• Journal of Korean Society of Water and Wastewater
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• v.38 no.1
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• pp.1-15
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• 2024

Occurrence of process environment changes, such as influent load variances and process condition changes, can reduce treatment efficiency, increasing effluent water quality. In order to prevent exceeding effluent standards, it is necessary to manage effluent water quality based on process operation data including influent and process condition before exceeding occur. Accordingly, the development of the effluent water quality prediction system and the application of technology to wastewater treatment processes are getting attention. Therefore, in this study, through the multi-channel measuring instruments in the bio-reactor and smart multi-item water quality sensors (location in bio-reactor influent/effluent) were installed in The Seonam water recycling center #2 treatment plant series 3, it was collected water quality data centering around COD, T-N. Using the collected data, the artificial intelligence-based effluent quality prediction model was developed, and relative errors were compared with effluent TMS measurement data. Through relative error comparison, the applicability of the artificial intelligence-based effluent water quality prediction model in wastewater treatment process was reviewed.

• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.91-98
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• 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 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.25 no.5
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• pp.674-681
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• 2009

Characteristics of organic matters (OM) in wastewater and the removal efficiencies were investigated using the influent and the effluent samples collected from 21 wastewater treatment plants. The OM characteristics investigated included biodegradability, humic content, specific UV absorbance (SUVA), the distribution percentage of refractory OM (R-OM), and synchronous fluorescence spectra. The types of wastewater (sewage, livestock waste/night soils, industrial waste) were easily distinguished by comparing the synchronous fluorescence spectra of the influent wastewater. The prominent peak of protein-like fluorescence (PLF) was observed for livestock waste/night soils whereas sewage exhibited a unique fluorescence peak at a wavelength of 370 nm. Irrespective of the wastewater types, the distribution percentage of R-OM increased from the influent to the effluent. Livestock waste/night soils showed the highest removal efficiency among all the three types of wastewater. There was no statistical difference of the removal efficiency between a traditional activated sludge and biological advanced treatment processes. Removal efficiency based on dissolved organic carbon DOC presented good correlations with the distribution percentage of R-OM and fulvic-like fluorescence (FLF) of the influent. The prediction for DOC removal efficiency was improved by using multiple regression analyses based on some selected OM characteristics and mixed liquid suspended solid (MLSS).

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.5
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• pp.96-105
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• 2002

As an useful water purification system for non-point source pollution in rural watersheds, interests in constructed wetlands are growing at home and abroad. It is well known that constructed wetlands are easily installed, no special managemental needs, and more flexible at fluctuating influent loads. They have a capacity for purification against nutrient materials such as phosphorus and nitrogen causing eutrophication of lentic water bodies. The Constructed Wetland Design Model (CWDM), developed through this study is consisted mainly of Database System, Runoff-discharge Prediction Submodel, Water Quality Prediction Submodel, and Area Assessment Submodel. The Database System includes data of watershed, discharge, water quality, pollution source, and design factors for the constructed wetland. It supplies data when predicting water quality and calculating the required areas of constructed wetlands. For the assessment of design flow, the GWLF (Generalized Watershed Loading Function) is used, and for water quality prediction in streams estimating influent pollutant load, Water Quality Prediction Submodel, that is a submodel of DSS-WQMRA model developed by previous works is amended. The calculation of the required areas of constructed wetlands is achieved using effluent target concentrations and area calculation equations that developed from the monitoring results in the United States. The CWDM is applied to Bokha watershed to appraise its application by assessing design flow and predicting water quality. Its application is performed through two calculations: one is to achieve each target effluent concentrations of BOD, SS, T-N and T-P, the other is to achieve overall target effluent concentrations. To prove the validity of the model, a comparison of unit removal rates between the calculated one from this study and the monitoring result from existing wetlands in Korea, Japan and United States was made. As a result, the CWDM could be very useful design tool for the constructed wetland in rural watersheds and for the non-point source pollution management.

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

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2003.11a
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• pp.73-77
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• 2003

In order to predict the dynamic behaviors of uranium and cobalt in a fixed bed at various influent pH values of liquid waste, the adsorption system was regarded as multi-component adsorption between each ionic species in a solution. Langmuir isotherm parameters of each species were extracted by incorporating equilibrium data with the solution chemistry of uranium and cobalt using IAST. Prediction results were in good agreement with the experimental data, except for a high concentration and pH. Although there was some limitations in predicting the cobalt adsorption, this method may be useful in analyzing a complex adsorption system where various kinds of ionic species exist in a solution.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.555-561
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• 2009

The established mathematical modeling methods have limitation to know the hydraulic characteristics at the wastewater treatment plant which are complex and nonlinear systems. So, an artificial neural network (ANN) model based on hydraulic characteristics is applied for modeling wastewater quality of a full-scale wastewater treatment plant using DNR (Daewoo nutrient removal) process. ANN was trained using data which are influents (TSS, BOD, COD, TN, TP) and effluents (COD, TN, TP) components in a year, and predicted the effluent results based on the training. To raise the efficiency of prediction, inputs of ANN are added the influent and effluent information that are in yesterday and the day before yesterday. The results of training data tend to have high accuracy between real value and predicted value, but test data tend to have lower accuracy. However, the more hydraulic characteristics are considered, the results become more accuracy.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.4
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• pp.429-437
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• 2007

This study aims to develop a sulfur-using denitrification process which is possible a renovation to advanced treatment plant submerging a simple module in activated sludge aeration tank. At first, the impact factor of sulfur-using denitrification was appreciated by the batch test. Secondly, reflecting a dissolved oxygen effect of sulfur-using denitrification that was confirmed by the batch test, in a continuous nitrification/sulfur-using denitrification, high-rate nitrogen removal reaction was induced at optimum condition controlling DO concentration according to phases. Also, inside and outside of sulfur-using denitrification module was covered with microfilter and the module was considered as an alternative of clarifier. Result of batch test for sulfur-using denitrification, $NO_2{^-}N$ was lower for consumption of alkalinity and sulfur than that of $NO_3{^-}-N$. These results revealed the accordance of theoretical prediction. In continuous nitrification/sulfur-using denitrification experiment, actual wastewater was used as a influent, and influent nitrogen loading rates were increased 0.04, 0.07, 0.11, $0.14kg\;N/m^3-day$ by changing hydraulic retention times. At this time, nitrogen loading rates of packed sulfur were increased 0.23, 0.46, 0.69, $0.93kg\;N/m^3-day$. As a result, nitrification efficiency was about 100% and denitrification efficiency was 93, 81, 79, 72%. Accordingly, nitrogen removal was a high-rate. Also the module of sulfur-using denitrification covered with microfilter did not make a fouling phenomena according to increased flux. And the module was achieved effluent suspended solids of below 10 mg/L without a clarifier. In conclusion, it is possible a renovation to advanced treatment plant submerging a simple module packed sulfur in activated sludge aeration tank of traditional facilities. And the plant used the module packed sulfur is expected as a effective facilities of high-rate and the smallest.

• 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.