

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Introduction: Failure modes and effects analysis (FMEA) method is used in industries to identify, assess and prioritize risks. Multi-criteria decision-making methods (MCDM) select the best option from different criteria. Therefore, this study aims to identify, assess and prioritize risks using FMEA based on SWARA-VIKOR multi-criteria decision-making methods in a gas pressure reduction station.
Material and Methods: In this descriptive and analytical study, stepwise weight assessment ratio analysis (SWARA) and decision-making optimization and compromise solution (VIKOR) methods were used to rank the risks of failure modes identified in FMEA. SWARA method was employed to determine the severity, probability and discovery weights, and VIKOR technique was applied to rank the failure modes of the system equipment. Finally, an operational example of the pressure reduction station was presented to show the application and feasibility of the proposed model. A comparative study was conducted to confirm the practicality and effectiveness of the proposed model.
Results: In total, 35 main failure modes were identified in the pressure reduction station. Failure of regulator sleeve and safety valve and regulator diaphragm rupture were assigned the first, second and third ranks of risk priority, respectively. The sensitivity analysis results showed the proposed approach had desirable stability and only the failure mode of increasing the heater flame temperature was very sensitive to changes in the weight of the criteria. Results of ranking the failure modes of the station indicated there were many changes in the ranking of failure modes based on the proposed approach.
Conclusion: The proposed approach could provide more reasonable and accurate results for ranking risks because the criteria were weighed step by step based on the experts’ opinion.

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Introduction: Reliability is always of particular importance in system design and planning; thus, improving reliability is among the approaches for achieving a safe system. Simulation methods are widely used in system reliability assessment. Therefore, this study aims to assess the reliability of the City Gate Gas Station (CGS) using Monte Carlo Simulation (MCS).
Material and Methods: This descriptive and analytical study was conducted in one of the CGSs of North Khorasan Province in 2021. The CGS process was carefully examined and its block diagram was plotted. Then, failure time data of CGS equipment were collected over 11 years and time between failures of subsystems was calculated. The failure probability distribution function of subsystems was determined using Easy Fit software and Kolmogorov-Smirnov test. Moreover, subsystems’ reliability was estimated by MCS. Finally, station reliability was calculated considering the series-parallel structure of the CGS.
Results: The results revealed that the failure probability density distribution function of CGS subsystems was based on gamma and normal functions. The reliabilities of filtration, heater, pressure reduction system, and odorize were calculated as 0.97, 0.987, 0.98, and 0.992 respectively, and their failure rates were 0.000003477, 0.0000014937, 0.0000023062, and 0.0000009169 failures per hour respectively. The station reliability was calculated as 0.93.
Conclusion: The failure probability distribution function and reliability assessment of subsystems were determined by data modeling and MCS respectively. Filtration and pressure reduction systems had the highest failure rate and required a proper maintenance program.

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Introduction: Iran has the most extensive maritime transport fleet in the Middle East, with 2700 km of water border with other countries in the region. However, the complex and hazardous marine environment has turned this advantage into a disadvantage. On the other hand, technological advancement has added to the complexity. Thus, new accident analysis tools should be developed to bring unity to marine casualty analysis and improve the analyst’s power of discovery from incident information. The current project aims to develop a specialized AcciMap-based marine accident investigation method.
Material and Methods: The primary stages of this applied descriptive study include data collection, method development, and validation. The necessary information about the factors leading to marine accidents was initially gathered through a review of previous studies, expert interviews, and analysis of actual cases. The AcciMap technique was then partially developed, and marine experts approved the designed model.
Results: This study’s results included an AcciMap model established on three levels: external influences (national and international), intra-organizational factors, and environmental/individual conditions and individual activities. Whereas external factors (international and national) are categorized into three main layers, two sublayers, and 13 secondary sublayers, intra-organizational factors are categorized into two main layers, 11 sublayers, and 35 secondary sublayers, and environmental/individual conditions and individual activities are organized in one main layer, three sublayers, and 11 secondary sublayers.
Conclusion: The developed approach can identify flawed levels and determine who is responsible for implementing corrective action. Because it includes emerging components that are effective in accidents, the method used in this study can better examine data from marine accidents.

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Introduction: Nanomaterials are widely applied across diverse scientific and industrial sectors; however, their emergence has introduced a new generation of occupational hazards for workers. Concurrent with discussions on the adverse effects of nanomaterials on human health, researchers have sought to develop methods for assessing occupational risks associated with these materials. Accordingly, this study aims to propose a general framework for the development of such methods.
Material and Methods: This is a critical analysis study designed to evaluate existing methods for assessing occupational risks related to nanomaterials and ultimately propose a modified framework for refining these methods. By examining current approaches and identifying their strengths and weaknesses, the authors have proposed an improved framework for occupational risk assessment of nanomaterials.
Results: The proposed framework is based on two key dimensions: “Severity/Hazard” and “Probability/Exposure.” The first dimension determines the potential risk level arising from exposure to nanomaterials, with the most critical factors being the intrinsic properties and toxicology of the nanomaterial itself, parent materials, and similar substances. The second dimension describes the likelihood and nature of exposure to nanomaterials during work activities, with the most influential factors being worker, job tasks, and workplace environment characteristics.
Conclusion: The lack of sufficient data and numerous uncertainties regarding bio-nano interactions make quantitative risk assessment (the traditional occupational health approach) difficult, less reliable, and in some cases unfeasible for nanomaterials—given current knowledge. Qualitative and semi-quantitative approaches, such as Control Banding, despite demonstrating positive aspects, have faced significant criticism. The framework-based method proposed herein appears capable of partially overcoming these challenges.

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Introduction: Fire risk assessment in oil storage tanks faces challenges due to incomplete, conflicting, and uncertain data, particularly when empirical evidence is limited. Traditional point-based likelihood estimates often fail to capture expert doubt and epistemic uncertainty. This study aims to develop and evaluate a novel hybrid framework combining Dempster-Shafer Theory (DST) and Bayesian Networks (BN) to improve the trustworthiness of fire risk prediction in such industrial settings.
Material and Methods: The proposed approach integrates DST to model expert uncertainty through interval probabilities (Bel–Pl) and BN to dynamically update causal relationships as new information appears. The study implements computational coding to enable DST calculations for five expert opinions across 243 scenarios, overcoming prior limitations in multi-expert modeling due to computational complexity.
Results: The hybrid DST-BN framework demonstrated superior ability to incorporate incomplete and conflicting expert data, reducing overconfidence linked to point estimates. Interval probabilities offered more trustworthy representations of epistemic uncertainty, while BN integration allowed traceable and adaptable causal modeling. The computational solution facilitated practical application of DST with multiple experts, enhancing the strength of the risk assessment.
Conclusion: This research provides an effective DST-BN hybrid methodology for assessing fire risk in fixed-roof oil tanks, improving accuracy and trustworthiness in complex industrial environments. By addressing the shortcomings of point-based methods and enabling multi-expert participation, the framework supports clearer and more defensible probabilistic inferences. Future work may focus on integrating real-time sensor data and AI-based decision systems to further strengthen dynamic risk assessment capabilities.
 

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Introduction: A gas pressure reduction station is an important facility in gas transmission systems. These systems consist of various sections, the reliability of each section affecting the station’s overall reliability. Therefore, this study aimed to assess the reliability of station sections using Markov chain Monte Carlo (MCMC) and the continuous-time Markov chain (CTMC) method.
Material and Methods: Equipment failure and repair rates were simulated using the MCMC method in WinBUGS14. Then, based on the failure and repair rates, the station reliability was evaluated using the CTMC. The results of the equipment failure rate simulation were validated using two criteria: MC Error and the Goleman-Rubin test. Also, the results of station reliability evaluation were validated using Reality Check and Partial Benchmark Exercise methods.
Results: Failures in the filtration and pressure reduction sections were more frequent than in other sections of the station. Therefore, these sections were considered the most critical sections in the reliability assessment. The posterior standard error was less than 0.01, indicating good convergence of the data for the parameter posterior distribution. The results of the Goleman-Rabin test showed values less than 1.2, indicating proper convergence of the chains. For all sections and stations, a systematic approach was determined using the Markov model. The results showed a strong correlation between the CTMC and the block diagram method (R2=0.9499).
Conclusion: The proposed approach combines failures of system components and can display multiple failures. It also accounts for time factors in its calculations and minimizes subjective expert evaluations.