• Journal of Korean Society for Atmospheric Environment
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• v.13 no.1
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• pp.51-63
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• 1997

Total suspended particulate matters in the ambient air were analyzed for eight chemical elements (Ca, Co, Cu, Fe, Mn, Pb, Si, and Zn) using an x-ray fluorescence spectrometry (XRF) at the Kyung Hee University - Suwon Campus during 1989 to 1994. To use these data as basis for source identification study, membership of each sample was selected to represent one of the well defined sample groups. The data sets consisting of 83 objects and 8 variables were initially separated into two groups, fine (d$_{p}$<3.3 ${\mu}{\textrm}{m}$) and coarse particle groups (d$_{p}$>3.3 ${\mu}{\textrm}{m}$). A hierarchical clustering method was examined to obtain possible member of homogeneous sample classes for each of the two groups by transforming raw data and by applying various distances. A disjoint principal component analysis was then used to define homogeneous sample classes after deleting outliers. Each of five homogeneous sample classes was determined for the fine and the coarse particle group, respectively. The data were properly classified via an application of logarithmic transformation and Euclidean distance concept. After determining homogeneous classes, correlation coefficients among eight chemical variables within all the homogeneous classes for calculated and meteorological variables (temperature. relative humidity, wind speed, wind direction, and precipitation) were examined as well to intensively interpret environmental factors influencing the characteristics of each class for each group. According to our analysis, we found that each class had its own distinct seasonal pattern that was affected most sensitively by wind direction.ion.

• Food Science and Biotechnology
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• v.16 no.3
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• pp.374-380
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• 2007

Chemical and sensory profiles of 5 Korean commercial rice wines (yakju) were developed using descriptive, physicochemical, and volatile analyses. Color, 6 aroma, and 5 taste attributes of these rice wines were evaluated by a panel of 13 judges. Sample wines were analyzed for titratable acidity, ethanol content, pH, Hunter colorimeter value, organic acids, and free sugars. Volatile analysis of the samples revealed the presence of 2 acids, 7 alcohols, 19 esters, and 5 miscellaneous compounds. Based on principal component analysis of the descriptive data, rice wines were primarily separated along the first principal component, which accounted for 57% of the total variance between the rice wines with high intensities of 'color' and 'sweet aroma' versus 'ginseng' aroma.

• Korean Chemical Engineering Research
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• v.60 no.2
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• pp.223-228
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• 2022

In chemical processes, unintended faults can make serious accidents. To tackle them, proper fault diagnosis models should be designed to identify the root cause of faults. To design a fault diagnosis model, a process and its data should be analyzed. However, most previous researches in the field of fault diagnosis just handle the data set of benchmark processes simulated on commercial programs. It indicates that it is really hard to get fresh data sets on real processes. In this study, real faulty conditions of an industrial polystyrene process are tested. In this process, a runaway reaction occurred and this caused a large loss since operators were late aware of the occurrence of this accident. To design a proper fault diagnosis model, we analyzed this process and a real accident data set. At first, a mode classification model based on support vector machine (SVM) was trained and principal component analysis (PCA) model for each mode was constructed under normal operation conditions. The results show that a proposed model can quickly diagnose the occurrence of a fault and they indicate that this model is able to reduce the potential loss.

• Transactions of the Korean hydrogen and new energy society
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• v.16 no.3
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• pp.209-218
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• 2005

To find a suitable metal component in oxygen carrier particles for chemical-looping hydrogen generation system(CLH), oxygen transfer capacities of metal components were compared and Ni has been selected as the best metal component. The proper operating conditions to achieve high hydrogen generation rate have been investigated based on the chemical-equilibrium composition analysis for water splitting reactor. Moreover, suitable compositions of syngas from gasifier of heavy residue to achieve high energy efficiency have been investigated by calculation of heat of reaction. Based on the selected operating conditions, the best configuration of two interconnected fluidized beds system for the chemical-looping hydrogen generator has been investigated as well.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.87-92
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• 2007

Recently, developments of process monitoring system in order to detect and diagnose process abnormalities has got the spotlight in process systems engineering. Normal data obtained from processes provide available information of process characteristics to be used for modeling, monitoring, and control. Since modern chemical and environmental processes have high dimensionality, strong correlation, severe dynamics and nonlinearity, it is not easy to analyze a process through model-based approach. To overcome limitations of model-based approach, lots of system engineers and academic researchers have focused on statistical approach combined with multivariable analysis such as principal component analysis (PCA), partial least squares (PLS), and so on. Several multivariate analysis methods have been modified to apply it to a chemical process with specific characteristics such as dynamics, nonlinearity, and so on.This paper discusses about missing value estimation and sensor fault identification based on process variable reconstruction using dynamic PCA and canonical variate analysis.

• Bulletin of the Korean Chemical Society
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• v.7 no.1
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• pp.73-77
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• 1986

Data on the concentration of Na, K, Sc, Cr, Fe, Co, Cu, Ga, Rb, Cs, Ba, La, Ce, Sm, Eu, Tb, Lu, Hf, Ta, and Th obtained by neutron activation analysis have been used to characterize Korean porcelainsherds by multivariate analysis. The mathematical approach employed is principal component analysis (PCA). PCA was found to be helpful for dimensionality reduction and for obtaining information regarding (a) the number of independent causal variables required to account for the variability in the overall data set, (b) the extent to which a given variable contributes to a component and (c) the number of causal variables required to explain the total variability of each measured variable.

• Journal of Soil and Groundwater Environment
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• v.7 no.3
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• pp.19-32
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• 2002

This study is objected to identify the relations between surface- and shallow ground-water and the seasonal variation of their qualities in watersheds near Muju area. The water type shows mainly Ca-$HCO_3$type. Heavy-metal contamination of surface water is locally detected, due to the mixing with mine drainage. In October nitrate concentration is especially high in densely populated area. Cluster Analysis and Principal Component Analysis are implemented to interpret the complexity of the chemical variation of surface- and ground-water with large amount of chemical data. Based on the cluster analysis, surface-water was divided into five groups and ground-water into three groups. Principal Component Analysis efficiently supports the result of cluster analysis, allowing the identification of three main factors controlling the water quality. There are (1) hydrogeochemical factor, (2) anthropogenic factor and (3) heavy metal contaminated by mine drainage.

• Journal of the Korean Applied Science and Technology
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• v.22 no.3
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• pp.241-249
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• 2005

This experiment extracted the natural green-tea using ethanol and obtained the refined oil component after filterated green-tea extract. This study tested the antimicrobial effect as characteristics experiment, and analyzed refined oil component with pH-meter and GC/MS. In the result of this experiment, it obtained the next conclusions. In the first result of extraction experiment, it could know that extraction ratio of refined oil component appeared about 9.0%. In the second result of characteristics experiment, it could certificate that in case of increasing the refined oil component in concentration of 100ppm and above, and according to passage of cultivation time, the number of S-aureus and E-coli in microbe decreased less and less. But in case of blank test not adding the refined oil component, the number of microbe increased more and more. In these phenomena, it could certificate that refined oil component of green-tea appeared antimicrobial effect against microbe. In the third result of instrumental analysis, refined oil component of green-tea appeared about 7.6 in 1% distilled water solution with pH-meter, and the aromatic components of benzene, bonyl acetate, campene, ${\alpha},{\beta},{\gamma}$-pinnene etcs from refined oil component of green-tea was detected with GC/MS.

• Journal of the Korean Chemical Society
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• v.36 no.1
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• pp.113-124
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• 1992

The pattern recognition methods of chemometrics have been applied to multivariate data, for which ninety four Korean ancient glass pieces have been determined for 12 elements by neutron activation analysis. For the purpose, principal component analysis and non-linear mapping have been used as the unsupervised learning methods. As the result, the glass samples have been classified into 6 classes. The SIMCA (statistical isolinear multiple component analysis), adopted as a supervised learning method, has been applied to the 6 training set and the test set. The results of the 6 training set were in accord with the results by principal component analysis and non-linear mapping. For test set, 17 of 33 samples were each allocated to one of the 6 training set.

• Transactions of Materials Processing
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• v.24 no.6
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• pp.418-425
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• 2015

Based on eco-friendly advantages and the enhanced development in the chemical and physical characteristics, liquid silicone rubber (LSR) is widely used in producing precision parts in the automotive, medical, electronics, aeronautical and many other industries. In the current work, a thermoplastic-thermoset multi component injection molding (MCM) was developed for a waterproof automotive connector housing using PBT and LSR resins. Measurements of the rheological characteristics of PBT and LSR were made to improve the reliability of the numerical analysis for the multi component injection process. With the measured viscosity, pvT and curing data, numerical analysis of the multi cycle injection molding was conducted using simulation software (Sigma V5.0).