• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1181-1188
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• 2021

When abnormal events occur in a nuclear power plant, operators must conduct appropriate abnormal operating procedures. It is burdensome though for operators to choose the appropriate procedure considering the numerous main plant parameters and hundreds of alarms that should be judged in a short time. Recently, various research has applied deep-learning algorithms to support this problem by classifying each abnormal condition with high accuracy. Most of these models are trained with simulator data because of a lack of plant data for abnormal states, and as such, developed models may not have tolerance for plant data in actual situations. In this study, two approaches are investigated for a deep-learning model trained with simulator data to overcome the performance degradation caused by noise in actual plant data. First, a preprocessing method using several filters was employed to smooth the test data noise, and second, a data augmentation method was applied to increase the acceptability of the untrained data. Results of this study confirm that the combination of these two approaches can enable high model performance even in the presence of noisy data as in real plants.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1201-1204
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• 2004

This paper suggests a DCS (Distributed Control System) model for steam temperature system of the thermal power plant. The model calculated within sectional range is linear. In order to calculate mathematical models, the system is partitioned into two or three sectors according to its thermal conditions, that is, saturated water/steam and superheating state. It is divided into three sections; water supply, steam generation and steam heating loop. The steam heating loop is called 'superheater' or steam temperature system. Water spray supply is the control input. A first order linear model is extracted. For linear approach, sectional linearization is achieved. Modeling methodology is a decomposition-synthetic technique. Superheater is composed of several tube-blocks. For this block, linear input-output model is to be calculated. Each tiny model has its transfer function. By expanding these block models to total system, synthetic DCS linear models are derived. Control instrument include/exclude models are also considered. The resultant models include thermal combustion conditions, and applicable to practical plant engineering field.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.3
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• pp.409-413
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• 2016

The 500 MW rated steam turbine model in coal fired power plant is developed to be used for validation and verification of controller rather than for the education of operator. The valve, steam turbine, reheater and generator are modeled and integrated into the simulator. And the data from the plant heat balance diagram are used for estimation of the model parameters together with actual operating data. It is found that the outputs of model such as pressure, temperature and speed are similar to the operating ones. So, it is expected that the developed model will play a very big role in controller development.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.485-485
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• 2000

A design method of model following control system using neural networks is proposed. An unknown nonlinear single-input single-output plant is identified using a multilayer neural networks. A linear controller is designed fer the linear approximation model obtained by linearinzing the identification model. The identification model is also used as a plant emulator to obtain the prediction error. Deficient servo performance due to controlling nonlinear plant with only linear controller is mended by adjusting the linear controller output using the prediction output and the parameters of the identification model. An optimal preview controller is adopted as the linear controller by reason of having good servo performance lowering the peak of control input. Validity of proposed method is illustrated through a numerical simulation.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.37-38
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• 2018

BIM(Building Information Modeling) in the architecture, VDC(Virtual Design and Construction) defined CIFE(Center for Integrated Facility Engineering) of Stanford university in USA, and Data-driven design definition issued by TECDOC-1284 of IAEA are doing data-level design generated by 3D CAD technology, integrating and managing related information based on the 3D model, and Using 3D models effectively during nuclear power plant life cycle. 3D model of domestic nuclear power industry is using interference review between design fields, 4D system linked 3D construction model and schedule activity, but the 3D model generated in the design phase is effectively not utilized during the construction, operation, decommissioning. therefore, This study is aimed to suggest 3D model LOD(Level of Detail) advancement method through the analysis of existing literature, 2D drawings, and 3D models throughout nuclear power plant lifecycle.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.84-87
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• 2002

This paper presents an adaptive PID control scheme with fuzzy model for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model was used to estimate the error of control input, and the parameter of PID controller was adapted from the error The parameter of TSK fuzzy model was also adapted to plant by comparing the activity output of plant and model output. PID controller which was adapted the uncertainty of nonlinear plant and the change of parameter can be designed by using the presented method. The usefullness of algorithm which was proposed by the simulation of several nonlinear system was also certificated.

• Coupled systems mechanics
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• v.4 no.2
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• pp.157-172
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• 2015

Control of potable water treatment plant (PWTP) is nowadays based on experience. The aim of this article is to show that model based control of treatment process is more efficient than process operation based on experience. Stimela environment is used for modeling of processes of potable water treatment. Application of the model was conducted on PWTP "Crkvice" in Zenica (BiH). This plant has used conventional rapid sand filters. By effective application of the model it is determined the optimal filter run time for different input turbidity of raw water. This results in the possibility of reducing the consumption of backwashing water, lower costs for its pumping and reducing the amount of coagulants. In the existing practice, based on experience, these benefits are not used.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.42 no.5
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• pp.494-502
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• 2009

A quantitative model was developed in order to estimate fishery production damage due to anthropogenically induced environmental changes. The model is described in the following equation, $Y_D=\frac{{\phi}_D}{{\phi}_G}[Y_0{\cdot}(t_p-t_0)-\frac{Y_0}{{\phi}_G}(1-e^{-{\phi}_G(t_p-t_0)})]$, where, $Y_D$ is annual amount of fishery production by nuclear power plant. ${\varphi}$ D and ${\varphi}$ G are instantaneous decreasing coefficient of fishery production by nuclear power plant and instantaneous decreasing coefficient of gross fishery production, respectively. $Y_0$ is annual mean fishery production without damages. $t_p$ is the present time, and $t_0$ is the starting time of damages. The model was applied to fishing grounds near a nuclear power plant on the east coast of Korea. Since fishery production damages have become bigger with increasing emission of thermal effluents from generators activities in the power plant, this factor has also been considered as, $\delta_{D_i}=\delta_D\({\sum}\limits_{i=0}^{n}\;W_i/W_T\)$, where, $\delta_{Di}$ is the cumulative damage rate in fishery production from generators, $\delta_D$ is the total cumulative damage rate in fishery production, $W_i$ is the emission amount of thermal effluents by generator i, and n is the number of generators in the nuclear power plant. This model can be used to conduct initial estimates of fishery production damages, before more detailed assessments are undertaken.

• Food Science of Animal Resources
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• v.35 no.5
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• pp.630-637
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• 2015

In the dairy industry, natural plant-based powders are widely used to develop flavor and functionality. However, most of these ingredients are water-insoluble; therefore, emulsification is essential. In this study, the efficacy of high pressure homogenization (HPH) on natural plant (chocolate or vanilla)-based model emulsions was investigated. The particle size, electrical conductivity, Brix, pH, and color were analyzed after HPH. HPH significantly decreased the particle size of chocolate-based emulsions as a function of elevated pressures (20-100 MPa). HPH decreased the mean particle size of chocolate-based emulsions from 29.01 μm to 5.12 μm, and that of vanilla-based emulsions from 4.18 μm to 2.44 μm. Electrical conductivity increased as a function of the elevated pressures after HPH, for both chocolate- and vanilla-based model emulsions. HPH at 100 MPa increased the electrical conductivity of chocolate-based model emulsions from 0.570 S/m to 0.680 S/m, and that of vanilla-based model emulsions from 0.573 S/m to 0.601 S/m. Increased electrical conductivity would be attributed to colloidal phase modification and dispersion of oil globules. Brix of both chocolate- and vanilla-based model emulsions gradually increased as a function of the HPH pressure. Thus, HPH increased the solubility of plant-based powders by decreasing the particle size. This study demonstrated the potential use of HPH for enhancing the emulsification process and stability of the natural plant powders for applications with dairy products.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.5
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• pp.1017-1022
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• 2011

This paper describes the improvement of the calculation by using $6{\sigma}$ method on steam turbine simulator in a nuclear power plant. The simulator is essential to not only verification and validation of control logic but also making sure of control constants in upgrading the long time used control system into the new one. And the dynamic model is a key point in that simulator. The model used during the retrofit period of the turbine controller in Kori Nuclear Power Plant makes difference in calculating generator output and control valve positions. That is because such operating data as the main steam pressure, the main steam temperature and control valve positions of Yongkwang #3 are different from those of Kori #4. Therefore, the model parameters must be tuned by using actual operating data for the high fidelity of simulator in calculating the dynamic characteristic of the model. This paper describes that the $6{\sigma}$ method is used in improvement of precision of generator output calculation in the steam turbine model of the simulator.