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.

1. 서 론
2. 연구 방법
    2.1 세라믹 정밀여과 파일럿 플랜트 운영 및 데이터수집
    2.2 파울링 조기 경보를 위한 여과 cycle 특성변수
    2.3 머신러닝 적용 방법
    2.4 파울링 조기 경보 시뮬레이션
3. 결과 및 고찰
    3.1 모델 파라미터 최적화
    3.2 여과 cycle 정상/비정상 예측 정확도 분석
    3.3 파울링 조기경보 성능 평가
4. 결 론
사 사

• 탁도현(부경대학교 지속가능공학부 토목공학전공) | Dohyun Tak (Major of Civil Engineering, Division of Sustainable Engineering, Pukyong National University)
• 김동건(부경대학교 지속가능공학부 토목공학전공) | Dongkeon Kim (Major of Civil Engineering, Division of Sustainable Engineering, Pukyong National University)
• 전종민(부경대학교 지속가능공학부 토목공학전공) | Jongmin Jeon (Major of Civil Engineering, Division of Sustainable Engineering, Pukyong National University)
• 김수한(부경대학교 지속가능공학부 토목공학전공) | Suhan Kim (Major of Civil Engineering, Division of Sustainable Engineering, Pukyong National University) Corresponding author