• Journal of Korean Society on Water Environment
• /
• v.37 no.5
• /
• pp.344-354
• /
• 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 on Water Environment
• /
• v.16 no.4
• /
• pp.491-499
• /
• 2000

Effluent concentration estimation equations for treatment wetland were reviewed with 3 -year experimental data. Four equations from USEPA, WPCF, Kadlec and Knight, and this study were applied to the over 100 data points of 1996 to 1999 study at the pilot plant in Konkuk University. The system was a subsurface flow type and consisted of 60cm depth of sand and reeds, and it worked continuously including winter with domestic sewage from school building. Generally, all the equations demonstrated reasonable agreement with experimental data and they could be used for design process if selected carefully. Among them, the equation from this study showed the best fit for the data. The reason might be not only the equation was derived from the experimental data, but also it included plant coverage parameter in the equation while others did not Plant coverage was proved to be an important parameter in the prediction of the treatment wetland system, and its inclusion in the estimation equation could improve the accuracy. Although existing equations could be used in the wetland design, pilot plant experiment for the anticipated condition and subsequent equation development can provide more reliable equation. It takes time to obtain meaningful data from wetland system. Therefore, timely onset of well organized study is recommended before large scale application of treatment wetland system to either point or nonpoint source pollution abatement.

• Journal of Korean Society of Water and Wastewater
• /
• v.26 no.6
• /
• pp.889-896
• /
• 2012

In this study, simulation results of nitrogen and phosphorus removals and microbial activities for an $A_2O$ process in wastewater treatment plant are presented by using Activated Sludge Models (ASMs). Simulations were performed using pre-calibrated model and layout implemented in GPS-X simulation software. The models were used to investigate variations of SRT, water temperature, DO and C/N ratio effect on nutrients removal and microbial activity. According to the simulated results, the successful nitrification required SRT higher than 10.3 days, whereas increase of $NO_3$-N loading in the anaerobic reactor caused phosphorus release by PAOs; the effluent $NH_4$-N showed rapid change between $12^{\circ}C$(21.7 mg/L) and $13^{\circ}C$(3.2 mg/L); the effluent phosphorus was increased up to 1.9 mg/L at water temperature of $25^{\circ}C$; the DO increase was positive for heterotrophs and autotrophs growths but negative for PAOs growth; the PAOs showed low activity when C/N ratio was lower than 2.5. The experimental results indicated that the calibrated models can assure the prediction quality of the ASMs and can be used to optimize the $A_2O$ process.

• Journal of the Korean Solar Energy Society
• /
• v.33 no.2
• /
• pp.78-83
• /
• 2013

A methodology to predict the output performance of small hydro power plant using treated effluent in waste water treatment plant has been studied. Existing waste water treatment plant located in Kyunggi-Do were selected and the output performance characteristics for these plants were analyzed. .Based on the models developed in this study, the hydrologic performance characteristics for SHP sites have been analyzed. The results show that the flow duration characteristics of small hydropower plant for waste water treatment plant have quite differences compared with small hydropower plant for the river. As a result, it was found that the developed model in this study can be used to analyze the output characteristics for small hydro power in waste water treatment plant. Additionally, primary design specifications such as design flowrate, capacity, operational rate and annual electricity production were estimated and discussed. It was found that the models developed in this study can be used to decide the design performance of small hydropower plant for waste water treatment plant effectively.

• Journal of the Korea Convergence Society
• /
• v.8 no.10
• /
• pp.45-52
• /
• 2017

Drifter is an equipment for observing the characteristics of seawater in the ocean, and it can be used to predict effluent oil diffusion and to observe ocean currents. In this paper, we design models or the prediction of drifter trajectory using machine learning. We propose methods for estimating the trajectory of drifter using support vector regression, radial basis function network, Gaussian process, multilayer perceptron, and recurrent neural network. When the propose mothods were compared with the existing MOHID numerical model, performance was improve on three of the four cases. In particular, LSTM, the best performed method, showed the imporvement by 47.59% Future work will improve the accuracy by weighting using bagging and boosting.

• 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.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.18 no.4
• /
• pp.338-347
• /
• 2006

A small hydro-power plant using the seawater used as the cooling (circulated) water and discharged is under construction. The bigger size of the small hydro-power plant, the better in order to maximize the efficiency and the electric power. The optimal size, however, should be determined in the constraints of the channel un-disturbed range. The water level change should be checked in detail based on the hydraulic behaviour. In this study, the FLOW3D model, three-dimensional flow model, is setup using the flow measurement data in the effluent discharge channel and the flow pattern changes due to the small hydro-power plant construction are predicted by the model. The plant construction makes the increasing of the water level, and the water level in the upstream of the channel weir is increased 65 cm from 4.32 m to 4.97 m, in the condition of the design discharge $156m^3/s$ and the movable weir height of the hydro-power plant 3.8 m.

• Journal of Korean Society on Water Environment
• /
• v.24 no.1
• /
• pp.91-98
• /
• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

• Journal of Microbiology and Biotechnology
• /
• v.16 no.11
• /
• pp.1720-1727
• /
• 2006

To set up a kinetic model that can provide a theoretical basis for developing a new mathematical model of the Cr(VI) biosorption column using brown seaweed Ecklonia biomass, a differential reactor system was used in this study. Based on the fact that the removal process followed a redox reaction between Cr(VI) and the biomass, with no dispersion effect in the differential reactor, a new mathematical model was proposed to describe the removal of Cr(VI) from a liquid stream passing through the differential reactor. The reduction model of Cr(VI) by the differential reactor was zero order with respect to influent Cr(IlI) concentration, and first order with respect to both the biomass and influent Cr(VI) concentrations. The developed model described well the dynamics of Cr(VI) in the effluent. In conclusion, the developed model may be used for the design and performance prediction of the biosorption column process for Cr(VI) detoxification.

• Journal of Korean Society of Water and Wastewater
• /
• v.31 no.4
• /
• pp.289-296
• /
• 2017

Pulp and paper industry produces large volumes of wastewater and residual sludge waste, resulting in many issues in relation to wastewater treatment and sludge disposal. Contaminants in pulp and paper wastewater include effluent solids, sediments, chemical oxygen demand (COD), and biological oxygen demand (BOD), which should be treated by wastewater treatment processes such as coagulation and biological treatment. However, few works have been attempted to predict the treatment efficiency of pulp and paper wastewater. Accordingly, this study presented empirical models based on experimental data in laboratory-scale coagulation tests and compared them with statistical models such as artificial neural network (ANN). Results showed that the water quality parameters such as turbidity, suspended solids, COD, and UVA can be predicted using either linear or expoential regression models. Nevertheless, the accuracies for turbidity and UVA predictions were relatively lower than those for SS and COD. On the other hand, ANN showed higher accuracies than the emprical models for all water parameters. However, it seems that two kinds of models should be used together to provide more accurate information on the treatment efficiency of pulp and paper wastewater.