• Membrane Journal
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• v.11 no.4
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• pp.190-203
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• 2001

In this study the discharged wastewater from a paper plant was filtrated by 4 kinds of tubular carbon ceramic ultrafiltration membranes with periodic water-back-flushing. We could investigate effects of watch-back-flushing period, transmembrane pressure (TMP) and flow rate, and find optimal operating conditions. The back-f1ushing time (BT) was fixed at 3 sec, and fi1tration times (FT) werc changed in 15~60 scc, TMP in 1.00~2.50$kg_{f}$/$cm^2$, and the flow rates in 0.27~1.75 L/min. The optimal conditions were discussed in 7he viewpoints of dimensionless permeate flux (J/J$_{0}$), total permeate volume ($V^T$) and resistance of membrane fouling ($R^f$). Optima1 back-flushing period was BT/FT=0.20, suggesting that the frequent back-flushing should decrease membrane fouling. Optimal TMP in the viewpoint of $V^T$ was 1.00~1.55$kg_{f}$/$cm^2$, suggesting that rising TMP should increase membrane fouling and decrease permeate flux. But, rising f1ow rate should decrease membrane fouling and increase permeate flux. Then, average rejection rates of pollutants filtratedby carbon ceramic membranes were 88~98 % for turbidity, 48~72% fort $COD_{cr}$ and 37~76% for TDS.

• Journal of Korea Water Resources Association
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• v.41 no.12
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• pp.1219-1230
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• 2008

Stream inflows induced by flood runoffs have a higher density than the ambient reservoir water because of a lower water temperature and elevated suspended sediment(SS) concentration. As the propagation of density currents that formed by density difference between inflow and ambient water affects reservoir water quality and ecosystem, an understanding of reservoir density current is essential for an optimization of filed monitoring, analysis and forecast of SS and nutrient transport, and their proper management and control. This study was aimed to quantify the characteristics of inflow density current including plunge depth($d_p$) and distance($X_p$), separation depth($d_s$), interflow thickness($h_i$), arrival time to dam($t_a$), reduction ratio(${\beta}$) of SS contained stream inflow for different flood magnitude in Daecheong Reservoir with a validated two-dimensional(2D) numerical model. 10 different flood scenarios corresponding to inflow densimetric Froude number($Fr_i$) range from 0.920 to 9.205 were set up based on the hydrograph obtained from June 13 to July 3, 2004. A fully developed stratification condition was assumed as an initial water temperature profile. Higher $Fr_i$(inertia-to-buoyancy ratio) resulted in a greater $d_p,\;X_p,\;d_s,\;h_i$, and faster propagation of interflow, while the effect of reservoir geometry on these characteristics was significant. The Hebbert equation that estimates $d_p$ assuming steady-state flow condition with triangular cross section substantially over-estimated the $d_p$ because it does not consider the spatial variation of reservoir geometry and water surface changes during flood events. The ${\beta}$ values between inflow and dam sites were decreased as $Fr_i$ increased, but reversed after $Fr_i$>9.0 because of turbulent mixing effect. The results provides a practical and effective prediction measures for reservoir operators to first capture the behavior of turbidity inflow.

• 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.

• Korean Journal of Ecology and Environment
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• v.44 no.1
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• pp.75-84
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• 2011

We examined the effect of the turbid water on the periphytic diatom community in an artificial stream system. The artificial stream was constructed with transparent acryl and composed of four channels. Each channel ($20\;cm{\times}200\;cm{\times}40\;cm$) was supplied continuously with eutrophic lake water. In order to the freely colonize and grow diatoms, artificial substrate was installed with commercial slide glass soaked in 1% agar. Prior to introducing turbid water, the artificial stream was operated with lake water for 6 days to permit the propagation of diatom community on the substrates. The turbid water prepared with sediment sieved with ${\varphi}$ $64\;{\mu}m$ at $2\;g\;L^{-1}$ (final concentration, 300 NTU) was provided daily for 50 minute duration. The experiment was conducted for 7 days with manipulated experimental condition of light ($50{\sim}80\;{\mu}mol\;m^{-2}s^{-1}$, light:dark=24:0), temperature ($10{\pm}1^{\circ}C$), and flow rate ($0.31\;cm\;s^{-1}$). Sampling and analysis were conducted daily for water quality and diatom. Turbidity of the water varied 162.2~173.2 NTU during the experiment. After introduction of turbid water, DO, pH and TN were decreased, while SS and TP increased significantly. A total of 14 genera and 47 species of diatoms was observed on the artificial substrates during the experimental period. Of these, Navicula appeared to be a most dominant genus with 10 species, followed by Cymbella (6 species), Fragilaria (6 species) and Gomphonema (5 species). Achnanthes minutissima was the most dominant species (>70% of total frequency) in both control and treatment experiments. Increase in diatom abundance lasted for three days since turbid water introduction, after that they gradually decreased by the termination of the experiment. These results suggest that frequent supply of highly-concentrated turbid water significantly decreases the periphytic diatom community, and retard the recovery of the stable food-web within the stream.