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.

1. 서 론
2. 연구방법
    2.1 연구대상 지역 및 데이터 수집
    2.2 방류 수질 예측 모델 선정
    2.3 방류 수질 예측 모델 개발 절차
    2.4 방류 수질 예측을 위한 입력 항목
3. 연구결과
    3.1 방류 수질 예측 모델 입력 항목 그룹 선정 결과
    3.2 학습 데이터 기간별 수질 예측 결과
4. 결 론
References

• 김민철(서울물재생시설공단 물재생연구소) | Mincheol Kim (Water Regeneration Research Center, Seoul Water Recycling Corporation) Corresponding author
• 박영호(서울물재생시설공단 물재생연구소) | Youngho Park (Water Regeneration Research Center, Seoul Water Recycling Corporation)
• 유광태((주)유앤유) | Kwangtae You (UnU Inc.)
• 김종락((주)유앤유) | Jongrack Kim (UnU Inc.)