Development of an improved deep network model as a general technique for thin film nanocomposite reverse osmosis membrane simulation

Due to the complexity of the inherent trade-off between water permeability and salt rejection in thin film nanocomposite reverse osmosis membranes (TFN-ROMs), conventional methods have not performed satisfac-torily in predicting the performance of TFN-ROMs. In this study, to address the limitations of traditional machine learning in predicting the performance of TFN-ROMs, we developed a three-component residual artificial neural network (R-TNN) and learned from membrane properties, nanoparticle properties, and membrane performance to capture the complex trade-off between water permeability and salt rejection of TFN-ROMs. The coefficients of determination of the trained R-TNN model for relative salt passage (RSP) and relative water permeability (RWP) of TFN-ROMs were 0.910 +/- 0.028 and 0.739 +/- 0.028, respectively, which showed good performance compared to fully-connected neural network and conventional machine learning models even with small sample data. Meanwhile, subsequent ablation experiments conducted verified that the improvement of the R-TNN model is practical and effective. In addition, we conducted data scale experiments, which showed that the R-TNN model has the potential to become a generalized technical framework for the initial informationization era and the future big data era. Subsequently, we employed the partial dependence plot to mechanistically interpret the model. Specifically, we observed a positive correlation between nanoparticle size and both RWP and RSP, while the loading of nanoparticles initially promoted and later inhibited these performances. The validated R-TNN model can better learn the inherent trade-off between salt rejection and water permeability of TFN-ROMs, which implies that this study can provide decision support for the fabrication of membranes with more outstanding performances, which greatly contributes to the promising applications of TFN-ROMs.