• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3543-3548
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• 2023

Shutdown chemistry evolution is performed in nuclear power plants at each refueling outage (RFO) to establish safe conditions to open system and minimize inventory of corrosion products in the reactor coolant system (RCS). After hydrogen peroxide is added to RCS during shutdown chemistry evolution, corrosion products are released and are removed by filters and ion exchange resins in the chemical volume control system (CVCS). Shutdown chemistry evolution including RCS clean-up time to remove released corrosion products impacts the critical path schedule during RFOs. The estimation of clean-up time prior to RFO can provide more reliable actions for RCS clean-up operations and transients to operators during shutdown chemistry. Electric Power Research Institute (EPRI) shutdown calculator (SDC) enables to provide clean-up time by Co-58 peak activity through operational data from nuclear power plants (NPPs). In this study, we have investigated the results of EPRI SDC by shutdown chemistry data of Co-58 activity using NPP data from previous cycles and modeled the estimated clean-up time by EPRI SDC using average Co-58 activity of the NPP. We selected two RFO data from the NPP to evaluate EPRI SDC results using the purification time to reach to 1.3 mCi/cc of Co-58 after hydrogen peroxide addition. Comparing two RFO data, the similar purification time between actual and computed data by EPRI SDC, 0.92 and 1.74 h respectively, was observed with the deviation of 3.7-7.2%. As the modeling the estimated clean-up time, we calculated average Co-58 peak concentration for normal cycles after cycle 10 and applied two-sigma (2σ, 95.4%) for predicted Co-58 peak concentration as upper and lower values compared to the average data. For the verification of modeling, shutdown chemistry data for RFO 17 was used. Predicted RCS clean-up time with lower and upper values was between 21.05 and 27.58 h, and clean-up time for RFO 17 was 24.75 h, within the predicted time band. Therefore, our calculated modeling band was validated. This approach can be identified that the advantage of the modeling for clean-up time with SDC is that the primary prediction of shutdown chemistry plans can be performed more reliably during shutdown chemistry. This research can contribute to improving the efficiency and safety of shutdown chemistry evolution in nuclear power plants.

• Journal of Food Hygiene and Safety
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• v.20 no.1
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• pp.22-35
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• 2005

A survey was conducted to investigate the perception of food sanitation officers toward the Genetically Modified Foods. They were mainly from Regional Agencies of KFDA, City/Province office, and National quarantine station. Some of them were professors of university and researchers of research institute. Most of respondents had experiences of hearing or reading GM foods (over $95\%$) and over $90\%$ of respondents much needed the label of GM foods. Although some of officers of city/province office and national quarantine station showed less knowledge than other respondent groups, most of respondents had basic knowledge about biology. The frequency of respondents worked over 20 years and worked in the general administration was higher than that of other groups in the question of unsafe of GM foods. The answer frequency of careless treatment of foods was highest in the question of risk factor, and the frequency of GM foods was lowest ($4.4\%$). It was concluded that food sanitation officers had positive opinion about GM foods, but there were some differences in the knowledge among agencies. Therefore, it is necessary more educations and informations are needed for food sanitation officers.

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.355-380
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• 2004

I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in

• Korean Journal of Microbiology
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• v.49 no.4
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• pp.313-320
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• 2013

Biofilm formed on stainless and copper in water treatment plant was investigated for sixteen weeks. Biofilm reactor was specially designed for this study. It was similar to that of a real distribution pipe. Raw water, coagulated, settled, filtered and treated water were used in this study. The average number of heterotrophic bacteria counts was $1.6{\times}10^4CFU/ml$, $5.8{\times}10^3CFU/ml$, $1.8{\times}10^3CFU/ml$, $1.3{\times}10^2CFU/ml$, 1 CFU/ml, respectively. Density of biofilm bacteria formed on stainless and copper pipes in raw, coagulated and settled water increased above $2.9{\times}10^3CFU/cm^2$ within second weeks while more biofilm bacteria counts were found on the stainless pipe than on the copper pipe. In case of filtered water (free residue chlorine 0.44 mg/L), there was no significant difference in the number of biofilm bacteria on both pipes and biofilm bacteria below $18CFU/cm^2$ were detected on both pipe materials after fifth weeks. Biofilm bacteria were not detected on both pipe materials in treated water (free residue chlorine 0.88 mg/L). According to the results of DGGE analysis, Sphingomonadacae was a dominant species of biofilm bacteria formed on the stainless pipe while the copper pipe had Bradyrhizobiaceae and Sphingomonadaceae as dominant bands. In case of filtered water, a few bands (similar to Propionibacterium sp., Sphingomonas sp., Escherichia sp., and etc.) that have 16S rRNA sequences were detected in biofilm bacteria formed on both pipes after fifth weeks. Stainless pipe had higher species richness and diversity than the copper pipe.

• Journal of Radiation Protection and Research
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• v.39 no.1
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• pp.21-29
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• 2014

A new cold neutron triple-axis spectrometer (Cold-TAS) was recently constructed at the 30 MWth research reactor, HANARO. The spectrometer, which is composed of neutron optical components and radiation shield, required a redesign of the segmented monochromator shield due to the lack of adequate support of its weight. To shed some weight, lowering the height of the segmented shield was suggested while adding more radiation shield to the top cover of the monochromator chamber. To investigate the radiological effect of such change, we performed MCNPX simulations of a few different configurations of the Cold-TAS monochromator shield and obtained neutron and photon intensities at 5 reference points just outside the shield. Reducing the 35% of the height of the segmented shield and locating lead 10 cm from the bottom of the top cover made of polyethylene was shown to perform just as well as the original configuration as radiation shield excepting gamma flux at two points. Using gamma map by MCNPX, it was checked that is distribution of gamma. Increased flux had direction to the top and it had longer distance from top of segmented shield. However, because of reducing the 35% of the height, height of dissipated gamma was lower than original geometry. Reducing the 35% of the height of the segmented shield and locating lead 10cm from the bottom of the top cover was selected. After changing geometry, radiation dose was measured by TLD for confirming tester's safety at any condition. Neutron(0.21 ${\mu}Svhr^{-1}$) and gamma(3.69 ${\mu}Svhr^{-1}$) radiation dose were satisfied standard(6.25 ${\mu}Svhr^{-1}$).

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.180-185
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• 2003

The increase of traffic over a bridge has been emerged as one of the most severe problems in view of bridge maintenance, since the load effect caused by the vehicle passage over the bridge has brought out a long-term damage to bridge structure, and it is nearly impossible to maintain operational serviceability of bridge to user's satisfactory level without any concern on bridge maintenance at the phase of completion. Moreover, bridge maintenance operation should be performed by regular inspection over the bridge to prevent structural malfunction or unexpected accidents front breaking out by monitoring on cracks or deformations during service. Therefore, technical breakthrough related to this uninterested field of bridge maintenance leading the public to the turning point of recognition is desperately needed. This study has the aim of development on automated inspection system to lower surface of bridge superstructures to replace the conventional system of bridge inspection with the naked eye, where the monitoring staff is directly on board to refractive or other type of maintenance .vehicles, with which it is expected that we can solve the problems essentially where the results of inspection are varied to change with subjective manlier from monitoring staff, increase stabilities in safety during the inspection, and make contribution to construct data base by providing objective and quantitative data and materials through image processing method over data captured by cameras. By this system it is also expected that objective estimation over the right time of maintenance and reinforcement work will lead enormous decrease in maintenance cost.

• Korean Chemical Engineering Research
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• v.56 no.2
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• pp.169-175
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• 2018

This study deals with a process for the recovery of hydrogen isotopes from methane ($CQ_4$) and water ($Q_2O$) containing tritium in the nuclear fusion exhaust gas (Q is Hydrogen, Deuterium, Tritium). Steam Methane Reforming and Water Gas Shift reactions are used to convert $CQ_4$ and $Q_2O$ to $Q_2$ and the produced $Q_2$ is recovered by the subsequent Pd membrane. In this study, one circulation loop consisting of catalytic reactor, Pd membrane, and circulation pump was applied to recover H components from $CH_4$ and $H_2O$, one of $CQ_4$ and $Q_2O$. The conversion of $CH_4$ and $H_2O$ was measured by varying the catalytic reaction temperature and the circulating flow rate. $CH_4$ conversion was 99% or more at the catalytic reaction temperature of $650^{\circ}C$ and the circulating flow rate of 2.0 L/min. $H_2O$ conversion was 96% or more at the catalytic reaction temperature of $375^{\circ}C$ and the circulating flow rate of 1.8 L/min. In addition, the amount of $CQ_4$ generated by Korean Demonstration Fusion Power Plant (K-DEMO) in the future was predicted. Then, the treatment process for the $CQ_4$ was proposed and HAZOP (hazard and operability) analysis was conducted to identify the risk factors and operation problems of the process.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.