Hazardous Chemical Accidents Research Articles

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.

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.

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.

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.

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.

24Model-based comparative analysis of two catastrophic hazardous chemical pipeline accidents

ABSTRACT Objectives. This study conducted a comparative analysis of two catastrophic pipeline accidents in China in order to identify some common mistakes and lessons learned to prevent similar accidents. Methods. The 24Model was used in this study, which provides a universal pathway for accident analysis from the individual level to the organizational level. Results. There were similarities between the two cases in the aspects of the occurrence, development, emergency and causation at different levels: both were caused by leaks of pipelines and evolved into multiple explosions during emergency response; both leaks were caused by the corrosion of pipelines in the confined space of a damp or salt-spray environment; both were classified as ‘responsibility accidents’, and unsafe acts, such as the failure to identify hidden hazards of pipelines that were the direct cause of accidents, reflected the shortcomings of individual safety habitual behaviour in terms of knowledge, awareness, habits and psychology; weaknesses in the organizational management mainly concerned hazard identification, pipeline maintenance, emergency disposal, etc.; and there is not a good safety climate within the organization. Conclusions. Organizations should develop a closed-loop management system and strengthen the construction of safety culture, and the government should supervise the implementation of procedures.

Modeling and Improvement Strategies for Safety Resilience in Maritime Hazardous Chemical Transportation System Based on Dissipative Structure Theory and System Dynamics

Maritime hazardous chemical transportation accidents have the characteristics of strong suddenness, wide influence, and great harm. To analyze the ability of a maritime hazardous chemical transportation system (MHCTS) to cope with sudden disturbance events, “resilience” is introduced into MHCTS safety research. The key to studying resilience is modeling its evolutionary process. Based on the dissipative structure theory, this study analyzes the entropy flow mechanism of MHCTS safety resilience evolution. Through a statistical analysis of 197 investigation reports on maritime hazardous chemical transportation accidents, the factors influencing the safety resilience of the MHCTS were determined. The entropy value and weight of each influencing factor were calculated using the entropy method and entropy weight method, respectively. Based on this, an entropy model of the safety resilience evolution of the MHCTS was established. The evolution process falls under four categories of disturbance strengths, which were simulated using the system dynamics method. The degree of contribution of absorptive, adaptive, and restorative capacities to the improvement of system safety resilience under four disturbance conditions and the sensitivity of each influencing factor to the absorptive, adaptive, and restorative capacities were analyzed. Based on the analyses, targeted resilience improvement strategies are proposed. The research results provide a theoretical reference for the study of safety resilience mechanisms and resilience management in the MHCTS.

Analyzing the Risk Factors of Hazardous Chemical Road Transportation Accidents Based on Grounded Theory and a Bayesian Network

With the increase in the production and use of hazardous chemicals in China, road transportation safety issues are becoming increasingly prominent. To study the causes of road transportation accidents involving hazardous chemicals, prevent accident occurrence, and realize the safety and sustainable development of the transportation of hazardous chemicals, we combined grounded theory (GT) and a Bayesian network (BN) model to quantify the causal relationship of the interactions among the influencing factors leading to hazardous chemical road transportation accidents that occurred in China in the period of 2017–2020. The influencing factors of these accidents were classified into 5 core categories, 12 main categories, and 28 categories through the GT method, and then a BN-based model was established for these collected accidents. The conditional probability and posterior probability of each influencing factor leading to an accident were determined through BN learning, and then the causal relationship of the interactions between the influencing factors was quantified. The results indicated that the probability of road transportation accidents involving hazardous chemicals considered in this study reaches 72.5% under the combined influence of various factors, and the most likely causal chain of an accident is that equipment failure during the hazardous chemical transportation process contributes to an Unsafe Tanker State, which in turn leads to an accident. The sensitivity analysis confirmed that the key impact factor of hazardous chemical road transportation accidents is equipment failure, followed by improper operation. Overall, this study presents a reference and a foundation for avoiding or reducing risks as much as possible during daily hazardous chemical road transportation operations and risk supervision, realizing safe, sustainable development.

Flame retardant, heat insulating and hydrophobic chitosan-derived aerogels for the clean-up of hazardous chemicals

Leakage of hazardous chemicals often causes significant casualties, enormous economic losses, and negative social benefits. Presently, fire rescue personnel lack efficient and eco-friendly disposal materials for hazardous chemical leakage accidents. In this study, chitosan (CS) aerogels with excellent flame-retardant performance were prepared via cross-linking by two phosphorus-containing vanillin-based compounds (DV and TV). The as-prepared chitosan aerogels were lightweight and porous. The introduction of DV and TV greatly enhanced the residual char yields of CS at 700 °C and the flame-retardant performance of chitosan aerogels. TCS-5.0 possessed the best flame-retardant performance, indicating that TV was more effective than DV in enhancing the flame-retardant performance of chitosan aerogels. The greatly improved flame-retardant properties could be attributed to TV effectively promoting the residual char formation of chitosan aerogels and reducing the formation of combustible gas phase products. To improve the hydrophobicity of chitosan aerogels, TCS-5.0 was treated with solution immersion to load siloxane molecules on its surface. The water contact angle of HTCS-5.0 was 136.1°. HTCS-5.0 had a high oil absorption multiplicity, absorbing up to 31 times its own weight of chloroform. HTCS-5.0 could continuously absorb organic solvents on the water surface with the assistance of a vacuum pump, indicating that HTCS-5.0 could be used for the clean-up of hazardous chemical leakage accidents.

Statistical investigation on the characteristics of hazardous chemical accidents in special work in China from 2008 to 2021

AbstractSpecial work involves hot work, confined space, blinding‐pipeline operation with stop plate, work at height, lifting work, temporary electricity, excavation work, work for road breaking, and other work, which may cause harm or damage to the operators themselves, surrounding buildings, equipment, and facilities. In this article, we conducted a statistical investigation on 154 large and above hazardous chemical accidents that occurred between 2008 and 2021 in China, and found that there were 58 accidents that occurred in special work alone, accounting for 37.7% of the total number of accidents. However, at present, no statistical analysis has been conducted on the characteristics of special work accidents in China. Therefore, this paper analyzes the special work accidents in terms of six aspects: categories of special work, industry distribution, enterprise scales, provincial location distribution, time‐volatility characteristics, and accident causes. Moreover, this article puts forward countermeasures and suggestions, aiming to provide useful information for the prevention of special work accidents in China.

Failure factor analysis of China hazardous chemicals safety regulation system based on FMEA

AbstractIn order to strengthen research on the identification and classification of the failure factors at the level of government safety regulation, comprehensively and systematically understand the failure of China's hazardous chemical safety regulation system, strengthen the prevention of hazardous chemical accidents, and construct China's hazardous chemical safety regulation system according to the principle of “Safety II”, the three subsystems of the regulation subject, regulatory object, and third‐party institution and their seven functional blocks are determined. Second, taking 26 dangerous chemical accidents as analysis materials, the failure mode and effect analysis method (FMEA) is used to analyze China's dangerous chemical safety regulation system. Nineteen failure modes of China's dangerous chemical safety regulation system are identified and the distribution of failure modes is analyzed and discussed. The results show that personnel failure is the main factor in the failure of the safety regulation system. Good institutional tools help to improve law enforcement work. The coordination between the regulatory agencies has an essential impact on the role of the regulatory body. It is necessary to strengthen the regulation of third‐party institutions.

Research on the prediction model of hazardous chemical road transportation accidents

In recent years, hazardous chemicals road transport accidents have occurred frequently, causing huge casualties and property losses, and accident risk assessment has become the focus of researchers' research. To predict the risk probability value of hazardous chemical road transport accidents, first, we compiled data on road transportation accidents of hazardous chemicals in China in the past five years. And the nine nodes in the Bayesian network (BN) structure were defined in combination with relevant classification standards. The optimal Bayesian network structure for hazardous chemical road transport accidents was determined based on the K2 algorithm and the causalities between the nodes. Second, the node conditional probabilities were derived by parameter learning of the model using Netica, and the validity of the model was verified using the 5-fold cross-validation method. Last, the Bayesian network model of hazardous chemical road transport accidents is used to analyze accident examples, and the accident chain of “rear-end-leakage” is predicted, and the accident is most likely to be disposed of within 3–9 h. The study shows that the derived accident prediction model for hazardous chemical road transportation can reason reasonably about the evolution of accident scenarios and determine the probability values of accident risks under different parameter conditions.

Smart chemical industry parks in China: Current status, challenges, and pathways for future sustainable development

The chemical industry has been pivotal to the rapid economic expansion and high standards of living in China. As an important carrier of the chemical industry, China has designated as many as 723 chemical industrial parks (CIPs). Unfortunately, safety concerns have become an obstacle to the sustainability of China's CIPs. Ever since the two devastating hazardous chemical accidents, namely “Tianjin Port 8·12 Explosion” and “Jiangsu 3·21 Explosion,” which occurred in 2015 and 2019 respectively, China has prioritized the safety of CIPs. The smart chemical industrial parks (SCIPs) are considered the optimal strategies toward the goal of sustainable development in China's CIPs. However, there has been a lack of research and subsequent discussion on the role of SCIPs in the forthcoming years. The period between 2020 and 2025 is considered a crucial period for the future development of CIPs and SCIPs in China mainly because the country has released a series of important government documents and national standards (such as “Fourteenth Five-Year (2020–2025) Plan for Hazardous Chemical Safety” and “Guidelines of SCIPs construction”) to promote hazardous chemical safety. With the aim of analyzing the future sustainable development for the construction of SCIPs in China, this paper proposes a systematic methodology in order to conduct an integrated and in-depth review on the standardization construction status (framework with Chinese characteristics, key events, spatial features, and national pilots), future tasks, problems, and sustainable development pathways of China's SCIPs. The method is implemented in accordance with the current scenarios of hazardous chemical safety in China and the latest government documents, regulations, and standards. Furthermore, this study provides basic data and a basis for future studies associated with the safety and sustainability of the SCIPs construction and chemical industry, both within China and in other countries.

Cause analysis framework from a safety capability perspective: Application to Tianjiayi hazardous chemical explosion accident

AbstractIdentifying the causes of accidents is essential for learning lessons and preventing similar accidents. Safety capability factors cannot be ignored when considering the causes of accidents. Therefore, safety capability is an important perspective to develop a cause analysis framework. This study focuses on a cause analysis framework based on safety capability in the 24Model. First, this study introduces the safety capability in the 24Model and elaborates on the five safety capability dimensions. Second, a 24Model‐based causal analysis framework from a safety capability perspective and its application process is proposed. Third, the Tianjiayi explosion accident is analyzed using the proposed framework. In this accident, fourteen safety capability factors in four categories: safety knowledge, safety awareness, safety habits, and safety psychology are identified and analyzed. The case study provides a critical process demonstration for analyzing safety capability factors and validates the usability of the proposed framework. Additionally, the strengths of the framework and the limitations of the study are discussed. In the future, this framework can facilitate the development of a safety capability factor database and data‐driven safety management approaches.

Exploring hazardous chemical explosion accidents with association rules and Bayesian networks

Hazardous chemical accidents pose a severe threat to human and economic activities. To explore the causes and paths of hazardous chemical explosion accidents (HCEAs) in China, a qualitative and quantitative model was proposed in this study. Firstly, 129 accident cases were collected from the official website of the Ministry of Emergency Management of the People's Republic of China, and the accident factors were screened based on association rule (AR). Then, the fault tree (FT) structure of HCEA was developed by the AR, and the qualitative model was transformed into a quantitative Bayesian network (BN) model. Finally, the accident path, the importance size of the basic events, and the sensitivity of the direct cause of the accident were uncovered based on the BN model. The results show that improper temperature control due to operating procedures is the most likely route to accidents. Among the basic events, process management and operating procedures issues ranked the first in importance, with the most significant impact on HCEA. The sensitivity of improper temperature control far exceeds other direct causes and is most likely to induce accidents directly. According to the study results, some feasible measures are put forward to improve the safety management of hazardous chemical production.

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach.

In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI. Clarifying the relationship between HFs can help in identifying the correlation between unsafe behaviors and influential factors in hazardous chemical warehouse accidents. This paper aims to investigate the impact of HFs that contribute to HE, which has caused FI disasters, explosions, and incidents in the past. This paper investigates why and how HEs contribute to the most severe accidents that occur while storing and using hazardous chemicals. The impact of fireworks and match industry disasters has motivated the planning of mitigation in this proposal. This analysis used machine learning (ML) and recommends an expert system (ES). There were many significant correlations between individual behaviors and the chance of HE to occur. This paper proposes an ML-based prediction model for fireworks and match work industries in Sivakasi, Tamil Nadu. For this study analysis, the questionnaire responses are reviewed for accuracy and coded from 500 participants from the fireworks and match industries in Tamil Nadu who were chosen to fill out a questionnaire. The Chief Inspectorate of Factories in Chennai and the Training Centre for Industrial Safety and Health in Sivakasi, Tamil Nadu, India, significantly contributed to the collection of accident datasets for the FI in Tamil Nadu, India. The data are analyzed and presented in the following categories based on this study's objectives: the effect of physical, psychological, and organizational factors. The output implemented by comparing ML models, support vector machine (SVM), random forest (RF), and Naïve Bayes (NB) accuracy is 86.45%, 91.6%, and 92.1%, respectively. Extreme Gradient Boosting (XGBoost) has the optimal classification accuracy of 94.41% of ML models. This research aims to create a new ES to mitigate HE risks in the fireworks and match work industries. The proposed ES reduces HE risk and improves workplace safety in unsafe, uncertain workplaces. Proper safety management systems (SMS) can prevent deaths and injuries such as fires and explosions.

Prediction of time in industrial chemical accidents: A survival analysis.

Chemical accidents have imposed casualties and high economic and social consequences to Iranian industries and society. This study investigated the effect of risk factors involved in occurrences of the chemical accidents and predicted the time of occurrences in Iranian chemical factories. A cross-sectional study was implemented in 574 chemical facilities with more than 25 employees from 2018 to 2020. Collecting data instruments were 2 checklists with 15 and 25 three-point Likert scale questions, respectively. Chi square and Monte Carlo tests assessed the relationships between independent risk factors and dependent hazardous chemical accidents. Cox semi-parametric and log-normal parametric models were used to predict the upcoming time of chemical accidents based on the impacts of risk factors understudy. Data analyses were performed using Stata and R software. The results showed that safety data sheets, labeling, fire extinguishing system, safe chemicals storage, separation, loading, transportation and training were statistically significant with occurrences of the chemical accidents (P-value < 0.05). Loading and transportation were mostly related to chemical incidents and reduced significantly the expected time of chemical events (P-value = 0.028). Establishing a comprehensive chemical accidents dataset and strict governmental supervision on chemical safety regulations are suggested to decrease the chemical accidents at regional and local levels in chemical plants.