Major Accidents Research Articles

Development of formation leakage model and process simulation for bullheading well control

In the drilling process of exploiting natural gas, improper wellbore pressure control will lead to gas kicks, which will cause formation gas to enter the wellbore. If the gas reaches the ground, toxic gases such as H2S will cause environmental pollution and even large amount of drilling fluid overflow will further cause major safety accidents such as blowout. Bullheading killing is to inject killing fluid by applying pressure at the wellhead, and push the gas in the wellbore back to the formation. However, it is difficult to obtain formation parameters in high temperature and high pressure environment, and the distribution in wellbore gas is unclear, which makes it difficult to accurately simulate the well killing process and provide reference for actual construction. Therefore, through the characteristics of wellhead overflow in the process of gas kicks, combined with particle swarm optimization (PSO) algorithm, the formation parameters are solved, and then a new leakage model is derived to describe the resistance of gas being pushed back to the formation. Well W2 and Well X3 are taken as examples to verify the model. The results show that the error between wellhead pressure and actual wellhead pressure data is 6.3% and 6.9% respectively, which shows satisfactory calculation accuracy. The specific process of obtaining construction parameters for this engineering technology is put forward. The research in this paper is of great significance for modeling and guiding well killing operation by bullheading killing.

Learning-based framework for industrial accident prevention: fuzzy cognitive mapping approach

PurposeDespite efforts to improve safety management practices in industrial companies, major accidents seem to be inevitable. Many accidents still occur because companies are unable to learn from past occurrences due to ineffective incident and accident learning processes. This study proposes a learning-based framework for industrial accidents investigation and contributes to accident prevention research.Design/methodology/approachThe proposed learning process includes the analysis of the industrial accident using the Event Tree Analysis (ETA) method, capitalisation of causative factors using the Swiss Cheese Model (SCM), and finally modelling the relationships among the accident causative factors and analysing their causality using the Fuzzy Cognitive Mapping (FCM) technique and running learning scenarios.FindingsThe proposed learning process was applied to an industrial accident, and the results showed that human unsafe behaviours and unsafe supervision were the principal causative factors of the blowout accident.Practical implicationsThe proposed learning-based framework provides a structured approach for oil and gas companies to systematically analyse and learn from past accidents, enhancing their prevention strategies. Theoretically, the framework bridges the gap between theory and practice by demonstrating how established accident analysis methods can be combined and applied in a real-world industrial context.Originality/valueThe proposed learning process combines accident analysis and investigation techniques with simulations for an in-depth and robust learning-based framework for accident prevention.

A Novel Framework for Identifying Major Fishing Vessel Accidents and Their Key Influencing Factors

This research addresses the critical issue of major fishing vessel accidents, which traditionally suffer from a lack of focused analysis due to their rarity and the subjective nature of their classification. We propose an innovative methodology of Peaks Over Threshold to overcome subjectivity in accident classification. This approach ensures a more representative and accurate analysis of major accidents, distinguishing them from more common, less severe incidents. Employing a Bayesian network model, we further explore the most influential factors contributing to these major accidents. The key innovation lies in our novel approach to data handling and analysis, enabling us to uncover hidden patterns and causal relationships that traditional methods often overlook. The results show that the approach proposed in this study can effectively capture the key factors of major fishing vessel accidents. This study identifies accident type, vessel-related factors, and accident location as the key influential factors leading to major accidents. The findings from our research are intended to inform sustainable fisheries management practices, promoting interventions that aim to decrease the occurrence and impact of severe maritime accidents while balancing economic, safety, and sustainable development considerations.

Zakłady niesevesowskie – stwarzające zagrożenie wystąpienia poważnej awarii przemysłowej

Non-Seveso establishments – plants posing a risk of a major industrial accident Various definitions and approaches used by organizations and control and supervisory institutions are reflected in the list of establishments posing a risk of a major industrial accident, which are not upper- and lower-tier establishments. The article proposes a definition that includes both sub-threshold establishments with smaller quantities of non-hazardous substances than qualify an establishment as a lower tier establishment and establishments containing other chemical substances that could be the source of a major accident. The results of analyses of databases of chemical substances present in non-Seveso establishments, the correlation of the definition of a major accident and chemical substances, and the failure rate for non-Seveso establishments are presented. Keywords: non-Seveso establishments, dangerous substances, major accidents

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.

The Important Role of Safety Culture in the Management of Major Industrial Accidents

Introduction: Major industrial accidents or disasters can have significant impacts on various aspects, including human health, the environment, societal well-being, and national economies. The concept of safety culture is increasingly receiving attention as a means of mitigating industrial mishaps. Nonetheless, a dearth of comprehensive systematic reviews exists that specifically examine the important role of safety culture in effectively mitigating severe industrial accidents across diverse industries on a global scale. Hence, the primary aim of this research is to examine the important role of safety culture in the effective management of industrial accidents across diverse sectors. Method: The systematic literature review (SLR) from the ScienceDirect database was established using the four basic procedures outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of thirty-three scholarly publications were retrieved from the database, encompassing the years 2017 to 2021, which specifically address the important role of safety culture in the effective management of major industrial incidents. Results: The findings yielded two primary themes, namely, the significance of safety culture at both the managerial and employee levels. These topics were then categorised into ten subthemes. The findings indicate that safety culture exerts a significant influence at the managerial level in mitigating industrial accidents through the enhancement of safety training. Conclusion: In summary, a safety culture holds significant importance within the realm of management as it serves to effectively mitigate the occurrence of industrial accidents in the years to come.

Study on Improving Fire Safety in Traditional Markets

In this study, we analyzed fire-safety management regulations and problems with fire-alarm equipment in traditional markets, as failures can cause significant damage in the event of a fire. We also present improvement measures based on analyzing major fire accidents. Traditional markets must designate the type of firefighting equipment installed at a store according to the number of stores, and apply this measure retroactively when the installation regulations are revised. If the market is designated as traditional, the equipment must be designated as a fire-safety management object, and the appointment of a fire safety manager, firefighting education and training, and self-inspection must be made mandatory. Fire-alarm equipment must be inspected regularly by a fire-safety manager, and fire-proof walls must be installed with self-supporting fire-resistant structures at certain sections. When designing a new traditional market, it must be designated after facility supplementation following safety inspection. In addition, traditional markets are required to have fire-detection equipment and automatic notification systems, and building-type traditional markets must retroactively install automatic sprinkler equipment.

산업재해 심리지원에 참여한 직업트라우마 상담자 경험에 대한 현상학적 연구

Industrial accidents often leave workers psychologically traumatized, and the occupational trauma counselors who assist them are exposed to ongoing stress. This study aims to explore the experiences of occupational trauma counselors who provide psychological support following industrial accidents. In-depth interviews were conducted with five counselors involved in crisis interventions during major industrial accidents, and the data were analyzed using van Manen's hermeneutic phenomenology method. The analysis revealed 24 subthemes and 5 overarching themes that encapsulate the essence of the counselors' experiences: “Facing the scene of suffering,” “Being thrown into an unfamiliar situation,” “Struggling amidst chaos,” “Experiencing anxiety and emptiness after intense immersion,” and “Reflecting on life and suffering, and becoming an advocate.” These findings have implications for enhancing psychological support in occupational trauma cases, and recommendations for future research are provided.

Resolving sustainable development conflicts caused by vinyl leakage accidents in the USA: From the perspective of graph model under fuzzy preferences with self-confidence

On February 3, 2023, a railroad accident led to the release and combustion of 115,580 gallons of vinyl chloride monomer in East Palestine, Ohio, USA. The large leakage and long-time combustion of vinyl chloride monomer harmed the stability of local sustainable ecosystems and the health of residents, as the burning produced deadly toxins, triggering dissatisfaction from residents and environmental protection organizations. With the COVID-19 pandemic still unfolding globally, decision makers (DMs) in vinyl chloride leakage accidents must consider the existing environmental health uncertainty, potentially leading to uncertainty in their preference cognitive. Thus, this paper proposes a novel fuzzy preference with a self-confidence structure of the graph model for conflict resolution (GMCR) to resolve the sustainable development conflicts caused by the vinyl chloride leakage accident in Ohio. Fully considering the uncertainty of option choice by DMs and the different levels of confidence in DMs’ preference selection, four different fuzzy stability definitions of self-confidence are defined in GMCR. The analysis results suggest the following: (1) During conflicts, the US Norfolk Southern Corp should assume corporate responsibility by setting up a health monitoring system for residents in Ohio; the company should pay for the medical examinations and related care expenses for residents within the scope of the vinyl chloride leakage accident. (2) The negotiation of all stakeholders in conflicts can promote reconciliation. Finally, the analysis process and results of this study can provide guidance to government departments for properly handling major accidents and appeasing affected residents.

CO2 Breathing Prior to Simulated Diving Increases Decompression Sickness Risk in a Mouse Model: The Microbiota Trail Is Not Forgotten.

Decompression sickness (DCS) with neurological disorders is the leading cause of major diving accidents treated in hyperbaric chambers. Exposure to high levels of CO2 during diving is a safety concern for occupational groups at risk of DCS. However, the effects of prior exposure to CO2 have never been evaluated. The purpose of this study was to evaluate the effect of CO2 breathing prior to a provocative dive on the occurrence of DCS in mice. Fifty mice were exposed to a maximum CO2 concentration of 70 hPa, i.e., 7% at atmospheric pressure, for one hour at atmospheric pressure. Another 50 mice breathing air under similar conditions served as controls. In the AIR group (control), 22 out of 50 mice showed post-dive symptoms compared to 44 out of 50 in the CO2 group (p < 0.001). We found that CO2 breathing is associated with a decrease in body temperature in mice and that CO2 exposure dramatically increases the incidence of DCS (p < 0.001). More unexpectedly, it appears that the lower temperature of the animals even before exposure to the accident-prone protocol leads to an unfavorable prognosis (p = 0.046). This study also suggests that the composition of the microbiota may influence thermogenesis and thus accidentology. Depending on prior exposure, some of the bacterial genera identified in this work could be perceived as beneficial or pathogenic.

CURRENT TRENDS IN FORENSIC ODONTOLOGY. SYSTEMATIC REVIEW

Abstract Background: Forensic odontology is a subfield of dental science that includes the relationship between dentistry and law. Forensic odontology has played a key role in famous criminal cases. Forensic odontology plays a critical role in the identification of human remains of victims (mutilated, burned and decomposed), when fingerprinting and visual recognition, cannot be fulfilled. This review article was conducted to show the implications of forensic dentistry in criminal investigations. Aim: The purpose of this article provides an overview of the evolving trends in traditional methods and the latest concepts used in forensic dentistry. Methodology: The systematic review included articles from Google Scholar, Medline, Scopus, Web Of Sciences, PubMed was conducted. For Search the following keywords: Forensic odontology, dental records, identification of human remains Estimate Age, Sex assessment, Identification following mass fatalities, assessing bite mark injuries, civil cases involving malpractice, analyze bite marks. Results: Conducted a preliminary search and reviewed 82 titles and abstracts in this review and 27 full-text articles were selected of high methodological quality. Teeth are often resistant to decay even in major accidents, crimes, burials, or other severe exposures to the elements. Therefore, a person's dentition is useful for individual identification and comparison if records exist for this purpose. Various methods have been developed forensic dentistry to determine the age, gender, and ethnicity of an individual using dental tissue. The need for documented procedures and protocols is important for the dentist to In the case of victim identification disaster identification indicators (DVI), allowing for process auditing, they also serve to ensure identification reliability. Dental professionals play a major role in keeping accurate dental records and providing all necessary information so that legal authorities may recognize malpractices, negligence, fraud child abuse and also, identify an individual. In order to serve the forensic operation and the legal authorities, dentists should be familiar with the basics of forensic dentistry, which will create awareness for the preservation of dental data. Conclusion: Forensic dentistry (odontology) plays an important role in resolving dental malpractice or negligence issues, including evaluation, report writing, and/or testifying in court. Every dentist has a responsibility to understand the forensic activities related to their dental practice and should be aware of the available technologies and their use in forensic dentistry. The data collection methods and additional technologies used in forensic dental identification have undergone significant transformation. Artificial intelligence is a technology that is still evolving in forensic dentistry, and its usefulness depends on the specific use case and implementation. New research in forensic dentistry should be encouraged.

The Influencing Mechanism of Household Food Purchasing Behavior and Household Reserve Efficiency under Non-Normal Conditions

Family reserves are an important part of national reserves, and how to do a good job in family reserves is a common concern of the government and society. Under the non-normal conditions of major accidents and disasters, wars, plagues, social unrest, etc., urban food supply mainly depends on external supply guarantee, and urban residents’ risk perception is more sensitive. Based on the Theory of Planned Behavior and Norm Activation Model, this study constructs an analytical framework for the risk perception, perceived behavior control, and family reserve efficacy of urban residents under non-normal conditions from the perspectives of rationality and sensibility, self-interest, and altruism. The perceived behavior control of household food reserves in non-normal conditions is affected by risk perception, subjective norms, and personal norms. On this basis, countermeasures and suggestions are put forward: Urban residents should strengthen their sense of risk and responsibility for storing food at home, reserve food appropriately, and develop a good habit of family saving. On the other hand, it is necessary to pay attention to personal norms, reduce the negative impact of subjective norms on residents, and avoid excessive food storage and food waste.

Design of intelligent detection method for electricity transmission line equipment defect based on data mining algorithm

Electricity transmission line is the most significant way of power transmission. Regular detection of it can find and eliminate its defects and hidden dangers in time and prevent major accidents, which is of great significance to the power system. In order to find the problems in the electricity transmission line in time, this paper applied the data mining algorithm to the intelligent detection method of electricity transmission line equipment defects. An electricity transmission line equipment defect intelligent detection and monitoring system was constructed, and the differences between clustering analysis image recognition technology in data mining algorithms and the XGBoost algorithm were analyzed. The results showed that compared with using XGBoost algorithms, the highest accuracy rate of the intelligent detection method of electricity transmission line equipment defects using data mining algorithm in the detection results was 98 %, which was generally higher than that of XGBoost algorithms, and could reduce the consumption of time. From the perspective of replication rate, the overall average value of XGBoost algorithms was 52.38 % and the overall average value of data mining algorithms was 7.63 %. The replica rate of data mining algorithm was much lower than that of XGBoost algorithm and the performance of fault signal detection was better. Therefore, the application of data mining algorithm to the intelligent detection method of electricity transmission line equipment defects can be more suitable, thus significantly improving the efficiency of all aspects. At the same time, the method has the advantages of simple operation, fast, reliable and not affected by region.

Research on work-stress recognition for deep ground miners based on depth-separable convolutional neural network

In deep mine production operations, the challenging operating environment intensifies the workload and pressure on coal miners. Long-term exposure to high-intensity operating pressure can seriously impact the physical and mental health of miners, leading to unsafe behaviors and accidents. To identify the pressure of miners' operations, this paper examines various driving scenarios, such as the deep-well tunneling machine cutting the wall and opening the alley, the shoveling machine shoveling ore, and the pickup truck driver transporting. The paper randomly collects facial images of miners during each operation using an explosion-proof CCD camera to obtain the facial expression characteristic data of miners. The Ferface2013 facial expression dataset was used to establish the dataset. The depth separable convolutional neural network MiniXception was used for training and to output the classification results of the pressure degree of deep shaft miners. A MiniXception-based miners' operating pressure recognition model was established. The training time, precision, recall, F1 score, and classification accuracy confusion matrix were selected. The study evaluated the effectiveness of the recognition model by measuring its training time, precision, recall, F1 score, and classification accuracy confusion matrix. The results indicate that the model has a correct recognition rate of 88% for the pressure state, 91% for the pleasure state, and 74% for the normal state. The overall accuracy of the model is 0.843. Therefore, the MiniXception recognition model is suitable for recognizing the pressure of miners' operations in deep mines. This can meet practical needs and is useful for preventing major accidents in mines, managing on-site safety, and managing safety in non-hazardous areas. It has important theoretical and practical significance.

Advancements in deep learning and natural language processing for effective disaster sentiment analysis: A review

This article comprehensively studies the application of deep learning (DL), natural language processing (NLP), and large language models (LLM) in sentiment analysis in disaster scenarios such as earthquakes and major accidents. The article focuses on the latest developments in these technologies and their role in strengthening disaster management and response. The article explores various methods, including BERT, LSTM, and convolutional neural networks, with a focus on their practicality, challenges, and potential for development. This review aims to provide researchers and relevant practitioners with a comprehensive understanding of this rapidly developing field.

Perancangan Sistem Pengendalian Risiko Menggunakan Simplified Bowtie Analysis pada Fasilitas FSO Federal II di PT XYZ

The oil and gas industry in Indonesia, including PT XYZ which manages domestic oil and gas, faces high complexity and risk. PT XYZ's operations are supported by several offshore platforms and facilities which have various potential risks. Process safety is a top priority to prevent major incidents or major accidents. PT XYZ uses Process Hazard Analysis (PHA) which needs to be revalidated every five years to ensure procedures comply with current conditions. This research focuses on the FSO Federal II facility owned by PT XYZ, a 200 thousand barrel capacity crude oil storage vessel with a high potential risk of major incidents. PT XYZ uses the HAZOP and HAZID methods in PHA to identify risks. However, PT XYZ does not yet have a direct control system for workers. This research aims to create a risk control system that can be used by Federal II FSO workers as a work guide. This work guide will help in handling undesirable events that have the potential to cause major incidents. Proposed improvements include creating a work guidance document based on risk identification using Bowtie Analysis, which produces a mapping diagram of causes, peak events, hazards and consequences. The bowtie diagram will be simplified to make things easier for workers, considering the large number of processes in FSO Federal II. This guideline will help workers prevent major incidents from occurring.

Estimating the Acute Health Effects of Smoke Exposure from an Urban Factory Fire Accident: A Case Study of a Tire Factory Fire in Korea.

A major industrial fire accident occurred in a tire manufacturing factory in Daejeon, Korea, on 12 March 2023 and lasted for 3 d, generating air pollutant emissions. Although evidence regarding the health effects of urban fires is limited, residents near tire factory may have experienced health hazards due to smoke exposure from fire plumes. Capitalizing on the timing of this fire incident as a natural experiment, we estimated the attributable excess air pollution exposure and associated disease development among residents living near the tire factory. We used the generalized synthetic control method to estimate air pollution exposure and health burden attributable to the accident among residents living in smoke-exposed districts. Based on satellite images and air pollution monitoring results, three administrative districts (within from the factory) were defined as smoke-exposed, and the other 79 districts of Daejeon were defined as controls. Among the 11 monitoring stations in Daejeon, the station located from the factory was used to estimate excess air pollution exposure (, , , , , and CO) for residents in the exposed districts. The number of daily district-level disease-specific incidence cases were acquired from the National Health Insurance Database and used to estimate excess health burden resulting from the fire. During the first week following the factory fire, residents of exposed districts had an estimated excess exposure to 125.2 [95% confidence interval (CI): 44.9, 156.7] of , 50.4 (95% CI: 12.7, 99.8) ppb of , and 32.0 (95% CI: 21.0, 35.9) ppb of . We also found an average increase in the incidence cases of other diseases of upper respiratory tract [20.6 persons (95% CI: 6.2, 37.4)], lung disease due to external agents [2.5 persons (95% CI: 2.1, 3.3)], urticaria and erythema [5.9 persons (95% CI: , 11.2)], and episodic and paroxysmal disorders [8.5 persons (95% CI: 3.7, 13.4)] in exposed districts. Excessive air pollution exposure and disease incidence were identified among residents living close to the tire factory. Preventive measures, such as a warning system, to avoid health impacts to people breathing fire-related pollution may be beneficial for communities impacted by such events. https://doi.org/10.1289/EHP14115.

Water transport and corrosion under insulation: Experimental investigations of drying in mineral wool

Corrosion under insulation (CUI) is a major risk factor for the material integrity of insulated equipment that is encountered i.a. in the chemical process industries. Undetected CUI has been the root cause for major accidents, including loss-of-site, loss-of-life, prolonged plant shutdown, and environmental pollution. Reducing CUI risk via labour-intensive inspection routines is associated with billions of dollars of annual maintenance costs worldwide. CUI is caused by water bypassing a weather shield, migrating through the insulation and contacting the equipment surface. The resulting corrosion rates are very high and the process is hidden from view. Surprisingly, the fundamental mechanisms that drive water migration through insulation have been largely unexplored. In this work we present novel experimental investigations of the evaporation and drying dynamics for a heated vertical pipe with preformed mineral wool insulation and metal cladding. Relative humidity and temperature sensors provide measurements of how the water evaporates and travels through the system during controlled water ingress experiments. The experimental data shows that the drying time of the insulation material is proportional to the amount of water added. We find that the drying process proceeds in two stages, where the length of the first drying regime is directly related to the diffusion-limited evaporation from the bulk liquid water, while the second drying time is influenced also by other factors such as the adsorption and desorption of water on the insulation material fibres. The findings have implications for how time of wetness, used in CUI risk-based inspection methods, can be obtained from humidity sensor data. The new insights into water transport in insulation enables a paradigm shift towards predictive CUI maintenance using humidity spot sensors.

Method for selecting emergency material reserve bases in chemical industry parks under typhoon scenarios

Rationally selecting emergency material reserve bases (EMSBs) for chemical industry parks is crucial to ensure timely emergency responses during major accidents. To identify practical site selection schemes for EMSBs under typhoon scenarios, a prioritization/sorting site selection model considering typhoon disasters for EMSBs is developed. Firstly, an improved k-means clustering algorithm is employed to cluster typhoon paths and classify them into several critical typhoon scenarios, which are used to assess the risk of typhoon disasters affecting chemical industrial parks in specific areas. Then, according to the region's functional characteristics of EMSBs, candidate areas with emergency capabilities are identified through kernel density analysis, and safe candidate areas are further determined based on the identified typhoon scenarios. Finally, the safe candidate areas are ranked based on emergency path distances, and the preferred site selection areas are ultimately obtained through administrative planning analysis of the top-ranked areas. Furthermore, a case study is conducted based on data from Zhejiang Province, verifying the effectiveness of the decision-making method.

Exploring the factors of major road traffic accidents: A case study of China

Exploring the factors of major road traffic accidents: A case study of China