Prediction of impacts of fabrication conditions on the filtration performance of homemade VC-co-VAc-OH microfiltration membrane by Support Vector Machine (SVM)

In this study, the support vector machine (SVM) model which was based on restricted data sets (the size of the training set is small or small training sample) was applied to predict the permeate flux and rejection of Bovine serum albumin (BSA) of homemade VC-co-VAc-OH microfiltration membrane as the function of fabrication conditions. The membrane preparation conditions (the solid content, the additive content, environmental temperature, the relative humidity, evaporation time of a volatile solvent, precipitation temperature, and precipitation time) were input variables; pure water flux and rejection of BSA were output variables. The results showed that the detailed relationships between fabrication conditions and filtration performance of the membranes could be established. Excellent agreements between the prediction of SVM model and the experiments validate that SVM model has sufficient accuracy. Furthermore, the results predicted by SVM model were compared with those predicted by artificial ne...