• Journal of Korean Society on Water Environment
• /
• v.39 no.6
• /
• pp.465-474
• /
• 2023

The chemical composition and molecular weight characteristics of dissolved organic matter (DOM) exert a profound influence on the efficiency of organic matter removal in water treatment systems, acting as efficiency predictive indicators. This research evaluated the primary chemical and molecular weight properties of DOM derived from diverse sources, including rivers, lakes, and biomasses, and assessed their relationship with the efficiency of coagulation/flocculation treatments. Dissolved organic carbon (DOC) removal efficiency through coagulation/flocculation exhibited significant correlations with DOM's hydrophobic distribution, the ratio of humic-like to protein-like fluorescence, and the molecular weight associated with humic substances (HS). These findings suggest that the DOC removal rate in coagulation/flocculation processes is enhanced by a higher presence of HS in DOM, an increased influence of externally sourced DOM, and more presence of high molecular weight compounds. The results of this study further posit that the efficacy of water treatment processes can be more accurately predicted when considering multiple DOM characteristics rather than relying on a singular trait. Based on major results from this study, a predictive model for DOC removal efficiency by coagulation/flocculation was formulated as: 24.3 - 7.83 × (fluorescence index) + 0.089 × (hydrophilic distribution) + 0.102 × (HS molecular weight). This proposed model, coupled with supplementary monitoring of influent organic matter, has the potential to enhance the design and predictive accuracy for coagulation/flocculation treatments targeting DOC removal in future applications.

• Journal of Korean Society of Environmental Engineers
• /
• v.36 no.8
• /
• pp.534-541
• /
• 2014

This study investigated a method to determine an efficient operating condition for the $UV/H_2O_2$ process. The OH radical scavenging factor is the most important factor to predict the removal efficiency of the target compound and determine the operating condition of the $UV/H_2O_2$ process. To rapidly and simply measure the scavenging factor, Rhodamine B (RhB) was selected as a probe compound. Its reliability was verified by comparing it with a typical probe compound (para-chlorobenzoic acid, pCBA); the difference between RhB and pCBA was only 1.1%. In a prediction test for the removal of Ibuprofen, the RhB method also shows a high reliability with an error rate of about 5% between the experimental result and the model prediction using the measured scavenging factor. In the monitoring result, the scavenging factor in the influent water of the $UV/H_2O_2$ pilot plant was changed up to 200% for about 8 months, suggesting that the required UV dose could be increased about 1.7 times to achieve 90% caffeine removal. These results show the importance of the scavenging factor measurement in the $UV/H_2O_2$ process, and the operating condition could simply be determined from the scavenging factor, absorbance, and information pertaining to the target compound.

• Journal of Microbiology and Biotechnology
• /
• v.11 no.6
• /
• pp.994-1001
• /
• 2001

An easier way of understanding the BNR system was proposed from the study on substrate, nutrient removal tendency, microbial community and its metabolic function by applying the municipal settled sewage. During the anaerobic period, the phosphorus release rate per VFACOD we varied depending on the phosphorus content in the sludge. When the phosphorus content in the sludge was $6\%$ VSS, according to influent VFACOD, the phosphorus release rate and PHA production were $0.35 gPO_4P/gVFACOD$ and 1.0 gPHA/gVFACOD, respectively. The $NO_3N$ requirement for the phosphorus uptake as an electron acceptor was about $0.5 gNO_3N/gPO_4P_{uptake}$ based on the proposed equation with PHA, biomass, production, and the concentration of phosphorus release/uptake. Bacterial-community analysis of the sludge, as determined by FISH and 16SrDNA characterization FISH, revealed that the beta-subclass proteobacteria were the most abundant group ($27.9\%$ of the proteobacteria-specific probe EUB338), and it was likely that representative of the beta-subclass played key roles in activated sludge. The next dominant group found was the gamma-protebacteria ($15.4\%$ of probe EUB338). 16S rDNA clone library analysis showed that the members of${\beta}$- and ${\gamma}$-proteobacteria were also the most abundant groups, and $21.5\%$ (PN2 and PN4) and $15.4\%$ (PN1 and PN5) of total clones were the genera of denitrifying bacteria and PAO, respectively. Prediction of the microbial community composition was made with phosphorus content (Pv, $\%$ P/VSS) in wasted sludge and profiles of COD, PHA, $PO_4P,\;and\;NO_3N$ in an anaerobic-anoxic SBR unit. Generally, the predicted microbial composition based upon metabolic function, i.e., as measured by stoichiometry, is fairly similar to that measure by the unculturable dependent method. In this study, a proposal was made on he microbial community composition that was more easily approached to analyze the reactor behavior.

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

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

• Journal of Food Hygiene and Safety
• /
• v.39 no.3
• /
• pp.260-265
• /
• 2024

During the coronavirus 2019 (COVID-19) pandemic, wastewater-based epidemiology was used for surveying infectious diseases. In this study, wastewater surveillance was employed to monitor COVID-19 outbreaks. Wastewater influent samples were collected from four sewage treatment plants in Ulsan (Gulhwa, Yongyeon, Nongso, and Bangeojin) between August 2022 and August 2023. The samples were concentrated using the polyethylene glycol-sodium chloride pretreatment method. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was extracted and detected using real-time polymerase chain reaction. Next generation sequences was used to perform correlation analysis between SARS-CoV-2 concentrations and COVID-19 cases and for COVID-19 variant analysis. A strong correlation was observed between SARS-CoV-2 concentrations and COVID-19 cases (correlation coefficient, r = 0.914). The COVID-19 variant analysis results were similar to the clinical variant genomes of three epidemics during the study period. In conclusion, monitoring COVID-19 via analyzing wastewater facilitates early recognition and prediction of epidemics.

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