• Nuclear Engineering and Technology
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• 제53권2호
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• pp.532-548
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• 2021

Nuclear power plants contain several monitoring systems that can identify the in-vessel phenomena of a severe accident (SA). Though a lot of analysis and research is carried out on SA, right from the development of the nuclear industry, not all the possible circumstances are taken into consideration. Therefore, to improve the efficacy of the safety of nuclear power plants, additional analytical studies are needed that can directly monitor severe accident phenomena. This paper presents an interacting multiple model (IMM) based fault detection and diagnosis (FDD) approach for the identification of in-vessel phenomena to provide the accident propagation information using reactor vessel (RV) out-wall temperature distribution during severe accidents in a nuclear power plant. The estimation of wall temperature is treated as a state estimation problem where the time-varying wall temperature is estimated using IMM employing three multiple models for temperature evolution. From the estimated RV out-wall temperature and rate of temperature, the in-vessel phenomena are identified such as core meltdown, corium relocation, reactor vessel damage, reflooding, etc. We tested the proposed method with five different types of SA scenarios and the results show that the proposed method has estimated the outer wall temperature with good accuracy.

• 한국안전학회지
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• 제37권3호
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• pp.9-15
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• 2022

The analysis of the causes of accidents in workplaces where machines and tools are used is essential to improve the effectiveness and efficiency of safety prevention policies in places of employment in Korea. The causes of workplace accidents are not fully understood mainly due to difficulties in analyzing available descriptive information. This study focuses on the automated accident cause analysis in workplaces based on the accident abstracts found in industrial accident reports written in an unstructured descriptive format. The method proposed in this paper is based on text data mining and uses the keyword search function of Excel software to automate the analysis. The analysis results indicate that the primary reason for the frequency of accidents is related to technical aspects at a stage in which dangerous situations occur in the workplace. Accidents due to managerial causes are typically observed when danger exists in the workplace; however, managerial actions play a more important role in reducing accident severity. A small company tends to use unsafe machines and devices, leading to further accidents due to technical causes, whereas managerial causes are more conspicuous as the company grows. To preclude the occurrence of accidents due to inadequate knowledge, the implementation of safety management and the provision of safety education to elderly workers at the early stage of their employment are particularly important for small companies with less than 100 workers.

• 방사성폐기물학회지
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• 제20권3호
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• pp.339-347
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• 2022

Major accidents at nuclear power plants generate huge amounts of radioactive waste in a short period of time over a wide area outside the plant boundary. Therefore, extraordinary efforts are required for safe management of the waste. A well-established remediation plan including radioactive waste management that is prepared in advance will minimize the impact on the public and environment. In Korea, however, only limited plans exist to systematically manage this type of off-site radioactive waste generating event. In this study, we developed basic strategies for off-site radioactive waste management based on recommendations from the IAEA (International Atomic Energy Agency) and NCRP (National Council on Radiation Protection and Measurements), experiences from the Fukushima Daiichi accident in Japan, and a review of the national radioactive waste management system in Korea. These strategies included the assignment of roles and responsibilities, development of management methodologies, securement of storage capacities, preparation for the use of existing infrastructure, assurance of information transparency, and establishment of cooperative measures with international organizations.

• 신재생에너지
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• 제19권3호
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• pp.22-33
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• 2023

The number of fire and explosion accidents caused by pyrolysis oil and gas at waste plastic pyrolysis plants is increasing, but accident status and safety conditions have not been clearly identified. Therefore, the aim of the study was to identify the risks of the waste plastic pyrolysis process and suggest appropriate safety management measures. We collected information on 19 cases of fire and explosion accidents that occurred between 2010 and 2021 at 26 waste plastic pyrolysis plants using the Korea Occupational Safety and Health Agency (KOSHA) database and media reports. The mechanical, managerial, personnel-related, and environmental problems within a plant and problems related to government agencies and the design, manufacturing, and installation companies involved with pyrolysis equipment were analyzed using the 4Ms of Machines, Management, Man, and Media, as well as the System-Theoretic Accident Model and Processes (STAMP) methodology for seven accident cases with accident investigation reports. Study findings indicate the need for establishing legal and institutional support measures for waste plastic pyrolysis plants in order to prevent fire and explosion accidents in the pyrolysis process. In addition, ensuring safety from the design and manufacturing stages of facilities is essential, as are measures that ensure systematic operations after the installation of safety devices.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.230-237
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• 2022

It is difficult to predict industrial accidents in the construction industry because many accident factors, such as human-related factors and environment-related factors, affect the accidents. Many studies have analyzed the severity of injuries and types of accidents; however, there were few studies on the prediction of injured body parts. This study aims to develop a classification model to predict the part of the injured body based on accident-related factors. Construction accident cases from June 2018 to July 2021 provided by the Korea Construction Safety Management Integrated Information were collected through web crawling and then preprocessed. A naïve Bayes classifier, one of the supervised learning algorithms, was employed to construct a classification model of the injured body part, which has four categories: 1) torso, 2) upper extremity, 3) head, and 4) lower extremity. The predictor variables are accident type, type of work, facility type, injury source, and activity type. As a result, the average accuracy for each injured body part was 50.4%. The accuracy of the upper extremity and lower extremity was relatively higher than the cases of the torso and head. Unlike the other classifications, such as spam mail filtering, a naïve Bayes classifier does not provide a good classification performance in construction accidents. The reasons are discussed in the study. Based on the results of this study, more detailed guidelines for construction safety management can be provided, which help establish safety measures at the construction site.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2022년도 추계학술대회
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• pp.229-230
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• 2022

미래환경에 부합하는 스마트 항로표지는 다양한 정보를 생성하고, 다양한 방식으로 제공될 것이다. 해양 이용자에게 적절한 서비스제공을 위해 관리자는 언제든지 항로표지를 확인하고, 원하는 형식으로 자료를 확인할 수 있어야 한다. 또한 미래의 항로표지가 적절히 관리되기 위해서는 항로표지사고의 원인을 파악하고, 재발 방지를 위해 노력해야 할 것이다. 이를 본 연구에서는 항로표지사고에 대하여 원인 7종과 사고종류를 11종으로 구분하여 제시하였다.

• 한국안전학회지
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• 제39권3호
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• pp.20-26
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• 2024

The Chemical Substances Control Act of South Korea mandates submission of transportation plans containing information on the transportation of hazardous chemicals, with over 600,000 submissions recorded annually. In this study, big data analysis was performed on 2,506,985 transportation plans to identify trends and assess their correlation with chemical transportation accidents. The analysis confirmed that despite NaOH accounting for 20.7% of transportation plans, HCl constitutes 40% of chemical transportation accidents, which indicates a correlation of these accidents with the chemical properties of hazardous substances rather than with the number of submitted transportation plans. Furthermore, chemical transportation accidents show a higher probability of occurrence in the 6-8 am and 6-8 pm windows, which is in agreement with higher incidence and fatality rates. The departure points of transportation plans are closely related to the characteristics of local chemical industrial complexes such as Ulsan, Yeosu, and Gunsan, whereas the arrival points are closely related to Pyeongtaek, Hwaseong, and Icheon, which are the locations of semiconductor industries. Ultimately, achievement of safety by consideration of characteristics of transported chemicals, enhancement of driver concentration during specific times, and implementation of preventive measures tailored to local government characteristics are strategies anticipated to contribute to a reduction in chemical transportation accidents.

• Nuclear Engineering and Technology
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• 제53권8호
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• pp.2534-2546
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• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.

• 한국항해항만학회지
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• 제47권1호
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• pp.1-9
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• 2023

해양사고 분석에 관한 많은 연구가 진행되고 있으며, 해양사고는 매년 업데이트되고 있어 주기적으로 원인을 분석하고 규명하는 것이 필요하다. 이 연구에서는 이전의 데이터와 새로운 데이터를 활용하여 해양사고를 파악·분석을 통해 어선 해양사고 원인을 규명하여 사고를 예방하는 것이다. 해양사고 데이터는 어선의 특수성을 고려하여 해양안전심판원의 어선에 대한 해양사고재결서 16년간의 1,921건을 수집하였으며, 해양수산부 종합상황실 사고알림문자 이력 3년간의 1,917건을 수집하였다. 재결서 데이터와 문자 데이터는 변수에 따라 분류하였으며, 수량화 작업을 수행하였다. 수량화 작업을 통한 데이터를 사용하여 베이지안 네트워크를 이용해 사전확률을 계산하였고, 후방 추론을 이용하여 어선 해양사고를 예측하였다. 두 가지 수집한 데이터 중 해양사고재결서는 모든 어선의 사고가 재결서에 포함되지 않았기 때문에 해양수산부 사고알림문자를 선택하였다. 분류한 데이터를 베이지안 네트워크를 사용하여 어선 해양사고의 사전 확률을 계산하였다. 후방 추론으로 계산한 기관손상이 서해 연안에서 발생할 어선 해양사고의 확률은 0.0000031%였다. 이 연구의 기대효과는 어선 해양사고를 분석하기 위하여 새로운 사고알림문자 데이터를 활용하여 실제 어선 특성에 맞는 해양사고를 분석할 수 있다는 것이다. 추후에는 어선 해양사고에 영향을 미치는 변수들 간의 인과관계에 관한 연구를 수행할 예정이다.

• 산업경영시스템학회지
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• 제40권4호
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• pp.147-153
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• 2017

The mortality rate in industrial accidents in South Korea was 11 per 100,000 workers in 2015. It's five times higher than the OECD average. Economic losses due to industrial accidents continue to grow, reaching 19 trillion won much more than natural disaster losses equivalent to 1.1 trillion won. It requires fundamental changes according to industrial safety management. In this study, We classified the risk of accidents in industrial complex of Ulju-gun using spatial analytics and data mining. We collected 119 data on accident data, factory characteristics data, company information such as sales amount, capital stock, building information, weather information, official land price, etc. Through the pre-processing and data convergence process, the analysis dataset was constructed. Then we conducted geographically weighted regression with spatial factors affecting fire incidents and calculated the risk of fire accidents with analytical model for combining Boosting and CART (Classification and Regression Tree). We drew the main factors that affect the fire accident. The drawn main factors are deterioration of buildings, capital stock, employee number, officially assessed land price and height of building. Finally the predicted accident rates were divided into four class (risk category-alert, hazard, caution, and attention) with Jenks Natural Breaks Classification. It is divided by seeking to minimize each class's average deviation from the class mean, while maximizing each class's deviation from the means of the other groups. As the analysis results were also visualized on maps, the danger zone can be intuitively checked. It is judged to be available in different policy decisions for different types, such as those used by different types of risk ratings.