• Nuclear Engineering and Technology
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• 제53권12호
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• pp.3892-3901
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• 2021

The pressure drop of a moisture separator in a steam generator is the important design parameter to ensure the successful performance of a nuclear power plant. The moisture separators have a wide range of operating conditions based on the arrangement of them. The prediction of the pressure drop in a moisture separator is challenging due to the complexity of the multi-dimensional two-phase vortex flow. In this study, the moisture separator test facility using the air/water two-phase flow was used to predict the pressure drop of a moisture separator in a Korean OPR-1000 reactor. The prototypical steam/water two-phase flow conditions in a steam generator were simulated as air/water two-phase flow conditions by preserving the centrifugal force and vapor quality. A series of experiments were carried out to investigate the effect of hydraulic characteristics such as the quality and liquid mass flux on the two-phase pressure drop. A new prediction model based on the scaling law was suggested and validated experimentally using the full and half scale of separators. The suggested prediction model showed good agreement with the steam/water experimental results, and it can be extended to predict the steam/water two-phase pressure drop for moisture separators.

• 상하수도학회지
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• 제36권4호
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• pp.239-248
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• 2022

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.

• 상하수도학회지
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• 제31권5호
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• pp.421-430
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• 2017

In this study, a model was developed to predict for Disinfection By-Products (DBPs) generated in water supply networks and consumer premises, before and after the introduction of advanced water purification facilities. Based on two-way ANOVA, which was carried out to statistically verify the water quality difference in the water supply network according to introduce the advanced water treatment process. The water quality before and after advanced water purification was shown to have a statistically significant difference. A multiple regression model was developed to predict the concentration of DBPs in consumer premises before and after the introduction of advanced water purification facilities. The prediction model developed for the concentration of DBPs accurately simulated the actual measurements, as its coefficients of correlation with the actual measurements were all 0.88 or higher. In addition, the prediction for the period not used in the model development to verify the developed model also showed coefficients of correlation with the actual measurements of 0.96 or higher. As the prediction model developed in this study has an advantage in that the variables that compose the model are relatively simple when compared with those of models developed in previous studies, it is considered highly usable for further study and field application. The methodology proposed in this study and the study findings can be used to meet the level of consumer requirement related to DBPs and to analyze and set the service level when establishing a master plan for development of water supply, and a water supply facility asset management plan.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.663-665
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• 2003

A field spectroradiometer SE-590 was used to measure the spectral reflectance of water body. The reflectance was calculated as the ratio of surface water radiance to the standard whiteboard radiance nearly measured at the same time. Water samples were taken simultaneously for determining their chlorophyll-a, suspended solid (SS) and transparency. The relationships between those water quality parameters and spectral reflectance were analy zed using stepwise multiple regression to derive optimal prediction models . The multiple regression was also applied to the SE-590 simulated SPOT bands. The SPOT image of the same day was also analyzed using the same method to compare the statistical results. It showed that the multiple regression models using the SE-590 reflectance data got the best water quality prediction results. The evaluated RMS error of chlorophyll-a, SS and transparency of water quality parameters were 0.57 ug/l, 0.2 mg/l and 0.17 m, respectively, and the RMS errors were 0.36 ug/l, 0.49 mg/l and 0.42 m for SPOT data, respectively. The SE-590 simulated SPOT three bands data obtained the worst results and the RMS errors were 1.77 ug/l, 0.49 mg/l and 0.37 m, respectively.

• 한국농공학회지
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• 제45권7호
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• pp.83-93
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• 2003

This research compared the observed and model predicted results that include; runoff, sediment yield, and nutrient losses from a 2.71 ha cattle grazing pasture field in North Alabama. Application of water quality computer simulation models can inexpensively and quickly assess the impact of pasture management practices on water quality. AGNPS single storm based model was applied to the three pasture species; Bermudagrass, fescue, and Ryegrass. While comparing model predicted results with observed data, it showed that model can reasonably predict the runoff, sediment yield and nutrient losses from the watershed. Over-prediction and under-prediction by the model occurred during very high and low rainfall events, respectively. The study concluded that AGNPS model can be reasonably applied to assess the impacts of pasture management practices and chicken litter application on water quality.

• 한국물환경학회지
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• 제23권5호
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• pp.591-599
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• 2007

The quality of water released from a stratified reservoir is dependent on various factors such as the location and shape of intake facility, structure of reservoir stratification, profile of water quality constituent, and withdrawal flux. Sometimes, selective withdrawal capabilities can provide the operational flexibility to meet the water quality demands both in-reservoir and downstream. The objective of this study was to evaluate the performance of a one-dimensional reservoir selective withdrawal model (SELECT) as a tool for supporting downstream water quality management for Daecheong and Imha reservoirs. The simulated water quality variables including water temperature, dissolved oxygen (DO), conductivity, turbidity were compared with the field data measured in tailwater. The model showed fairly satisfactory results and high reliability in simulating observations. The coefficients of determinant between simulated and observed turbidity values were 0.93 and 0.95 for Daecheong and Imha reservoirs, respectively. The outflow water quality was significantly influenced by water intake level under fully stratified condition, while the effect of intake amount was minor. In conclusion, the SELECT is simple but effective tool for supporting downstream water quality prediction and management for both reservoirs.

• Water Engineering Research
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• 제4권2호
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• pp.59-68
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• 2003

The applicabilities and validities of two methodologies fur the prediction of THM (trihalomethane) formation in a water pipeline system were proposed and discussed. One is the multiple regression technique and the other is an artificial neural network technique. There are many factors which influence water quality, especially THMs formations in water pipeline systems. In this study, the prediction models of THM formation in water pipeline systems are developed based on the independent variables proposed by American Water Works Association(AWWA). Multiple linear/nonlinear regression models are estimated and three layer feed-forward artificial neural networks have been used to predict the THM formation in a water pipeline system. Input parameters of the models consist of organic compounds measured in water pipeline systems such as TOC, DOC and UV254. Also, the reaction time to each measuring site along pipeline is used as input parameter calculated by a hydraulic analysis. Using these variables as model parameters, four models are developed. And the predicted results from the four developed models are compared statistically to the measured THMs data set. It is shown that the artificial neural network approaches are much superior to the conventional regression approaches and that the developed models by neural network can be used more efficiently and reproduce more accurately the THMs formation in water pipeline systems, than the conventional regression methods proposed by AWWA.

• 상하수도학회지
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• 제34권1호
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• 한국환경과학회지
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• 제20권5호
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• pp.589-598
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• 2011

Milyanggang mid-watershed is located in downstream of Nakdong river basin. The pollutants from that watershed have an direct effect on Nakdong river water quality and it's control is important to manage a water quality of Nakdong river. A target year of Milyanggang mid-watershed water environment management plan is 2013. To predict a water quality at downstream of Milyang river, we have investigated and forecasted the pollutant source and it's loading. There are some plan to construction the sewage treatment plants to improve the water quality of Milyang river. Those are considered on predicting water quality. As results, it is shown that the population of Milyanggang mid-watershed is 131,857 and sewerage supply rate is 62.2% and the livestock is 1,775.300 in 2006. It is estimated that the population is 123,921, the sewerage supply rate is 75.5% in 2013. The generated loading of BOD and TP is 40,735 kg/day and 2,872 kg/day in 2006 and discharged loading is 11,818 kg/day and 722 kg/day in 2006 respectively. Discharged loadings were forecasted upward 1.0% of BOD and downward 2.7% of TP by 2013. The results of water quality prediction of Milyanggang 3 site were 1.6 mg/L of BOD and 0.120 mg/L of TP in 2013. It is over the target water quality at that site in 2015 about 6.7% and 20.0% respectively. Consequently, there need another counterplan to reduce the pollutants in that mid-watershed by 2015.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2001년도 학술발표회 논문집(II)
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• pp.1189-1194
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• 2001