• 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.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.

• 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.

• 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.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.10
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• pp.916-927
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• 2010

In general water treatment process, the disinfection process by chlorine is used to prevent water borne disease and microbial regrowth in water distribution system. Because chlorines were reacted with organic matter, carcinogens such as disinfection by-products (DBPs) were produced in drinking water. Therefore, a suitable injection of chlorine is need to decrease DBPs. Rechlorination in water pipelines or reservoirs are recently increased to secure the residual chlorine in the end of water pipelines. EPANET 2.0 developed by the U.S. Environmental Protection Agency (EPA) is used to compute the optimal chlorine injection in water treatment plant and to predict the dosage of rechlorination into water distribution system. The bulk decay constant ($k_{bulk}$) was drawn by bottle test and the wall decay constant ($k_{wall}$) was derived from using systermatic analysis method for water quality modeling in target region. In order to predict water quality based on hydraulic analysis model, residual chlorine concentration was forecasted in water distribution system. The formation of DBPs such as trihalomethanes (THMs) was verified with chlorine dosage in lab-scale test. The bulk decay constant ($k_{bulk}$) was rapidly decreased with increasing temperature in the early time. In the case of 25 degrees celsius, the bulk decay constant ($k_{bulk}$) decreased over half after 25 hours later. In this study, there were able to calculate about optimal rechlorine dosage and select on profitable sites in the network map.

• Journal of Korean Society of Water and Wastewater
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• v.23 no.3
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• pp.331-337
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• 2009

Electrolysis produces hypochlorous acid by using a small quantity of NaCl as electrolyte. This process maximizes the stabilization of drinking water through the control of chlorine residual concentration. This study investigated free chlorine generation by an electrolytic method using $Ti/IrO_2$ and stainless steel. The generation of free chlorine was increased with increasing hydraulic retention time, voltage, chlorine ion concentration and the number of electrodes. However, the change of pH did not affect the generation of free chlorine. There was no significant difference on the behavior of chlorine concentration between electrolytic method and NaOCl injection. In this study, the concentration of free chlorine predicted model based on power functional model was developed various under conditions. Electrolysis free chlorine generation model can be effective tool in the estimation of free chlorine generation.

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.643-656
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• 2021

Recently, water supply management technology is highly developed, and a computer simulation model plays a critical role for estimating hydraulics and water quality in water distribution networks (WDNs). However, a simulation of complex large water networks is computationally intensive, especially for the water quality simulations, which require a short simulation time step and a long simulation time period. Thus, it is often prohibitive to analyze the water quality in real-scale water networks. In this study, in order to improve the computational efficiency of water quality simulations in complex water networks, a hierarchical water-quality-simulation technique was proposed. The water network is hierarchically divided into two sub-networks for improvement of computing efficiency while preserving water quality simulation accuracy. The proposed approach was applied to a large-scale real-life water network that is currently operating in South Korea, and demonstrated a spatiotemporal distribution of chlorine concentration under diverse chlorine injection scenarios.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.723-728
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• 2019

Citizens' distrust of water pollution is very high in tap water that we routinely drink. In addition, water pollution accidents of tap water are difficult to predict and the risk is high, so real-time monitoring and management are needed. Therefore, it is necessary to introduce real-time water quality monitoring using the Internet of things(IoT). Residual chlorine is more persistent and economical than other disinfectants and it is easy to check residual effect, so it is mainly used as a disinfection index in waterworks. It can be monitored in real time by using IoT technology in order to secure the safety of tap water. In this study, we developed smart device for real-time water quality monitoring using amperometry sensor and analyzed its performance.

• Journal of Korean Society of Environmental Engineers
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• v.35 no.4
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• pp.276-282
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• 2013

The reclaimed water has been highlighted as a representative alternative to solve the lacking water resources. This study examined the reduction of residual free chlorine by temperature (5, 15, $25^{\circ}C$) and initial injection concentration (1, 2, 4, 6 mg/L) in the reclaimed water and carried out propose on the calculating method of the optimal chlorine dosage. As the reclaimed water showed a very fast reaction with chlorine at the intial time comparing to that of drinking water, the existing general first-order decay model ($C_t=C_o(e^{-k_bt})$) was not suitable for use. Accordingly, the reduction of residual free chlorine could be estimated in a more accurate way as a result of applying the exponential first-order decay model ($C_t=a+b(e^{-k_bt})$). ($r^2$=0.872~0.988). As a result of calculating the bulk decay constant, it showed the highest result at 653 $day^{-1}$ under the condition of 1 mg/L, $25^{\circ}C$ for the initial injection whereas it showed the lowest result at 3.42 $day^{-1}$ under the condition of 6 mg/L, $5^{\circ}C$ for the initial injection. The bulk decay constant tends to increase as temperature increases, whereas the bulk decay constant tends to decrease as the initial injection concentration increases. More accurate calculation for optimal chlorine dosage could be done by using the experimental results for 30~5,040 min, after the entire response time is classified into 0~30 min and 30~5,040 min to calculate the optimal chlorine dosage. In addition, as a result of calculating the optimal chlorine dosage by temperature, the relationships of initial chlorine demand (y) by temperature (x) could be obtained such as y=1.409+0.450x to maintain 0.2 mg/L of residual free chlorine at the time after 4 hours from the chlorine injection.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.2
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• pp.87-95
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• 2016

Turbidity is a key indicator to the user that the 'Discolored Water' phenomenon known to be caused by corrosion of the pipeline in the water supply system. 'Discolored Water' is defined as a state with a turbidity of the degree to which the user visually be able to recognize water. Therefore, this study used data mining techniques in order to estimate turbidity changes in water supply system. Decision tree analysis was applied in data mining techniques to develop estimation models for turbidity changes in the water supply system. The pH and residual chlorine dataset was used as variables of the turbidity estimation model. As a result, the case of applying both variables(pH and residual chlorine) were shown more reasonable estimation results than models only using each variable. However, the estimation model developed in this study were shown to have underestimated predictions for the peak observed values. To overcome this disadvantage, a high-pass filter method was introduced as a pretreatment of estimation model. Modified model using high-pass filter method showed more exactly predictions for the peak observed values as well as improved prediction performance than the conventional model.