간행물
상하수도학회지 KCI 등재 Journal of the Korean Society of Water and Wastewater
- 발행기관 대한상하수도학회
- 자료유형 학술지
- 간기 격월간
- ISSN 1225-7672 (Print)2287-822X (Online)
- 수록기간 1993 ~ 2024
- 주제분류 공학 > 건축공학 공학 분류의 다른 간행물
- 십진분류KDC 539DDC 628
권호리스트/논문검색
제35권 제1호 (2021년 2월) 8건
Research Papers
1.
2021.02
구독 인증기관 무료, 개인회원 유료
Eutrophication and algal blooms can lead to increase of taste and odor compounds and health problems by cyanobacterial toxins. To cope with these eco-social issues, Ministry of Environment in Korea has been reinforcing the effluent standards of wastewater treatment facilities. As a result, various advanced phosphorus removal processes have been adopted in each wastewater treatment plant nation-widely. However, a lot of existing advanced wastewater treatment processes have been facing the problems of expensive cost in operation and excessive sludge production caused by high dosage of coagulant. In this study, the sedimentation and dissolved air flotation (SeDAF) process integrated with sedimentation and flotation has been developed for enhanced phosphorus removal in wastewater treatment facilities. Design and operating parameters of the SeDAF process with the capacity of 100 m3/d were determined, and a demonstration plant has been installed and operated at I wastewater treatment facility (located in Gyeonggi-do) for the verification of field applicability. Several empirical evaluations for the SeDAF process were performed at demonstration-plant scale, and the results showed clearly that T-P and turbidity values of treated water were to satisfy the highest effluent standards below 0.2 mg/L and 2.0 NTU stably for all of operation cases.
4,600원
2.
2021.02
구독 인증기관 무료, 개인회원 유료
In this study, a method of leakage detection was proposed to locate leak position for a reservoir pipeline valve system using wavelet coherence analysis for an injected pressure wave. An unsteady flow analyzer handled nonlinear valve maneuver and corresponding experimental result were compared. Time series of pressure head were analyzed through wavelet coherence analysis both for no leak and leak conditions. The leak information can be obtained through either time domain reflectometry or the difference in wavelet coherence level, which provide predictions in terms of leak location. The reconstructed pressure signal facilitates the identification of leak presence comparing with existing wavelet coherence analysis.
4,200원
3.
2021.02
구독 인증기관 무료, 개인회원 유료
Tetracycline is one of the most commonly used as antibiotics for the livestock industry and it is still widely used nowadays. Tetracycline and its metabolites are excreted with excrement, which is difficult to completely removed with conventional sewage treatment, therefore it is apprehended that the tetracycline-resistant bacteria occurs. In this study, the oxidant named ferrate(VI) was used to degrade the tetracycline and investigate the reaction between ferrate(VI) and tetracycline under various aqueous conditions. The highest degradation efficiency of tetracycline occurred in basic condition (pH 10.1 ± 0.1) because of the pKa values of tetracycline and ferrate(VI). The results also showed the effect of water temperature on the degradation of tetracycline was not significant. In addition, the dosage of ferrate(VI) was higher, the degradation of tetracycline and the self-degradation of ferrate(VI) also higher, finally the efficiency of ferrate(VI) was lower. The results said that the various mechanisms effects the reaction of ferrate(VI) oxidation, it required the consideration of the characteristics of the target compound for optimal degradation efficiency. Additionally, intermediate products were detected with LC/MS/MS and three degradation pathways were proposed.
4,200원
4.
2021.02
구독 인증기관 무료, 개인회원 유료
The global water shortage is getting more attention by global climate change. And water demand rapidly increases due to industrialization and population growth. Desalination technology is being expected as an alternative water supply method. Desalination technology requires low energy or maintenance costs, making it a competible next generation technology, with examples such as forward osmosis (FO), membrane distillation (MD), capacitive deionization (CDI), and electrodialysis (ED) to compete with reverse osmosis (RO). In order to identify recent research trends in desalination technologies (FO, MD, RO, CDI, and ED) between 2000-2020, a bibliometric analysis was conducted in the current study. The number of published papers in desalination technology have increased in Desalination and Journal of Membrane Science mainly. Moreover, it was found that FO, MD, RO, CDI, and ED technologies have been applied in various research areas including electrochemical, food processing and carbon-based material synthesis. Recent research topics according to the desalination technologies were also identified.
4,600원
5.
2021.02
구독 인증기관 무료, 개인회원 유료
The quantified analysis of damages to wastewater treatment plants by natural disasters is essential to maintain the stability of wastewater treatment systems. However, studies on the quantified analysis of natural disaster effects on wastewater treatment systems are very rare. In this study, a total disaster index (DI) was developed to quantify the various damages to wastewater treatment systems from natural disasters using two statistical methods (i.e., AHP: analytic hierarchy process and PCA: principal component analysis). Typhoons, heavy rain, and earthquakes are considered as three major natural disasters for the development of the DI. A total of 15 input variables from public open-source data (e.g., statistical yearbook of wastewater treatment system, meteorological data and financial status in local governments) were used for the development of a DI for 199 wastewater treatment plants in Korea. The total DI was calculated from the weighted sum of the disaster indices of the three natural disasters (i.e., TI for typhoon, RI for heavy rain, and EI for earthquake). The three disaster indices of each natural disaster were determined from four components, such as possibility of occurrence and expected damages. The relative weights of the four components to calculate the disaster indices (TI, RI and EI) for each of the three natural disasters were also determined from AHP. PCA was used to determine the relative weights of the input variables to calculate the four components. The relative weights of TI, RI and EI to calculate total DI were determined as 0.547, 0.306, and 0.147 respectively.
4,000원
6.
2021.02
구독 인증기관 무료, 개인회원 유료
Microalgae are primary producers of aquatic ecosystems, securing biodiversity and health of the ecosystem and contributing to reducing the impact of climate change through carbon dioxide fixation. Also, they are useful biomass that can be used as biological resources for producing valuable industrial products. However, harvesting process, which is the separation of microalgal biomass from mixed liquor, is an important bottleneck in use of valorization of microalgae as a bioresource accounting for 20 to 30% of the total production cost. This study investigates the applicability of sewage sludge-derived extracellular polymeric substance (EPS) as bioflucculant for harvesting microalgae. We compared the flocculation characteristics of microalgae using EPSs extracted from sewage sludge by three methods. The flocculation efficiency of microalgae is closely related to the carbohydrate and protein concentrations of EPS. Heat-extracted EPS contains the highest carbohydrate and protein concentrations and can be a best-suited bioflocculant for microalgae recovery with 87.2% flocculation efficiency. Injection of bioflocculant improved the flocculation efficiency of all three different algal strains, Chlorella Vulgaris, Chlamydomonas Asymmetrica, Scenedesmus sp., however the improvement was more significant when it was used for flocculation of Chlamydomonas Asymmetrica with flagella.
4,000원
7.
2021.02
구독 인증기관 무료, 개인회원 유료
The increased turbidity in rivers during flood events has various effects on water environmental management, including drinking water supply systems. Thus, prediction of turbid water is essential for water environmental management. Recently, various advanced machine learning algorithms have been increasingly used in water environmental management. Ensemble machine learning algorithms such as random forest (RF) and gradient boosting decision tree (GBDT) are some of the most popular machine learning algorithms used for water environmental management, along with deep learning algorithms such as recurrent neural networks. In this study GBDT, an ensemble machine learning algorithm, and gated recurrent unit (GRU), a recurrent neural networks algorithm, are used for model development to predict turbidity in a river. The observation frequencies of input data used for the model were 2, 4, 8, 24, 48, 120 and 168 h. The root-mean-square error-observations standard deviation ratio (RSR) of GRU and GBDT ranges between 0.182~0.766 and 0.400~0.683, respectively. Both models show similar prediction accuracy with RSR of 0.682 for GRU and 0.683 for GBDT. The GRU shows better prediction accuracy when the observation frequency is relatively short (i.e., 2, 4, and 8 h) where GBDT shows better prediction accuracy when the observation frequency is relatively long (i.e. 48, 120, 160 h). The results suggest that the characteristics of input data should be considered to develop an appropriate model to predict turbidity.
4,000원
8.
2021.02
구독 인증기관 무료, 개인회원 유료
Forward osmosis (FO) process is a chemical potential driven process, where highly concentrated draw solution (DS) is used to take water through semi-permeable membrane from feed solution (FS) with lower concentration. Recently, commercial FO membrane modules have been developed so that full-scale FO process can be applied to seawater desalination or water reuse. In order to design a real-scale FO plant, the performance prediction of FO membrane modules installed in the plant is essential. Especially, the flux prediction is the most important task because the amount of diluted draw solution and concentrate solution flowing out of FO modules can be expected from the flux. Through a previous study, a theoretical based FO module model to predict flux was developed. However it needs an intensive numerical calculation work and a fitting process to reflect a complex module geometry. The idea of this work is to introduce deep learning to predict flux of FO membrane modules using 116 experimental data set, which include six input variables (flow rate, pressure, and ion concentration of DS and FS) and one output variable (flux). The procedure of optimizing a deep learning model to minimize prediction error and overfitting problem was developed and tested. The optimized deep learning model (error of 3.87%) was found to predict flux better than the theoretical based FO module model (error of 10.13%) in the data set which were not used in machine learning.
4,000원
