• Journal of the Korean Crystal Growth and Crystal Technology
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• v.13 no.4
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• pp.199-204
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• 2003

Microstructure and chemical analysis of sintered bricks containing recycled wastes were investigated by SEM and EDS. The recycled wastes for which substitute ceramic raw materials were EAF (electric arc furnace) dust, fly ash and stone ash. Yellowish and brownish regions on the surface and brownish and blackish regions in the inside of bricks were observed. Main component of yellowish region on the surface turned out to be Zn. No chemical difference between the black-core region and brownish matrix. Mullite crystallites of 1 fm size were distributed in the inside of bricks and enclosed by glass phases. It seems that alumine-silicate mixtures of kaolin and fly ash were transformed to mullite crystallites during the sintering. Relatively large pores ot several ten fm size were observed in the black-core region in the inside of bricks. The main components of the inside of brick were Al and Si. The minor components were C, Na, Mg, K, Ca, and Fe. Particularly, the precipitates of Fe-rich crystallites were observed in the amorphous matrix. These precipitates were formed due to the local reduction atmosphere in the inside of bricks. Zn-rich covers were found on the surface of bricks because Zn diffused from the inside of bricks to the surface under the reduction atmosphere.

• Journal of Electrochemical Science and Technology
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• v.13 no.1
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• pp.148-158
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• 2022

The electrochemical CO₂ reduction (ECR) to produce value-added fuels and chemicals using clean energy sources (like solar and wind) is a promising technology to neutralize the carbon cycle and reproduce the fuels. Presently, the ECR has been the most attractive route to produce carbon-building blocks that have growing global production and high market demand. The electrochemical CO₂ reduction could be extensively implemented if it produces valuable products at those costs which are financially competitive with the present market prices. Herein, the electrochemical conversion of CO₂ obtained from flue gases of a power plant to produce diesel and formic acid using a consistent techno-economic approach is presented. The first scenario analyzed the production of diesel fuel which was formed through Fischer-Tropsch processing of CO (obtained through electroreduction of CO₂) and hydrogen, while in the second scenario, direct electrochemical CO₂ reduction to formic acid was considered. As per the base case assumptions extracted from the previous outstanding research studies, both processes weren't competitive with the existing fuel prices, indicating that high electrochemical (EC) cell capital cost was the main limiting component. The diesel fuel production was predicted as the best route for the cost-effective production of fuels under conceivable optimistic case assumptions, and the formic acid was found to be costly in terms of stored energy contents and has a facile production mechanism at those costs which are financially competitive with its bulk market price. In both processes, the liquid product cost was greatly affected by the parameters affecting the EC cell capital expenses, such as cost concerning the electrode area, faradaic efficiency, and current density.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.7
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• pp.917-925
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• 2014

An accurate feed formulation is essential for optimizing feed efficiency and minimizing feed cost for swine and poultry production. Because energy and amino acid (AA) account for the major cost of swine and poultry diets, a precise determination of the availability of energy and AA in feedstuffs is essential for accurate diet formulations. Therefore, the methodology for determining the availability of energy and AA should be carefully selected. The total collection and index methods are 2 major procedures for estimating the availability of energy and AA in feedstuffs for swine and poultry diets. The total collection method is based on the laborious production of quantitative records of feed intake and output, whereas the index method can avoid the laborious work, but greatly relies on accurate chemical analysis of index compound. The direct method, in which the test feedstuff in a diet is the sole source of the component of interest, is widely used to determine the digestibility of nutritional components in feedstuffs. In some cases, however, it may be necessary to formulate a basal diet and a test diet in which a portion of the basal diet is replaced by the feed ingredient to be tested because of poor palatability and low level of the interested component in the test ingredients. For the digestibility of AA, due to the confounding effect on AA composition of protein in feces by microorganisms in the hind gut, ileal digestibility rather than fecal digestibility has been preferred as the reliable method for estimating AA digestibility. Depending on the contribution of ileal endogenous AA losses in the ileal digestibility calculation, ileal digestibility estimates can be expressed as apparent, standardized, and true ileal digestibility, and are usually determined using the ileal cannulation method for pigs and the slaughter method for poultry. Among these digestibility estimates, the standardized ileal AA digestibility that corrects apparent ileal digestibility for basal endogenous AA losses, provides appropriate information for the formulation of swine and poultry diets. The total quantity of energy in feedstuffs can be partitioned into different components including gross energy (GE), digestible energy (DE), metabolizable energy (ME), and net energy based on the consideration of sequential energy losses during digestion and metabolism from GE in feeds. For swine, the total collection method is suggested for determining DE and ME in feedstuffs whereas for poultry the classical ME assay and the precision-fed method are applicable. Further investigation for the utilization of ME may be conducted by measuring either heat production or energy retention using indirect calorimetry or comparative slaughter method, respectively. This review provides information on the methodology used to determine accurate estimates of AA and energy availability for formulating swine and poultry diets.

• Korean Journal of Environmental Agriculture
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• v.15 no.2
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• pp.188-197
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• 1996

This study was carried out to investigate yearly changes in the precipitation component and the source strength to acid precipitation in the rural area of Chinbuk province by analysis of the chemical components in the precipitation at National Honam Agricultural Experiment Station RDA in the suburbs of Iksan from 1991 to 1995. The average ratio of acid precipitation was 47.9% from 1991 to 1995. pH of the rain water in precipitation below 5mm was higher than that above 5mm and the concentration of the ions in the rain water was the highest in the first fraction$(0{\sim}5mm)$ of precipitation. The amount and ratio of the precipitation below pH 4.0 from 1991 to 1995 were 64mm and 1.4%, respectively. The order of the major ions concentration in the precipitation was $SO_4\;^{2-}$ > $NO_3\;^-$ > $Cl^-$ > $NH_4\;^+$ > $Ca^{2+}$ > $K^+$ ${\lrcorner}\;Mg^{2+}$. The relative contributions to the acidification of the rain in Iksan were 52% from $SO_4\;^{2-}$, 25% from $NO_3\;^-$ and 23% from $Cl^-$.

• Korean Journal of Plant Resources
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• v.18 no.1
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• pp.161-168
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• 2005

Tuber yield and content of general component and diosgenin which is a main bioactive property were investigated in order to determine the growth characteristics of round typed yam(Dioscorea opposita L.) and the potential of artificial culture at Suwon, Korea. Tubers of round yam were initiated to form at 60 days after planting and then enlargement of tubers lasted by 160 days after planting. Compared to short typed yam(108g), tuber weight of round yam was higher(127g) on the basis of dry weight at 200 days after planting. In comparison of general component between round yam and short yam, protein of round yam$(3.62\%)$ was higher than short yam$(2.10\%)$. Water content in round yam$(64.5\%)$ was lower in short yam$(79.4\%)$, indicating a higher dry weight ratio of round yam. Hardness of round yam was 2787.6 while short yam showed about two times higher hardness(4946.9). Lightness was higher in round yam(77.4). In tuber extracts analysis, diosgenin content was respectively $3.32\%$ in round yam and $2.61\%$ in short yam.

• Journal of Practical Agriculture & Fisheries Research
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• v.6 no.1
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• pp.50-62
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• 2004

Tuber yield and content of general component and diosgenin which is a main bioactive property were investigated in order to determine the growth characteristics of round typed yam(Dioscorea opposita L.) and the potential of artificial culture at Suwon, Korea. Tubers of round yam was initiated to form at 60 days after planting and then enlargement of tubers lasted by 160 days after planting. Compared to short typed yam(108g), tuber weight of round yam was higher(127g) on the basis of dry weight at 200 days after planting. In comparison of general component between round yam and short yam, protein of round yam(3.62%) was higher than short yam(2.10%). Water content in round yam(64.5%) was lower in short yam(79.4%), indicating a higher dry weight ratio of round yam. Hardness of round yam was 2787.6 while short yam showed about two times higher hardness(4946.9). Lightness was higher in round yam(77.4). In tuber extracts analysis, diosgenin content was respectively 3.32% in round yam and 2.61% in short yam.

• Food Science and Preservation
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• v.13 no.2
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• pp.186-191
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• 2006

Prunus mume is extensively cultivated as a fruit and medicinal plant in Korea. Recently, prunus mume has a pressing problem with an increase of prunus mume cultivation area in southern part in Korea. Chemical properties of prunus mume flower to determine the optimum processing varieties for tea were investigated. Three kinds of samples treated with fresh, freeze dry and shade dry were used. The content of moisture, crude ash, crude protein, crude fiber, crude fat and nitrogen free extract of prunus mume flower varieties were to $82{\sim}85%,\;0.2{\sim}0.6%,\;2.5{\sim}3.1%,\;2.5{\sim}3.1%,\;0.6{\sim}0.8%\;and\;10{\sim}11%$ respectively. The main component of free sugars in prunus mume flower was glucose and those of organic acids were citric and malic acids. 17 kinds of amino acids were determined from prunus mume flower. The total amino acid contents of Cheongchuk, Baeagaha and Goseong were 760.47 mg%, 624.01 mg% and 807.41 mg%, respectively. Aspartic acid, glutamic acid and lysine were the major component in 3 cultivars. The content of K was much higher than Ca, Mg, Na, fe and Zn. The major fatty acids of prunus mume flower were myristic acid, palmitoleic acid me oleic acid. As a result of analysis, there were no significant differences among the three cultivars of prunus mume flower and drying method.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1244-1244
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from mastitic and healthy cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from mastitic and healthy cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA and FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference, thereby providing a useful means for spectroscopy-based clinic applications.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1281-1281
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• 2001

Near-infrared (NIR) spectra have bean measured for high-density (HDPE), linear low-density (LLDPE), and low-density (LDPE) polyethylene in pellet or thin films. The obtained spectra have been analyzed by conventional spectroscopic analysis methods and chemometrics. By using the second derivative, principal component analysis (PCA), and two-dimensional (2D) correlation analysis, we could separate many overlapped bands in the NIR. It was found that the intensities of some bands are sensitive to density and crystallinity of PE. This may be the first time that such bands in the NIR region have ever been discussed. Correlations of such marker bands among the NIR spectra have also been investigated. This sort of investigation is very important not only for further understanding of vibration spectra of various of PE but also for quality control of PE by vibrational spectroscopy. Figure 1 (a) and (b) shows a NIR reflectance spectrum of one of the LLDPE samples and that of PE, respectively. Figure 2 shows a PC weight loadings plot of factor 1 for a score plot of PCA for the 16 kinds of LLDPE and PE based upon their 51 NIR spectra in the 1100-1900 nm region. The PC loadings plot separates the bands due to the $CH_3$ groups and those arising form the $CH_2$ groups, allowing one to make band assignments. The 2D correlation analysis is also powerful in band enhancement, and the band assignments based upon PCA are in good agreement with those by the 2D correlation analysis.(Figure omitted). We have made a calibration model, which predicts the density of LLDPE by use of partial least square (PLS) regression. From the loadings plot of regression coefficients for the model , we suggest that the band at 1542, 1728, and 1764 nm very sensitive to the changes in density and crystalinity.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1042-1042
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from daily monitoring of two Japanese cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from two cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA md FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference.