Chemical Accidents Research Articles

Analysis of the Causes and Configuration Paths of Explosion Accidents in Chemical Companies Based on the REASON Model

Explosion accidents, representing one of the most severe accident types within the chemical industry, pose substantial threats to personnel safety, economic losses, and environmental pollution, among other consequences. This paper constructs a research framework based on the REASON theory, utilizing accident investigation reports of 30 typical chemical enterprise explosion accidents in China from 2013–2022 as research samples. The fsQCA method is employed to deeply explore the influencing factors and causal configuration pathways of chemical explosion accidents from a configurational perspective. The study findings indicate that the occurrence of explosions in chemical enterprises is the result of the coupled effects of multiple factors, with five complex accident causation configurations, which can be summarized into the following three categories: organizational management deficiencies, supervision deficiencies, and behavior–risk linkages. Organizational management and safety supervision have a direct and significant impact on the occurrence of explosions in chemical enterprises and are key factors in the accidents. The research conclusions contribute to a rational understanding of the complex causes of explosions in chemical enterprises and provide practical guidance for the prevention and control of such accidents.

A Study on the Development of a Filtration System for Drainage Discharge Capacity

This study analyzes the social and economic characteristics of an area affected by chemical emission facilities and proposes policy measures to respond to social disasters such as hazardous chemical emission accidents. Even within the area affected by chemical discharge facilities, social and economic disparities existed depending on the proximity to the chemical discharge facility, child population ratio, and land value. However, the elderly population ratio decreased. It is believed that the presentation of response plans based on the study’s analysis will contribute to protecting people's health and property, which is the ultimate goal of chemical management.

Application of STPA for Comprehensive Risk Analysis of Naphtha Explosion Hazards

Chemical accidents always result in significant losses due to the flammable, explosive, and toxic characteristics of hazardous chemicals. Analysis of process safety parameters is an effective way to prevent hazardous chemical accidents and reduce losses. System-theoretic process analysis (STPA) is a newer hazard analysis technique that is based on systems theory. It has been shown to be effective in identifying hazards in other industries, but its application in oil and gas plants is still rare and limited due to systems complexities and other challenges. This paper aims to apply the STPA method to a complex system “column C-63” at the Skikda RA1K refinery to prevent the explosion scenario of naphtha. The results show that STPA was able to identify the root causes of the explosion scenario, which is important for preventing chemical risks.

Simulation of a Hazardous Chemical Cascading Accident Using the Graph Neural Network

In the storage of hazardous chemicals, due to space limitations, various hazardous chemicals are usually mixed stored when their chemical properties do not conflict. In a fire or other accidents during storage, the emergency response includes two key steps: first, using fire extinguishers like dry powder and carbon dioxide to extinguish the burning hazardous chemicals. In addition, hazardous chemicals around the accident site are often watered to cool down to prevent the spread of the fire. But both the water and extinguishers may react chemically with hazardous chemicals at the accident site, potentially triggering secondary accidents. However, the existing research about hazardous chemical domino accidents only focuses on the pre-rescue stage and ignores the simulation of rescue-induced accidents that occur after rescue. Aiming at the problem, a quantitative representation algorithm for the spatial correlation of hazardous chemicals is first proposed to enhance the understanding of their spatial relationships. Subsequently, a graph neural network is introduced to simulate the evolution process of hazardous chemical cascade accidents. By aggregating the physical and chemical characteristics, the initial accident information of nodes, and bi-temporal node status information, deep learning models have gained the ability to accurately predict node states, thereby improving the intelligent simulation of hazardous chemical accidents. The experimental results validated the effectiveness of the method.

Quantitative Risk Assessment of an Oil-Gas-Hydrogen-Electricity Integrated Energy Station in China.

This paper presents a quantitative risk assessment (QRA) study of an oil-gas-hydrogen-electricity integrated energy station in China. A comprehensive assessment of the station was made in terms of consequences and risks, respectively. Consequence analysis shows that the severity of hazardous chemical leakage accidents in the station is in the order of natural gas, hydrogen, gasoline, and diesel fuel, especially in the liquefied natural gas (LNG) and compressed natural gas (CNG) storage tank areas, which may cause major accidents in the event of leakage. The results of the risk assessment showed that the individual and societal risks of the integrated oil-gas-hydrogen-electricity energy station exceeded the risk acceptance criteria. Moreover, the catastrophic rupture of LNG and CNG storage tanks is the main cause of the unacceptable risk. Additional mitigation measures for them, mainly including the installation of emergency manual shut-off valves, the installation of combustible gas detection and alarm devices, and pressure relief protection devices, can effectively reduce the risk to an acceptable level.

Agricultural Health and Safety: Evaluating Farmers’ Knowledge, Attitude, and Safety Behavior in Northern Thailand

BackgroundAgriculture is vital for economic growth and livelihoods in northern Thailand, yet it is challenged by hazardous practices such as improper chemical use and machinery accidents. This study aimed to evaluate the risk and determine the relationship between pesticide exposure and knowledge, attitudes, and safety behaviors. MethodsThis cross-sectional study was conducted between October and December 2017 in the San Pa Tong district of Chiang Mai, Thailand. Data were collected through structured questionnaires and face-to-face interviews with 280 farmers. ResultsThe findings indicated that knowledge and safety behavior scores varied significantly with educational attainment and training received. Farmers with higher education levels or those who had undergone training exhibited better knowledge and safer behavioral practices, whereas income levels did not significantly affect knowledge or safety behaviors. The pesticide exposure risk level was associated with the study variables, with those recognizing a ‘normal’ risk level demonstrating greater knowledge and safer behaviors than those who reported the risk level as safe or at risk. A strong positive correlation between knowledge and safety behavior was observed, indicating that increased awareness can result in more cautious practices. These findings suggest that educational and training programs are crucial for promoting safer pesticide use among farmers. Although attitudes toward pesticide safety varied based on income, education, and training positively influenced knowledge and behavior. ConclusionThe study concludes that understanding farmers’ perceptions of pesticide risks is essential for designing and implementing effective educational interventions.

Quantitative risk analysis of road transportation of hazardous materials in coastal areas

Given the complex climate conditions in coastal areas and their role as key transportation hubs for hazardous chemicals, this study proposes a method to quantitatively and comprehensively evaluate transportation risks. Initially, accident data were analyzed to identify risk factors from five aspects: human, vehicle, materials, environment, and management, based on system safety theory. Subsequently, a risk analysis model was developed using Decision-making Trial and Evaluation Laboratory, interpretive structural model theory, and Bayesian theory to quantitatively assess accident risk levels. The model was applied to a case involving a hazardous chemical accident on a cross-sea bridge, where Bayesian backward reasoning was used to analyze the sensitivity and importance of risk factors. This approach facilitated the key risk factors affecting the safety of hazardous chemical transportation systems. Notably, the study incorporated scenarios involving hazardous material transport vehicles crossing sea bridges into the risk assessment framework, offering valuable insights for management authorities. It also considered the impact of strong side winds-a factor often overlooked-in hazardous material transport. The validation process demonstrated that the method accurately quantifies the risk of hazardous chemical transportation and identifies the key factors influencing accident occurrence. The research highlights that strong gusts of wind significantly impact safety, and human factors are crucial in the overall risk system.

Aggregate risk assessment for multi-route exposure to hazardous chemicals caused by chemical accidents, with a focus on toluene

Chemical accidents significantly impact environmental and human health. However, studies investigating the impacts of such accidents have primarily focused on single-route exposures, potentially underestimating the extent of damage. This study aimed to conduct an aggregate risk assessment for multi-route exposure to hazardous chemicals to ensure systematic and rational management of the health impacts on residents exposed to chemical accidents, considering the behavior of a hazardous chemical from a chemical accident within environmental media. Drawing upon a real chemical accident that occurred in Siheung, Gyeonggi-do, in 2019, leakage of 500 L of toluene over an hour was assumed. Employing a multimedia environmental dynamics model, the time-dependent concentrations across various environmental media were calculated, and the average daily dose (ADD), hazard quotient (HQ), and hazard index (HI) for each exposure route included in the multi-route exposure assessment were derived. Health risks were deemed present if the calculated HQ and HI values exceeded the threshold of 1. The results indicated the highest ADD values among the 0–9 age group, with inhalation exposure registering the highest ADD across all exposure routes. However, no significant health risks were observed, with both HQ and HI values not exceeding 1. This aggregate risk assessment approach is proposed as an effective preliminary evaluation method for health impact assessments in areas affected by chemical accidents.Graphical

Intelligent Emergency Assisted Decision-Making Method Based on Standard Digitalization: Hazardous Chemical Accidents in Industrial Parks

Contemporary society is confronted with multifaceted challenges, and the intricate interplay of interconnected factors significantly complicates emergency response efforts. Current practices rely on quick decisions by domain experts; however, the limitations of individual expertise and the urgency of crises hinder both precision and standardization. To address these issues, we propose a novel approach: an intelligent method for emergency decision making grounded in a standardized digital knowledge graph. First, our study examined the underlying theory of standardized digital transformation and event-chain evolution. This led to the construction of a knowledge graph encompassing standard emergency knowledge, as well as supplementary derivative data pertinent to event response. Second, through the application of semantic analysis and intention recognition of the decision target, coherent and interpretable query sentences for the decision system were crafted. These query sentences then served as a conduit for retrieving standard emergency knowledge relevant to the current emergency situation, as well as potential secondary disasters. The overarching goal is to provide emergency decision makers with effective support mechanisms that are both well informed and tailored to the specific demands of each situation.

Improving the industrial safety management system at enterprises with chemically hazardous sites

This study focuses on developing an industrial and occupational safety management system for enterprises that contain chemically hazardous sites. The methodology, based on an expert approach, enabled the authors to design the structure of the risk management system at such enterprises. It also facilitated the identification of clusters and their descriptors, along with their roles in evaluating the state of the safety management system. The proposed methodology features a flexible and universal structure, making it applicable for assessing industrial and occupational safety across different enterprises, taking into account the specific technological aspects of production processes. In this case study, the authors examined the accident rates, injury hazards, and health risks associated with chemically hazardous sites in enterprises located in the Republic of Kazakhstan. The findings of this study provide a methodological approach that industrial enterprises can use to evaluate the effectiveness of their safety management systems. This allows for the development of measures aimed at preventing chemical accidents and reducing their impacts.

Quantitative assessment of human error of emergency behavior for hazardous chemical spills in chemical parks

In-depth research on human error is of great significance to improve the human reliability in the emergency rescue as well as to avoid the occurrence of secondary accidents. Therefore, the standardized process and key emergency behaviors in the emergency rescue process of hazardous chemical leakage accidents are first analyzed. Based on the Cognitive Reliability and Error Analysis Method, the human error mode was sorted out, and the human error probability estimation model was established. Combining the improved Analytic Hierarchy Process and entropy weight method, a model for estimating the severity of human error was established, realizing the quantitative analysis of the human reliability of the emergency behavior. The study showed that the 21 emergency behaviors were clustered into 3 categories, with clustering 1 including 2 emergency behaviors, which have the highest level of severity, clustering 2 including 11 emergency behaviors, and clustering 3 including 8 emergency behaviors. Finally, countermeasures were proposed in four areas, including increasing the level of Common Performance Conditions and the ability to transmit information on accidents, etc. It indicates that the method can widely guide the improvement of emergency response capability in chemical parks.

Improving implementation of occupational health and safety of construction company by Job Safety Analysis (JSA) method (study case at PT Arto Moro Sentosa)

The construction sector is a sector that employs many people who have high risk of work accidents, because a lot of work in this sector is possibility carried out by unsafe actions or unsafe conditions. PT. Arto Moro Sentosa is company which operates in general trading, general construction, suppliers, outsourcing, etc., is aware that the business has a high risk of work accidents. The lack of employee awareness will have an impact on the magnitude of the risks that must be borne by the worker and the company. The purpose of this research is determining work accident records for the Occupational Health and Safety (OHS) system, employee risks, and existing improvements at company. By knowing the classification of hazards based on the type of accident, this research uses the Job Safety Analysis (JSA) method, histograms, Pareto diagrams, and cause-and-effect (fish bone) diagrams. Based on the author's observations, company data related to OHS (Occupational Health and Safety) and interviews with the directors, could be obtained a history of work accidents based on the type of accident. Here are ergonomic work accidents (57.89%), physical factor accidents ( 31.58%), chemical factor accidents (5.26%), biological factor accidents (5.26%), and psychological factor accidents (0%). Several recommendation is performed to avoid repeated accident by providing training programs on OHS to workers, paying more attention to OHS supervision, giving sanctions to workers who violate OHS.

Optimization and standardization of the work of a health care facility during a chemical accident

During the years of war, as a result of aviation and missile attacks, a significant number of health care facilities in various regions of Ukraine were destroyed, and the complexity and intensity of their work increased significantly. If the readiness of hospitals and their employees to treat gunshot and explosive injuries is established, in the case of mass destruction of chemically hazardous enterprises and the threat of the enemy using weapons of mass destruction, including chemical weapons, there is a need for special training of health care facilities to work in the in conditions of chemical threats. The aim of the work was justification of the development and set of measures for the implementation of standard operating procedures in the event of a chemical incident. The legal documents and recommendations of the Ministry of Health of Ukraine, management of the World Health Organization and the Red Cross on the topic of the study were used. The research methods: bibliosemantic, analytical, modeling were applied. It is established that the special management group, which includes representatives of various services, is of decisive impor­tance in the effective management of a health care facility during a chemical incident: administration; communication department; security department; management of nurses and the department of infection control; human resources department; pharmacy; intensive care unit; department of engineering maintenance and repair; laboratory; kitchen; laundry; cleaning service and waste management. The most important measures of response to a chemical incident that ensure the safety of medical personnel and patients are the organization of a triage zone and a decontamination area for chemically affected persons. Based on the above, to prepare a health care facility for work during a chemical incident, it is necessary to develop a response plan, which consists of special administrative and management decisions and provides for regular staff training.

Dynamic scenario deduction analysis for hazardous chemical accident based on CNN‐LSTM model with attention mechanism

AbstractThe evolution of hazardous chemical accidents (HCAs) is characterized by uncertainty and complexity. It is challenging for decision‐makers to expeditiously adapt emergency response plans in response to dynamically changing scenario states. This study proposes a data‐driven methodology for constructing accident scenarios and develops a novel hybrid deep learning model for scenario deduction analysis. This model aids in accurately predicting the evolution of HCAs, enabling emergency responders to prepare and implement targeted interventions proactively. First, a framework for constructing an accident scenario database is presented, based on the time‐sequential characteristics of accident progression. This framework employs a data‐driven approach to describe the evolution process of accident scenarios. Second, a deep learning model (CNN‐LSTM‐Attention) that integrates convolutional neural network (CNN), long short‐term memory (LSTM), and attention mechanism (AM) is developed for accident scenario deduction analysis. Finally, to illustrate practical application, a scenario database for HCAs is established. A major HCA case study is conducted to demonstrate the ability of this model to analyze various scenarios, thereby improving emergency decision‐making efficiency. Compared with algorithms such as CNN, LSTM, and CNN‐LSTM, the prediction accuracy of this method ranges from 86% to 93%, signifying an improvement of over 7%. This work provides a reliable framework for supporting decision‐making in emergency management.

GD-YOLO: An improved convolutional neural network architecture for real-time detection of smoking and phone use behaviors

Chemical safety accidents cause significant socio-economic and environmental hazards, and safety accidents caused by unsafe worker behaviors are preventable. Detection of workers' smoking and phone use behaviors in chemical parks can avoid such safety accidents from the root. Aiming at the issues of reduced accuracy in detecting small objects and low arithmetic capability within industrial equipment. This paper proposes a GD-YOLO network based on YOLOv7 for detecting smoking and phone use behaviors. First, an efficient feature extraction module, D-LAN, is designed to solve the problem of difficult extraction of small object features. Second, a lightweight G-LAN module is proposed to reduce the complexity of the model. Third, WIoU and Mish are introduced to improve the model's performance further, creating a more efficient model. The experimental results show that the mean average precision (mAP) and the number of computational parameters increased by 16.80% and decreased by 21.54%, respectively, compared with YOLOv7. GD-YOLO performs better than other existing mainstream methods in precision and complexity.

Research progress in the development of 3D skin models and their application to in vitro skin irritation testing.

Toxicological assessment of chemicals is crucial for safeguarding human health and the environment. However, traditional animal experiments are associated with ethical, technical, and predictive limitations in assessing the toxicity of chemicals to the skin. With the recent development of bioengineering and tissue engineering, three-dimensional (3D) skin models have been commonly used as an alternative for toxicological studies. The skin consists of the subcutaneous, dermis, and epidermis. All these layers have crucial functions such as physical and biological protection and thermoregulation. The epidermis is the shallowest layer protecting against external substances and media. Because the skin is the first contact point for many substances, this organ is very significant for assessing local toxicity following skin exposure. According to the classification of the United Nations Global Harmonized System, skin irritation is a major potentially hazardous characteristic of chemicals, and this characteristic must be accurately assessed and classified for enhancing chemical safety management and preventing and reducing chemical accidents. This review discusses the research progress of 3D skin models and introduces their application in assessing chemical skin irritation.

Temperature control for the synthesis of n-butylmagnesium bromide Grignard reagent based on n-octadecane/polystyrene microPCMs

Thermal runaway accounts for a high proportion of chemical safety accidents and poses a serious threat to the operational safety of personnel. Phase change materials (PCMs) have a high enthalpy of melting, if the latent heat energy of their phase change can be stored and used to absorb the heat released during thermal runaway, this phenomenon can provide a new approach for the control of reaction thermal runaway. In this work, microencapsulated phase change materials (microPCMs) were prepared by the suspension polymerization method using n-octadecane as the core material and polystyrene (PS) as the shell material. The prepared microPCMs had regular spherical morphologies with smooth surfaces, and the average particle size was 7.54 μm, the thermal conductivity was 0.05964 W/m∙K. The melting enthalpy of the microPCMs was 142.2 J/g, and the encapsulation percentage of the PCMs was 62.15%. The synthesis of the n-butylmagnesium bromide Grignard reagent occurred via a strong exothermic reaction, which was very likely to lead to overpressure venting once control was lost. The prepared microPCMs were applied as inhibitors in this reaction to investigate the temperature control effect under different operating conditions. The results showed that the amount of microPCMs added, and the timing of the addition affected the control effect. The addition of microPCMs could effectively reduce the reaction temperature and slow the rate of temperature increase. Even in the event of an emergency situation, such as a control failure, time could be gained for other emergency measures, effectively reducing the probability of thermal runaway accidents.

Emergency response actions modeling and time analysis: Considering priority of actions

When a major chemical accident occurs, emergency response is an important measure to reduce accident losses. Some actions in an emergency response may be more important than others in reducing accident losses, so they need to be carried out as a matter of priority. The impact of the number of emergency personnel on the relationship between emergency response actions and time of emergency response process is analyzed in this paper in the circumstance that the number of responders is not enough to carry out emergency actions with priority at the same time, and a Petri-net based approach is proposed to model and analyze the actions. An emergency response action that needs to consider its priority is divided into two stages, one is to determine the personnel for executing the action, the other is the execution of the action. This division facilitates the handling of dynamic changes in the relationship between actions and differences in start time of the actions. The approach is illustrated by taking the emergency response of on-site emergency personnel of a chemical plant to a fire as an example. The efficiency of the emergency process is analyzed, and for the action of rescuing the wounded, which has the highest priority in the example, the time and the probability when considering the priority of emergency response actions are compared with those when not considering the priority.

Predicting possible recommendations related to causes and consequences in the HAZOP study worksheet using natural language processing and machine learning: BERT, clustering, and classification

A set of recommendations is one of the most valuable outputs of the hazard and operability (HAZOP) study. The HAZOP study team provides recommendations when deficiencies are detected in the chemical process plant. These deficiencies can cause chemical process accidents and operability issues. This study employed a data-driven approach using natural language processing (NLP) and machine learning (ML) to predict potential recommendations based on causes and consequences. The dataset had no label; thus, clustering was used to label it. Firstly, bidirectional encoder representations from transformers (BERT) converted recommendation sentences into vectors. Secondly, uniform manifold approximation and projection (UMAP) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) were utilized to determine recommendation categories and label the dataset. Then, BERT was used to convert causes and consequences into vectors. Finally, a multi-layer perceptron (MLP) classifier was employed to predict possible recommendations based on causes and consequences. The class imbalance problem was handled by random over-sampling. The prediction accuracy of possible recommendations based on causes and consequences equals 93.7% and 89.5%, respectively. As a result of predicting potential recommendations utilizing causes and consequences, major recommendations will not be overlooked during the HAZOP study. This can further expand NLP and ML applications in HAZOP study automation.

An integrated risk assessment method for urban areas due to chemical leakage accidents

Chemical parks which hold large inventories of hazardous materials have become one important type of urban critical infrastructure. The hazardous chemical leakage such as toxic gas leakage has emerged in recent years, resulting in not only serious environmental pollution but also catastrophic consequences to certain urban areas. It is critical to make scientific risk assessment for such events to reduce negative effects. To address this issue, an integrated urban risk assessment (IURA) method coupling consequences of chemical leakage accidents and the vulnerability of local population is proposed in this work. The threat zones and hazard maps of toxic gas dispersion are present by using the ALOHA software. The population vulnerability is decided by three indicators including exposure, sensitivity, and adaptive capacity of residents in affected areas, the Grey Correlation-TOPSIS (GC-TOPSIS) method is used to rank the vulnerability of different regions. The ArcGIS software is applied to generate an integrated risk map through superimposing the hazard map and the population vulnerability map. The original risk matrix evaluation results were subdivided using the Borda count method. A case about an industrial city in Hubei, China is analyzed to test the method. Result shows the new integrated risk assessment method is more accurate and effective, which is beneficial for the government to develop emergency plans and enhance the safety of people.