• Journal of Environmental Science International
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• v.22 no.12
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• pp.1643-1650
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• 2013

This study investigated water quality of effluents and stream from the sewage treatment plants located at Gumi Complex 4, Gumi, and Wonpyeong in Gumi. Downstream region was found to increase the concentration of nutrients for sewage treatment plant effluent. Both phosphorus and nitrogen were accounted most as soluble form. In particular, the high ratio of dissolved effluent of sewage treatment plants were investigated. In the streams, Phosphorus concentration was high during rainy season and nitrogen concentration was high in the dry season. Sewage treatment plant effluent was relatively less microbial activity and nutrient concentrations were higher in the winter. TN/TP ratio was the highest in the upstream region and the lowest in the sewage treatment plant effluent. The effect of the nutrient matter from a discharge of a sewage treatment plant on rivers varied depending on the size of the river and the treatment plant. However, the influence of the concentration was greater than that of flowrate. Sewage treatment plant effluent loads phosphorus, nitrogen accounted for 8% and 6% respectively at the point N3 of the Nakdong river.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.493-493
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• 2018

The purpose of this study was to analyze the correlation between the effluent of the sewage treatment plant (STP) and the adjacent stream located downstream of the STP in Nakdong River. The flow and water quality such as BOD, COD, SS, T-N, and T-P data for 12 STPs and adjacent downstream monitoring stations in the main stream and tributaries of Nakdong River were collected from 2012 to 2015. As a result of correlation analysis between river flow and water quality at the river water quality measurement point, COD, SS and T-P were correlated positively with river flow rate at 6, 8, and 6 points, respectively. As a result of analyzing the water quality of sewage treatment plant effluent and downstream stream, BOD and COD were correlated at 2 and 3 points, respectively. T-N showed a positive correlation at 9 points, and 7 of them had a strong positive correlation, indicating that sewage treatment effluent had a large effect on downstream streams. In this study, we found that the correlation between river flow rate and water quality factors (COD, SS, TP) was high for river water measurement points, and the sewage treatment plant effluent was correlated with the T-N value of adjacent streams.

• Microbiology and Biotechnology Letters
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• v.28 no.6
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• pp.361-366
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• 2000

Genera and number of protozoa were investigated in the conventional activated sludge pilot plant used for the treatment of municipal sewage and pre-treated night soil-containing sewage. In both case, the predominant protozoa was ciliates and among them Vorticella was the most common. In the pilot plant where pre-treated night soil was mixed with municipal sewage, genera of free-swimming ciliates, flagellates and amoeba was higher than in those withour night soil. Correlation analysis on the quality of effluent and protozoa indicates that municipal sewage has positive correlation with protozoa. However in the pilot plan 샐 sewage contatinin pre-treated night coil soil more samples show negative correlation. Followed equations were derived by the regression analysis of BOD in both the pilot plants. In case of pilot plant A of municipal sewage, the analysis B of munici-pal and pre-treated night soil-containing sewage, the analysis of BOD was $6.731$\times$10_{-2}$ $\times$Bodo+0.306(Adjusted $R^2$=0.864). At low temperature, number of protozoa was decreased to 35% and among therm, Aspidisca was the most common genus. Therefore, protozoa can be used as indicator of quality of the effluent in sewage treatment plants.

• Journal of Korean Society on Water Environment
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• v.26 no.5
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• pp.871-879
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• 2010

To figure out the removal efficiency of indicator and pathogenic bacteria by unit processes of a sewage treatment plant using activated sludge process, analyses were done for incoming sewage, influent and effluent of primary clarifier, aeration tank, secondary clarifier and final discharge conduit of the plant. A matrix of bacterial items (average of bacterial reduction [log/ml], p value of paired t-test, number of decreased cases of twenty analyses, removal percentage only for decreased cases) between incoming sewage and final effluent of the plant were heterotrophic plate counts (1.54, 0.000, 20, 95.01), total coliforms (1.38, 0.000, 19, 83.94), fecal coliforms (0.90, 0.000, 20, 94.84), fecal streptococci (0.90, 0.000, 20, 98.08), presumptive Salmonella (0.23, 0.561, 7, 99.09), and presumptive Shigella (1.02, 0.002, 15, 92.98). Total coliforms, fecal coliforms, heterotrophic plate counts, and fecal streptococci showed highest decrease through secondary clarifier about 1-log (p<0.001) between 88% and 96%, and primary clarifier represented the significant (p<0.05) decrease. However, final effluent through discharge conduit showed higher total coliforms and fecal streptococci than effluent of secondary clarifier (p<0.05). In addition, final effluent once violated the water quality standard while effluent of secondary clarifier satisfied the standard. Hence some control measures including elimination of deposits in discharge conduit or disinfection of final effluent are necessary.

• Journal of Environmental Science International
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• v.19 no.3
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• pp.323-329
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• 2010

To acquire preliminary data for the control of total nitrogen (TN) in S sewage treatment plant, which processes merging food waste and sewage, the effect of reject water on the total nitrogen in the effluent was examined in this study. Water quality data for the plant during the winter period were applied to calculate the mass balance. It was calculated that at least more than 231 kg/d TN should be removed to control the TN concentration in the effluent. Assuming 18 ppm as the goal TN concentration in the effluent, about 941 kg/d TN should be removed from this plant. Approximately 10% more TN should be removed than at present to achieve this result. It was observed that dewatering the filtrate had a considerably greater effect on the total nitrogen in the effluent than the reject waters. The dewatered filtrate contained 1,399kg/d TN. The contribution of the dewatered filtrate to the TN concentration in the effluent was 0.183, which was 7 to 23 times greater than the other reject waters. In addition, the amount of total nitrogen from the reject water, with the exception of the dewatering filtrate, was lower than the amount of TN that should be removed from S sewage treatment plant. Therefore, it was concluded that one of the most effective methods for controlling the TN concentration in effluent was the removal of the TN contained in the dewatering filtrate.

• Journal of Environmental Science International
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• v.11 no.1
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• pp.63-68
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• 2002

In this study, we examined statistical analysis between sewage plant operations parameters and effluent quality We got six components from principle component analysis of the operation parameters and secondary effluent quality. 91.8% of the total variance was explained by the six components. The components were identified in the following order : 1) organic matter removal by aeration basin microbe, 2) settleability on secondary clarifier load, 3) removal of nutrients, 4) microbial number increasement and species diversity, 5) microbial activity in aeration basin, 6) oxidation in aeration basin.

• Journal of Environmental Health Sciences
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• v.28 no.3
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• pp.34-38
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• 2002

Statistical analysis between sewage plant operating parameters and the effluent quality was performed. We extracted two factors from principal component analysis of operating parameters and effluent quality from each plant. The total variance of 84.7%, 79.2% was explained by the two factors at SB plant and SC plant, respectively. The factors were identified at SB plant in the following order 1) the oxidation of organic material by aeration basin microbe,2) biomass in aeration basin and at SC plant 1) the oxidation of organic material by aeration basin microbe, 2) thickening of acti-vated sludge. These results suggested that the control of microbial composition might be critical on the improvement of the effluent quality and plant operating efficiency because most of the factors were related with microbes.

• Journal of Korean Society on Water Environment
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• v.34 no.2
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• pp.202-209
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• 2018

The purpose of this study was to analyze the correlation between the effluent of a sewage treatment plant (STP) and the adjacent stream located downstream of the STP in Nakdong River. Flow and water quality data, such as BOD, COD, SS, T-N, and T-P data, for 12 STPs and adjacent downstream monitoring stations in the main stream and tributaries of Nakdong River were collected from 2012 to 2015. As a result of correlation analysis between river flow and water quality at the river water quality measurement point, COD, SS, and T-P were correlated positively with the river flow rate at 6, 8, and 6 points, respectively. As a result of analyzing the water quality of sewage treatment plant effluents and downstream streams, BOD and COD were correlated at 2 and 3 points, respectively. T-N showed a positive correlation at 9 points, and 7 of them had a strong positive correlation, indicating that sewage treatment effluent had a large effect on downstream streams. In this study, we found that the correlation between the river flow rate and the water quality factors (COD, SS, TP) was high at river water measurement points, and the sewage treatment plant effluent was correlated with the T-N value of adjacent streams.

• Journal of Environmental Health Sciences
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• v.27 no.2
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• pp.92-99
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• 2001

This present study was aimed to investigate the characteristics of dissoloved organic matter (DOC) in sewage. The results are summarized as follows ; The plateaux reached in 3~4 days by the biodegradability test on sewage samples based on DOC. 쏭 rations of BDOC to DOC were 48, 21, 13 and 11% for raw sewage, primary treatment effluent, secondary treatment effluent and final treatment effluent, respectively. As the SUVA values ranged less 3L/m.mg for the effluent of sewage treatment plant, the DOC is composed largely of non-humic materials, hydrophilic, less aromatic as compared to waters with higher SUVA values. Through the biodegradability test, Dissolved organics showed that the quantity of LMW(Low Molecular Weight) less than 1,000 daltons was decreased, HMW(High Molecular Weight) more than 30,000 daltons had a tendency to increase. Large portion of UV$^{254}$ in final treatment effluent was increased of MMW(Medium Molecular Weight). Also, average removal efficiency of DOC was 32% during sewage treatment.

• Journal of Korean Society on Water Environment
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• v.35 no.4
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• pp.370-378
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• 2019

In the recent years, various studies have presented stable and economic methods for increased regulations and compliance in sewage treatment plants. In some sewage treatment plants, the effluent concentration exceeded the regulations, or the effluent concentration was manipulated. This indicates that the process is currently inefficient to operate and control sewage treatment plants. The operation and control method of sewage treatment plant is mathematically dealing with a physical and chemical mechanism for the anticipated situation during operation. In addition, there are some limitations, such as situations that are different from the actual sewage treatment plant. Therefore, it is necessary to find a more stable and economical way to enhance the operational and control method. AI (Artificial Intelligence) technology is selected among various methods. There are very few cases of applying and utilizing AI technology in domestic sewage treatment plants. In addition, it failed to define specific definitions of applying AI technologies. The purpose of this study is to present the application of AI technology to domestic sewage treatment plants by comparing and analyzing various cases. This study presented the AI technology algorithm system, verification method, data collection, energy and operating costs as methods of applying AI technology.