• Journal of the Korean Society for Precision Engineering
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• v.25 no.7
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• pp.27-32
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• 2008

• Proceedings of the Korean Fiber Society Conference
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• 2001.10a
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• pp.33-36
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• 2001

고흡수성 고분자는 이온성기를 가진 수용성 고분자에 부분적인 가교결합을 도입하여 일반적으로 카르복실기 이온 (-COO-)등과 같은 친수성기를 다량으로 지닌 3차원 망상구조를 지니는 수용성 고분자이다[1]. 여지껏 물을 흡수하는 목적으로 사용된 흡수소재는 면, pulp, sponge 등이 일반적으로 알려져 있다. 이들은 모세관 현상에 의해 물을 흡수하는 것으로 알려져 있으며, 이들 흡수 재료의 흡수능력은 자기 무게의 수백 배로부터 천 배까지의 물도 흡수하며 외압하에서도 잘 탈수되지 않는 고기능성 고분자이며 그 원리는 다음과 같다. (중략)

• Proceedings of the Korean Vacuum Society Conference
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• 2007.08a
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• pp.213-213
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• 2007

• Proceedings of the Korean Vacuum Society Conference
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• 2007.08a
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• pp.215-215
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• 2007

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2010.04a
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• pp.418-420
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• 2010

• Proceedings of the Korean Vacuum Society Conference
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• 1992.07a
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• pp.23-27
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• 1992

• Proceedings of the Korean Vacuum Society Conference
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• 2006.08a
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• pp.207-207
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• 2006

• Journal of the Korean institute of surface engineering
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• v.36 no.6
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• pp.437-443
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• 2003

The effects of additive(grain refiner, GR) on process efficiency of the Ni-Fe-P alloy electrodeposition and the material properties of the deposit were investigated. Electrochemical properties of the deposits were investigated using polarization and electrochemical impedance techniques, and the material properties of the deposits were characterized through inductively coupled plasma(ICP), spiral contractometer, XRD, SEM and TEM. When the additive was added into the electrodeposition bath, current efficiency, Ni content and corrosion resistance of the deposit increased, whereas residual stress, surface roughness and grain size of the deposit decreased.

• Korean Journal of Microbiology
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• v.31 no.3
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• pp.245-254
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• 1993

Distribution and variation of bacterial densities of heterotrophic plate count (HPC) and Enterobacteriaceae in the water supply systems comprising raw, treated, and three tap water samples of a water treatment plant in Seoul were studied 23 times from 1991 to 1992. HPC bacteria of raw. treated, and tap waters on $R_{2}A$ agar media were at a density of $1.22{\times}10^{3} to 3.05{\times}10^{5}$, $1.50{\times}10^{1} to 4.29{\times}10^{3}$ and 2 to $5.41{\times}10^{3}$ cfu/ml, respectively. Densities of Enterobacteriaceae in raw, treated, and tap waters on mENDO-LES agar media ranged from 0.] to 8200 cfu/ml, 0 to 17.5 cfu/JOO mI. and 0 to 47.5 cfu/IOO ml, respectively. Injured Enterobacteriaceae of treated and tap waters on m-T7 agar media were at a density of o to 27 and 0 to 35 cfu/100 mI. These results showed that the density of bacteria in the treated water outflowing from the water plant significantly increased as the water flowed along the distribution sytems, which is so-called bacterial regrowth. The predominant bacteria] types in the water supply system were Pw'udomonas and Acinerobacter. In raw water, the ratio of Pseudomonas was higher than that of Acinetobaeter, but in treated and tap waters. both ratios were reversed. The most predominant species of Enterobacteriaceae was Enterobacter agglomerans. Some species such as Citrobacter freundii. Escherichia coli. Klebsiella pneumoniae. and Shigella dysenteriae which are opportunistic pathogens or pathogens were not found in the treated water but additionally detected in tap waters.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1283-1293
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• 2022

The purpose of this study is to predict residual chlorine in order to maintain stable residual chlorine concentration in sedimentation basin by using artificial intelligence algorithms in water treatment process employing pre-chlorination. Available water quantity and quality data are collected and analyzed statistically to apply into mathematical multiple regression and artificial intelligence models including multi-layer perceptron neural network, random forest, long short term memory (LSTM) algorithms. Water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage data are used as the input parameters to develop prediction models. As results, it is presented that the random forest algorithm shows the most moderate prediction result among four cases, which are long short term memory, multi-layer perceptron, multiple regression including random forest. Especially, it is result that the multiple regression model can not represent the residual chlorine with the input parameters which varies independently with seasonal change, numerical scale and dimension difference between quantity and quality. For this reason, random forest model is more appropriate for predict water qualities than other algorithms, which is classified into decision tree type algorithm. Also, it is expected that real time prediction by artificial intelligence models can play role of the stable operation of residual chlorine in water treatment plant including pre-chlorination process.