Modeling and optimizing the performance of PVC/PVB ultrafiltration membranes using supervised learning approaches

Abstract

Mathematical models play an important role in performance prediction and optimization of ultrafiltration (UF) membranes fabricated via dry/wet phase inversion in an efficient and economical manner. In this study, a systematic approach, namely, a supervised, learning-based experimental data analytics framework, is developed to model and optimize the flux and rejection rate of poly(vinyl chloride) (PVC) and polyvinyl butyral (PVB) blend UF membranes. Four supervised learning (SL) approaches, namely, the multiple additive regression tree (MART), the neural network (NN), linear regression (LR), and the support vector machine (SVM), are employed in a rigorous fashion. The dependent variables representing membrane performance response with regard to independent variables representing fabrication conditions are systematically analyzed. By comparing the predicting indicators of the four SL methods, the NN model is found to be superior to the other SL models with training and testing R-squared values as high as 0.8897 and 0.6344, respectively, for the rejection rate, and 0.9175 and 0.8093, respectively, for the flux. The optimal combination of processing parameters and the most favorable flux and rejection rate for PVC/PVB ultrafiltration membranes are further predicted by the NN model and verified by experiments. We hope the approach is able to shed light on how to systematically analyze multi-objective optimization issues for fabrication conditions to obtain the desired ultrafiltration membrane performance based on complex experimental data characteristics.