• Journal of the Korean Applied Science and Technology
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• v.32 no.1
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• pp.148-156
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• 2015

In this study the differences in the sample size and sample input changes as characteristics of bio-oil oak(Quercus variabilis), the oak 0.5~2.0 mm of the oak weighing 300~900g was processed into bio-oil via fast pyrolysis for 1.64 seconds. In this study, the physico-chemical properties of biooil using oak were investigated. Fast pyrolysis was adopted to increase the bio-oil yield from raw material. Although the differences in sample size and sample input changes in the yield of pyrolysis products were not significantly noticeable, increases in the yield of bio-oil accounted for approximately 60.3 to 62.1%, in the order of non-condensed gas, and biochar. When the primary bio-oil obtained by the condensation of the cooling tube and the seconary bio-oil obtained from the electric dust collector were measured separately, the yield of primary bio-oil was twice as higher than that of the secondary bio-oil. However, HHV (Higher Heating Value) of the secondary bio-oil was approximately twice as higher than that of the primary bio-oil by up to 5,602 kcal/kg. The water content of the primary bio-oil was more than 20% of the moisture content of the secondary bio-oil, which was 10% or less. In addition, the result of the elemental analysis regarding the secondary bio-oil, its primary carbon content was higher than that of the primary bio-oil, and since the oxygen content is low, the water content as well as elemental composition are believed to have an effect on the calorific value. The higher the storage temperature or the longer the storage period, the degree of the viscosity of the secondary bio-oil was higher than that of the primary bio-oil. This can be the attributed to the chemical bond between the polymeric bio-oil that forms during the storage period.

• Analytical Science and Technology
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• v.11 no.1
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• pp.62-72
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• 1998

This study was performed to characterize the trace organic pollutants in the industrial wastewater and to establish the database of the trace organic pollutants. The four manufacturing industries, which are refined petroleum, industrial chemicals, rubber & plastics and fabricated metals, were surveyed. The wastewater and discharging water of these 30 factories are analyzed to characterize the trace organic pollutants. In industrial chemicals, the kinds of products and organic pollutants are very various. Therefore to select the characteristic organic pollutants in this categories are also very difficult. In industrial chemicals, the gas chromatograpic peak patterns of wastewater are represented the various type according to their products, therefore the typical patterns of the characteristic organic pollutants could not be obtained because the kinds of manufactured goods and organic pollutants are very various. In refined petroleum, the effluent is discharged in the distillatory process of atmosphere pressure and contained the saturated hydrocarbons, phenol compounds, benzene compounds and naphtalene compounds. The saturated hydrocarbons peaks from $C_{15}$ to $C_{35}$ are represented the typical oil patterns by the uniform intervals therefore the peak can be easily distinguished. In rubber & plastics, the wastewater is discharged in the washing process which contains the additives. The problem of wastewater is not serious because the manufacturing process is not produced the effluent or the produced cooling water is recycled in that process.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.6
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• pp.1195-1206
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• 2011

This study was conducted to apply LCA (Life cycle assessment) methodology to lettuce (Lactuca sativa L.) production systems in Namyang-ju as a case study. Five lettuce growing farms with three different farming systems (two farms with organic farming system, one farm with a system without agricultural chemicals and two farms with conventional farming system) were selected at Namyangju city of Gyeonggi-province in Korea. The input data for LCA were collected by interviewing with the farmers. The system boundary was set at a cropping season without heating and cooling system for reducing uncertainties in data collection and calculation. Sensitivity analysis was carried out to find out the effect of type and amount of fertilizer and energy use on GHG (Greenhouse Gas) emission. The results of establishing GTG (Gate-to-Gate) inventory revealed that the quantity of fertilizer and energy input had the largest value in producing 1 kg lettuce, the amount of pesticide input the smallest. The amount of electricity input was the largest in all farms except farm 1 which purchased seedlings from outside. The quantity of direct field emission of $CO_2$, $CH_4$ and $N_2O$ from farm 1 to farm 5 were 6.79E-03 (farm 1), 8.10E-03 (farm 2), 1.82E-02 (farm 3), 7.51E-02 (farm 4) and 1.61E-02 (farm 5) kg $kg^{-1}$ lettuce, respectively. According to the result of LCI analysis focused on GHG, it was observed that $CO_2$ emission was 2.92E-01 (farm 1), 3.76E-01 (farm 2), 4.11E-01 (farm 3), 9.40E-01 (farm 4) and $5.37E-01kg\;CO_2\;kg^{-1}\;lettuce$ (farm 5), respectively. Carbon dioxide contribute to the most GHG emission. Carbon dioxide was mainly emitted in the process of energy production, which occupied 67~91% of $CO_2$ emission from every production process from 5 farms. Due to higher proportion of $CO_2$ emission from production of compound fertilizer in conventional crop system, conventional crop system had lower proportion of $CO_2$ emission from energy production than organic crop system did. With increasing inorganic fertilizer input, the process of lettuce cultivation covered higher proportion in $N_2O$ emission. Therefore, farms 1 and 2 covered 87% of total $N_2O$ emission; and farm 3 covered 64%. The carbon footprints from farm 1 to farm 5 were 3.40E-01 (farm 1), 4.31E-01 (farm 2), 5.32E-01 (farm 3), 1.08E+00 (farm 4) and 6.14E-01 (farm 5) kg $CO_2$-eq. $kg^{-1}$ lettuce, respectively. Results of sensitivity analysis revealed the soybean meal was the most sensitive among 4 types of fertilizer. The value of compound fertilizer was the least sensitive among every fertilizer imput. Electricity showed the largest sensitivity on $CO_2$ emission. However, the value of $N_2O$ variation was almost zero.