• 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 Ocean Engineering and Technology
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• v.22 no.2
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• pp.34-41
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• 2008

As a basic study for developing a forecasting/estimating system that predicts water quality changes when Deep Sea Water (DSW) drains to the ocean after using it, this study was carried out as follows: 1) numerical simulation of the present state at DSW developing region in the East sea using SWEM, 2) numerical prediction of water quality changes by effluent DSW, 3) analysis of influence degree 'With defined DEI (DSW effect index) at F station. On the whole, when DSW drained to the ocean, Chl-a, COD and water-temperature were decreased and DIN, DIP and DO were increased by effluent DSW, and Salinity was steady. According to analysis of influence degree, the influence degree of DIN was the highest and it was high in order of Chl-a, COD, Water-temperature, DO, DIP and Salinity. The influence degree classified by DSW effluent position was predicted that suiface outflow was lower than bottom outflow. Ad When DSW discharge increased 10 times, the influence degree increased about $5{\sim}14$ times.

• 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.

• Environmental Engineering Research
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• v.19 no.1
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• pp.31-36
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• 2014

In this study, the influence of the control and operational parameters within a sewage treatment plant were reviewed by performing multiple regression analysis on the effluent quality of the sewage treatment. The data used for this review are based on the actual data from a sewage treatment plant using the media process within the year 2012. The prediction models of chemical oxygen demand ($COD_{Mn}$) and total nitrogen (T-N) within the effluent of the 2nd settling tank based on the multiple regression analysis yielded the prediction accuracy measurements of 0.93 and 0.84, respectively; and it was concluded that the model was accurately predicting the variances of the actual observed values. If the data on the energy spent on each operating condition can be collected, then the operating parameter that conserves energy without violating the effluent quality standards of COD and T-N can be determined using the regression model and the standardized regression coefficients. These results can provide appropriate operation guidelines to conserve energy to the operators at sewage treatment plants that consume a lot of energy.

• Journal of Korean Society of Environmental Engineers
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• v.34 no.5
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• pp.312-315
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• 2012

In this study, the model which can predict the quality of effluent has been implemented through multiple regression analysis to use operation data of a sewage treatment plant, to which a media process is applied. Multiple regression analysis were carried out by cases according to variable selection method, removal of outliers and log transformation of variables, with using data of one year of 2011. By reviewing the results of predictable models, the accuracy of prediction for $COD_{Mn}$ of treated water of secondary clarifiers was over 0.87 and for T-N was over 0.81. Using this model, it is expected to set the range of operating conditions that do not exceed the standards of effluent quality. In conclusion, the proper guidance on the effluent quality and energy costs within the operating range is expected to be provided to operators.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.11
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• pp.1174-1179
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• 2008

Many modeling and calibration methods have been developed to analyze and design the biological wastewater treatment process. For the systematic use of activated sludge model (ASM) in a real treatment process, a most important step in this usage is a calibration which can find a key parameter set of ASM, which depends on the microorganism communities and the process conditions of the plants. In this paper, a standardized calibration protocol of the ASM model is developed. First, a weighted effluent quality index(WEQI) is suggested far a calibration protocol. Second, the most sensitive parameter set is determined by a sensitive analysis based on WEQI and then a parameter optimization method are used for a systematic calibration of key parameters. The proposed method is applied to a calibration problems of the single carbon removal process. The results of the sensitivity analysis and parameter estimation based on a WEQI shows a quite reasonable parameter set and precisely estimated parameters, which can improve the quality and the efficiency of the modeling and the prediction of ASM model. Moreover, it can be used for a calibration scheme of other biological processes, such as sequence batch reactor, anaerobic digestion process with a dedicated methodology.

• 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 the Korean Society of Urban Environment
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• v.18 no.4
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• pp.401-408
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• 2018

EQPS (Effluent Quality Prediction System, Dynamita, France) was applied to analyze the appropriateness of the design of a bioreactor in A sewage treatment plant. A sewage treatment plant was designed by setting the design concentration of the secondary clarifier effluent to total nitrogen and total phosphorus, 10 mg/L and 1.8 mg/L, respectively, in order to comply with the target water quality at the level of the hydrophilic water. The retention time of the 4-stage BNR reactor was 9.6 hours, which was 0.5 for the pre-anoxic tank, 1.0 for the anaerobic tank, 2.9 for the anoxic tank, and 5.2 hours for the aerobic tank. As a result of the modeling of the winter season, the retention time of the anaerobic tank was increased by 0.2 hours in order to satisfy the target water quality of the hydrophilic water level. The default coefficients of the one step nitrification denitrification model proposed by the software manufacturer were used to exclude distortion of the modeling results. Since the process modeling generally presents optimal conditions, the retention time of the 4-stage BNR should be increased to 9.8 hours considering the bioreactor margin. The accurate use of process modeling in the design stage of the sewage treatment plant is a way to ensure the stability of the treatment performance and efficiency after construction of the sewage treatment plant.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.12 no.3
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• pp.175-179
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• 1979

One of the major problems in tile activated sludge system has been difficulty in separating the microbial solids from the treated effluent and in returning them to the aeration tank. Another problem has been the digestion of the excess activated sludge. In constrast, it has not been difficult to separate the microbial solids from the treated effluent from the biological fixed-film systems(RBC process, Trickling Filter, FAST process). These systems have also featured less sludge production. Recently, it was proposed to experiment with the RESMAS process in order to eliminate the settling tank and sludge concentration facilities and to reduce the quantity of excess sludge for final disposal. The effluent quality could be predicted by .the concept of the maximum accumulation capacity. However, the hydraulic characteristics of the screen media in the RESMAS reactor were not dynamic. The object of the present study is to evalute the sludge accumulation rate and effluent quality prediction in the REMSMAS process designed in the dynamic hydraulic structure. This process can eliminate the final sedimentation tank and sludge concentration tank needed in the RBC, CMAS, Trickling Filter and FAST processes. Also, the effluent quality is desirable to compare with other processes. It appeared that the value of the sludge holding capacity was higher than those of the RESMAS and FAST processes, and the periods of the critical operating time were proportional to the substrate hydraulic loadings.

• Journal of the Korean Society of Marine Environment & Safety
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• v.15 no.2
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• pp.91-98
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• 2009

The long-term water-quality change of Asan Bay by the influx of polluted disposal water was predicted through a simulation with an Eco-hydrodynamic model. Eco-hydrodynamic model is composed of a multi-level hydrodynamic model to simulate the water flow and an ecosystem model to simulate water quality. The water quality simulation revealed that the COD(Chemical Oxygen Demand), dissolved inorganic nitrogen(DIN) and dissolved inorganic phosphorus(DIP) are increased at 5 stations for the subsequent 6 months after the influx of the effluent. COD, DIN and DIP showed gradual decreases in concentration during the period of one to two years after the increase of last 6 months and reached steady state for next three to ten years. Concentration levels of COD, DIN, and DIP showed the increase by the ranges of $11{\sim}67%$, $10{\sim}67%$, and $0.5{\sim}7%$, respectively, which represents that the COD and DIN are the most prevalent pollutants among substances in the effluent through the sewage treatment plant. The current water quality of Asan Bay based on the observed COD, TN and TP concentrations ranks into the class II of the Korean standards for marine water quality but the water quality would deteriorate into class III in case that the disposal water by the sewage plant is discharged into the Bay.