• Journal of Environmental Impact Assessment
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• v.19 no.6
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• pp.617-623
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• 2010

It is the most important subject to figure out characteristics of the wastewater inflows of sewerage treatment plant(STP) when situation models are applied to operation of the biological processes and in the automatic control based on ICA(Instrument, Control and Automation). For the purposes, real-time influent monitoring method has been applied by using on-line monitoring equipments for the process optimization in conventional STP. Since, the influent of STP is consist of complex components such as, COD, BOD, TN, $NH_4$-N, $NO_3$-N, TP and $PO_4$-P. MRA2(Microbial Respiration Analyzer 2), which is capable of real-time analyzing of wastewater characteristics is used to overcome the limitations and defects of conventional online monitoring equipments in this study. Rapidity, accuracy and stability of developed MRA2 are evaluated and compared with the results from on-line monitoring equipments for seven months after installation in Full-scale STP.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.344-354
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• 2021

Optical methods such as UV and fluorescence spectrophotometers can be applied not only in the qualitative analysis of dissolved organic matter (DOM), but also in real-time quantitative DOM monitoring for wastewater and natural water. In this study, we measure the UV₂₅₄ and fluorescence excitation emission spectra for a sewage treatment plant influent and effluent, and river water before and after sewage effluent flows into the river to examine the composition and origin of DOM. In addition, a correlation analysis between quantified DOM characteristics and dissolved organic carbon (DOC) was conducted. Based on the fluorescence excitation emission spectra analysis, it was confirmed that the protein-type tryptophan-like DOM was the dominant substance in the influent, and that the organic matter exhibited relatively more humic properties after biological treatment. However, DOM in river water showed the fluorescence characteristics of terrestrial humic-like and algal tyrosine-like (protein-like) organic matter. In addition, a correlation analysis was conducted between the DOC and optical indices such as UV₂₅₄, the fluorescence intensity of protein-like and humic-like organic matter, then DOC prediction models were suggested for wastewater and river monitoring during non-rainfall and rainfall events. This study provides basic information that can improve the understanding of the contribution of DOC concentration by DOM components, and can be used for organic carbon concentration management in wastewater and natural water.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.9
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• pp.630-635
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• 2011

As this study was estimation of factors of rapid OUR (Oxygen Uptake Rate) monitoring method. Experiment for estimating factors of optimal microorganism activity was carried out in this study. In addition to comparison and estimation of SCOD variation by OUR variation using real wastewaters. In consequence OUR value was highest when F/M ratio, pH and temperature were 0.03~0.05, 6.0~8.5 and $20{\sim}30^{\circ}C$ respectively. Oxygen consumption by nitrification was incomplete. OUR variation of SCOD was recognizable difference of degradable rate at before and after of inflection point OUR. This study used an experimental method for real time prediction of the influent of the sewage treatment plant for optimal operation is expected to be able to do.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.581-589
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• 2013

Generally, Sewage Treatment Plants(STPs) are complexes systems in which a range of physical, chemical and biological processes occur. However, their performance strongly depends on the know-how acquired by the field-engineer. Recently, in order to solve this situations, various operation and management technologies based on the Instrumentation, Control and Automation(ICA) have been developed. As a economies-environmental affect point of view, this study was for the performance evaluation and assessment of results from the Smart Operation System(SOS) in full-scale STP. The SOS in STP consisted of the process monitoring module, including real-time influent prediction and effluent simulation, and the Smart Air Control(SAC) module. According to the results from field test for 2 years, the results of economical evaluation, amount of benefits and cost saving by the SOS have shown to be much higher than that of traditional operation. Nevertheless, the removal load(kg/yr) of BOD 13.3 %, COD 28.2 %, TN 44.4 % and TP 20.8 % were increased, respectively. Remarkable improvement of removal load could be achieved after the SOS was adapted. It was concerned that the SOS offer a user friendly functionalities and cost saving needed by the field-engineers. In addition, it was expected that the results of this study would supply helpful information for design and cost saving for the SOS in full-scale STP.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.3
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• pp.308-315
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• 2006

Since sewage characteristics are the most important factors that can affect the biological reactions in wastewater treatment plants, a detailed understanding on the characteristics and on-line measurement techniques of the influent sewage would play an important role in determining the appropriate control strategies. In this study, samples were taken at two hour intervals during 51 days from $1^{st}$ October to $21^{st}$ November 2005 from the influent gate of sewage treatment plant. Then the characteristics of sewage were investigated. It was found that the daily values of flow rate and concentrations of sewage components showed a defined profile. The highest and lowest peak values were observed during $11:00{\sim}13:00$ hours and $05:00{\sim}07:00$ hours, respectively. Also, it was shown that the concentrations of sewage components were strongly correlated with the absorbance measured at 300 nm of UV. Therefore, the objective of the paper is to develop on-line estimation technique of the concentration of each component in the sewage using accumulated profiles of sewage, absorbance, and flow rate which can be measured in real time. As a first step, regression analysis was performed using the absorbance and component concentration data. Then a neural network trained with the input of influent flow rate, absorbance, and inflow duration was used. Both methods showed remarkable accuracy in predicting the resulting concentrations of the individual components of the sewage. In case of using the neural network, the predicted value md of the measurement were 19.3 and 14.4 for TSS, 26.7 and 25.1 for TCOD, 5.4 and 4.1 for TN, and for TP, 0.45 to 0.39, respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1706-1709
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• 2010

Sewerage Treatment Plants(STPs) are complexes systems in which a range of physical, chemical and biological processes occur. Since Activated Sludge Model(ASM) No.1 was published, a number of new mathematical models for simulating biological processes have been developed. However, these models have disadvantages in cost and simplicity due to the laboriousness and tediousness of their procedures. One of the major difficulties of these mathematical model based tools is that the field-operators mostly don't have the time or the computer-science skills to handle there models, so it mainly remains on experts or special engineers. In order to solve these situations and help the field-operators, the $KM^2BM$(K-water & More-M Mass Balance Model) based on the dynamic-mass balance model was developed. This paper presents $KM^2BM$ as a simulation tools for STPs design and optimization. This model considers the most important microbial behavioral processes taking place in a STPs to maximize potential applicability without increasing neither model parameter estimation nor wastewater characterization efforts.

• Clean Technology
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• v.28 no.1
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• pp.79-93
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• 2022

In this study, an electronics industrial wastewater activated sludge model (e-ASM) to be used as a Water Digital Twin was calibrated based on real high-tech electronics industrial wastewater treatment measurements from lab-scale and pilot-scale reactors, and examined for its treatment performance, effluent quality prediction, and optimal process selection. For specialized modeling of a high-tech electronics industrial wastewater treatment system, the kinetic parameters of the e-ASM were identified by a sensitivity analysis and calibrated by the multiple response surface method (MRS). The calibrated e-ASM showed a high compatibility of more than 90% with the experimental data from the lab-scale and pilot-scale processes. Four electronics industrial wastewater treatment processes-MLE, A2/O, 4-stage MLE-MBR, and Bardenpo-MBR-were implemented with the proposed Water Digital Twin to compare their removal efficiencies according to various electronics industrial wastewater characteristics. Bardenpo-MBR stably removed more than 90% of the chemical oxygen demand (COD) and showed the highest nitrogen removal efficiency. Furthermore, a high concentration of 1,800 mg L^-1 T MAH influent could be 98% removed when the HRT of the Bardenpho-MBR process was more than 3 days. Hence, it is expected that the e-ASM in this study can be used as a Water Digital Twin platform with high compatibility in a variety of situations, including plant optimization, Water AI, and the selection of best available technology (BAT) for a sustainable high-tech electronics industry.