• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.47-56
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• 2023

Hydrogen energy is an eco-friendly energy that produces heat and electricity with high energy efficiency and does not emit harmful substances such as greenhouse gases and fine dust. In particular, smart hydrogen energy is an economical, sustainable, and safe future smart hydrogen energy service, which means a service that stably operates based on 'data' by digitally integrating hydrogen energy infrastructure. In this paper, in order to implement a data-based hydrogen charging station demand forecasting model, three hydrogen charging stations (Chuncheon, Sokcho, Pyeongchang) installed in Gangwon-do were selected, supply and demand data of hydrogen charging stations were secured, and 7 machine learning and deep learning algorithms were used. was selected to learn a model with a total of 27 types of input data (weather data + demand for hydrogen charging stations), and the model was evaluated with root mean square error (RMSE). Through this, this paper proposes a machine learning-based hydrogen charging station energy demand prediction model for optimal hydrogen energy supply and demand.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2008.04a
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• pp.117-122
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• 2008

본 연구에서는 수소 경제의 핵심 인프라 시설인 수소 충전소의 조업자 안전 운전을 위한 교육 및 훈련 프로그램을 구축하였으며, 개발된 프로그램은 크게 수소안전교육 모듈, 수소 충전소 가상체험 모듈, 가상 사고 시나리오 모듈로 구성되어 있다. 수소안전교육 모듈에서는 수소 사고의 특징과 안전 물성 등의 자료를 수록함으로써 수소에 안전에 대한 이해를 돕도록 하였으며, 가상체험 모듈에서는 충전소의 구성과 용도를 3D Virtual Reality 기술을 도입해 간접 체험할 수 있도록 하였다. 또한 가상사고 모듈에서는 수소 충전소에서 일어날 수 있는 사고에 대한 동적 모사를 수행하여 사고의 전개 과정 및 결과를 체험해 볼 수 있도록 하였다. 이와 함께 사고가 일어났을 경우 신속하고 정확한 대응을 통해 피해를 최소화하기 위한 ERP(Emergency Response Plan)과 SOP(Standard Operating Procedure)를 개발하였다.

• Journal of the Korean Institute of Gas
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• v.27 no.1
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• pp.38-47
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• 2023

In the hydrogen filling process, hydrogen flows by the pressure difference between the supply pressure at a filling station and a storage tank in the vehicle, and the flow rate depends on the pressure difference. Therefore, it is essential to consider the pressure drop of hydrogen occurring during the filling process, and the efficiency of the hydrogen filling process can be improved through its analysis. In this study, the pressure drop was analyzed for a hose, a nozzle/receptacle coupling, a pipe, and a valve in a filling line. The pressure drops through hose and pipe, the nozzle,receptacle coupling, and the valve were calculated by using a equation for a straight conduit, a flow nozzle formula, and a gas flow respectively. In addition, as a result of comprehensive analysis of the pressure drop effect occurring in each component, it was found that the factor that has the greatest influence on the pressure drop in the entire filling line is the pressure drop through the valve. This study can be used to develop a model of the hydrogen filling process by analyzing hydrogen flow including hydrogen filling in the future.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.611-618
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• 2023

The purpose of the refueling protocol and the contents of SAE J2601, which is used as the basis for hydrogen vehicles refueling around the world, were investigated, and research contents related to domestic protocols were also investigated. In addition, the components of the hydrogen refueling performance evaluation device developed in Korea and the method for evaluating the performance and safety of hydrogen refueling stations were reviewed. And, the result were analyzed by applying it to the hydrogen refueling stations currently operating in Korea. In addition, an economic feasibility analysis was conducted using data collected from domestic hydrogen refueling stations. In order to secure the safety and economy of a hydrogen refueling station, the protocol must be satisfied, and in order to satisfy the protocol, it is necessary to evaluate whether the refueling temperature, refueling pressure, and refueling flow are controlled within a safe range.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.731-732
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• 2022

최근 탄소중립을 위한 친환경 자동차 권장 정책으로 인해서 전기차와 수소차 이용자가 증가하고 있다. 그러나 기존 내연기관차, 또는 전기차와 비교했을 때 수소차에 대한 인프라는 매우 부족한 현실이다. 따라서 출장이나 여행 등 기존 주거지를 벗어나는 상황에서는 수소차 충전소를 찾는데 어려움을 겪을 수 있다. 본 논문에서는 수소차 이용자들에게 편의를 제공하기 위해서 전국의 수소차 충전소의 위치를 구글 지도에서 보여주고 충전소의 사진과 전화번호를 쉽게 확인할 수 있는 애플리케이션을 설계 및 구현하였다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.7
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• pp.813-819
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• 2011

In this research, we investigated the hydrogen-gas filling characteristics of ultra-light composite tanks that have a plastic or aluminum liner inside the composite shell. The study was performed for different gas and tank temperatures. The temperature changes at various positions in the Type-4 tank during hydrogen-gas filling were monitored in order to understand the effects of the filling conditions. The results were compared with those obtained for a Type-3 tank. As the filling speed was increased, a quicker temperature rise was observed, and the temperature distribution over the entire region showed significant discrepancies.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.83-106
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• 2021

Interests in clean fuels have been soaring because of environmental problems such as air pollution and global warming. Unlike fossil fuels, hydrogen obtains public attention as a eco-friendly energy source because it releases only water when burned. Various policy efforts have been made to establish a hydrogen based transportation network. The station that supplies hydrogen to hydrogen-powered trucks is essential for building the hydrogen based logistics system. Thus, determining the optimal location of refueling stations is an important topic in the network. Although previous studies have mostly applied optimization based methodologies, this paper adopts machine learning to review spatial attributes of candidate locations in selecting the optimal position of the refueling stations. Machine learning shows outstanding performance in various fields. However, it has not yet applied to an optimal location selection problem of hydrogen refueling stations. Therefore, several machine learning models are applied and compared in performance by setting variables relevant to the location of highway rest areas and random points on a highway. The results show that Random Forest model is superior in terms of F1-score. We believe that this work can be a starting point to utilize machine learning based methods as the preliminary review for the optimal sites of the stations before the optimization applies.

• Journal of the Korean Institute of Gas
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• v.28 no.1
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• pp.85-99
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• 2024

As the proliferation of hydrogen electric vehicles accelerates, there is observed diversification in hydrogen refueling station models. This diversification raises safety concerns for different types of stations. This study conducted a quantitative risk assessment of a multi-vehicle hydrogen station, capable of simultaneously refueling cars, buses, and trucks. Utilizing Gexcon's Effects&Riskcurves Software, scenarios of fire and explosion due to hydrogen leaks were assessed. The study calculated the impact distances from radiative heat and explosion overpressure, and measured risks to nearby buildings and populations. The largest impact distance was from fires and explosions at dispensers and high-pressure storage units. High-pressure storage contributes most significantly to personal and societal risk. The study suggests that conservative safety distances and proper protective measures for these facilities can minimize human and material damage in the event of a hydrogen leak.

• Journal of the Korean Institute of Gas
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• v.23 no.6
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• pp.67-73
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• 2019

Hydrogen is difficult to accurately measure the amount of charge due to sudden temperature changes and pressure rise when charging the vehicle. In order to construct a hydrogen infrastructure, it is important to precisely measure the amount of charge that can be a sensitive issue in commercial transactions. In this study, the accuracy of metering of domestic hydrogen stations was evaluated as a study for metering management of hydrogen dispenser. For the experiment, we constructed metering system using master meter method and measured the flow rate in the actual hydrogen vehicle charging environment. As a result of error occurred about 10% on average, and the hydrogen loss per one charge was found to be up to 60g.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.573-580
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• 2020

Hydrogen emerged as a sustainable transport energy source. To increase hydrogen utilization, hydrogen refueling stations must be available in many places. However, this requires large-scale financial investment. This paper proposed a methodology for selecting the optimal location to maximize the use of hydrogen charging stations. The location of gas stations and natural gas charging stations, which are competing energy sources, was first considered, and the expected charging demand of hydrogen cars was calculated by further reflecting data such as population, number of registered vehicles, etc. Using k-medoids clustering, one of the machine learning techniques, the optimal location of hydrogen charging stations to meet demand was calculated. The applicability of the proposed method was illustrated in a numerical case of Seoul. Data-based methods, such as this methodology, could contribute to constructing efficient hydrogen economic systems by increasing the speed of hydrogen distribution in the future.