논문 상세보기
pp.93-100
Forward osmosis (FO) process is a chemical potential driven process, where highly concentrated draw solution (DS) is used to take water through semi-permeable membrane from feed solution (FS) with lower concentration. Recently, commercial FO membrane modules have been developed so that full-scale FO process can be applied to seawater desalination or water reuse. In order to design a real-scale FO plant, the performance prediction of FO membrane modules installed in the plant is essential. Especially, the flux prediction is the most important task because the amount of diluted draw solution and concentrate solution flowing out of FO modules can be expected from the flux. Through a previous study, a theoretical based FO module model to predict flux was developed. However it needs an intensive numerical calculation work and a fitting process to reflect a complex module geometry. The idea of this work is to introduce deep learning to predict flux of FO membrane modules using 116 experimental data set, which include six input variables (flow rate, pressure, and ion concentration of DS and FS) and one output variable (flux). The procedure of optimizing a deep learning model to minimize prediction error and overfitting problem was developed and tested. The optimized deep learning model (error of 3.87%) was found to predict flux better than the theoretical based FO module model (error of 10.13%) in the data set which were not used in machine learning.
1. 서 론
2. 연구 방법
2.1. 딥러닝에 사용된 FO 막모듈 실험 데이터
2.2 딥러닝 모델 구축
2.3 딥러닝 모델의 변동성 및 과적합 분석
3. 결과 및 토의
3.1 딥러닝 모델의 변동성 평가 및 최소화 방안
3.2 검증 오차를 최소화하기 위한 딥러닝 모델 최적화
3.3 딥러닝 모델의 과적합 분석
4. 결 론
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