• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.156-159
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• 1996

An adaptive predictive control for steam-reforming plant which consist of a steam-gas reformer and a waste heat steam-boiler was studied by using MIMO bilinear model. The simulation experiments of the process identification were performed by using linear and bilinear models. From the simulation results it was found that the bilinear model represented the dynamic behavior of a steam-reforming plant very well. ARMA model was used in the process identification and the adaptive predictive control. To verify the performance and effectiveness of the adaptive predictive controller proposed in this study the simulation results of steam-reforming plant control based on bilinear model were compared to those of linear model. The simulation results showed that the adaptive predictive controller based on bilinear model provides better performance than those of linear model.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.203-203
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• 2000

The P&S(Power Plant Simulation System) is a powerful simulation software system for the dynamic behavior of power plants. The P&S module libraries provide plant models with higher flexibility of dynamic simulations for process and control designs. The P&S software was effectively available for PCS based on Linux and modem workstations based on Unix. The P&S was applied for simulating the dynamic behaviors of the SOVR(Supercritical Once-Through Variable Pressure Reheater) according to the operations such as stan-up, shutdown, load following, load change and trip in order to obtain an optimal operation procedure for Unit 5/6 of Taeahn fossil power plant consisted of SOVRs and steam turbines.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2285-2290
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• 2008

Even though efficiency of coal-fired power plant is proportional to operating temperature, increasement of operating temperature is limited by a technological level of each power plant component. It is an alternative plan to increase operating pressure up to ultra super critical point for efficiency enhancement. It is difficult to control process of power plant in ultra super critical point because that point has highly nonlinear characteristics. In this paper, new control logic, Unit Response Optimizer Controller(URO Controller) which is based on Fuzzy logic and Model Predictive Controller, is introduced for better performance. Then its performance is tested and analyzed with design guideline.

• 한국신재생에너지학회:학술대회논문집
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• 2006.06a
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• pp.576-579
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• 2006

This paper describes characteristics and procedure of the basic design of large scale solar thermal power plant system. The evaluation is based on the operating data of CESA-I, solar central receiver plant. In order to evaluate the solar irradiation on the receiver, it is necessary to calculate the amount of thermal energy consumption at steam turbine and storage system in the STPPS. Especially, it is need to take into account of the storage and operating time to design a plant efficiently. In addition, basic design is performed for the CESA-I using the software tool of THERMOFLEX program. Based on the results, It is at lowed to use the program to investigate detail performance of each units of the STPPS by varying the operating conditions.

• Journal of the Korean Society of Propulsion Engineers
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• v.26 no.6
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• pp.28-33
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• 2022

In this study, the curing characteristics of commonly used Hydroxyl terminated polybutadiene(HTPB)-based solid propellant according to the curing temperature and Equivalent ratio change were investigated. In addition, the effect of curing reaction according to their ratio and content in the Triphenyl bismuth(TPB), Maleic anhydride(MA) and Magnesium oxide(MgO) catalyst systems was confirmed. Finally, moisture was added for each propellant mixing process to check the effect of moisture on propellant curing.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.72-84
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• 2024

Plant leaf classification is an imperative task when their use in real world is considered either for medicinal purposes or in agricultural sector. Accurate identification of plants is, therefore, quite important, since there are numerous poisonous plants which if by mistake consumed or used by humans can prove fatal to their lives. Furthermore, in agriculture, detection of certain kinds of weeds can prove to be quite significant for saving crops against such unwanted plants. In general, Artificial Neural Networks (ANN) are a suitable candidate for classification of images when small datasets are available. However, these suffer from local minima problems which can be effectively resolved using some global optimization techniques. Considering this issue, the present research paper presents an automated plant leaf classification system using optimized soft computing models in which ANNs are optimized using Grasshopper Optimization algorithm (GOA). In addition, the proposed model outperformed the state-of-the-art techniques when compared with simple ANN and particle swarm optimization based ANN. Results show that proposed GOA-ANN based plant leaf classification system is a promising technique for small image datasets.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.598-603
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• 2003

During the last ten years, the need has been increased for reducing maintenance cost for aged equipments and ensuring safety, efficiency and profitability of petrochemical and refinery plants. RBI (Risk Based Inspection) methodology is one of the most promising technologies satisfying the need in the field of integrity management. In this study, a user-friendly software, realRBI for RBI based on the API 581 code was developed and a quantitative analysis was performed for over 500 equipments in a domestic plant whose operating time reaches about 13 years. Current risks for each equipment parts were evaluated and risk based prioritization were determined as a conclusion.

• Corrosion Science and Technology
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• v.11 no.5
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• pp.179-183
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• 2012

As one of promising solutions to overcome high oil price and energy crisis, the construction market of high value-added LNG plants is spotlighted world widely. The purpose of this study is to introduce LNG-RBI system to develop risk assessment technology with RAM(Reliability, Availability, Maintainability) modules against overseas monopolization. After analyzing relevant specific features and their technical levels, risk assessment program, non-destructive reliability evaluation strategy and safety criteria unification class are derived as core technologies. These IT-based convergence technologies can be used for enhancement of LNG plant efficiency, in which the modular parts are related to a system with artificial optimized algorithms as well as diverse databases of facility inspection and diagnosis fields.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6297-6304
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• 2015

Nuclear power plant decommissioning has attracted attention according to the shutdown decision of Kori 1 which is Korea's first nuclear power plant. Nuclear power plant decommissioning is the one who never experienced ever in our country. So, its process is difficult and time-consuming. In addition, it is difficult to determine the decommissioning quantity. This study proposed the plan that can be used in quantity take-off for nuclear power plant decommissioning using BIM technology being utilized in recent construction industry. As a result, we suggested the method of BIM-based quantity take-off such as the selection decommissioning method and process, setting up of BIM modeling environment, establishment of OBS & WBS, integrated BIM modeling, the definition of quantity property. The proposed plan can be utilized usefully from when permanent stopping nuclear power plant occurs intensively. Furthermore, the overseas nuclear power plant decommissioning project order also are expected through technology securement based on this plan.

• Korean Journal of Plant Tissue Culture
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• v.27 no.5
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• pp.387-393
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• 2000

The process by which Agrobacterium tumefaciens genetically transforms plants involves a complex series of reactions communicated between the pathogen and the plants. To identify plant factors involved in agrobacterium-mediated plant transformation, a large number of T-DNA inserted Arabidopsis thaliana mutant lines were investigated for susceptibility to Agrobacterium infection by using an in vitro root inoculation assay. Based on the phenotype of tumorigenesis, twelve T-DNA inserted Arabidopsis mutants(rat) that were resistant to Agrobacterium transformation were found. Three mutants, rat1, rat3, and rat4 were characterized in detail. They showed low transient GUS activity and very low stable transformation efficiency compared to the wild-type plant. The resistance phenotype of rat1 and rats resulted from decreased attachment of Agrobacterium tumefaciens to inoculated root explants. They may be deficient in plant actors that are necessary for bacterial attachment to plant cells. The disrupted genes in rat1, rat3, and rat4 mutants were coding a arabinogalactan protein, a likely cell wall protein and a cellulose synthase-like protein, respectively.