• Journal of Korean Society for Atmospheric Environment
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• v.6 no.1
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• pp.85-96
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• 1990

Simultaneous removal efficiencies of hydrophilic and hydrophobic gaseous pollutants are experimentally determined, and the macroscopic removal mechanism of pollutants in a dry scrubber is analyzed using the extended model of three phase equilibrium distribution of pollutant at high temperatures that can describe the different morphological conditions of adsorbent and water at varying relative humidities. For the simplicity, the inside of spray dryer is divided into three regions of ; (1) absorption, (2) three-phase equilibrium, and (3) adsorption, and the removal efficiencies of each pollutants at three regions are observed at different experimental conditions to estimate the effects of important parameters of dry scrubber. The laboratory experiments simulate the three regions of spray dryer with the temperature control and thus evaporation rate of water from the slurry particle. $SO_2$ as a hydrophilic gaseous pollutant and vinyl chloride as a hydrophobic toxic gas are selected for the future field application to soid waste incineration, and the two types of slurry are made of the two sorbents ; 10 wt.% $Ca(OH)_2$, and 10 wt.% NaOH. Result of temperature effect shows the height of absorption plus three-phase region is decreased as the operation temperature is increased, which results in the lower removal efficiency of $SO_2$ but higher removal for vinyl chloride in the adsorption region of dry scrubber. The removal efficiency of $SO_2$ is higher by NaOH slurry than by $Ca(OH)_2$ slurry due to the hygroscopic nature of NaOH, while the removal of vinyl chloride is higher in $Ca(OH)_2$ case. From the analysis of redults using three-phase equilibrium distribution model, the effective two-phase partition coefficients can be obtained, and the possible extention in the application of the three-phase equilibrium model in a dry scrubber design has been demonstrated.

• Journal of Environmental Science International
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• v.19 no.4
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• pp.447-457
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• 2010

This study aims to monitor the variation of concentration of PCDD/DFs between the gaseous phase-particulate phases in the ambient air of urban area in Korea. This monitoring is evaluated by using the Junge-Pankow model and the Koa absorption model with the application of the Octanol-air partition coefficient. In this study, the ambient air samples were analyzed according to each congener group of the PCDD/DFs by HRGC/HRMS, which have been investigated for the past 5 years. In the results, the annual variation in the concentration level of $\Sigma$PCDD/DFs in TSP was increased from $1588\;fg/m^3$ in 1998 to $5123\;fg/m^3$ in 2002, and from 31 fg I-TEQ/$m^3$ to 94 fg I-TEQ/$m^3$ in the $\Sigma$I-TEQ. In the case of PUF of gaseous phase sample, their variation was increased from $1615\;fg/m^3$ in 1998 to $2237\;fg/m^3$ in 2002, and in the $\Sigma$I-TEQ from 12 fg I-TEQ/$m^3$ to 17 fg I-TEQ/$m^3$. The relative coefficient between the gas phase concentration of PCDD/DFs and the temperature was a value of 0.744; the contributive rate of the temperature to the gaseous phase concentration was 0.554. According to the results, the pattern of the coefficient of distribution based on log $p_L^0$ is similar to the ambient air of the urban areas.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.3
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• pp.430-446
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• 2018

To improve understanding of the sources and chemical properties of particulate pollutants on the Korean Peninsula, An Aerodyne High Resolution Time of Flight Aerosol Mass Spectrometer (HR-ToF-AMS) measured non-refractory fine particle ($NR-PM_1$) from 2013 to 2015 at Baengnyeong Island and Seoul metropolitan area (SMA), Korea. The chemical composition of $NR-PM_1$ in Baengnyeong island was dominated by organics and sulfate in the range of 36~38% for 3 years, and the organics were the dominant species in the range of 44~55% of $NR-PM_1$ in Seoul metropolitan area. The sulfate was found to be more than 85% of the anthropogenic origin in the both areas of Baengnyeong and SMA. Ratio of gas to particle partition of sulfate and nitrate were observed in both areas as more than 0.6 and 0.8, respectively, representing potential for formation of additional particulate sulfate and nitrate. The high-resolution spectra of organic aerosol (OA) were separated by three factors which were Primary OA(POA), Semi-Volatility Oxygenated Organic Aerosol (SV-OOA), and Low-Volatility OOA(LV-OOA) using positive matrix factorization (PMF) analysis. The fraction of oxygenated OA (SOA, ${\fallingdotseq}OOA$=SV-OOA+LV-OOA) was bigger than the fraction of POA in $NR-PM_1$. The POA fraction of OA in Seoul is higher than it of Baengnyeong Island, because Seoul has a relatively large number of primary pollutants, such as gasoline or diesel vehicle, factories, energy facilities. Potential source contribution function (PSCF) analysis revealed that transport from eastern China, an industrial area with high emissions, was associated with high particulate sulfate and organic concentrations at the Baengnyeong and SMA sites. PSCF also presents that the ship emissions on the Yellow Sea was associated with high particulate sulfate concentrations at the measurement sites.