• Journal of Korean Society of Water and Wastewater
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• v.21 no.5
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• pp.601-607
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• 2007

In order to maintain constant residual chlorine in sedimentation basin, It is necessary to develop real time prediction model of residual chlorine considering water treatment plant data such as water qualities, weather, and plant operation conditions. Based on the operation data acquired from K water treatment plant, prediction models of residual chlorine in sediment basin were accomplished. The input parameters applied in the models were water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage. The multiple regression models were established with linear and non-linear model with 5,448 data set. The corelation coefficient (R) for the linear and non-linear model were 0.39 and 0.374, respectively. It shows low correlation coefficient, that is, these multiple regression models can not represent the residual chlorine with the input parameters which varies independently with time changes related to weather condition. Artificial neural network models are applied with three different conditions. Input parameters are consisted of water quality data observed in water treatment process based on the structure of auto-regressive model type, considering a time lag. The artificial neural network models have better ability to predict residual chlorine at sediment basin than conventional linear and nonlinear multi-regression models. The determination coefficients of each model in verification process were shown as 0.742, 0.754, and 0.869, respectively. Consequently, comparing the results of each model, neural network can simulate the residual chlorine in sedimentation basin better than mathematical regression models in terms of prediction performance. This results are expected to contribute into automation control of water treatment processes.

• Journal of Korean Society of Water and Wastewater
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• v.12 no.3
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• pp.118-124
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• 1998

To use chlorine residual as an surrogate parameter of the water quality change during the transportation in the water distribution system(WDS), the correct prediction model of chlorine residual must be established in advance. This paper shows the procedure and the result of applying the water quality model to the field WDS. To begin with, hydraulic model was calibrated and verified using fluoride as an tracer. And chlorine residual was predicted through simulation of water quality model. This predicted value was compared with the observed value. With adjusting the bulk decay coefficient(kb) and the wall decay coefficient(kw) according to the pipewall environment, the predicted chlorine residual can represent the observed value relatively well.

• Journal of Korean Society of Water and Wastewater
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• v.33 no.1
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• pp.17-29
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• 2019

This study developed prediction models of chlorine bulk decay coefficient by each condition of water quality, measuring chlorine bulk decay coefficients of the water and water quality by water purification processes. The second-reaction order of chlorine were selected as the optimal reaction order of research area because the decay of chlorine was best represented. Chlorine bulk decay coefficients of the water in conventional processes, advanced processes before rechlorination was respectively $5.9072(mg/L)^{-1}d^{-1}$ and $3.3974(mg/L)^{-1}d^{-1}$, and $1.2522(mg/L)^{-1}d^{-1}$ and $1.1998(mg/L)^{-1}d^{-1}$ after rechlorination. As a result, the reduction of organic material concentration during the retention time has greatly changed the chlorine bulk decay coefficient. All the coefficients of determination were higher than 0.8 in the developed models of the chlorine bulk decay coefficient, considering the drawn chlorine bulk decay coefficient and several parameters of water quality and statistically significant. Thus, it was judged that models that could express the actual values, properly were developed. In the meantime, the chlorine bulk decay coefficient was in proportion to the initial residual chlorine concentration and the concentration of rechlorination; however, it may greatly vary depending on rechlorination. Thus, it is judged that it is necessary to set a plan for the management of residual chlorine concentration after experimentally assessing this change, utilizing the methodology proposed in this study in the actual fields. The prediction models in this study would simulate the reduction of residual chlorine concentration according to the conditions of the operation of water purification plants and the introduction of rechlorination facilities, more reasonably considering water purification process and the time of chlorination. In addition, utilizing the prediction models, the reduction of residual chlorine concentration in the supply areas can be predicted, and it is judged that this can be utilized in setting plans for the management of residual chlorine concentration.

• Journal of Environmental Health Sciences
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• v.29 no.1
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• pp.8-15
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• 2003

In this study, chlorine dose at water storage tank was predicted to meet the recommended guideline for free chlorine residual in drinking water distribution system, using EPANET which is a computer program that performs extended Period simulation of hydraulic and water quality behavior within pressurized pipe networks. The results may be summarized as follows. The decay of chlorine residual by season varied considerably in the following order; in summer ($25^{\circ}C$) > spring and fall (15$^{\circ}C$) > winter (5$^{\circ}C$). For re-chlorination at water storage tank by season, season-varying chlorine dose was required at its maximum of 1.00 mg/l in summer and minimum of 0.40 mg/l in winter as free chlorine residual. The decay of chlorine residual through out the networks increased with water age spent by a parcel of water in the network except for some points with low water demand. In conclusion, the season-varying chlorine dose as well as the monitoring of water quality parameters at the some points which showed high decay of chlorine residual may be necessary to deliver the safe drinking water.

• Journal of Korean Society of Water and Wastewater
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• v.26 no.6
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• pp.861-869
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• 2012

The chlorine's residual concentration prevents the regrowth of microorganism in water transport along the pipeline system. Precise prediction of chlorine concentration is important in determining disinfectant injection for the water distribution system. In this study, a pilot scale water distribution system was designed and fabricated to measure the temporal variation of chlorine concentration for three flow conditions (V = 0.88, 1.33, 1.95 m/s). Various kinetic models were applied to identify the relationship between hydraulic condition and chlorine decay. Genetic Algorithm (GA) was integrated into five kinetic models and time series of chlorine were used to calibrate parameters. Model fitness was compared by Root Mean Square Error (RMSE) between measurement and prediction. Limited first order model and Parallel first order showed good fitness for prediction of chlorine concentration.

• 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 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 Water and Wastewater
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• v.14 no.1
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• pp.108-116
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• 2000

Chlorine is widely used as a disinfectant in drinking-water systems throughout the world. Chlorine residual was used as an indicator for prediction of water quality in water distribution systems. The variation of chlorine residual in drinking water distribution systems of Suwon city was simulated using EPANET. EPANET is a computerized simulation model which predicts the dynamic hydraulic and water quality behavior within a water distribution system operating over an extended time period. Sampling and analysis were performed to calibrated the computer model in 1999 (Aug. Summer). Water quality variables used in simulations are temperature, roughness coefficient, pipe diameter, pipe length, water demand, velocity and so on. Extended water residence time affected water quality due to the extended reaction time in some areas. All area showed the higher concentration of chlorine residual than 0.2mg/l(standard). So it can be concluded that any area in Suwon city is not in biological regrowth problem. Rechlorination turned out to be an useful method for uniform concentration of free chlorine residual in distribution system. The cost of disinfectant could be saved remarkably by cutting down the initial chlorine concentration to the level which guarantees minimum concentration (0.2mg/l) throughout the distribution system.

• Journal of Korean Society of Water and Wastewater
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• v.28 no.3
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• pp.287-297
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• 2014

It is important to predict chlorine decay with different water purification processes and distribution pipeline materials, especially because chlorine decay is in direct relationship with the stability of water quality. The degree of chlorine decay may affect the water quality at the end of the pipeline: it may produce disinfection by-products or cause unpleasant odor and taste. Sand filtrate and dual media filtrate were used as influents in this study, and cast iron (CI), polyvinyl chloride (PVC), and stainless steel (SS) were used as pipeline materials. The results were analyzed via chlorine decay models by comparing the experimental and model parameters. The models were then used to estimate rechlorination time and chlorine decay time. The results indicated that water quality (e.g. organic matter and alkalinity) and pipeline materials were important factors influencing bulk decay and sand filtrate exhibited greater chlorine decay than dual media filtrate. The two-component second-order model was more applicable than the first decay model, and it enabled the estimation of chlorine decay time. These results are expected to provide the basis for modeling chlorine decay of different water purification processes and pipeline materials.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.2
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• pp.213-218
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• 2008

Prediction models for post-chlorination require complicated information of reaction time, chlorine dosage considering flow rate as well as environmental conditions such as turbidity, temperature and pH. In order to operate post-chlorination process effectively, the correlations between inlet and outlet of clear well were investigated to develop prediction models of chlorine dosages in post-chlorination process. Correlations of environmental conditions including turbidity and chlorine dosage were investigated to predict residual chlorine at the outlet of clear well. A linear regression model and autoregressive model were developed to apply for the post-chlorination which take place time delay due to detention in clear well tank. The results from autoregressive model show the correlationship of 0.915~0.995. Consequently, the autoregressive model developed in this study would be applicable for real time control for post chlorination process. As a result, the autoregressive model for post chlorination which take place time delay and have multi parameters to control system would contribute to water treatment automation system by applying the process control algorithm.