• Journal of Food Hygiene and Safety
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• v.1 no.2
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• pp.163-169
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• 1986

소, 돼지, 닭 및 염소의 근육, 지방조직 및 내장부위에서 11가지 유기염소계 잔류농약을 분석하여 다음과 같은 결과를 얻었다. 1. 육류시료 모두에서 검출된 유기염소계의 잔류량은 $\alpha$-BHC, $eta$-BHC, pp'-DDE, Heptachlor epoxide, ${\gamma}$-BHC순으로 검출되었고, Heptachlor, pp'-DDE 및 pp'-DDD는 극미량 내지는 흔적 정도 검출되었고 Drins류는 검출되지 않았다. 2. Total BHC 잔류량의 범위는 소의 경우 0.389ppb~1.111ppb로 평균 0.713ppb였고, 돼지는 0.139ppb~0.150ppb로 평균 0.631ppb였으며 닭은 0.312ppb~0.80ppb로서 평균 0.517ppb였다. 그리고 염소의 경우에는 0.238ppb~1.134ppb로써 평균 0.586ppb의 수준이었다. 3. 육류별 잔류량은 소, 돼지, 염소, 닭의 순서이고 부위별 잔류량은 지방조직이 가장 많았고 그 다음이 근육, 그외 부위별 잔류량의 차이는 거의 없었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.147-154
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• 2014

In this study, chlorine modeling technique based on fuzzy system is proposed to reduce the carcinogenic substance and decide the optimal chlorine injection rate, which is affected by chlorine evaporation rate in sedimentation basin according to detention time, weather and water quality. The additional chlorine meter is installed in the inlet part of sedimentation to reduce the feedback time and implement cascade control, which leads to maintaining the residual chlorine concentration decided by fuzzy rule. It helps to take a preemptive action about long time delay, the characteristics of the disinfection process, and reduce the variation of residual chlorine rate by 7.3 times and the chlorine consumption by 40,000 dollars. It made a significant contribution to supply hygienically safe drinking water.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1431-1438
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• 2006

유역에서의 하천 프랙탈은 본 연구의 목적은 상수도 배수시스템의 수질예측 모형인 EPANET의 수질보정을 위한 염소분해계수의 효율적인 적용을 평가하기 위한 것이다. 이를 위해 우선적으로 연구대상시스템의 특성에 따른 수질 및 관종별 염소분해계수를 실험에 의하여 분석하고, 대상블록에 대한 EPANET 모형의 수질보정을 위한 잔류염소분해계수의 3가지 적용방법을 검토하여 효율적인 적용방안을 도출하였다. 연구결과, 실험에 의한 염소분해계수는 계절적 특성과 관종 및 관경에 따른 다양한 결과를 보였으며, 각 방법에 따른 모의결과도 다양하게 나타났으며, 관종, 관경, 계절적 특성을 반영한 분해계수를 적용한 모의 결과가 현장분석된 잔류염소농도와 더 가깝게 예측되는 것으로 나타났다. 따라서 EPANET을 이용하여 잔류염소농도를 예측하기 위해서는 대상수질 및 관망의 특성을 반영한 잔류염소분해계수를 사용하는 방법이 가장 효율적일 것으로 사료된다.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1197-1210
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• 2022

Recently, users' complaints on drinking water quality are increasing according to emerging interest in the drinking water service issues such as pipe aging and various water quality accidents. In the case of drinking water quality complaints, not only the water pollution but also the inconvenience on the chlorine residual for disinfection are included, thus various efforts, such as rechlorination treatment, are being attempted in order to keep the chlorine concentration supplied evenly. In this research, for a more accurate water quality simulation of water distribution network, the water quality reaction coefficients were estimated, and an optimization method of chlorination/ rechlorination scheduling was proposed consideirng satisfaction of water quality standards and chlorine residual equalization. The proposed method was applied to a large-scale real water network, and various chlorination schemes were comparatively analyzed through the grid search algorithm and optimized based on the suitability and uniformity of supplied chlorine residual concentration.

• Journal of Korean Society of Disaster and Security
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• v.9 no.2
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• pp.63-75
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• 2016

For safe water supply, residual chlorine has to be maintained in tap-water above a certain level from drinking water treatment plants to the final tap-water end-point. However, according to the current literature, approximately 30-60% of residual chlorine is being lost during the whole water supply pathways. The losses of residual chlorine may have been attributed to the current tendency for water supply managers to reduce chlorine dosage in drinking water treatment plants, aqueous phase decomposition of residual chlorine in supply pipes, accelerated chlorine decomposition at a high temperature during summer, leakage or losses of residual chlorine from old water supply pipes, and disappearances of residual chlorine in water storage tanks. Because of these, it is difficult to rule out the possibility that residual chlorine concentrations become lower than a regulatory level. In addition, it is concerned that the regulatory satisfaction of residual chlorine in water storage tanks can not always be guaranteed by using the current design method in which only storage capacity and/or hydraulic retention time are simply used as design factors, without considering other physico-chemical processes involved in chlorine disappearances in water storage tank. To circumvent the limitations of the current design method, mathematical models for aqueous chlorine decomposition, sorption of chlorine into wall surface, and mass-transfer into air-phase via evaporation were selected from literature, and residual chlorine reduction behavior in water storage tanks was numerically simulated. The model simulation revealed that the major factors influencing residual chlorine disappearances in water storage tanks are the water quality (organic pollutant concentration) of tap-water entering into a storage tank, the hydraulic dispersion developed by inflow of tap-water into a water storage tank, and sorption capacity onto the wall of a water storage tank. The findings from his work provide useful information in developing novel design and technology for minimizing residual chlorine disappearances in water storage tanks.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.705-720
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• 2023

In South Korea, ongoing incidents related to drinking water quality have eroded consumer trust. Specifically, beyond quality incidents, there have been complaints about taste, odor, and other issues stemming from the presence of chlorine. To address this, water service operators are employing various management strategies from both temporal (scheduling) and spatial (rechlorination) perspectives to ensure uniform and safe distribution of chlorine residuals. In this study, we focus on the optimal monthly management of chlorine residuals, based on water distribution network analysis. Water quality reaction coefficients, including bulk fluid and wall reaction coefficients, were estimated through lab-scale tests and EPANET water quality simulations, respectively, accounting for temperature variations in a large-scale water distribution network. Utilizing these estimated coefficients, we examined the monthly variations in chlorine residual distribution under different chlorine injection conditions. The results indicate that the efficient concentration for chlorine injection, which satisfies the residual chlorine limit range, varies with temperature changes. Consequently, it is imperative to establish a specific and quantitative chlorine injection plan that considers the accurate spatial distribution of monthly chlorine residuals.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2001.11a
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• pp.87-89
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• 2001

온도조건에 따른 염소투입량과 염소투입 후 방치조건에 따른 유기할로겐화합물의 발생특성에 대해 검토하였다. 원수의 수질특성에 있어서 암모니아성 질소와 TOC의 농도가 높을수록 TOXs의 발생량은 높았고, 온도조건에 따른 TOXs의 발생량은 그다지 큰 차이는 없는 반면 염소투입량에 따른 실험에서는 큰 차이를 보였다. 방치조건에 따른 TOXs의 발생량은 실외에서 방치했을 경우 조금 높게 나타났다. 따라서 10～$30^{\circ}C$의 온도조건보다는 유기물의 농도와 염소투입량과 TOXs의 발생량과의 관계가 밀접하고, 실외방치시 시료수의 잔류염소와 외부로부터의 유기물등의 유입으로 인한 반응으로 인해 TOXs 발생량이 높아졌다고 추정되나 추후 면밀한 검토가 요구된다.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• 수도
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• v.22 no.1 s.71
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• pp.71-81
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• 1995

• Journal of Korean Society of Environmental Engineers
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• v.29 no.6
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• pp.706-714
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• 2007

This study suggested a method for prediction of residual chlorine and THMs in water distribution system by measurement of residual chlorine, THMs, and other parameters, estimation of chlorine decay coefficients and THM formation coefficients, and simulation of water qualities using pipe network analysis. Bulk decay coefficients of parallel first-order were obtained by bottle tests, and pipe wall decay coefficients of first-order were estimated through evaluation of 5 models, which showed the lowest values of 0.03 for MAE(mean absolute error) and 0.037 MAE in comparison with the observed in field. And bottle tests were conducted to model first-order reaction of THM formation by nonlinear least square regression and the resultant coefficients were compared with the observed in field. As a result, the coefficients of determination$(R^2)$ for the observed and the predicted values were 0.98 in September and 0.82 in November, and the formation of THMs was predicted by modeling.