• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.225-231
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• 2002

POSCO $\#2$ Stainless steel making plant produces more than 600 thousand ton per year with a variety of products consisting of austenite and ferrite stainless steel to meet custrmers' needs since 1996. The plant has four different major processes, that are, EAF-AOD-VOD-CC to finally produce semi-product called as slab. In this study, we importantly took AOD process into consideration due to its roles such as to check and verify the final qualities through sampling inspection. But the lead-time from sampling to its verification takes five to ten minutes causing produrtivity loss as muck as the lead-time as a result. Of all indices for quality and process control the plant has, carbon ingredient in liquid type of steel is the most important since it affects in a great way to the characteristics of steel, if any problem. customers not satisfied with quality could issue a claim; therefore there is no way hut to guarantee it before delivery. in this study, to reasonably reduce lead-time ran save a cycle time and finally improve our productivity from a state-or-art alternative just such as applying statistical model based on multi-regression analysis into the A.O.D line by analyzing the statistical and technical relationship between carbon and the relevant some vital independent variables. In consequence, the model with R-square $87\%$ allowed the plant to predict, abbreviating the process in relations to sampling to verification. approximately the value of [C] so that operators could run the process line with reliability on data automatically calculated instead of actual inspection. In the future, we are going to do the best to share this type of methodology with other processes, if possible, to apply into them.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.6
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• pp.463-471
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• 2020

A 1,000 ㎥/d DAF(dissolved air flotation) pilot plant was installed to evaluate the performance of the floating process using the Nakdong River. Efficiency of various DAF operations under different conditions, such as hydraulic loading rate, coagulant concentration was evaluated in the current research. The operation conditions were evaluated, based on the removal or turbidity, TOC(total organic carbon), THMFP(trihalomethane formation potential), Mn(manganese), and Al(aluminum). Also, particle size analysis of treated water by DAF was performed to examine the characteristics of particles existing in the treated water. The turbidity removal was higher than 90%, and it could be operated at 0.5 NTU or less, which is suitable for the drinking water quality standard. Turbidity, TOC, and THMFP resulted in stable water quality when replacing the coagulant from alum to PAC(poly aluminum chloride). A 100% removal of Chl-a was recorded during the summer period of the DAF operations. Mn removal was not as effective as where the removal did not satisfy the water quality standards for the majority of the operation period. Hydraulic loading of 10 m/h, and coagulant concentrations of 40 mg/L was determined to be the optimal operating conditions for turbidity and TOC removal. When the coagulant concentration increases, the Al concentration of the DAF treated water also increases, so coagulant injection control is required according to the raw water quality. Particle size distribution results indicated that particles larger than 25 ㎛ showed higher removal rates than smaller particles. The total particel count in the treated water was 2,214.7 counts/ml under the operation conditions of 10 m/h of hydraulic loading rate and coagulant concentrations of 60 mg/L.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.4
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• pp.239-250
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• 2020

In this study, effects of five raw water quality parameters (turbidity, odor compounds caused by algae, filter clogging caused by algae, pH increase caused by algae, and organic matter) on improvements and operations costs of typical water treatment plant (WTP) were estimated. The raw water quality parameters were assumed the worst possible conditions based on the past data and costs were subsequently estimated. Results showed that new water treatment facilities were needed, such as a selective intake system, an advanced water treatment processes, a dual media filter, a carbonation facility, and a re-chlorination facility depending on water quality. Furthermore, changes needed to be made in WTP operations, such as adding powered activated carbon, increasing the injection of chlorine, adding coagulation aid, increasing the discharge of backwashed water, and increasing the operation time of dewatering facilities. Such findings showed that to reliably produce high-quality tap water and reduce water treatment costs, continuous improvements to the quality of water sources are needed.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.135-150
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• 2012

This paper presents a portfolio model for a long-term power generation mix problem. The proposed portfolio model evaluates generation mix by considering the tradeoffs between the expected cost for power generation and its variability. Unlike conventional portfolio models measuring variance, we introduce Conditional Value-at-Risk (CVaR) in designing the variability with aims to considering events that are enormously expensive but are rare such as nuclear power plant accidents. Further, we consider uncertainties associated with future electricity demand, fuel prices and their correlations, and capital costs for power plant investments. To obtain an objective generation by each energy source, we employ the sample average approximation method that approximates the stochastic objective function by taking the average of large sample values so that provides asymptotic convergence of optimal solutions. In addition, the method includes Monte Carlo simulation techniques in generating random samples from multivariate distributions. Applications of the proposed model and method are demonstrated through a case study of an electricity industry with nuclear, coal, oil (OCGT), and LNG (CCGT) in South Korea.

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.896-904
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• 2017

Several Generation IV nuclear reactor concepts have goals for optimizing investment recovery through phased introduction of multiple units on a common site with shared facilities and/or reconfigurable energy conversion systems. Additionally, small modular reactors are suitable for remote deployment to support highly localized microgrids in isolated, underdeveloped regions. The long-term economic viability of these advanced reactor plants depends on significant reductions in plant operations and maintenance costs. To accomplish these goals, intelligent control and diagnostic capabilities are needed to provide nearly autonomous operations with anticipatory maintenance. A nearly autonomous control system should enable automatic operation of a nuclear power plant while adapting to equipment faults and other upsets. It needs to have many intelligent capabilities, such as diagnosis, simulation, analysis, planning, reconfigurability, self-validation, and decision. These capabilities have been the subject of research for many years, but an autonomous control system for nuclear power generation remains as-yet an unrealized goal. This article describes a functional framework for intelligent, autonomous control that can facilitate the integration of control, diagnostic, and decision-making capabilities to satisfy the operational and performance goals of power plants based on multimodular advanced reactors.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.9
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• pp.413-422
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• 2002

In a power plant, disturbance detection and diagnosis are massive and complex problems. Once a disturbance occurs, it can be either persistent, self cleared, cleared by the automatic controllers or propagated into another disturbance until it subsides in a new equilibrium or a stable state. In addition to the Physical complexity of the power plant structure itself, these dynamic behaviors of the disturbances further complicate the fault monitoring and diagnosis tasks. A data structure called a disturbance interrelation analysis graph(DIAG) is proposed in this paper, trying to capture, organize and better utilize the vast and interrelated knowledge required for power plant disturbance detection and diagnosis. The DIAG is a multi-layer directed AND/OR graph composed of 4 layers. Each layer includes vertices that represent components, disturbances, conditions and sensors respectively With the implementation of the DIAG, disturbances and their relationships can be conveniently represented and traced with modularized operations. All the cascaded disturbances following an initial triggering disturbance can be diagnosed in the context of that initial disturbance instead of diagnosing each of them as an individual disturbance. DIAG is applied to a typical cooling water system of a thermal power plant and its effectiveness is also demonstrated.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.7
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• pp.401-406
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• 2003

Electric power industries in several countries are currently undergoing major changes, mainly represented by the privatizations of the power plants and distribution systems. Reliable operations of the power plants directly contribute to the revenue increases of the generation companies in such competitive environments. Strategic optimizations should be performed between the levels of the reliabilities to be maintained and the various preventive maintenance costs, which require the accurate estimations of the power plant reliabilities. However, accurate estimations of the power plant reliabilities are often limited by the lack of accurate power plant failure data. A power plant is not supposed to be failed that often. And if it fails, its impact upon the power system stability is quite substantial in most cases, setting aside the significant revenue losses and lowered company images. Reliability assessment is also important for Independent System Operators(ISO) or Market Operators to properly assess the level of needed compensations for the installed capacity based on the availability of the generation plants. In this paper, we present a power plant reliability estimation technique that can be applied when the failure data is insufficient. Median rank and Weibull distribution are used to accommodate such insufficiency. The Median rank is utilized to derive the cumulative failure probability for each ordered failure. The Weibull distribution is used because of its flexibility of accommodating several different distribution types based on the shape parameter values. The proposed method is applied to small size failure data and its application potential is demonstrated.

• Korean Journal of Computational Design and Engineering
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• v.20 no.4
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• pp.420-430
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• 2015

Operator Training Simulators (OTSs) provide macroscopic training environment for plant operation. They are equipped with simulation systems for the emulation of remote monitoring and controlling operations. OTSs typically provide 2D block diagram-based graphic user interface (GUI) and connect to process simulation tools. However, process modeling for OTSs is a difficult task. Furthermore, conventional OTSs do not provide real plant field information since they are based on 2D human machine interface (HMI). In order to overcome the limitation of OTSs, we propose a new type of plant training system. This system has the capability required for collaborative training between operators and field workers. In addition, the system provides 3D virtual training environment such that field workers feel like they are in real plant site. For this, we designed system architecture and developed essential functions for the system. For the verification of the proposed system design, we implemented a prototype training system and performed experiments of collaborative training between one operator and two field workers with the prototype system.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.4
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• pp.180-185
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• 2012

Tidal power is one of new and renewable energy sources. The seawater is stored inside a tidal embankment built at the mouth of a river or bay, where tides ebb and flow. The water turbine-generators produce power by exploiting the gap in the water level between the water outside and inside the embankment. Tidal power plant is a large plant that is installed on the sea. And then, the facility's operations and a separate control system for monitoring and maintenance is required. However, this plant predictive control of building systems and technologies have been avoided the transfer of technology from advanced global companies. Accordingly, the control system for core technology development and localization is urgently needed. This paper presents modeling and simulation using by PSS/E about generator, governor, exciter, and power system stabilizer for control system in Sihwa tidal power plant to improve the efficiency and develope of core technology. And the dynamic characteristics of governor and exciter were analyzed.

• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2262-2273
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• 2020

Digital twin technology can provide significant value for the prognostics and health management (PHM) of critical plant components by improving insight into system design and operating conditions. Digital twinning of systems can be utilized for anomaly detection, diagnosis and the estimation of the system's remaining useful life in order to optimize operations and maintenance processes in a nuclear plant. In this regard, a conceptual framework for the application of digital twin technology for the prognosis of Control Element Drive Mechanism (CEDM), and a data-driven approach to anomaly detection using coil current profile are presented in this study. Health management of plant components can capitalize on the data and signals that are already recorded as part of the monitored parameters of the plant's instrumentation and control systems. This work is focused on the development of machine learning algorithm and workflow for the analysis of the CEDM using the recorded coil current data. The workflow involves features extraction from the coil-current profile and consequently performing both clustering and classification algorithms. This approach provides an opportunity for health monitoring in support of condition-based predictive maintenance optimization and in the development of the CEDM digital twin model for improved plant safety and availability.