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        검색결과 189

        10.
        2023.12 KCI 등재 구독 인증기관 무료, 개인회원 유료
        Membrane bioreactor (MBR) provides the benefits on high effluent quality and construction cost without the secondary clarification. Despite of these advantages, fouling, which clogs the pore in membrane modules, affects the membrane life span and effluent quality. Studies on the laboratory scale MBR were focused on the control of particulate fouling, organic fouling and inorganic fouling. However, less studies were focused on the control of biofouling and microbial aspect of membrane. In the full scale operation, most MBR produces high effluent quality to meet the national permit of discharge regulation. In this study, the performance and microbial community analysis were investigated in two MBRs. As the results, the performance of organic removal, nitrogen removal, and phosphorus removal was similar both MBRs. Microbial community analysis, however, showed that Azonexus sp. and Propionivibrio sp. contributed to indirect fouling to cause the chemical cleaning in the DX MBR.
        4,000원
        20.
        2023.10 KCI 등재 구독 인증기관 무료, 개인회원 유료
        Fouling is an inevitable problem in membrane water treatment plant. It can be measured by trans-membrane pressure (TMP) in the constant flux operation, and chemical cleaning is carried out when TMP reaches a critical value. An early fouilng alarm is defined as warning the critical TMP value appearance in advance. The alarming method was developed using one of machine learning algorithms, decision tree, and applied to a ceramic microfiltration (MF) pilot plant. First, the decision tree model that classifies the normal/abnormal state of the filtration cycle of the ceramic MF pilot plant was developed and it was then used to make the early fouling alarm method. The accuracy of the classification model was up to 96.2% and the time for the early warning was when abnormal cycles occurred three times in a row. The early fouling alram can expect reaching a limit TMP in advance (e.g., 15-174 hours). By adopting TMP increasing rate and backwash efficiency as machine learning variables, the model accuracy and the reliability of the early fouling alarm method were increased, respectively.
        4,000원
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