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.

투과증발공정에서 polydimethylsiloxane(PDMS)막에 대한 용매의 수착특성과 투과 플럭스를 예측하는 방법을 제시하였다. 이 방법을 이용하여 chloroform, toluene, methoanol, n-butanol의 수착량과 투과 플럭스를 계산하였으며, 계산값과 실험값을 비교하였다. 팽윤을 촉진시키는 정용매(good solvent)인 toluene과 chloroform의 경우 계산된 수착량과 투과 플럭스는 실험값과 잘 일치하였다. 막의 밀도가 작을수록 수착량과 투과 플럭스는 증가하였다. 팽윤을 억제시키는 부용매(poor solvent)인 methanol, n-butanol의 경우는 실험값과 상당한 오차가 있었다. 따라서, 본 미케니즘에 의해 PDMS막에 대한 정용매의 수착량과 투과 플럭스는 실험에 의하지 않고도 이론적으로 예측할 수 있는 가능성을 보여주었다.