Dangerous Accidents Research Articles

Knowledge and Attitude of the First Aid Among School Teachers in Saudi Arabia: Systematic Review

Aim: Injury poses a serious threat to health as it is the main contributor to early mortality and morbidity as well as the main reasons for the death of adolescents. About one- third time of school children is spent in schools, where they are frequently in danger of accidents and medical issues. First-aid (FA) has been shown to be beneficial in the prevention and management of injuries. Teachers are the bystanders or first responders in case a mishap occurs in schools. It is therefore very crucial for teachers to have sound knowledge of first aid. This systematic review aims to assess the status of knowledge among Saudi Arabia school teachers and for studies to be conducted in future it aims to provide recommendations. Methods: The PRISMA guidelines were followed in conducting the systematic review. For the search of relevant studies, the strategy of using text words and MeSH terms was included. The studies included in the review were cross-sectional studies and survey research studies. Studies in government as well as private schools of every level, primary, intermediate, secondary, higher secondary, in any part of Saudi Arabia with more than 100 subjects were included in the systematic review. Studies carried out on populations in school settings apart from teachers, such as sports instructors, helpers, or students were excluded. The risk of bias was assessed using “the Joanna Briggs Institute (JBI) critical appraisal checklist for analytic cross-sectional studies”. Results: Twenty-nine studies were incorporated for assessment. Largest number of studies were conducted in Riyadh, the capital of Saudi Arabia. Questionnaires from previous studies were adapted in seventeen studies. Six studies utilized case-based questionnaires. 11 studies showed high risk of bias. Conclusion: The studies lacked well-developed, standard questionnaires containing uniformity in questions for similar kind of study. Level of knowledge was found to be low or barely adequate in major proportion of the studies

High-frequency Resonance Suppression of Railway Traction Power Supply System Based on the Combination of Single-tuned and C-type Filters

Background: Railroad transportation in the actual operation process, there are also many dangerous accidents caused by resonance, which greatly affect the safety of railroad transportation. A comprehensive examination of the operational dynamics within the power branch of a traction substation is imperative for sustaining system equilibrium. Discrepancies between these facets pose a potential threat to the safety of railway transport. Thus, a meticulous analysis of highfrequency resonance characteristics and the formulation of effective suppression techniques become paramount. Objective: Harmonics from grid-side locomotive traction braking are scrutinized under different operational scenarios and different train runs. The aim is to develop an effective harmonic management strategy to normalize the THD and thus maintain the traction power supply system to become more stable. Methods: This study investigates the high-frequency harmonic resonance characteristics using a control variable approach. Harmonics and negative sequences generated during locomotive traction braking under different operating conditions are investigated by means of extensive analysis. These challenges require the implementation of a harmonic management method. This method uses a combination of two monotonic filters and a C filter in parallel. This method applies to different operating conditions and can dynamically adapt harmonics to changes in the number of trains, which is related to the actual dynamics of the traction power system closely. Results: A comparative evaluation of seven operating conditions shows that the filter in this patent exceeds the efficiency of existing methods. The filter reduces the fluctuations during spectral changes, and the voltage distortion rate decreases from the previous 2.33% to 0.55%, making it more adaptable and promising effective harmonic management in high voltage and high current situations. Conclusion: Through multiple simulation tests, a synergistic configuration involving the C-type filter and two single-tuned parallel connections under regenerative braking conditions emerges. This refined, patented filter design not only mitigates the impact of negative sequence during the filtration process but also substantially diminishes high harmonics and THD.

Drone Swarm Classification from ISAR Imaging

In today's technology, the use of drones has become very popular for they can be easily purchased over the Internet and can be easily developed. With drones that have wide usage areas, swarm structures have become popular. However, this has brought about some problems. The issue of drone detection has emerged in order to prevent the uncontrolled use of drone swarms in the airspace. Drone swarm detection is important to prevent dangerous accidents or criminal acts. In this study, a new classification algorithm is proposed with deep learning using inverse synthetic aperture radar (ISAR) images of drone swarms based on various formation swarm types. ISAR images are created using ANSYS simulation. Additionally, high frequency structural simulator (HFSS) - shooting bouncing ray (SBR+) solver is used for high-speed computation. Radar and simulation parameters to obtain ISAR images are discussed. Especially, down-range and cross-range resolution parameters are taken into account to achieve high resolution. ISAR images are classified using deep learning methods in terms of formation. Formation types include Line, Square, Cross, and Triangle. The convolutional neural network (CNN) model is used to solve classification problems. The model consists of train, validation, and test steps. Classification performance results are presented with high accuracy. The developed method can be used for anti-drone technologies.

Investigating the effective consequences on the assets of an urban system in facing scenario-based hazards

Urban assets are generated through physical elements in a city as part of critical infrastructure. To ensure a sustainable urban future, innovative businesses and people are also essential urban assets. In the possible occurrence of natural and manborne hazards, architecture, population density, gathering locations, and connected infrastructure systems in cities are efficient. Manmade hazards are accidents caused by people that happen in or close to human settlements. These are things that have a major impact on the quality of life, health status, and even mortality. The consequences of such hazards in the town will have to be analyzed. To this end, causal relationships between a town's assets against man-made and natural hazards have been analyzed using the Fuzzy DEMATEL method. According to the results of this research, physical elements have a more important effect on other assets in cities at risk from man-made hazards than any other factor. In the face of man's hazards, economic activity can be more closely linked to population and physical elements. Physical elements have a higher impact on the population and economic activities when confronted with environmental hazards. Physical elements interact more with population and economic activities. In line with the results, more important than physical elements are population and economic activities. Lastly, it is suggested that the risks of dangerous accidents should be assessed and their consequences determined by taking into account the worst-case scenario.

The impact of the environment of use on the behavior of polychlorinated (vinyl chloride).

Polymers are omnipresent in our daily life thanks to their low production cost and their ease of implementation, which makes thermoplastics more usable in supplying drinking water, distributing cold and hot water, and transporting hot corrosive fluids and industrial chemicals, including inorganic acids. The material of our study is chlorinated polyvinyl chloride (CPVC); it has better thermal insulation and a wide operating temperature range. This paper deals with the impact of thermal aging over time on the behavior of CPVC by performing two types of tests according to ISO 15877 (EN ISO 15877) two types of tests; the first is thermal aging (Fig.1) at a temperature of 95°C and a pressure of 10 bars for periods of up to 2000h and the second test exposure of the pipeline to pressure until bursting. On the basis of the test results, we can predict the rupture of the pipeline and thus avoid the dangerous accidents due to the bursting under the effect of the pressure and the high temperature; by using the results of this study, the inspectors change CPVC structures at the right time, neither too early when the part is still in good condition nor too late after failure; this will be very useful to save water and money and of course save the environment.

Visual Illusion Created by a Striped Pattern Through Augmented Reality for the Prevention of Tumbling on Stairs.

A fall on stairs can be a dangerous accident. An important indicator of falling risk is the foot clearance, which is the height of the foot when ascending stairs or the distance of the foot from the step when descending. We developed an augmented reality system with a holographic lens using a visual illusion to improve the foot clearance on stairs. The system draws a vertical striped pattern on the stair riser as the participant ascends the stairs to create the illusion that the steps are higher than the actual steps, and draws a horizontal striped pattern on the stair tread as the participant descends the stairs to create the illusion of narrower stairs. We experimentally evaluated the accuracy of the system and fitted a model to determine the appropriate stripe thickness. Finally, participants ascended and descended stairs before, during, and after using the augmented reality system. The foot clearance significantly improved, not only while the participants used the system but also after they used the system compared with before.

A wall climbing robot based on machine vision for automatic welding seam inspection

With the ongoing progress of industrial technology such as shipbuilding, the importance of weld quality in industrial production is becoming increasingly prominent. Intelligent and automated welding seam inspection robots are more efficient than traditional manual inspection and can avoid dangerous accidents. This article describes the design of a welding seam inspection robot suitable for high-altitude ship operation. The robot uses machine vision and object segmentation models to automatically detect the position of welding seams, and uses a cubic polynomial to fit the welding seam path. The upper and lower computers of the robot communicate through WIFI transmission and TCP protocol, which can realize remote real-time detection of weld surface defects. In addition, this article designs a permanent magnet adsorption structure for robot high-altitude wall climbing, which has been verified through simulation and experimental verification. To verify the intelligence of the robot, this paper conducted performance analysis experiments on weld line recognition and tracking models and surface defect models. The experimental results showed that the average detection accuracy of the weld line recognition and tracking algorithm was 96.8%, and the average detection accuracy of surface defects in the three types of welds was 94.2%. The method proposed in this article combines two algorithms and a special robot structure, providing a new approach for the automated inspection of welds in large industrial products such as ships.

Key components identification of EMU complex system faults with interval intuitionistic fuzzy set and multi-attribute group decision-making based on FMECA method

With the continuous acceleration of high-speed railway, the high-voltage traction system of the EMU is an important part for ensuring the operation speed and safety. If the failure does not discontinued effectively, it will cause major dangerous accidents, so the key components identification of system is crucial. This paper focus on the contradictions of the expert evaluation information ambiguity, the difference of expert risk appetite and the rationality of risk priority number (RPN) calculation method in the traditional failure analysis method FMECA. The interval intuitionistic fuzzy set (IIFS) is introduced to transform the expert evaluation into the form of membership interval and non-membership interval, which reduced the ambiguity of the specific numerical score. The interval intuitive fuzzy entropy was used to determine the entropy values of the occurrence (O), severity (S), and undetectable degree (D) of each failure mode under each expert score, which was used to calculate the weight value [Formula: see text], to weaken the influence caused by subjective risk preference. The interval intuition fuzzy ensemble operator (AIVIFWM) is used to assemble a single scoring matrix into a comprehensive score, which weakens the subjective influence of expert evaluation. Combined with the multi-attribute group decision-making idea, the score function [Formula: see text] is calculated for each comprehensive evaluation interval of each failure mode after assembly, so as to sort the failure mode risk and finally identify the key components. Based on the fault data of the high-voltage traction system of a certain type of EMU in 2022, 39 failure modes of 30 components are researched and summarized. The results show that rectifier, converter cooling unit, and carbon skateboard are the key components of EMU high-voltage traction system, which provided basic support for the detection and maintenance decision.

Effective ignition energy for capacitor short-circuit discharge in explosive environments

Capacitors short-circuit discharge in an explosive environment can ignite and detonate the surrounding explosive media, causing dangerous accidents. At low voltages, this kind of discharge constitutes a micro-nano discharge; because the discharge gaps here are of the order of only microns to nanometers, the discharge process, electrode energy consumption, explosive media ignition energy, and other energy relationships are unclear. To study the relationships between the capacitor storage energy and various kinds of dissipation energies under short-circuit discharge, a model comprising conical and spherical cylinder microbumps is proposed based on the cathode surface morphology obtained by three-dimensional profiling and scanning electron microscopy, respectively. Then, the second-order non-chi-squared differential equations were established based on the principle of energy conservation and heat balance to deduce the relationships between the cathode surface temperature and height of the microbump, conical angle, and spherical radius; further, the energy consumed by the anode surface is calculated based on the theory of heat transfer. Using heat conduction theory, the energy consumed by the microbumps on the cathode surface is calculated, and the energy consumed on the anode surface is deduced using the surface heat source as the loading heat source. The residual energy of the capacitor is calculated from the discharge time and voltages before and after discharge, and the effective energy of the gas is calculated using the law of conservation of energy. Finally, the discharge channel energy, electrode energy consumption, and end residual energy of the discharge capacitor are used to derive the effective ignition energy of the explosive gas. This research is of great significance for the design of intrinsically safe circuits with high power.

The Prevalence of Distracted Walking While Using Mobile Technology: An Observational Study of High School Students in Kahramanmaras, Türkiye

Objective: Distracted walking is defined as any activity that diverts a pedestrian's attention from walking. The increasing addiction to mobile phone use has recently led to a rise in dangerous accidents caused by distracted walking. This study aimed to assess the prevalence and preventable causes of distracted walking among high school students crossing intersections with pedestrian crossings. Material and Methods: High school students (n=3237) between the ages of 14 and 18 from 10 different high schools, including 5 high schools in the Müze crossing area and 5 high schools in the Belediye crossing area, were observed while crossing the pedestrian crossing. Poisson regression was applied to determine the association between distracted walking and various factors, including the city, average household income in the surrounding area, type of pedestrian crossing, and the gender of pedestrians. Results: A total of 3237 students were observed walking near 10 high schools in the city centre of Turkey, of whom 1514 exhibited distracted walking (45.77%). The estimated prevalence of distracted walking was 45.77% (95% CI 45.06-48.49%). Of the students who exhibited distracted walking, 85.5% were engaged in some way with their mobile phones. The highest rate was observed in the group looking down and/or using their phones (62.81%). Conclusion: Our study reveals striking results about the distracted walking behavior of pedestrians in dense urban areas. The high rate of distracted walking among students indicates significant pedestrian-oriented security weaknesses. This study underscores the importance of addressing distracted walking to improve pedestrian safety and prevent potential accidents in urban environments.

EAFNet: Extraction-amplification-fusion network for tiny cracks detection

Tiny cracks are often overlooked in the inspection process, causing huge economic losses and dangerous accidents. Therefore, tiny cracks should be detected in a timely and accurate manner to eliminate the disease at the initial stage. Inspired by the fact that humans are more likely to capture conspicuous information when observing objects, we propose a novel three-stage Extraction-Amplification-Fusion network (EAFNet). Specifically, in the extraction stage, we utilize an effective backbone network for feature extraction of tiny cracks. In the amplification stage, we design a Tiny Feature Amplification (TFA) module to amplify the extracted features. In the fusion stage, we propose a Two-Branch Fusion (TBF) module to fully fuse the feature maps at different resolutions. To make tiny crack information more ‘conspicuous’, we propose an activation function TinyReLU to enhance the contrast of the tiny cracks with the background. In addition, we construct a Tiny Crack (T-CRACK) dataset with six different backgrounds and a Cross-scale Crack (C-CRACK) dataset. On both datasets, EAFNet achieves an advantage over the existing 8 advanced networks. The two datasets are available at: https://github.com/EAFNet/EAFNet.

Оценка влияния различных газов на давление в шинах при выделении тепла

Transportation of minerals during open-pit mining works is mainly provided by heavy vehicles. One of the most dangerous accidents during the operation of vehicles in open-pit mines is the destruction of large-scale tires. The main cause of the destruction of tires is heating of rubber causing the increase of pressure of injected gas and change of properties of tire material. Apart from friction processes and rubber deformation causing heating of wheels, the temperature of tires can increase when affected by external heat sources. At coal mines, sources of auto-ignition of fossil fuels often located along the road shoulders and sometimes on the road surface can be additional sources of heat for the vehicles. According to the calculations, the increase in tire pressure does not depend on the type of gas injected. The temperature rise in tires, however, is significantly affected by the specific heat capacity and the density of the gas used. According to the conducted studies, carbon dioxide is a more effective gas compared with air or nitrogen. With a certain amount of heat emitted in the tire, air or nitrogen heats up from 0 to 100 °С, while the temperature of carbon dioxide will increase only to 72 °С. Due to heating, the air and nitrogen pressure in the tires will increase from 6 to 8.2 atm, whereas carbon dioxide pressure will increase only to 7.5 atm. Unlike air, carbon dioxide is not involved in the combustion and oxidation of heated rubber. Methane and its homologs are even more effective. For example, propane injected into tires, when emitting the same amount of heat, will heat up from 0 only to 28 °С, and its pressure will increase from 6 to 6.6 atm. The combustion of these hydrocarbons can be prevented by using inhibitors. Thus, adding 2–3 % of freons will make these gases non-flammable and explosion-proof.

중대재해처벌 등에 관한 법률 시행에 따른 사회복지조직의 대응방안

The Serious Accidents Punishment Act was expanded to apply to all workplaces with five or more employees on January 27, 2024. This paper examines the major legal issues surrounding senior employment support agencies, especially senior clubs, which are social welfare facilities particularly vulnerable to major accidents, and proposes organizational countermeasures for preventing major accidents. To this end, this paper analyses the main contents of the Serious Accidents Punishment Act through academic literatures, and reviews various legal principles and key issues related to the implementation of the Act on senior clubs through the case laws. This paper aims to seek effective preventive measures and responses to the accidents that occur in senior clubs. The research revealed that the characteristics of senior clubs include continuous increasing trend in the number of elderly participants in senior job programs, full-time employments more than three hundred employees, various types of business operations such as public service, social service, and private business, and continuous occurrence of dangerous accidents. The key issues of the Serious Accidents Punishment Act relevant to the senior clubs are identifying an employee with legal responsibility, types of business subject to the Act, establishment of a dedicated occupational health and safety board appropriate to the type of business, a joint penal provision, and punitive damages. This paper proposes the following responsive measures for senior clubs in response to the implementation of the Act; changing employees’ perspective towards the Serious Accidents Punishment Act, building a health and safety management system reflecting characteristics of senior clubs, utilizing scientific risk management techniques in establishing a health and safety management system and support and management from the Ministry of Health and Welfare as well as local governments.

The effects of pressure and release training on equine behavior and discipline

In America, there are over 100,000 horse-related injuries per year (Mendell, 2006). It is important that all horses are properly trained to avoid dangerous accidents resulting from poor training involving the horse, rider, or spectators. This study aims to understand how a person should approach properly training a horse. It determines what training methods are the most effective and why it is important that owners understand how horses process discipline. Previous research indicates the common need for pressure, but fails to explore it as a training method. As such, a definite answer that explains how Pressure and Release Training affects equine behavior and discipline has not been determined. A physical experiment collecting and analyzing quantitative data was conducted in order to obtain the most accurate and specific results. Horses of various breeds, ages, sexes, and training levels were subjected to five different Pressure and Release Training exercises and scored on their completion (attempt > accuracy). Each horse was tested twice on separate days to ensure replicability. All horses completed every exercise successfully without conflict behaviors or refusal (50/50). The experiment reveals that regardless of traits, Pressure and Release Training is an effective training method that horses understand even without previously consistent training. However, it is vital that trainers understand how horses process discipline so that they know if the training is effective and low stress for the horse.

Machine vision-based detections of transparent chemical vessels toward the safe automation of material synthesis

Although robot-based automation in chemistry laboratories can accelerate the material development process, surveillance-free environments may lead to dangerous accidents primarily due to machine control errors. Object detection techniques can play vital roles in addressing these safety issues; however, existing detection models still suffer from insufficient accuracy in environments involving complex and noisy scenes. With the aim of improving safety in a surveillance-free laboratory, we report a deep learning (DL)-based object detector, namely, DenseSSD. For the foremost and frequent problem of detecting positions of transparent chemical vessels, DenseSSD achieved a mean average precision (mAP) over 95% based on a complex dataset involving both empty and solution-filled vials, greatly exceeding those of conventional detectors; such high precision is critical to minimizing failure-induced accidents. Additionally, DenseSSD was observed to be generalizable to other laboratory environments, maintaining its high precisions under the variations of solution colors, camera view angles, background scenes, experiment hardware and type of chemical vessels. Such robustness of DenseSSD supports that it can universally be implemented in diverse laboratory settings. This study conclusively demonstrates the significant utility of DenseSSD in enhancing safety within automated material synthesis environments. Furthermore, the exceptional detection accuracy of DenseSSD opens up possibilities for its application in various other fields and scenarios where precise object detection is paramount.

An integrated group decision-making framework for evaluating the urban fire emergency management capability based on PDHLWPHM and PDHLWPGHM operator

Urban fire accident is a common dangerous accident in urban sudden accidents, which threatens the safety of people’s lives and property. For this reason, in recent years, all cities have incorporated the prevention and emergency management of urban fire accidents into their urban development planning, and actively improved their fire accident emergency management capabilities. However, how to evaluate the urban fire accident emergency management capacity of each city to ensure that people’s lives and property are protected to the greatest extent is an urgent problem to be considered and solved. Therefore, this paper defines a class of probabilistic double hierarchy linguistic Heronian mean (PDHLHM) operator, probabilistic double hierarchy linguistic Power Heronian mean (PDHLPHM) operators, and their dual operators that can reflect the relationship between two attributes during aggregation. Taking urban fire accident risk monitoring and early warning capability, fire infrastructure and communication system, fire-fighting and rescue capability, recovery and reconstruction capability as evaluation attributes, the probabilistic double hierarchy linguistic weight Power Heronian mean (PDHLWPHM) operator model and the probabilistic double hierarchy linguistic weight Power geometric Heronian mean (PDHLWPGHM) operator model are constructed for group decision-making. In addition, the idempotence, boundedness and monotonicity of these operators are studied, and the sensitivity of the parameters involved in the operator model is analyzed. Finally, the new model proposed in this paper is compared with the existing model to verify its scientificity.

An intelligent water supply pipeline leakage detection method based on SV-WTBSVM

Water supply pipeline leakage not only wastes resources but also causes dangerous accidents. Therefore, detecting the state of pipelines is a critical task. With the expansion of the scale of the water supply pipeline, the amount of data collected by the leak detection system is gradually increasing. Moreover, there is an imbalance of sample in the data. This makes the detection performance of traditional leakage detection methods deteriorate. To solve the above issues, this paper proposes a pipeline leakage intelligent detection method based on a support vector weighted twin-bound support vector machine (SV-WTBSVM). Noise in the data negatively affects the performance of the classifier. To eliminate the effect of noise, a hybrid denoising algorithm based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used for denoising to filter out the noise in the data. Twin bound support vector machine (TBSVM) is a classical classification algorithm that has been widely used in the detection of pipeline leakage. To solve the decrease in classification accuracy caused by sample imbalance, the SV-WTBSVM algorithm oversamples the minority class samples based on the distance density and integrally undersamples the majority class samples to obtain a balanced sample. Since pipelines often have multiple working conditions, the SV-WTBSVM used for binary classification cannot meet this requirement, and this paper combines the SV-WTBSVM with the ‘one-to-one’ strategy to address the multi-classification problem. Finally, experiments have verified that the SV-WTBSVM algorithm not only retains the advantages of fast training speed and simple operation of the TBSVM but also improves the classification accuracy and generalization ability of the algorithm when dealing with imbalanced data.

인명, 환경, 재산피해를 고려한 고위험 화학사고의 원인과 동향 분석

This study analyzed the causes and trends of chemical accidents that occurred in Korea from 2014 to 2022. In particular, by comprehensively considering human casualties, environmental damage, and property damage, the relative risks and accident trends of each type of accident were identified. Utilizing the relative risk, the most dangerous accident type for each type of accident were derived. And, LDA topic modeling and network analysis were used to analyze accident trends for fire accidents identified as the most dangerous accident types. Results showed that the most dangerous accident type in terms of human casualties was explosion accidents, and the type of accident with a large impact on the environment and property was analyzed as fire accidents. As a result of LDA topic modeling, chemical fire accidents were broadly classified into four major topics. It is possible to analyze the complex causes and patterns of chemical accidents through methods using LDA topic modeling and network analysis, and it is possible to effectively establish prevention and response strategies for chemical accidents through the methods used.

ОЦЕНКА ВЕРОЯТНОСТИ РЕАЛИЗАЦИИ НА ГОЛОВНОЙ НЕФТЕПЕРЕКАЧИВАЮЩЕЙ СТАНЦИИ АВАРИЙНЫХ СИТУАЦИЙ, ПРОТЕКАЮЩИХ ПО РАЗЛИЧНЫМ СЦЕНАРИЯМ

The work of enterprises in the agro-industrial complex is closely connected with the use of petroleum products, the use of which is ensured through oil pumping stations. The assessment of the probability of emergency situations at such enterprises was carried out using the example of the main oil pumping station GNPS-1 “Taishet” of TransnefteVostok LLC. It is shown that the enterprise belongs to objects of increased fire danger, the most dangerous technological areas of which are the tank farm, the main oil pumping station and the booster pumping station. A fire and explosive substance - oil is circulated in a technological scheme. Possible ways of developing emergency situations associated with the failure of equipment in individual technological sections of an oil pumping station were investigated in the work. The most dangerous accidents occur in three areas: in the tank farm, at the main and booster pumping stations. The probabilities for the development of emergency situations have been determined. It was revealed that the most dangerous scenarios are possible at any of the reservoirs of the tank farm and at the main oil pumping and booster pumping station. In both scenarios, an instant spill fire, a spill fire with delayed ignition, and an explosion of a vapor-air cloud are possible. The cause may be a failure of emergency ventilation and a vapor-air cloud gas analyzer. The studied options for the development of an accident on technological equipment will make it possible to establish the scale of the consequences of the spread of dangerous factors of fire and explosion, as well as to determine a set of measures to prevent emergency situations, timely response and minimize their consequences.

Safety, health, and environmental risk assessment of the aromatic outlet of Imam Khomeini Petrochemical Company using LOPA and fuzzy-LOPA and pollutants and control actions using the Bow-tie method.

Today, with the development of the industry, the occurrence of accidents caused by the release and explosion of chemical and toxic substances in industrial units has increased, and these accidents sometimes cause irreparable damage to human life and the environment. According to a study by the American Petroleum Institute, of the recent major accidents in the last 30 years, 44% are related to machinery failure and 12% are caused by unknown factors and lack of information. Therefore, equipment risk control is aimed at preventing large and dangerous accident. The present study, the performance of LOPA and fuzzy-LOPA methods was compared toward the risk assessment of Imam Khomeini Petrochemical Company under certainty and uncertainty of data. This comparison was done in order to a conceptual method with high certainty to assess high-level hazards leading to health and safety risks and environmental pollution. First, the health, safety hazards and environmental aspects were identified via the HAZOP method. Then, a risk assessment was performed using the LOPA method. The fuzzification, severity, and likelihood of each risk were considered as an input variable and risk probability as an output variable. Finally, was the methods used in our analysis were compared and the Bow-tie software was used to draw a Bow-tie diagram to control and reduce the risks. As a result, a total of 50 safety and health hazards and 37 environmental aspects were identified in the aromatic outlet of the studied company using the HAZOP method. The most critical risks identified were operational activities in feed and product tanks; flammable materials pumping; blocking the flare path; and releasing H2S gas. The results showed that the production of air pollutants in the power supply unit, disposal of waste from reactor tanks, disposal of waste from condensate tanks, and fire and explosion of the reactor are high-level environmental risks. In the conditions of uncertainty or the absence of information related to the probability and severity of the risk scenario, among the mentioned methods. The result showed that errors in the risk assessment were reduced to an acceptable extent by using Fuzzy LOPA method.