• Advances in Energy Research
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• v.8 no.3
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• pp.185-202
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• 2022

This research work focuses to design and simulate a 200W solar power system with electrical power conservation scheme as well as thermal power conservation modeling to improve power extraction from solar power plant. Many researchers have been already designed and developed different methods to extract maximum power while there were very researches are available on improving solar power thermally and mechanically. Thermal parameters are also important while discussing about maximizing power extraction of any power plant. A specific type of coolant which have very high boiling point is proposed to be use at the bottom surface of solar panel to reduce the temperature of panel in summer. A comparison between different maximum power point tracking (MPPT) technique and proposed MPPT technique is performed. Using this proposed Thermo-electrical MPPT (TE-MPPT) with Deep Learning Algorithm model 40% power is conserved as compared to traditional solar power system models.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1285-1285
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• 2024

In process plant construction, the implementation of design automation technologies is pivotal in reducing the timeframes associated with the design phase and in enabling the generation and evaluation of a variety of design alternatives, thereby facilitating the identification of optimal solutions. These technologies can play a crucial role in ensuring the successful delivery of projects. Previous research in the domain of design automation has primarily focused on parametric design in architectural contexts and on the automation of equipment layout and pipe routing within plant engineering, predominantly employing rule-based algorithms. Nevertheless, these studies are constrained by the limited flexibility of their models, which narrows the scope for generating alternative solutions and complicates the process of exploring comprehensive solutions using nonlinear optimization techniques as the number of design and engineering parameters increases. This research introduces a framework for automating plant design through the use of generative neural network models to overcome these challenges. The framework is applicable to the layout problems of process plants, covering the equipment necessary for production processes and the facilities for essential resources and their interconnections. The development of the proposed Neural-network (NN) based Generative Design Model unfolds in four stages: (a) Rule-based Model Development: This initial phase involves the development of rule-based models for layout generation and evaluation, where the generation model produces layouts based on predefined parameters, and the evaluation model assesses these layouts using various performance metrics. (b) Neural Network Model Development: This phase transitions towards neural network models, establishing a NN-based layout generation model utilizing Generative Adversarial Network (GAN)-based methods and a NN-based layout evaluation model. (c) Model Optimization: The third phase is dedicated to optimizing the models through Bayesian Optimization, aiming to extend the exploration space beyond the limitations of rule-based models. (d) Inverse Design Model Development: The concluding phase employs an inverse design method to merge the generative and evaluative networks, resulting in a model that outputs layout designs to meet specific performance objectives. This study aims to augment the efficiency and effectiveness of the design process in process plant construction, transcending the limitations of conventional rule-based approaches and contributing to the achievement of successful project outcomes.

• Plant Journal
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• v.9 no.4
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• pp.37-42
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• 2013

Power plants have many components and equipment. It is difficult for operators to know the each equipment fails or what equipment fails. It is important to prevent failure in advance. Recently, outlook of maintenance tasks is changing from time based maintenance to condition based maintenance. In this study, we selected RCM-based maintenance intervals for coal handling equipment at coal power plant. For RCM analysis, we have made great progress in a maintenance task and interval. If we apply RCM analysis to the whole plant system, we can expect qualitative improvement and efficient operation of power plant system.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2009.05b
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• pp.93-96
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• 2009

This study is to examine a change of quality and a material performance of fiber reinforced cement composite for mass production. It is necessary to make Strain-hardening cementitious composite(SHCC) by batcher plant for ready-mixed concrete and use the performance of SHCC which made based on laboratory level. This study makes a comparative performance of press and mechanics that is the property of Strain-hardening by direct tension. In case of making by batcher plant. This experiment has demonstrated that even if it takes long after being mixed small and compared with the one which made based on laboratory, it has a tendency to be dissatisfied with fiver's dispersion and lower its performance of Strain-hardening. The reason why the material performance of SHCC for mass production went down is through SHCC that mixed sometimes matrix's viscosity and fiber's dispersion.

• Journal of the Earthquake Engineering Society of Korea
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• v.27 no.1
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• pp.13-24
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• 2023

In this study, alternative seismic force-resisting systems for plant structure supporting equipment were designed, and the seismic performance thereof was compared using nonlinear dynamic analysis. One alternative seismic force-resisting system was designed per the requirement for ordinary moment-resisting and concentrically braced frames but with a reduced base shear. The other seismic force-resisting system was designed by accommodating seismic details of intermediate and unique moment-resisting frames and special concentrically braced frames. Different plastic hinge models were applied to ordinary and ductile systems based on the validation using existing test results. The control model obtained by code-based flexible design and/or reduction of base shear did not satisfy the seismic performance objectives, but the alternative structural system did by strengthened panel zones and a reduced effective buckling length. The seismic force to equipment calculated from the nonlinear dynamic analysis was significantly lower than the equivalent static force of KDS 41 17 00. The comparison of design alternatives showed that the seismic performance required for a plant structure could be secured economically by using performance-based design and alternative seismic-force resisting systems adopting minimally modified seismic details.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.3
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• pp.68-74
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• 2024

This study develops a model to determine the input rate of the chemical for coagulation and flocculation process (i.e. coagulant) at industrial water treatment plant, based on real-world data. To detect outliers among the collected data, a two-phase algorithm with standardization transformation and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied. In addition, both of the missing data and outliers are revised with linear interpolation. To determine the coagulant rate, various kinds of machine learning models are tested as well as linear regression. Among them, the random forest model with min-max scaled data provides the best performance, whose MSE, MAPE, R² and CVRMSE are 1.136, 0.111, 0.912, and 18.704, respectively. This study demonstrates the practical applicability of machine learning based chemical input decision model, which can lead to a smart management and response systems for clean and safe water treatment plant.

• 한국생물공학회:학술대회논문집
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• 2005.04a
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• pp.218-218
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• 2005

• Proceedings of the Korean Society of Potoscience Conference
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• spring
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• pp.77-85
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• 2003

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2006.06a
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• pp.11-11
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• 2006

• Proceedings of the Korean Society of Crop Science Conference
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• 2018.10a
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• pp.33-33
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• 2018