• New & Renewable Energy
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• v.18 no.4
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• pp.12-21
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• 2022

The pyrolysis characteristics of high-density polyethylene (HDPE), low-density polyethylene (LDPE), and polypropylene (PP) were analyzed numerically using a 1D plug flow reactor (PFR) model. A lumped kinetic model was selected to simplify the pyrolysis products as wax, oil, and gas. The simulation was performed in the 400-600℃ range, and the plastic pyrolysis and product generation characteristics with respect to time were compared at various temperatures. It was found that plastic pyrolysis accelerates rapidly as the temperature rises. The amounts of the pyrolysis products wax and oil increase and then decrease with time, whereas the amount of gas produced increases continuously. In LDPE pyrolysis, the pyrolysis time was longer than that observed for other plastics at a specified temperature, and the amount of wax generated was the greatest. The maximum mass fraction of oil was obtained in the order of HDPE, PP, and LDPE at a specified temperature, and it decreased with temperature. Although the 1D model adopted in this study has a limitation in that it does not include material transport and heat transfer phenomena, the qualitative results presented herein could provide base data regarding various types of plastic pyrolysis to predict the product characteristics. These results can in turn be used when designing pyrolysis reactors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.4
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• pp.505-514
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• 2015

This paper proposes a probabilistic generation assessment model of renewable energy generators(REGs) considering uncertainty of resources, mainly focused on Wind Turbine Generator(WTG) and Solar Cell Generator(SCG) which are dispersed widely in South Korea The proposed numerical analysis method assesses the one day-ahead generation by combining equivalent generation characteristics function and probabilistic distribution function of wind speed(WS) and solar radiation(SR) resources. The equivalent generation functions(EGFs) of the wind and solar farms are established by grouping a lot of the farms appropriately centered on Weather Measurement Station(WMS). First, the EGFs are assessed by using regression analysis method based on typical least square method from the recorded actual generation data and historical resources(WS and SR). Second, the generation of the REGs is assessed by adding the one day-ahead resources forecast, announced by WMS, to the EGFs which are formulated as third order degree polynomials using the regression analysis. Third, a Renewable Energy Generation Assessment System(REGAS) including D/B of recorded actual generation data and historical resources is developed using the model and algorithm predicting one day-ahead power output of renewable energy generators.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.859-866
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• 2021

Agricultural Photovoltaic power generation is a new model that installs solar power generation facilities on top of farmland. Through this, it is possible to increase farm household income by producing crops and electricity at the same time. Recently, various attempts have been made to utilize agricultural solar power generation. Agricultural photovoltaic power generation has a disadvantage in that maintenance is relatively difficult because it is installed on a relatively high structure unlike conventional photovoltaic power generation. To solve these problems, intelligent and efficient operation and diagnostic functions are required. In this paper, we discuss the design and implementation of a prediction and diagnosis system to collect and store the power output of agricultural solar power generation facilities and implement an intelligent prediction model. The proposed system predicts the amount of power generation based on the amount of solar power generation and environmental sensor data, determines whether there is an abnormality in the facility, calculates the aging degree of the facility and provides it to the user.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.03a
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• pp.101-105
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• 1998

A large system predictor, which can perform prediction of sales trend in a huge number of distribution centers, is presented using neural predictive model. There are 20,000 number of distribution centers, and each distribution center need to forecast future demand in order to establish a reasonable inventory policy. Therefore, the number of forecasting models corresponds to the number of distribution centers, which is not possible to estimate that kind of huge number of accurate models in ERP (Enterprise Resource Planning)module. Multilayer neural net as universal approximation is employed for fitting the prediction model. In order to improve prediction accuracy, a sequential simulation procedure is performed to get appropriate network structure and also to improve forecasting accuracy. The proposed simulation procedure includes neural structure identification and virtual predictive model generation. The predictive model generation consists of generating virtual signals and estimating predictive model. The virtual predictive model plays a key role in tuning the real model by absorbing the real model errors. The complement approach, based on real and virtual model, could forecast the future demands of various distribution centers.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.243-248
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• 2014

Recently, solar energy is expanding to combination of computing in real time by tracking the position of the sun to estimate the angle of inclination and make up freshly correcting a part of the solar radiation. Solar power is need that reliably linked technology to power generation system renewable energy in order to efficient power production that is difficult to output predict based on the position of the sun rise. In this paper, we analysis of prediction model for solar power generation to estimate the predictive value of solar power generation in the development of real-time weather data. Photovoltaic power generation input the correction factor such as temperature, module characteristics by the solar generator module and the location of the local angle of inclination to analyze the predictive power generation algorithm for the prediction calculation to predict the final generation. In addition, the proposed model in real-time national weather service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• Journal of computational fluids engineering
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• v.2 no.2
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• pp.18-25
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• 1997

A pressure-based Navier-Stokes numerical solver was used to compare solutions of the k-ε/RNG k-ε turbulence models. An efficient grid generation scheme, the transient grid generation with full boundary control, was used to solve the flows in the tip clearance region. Results indicate that the calculations using k-ε model captures various phenomena related to the tip clearance with good accuracy.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.743-747
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• 1994

A process planning system that generates alternative process plans offers multiple process plans for a part, thereby provides the flexibility to cope with the changes in shop floor status. In this paper, we introduce the concept of process net as a model for the generation of alternative process plans. We also show the usefulness of process net model by implementing the developed system to generate alternative process plans for rotational parts.

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.640-643
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• 1987

A probabilistic production costing model based on the economic load dispatch has been developed. Objective function is composed of fuel cost which is a function of generation output and the failure cost. Coefficients of the failure cost is determined from the known equivalent generation cost. The model is compared with other existing methodolgies and the excellent results are obtained.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.7
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• pp.957-965
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• 2012

Korean government has been preparing the introduction of Emission Trading as part of the framework convention on Climate Change as a relief of negative downstream effect over electricity industry. This paper develops a mathematical model amenable to analyzing the economic impact of introduced emission trading system on the national generation expansion planning. The developed model was also employed with a case study to verify its applicability.

• Proceedings of the Technology Innovation Conference
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• 2003.02a
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• pp.173-190
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• 2003

Demand forecasting is essential in establishing national and corporate strategy as well as the management of their resource. We forecast demand for multi-generation product using discrete choice model combining diffusion model The discrete choice model generally captures consumers'valuation of the product's qualify in the framework of a cross-sectional analysis. We incorporate diffusion effects into a discrete choice model in order to capture the dynamics of demand for multi-generation products. As an empirical application, we forecast demand for worldwide DRAM (dynamic random access memory) and each of its generations from 1999 to 2005. In so doing, we use the method of 'Technological Forecasting'for DRAM Density and Price of the generations based on the Moore's law and learning by doing, respectively. Since we perform our analysis at the market level, we adopt the inversion routine in using the discrete choice model and find that our model performs well in explaining the current market situation, and also in forecasting new product diffusion in multi-generation product markets.