• 한국생물공학회:학술대회논문집
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• 2000.04a
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• pp.125-128
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• 2000

A recycling reactor system operated under sequential anoxic and oxic conditions for the swine wastewater has been developed, in which piggery slurry is fermentatively and aerobically treated and then part of the effluent recycled to the pigsty. This system significantly removes offensive smells (at both pigsty and treatment plant), BOD and other loads, and appears to be costeffective for the small-scale farms. The most dominant heterotrophs were Alcaligenes faecalis, Brevundimonas diminuta and Streptococcus sp. in order while lactic acid bacteria were dominantly observed in the anoxic tank. We propose a novel monitoring system for a recycling piggery slurry treatment system through neural networks. Here we tried to model treatment process for each tank(influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) in the system based on population densities of heterotrophic and lactic acid bacteria. Principle component analysis(PCA) was first applied to identify a relation between input(microbial densities and parameters for the treatment such as population densities of heterotrophic and lactic acid bacteria, suspended solids (SS), COD, $NH_3-N$, ortho-P, and total-P) and output, and then multilayer neural networks were employed to model the treatment process for each tank. PCA filtration of input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of input. Neural networks independently trained for each treatment tank and their subsequent combinatorial data analysis allowed a successful prediction of the treatment system for at least two days.

• 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 Environmental Impact Assessment
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• v.25 no.5
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• pp.345-356
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• 2016

In this study, auto-calibration method for water quality model was compared and analyzed using QUAL2Kw, which can estimate the optimum parameters through the integration of genetic algorithm and QUAL2K. The QUAL2Kw was applied to the Sum River which is greatly affected by the pollution loads of Wonju city. Two auto-calibration methods were examined: single parameter application for the whole river reach and separate parameter application for each reach of multiple reaches. The analysis about CV(RMSE) and fitness of the GA show that the separate parameter auto-calibration method is better than the single parameter method in the degree of precision. Thus the separate parameter auto-calibration method is applied to the water quality modelling of this study. The calibrated QUAL2Kw was used for the three scenarios for the water quality management of the Sum River, and the water quality impact on the river was analyzed. In scenario 1, which improve the effluent water quality of Wonju WWTP, BOD and TP concentrations of the Sum River 4-1 station which is representative one of Mid-Watershed, are decreased 17.7% and 29.1%, respectively. And immediately after joining the Wonjucheon, BOD and TP concentrations are decreased 50.4% and 40.5%, respectively. In scenario 2, Wonju water supply intake is closed and multi-regional water supply, which come from other watershed except the Sum River, is provided. The Sum River water quality in scenario 2 is slightly improved as the flow of the river is increased. Immediately after joining the Wonjucheon, BOD and TP concentrations are decreased 0.18mg/L and 0.0063mg/L, respectively. In scenario 3, the water quality management alternatives of scenario 1 and 2 are planned simultaneously, the Sum River water quality is slightly more improved than scenario 1. Water quality prediction of the three scenarios indicates that effluent water quality improvement of Wonju WWTP is the most efficient alternative in water quality management of the Sum River. Particularly the Sum River water quality immediately after joining the Wonjucheon is greatly improved. When Wonju water supply intake is closed and multi-regional water supply is provided, the Sum River water quality is slightly improved.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.9
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• pp.940-948
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• 2006

Fluorescence measurements of dissolved organic matter(DOM) have the superior advantages over other analysis tools for applying to water quality management. They are simple and fast and require minimal pretreatment of samples. Fluorescence index($F_{450}/F_{500}$), synchronous spectra, and fluorescence excitation-emission matrices(EEM) of various DOM samples were investigated to discriminate autochthonous/allochthonous composition, protein-like fluorescence, fulvic-like fluorescence, humic-like fluorescence, terestrial humic-like fluorescence by comparing among the real DOM samples of different origins with the help of literature. The samples used included standard purified DOM, lake, river and wastewater treatment effluent. The relative distribution of various DOM composition was derived from the ratios of each fluorescence region. The results were very consistent with those expected from the sample properties. Allochthonous and terrestrial humic-like fluorescence were more prominent in the samples with abundant soil-derived DOM components. In addition, the protein-like fluorescence property was more pronounced in the samples where strong algal or microbial activities were expected. It was also shown that the ratio of protein-like/terrestrial humic-like fluorescence obtained from synchronous spectrum and fluorescence EEM could be used as an indicator for the evaluation of wastewater treatment on the downstream water quality of rivers and for the prediction of the degree of algal/microbial activities in lakes. It is expected that the results of this study will provide the basic information to develop the future water quality management techniques using DOM fluorescence measurements.

• Journal of Environmental Impact Assessment
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• v.26 no.5
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• pp.331-343
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• 2017

The important changes in water environment management in Korea can be summarized as the enactment of Act on the Integrated Control of Pollutant-Discharging Facilities. Therefore water quality impact assessment should be reexamined and be revised. This study examines the present water quality impact assessment items (permissible discharge limits, standards for effluent water quality including Total Pollutant Load Management System) and considers the land use regulation for water quality conservation and NVZs(Nitrate Vulnerable Zones of EU and England). It also considers lately adopted standards(maximum discharge standards, permissible discharge standards, and marginal discharge standards etc) based on Act on the Integrated Control of Pollutant-Discharging Facilities and then compares Korean BAT and its counterpart control technology of U.S.A. And it also compares the items of water quality impact assessment with those of Integrated Control of Pollutant-Discharging Facilities, based on EIS reporting items. This study suggests five improvement measures for water quality impact assessment. First reflection of discharge impact analysis on impact prediction and assessment, second reflection of permissible discharge standards on agreed standards in the EIA procedure, third, reflection of diversified BAT on mitigation measures in the EIA procedure, forth introduction of land use regulation such as NVZs, finally strengthening linkage between water quality items and land use items etc.

• Korean Journal of Environmental Agriculture
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• v.10 no.1
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• pp.67-75
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• 1991

The QUAL-II E Model was applied to predict the water quality of the Mankyong drainage System, and lead to following conclusion. 1. The difference between computed and measured BOD at the M-3 (Bakgugeong) station was within 10%, indicating that the application of the QUAL-IIE Model for the prediction of water quality was satisfactory thus far. 2. The application of the model states that the discharge of concentrated pollutants at the M-1 station on the Jeonju stream, located 41Km upstream from the estuary, causes the worst problems. The sluice which extends residence time and enlarges watery surface improves water quality by a Self-purification process at the M-3 station, 28km upstream from the estuary. 3. The accuracy of the model diminished when this model was applied on the estuary downstream of the sluice. Hence, the application of the model on the estuary needs to be used with caution. 4. Among the conputed water quality parameters, BOD is the worst problem. At the M-3 station, BOD is computed to be 26.6 mg/1 in 1996, 30.7 mg/1 in 2,001, 33.0 mg/l in 2006, and 37.5 mg/1 in 2011. When preventive measures against water pollution are not properly exercised, severe problems in irrigation and water resources are expected. This study will be of used in the selection of irrigation water intake points, the criteria of effluent treatment, the management of water resources, and the establishment of water quality managemont policy.

• Clean Technology
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• v.28 no.1
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• pp.63-78
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• 2022

Electronics industrial wastewater treatment facilities release organic wastewaters containing high concentrations of organic pollutants and more than 20 toxic non-biodegradable pollutants. One of the major challenges of the fourth industrial revolution era for the electronics industry is how to treat electronics industrial wastewater efficiently. Therefore, it is necessary to develop an electronics industrial wastewater modeling technique that can evaluate the removal efficiency of organic pollutants, such as chemical oxygen demand (COD), total nitrogen (TN), total phosphorous (TP), and tetramethylammonium hydroxide (TMAH), by digital twinning an electronics industrial organic wastewater treatment facility in a cyber physical system (CPS). In this study, an electronics industrial wastewater activated sludge model (e-ASM) was developed based on the theoretical reaction rates for the removal mechanisms of electronics industrial wastewater considering the growth and decay of micro-organisms. The developed e-ASM can model complex biological removal mechanisms, such as the inhibition of nitrification micro-organisms by non-biodegradable organic pollutants including TMAH, as well as the oxidation, nitrification, and denitrification processes. The proposed e-ASM can be implemented as a Water Digital Twin for real electronics industrial wastewater treatment systems and be utilized for process modeling, effluent quality prediction, process selection, and design efficiency across varying influent characteristics on a CPS.