• Journal of the Korean Geosynthetics Society
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• v.13 no.4
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• pp.21-31
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• 2014

Recently, the research on renewable energy against depletion of fossil fuel have been actively carried out in the world. Especially, offshore wind turbines are very economical and innovative technology. However, offshore wind turbines experience large base moments due to the wind and wave loading, so the monopile with large diameter needs to be applied. For the economical design of the large diameter pile, it is important to consider the flexibility of the foundation to estimate the maximum moment accurately, based on studies conducted so far. In this paper, the foundation was modeled using the finite element method in order to better describe the large diameter effect of a monopile and the results were compared with those of p-y method. For the examples studied in this paper, the change in maximum moment was insignificant, but the maximum tilt angle from the finite element method was over 14% larger than that of p-y method. Therefore, the finite element approach is recommended to model the flexibility effect of the pile when large tilt angles may cause serviceability issues.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.7
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• pp.199-204
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• 2021

Digital substation refers to a substation that digitizes functions and communication methods of power facilities such as monitoring, measuring, control, protection, and operation based on IEC 61850, an international standard for the purpose of intelligent power grids. Based on the intelligent operating system, efficient monitoring and control of power facilities is possible, and automatic recovery function and remote control are possible in the event of an accident, enabling rapid power failure recovery. With the development of digital technology and the expansion of the introduction of eco-friendly renewable energy and electric vehicles, the spread of direct current distribution systems is expected to expand. MVDC is a system that utilizes direct current lines with voltage levels and transmission capacities between HVDCs applied to conventional transmission systems and LVDCs from consumers. Converting existing lines in substations, where most power equipment is alternating current centric, to direct current lines will reduce transmission losses and ensure greater current capacity. The process bus of a digital substation is a communication network consisting of communication equipment such as Ethernet switches that connect installed devices between bay level and process level. For MVDC linkage to existing digital substations, the process level was divided into two buses: AC and DC, and a system that can be comprehensively managed in conjunction with diagnostic IEDs as well as surveillance and control was proposed.

• Clean Technology
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• v.27 no.1
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• pp.1-8
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• 2021

Due to the negative impacts of microplastics (MPs) on the ecosystem, the investigation of its occurrence and its treatment from sewage and wastewater treatment plants (WWTPs) have received a lot of attention in the recent years. Most MPs are precipitated and removed with the sludge during the treatment process. Proper sludge management is immensely necessary to avoid MP exposure in the environment. However, the domestic research on this aspect is limited. This study reviews appropriate sludge management approaches to decrease environmental MP exposure. This can be achieved through investigating sludge generation and treatment, regulation laws and government policy trends with an emphasis on WWTPs. The ratio of sludge in sewage treatment plants has been observed to be highest in recycling followed by incineration and landfills. Recycling is the highest in fuel followed by construction materials and composting. For WWTPs, the highest ratio is in recycling followed by fuel and landfills, and recycling is confirmed in the following order: incineration > after composting > after solidification > earthworm breeding. Treatment approaches that can increase the exposure of MPs to the ecosystem are considered to be used in landfills and agricultural fields. However, this method is not appropriate given the insufficient capacity of domestic landfills and the sufficient supply of existing chemical and animal manure fertilizers. Instead, it would be rational in terms of environmental preservation to expand the use of fuel and energy in connection with the new and renewable energy policy, and to actively seek the use of sub-materials for construction materials. In order to secure the basic data for the effectiveness of future planning and revision of related laws, it is required to perform an in-depth investigation of the sludge supply and demand status along with the environmental and economic effects.

• Journal of Venture Innovation
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• v.5 no.1
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• pp.91-106
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• 2022

Cryptocurrency, stood out the sharp cost rising of Bitcoin has been spotlighted by means of the solution for stagflation because it is decentralized with an existing currency differently. Especially getting into 4th industrial revolution, technologies using block chain and internet of things have been used in the many fields, and the power of influence is also widespread. Nevertheless like a remark of Elon Musk of Tesla CEO, the problems of environmental contamination for cryptocurrency have been pointed out continuously and the most representative of them is an enormous electric usage as the use of fossil fuels. Also the amount generated of carbon dioxide result in the acceleration of global warming mainly based on the climate changes of earth if the existing mining method is continued. On the other hand, review researches have been conducted restrictively as the connection with environmental contamination as the mining of cryptocurrency. In this study, it intended to review problems for environmental contamination as the diversification of ecological system of cryptocurrency concretely. Upon investigation existing prior documents on the putting recent data first, the mining of cryptocurrency has affected on the environmental contamination conflicting with carbon neutrality as increasement of the electric usage and electronic wastes. And POS method without the mining process appeared, but it had a demerit collapsing a decentralization and then we met turning point on appearing various environmental-friendly cryptocurrency. Finally the appearance of cryptocurrency using new renewable energy acted on the opportunity of the usage maximization of energy storage apparatus and the birth of national government intervention. Based on these results, we mention clearly that hereafter cryptocurrency will regress if not go abreast the value of currency as well as environmental approach.

• Journal of the Korean Applied Science and Technology
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• v.34 no.1
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• pp.1-11
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• 2017

Bio-oil has attracted considerable interest as one of the promising renewable energy resources because it can be used as a feedstock in conventional petroleum refineries for the production of high value chemicals or next-generation hydrocarbon fuels. Zeolites have been shown to effectively promote cracking reactions during pyrolysis resulting in highly deoxygenated and hydrocarbon-rich compounds and stable pyrolysis oil products. In this study, catalytic pyrolysis was applied to upgrade bio-oil from yellow poplar and then fuel characteristics of upgraded bio-oil was investigated. Yellow Poplar(500 g) which ground 0.3~1.4 mm was processed into bio-oil by catalytic pyrolysis for 1.64 seconds at $465^{\circ}C$ with Control, Blaccoal, Whitecoal, ZeoliteY and ZSM-5. Under the catalyst conditions, bio-oil productions decreased from 54.0%(Control) to 51.4 ~ 53.5%, except 56.2%(Blackcoal). HHV(High heating value) of upgraded bio-oil was more lower than crude bio-oil while the water content increased from 37.4% to 37.4 ~ 45.2%. But the other properties were improved significantly. Under the upgrading conditions, ash and TAN(Total Acid Number) is decrease and particularly important as transportation fuel, the viscosity of bio-oil decreased from 6,933 cP(Control) to 2,578 ~ 4,627 cP. In addition, ZeoliteY was most effective on producing aromatic hydrocarbons and decreasing of from the catalytic pyrolysis.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.4
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• pp.333-340
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• 2010

Wastewater treatment using algal communities and biodiesel production from wastewater-cultivated algal biomass is a promising green growth technology. In literature, there are many studies providing information on algal species producing high content of lipid. However, very little is known about adaptability and wastewater treatability of such high-lipid algal species. In this study, we attempted to characterize algal biomass production and wastewater treatability of high-lipid algal species under municipal wastewater condition. For this, four known high-lipid algal strains including Chlorella vulgaris AG 10032, Ankistrodesmus gracilis SAG 278-2, Scenedesmus quadricauda, and Botryococcus braunii UTEX 572 were individually inoculated into municipal wastewater where its indigenuous algal populations were removed prior to the inoculation, and the algae-inoculated wastewater was incubated in the presence of light source (80${\mu}E$) for 9 days in laboratory batch reactors. During the incubations, algal biomass production (dry weight) and the removals of dissolved organics (COD), nitrogen and phosphorous were measured in laboratory batch reactors. According to algal growth results, C. vulgaris, A. gracilis and S. quadricauda exhibited faster growth than indigenuous wastewater algal populations while B. braunii did not. The wastewater-growing strains exhibited efficient removals of total-N, ${NH_4}^+$-N, Total-P and ${PO_4}^{3-}$-P which satisfy the Korea water quality standards for effluent from municipal wastewater treatment plants. A. gracilis and S. quadricauda exhibited efficient and stable treatability of COD but C. vulgaris showed unstable treatability. Taken together with the results, A. gracilis and S. quadricauda were found to be suitable species for biomass production and wastewater treatment under municipal wastewater condition.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.