• 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 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.

• Preventive Nutrition and Food Science
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• v.21 no.2
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• pp.147-154
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• 2016

In this study, the effects of a pre-filter system and electrolysis system on the safe and efficient reuse of brine in the cabbage salting process were investigated. First, sediment filter-electrolyzed brine (SF-EB) was selected as brine for reuse. Then, we evaluated the quality and microbiological properties of SF-EB and Chinese cabbage salted with SF-EB. The salinity (9.4%) and pH (4.63) of SF-EB were similar to those of control brine (CB). SF-EB turbidity was decreased (from 0.112 to 0.062) and SF-EB residual chlorine (15.86 ppm) was higher than CB residual chlorine (0.31 ppm), and bacteria were not detected. Salinity (2.0%), pH (6.21), residual chlorine (0.39 ppm), chromaticity, hardness, and chewiness of cabbage salted with SF-EB were similar to those of cabbage salted with CB. The total bacterial count in cabbage salted with CB was increased as the number of reuses increased (from 6.55 to 8.30 log CFU/g), whereas bacteria in cabbage salted with SF-EB was decreased (from 6.55 to 5.21 log CFU/g). These results show that SF-EB improved the reusability of brine by removing contaminated materials and by sterilization.

• The Korean Journal of Food And Nutrition
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• v.21 no.4
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• pp.499-509
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• 2008

The purpose of this study was to investigate the changes in physicochemical and sensory properties of raw fruits during washing and chlorine treatments. Strawberry and banana were pre-prepared at different concentration of chlorinated water(0 ppm, 50 ppm and 100 ppm), immersion time(3 min and 5 min), and number of post-rinsing(1 time, 2 times and 3 times). The physicochemical properties such as pH, sugar contents, residual chlorine contents, color values and hardness of the fruits were analyzed, and the sensory quality were evaluated throughout the sterilization treatment process. After washing strawberry with 100 ppm chlorinated water and 3 times of post-rinsing, pH and residual chlorine contents were showed a little difference, while sugar contents, hardness, and color values(L, a and b) were reduced. In case of banana, pH, sugar contents and residual chlorine contents were not affected, and hardness and L color value were reduced. However, a and b color values of banana were gradually increased as the development of brown discoloration. Sensory properties of the samples were affected by the chlorine sterilization treatment. In overall acceptance, strawberry and banana treated with 100 ppm chlorinated water showed the lowest scores among treatments. Therefore it could be suggested that the application of 50 ppm chlorinated water for $3{\sim}5$ minutes with over 3 times of post-rinsing was the effective pre-preparation method without affecting the quality of the fruits.

• Journal of the Korean Society of Marine Environment & Safety
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• v.2 no.1
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• pp.89-95
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• 1996

This paper describes a fuzzy controlled pre-chlorination technique for purifying the pulluted raw water in water purification lants. For the purpose of obtaining the high quality water, the appropriate pre-chlorine dosage rate has to be continuously adjusted according to a change in quality of a intake raw water, weather, solar nergy mount, temperature and etc. Therefore, the method of expressing an expert's empirical knowledge cumulated from his past carrier by fuzzy reasoning and the fuzzy controller design technique is necessary.In this paper fuzzy membership functions and rules accordingto emprircal knowledge and experimental field data were obtained, And also fuzzy cintriller design using four feedforward components for the determination of pre-chlorine dosage rate and four feedback ones for the compensation of its dosage rate with residual chlorine and its change rate, was executed.

• 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.22 no.3
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• pp.505-510
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• 2000

In this study the effects of pre-chlorination on the coagulation of water which contain Microcystis aeruginosa. were investigated on the laboratory scale. We prepared the sample of $10^5cell/mL$ Microcystis aeruginosa and then applied 0.2, 1.0, 10 mg-Cl/L chlorine on the sample After reaction period(1 minute and 1 hour), each sample was coagulated. As a result, after 0.4 mg-Al/L coagulant dose, turbidities of all samples were below 2 mg-Kaolin/L. Turbidity was not affected by chlorine dose. As the dose of chlorine was increased, the residual aluminum was decreased, but result of $UV_{254}$ was adverse.

• Journal of Korean Society on Water Environment
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• v.38 no.6
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• pp.267-274
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• 2022

This paper is a result of research conducted on the 800,000 m³/d capacity of A Water Treatment Plant (WTP) and 400,000 m³/d capacity of B WTP plant in operation in the Nakdong River region. We evaluated the effect of algae broom on the WTP operation based on the running data of both WTP and the data on the pre-oxidation process field test for algae control using sodium permanganate (SPM) at the B WTP. The study results showed that during the algal bloom period, the coagulant dose increased by 102% in A WTP and 58% in B WTP, respectively, and the chlorine dose also increased by 38% and 29%, respectively, which may affect Total trihalomethane (THM) production. Data such as algal populations and Chl-a, residual chlorine and THM, algal populations, and ozone dose appeared also highly correlated, confirming that algal broom affects WTP operations, including water quality and chemical dosage. As a result of the field test of B WTP, THMs appeared lower than that of the control, suggesting the possibility of the SPM pre-oxidation process as an alternative to algae-related water quality management. Furthermore, in terms of GHG emissions due to energy consumption, it was observed that the pre-oxidation process using SPM was approximately 10.8%, which is a very low ratio compared to the pre-ozonation process. Therefore, these results suggest that the SPM pre-oxidation process can be recommended as an alternative to low-carbon water purification technology.

• Environmental Engineering Research
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• v.24 no.3
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• pp.501-512
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• 2019

The seasonal effects on the biostability of drinking water were investigated by comparing the seasonal variation of assimilable organic carbon (AOC) in full-scale water treatment process and adsorption of AOC by three filling materials in lab-scale column test. In full-scale, pre-chlorination and ozonation significantly increase $AOC_{P17\;(Pseudomonas\;fluorescens\;P17)}$ and $AOC_{NOX\;(Aquaspirillum\;sp.\;NOX)}$, respectively. AOC formation by oxidation could increase with temperature, but the increased AOC could affect the biostability of the following processes more significantly in winter than in warm seasons due to the low biodegradation in the pipes and the processes at low temperature. $AOC_{P17}$ was mainly removed by coagulation-sedimentation process, especially in cold season. Rapid filtration could effectively remove AOC only during warm seasons by primarily biodegradation, but biological activated carbon filtration could remove AOC in all seasons by biodegradation during warm season and by adsorption and bio-regeneration during cold season. The adsorption by granular activated carbon and anthracite showed inverse relationship with water temperature. The advanced treatment can contribute to enhance the biostability in the distribution system by reducing AOC formation potential and helping to maintain stable residual chlorine after post-chlorination.

• Journal of Environmental Health Sciences
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• v.46 no.6
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• pp.710-718
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• 2020

Objectives: We analyzed water in waterscape facilities to investigate contamination levels of water-borne pathogens and four test items (pH, turbidity, residual chlorine, and Escherichia coli) at facilities including play fountains, splash parks, and artificial streams from June to October in Suwon City and in the whole of Gyeonggi-do. Methods: A total of 62 waterscape facility samples were collected from 36 sites and tested for pathogenic Escherichia coli and water-borne viruses that cause hand-foot-and-mouth disease, eye disease, and acute enteritis. Results: None of the water-borne pathogens were detected in waterscape facility samples collected from across Gyeonggi-do that were for pre-inspection for facility management. However, the results of samples from Suwon collected in hot weather and during the school vacation period showed five total inconsistencies in turbidity (four cases) and Escherichia coli (one case). Three out of the four inconsistent samples in turbidity were from the same facility which operated a sand filtration system due to its locational factors close to mountains. Conclusion: We suggest that the waterscape facilities in Gyeonggi-do are managed properly in the respect of microbial contamination and water quality.