• Journal of the Korean Institute of Gas
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• v.23 no.2
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• pp.74-81
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• 2019

Expansion turbine system to recover the electricity energy from natural gas transmission stations is a well-known technique. The turbo-expander efficiency depends on the ratio of the natural gas flow rates to the design flow rate of the turbo-expander. However, if there is a big difference of the natural gas flow rate through the pressure letdown station because of seasonal supply pattern, that is, high flow rate in winter while low flow rate in summer, single turbo-expander system is not so efficient as to recover the pressurized energy from the low flow-rate natural gas. Therefore, we have proposed a new concept of double turbo-expander system: one is a big capacity and the other a small capacity. Here we have theoretically computed the electric powers at the pressure reduction from 18.5 bar to 7.5 bar depending on the inlet conditions of temperature and flow rate. The calculated electricity generation has been increased by 30% from 12.4 MW in a single turbo expander to 16.1 MW in the proposed double turbo-expander system when a minimal design efficiency of 0.72 is applied.

• Environmental and Resource Economics Review
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• v.30 no.4
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• pp.627-648
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• 2021

The fine-dust season management system refers to the policy of implementing enhanced reduction measures in transportation, power, business and living sectors in winter, when fine dust levels are high. The fine dust season management system is a regulatory policy that causes social costs and transfers to various economic players. Equity is an important issue for the cost burden. Therefore, in this study, the cost of each power generator was analyzed using the coal power generation reduction amount of each power generator to verify that the cost of the power sector is evenly distributed. In particular, the effect of the fine dust season management system on coal power generation of power generators was analyzed by applying a synthetic control method that can identify the time-variable effect of the policy. It was confirmed that the fine dust season management system reduced volume of fuel and power generation in coal power plants, resulting in an increase in the cost of the power generation sector, even considering the effect of some power demand due to the COVID-19 crisis. However, it could be seen that these costs were not distributed equally among the generators, and that they were more costly to the specific generators.Social costs incurred by fine dust season management need to be improved so that stakeholders are equally burdened.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.225-232
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• 2024

The reduction of CO₂ emissions in the energy production sector, which accounts for 86.8% of total greenhouse gas emissions, is important to achieve carbon-neutrality. At present, 60% of total power generation in South Korea is coal and natural gas. Replacing fossil fuel with renewable energy such as wind and solar has disadvantages of unstable energy supply and high costs. Therefore, this study was conducted through the co-firing of natural gas, ammonia and hydrogen utilizing the natural gas combined cycle process. The results demonstrated reduction in CO₂ emissions and 34%~238% of the power production compared to using only natural gas. Case studies on mass fractions of natural gas, ammonia and hydrogen indicated that power production and NO_x emissions were inversely proportional to the ammonia ratio and directly proportional to the hydrogen ratio. This study provides guidelines for the use of various fuel mixtures and economic analysis in co-firing power generation.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.4
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• pp.589-593
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• 2016

Microbial fuel cell (MFC) can produce electricity from oxidation-reduction of organic and inorganic matters by electrochemically active bacteria as catalyst. Stacked MFCs have been investigated for overcoming low electricity generation of single MFC. In this study, two-typed stacked-MFCs (submerged-flat and stand-falt) were operated according to electrode connection for optimal stacked technology of MFC. In case of submerged-flat MFC with all separator electrode assembly (SEA) sharing anode chamber, MFC with mixed-connection showed more voltage loss than MFC with single-connection method. And MFC stacked in parallel showed better voltage production than MFC stacked in series. In case of stand-flat MFC, voltage loss was bigger when SEAs sharing anodic chamber only were connected in series. Voltage loss was decreased when SEAs parallel connected SEAs sharing anodic chamber were connected in series.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.632-640
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• 2017

Although solar insolation is the weather factor with the greatest influence on power generation in photovoltaic systems, the Meterological Agency does not provide solar insolation data for future dates. Therefore, it is essential to research prediction methods for solar insolation to efficiently manage photovoltaic systems. In this study, we propose a Dynamic Piecewise Prediction Model that can be used to predict solar insolation values for future dates based on information from the weather forecast. To improve the predictive accuracy, we dynamically divide the entire data set based on the sun altitude and cloudiness at the time of prediction. The Dynamic Piecewise Prediction Model is developed by applying a polynomial linear regression algorithm on the divided data set. To verify the performance of our proposed model, we compared our model to previous approaches. The result of the comparison shows that the proposed model is superior to previous approaches in that it produces a lower prediction error.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.3
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• pp.271-278
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• 2016

Recently, reverse osmosis (RO) is the most common process for seawater desalination. A common problem in both RO and thermal processes is the high energy requirements for seawater desalination. The one energy saving method when utilizing the osmotic power is utilizing pressure retarded osmosis (PRO) process. The PRO process can be used to operate hydro turbines for electrical power production or can be used directly to supplement the energy required for RO desalination system. This study was carried out to evaluate the performance of both single-stage PRO process and two-stage PRO process using RO concentrate for a draw solution and RO permeate for a feed solution. The major results, were found that increase of the draw and feed solution flowrate lead to increase of the production of power density and water permeate. Also, comparison between CDCF and CDDF configuration showed that the CDDF was better than CDCF for stable operation of PRO process. In addition, power density of two-stage PRO was lower than the one of single-stage. However, net power of two-stage PRO was higher than the one of single-stage PRO.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.695-702
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• 2020

Recently, as research on smart farms has been actively conducted, indoor environment control, such as a green house, has reached a high level. However, In the field of forestry where cultivation is carried out in outdoor, the use of ICT is still insufficient. In this paper, we propose LPWA-based forest growth environment collection and big data analysis system using ICT technology. The proposed system collects and transmits the field cultivation environment data to the server using small solar power generation and LPWA technology based on the oneM2M architecture. The transmitted data is constructed as big data on the server and utilizes it to predict the production and quality of forest products. The proposed system is expected to contribute to the production of low-cost, high-quality crops through the fusion of renewable energy and smart farms. In addition, it can be applied to other industrial fields that utilize the oneM2M architecture and monitoring the growth environment of agricultural crops in the field.

• Resources Recycling
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• v.4 no.4
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• pp.37-58
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• 1995

Whole tues were put uto cement kiln inlet where the tempmalures or gas and cemcnt-raw-materials were 1050 and 800- 850.C. respcclrvely. Tl~ck iln consisls of \ulcorner-stage suspension preheatel- and air quenching coolers The amount of wusle tlrcs added in lhc normal encrgy in lhc ce~ncnlk iln was 3, 5, 7% by volume Welght and steel contents of tiles. ulti~~iaalcn d elemental analysis, ash contents. ash hsion temperature. etc, wete detcnutned to inveshgate thc prnpcrlics a1 tires and ilreir ashes. Flucluat~ons of cement kiln placess, cement quality and an pollulton were invesligalerl during lhc burning tins. When the Ieeding ralio ol wasle lires to normal cncrgy was 50'0, there was nn wlde d~ffereilces m the cemmt quctlity and air pollutcon between operation with tiles and withoul tires. Tl~ch cal iccovcry was uhout 50% w~th5 % add~tionI n the nonndl energy. There was a little lxt fluctuation of cement quultty ncld an pollution at olher feeding ralios.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.825-832
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• 2022

In this paper, we discuss the design and implementation of predictive and diagnostic models for realizing intelligent predictive models by collecting and storing the power output of agricultural photovoltaic power generation systems. Our model predicts the amount of photovoltaic power generation using RNN, LSTM, and GRU models, which are recurrent neural network techniques specialized for time series data, and compares and analyzes each model with different hyperparameters, and evaluates the performance. As a result, the MSE and RMSE indicators of all three models were very close to 0, and the R2 indicator showed performance close to 1. Through this, it can be seen that the proposed prediction model is a suitable model for predicting the amount of photovoltaic power generation, and using this prediction, it was shown that it can be utilized as an intelligent and efficient O&M function in an agricultural photovoltaic system.