• Environmental Engineering Research
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• v.22 no.3
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• pp.302-311
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• 2017

Nowadays, with the burgeoning development of economy, $CO_2$ emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast $CO_2$ emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make $CO_2$ emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson coefficient test. Partial autocorrelation function (PACF) is utilized to determine the lag phases of historical $CO_2$ emissions so as to improve the rationality of input selection. Then PCA is employed to reduce the dimensionality of the influential factors. Finally RELM is applied to forecast $CO_2$ emissions. According to the modeling results, the proposed model outperforms a single RELM model, extreme learning machine (ELM), back propagation neural network (BPNN), GM(1,1) and Logistic model in terms of errors. Moreover, it can be clearly seen that ELM-based approaches save more computing time than BPNN. Therefore the developed model is a promising technique in terms of forecasting accuracy and computing efficiency for $CO_2$ emission prediction.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.107-108
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• 2020

The severity of the global climate crisis is increasing due to greenhouse gases caused by human activities. As a result, countries and industries are making efforts to reduce carbon dioxide emissions, the biggest cause of global warming. Many studies have been conducted to predict carbon emissions in the construction sector to reduce this, but they have not actually produced a highly usable formula in the field. Therefore, the two variables 'Curve Fitting' were performed based on the data of excavators and trucks measured at the field. As a result, we have obtained a carbon dioxide emission prediction model for construction equipment, and we would like to use it to help establish an eco-friendly process plan.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.38-39
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• 2019

Environmental problems caused by GHG emitted by various industries are emerging around the world, and accordingly, relevant regulations are being applied by countries around the world. Korea is operating a carbon credit system that trades GHG in industry for money, which is expected to be applied to the construction industry. In addition, construction equipment using fossil fuels accounts for the largest portion of $CO_2$ emissions in the construction industry, and the importance of $CO_2$ reduction and prediction is increasing. However, there is a lack of data on the directly measured $CO_2$ emissions of construction equipment and there is no accurate methodology for measuring methods. Therefore, in this study, independent variables were derived based on the $CO_2$ emission data. In addition, multiple linear regression is performed for each independent variable to derive a predictive model of carbon dioxide emission by work type of construction equipment. It is expected that the construction process plan based on environmental factors in the construction industry can be established in the future.

• Journal of the korean Society of Automotive Engineers
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• v.8 no.2
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• pp.31-42
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• 1986

The prediction of performance, exhaust emissions and EGR effect is made by the SI engine cycle simulation. In this simulation several models are employed - two zome, thermodynamic combustion, mass fraction burned, heat transfer, chemical equilibrium, chemical kinetics for NOx, laminar flame speed for ignition delay. The chemical species in burned gas considered are 13 species-CO$_{2}$, CO, $O_{2}$, H$_{2}$O, H$_{2}$,OH, H, O, N$_{2}$, NO$_{2}$, N, Ar - and the cylinder pressure, burned and unburned zone temperature and composition of gas are calculated at each crank angle through the compression, ignition delay, combustion and expansion process. To check the validity of the model, experimental study is done for measuring emissions, combustion pressure and engine output. The predicted values for pressure and emissions show qualitative agreement with the measured data and the EGR effect also shows similar tendency.

• Journal of the korean Society of Automotive Engineers
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• v.5 no.2
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• pp.48-55
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• 1983

The prediction of performance and emissions is presented for a spark ignition engine. a two zone, zero-dimensional model was employed which included thermodynamics, combustion and hear transfer, and a kinetic model employed for NOx. The model was used to analyze the processes of compression, combustion and expansion. Cylinder pressures and temperatures were calculated as a function of crankangle as well as engine performance and emissions. Predictions made with the simulation were compared with experimental data from a four cylinder spark ignition engine. Calculated pressures and, Co and Co$_{2}$ concentrations showed acceptable quantitative agreement with data. But calculated No concentrations were slightly different. A parametric study of the effect of variations in speed, combustion duration and spark timing was carried out. This simulation can be useful for design of spark ignition engines.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.66-79
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• 2024

The model developed in this study holds significant importance in predicting carbon emissions in maritime transport. By utilizing ship data and EEDI (Energy Efficiency Design Index) guidelines, the model presents a highly accurate prediction tool, providing a solid foundation for maximizing operational efficiency and effectively managing carbon emissions in ship operations. The model's accuracy was demonstrated by an R² score of 0.95 and a Mean Absolute Percentage Error (MAPE) of 1.4%. Through SHAP (SHapley Additive exPlanations) and Partial Dependence Plots (PDP), it was identified that Speed Over Ground and relative wind speed are the most significant variables, both showing a positive correlation with increased CO2 emissions. Additionally, environmental factors such as exceeding an average draft of 22(m), a Leeway over 5°, and a current angle exceeding 200° were found to increase emissions significantly. Specific ranges of wind and swell wave angles also notably affected emissions. Conversely, lower pitch, roll, and rudder angle were associated with reduced emissions, indicating that stable ship operation enhances efficiency.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.44-45
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• 2020

Korea has the fourth highest CO₂ emission among OECD countries in 2018, As of 2019, total greenhouse gas emissions per capita increased by about 98.2% in comparison to 1990. Korea has promised a 37% reduction in greenhouse gas emissions in 2030 from the projected Paris Climate Change Accord. Currently, many countries use the emissions trading system(ETS) for international carbon management. In 2015, ETS has been implemented in Korea, and the importance of calculating CO₂ emissions from construction machinery has increased. So, we require an accurate calculation of the environmental charges through the allocated CERs. Using the CER price and related search keywords, this paper derive about prediction models of CER price and compare and focus on more accurate prediction about CER price. By this method, the budget needed to establish the initial construction process plan can be calculated based on more accurate predicted CER price.

• Journal of the Korean Solar Energy Society
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• v.31 no.1
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• pp.23-30
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• 2011

International cooperation to reduce greenhouse gas emissions is expected to provide a big crisis and a great opportunity at the same time for our industry that heavily consumes energy. To cope actively with the international environmental regulation, such as the Framework Convention on Climate Change, quantitative measurement of the volume of greenhouse gases emitted by various industries and quantitative prediction of the greenhouse gas emissions of the future are becoming more important than anything else at the national level. This study aims to propose the calculation process of carbon dioxide($CO_2$) emission for building in life cycle. This paper describes and compares 9 different tool for environmental load estimation with LCA. This study proposed the calculation process for quantitatively predicting and assessing $CO_2$ emissions during the life cycle of buildings based on the life cycle assessment(LCA). The life cycle steps of buildings were divided into the design/supervision, new construction, repair, renovation, use of operating energy in buildings, maintenance, and reconstruction stage in the life cycle inventory analysis and the method of assessing the environmental load in each stage was proposed.

• Journal of the Korean Society of Safety
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• v.31 no.4
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• pp.48-54
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• 2016

The prediction of various emissions from coal combustion is an important subject of researchers and engineers because of environmental consideration. Therefore, the development of the models for predicting pollutants very fast has received much attention from international research community, especially in the field of safety assessment. In this work, response surface method was introduced as a design of experiment, and the database for RSM was set with the numerical simulation of a drop tube furnace (DTF) to predict the spatial distribution of pollutant concentrations as well as final ones. The distribution of carbon dioxide in DTF was assumed to have Boltzman function, and the resulted function with parameters of a high $R^2$ value facilitates predicting an accurate distribution of $CO_2$. However, CO distribution had a difference near peak concentration when Gaussian function was introduced to simulate the CO distribution. It might be mainly due to the anti-symmetry of the CO concentration in DTF, and hence Extreme function was used to permit the asymmetry. The application of Extreme function enhanced the regression accuracy of parameters and the prediction was in a fairly good agreement with the new experiments. These results promise the wide use of statistical models for the quantitative safety assessment.

• International Journal of Automotive Technology
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• v.6 no.3
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• pp.205-213
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• 2005

This study deals with the result of exhaust emissions and performance calculated by simulation of the fuel injection characteristics of the inline injection system in a marine diesel engine. The emissions are calculated through non-equilibrium by using the extended Zel'dovich kinetic mechanism for NOx and equilibrium method for OH, CO, $H_2$, Hand soot concentrations. Comparisons of the model predictions with the experimental values show reasonable agreement. Detailed prediction results showing the sensitivity of the model by injection rates are presented and discussed.