• Journal of Advanced Marine Engineering and Technology
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• v.31 no.5
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• pp.514-521
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• 2007

There has been considerable interest recently in the topic of renewable energy. This is primarily due to concerns about environmental impacts. Moreover, fluctuating and rising oil prices, increases in demand, supply uncertainties and other factors have led to increased calls for alternative energy sources. Small hydropower, especially using water supply system, attracts high attentions because of relatively lower cost and smaller space requirements to construct the plant. Moreover. newly developed positive displacement turbine has high acceptability for the system. Therefore, the purpose of this study is focused on the examination of the performance characteristics and proposition of a optimum design method of the turbine for the improvement of the performance. The results show that newly proposed optimum design method for the turbine has high accuracy of performance prediction and good applicability for the performance improvement of the turbine.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.1037-1042
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• 2009

In connection with a recent research project dealing with heating system in apartment buildings by district heating, it was realised that in general very little information on the actual performance of heating and hot water systems in apartment buildings has been documented. In order to improve of district heating systems, a prediction of the heat demand first needs to be determined before a production plan. this is hot water heat system developed in this paper. this is also analyzed relation heat load with preheat load in hot water heating exchanger system.

• Environmental Engineering Research
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• v.17 no.2
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• pp.95-102
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• 2012

To evaluate the effect of the 4 major rivers restoration project in the Nakdong River to water quality of the river, the Environmental Fluid Dynamics Code (EFDC) and Water Quality Analysis Simulation Program (WASP), are applied in series. Results showed overall decrease in biochemical oxygen demand ($BOD_5$) concentrations and increase in chlorophyll-a concentrations, while total nitrogen and total phosphorous concentrations did not show significant changes, relatively. Decrease in $BOD_5$ concentrations seems to be influenced by an increased hydraulic residence time, which may allow more time for the degradation of organic material. Changes in Chlorophyll-a (Chl-a) concentration, due to the project were more significant for the upper stream areas that show relatively low Chl-a concentration ranges (less than 20 g/L). After the introduction of the Geumho River in the middle part of the Nakdong River, rapid growth of phytoplankton was observed. However, in this middle part of the Nakdong River, the ratio of Chl-a concentration change are less significant, compared to the upper stream areas, due to the project. In the lower stream area, Chl-a concentration decreased after the project. This seems to be resulted from the decreased light availability, due to increased depth, while the nutrient concentrations have been high enough to support phytoplankton growth.

• Advances in environmental research
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• v.5 no.3
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• pp.153-167
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• 2016

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination ($R^2$) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.

• Water for future
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• v.26 no.3
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• pp.75-85
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• 1993

In this study, reach boundaries in QUAL2E Model were set by the locations of point-waste discharge or tributary input and measured or direct calculated hydrologic factors were used in computation as much as possible. South Platte Experimental River in Colorado, USA was selected as a target river and data collected during September 1991 and January 1992 periods were used for calibration and verification, respectively. Constituents modeled in this study are 5-day carbonaceous biochemical oxygen demand(CBOD$) and dissolved oxygen(DO). The good agreement was obtained between a calculated using this model and observed, less than 5% to DO and about 20% to CBOD$. According to the result of water-quality prediction, experimental river is classified as the 4th category by the criteria of environmental protection agency in the USA in 2001.

• Journal of Environmental Health Sciences
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• v.27 no.1
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• pp.27-35
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• 2001

This aim of the work presented in this paper is to investigate the factors that affcet chlorine decay and to develop functional relationships that can be used to enhance the durability of network models. predictive relationships were established that correlated the rate of chlorine decay to the various water conditions such as DOC, N $H_3$-N, initial chlorine, contact time, temperature and pH values. Free chlorine residual decreased with increasing temperature, DOC, N $H_3$-N, reaction time and chlorine dose. At 2$0^{\circ}C$, pH 7, The initial chlorine demand per mg as DOC/L and mg as N $H_3$-N/L was about 0.43, 2.69 mg/$\ell$ respectively at 180 minutes contact time. The Reaction between chlorine and humic acids was lasted intil 48hr, but the reaction between chlorine and N $H_3$-N was almost completed in 180 min. When the temperature is raised by 1$0^{\circ}C$, chlorine is more consumed about 0.25 mg/$\ell$ in the absence of organic substances and it is more consumed about 3.4 mg/$\ell$ in the presence of humic acid (5 mg/$\ell$) in water at pH 7 for 180 min. Regression Analysis created the resulting prediction equation for the chlorine decay in a SPSS package of the computer system. The model is as follows; $C_{t}$=1.239+0.707(Co)-0.000529(Time)-0.0112(Temp)+0.02227(pH)-0.42(DOC)-2.132(N $H_3$-N).).

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1271-1271
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• 2001

Water pollutants in drainage mainly consist of organic compounds. Hence, total organic carbon (TOC), chemical oxygen demand (COD), and biochemical oxygen demand (BOD) were generally used as the indices of pollution. However, these values are determined by special analyzer (TOC), titration method (COD), or microbe culture (BOD). Therefore, the development of simple and easy methods for the determination of water pollution is required. The authors reported the evaluation of water pollution by near infrared (NIR) spectroscopy in a model system with food components (Takamura et al. (200) Near Infrared Spectroscopy: Proceedings of 9th International Conference, pp. 503-507). In this study, the relationship between NIR spectra and drainage was investigated in order to develop a method for evaluation of drainage by NIR. Drainage was obtained in Nara Purification Center. The ranges of TOC, COD, and BOD were 0-130, 0-100 and 0-200, respectively. NIR transmittance spectra were recorded on NIR Systems Model 6250 Research Composition Analyzer in the wavelength range of 680-1235 and 1100-2500 nm with a quartz cell (light path: 0.5, 1, 2, 4 and 10mm) at 10-40. Statistical analysis was performed using NSAS program. A partial least squares (PLS) regression analysis was used for calibration. As the result, a good correlation between the raw NIR spectra and OC was obtained in the calibration. The best light path was 10 and 0.5mm in the wavelength range of 680-1235 and 110-2500nm, respectively. In the calibration, correlation coefficients(R) were 096-0.97 in the both range. In the prediction, however, a good correlation (R=0.89-0.96) was obtained only in the range of 6801235 nm, Similar results were obtained in the cases of COD and BOD. These results suggest the possibility that NIR spectroscopy can be used to evaluate drainage.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.46-46
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• 2022

유기물의 복잡한 혼합물인 CDOM(Colored or Chromophoric Dissolved Organic Matter)은 하천 내 BOD(Biological Oxygen Demand), COD(Chemical Oxygen Demand) 및 유기 오염물질과 상당한 관련이 있다. CDOM은 가시광선 영역에서 빛을 흡수하는 성질을 가지고 있으며, 최근 원격감지 기술로 CDOM을 모니터링하기 위한 연구가 진행되고 있다. 본 연구에서는 백제보 상류 23km 구간에서 3년(2016~2018) 중 13일의 초분광영상을 활용하여 머신러닝 기반 CDOM을 추정 알고리즘을 개발하고자 한다. 초분광영상은 400~970 nm의 범위의 4 nm 간격 127개 대역의 분광해상도와 2 m의 공간해상도를 가진 항공기 탑재 AsiaFENIX 초분광 센서를 통해 수집하였으며 CDOM은 Millipore polycarbonate filter (𝚽47, 0.2 ㎛)에서 여과된 CDOM 샘플 자료를 200~800 nm의 흡수계수 스펙트럼으로 추출하여 사용하였다. CDOM 값은 전체기간 동안 2.0~11.0 m^-1의 값 분포를 보였으며 5 m^-1이상의 고농도 구간 자료개수가 전체 153개 샘플자료 중 21개로 불균형하다. 따라서 ADASYN(Adaptive Synthesis Sampling Approach)의 oversampling 방법으로 생성된 합성 데이터를 사용하여 원본 데이터의 소수계층 데이터 불균형을 해결하고 모델 예측 성능을 개선하고자 하였다. 생성된 합성 데이터를 입력변수로 하여 ANN(Artificial Neural Netowk)을 활용한 CDOM 예측 알고리즘을 구축하였다. ADASYN 기법을 통한 합성 데이터는 관측된 데이터의 불균형을 해결하여 기계학습 모델의 CDOM 탐지 성능을 향상시킬 수 있으며, 저수지 내 유기 오염물질 관리를 위한 설계를 지원하는데 사용할 수 있을 것으로 판단된다.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.5
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• pp.1-9
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• 2014

The DIROM (Daily Irrigation Reservoir Operation Model) was modified to estimate amounts of water release from reservoirs, considering customary irrigation water management practices, such as water supply for puddling and transplanting paddy rice from seeding beds and mid-season drainage. The applicability of the modified model was investigated by simulating amounts of water release from three study reservoirs: Hwamae, Ogi, and Doya Reservoirs. In terms of annual amounts of water release, the relative errors between the observed and simulated values in 2012 and 2013 ranged -26.20 % to 10.28 % and 4.90 % to 30.06 %, respectively; in case of reservoir water levels, the RMSE values ranged 0.45 m to 1.34 m and 0.40 m to 1.27 m, respectively. Also, it was revealed that the model provided better simulation results for monthly water releases than the original model. In addition, the model presented better performance in simulating 10-day amounts of water release from April to June. However, the model had still significant errors in the simulation results from July to September because the reservoirs were practically operated to adapt to water management circumstances. Finally, it is concluded that the modified DIROM can estimate the amounts of water release from reservoirs, reflecting irrigation water management customs in paddy-field districts. To achieve higher prediction accuracy of the model, it is necessary to incorporate practical reservoir operation rules into the model.

• Clean Technology
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• v.28 no.1
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• pp.79-93
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• 2022

In this study, an electronics industrial wastewater activated sludge model (e-ASM) to be used as a Water Digital Twin was calibrated based on real high-tech electronics industrial wastewater treatment measurements from lab-scale and pilot-scale reactors, and examined for its treatment performance, effluent quality prediction, and optimal process selection. For specialized modeling of a high-tech electronics industrial wastewater treatment system, the kinetic parameters of the e-ASM were identified by a sensitivity analysis and calibrated by the multiple response surface method (MRS). The calibrated e-ASM showed a high compatibility of more than 90% with the experimental data from the lab-scale and pilot-scale processes. Four electronics industrial wastewater treatment processes-MLE, A2/O, 4-stage MLE-MBR, and Bardenpo-MBR-were implemented with the proposed Water Digital Twin to compare their removal efficiencies according to various electronics industrial wastewater characteristics. Bardenpo-MBR stably removed more than 90% of the chemical oxygen demand (COD) and showed the highest nitrogen removal efficiency. Furthermore, a high concentration of 1,800 mg L^-1 T MAH influent could be 98% removed when the HRT of the Bardenpho-MBR process was more than 3 days. Hence, it is expected that the e-ASM in this study can be used as a Water Digital Twin platform with high compatibility in a variety of situations, including plant optimization, Water AI, and the selection of best available technology (BAT) for a sustainable high-tech electronics industry.