Deep learning models for assisted decision-making in performance optimization of thin film nanocomposite membranes

Abstract

The performance of thin film nanocomposite nanofiltration membranes (TFNNMs) is frequently constrained by the trade-off between the permeability and rejection/selectivity of the membrane. In this study, to provide assisted decision-making in performance optimization of TFNNMs, we utilized a dataset comprising four categories of impact parameters, including membrane manufacturing conditions, membrane performance, nanoparticle material performance, and organic solvent nanofiltration conditions, and trained a dual output neural network (D-ANN) to simulate the relative permeability (RP) and relative rejection/selectivity (RR) of TFNNMs. The D-ANN model outperformed conventional machine learning models and single-output neural networks in predicting both RP and RR, indicating its potential in uncovering correlations between output variables. The Shapley additive explanation method was employed to elucidate the contribution and marginal effects of each influencing parameter on RP and RR, among them, NP size, NP loading, water contact angle, and chloride concentration were influential parameters that are important for both RP and RR. Based on the D-ANN model, we extrapolated two sets of unknown data and achieved good predictive performance (RP-error <15%, RS-error <7% for most sample points). Furthermore, we observed that the predicted performance of the data points rarely exceeded the actual performance, indicating a low probability of overestimation. Based on these premises, the influential parameters can be optimized using the D-ANN model, which ultimately led to exceptional performance for a total of 22 datasets in four pairs of organic solvent and solute purifications guided by the D-ANN model. This study demonstrates that the D-ANN model can provide decisions for the performance optimization of TFNNMs while highlighting the enhancement of nanofiltration performance by thin film nanocomposites, significantly facilitating the design of high-performance TFNNMs.