Journal of Health and Safety at Work

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Introduction: Many methods exist for evaluating ergonomic risk factors for LBP at workplace, including biomechanical, physiological and psycho-physical methods. Digital Human Modeling (DHM) as a tool based on computer for ergonomic evaluation that Because having advantages such as saving time and costs in assessment and actively evaluation of ergonomic solutions in the digital environment.Aim of this study was evaluation occupational causes of LBP with the use of digital human modeling.

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Method and Materials: This study was a case quasi-experimental study in the engine assembly of the car manufacturing industry was conducted. First, The characteristics of job and risk factors for low back pain In all workstations were documented, then workstations with high risk of low back disorders were selected. Finally, a workstation for simulating and evaluating in the human digital modeling softwares, which includes 3DSSPP and Catia were selected. A total 22 posture of the lifting and lowering moment of the three tasks of workstation selected for simulation. After evaluation in the digital environment, the risk areas identified and solutions were presented.

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Results: The results showed that compressive and shear forces on the L5/S1 disc increased with increase of anthropometric dimensions and Horizontal distance from the body and decrease of height Lowering the site, and the percentage of people capable to perform a task in joint, decreased with increase of anthropometric dimensions. Ligament strain in postures with sever bending trunk were more increasing. RULA scores increased with the Unsuitable conditions of back and arms. High risk areas, were mainly related to the low height of pallets in lifting and lowering and high Horizontal distance from the body.

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Conclusion: According to The results of this study, Biomechanical Causes for LBP, including anthropometric characteristics (height and weight), Horizontal distance of object to the body, height of lifting and lowering location and trunk bending and torsion. This study showed that DHM is an effective tool in the evaluation of job tasks and workplace design, that can be identified risk area in each task and to achieve the ideal design. Using DHM can be implement Desired solution in a virtual environment and With the re-evaluation, Be sure of having effective solutions in the real environment.

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Introduction: Although human industrial activities are as a part of efforts to achieve greater prosperity, the risks related to these activities are also expanding. Hazard identification and risk assessment in the oil and gas industries are essential to reduce the frequency and severity of accidents and minimize damage to people and property before their occurrence. The aim of this study was to evaluate the liquefied and pressurized petroleum gas spherical tanks in a refinery and assessing the risks of Boiling Liquid Expanding Vapor Explosion (BLEVE) phenomenon.

Material and Method: In this study, the risks of BLEVE phenomenon were assessed, using the Bowtie method. The consequences of explosion wave phenomenon and the resulting wave quantity and its impacts on the neighboring machineries and equipment were analyzed. PHAST software version 6.54 has been used for modeling the BLEVE phenomenon.

Result: In this evaluation, generally five causes and two consequences were identified for BLEVE phenomenon. In order to reduce its consequences, forty-three controlling measures were introduced to prevent the BLEVE phenomenon and the impacts of 31 control measures were identified. According to the conducted analysis, it was found that the spherical tank blast wave caused by LPG can lead to explosion of close located tanks which can create a chain of explosions.

Conclusion: The results of modeling and risk assessment can be used to identify the BLEVE phenomenon causes and its effects on nearby people and equipment. Based on these results, preventive controlling measures can be implemented and also be determined by adopting proper design and layout, margin of safety for personnel, equipment and accessories.

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Introduction: Nowadays, many modern industries require an environment with no contamination by particles and bacteria. An enclosed clean room environment is a place where parameters such as airborne particles, temperature, humidity, air pressure and air flow pattern is controlled. The aim of this study was to evaluate functional parameters of a clean room  in a selected pharmaceutical industry.  

Material and Method: This study was an experimental study conducted in 2015 in a pharmaceutical industry.  The air flow rate and flow rate with airflow capture hood was used and multi sensor devices for measuring temperature, humidity and pressure of multi-sensor device. HEPA filter leakage test and counting concentration of particles in the cleanroom was done according to the ISO 14644 – 3(2005) standards using aerosol photometer and aerosol generator. In this study, 6 clean room relating to the 3 cleanliness classes B, C and D (in accordance with standard EU GMP) were evaluated. Meanwhile, both the 2 and 3-dimensional flow model using Computational Fluid Dynamics Software was simulated in this study.   

Result: Measuring the parameters flow rate and air velocity, temperature (average temperature 20 ° C), relative humidity (below 50%), pressure (pressure less than 15 psi) for every three classes of cleanliness are all acceptable and less than the proposed standard. Furthermore, the results of modelling showed that the pattern of air flow in the room is correct paths in circulation. In the case of leakage test filters, the filter 29 filters tested 5 was leaking and ultimately determine the HEPA filters remove particles that average efficiency is 99.99%.

Conclusion: This study showed that the high volume and good quality of air entering the clean room affect the optimal efficiency and air flow rate, pressure drop and air penetration of the HEPA filters Also, the results of study show that the concentration of airborne particles in clean room is depend on the air flow rate and speed and adopting a good air flow pattern will affect the particle concentration.

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Introduction: Being aware of the explosion, fire radius, and their damages, has an important role in accident prevention methods. Therefore, the aim of this study was modeling and evaluation of the consequences of propylene oxide spill in a petrochemical company.
 

Material and Method: The QRA method including seven steps was used in this study. In the present study, in order to examine and modeling of the propagation propylene oxide, first a familiarization with the process information of the unit was done then, a risk assessment was carried out adopting HAZOP technique to identify existing hazards. Consequence analysis in a process unit includes: selecting important scenarios, characterizing scenario, modeling the consequences of scenarios, analyzing the results and determining the percentage of mortality. PHAST software version 6.51 was used for modeling of outcomes and assessment propylene leak.
 

Result: urves of the firing zones of sudden release of propylene oxide showed that the influence puts are included up to radius of 0.15 meters in the scenario of leakage 5 mm, in scenarios with leaks 25 mm to a radius of 1.1 meters and in scenarios with leakage of 100 mm to a radius of 39 meters. The maximum intensity of flash fire in the initial point Scenario 5 mm was 4.2 kW/m2, in the scenario of radiation leakage was 25 mm at the distance to 5 meters from the fire intensity up to maximum of 9 kW/m2, and also in the scenario with 100 mm flash fire radiation leak at an earlier point fire was 14 kW/m2. The maximum intensity of thermal radiation at the distance to 5 meters up to 16.5 kW/m2, and maximum distance of 80 meters around the reservoir affected. The mortality rate of flash fire has exposed employees, was 50 percent.
 

Conclusion: Many accidents caused by leakage and explosion were due to corrosion, spoil tanks and equipment, and the majority of such accidents can be prevented by technical inspections and continuous audits.

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Introduction: Risk assessment is a main tool in safety management process as it can help managers to choose corrective actions by providing appropriate information. The purpose of this paper was to select the optimal corrective actions among the proposed ones by the experts based on mathematical modeling, taking into account the standards and also the limitations including the cost.

Material and Method: In this paper, a model was presented to find the optimal corrective actions regarding the organization goals (maximum in risk reduction value) and the limitations such as cost and level of acceptable risk. Due to extensive number of solutions, Genetic Algorithm (GA) is used for solving the problem.

Result: To show the capability of this method in an industrial environment, a power generation industry with 40 hazards was considered as the case study. Then, the risk of hazards was estimated and corrective actions were determined for each of them. Using the proposed model, corrective actions were selected optimally, with the least possible cost; all risks were reduced below the level of organizational acceptable risk.

Conclusion: It was shown that the optimal corrective actions using mathematical modeling are selected with high precision in acceptable time. This method is suggested as an alternative for conventional qualitative methods based on expert’s opinions.

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Introduction: Nearly half of occupational accidents in Iran occur in construction sites. Therefore, modeling of occupational accidents in these sites is one of the solutions to design safety strategies to reduce occupational accidents in the field of construction. This study was designed and conducted with the aim of modeling the cause-consequence of accidents in construction sites.
Material and Methods: This study was conducted based on a retrospective analysis of 10-year accident data (2010-2019) in Iranian construction sites in 2020. The main variable included the types of occupational accidents in construction sites. The study tool included accidents checklist as well as a detailed report of the studiedaccidents. The required data were collected based on a conceptual model designed to model the cause-consequence of accidents in the construction sites. Cause-consequence modeling of the studied accidents has been done based on the structural equation modeling and using IBM SPSS AMOS v. 22.0.
Results: The frequency of the studied accidents was 3854 accidents. The annual averages of AFR and ASR indices were 17.27 ± 8.54 and 322.42 ± 44.23 days, respectively. The results of cause-consequence modeling of these construction accidents showed that individual and occupational, safety training and risk assessment factors as well as variables related to these factors have a negative and significant relationship with the indicators of the construction accidents, and the factors of environmental conditions and unsafe acts and variables belonged to these factors have a positive and significant relationship with these indicators (p < 0.05).
Conclusion: The findings of the study revealed that the highest impact factors on accident indicators were related to safety training, risk assessment and unsafe acts and their variables. Therefore, the results of this modeling can help to design safety strategies in construction sites.

 

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Introduction: Unit risk management is a critical component of gas refining management, as risks that are not well-managed may lead to trip production failures. The present study aimed to provide a structural model for investigating the role and effect of different variables on stopping the gas production process in the gas refinery.
Material and Methods: This study was a retrospective cross-sectional and systematic analysis, which was carried out on key risks in the trip gas sweetening unit in a gas refinery industry located in Asaluyeh, Iran. The systems analysis was applied by using Fishbone Diagram, and then data modeling was prepared by Structural Equation Modeling (SEM) for an incident that occurred during gas sweetening production. Tools for the data analysis included the SPSS 24 and Smart PLS 2 software.
Results: Results of this research indicate that “Environment Risk” with a path coefficient of 0.943 and T- Value of 103.791; “Cost Risk” with a path coefficient of 0.937 and T- Value of 95.168; “Implementation of management system Risk” with a path coefficient of 0.847 and T- Value of 35.23; “Accident Risk” with path coefficient of 0.577 and T- Value of 25.410; “Time Risk” with path coefficient of 0.758 and T- Value of 15.121; “Human Error Risk” with path coefficient of 0.712 and T- Value of 11.215 had the most important coefficients of the paths respectively, that are effective in stopping production concerning other risks. Also, by comparing the path coefficients of the risks we can see that the impact of each of the risks on stopping production is different.
Conclusion: The findings of the present study revealed that a combination of variables can affect stopping production in the gas industry. Therefore, the role of these risks in losses in the refinery system should be investigated.

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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: Cognitive functions play a vital role in how tasks are performed; for this, temporary cognitive and mental dysfunctions could lead to grave consequences, especially when an accurate and prompt response is required. Attention and reaction time to noise are among the most effective exogenous factors on the brain processing mechanism. This study aimed to measure the sustained attention of workers in the steel industry exposed to different sound pressure levels. 
Material and Methods: The study was conducted in 4 general stages, including 1- Selecting predictive orientation variables (age, work history, different sound pressure levels); 2- Conducting the Cognitive Performance Test (CPT); 3 Conducting N-BACK Cognitive Performance Test and 4- Modeling cognitive performance changes using model precision methods.
Results: Continuous Performance Test (CPT) results indicated that all three groups’ omission error, commission error, and response time were affected by shift time. All three components increased significantly as the shift ended, decreasing individuals’ cognitive function. Also, the higher noise impact in modeling CPT and N-Back tests indicated reduced workers’ concentration.
Conclusion: These study findings suggested that greater noise weight obtained in test modeling in three-time intervals, i.e., in the beginning, middle, and end of the shift, affected the continuous performance components of the CPT and working memory performance of the N-back test, including workers’ response time and reaction time, with workers’ rate of error increasing and their focus decreasing during the shift.