• Journal of Powder Materials
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• v.30 no.6
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• pp.509-515
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• 2023

Lead-free perovskite ceramics, which have excellent energy storage capabilities, are attracting attention owing to their high power density and rapid charge-discharge speed. Given that the energy-storage properties of perovskite ceramic capacitors are significantly improved by doping with various elements, modifying their chemical compositions is a fundamental strategy. This study investigated the effect of Zn doping on the microstructure and energy storage performance of potassium sodium niobate (KNN)-based ceramics. Two types of powders and their corresponding ceramics with compositions of (1-x)(K,Na)NbO₃-xBi(Ni_2/3Ta_1/3)O₃ (KNN-BNT) and (1-x)(K,Na)NbO₃-xBi(Ni_1/3Zn_1/3Ta_1/3)O₃ (KNN-BNZT) were prepared via solid-state reactions. The results indicate that Zn doping retards grain growth, resulting in smaller grain sizes in Zn-doped KNN-BNZT than in KNN-BNT ceramics. Moreover, the Zn-doped KNN-BNZT ceramics exhibited superior energy storage density and efficiency across all x values. Notably, 0.9KNN-0.1BNZT ceramics demonstrate an energy storage density and efficiency of 0.24 J/cm³ and 96%, respectively. These ceramics also exhibited excellent temperature and frequency stability. This study provides valuable insights into the design of KNN-based ceramic capacitors with enhanced energy storage capabilities through doping strategies.

• Resources Recycling
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• v.32 no.6
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• pp.79-89
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• 2023

One of the foremost environmental challenges, alongside the contemporary focus on achieving carbon neutrality, pertains to the pervasive issue of plastic waste. Thermochemical recycling technology, operating under high-temperature conditions to covert organic matter and recycle it into raw materials and energy, represents a transformative approach surpassing the conventional bounds of material recycling predominantly applied in plastic waste management. The thermochemical recycling paradigm is emerging as a pivotal technology within the circular economy, capable of transforming waste plastics into raw materials for producing original plastics. Its significance extends beyond national borders, garnering global attention due to its versatility as a chemical or energy recycling method, contingent upon the subsequent processes and final products. This study aims to scrutinize three quintessential thermochemical recycling technologies: combustion, gasification, and pyrolysis. Furthermore, the study discusses the recent major technology trends of these technologies.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.17-31
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• 2023

Amidst escalating global warming fueled by indiscriminate fossil fuel consumption, concerted efforts are underway worldwide to mitigate atmospheric carbon dioxide (CO₂) levels. Electrochemical CO₂ reduction technology is recognized as a promising and environmentally friendly approach to convert CO₂ into valuable hydrocarbon compounds, deemed essential for achieving carbon neutrality. Copper, among the various materials used as CO₂ reduction electrodes, is known as the sole metal capable of generating C₂₊ compounds. However, low conversion efficiency and selectivity have hindered its widespread commercialization. This review highlights diverse research endeavors to address these challenges. It explores various studies focused on utilizing copper-based electrodes for CO₂ reduction, offering insights into potential solutions for advancing this crucial technology.

• Clean Technology
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• v.29 no.4
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• pp.321-326
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• 2023

Recently, there has been a growing interest in clean hydrogen energy that does not emit carbon dioxide during combustion due to the increasing focus on carbon neutral. Research related to hydrogen production continues, and in this study, we applied waste-derived synthesis gas to the water-gas shift reaction to simultaneously treat waste and produce high-purity hydrogen. To enhance catalytic activity in the high-temperature water-gas shift (HT-WGS) reaction, magnesium was used as a support material alongside cerium. Cu-CeO₂-MgO catalysts were synthesized, with copper acting as the active component for the HT-WGS reaction. A study on the catalytic activity based on the preparation method was conducted, and the Cu-CeO₂-MgO catalyst prepared by impregnation method exhibited the highest activity in the HT-WGS reaction. The observed superior performance of the Cu-CeO₂-MgO catalyst prepared through the impregnation method can be attributed to its significantly higher oxygen storage capacity and amount of active Cu species.

• Journal of Advanced Technology Convergence
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• v.2 no.4
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• pp.43-48
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• 2023

Globally, there is a collaborative effort to achieve global carbon neutrality in response to climate change. In the case of South Korea, greenhouse gas emissions are rapidly increasing, presenting an urgent situation that requires resolution. In this context, this study developed a thermal energy collection device named a 'steam trap' and created an AI model capable of predicting future electricity usage by collecting energy usage data through steam traps. The average accuracy of electricity usage prediction with this AI model was 96.7%, demonstrating high precision. Consequently, the AI model enables the prediction and management of days with high electricity consumption and identifies which facilities contribute to elevated power usage. Future research aims to optimize energy consumption efficiency through efficient equipment operation using anomaly detection in steam traps and standardizing energy management systems, with the ultimate goal of reducing greenhouse gas emissions.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.841-852
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• 2023

Gas turbines, which are used as generators for frequency regulation of the domestic power system, are increasing in use due to the carbon-neutral policy, quick startup and shutdown, and high thermal efficiency. Since the gas turbine rotates the turbine using high-temperature flame, the turbine inlet temperature is acting as a key factor determining the performance and lifespan of the device. However, since the inlet temperature cannot be directly measured, the temperature calculated by the manufacturer is used or the temperature predicted based on field experience is applied, which makes it difficult to operate and maintain the gas turbine in a stable manner. In this study, we present a model that can predict the inlet temperature of a reheat gas turbine based on Deep Neural Network (DNN), which is widely used in artificial neural networks, and verify the performance of the proposed DNN based on actual data.

• Journal of the Korea Institute of Building Construction
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• v.23 no.6
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• pp.715-726
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• 2023

This study suggested that protective panels should be installed as sacrificial members as a safety design method for structures with potential explosions such as hydrogen charging stations to minimize direct damage to the structure and have resilience. To this end, the focus of the experiment is on quantitatively evaluating the impact of the structure when the protection panel is installed closely or spaced apart from the structure in a high-speed collision situation of the projectile. The experimental design used steel plates instead of concrete structural members mainly used in the past for excellent reproducibility, and the impact of structural members was compared and analyzed through deformation differences on the back of the steel plate. In addition, the impact of changes in the physical properties of the elastic body used as a separation material for the protective member and the difference in shock wave transmission time according to the protective member and the elastic body on the structural member was investigated.

• Journal of the Korean Institute of Gas
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• v.27 no.4
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• pp.92-101
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• 2023

To solve the problem of climate change, carbon neutrality has now become a necessity rather than an option. Hydrogen is not only a energy storage that can supplement the intermittent production of renewable energy, but is also considered a good alternative in the field of utilization as it does not emit carbon dioxide after reaction. In order to revitalize hydrogen vehicles, one of the fields of hydrogen utilization, the construction of hydrogen station infrastructure must be preceded. Prioritization of risk factors is necessary for efficient operation and risk assessment of hydrogen stations, but due to the short operation period of domestic hydrogen stations, there is a lack of frequency data on accidents and their reliability is low. In this study, we aim to identify the causes and consequences of deviations in hydrogen stations through HAZOP analysis. Additionally, we intend to analyze them using Fuzzy-AHP. Through this, we intend to derive the decision values for the causes of deviations in hydrogen stations and apply them to hydrogen accident cases and risk assessments to confirm the reliability and utility of the data.

• Journal of the Korean Institute of Gas
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• v.27 no.4
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• pp.116-122
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• 2023

In addition to analyzing the hydrogen economy trends of the international community (Korea, the United States, Europe, Japan, etc.), which is being promoted to realize a carbon-neutral society, this study compared and analyzed the differences between the gaseous hydrogen refueling station, which is a key hydrogen-using facility close to the people, and a liquefied hydrogen refueling station that is scheduled to be built in the future. In addition, SAFETI, a quantitative risk assessment program, was used to analyze the safety of liquefied hydrogen refueling stations and In consideration of the individual and societal risks and the ranking of risks by facility, which are conditional allowable areas, a plan to improve safety such as facility layout was proposed

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.31-34
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• 2024

태양광 에너지는 탄소 중립 이행을 위한 주요 방안으로 많은 주목을 받고 있다. 태양광 발전량은 여러 환경적 요인에 따라 크게 달라질 수 있으므로, 정확한 발전량 예측은 전력 네트워크의 안정성과 효율적인 에너지 관리에 근본적으로 중요하다. 대표적인 인공지능 기술인 신경망(Neural Network)은 불안정한 환경 변수와 복잡한 상호작용을 효과적으로 학습할 수 있어 태양광 발전량 예측에서 우수한 성능을 도출하였다. 하지만, 신경망은 모델의 구조나 초매개변수(Hyperparameter)를 최적화하는 것은 복잡하고 시간이 많이 드는 작업이므로, 에너지 분야에서 실제 산업 적용에 한계가 존재한다. 본 논문은 2단계 신경망 최적화를 통한 태양광 발전량 예측 기법을 제안한다. 먼저, 태양광 발전량 데이터 셋을 훈련 집합과 평가 집합으로 분할한다. 훈련 집합에서, 각기 다른 은닉층의 개수로 구성된 여러 신경망 모델을 구성하고, 모델별로 Optuna를 적용하여 최적의 초매개변숫값을 선정한다. 다음으로, 은닉층별 최적화된 신경망 모델을 이용해 훈련과 평가 집합에서는 각각 5겹 교차검증을 적용한 발전량 추정값과 예측값을 출력한다. 마지막으로, 스태킹 앙상블 방식을 채택해 기본 초매개변숫값으로 설정해도 우수한 성능을 도출하는 랜덤 포레스트를 이용하여 추정값을 학습하고, 평가 집합의 예측값을 입력으로 받아 최종 태양광 발전량을 예측한다. 인천 지역으로 실험한 결과, 제안한 방식은 모델링이 간편할 뿐만 아니라 여러 신경망 모델보다 우수한 예측 성능을 도출하였으며, 이를 바탕으로 국내 에너지 산업에 이바지할 수 있을 것으로 기대한다.