지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학위논문
- 저자정보
- 지도교수
- 김수한
- 발행연도
- 2021
- 저작권
- 부경대학교 논문은 저작권에 의해 보호받습니다.
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초록· 키워드
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The low-pressure membrane filtration process includes microfiltration and ultrafiltration, which are mainly operated at pressures within 200 KPa. Membrane fouling reduces the operating efficiency of the low pressure membrane filtration process, so it is important to determine an proper flux to minimize that. The best way to determine the proper flux is to go through a long-term pilot test with one or two membrane modules applied in the field, which can take at least 6 months to over a year and pilot. There is a disadvantage that it is difficult to apply if the quality of the raw water in the water changes.
In this study, by using the characteristic that fouling increases or decreases when the flux is increased or decreased, an algorithm is developed to lower the flux when the fouling index exceeds the limit value through artificial intelligence automatic control, and to increase the flux when the value is not reached. It was attempted to derive an proper flux.
The reference value of the fouling index was calculated through the low-pressure membrane filtration model, and the flux fluctuation was made to find an appropriate value by using deep learning. The membrane filtration process operation result when the flux fluctuates was scored through a function of the fouling index, and the subsequent operation result when the artificial intelligence fluctuated the flux was feedback, and through this feedback result, the appropriate flux fluctuation value was found by itself.
Finally, an proper flux suitable for operating conditions such as raw water quality was derived.
In this study, by using the characteristic that fouling increases or decreases when the flux is increased or decreased, an algorithm is developed to lower the flux when the fouling index exceeds the limit value through artificial intelligence automatic control, and to increase the flux when the value is not reached. It was attempted to derive an proper flux.
The reference value of the fouling index was calculated through the low-pressure membrane filtration model, and the flux fluctuation was made to find an appropriate value by using deep learning. The membrane filtration process operation result when the flux fluctuates was scored through a function of the fouling index, and the subsequent operation result when the artificial intelligence fluctuated the flux was feedback, and through this feedback result, the appropriate flux fluctuation value was found by itself.
Finally, an proper flux suitable for operating conditions such as raw water quality was derived.
목차
제1장 서론 11.1 연구배경 11.2 연구의 기본 가설 설정 21.3 연구내용 3제2장 문헌연구 42.1 막여과 42.1.1 개요 42.1.2 파울링 112.2 기계 학습 162.2.1 기계학습 알고리즘 162.2.2 딥 러닝 182.2.3 Python 24제3장 연구방법 293.1 연구 개요 293.2 연구 방법 293.2.1 저압 막여과 공정 시뮬레이터 293.2.2 Step feedback 393.2.3 AI feedback 48제4장 연구 결과 604.1 저압 막여과 공정 시뮬레이터 604.2 Step feedback 644.3 AI feedback 70제5장 결론 805.1 연구 결과 요약 805.2 향후 연구 내용 82참고문헌 83
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