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J Korean Soc Environ Eng > Volume 38(8); 2016 > Article
J Korean Soc Environ Eng 2016;38(8): 444-451. doi: https://doi.org/10.4491/KSEE.2016.38.8.444
유전자알고리즘을 이용한 막오염 시계열 예측 연구
이진숙1, 김준현1, 전용성1, 곽영주1, 이진효2
1인천광역시상수도사업본부 수질연구소
2서울특별시보건환경연구원 대기환경연구부
A Study on Time Series Analysis of Membrane Fouling by using Genetic Algorithm in the Field Plant
Jin Sook Lee1, Jun Hyun Kim1, Yong Seong Jun1, Young Ju Kwak1, Jin Hyo Lee2
1Water Quality Institute, Waterworks Headquarters, Incheon Metropolitan City
2Atmospheric Research Department, Seoul Metropolitan Government Research Institute of Public Health and Environment
Corresponding author  Jin Sook Lee ,Tel: 02-720-2231, Fax: 032-440-8740, Email: july8099@korea.kr
Received: April 21, 2016;  Revised: June 14, 2016;  Accepted: July 18, 2016.  Published online: August 31, 2016.
Most research on membrane fouling models in the past are based on theoretical equations in lab-scale experiments. But these studies are barely suitable for applying on the full-scale spot where there is a sequential process such as filtration, backwash and drain. This study was conducted in submerged membrane system which being on operation auto sequentially and treating wastewater from G-water purification plant in Incheon. TMP had been designated as a fouling indicator in constant flux conditions. Total volume of inflow and SS concentration are independent variables as major operation parameters and time-series analysis and prediction of TMP were conducted. And similarity between simulated values and measured values was assessed. Final prediction model by using genetic algorithm was fully adaptable because simulated values expressed pulse-shape periodicity and increasing trend according to time at the same time. As results of twice validation, correlation coefficients between simulated and measured data were r2 = 0.721, r2 = 0.928, respectively. Although this study was conducted limited to data for summer season, the more amount of data, better reliability for prediction model can be obtained. If simulator for short range forecast can be developed and applied, TMP prediction technique will be a great help to energy efficient operation.
Key Words: Membrane, Fouling Model, Genetic Algorithm, Time-series Analysis, Field Plant
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