• Plant Journal
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• v.17 no.2
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• pp.42-47
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• 2021

In terms of district heating operation, efficient production and supply of heat by alleviating the peak load at a specific time require an application technology that can solve the inconvenience of the user and the difficulties of the supplier. In this study, a 78 ℃ class PCM heat storage tank suitable among the technologies that can solve these problems was manufactured and applied to the hot water supply facility for apartments in district heating users. As a result of the application of this system, it was confirmed that the supply temperature was constant to the user compared to the existing supply method. In addition, it was confirmed that the reduction of the peak load due to load equalization reduced the heat supply margin of 10% in the existing heat supply facility. And the construction cost of the new heat supply facility and the construction cost of heat users is decreased by 5% and 10%, respectively.

• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.54-63
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• 2021

Since the recent COVID-19 pandemic, countries have been strengthening trade protection for their security, and the importance of securing strategic materials, such as food, is drawing attention. In addition to the cultural aspects, the global preference for food produced in Korea is increasing because of the Korean Wave. Thus, the Korean food industry can be developed into a high-value-added export food industry. Currently, Korea has a low self-sufficiency rate for foodstuffs apart from rice. Korea also suffers from problems arising from population decline, aging, rapid climate change, and various animal and plant diseases. It is necessary to develop technologies that can overcome the production structures highly dependent on the outside world of food and foster them into export-type system industries. The global agricultural industry-related technologies are actively being modified via data accumulation, e.g., environmental data, production information, and distribution and consumption information in climate and production facilities, and by actively expanding the introduction of the latest information and communication technologies such as big data and artificial intelligence. However, long-term research and investment should precede the field of living organisms. Compared to other industries, it is necessary to overcome poor production and labor environment investment efficiency in the food industry with respect to the production cost, equipment postmanagement, development tailored to the eye level of field workers, and service models suitable for production facilities of various sizes. This paper discusses the flow of domestic and international technologies that form the core issues of the site centered on the 4th Industrial Revolution in the field of agriculture, livestock, and fisheries. It also explains the environmental awareness production technologies centered on sustainable intelligence platforms that link climate change responses, optimization of energy costs, and mass production for unmanned production, distribution, and consumption using the unstructured data obtained based on detection and growth measurement data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.35-42
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• 2022

When an accident occurs, the associated human activity is typically regarded as a "human error," or a temporal deviation. On the other hand, if the accident results in a serious loss or if it evokes a social issue, the person determined to be responsible may be punished with a "violation" of related laws or regulations. However, as Heinrich stated, it is neither appropriate nor reasonable in terms of probability theory and cognitive science to distinguish whether it is a "human error" or a "violation" with a criterion of resultant accident severity. Nonetheless, some in society get on the social climate to strengthen regulations on workers who have caused accidents, especially violations. This response can present a social issue due to the lack of systematic judgment procedure which distinguishes violations from human errors. The purpose of this study was to develop an objective and systematic procedure to assess whether workers' activities which induced industrial accidents should be categorized as violations rather than human errors. Various analysis techniques for the determination of violation procedure were investigated and compared using an analysis approach method. An appropriate technique was not found, however, for judging the culpability of intentional violations. As an alternative, this study developed the process of creating violations, based on cognitive procedure, as well as the criteria to determine and categorize an activity as a violation. In addition, the developed procedure was applied to cases of industrial accidents and nuclear power plant issues to test its practical applicability. The study demonstrated that the proposed model could be used to determine the existence of a violation even in the case of multiple workers who work simultaneously.

• Membrane Journal
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• v.32 no.2
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• pp.83-90
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• 2022

Wastewater from refineries and petroleum plant lead to severe environmental pollution. There are various existing processes applied for oily water treatment, but membrane-based technology is one of the most efficient methods. Polymeric membranes prepared from organic materials for the separation of oil in water often face chronic problem of membrane fouling. Inorganic membranes are considered to be more efficient due to longer lifetime than organic membranes. Zeolite membranes have better chemical stability and long-term recyclability. The presence of hydrophilicity enhances the water flux of membrane. Ceramic membranes prepared from zeolites are another efficient class of inorganic membranes applied for oil water separation. This review is focused on oily wastewater separation based on zeolite membrane which classified into two categories, i) neat zeolite and ii) zeolite composites with other materials.

• CELLMED
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• v.11 no.4
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• pp.18.1-18.8
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• 2021

Jawarishe Jalinoos (JJ) is an orally used formulation available in semisolid dosage form, prepared with powdered plant materials mixed in honey or sugar syrup. It has many admirable pharmacological effects and used in Unani medicine to treat various acute and chronic disorders since ancient times. The ICH Harmonised Tripartite Guideline stated that photostability testing should be an essential part of stability testing to confirm that light exposure does not result in an unacceptable change in drugs substance and finished products. To date, the effect of light on JJ is not studied, in this study photostability evaluation of JJ was carried out. The test sample was manufactured with genuine ingredients in the in-door pharmacy of the National Institute of Unani Medicine. JJ was packed in two transparent polyethylene terephthalate airtight containers. The first sample was analysed at zero-day and the second sample was placed in a stability chamber subjected to light challenge with an overall illumination of 1.2 million lux hours combined with near ultraviolet energy of 200-watt hours per square meter by using option 2, along with 30±2℃ temperature and relative humidity 70±5%. Analysis of both finished products showed no considerable changes in organoleptic characters. Less than 5% variation was observed in physicochemical parameters. HPTLC fingerprinting showed justifiable variation. Microbial load and specific counts were within the limit prescribed by WHO. As no unacceptable changes were noted in JJ subjecting to light challenge, it is concluded that JJ is a photostable Unani compound formulation.

• Journal of the Earthquake Engineering Society of Korea
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• v.26 no.1
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• pp.13-22
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• 2022

This study proposes a low-cycle fatigue life derived from measurement points on pipe elbows, which are components that are vulnerable to seismic load in the interface piping systems of nuclear power plants that use seismic isolation systems. In order to quantitatively define limit states regarding leakage, i.e., actual failure caused by low-cycle fatigue, in-plane cyclic loading tests were performed using a sine wave of constant amplitude. The test specimens consisted of SCH40 6-inch carbon steel pipe elbows and straight pipes, and an image processing method was used to measure the nonlinear behavior of the test specimens. The leakage lines caused by low-cycle fatigue and the low-cycle fatigue curves were compared and analyzed using the relationship between the relative deformation angles, which were measured based on each of the measurement points on the straight pipe, and the moment, which was measured at the center of the pipe elbow. Damage indices based on the combination of ductility and dissipation energy at each measurement point were used to quantitatively express the time at which leakage occurs due to through-wall cracking in the pipe elbow.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2387-2395
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• 2021

Seismic qualifications of electrical equipment, such as cabinet systems, have been emerging as the key area of nuclear power plants in Korea since the 2016 Gyeongju earthquake, including the high-frequency domain. In addition, electrical equipment was sensitive to the high-frequency ground motions during the past earthquake. Therefore, this paper presents the rocking behavior of the electrical cabinet system subjected to Reg. 1.60 and UHS. The high fidelity finite element (FE) model of the cabinet related to the shaking table test data was developed. In particular, the first two global modes of the cabinet from the experimental test were 16 Hz and 24 Hz, respectively. In addition, 30.05 Hz and 37.5 Hz were determined to be the first two local modes in the cabinet. The high fidelity FE model of the cabinet using the ABAQUS platform was extremely reconciled with shaking table tests. As a result, the dynamic properties of the cabinet were sensitive to electrical instruments, such as relays and switchboards, during the shaking table test. In addition, the amplification with respect to the vibration transfer function of the cabinet was observed on the third floor in the cabinet due to localized impact corresponding to the rocking phenomenon of the cabinet under Reg.1.60 and UHS. Overall, the rocking of the cabinet system can be caused by the low-frequency oscillations and higher peak horizontal acceleration.

• Nuclear Engineering and Technology
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• v.53 no.5
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• pp.1676-1685
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• 2021

We develop a new high-fidelity multiphysics model to simulate boron chemistry in the porous Chalk River Unidentified Deposit (CRUD) deposits. Heat transfer, capillary flow, solute transport, and chemical reactions are fully coupled. The evaporation of coolant in the deposits is included in governing equations modified by the volume-averaged assumption of wick boiling. The axial offset anomaly (AOA) of the Seabrook nuclear power plant is simulated. The new model reasonably predicts the distributions of temperature, pressure, velocity, volumetric boiling heat density, and chemical concentrations. In the thicker CRUD regions, 60% of the total heat is removed by evaporative heat transfer, causing boron species accumulation. The new model successfully shows the quantitative effect of coolant evaporation on the local distributions of boron. The total amount of boron in the CRUD layer increases by a factor of 1.21 when an evaporation-driven increase of soluble and precipitated boron concentrations is reflected. In addition, the concentrations of B(OH)₃ and LiBO₂ are estimated according to various conditions such as different CRUD thickness and porosity. At the end of the cycle in the AOA case, the total mass of boron incorporated in CRUD deposits of a reference single fuel rod is estimated to be about 0.5 mg.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.