• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.25-33
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• 2020

In most plant layout optimization researches, MILP(Mixed Integer Linear Programming) problems, in which the objective function includes the costs of pipelines connecting process equipment and cost associated with safety issues, have been employed. Based on these MILP problems, various optimization solvers have been applied to investigate the optimal solutions. To consider safety issues on the objective function of MILP problems together, the accurate information about the impact and the frequency of potential accidents in a plant should be required to evaluate the safety issues. However, it is really impossible to obtain accurate information about potential accidents and this limitation may reduce the reliability of a plant layout problem. Moreover, in real industries such as plant engineering companies, the plant layout is previously fixed and the considerations of various safety instruments and systems have been performed to guarantee the plant safety. To reflect these situations, the two step optimization problems have been designed in this study. The first MILP model aims to minimize the costs of pipelines and the land size as complying sufficient spaces for the maintenance and safety. After the plant layout is determined by the first MILP model, the optimal locations of blast walls have been investigated to maximize the mitigation impacts of blast walls. The particle swarm optimization technique, which is one of the representative sampling approaches, is employed throughout the consideration of the characteristics of MILP models in this study. The ethylene oxide plant is tested to verify the efficacy of the proposed model.

• The Plant Pathology Journal
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• v.17 no.6
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• pp.370.4-370
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• 2001

• The Plant Pathology Journal
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• v.17 no.6
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• pp.372.3-372
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• 2001

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.12
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• pp.1625-1634
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• 2000

Verification of measurements is required for precise evaluation of turbine cycle performance in nuclear power plant. We assumed that initial acceptance data and design data of the plant could provide correlation information between performance data. The data can be used as sample sets for the correct estimation model of measurement value. The modeling was done practically by using regression model based on plant design data, plant acceptance data and verified plant performance data of domestic nuclear power plant. We can construct more robust performance analysis system for an operation nuclear power plant with this validation scheme.

• Journal of the Ergonomics Society of Korea
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• v.32 no.1
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• pp.9-16
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• 2013

Objective: The aim of this study is to investigate a trend of human error types observed in a series of verification and validation experiments for an Advanced Control Room(ACR) equipped with Lager Display Panel(LDP), Work Station Flat Panel Display(WS FPD), list type Alarm System(AS), Soft Control(SC) and Computerized Procedure System(CPS). Background: Operator behaviors in a fully computerized control room are quite different from those in a traditional hard-wired control room. Operators in an ACR all together monitor plant status and variables through their own interface system such as LDP and WS FPD, are notified of abnormal plant status through their own list type AS, control the plant through their own SC, and follow the structured procedure through their own CPS whereas operators in a traditional control room only separately do their duty directed by their supervisor. Especially the secondary task such as manipulating the user interface of ACR can be an extra burden to all the operators including the supervisor. Method: The Reason's human error classification method was applied to operators' behavioral data collected from a series of verification and validation experiments where operators showed their plant operational behaviors under a couple of harsh scenarios using the ACR simulator. Results: As operators accustomed to the new ACR system, knowledge or rule based mistakes appearing frequently in the early series of experiments decreased drastically in the latest stage of the series. Slip and lapse types of errors were observed throughout the series of experiments. Conclusion: Education and training can be one of the most important factors for the operators accustomed to the traditional control room to be adapted to the new system and to run the ACR successfully. Application: The results of this study implied that knowledge or rule based mistakes can be reduced by training and education but that lapse type errors might be reduced only through innovative improvement in human-system interface design or teamwork culture design including a new leadership style suitable for ACR.

• Journal of Microbiology and Biotechnology
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• v.32 no.5
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• pp.582-593
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• 2022

Among abiotic stresses in plants, drought and chilling stresses reduce the supply of moisture to plant tissues, inhibit photosynthesis, and severely reduce plant growth and yield. Thus, the application of water stress-tolerant agents can be a useful strategy to maintain plant growth under abiotic stresses. This study assessed the effect of exogenous bio-based 2,3-butanediol (BDO) application on drought and chilling response in tomato and turfgrass, and expression levels of several plant signaling pathway-related gene transcripts. Bio-based 2,3-BDOs were formulated to levo-2,3-BDO 0.9% soluble concentrate (levo 0.9% SL) and meso-2,3-BDO 9% SL (meso 9% SL). Under drought and chilling stress conditions, the application of levo 0.9% SL in creeping bentgrass and meso 9% SL in tomato plants significantly reduced the deleterious effects of abiotic stresses. Interestingly, pretreatment with levo-2,3-BDO in creeping bentgrass and meso-2,3-BDO in tomato plants enhanced JA and SA signaling pathway-related gene transcript expression levels in different ways. In addition, all tomato plants treated with acibenzolar-S-methyl (as a positive control) withered completely under chilling stress, whereas 2,3-BDO-treated tomato plants exhibited excellent cold tolerance. According to our findings, bio-based 2,3-BDO isomers as sustainable water stress-tolerant agents, levo- and meso-2,3-BDOs, could enhance tolerance to drought and/or chilling stresses in various plants through somewhat different molecular activities without any side effects.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.133-142
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• 2023

The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.

• The Plant Pathology Journal
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• v.26 no.3
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• pp.209-215
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• 2010

Plant pathogenic oomycetes, such as Phytophthora spp., are the causal agent of the most devastating plant diseases. During infection, these pathogens accomplish parasitic colonization of plants by modulating host defenses through an array of disease effector proteins. These effectors are classified in two classes based on their target sites in the host plant. Apoplastic effectors are secreted into the plant extracellular space, and cytoplasmic effectors are translocated inside the plant cell, through the haustoria that enter inside living host cell. Recent characterization of some oomycete Avr genes showed that they encode effector protein with general modular structure including N-terminal conserved RXLR-DEER motif. More detailed evidences suggest that these AVR effectors are secreted by the pathogenic oomycetes and then translocated into the host plant cell during infection. Recent findings indicated that one of the P. infestans effector, Avrblb2, specifically induces hypersensitive response (HR) in the presence of Solanum bulbocastanum late blight resistance genes Rpi-blb2. On the other hand, another secreted RXLR protein PexRD8 originated from P. infestans suppressed the HCD triggered by the elicitin INF1. In this review, we described recent progress in characterized RXLR effectors in Phytophthora spp. and their dual functions as modulators of host plant immunity.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2490-2495
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• 2005

The scheduling of plant should be determined based on the product demands correctly forecasted by reasonable methods. However, because most existing forecasting packages need user's knowledge about forecasting, it has been hard for plant engineers without forecasting knowledge to apply forecasted demands to scheduling. Therefore, a forecasting module has been developed for plant engineers without forecasting knowledge. In this study, for the development of the forecasting module, an automatic method using the ARIMA model that is framed from the modified Box-Jenkins process is proposed. And a new method for safety inventory determination is proposed to reduce the penalty cost by forecasting errors. Finally, using the two proposed methods, the web-based automatic module has been developed.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.493-495
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• 2005

This paper proposes a swarm-based fuzzy system modeling technique for coke oven combustion control diagnosis. The coke plant produces coke for the blast furnace plant in steel making process by charging coal into oven and supplying gas to carbonize it. A conventional mathematical model for coke oven combustion control has been used to control the amount of gas input, but it does not work well because of highly nonlinear feature of coke plant. To solve this problem, swarm-based fuzzy system modeling technique is suggested to construct a diagnosis model of coke oven combustion control. Based on the measured input-output data pairs, the fuzzy rules are generated and the parameters are tuned by the PSO(Particle Swarm Optimizer) to increase the accuracy of the fuzzy system is operated. This system computes the proper amount of gas input taking the operation conditions of coke oven into account, and compares the computed result with the supplied gas input.