• Journal of Korea Ship Safrty Technology Authority
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• s.27
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• pp.15-24
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• 2009

최근 IMO에서는 온실가스 배출을 규제화 하고자 하는 동향이 있으며 조만간 선박에서 온실가스 배출의 규제가 실현될 것으로 예상된다. 선박에서 배출되는 온실가스는 CO$_2$가 대부분을 차지하고 있으며 세계경제상황에 따른 영향이 고려되지만 지속적으로 증가추세에 있는 CO$_2$를 감소시키기 위한 아국의 대처방안이 필요한 시점이다. 또한 향후 발효될 EEDI를 소개하고 결과적으로 GHG를 저감시키기 위한 방안인 선형개선, 폐열회수시스템, 친환경연료사용기관, SEMP 등에 관한 내용을 다루어 보았다. 이 보고서에서는 GHG를 비롯한 NOx, SOx 및 PM과 같은 유해배출물질을 Top Down방식으로 평가함으로써 선박에서 기인하는 대기오염물질 관련 국내정책 및 해운산업의 장기적인 발전전략에 유용하게 사용될 것으로 기대한다.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.171-178
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• 2014

From now on, in order to meet more stringer diesel emission standard, diesel vehicle should be equipped with emission after-treatment devices as NOx reduction catalyst and particulate filters. Urea-SCR is being developed as the most efficient method of reducing NOx emissions in the after-treatment devices of diesel engines, and recent studies have begun to mount the urea-SCR device for diesel passenger cars and light duty vehicles. That is because their operational characteristics are quite different from heavy duty vehicles, urea solution injection should be changed with other conditions. Therefore, the number and diameter of the nozzle, injection directions, mounting positions in front of the catalytic converter are important design factors. In this study, major design parameters concerning urea solution injection in front of SCR are optimized by using a CFD analysis and Taguchi method. The computational prediction of internal flow and spray characteristics in front of SCR was carried out by using STAR-CCM+7.06 code that used to evaluate $NH_3$ uniformity index($NH_3$ UI). The design parameters are optimized by using the $L_{16}$ orthogonal array and small-the-better characteristics of the Taguchi method. As a result, the optimal values are confirmed to be valid in 95% confidence and 5% significance level through analysis of variance(ANOVA). The compared maximize $NH_3$ UI and activation time($NH_3$ UI 0.82) are numerically confirmed that the optimal model provides better conversion efficiency of $NH_3$. In addition, we propose a method to minimize wall-wetting around the urea injector in order to prevent injector blocks caused by solid urea loading. Consequently, the thickness reduction of fluid film in front of mixer is numerically confirmed through the mounting mixer and correcting injection direction by using the trial and error method.

• Journal of the Korea Organic Resources Recycling Association
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• v.13 no.1
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• pp.115-123
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• 2005

In this study, life cycle assessment(LCA) technique was employed to evaluate the environmental impact of material recycling of polyethylene terephthalate(PET) bottle. Life cycle inventory was established based on the data collected from recycling companies in Korea. Simapro 5.0 LCA software and Eco-indicator 95 index were used for the analysis. The biggest impact by the material recycling of PET bottle on the environmental category was the global warming. It is because melting and production of the recycled PET product consume a significant amount of electricity and energy. In the environmental pollution discharge, $CO_2$ emission was the highest, followed by NOx. This is probably due to the use of diesel and gasoline in the consumption of electricity and transportation. All the environmental impact showed (-) value except the ozone layer depletion, which means that the material recycling of PET bottle is environmentally fair. The use of recycled PET product greatly reduced the environmental impact.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.33 no.7
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• pp.486-494
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• 2009

One-dimensional modeling of $CH_4$/air premixed flame was conducted to validate the heat loss model and investigate NOx formation characteristics in the postflame region. The predicted temperature and NO concentration were compared to experimental data and previous heat loss model results using a constant gradient of temperature (100 K/cm). The following conclusions were drawn. In the heat loss model using steady-state heat transfer equation, the numerical results using the effective heat loss coefficient ($h_{eff}$) of $1.0\;W/m^2K$ were in very good agreement with the experiments in terms of temperature and NO concentration. On the other hand, the calculated values using the constant gradient of temperature (100 K/cm) were lower than that in the experiments. Although the effects of heat loss suppress NO production near the flame region, a significant difference in NO concentration was not found compared to that under adiabatic conditions. In the postflame region, however, there were considerable differences in NO emission index as well as the contribution of NO formation mechanisms. In particular, in the range of ${\phi}\;{\geq}\;0.8$, the prompt NO mechanism plays an important role in the NO reduction under the adiabatic condition. On the other hand, the mechanism contributes to the NO production under the heat loss conditions.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• 한국연소학회:학술대회논문집
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• 2005.10a
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• pp.275-280
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• 2005

At previous study, nitrogen oxide emission was decreased with decreasing pressure index. This tendency was explained by the flame oscillation with changing combustor pressure. In this study, the characteristics of flame oscillation with changing combustor pressure were investigated. It can be found that flame length is extended and flame width is narrowed by decreasing combustor pressure. It can be observed that pilot flame and the surrounding air converge on the inner flame in the $P^{\ast}{\geqq}1$ conditions and that surrounding air and flow pattern was widely dispersed in the $P^{\ast}<1$ conditions. In the respect of average flame length, low fluctuation was shown in the $P^{\ast}<1$ conditions. On the other hands, large fluctuation was shown in the $P^{\ast}<1$ conditions. Flame oscillation are observed from $P^{\ast}=$ 0.98 in the condition of $P^{\ast}<1$ and the amplitude of flame oscillation becomes larger when $P^{\ast}$ is lowered. These results demonstrate that low NOx phenomenon was caused by flame oscillation with changing combustor pressure.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2821-2823
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• 2002

In this paper we propose the Multi-FNN (Fuzzy-Neural Networks) for optimal identification modeling of complex system. The proposed Multi-FNNs is based on a concept of FNNs and exploit linear inference being treated as generic inference mechanisms. In the networks learning, backpropagation(BP) algorithm of neural networks is used to updata the parameters of the network in order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM(Hard C-Means)clustering algorithm which carry out the input-output dat a preprocessing function and Genetic Algorithm which carry out optimization of model The HCM clustering method is utilized to determine the structure of Multi-FNNs. The parameters of Multi-FNN model such as apexes of membership function, learning rates, and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization abilities of the model. NOx emission process data of gas turbine power plant is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.1
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• pp.48-61
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• 2022

South Korea is quite vegetation rich country which has 63% forests and 16% cropland area. Massive NOx emissions from megacities, therefore, are easily combined with BVOCs emitted from the forest and cropland area, then produce high ozone concentration. BVOCs emissions have been estimated using well-known emission models, such as BEIS (Biogenic Emission Inventory System) or MEGAN (Model of Emission of Gases and Aerosol from Nature) which were developed using non-Korean emission factors. In this study, we ran MEGAN v2.1 model to estimate BVO Cs emissions in Korea. The MO DIS Land Cover and LAI (Leaf Area Index) products over Korea were used to run the MEGAN model for June 2012. Isoprene and Monoterpenes emissions from the model were inter-compared against the enclosure chamber measurements from Taehwa research forest in Korea, during June 11 and 12, 2012. For estimating emission from the enclosed chamber measurement data. The initial results show that isoprene emissions from the MEGAN model were up to 6.4 times higher than those from the enclosure chamber measurement. Monoterpenes from enclosure chamber measurement were up to 5.6 times higher than MEGAN emission. The differences between two datasets, however, were much smaller during the time of high emissions. More inter-comparison results and the possibilities of improving the MEGAN modeling performance using local measurement data over Korea will be presented and discussed.

• Journal of Environmental Impact Assessment
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• v.11 no.1
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• pp.25-36
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• 2002

The purpose of this study is to assess the environmental carrying capacity of Chongju City for the environmental management and the urban growth management. The urban environmental carrying capacity assessment of the city by the index of ecological footprint(EF), shows that the ecosystem of the city has been overloaded and most of the deficiencies has come from outside of the city. The EF index, the area of land per capita required for production and consumption in the city, was 1.731 ha per capita in 1989 and 1.901 ha per capita in 1999. On the other side, the ecologically productive land is 0.0175 ha per capita. It means that every citizen owes 1.88 ha per capita to the ecosystem in 1999. The land consumption of the city has increased by 0.1705 ha per capita during the last 10 years. The capacity of infrastructure and the service supply estimated by the Onishi model does not exceed the demand of the city in 1999. But the rapidly increasing population and fast urban growth need the expansion of the capacity. The water supply capacity of the city appears to be sufficient in 1999, but the water supply demand will increase in the future. The capacity of sewage treatment facilities seems to be sufficient, but the higher level of sewage treatment facilities should be adopted for the improvement of water quality as the generation of sewage will increase and its characteristics will also make the wastewater treatment difficult. Due to the decrease of solid waste generated, the land fill capacity for solid waste disposal is not insufficient at present, but the capacity will be saturated in the near future. Therefore, the scientific management system of solid wastes should be introduced. The air quality of the city meets both the national air quality standard and WHO recommendation standard, but the strong regulation and control of automobile emission gas such as CO, $CO_2$, NOx and HC is required for clean air.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.40-49
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• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.