• Environmental Engineering Research
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• v.24 no.2
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• pp.181-190
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• 2019

Constructed wetlands are improvised man-made systems, designed for adopting the principle of natural wetlands for purifying wastewater - the elixir of life. They are used widely as a cost-effective and energy-efficient solution for treating greywater generated from different tertiary treatment sources. It provides an elaborate platform for research activities in an attempt to recycle earth's natural resources. Among the several organic impurities removal mechanisms existing in constructed wetland systems, the earth's active microbial population plays a vital role. This review deals with the recent advancements in constructed wetland systems from a microbiological perspective to (effect/ devise/ formulate) chemical and physical treatment for water impurities. It focuses on microbial diversity studies in constructed wetlands, influence of wetland media on microbial diversity and wetland performance, role of specific microbes in water reuse, removal of trace elements, some heavy metals and antibiotics in constructed wetlands. The impurities removal processes in constructed wetlands is achieved by combined interactive systems such as selected plant species, nature of substrate used for microbial diversity and several biogeochemical effected reaction cycles in wetland systems. Therefore, the correlation studies that have been conducted by earlier researchers in microbial diversity in wetlands are addressed herewith.

• Nuclear Engineering and Technology
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• v.51 no.6
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• pp.1554-1560
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• 2019

Development of digital main control rooms (MCRs) has greatly changed operating environments by altering operator tasks, and thus the unique characteristics of digital MCRs should be considered in terms of human reliability analysis. Digital MCR tasks can be divided into primary tasks that directly supply control input to the plant equipment, and secondary tasks that include interface management conducted via soft controls (SCs). Operator performance regarding these secondary tasks must be evaluated since such tasks did not exist in previous analog systems. In this paper, we analyzed SC-related tasks based on simulation data, and classified the error modes of the SCs following analysis of all operational tasks. Then, we defined the factors to be considered in human reliability analysis methods regarding the SCs; such factors are mainly related to interface management and computerized operator support systems. As these support systems function to reduce the number of secondary tasks required for SC, we conducted an assessment to evaluate the efficiency of one such support system. The results of this study may facilitate the development of training programs as well as help to optimize interface design to better reflect the interface management task characteristics of digitalized MCRs.

• Nuclear Engineering and Technology
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• v.51 no.7
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• pp.1765-1775
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• 2019

Multiple spurious operations (MSOs) mean multiple fire induced circuit faults causing an undesired operation of one or more systems or components. The Nuclear Energy Institute (NEI) of the United States published NEI 00-01 as guidelines for solving MSOs. And this guideline includes MSO scenarios of pressurized water reactor (PWR) and boiling water reactor (BWR). Nuclear power plant operators in U.S. analyzed MSOs under MSO scenarios included in NEI 00-01 and operators of PWRs in Korea also analyzed MSOs under the scenarios of NEI 00-01. As there are no pressurized heavy water reactors (PHWRs) in the United States, MSO scenarios of PHWRs are not included in the NEI 00-01 and any feasible scenarios have not been developed. This paper developed MSO scenarios which can be applied to PHWRs by reviewing the 63 MSO scenarios included in NEI 00-01. This study found that seven scenarios out of the 63 MSO scenarios can be applied and three more scenarios need to be developed.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.24-30
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• 2021

Heat is generated during the synthesis and mixing process of chemical compounds due to a change in activation energy during the reaction. A runaway reaction occurs when sufficient heat is not removed during the heat control process within a reactor, rapidly increasing the temperature, reaction speed, and rate of heat generation inside the reactor. A risk assessment was executed using an RC-1 (Reaction Calorimeter) during Friedel-Crafts acylation. Friedel-Crafts acylation runs the risk of rapid heat generation during Active Pharmaceutical Ingredient (API) manufacturing; it was used to confirm the risk of a runaway reaction at each synthesis stage and during the mixing process. This study used experimental data to develop a safety efficiency improvement plan to control the risks of runaway and other exothermic reactions, which was implemented at the production site of a chemical plant.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2859-2870
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• 2022

Because nuclear power plants (NPPs) are safety-critical infrastructure, it is essential to increase their safety and minimize risk. To reduce human error and support decision-making by operators, several artificial-intelligence-based diagnosis methods have been proposed. However, because of the nature of data-driven methods, conventional artificial intelligence requires large amount of measurement values to train and achieve enough diagnosis resolution. We propose a graph neural network (GNN) based accident diagnosis algorithm to achieve high diagnosis resolution with limited measurements. The proposed algorithm is trained with both the knowledge about physical correlation between components and measurement values. To validate the proposed methodology has a sufficiently high diagnostic resolution with limited measurement values, the diagnosis of multiple accidents was performed with limited measurement values and also, the performance was compared with convolution neural network (CNN). In case of the experiment that requires low diagnostic resolution, both CNN and GNN showed good results. However, for the tests that requires high diagnostic resolution, GNN greatly outperformed the CNN.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1221-1229
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• 2022

In this work, the guided wave de-bonding area-detecting technique was studied for application to containment liner plates in nuclear power plant areas. To apply this technique, an appropriate Lamb wave mode, symmetric and longitudinal dominance, was verified by the frequency shifting technique. The S0 2.7 MHz mm Lamb wave mode was chosen to realize quantitative experimental results and their visualization. Results of the bulk wave, longitudinal wave mode, and comparison experiments indicate that the wave mode was able to distinguish between the de-bonded and bonded areas. Similar to the bulk wave cases, the bonded region could be distinguished from the de-bonded region using the Lamb wave approach. The Lamb wave technique results showed significant correlation to the de-bonding area. As the de-bonding area increased, the Lamb wave energy attenuation effect decreased, which was a prominent factor in the realization of quantitative tomographic visualization. The feasibility of tomographic visualization was studied via the application of Lamb waves. The reconstruction algorithm for the probabilistic inspection of damage (RAPID) technique was applied to the containment liner plate to verify and visualize the de-bonding condition. The results obtained using the tomography image indicated that the Lamb wave-based RAPID algorithm was capable of delineating debonding areas.

• Journal of Environmental Science International
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• v.31 no.7
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• pp.593-607
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• 2022

Climate change has recently become a serious global issue, and carbon emissions and energy consumption are increasing, particularly in cities where economic activities and populations are concentrated. Accordingly, various countries worldwide are promoting the Green New Deal and promoting urban-centered climate change response policies with the aim of carbon neutrality. In Korea, following the "smart green city" project that creates a city where humans and the environment coexist, a similar "carbon neutral green city" policy is set to be introduced. Therefore, in this study, implications and directions for the sustainable introduction of the carbon neutral green city policy will be derived by comparing and analyzing the State and Tribal Assistance Grants of the U.S. bipartisan infrastructure law and the smart green city of the Korean new deal.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.3944-3951
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• 2021

Several reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS) technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, in presence of large number of FS techniques, are very tedious and time consuming task. In this study to tackle the issue of dependency of estimation accuracy on the selected FS technique, a methodology based on the heterogeneous ensemble is proposed. The performance of the major learning algorithms of neural network (i.e. the FFNN-BR, the FFNN-LM) in combination with the diverse FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) and different combination techniques of the heterogeneous ensemble (i.e. the Min, the Median, the Arithmetic mean, and the Geometric mean) are considered. The target parameters/transients of Bushehr nuclear power plant (BNPP) are examined as the case study. The results show that the Min combination technique gives the more accurate estimation. Therefore, if the number of FS techniques is m and the number of learning algorithms is n, by the heterogeneous ensemble, the search space for acceptable estimation of the target parameters may be reduced from n × m to n × 1. The proposed methodology gives a simple and practical approach for more reliable and more accurate estimation of the target parameters compared to the methods such as the use of synthetic dataset or trial and error methods.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4179-4188
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• 2021

This study identifies efficient earthquake intensity measures (IMs) for seismic performances and fragility evaluations of the reactor containment building (RCB) in the advanced power reactor 1400 (APR1400) nuclear power plant (NPP). The computational model of RCB is constructed using the beam-truss model (BTM) for nonlinear analyses. A total of 90 ground motion records and 20 different IMs are employed for numerical analyses. A series of nonlinear time-history analyses are performed to monitor maximum floor displacements and accelerations of RCB. Then, probabilistic seismic demand models of RCB are developed for each IM. Statistical parameters including coefficient of determination (R²), dispersion (i.e. standard deviation), practicality, and proficiency are calculated to recognize strongly correlated IMs with the seismic performance of the NPP structure. The numerical results show that the optimal IMs are spectral acceleration, spectral velocity, spectral displacement at the fundamental period, acceleration spectrum intensity, effective peak acceleration, peak ground acceleration, A95, and sustained maximum acceleration. Moreover, weakly related IMs to the seismic performance of RCB are peak ground displacement, root-mean-square of displacement, specific energy density, root-mean-square of velocity, peak ground velocity, Housner intensity, velocity spectrum intensity, and sustained maximum velocity. Finally, a set of fragility curves of RCB are developed for optimal IMs.