Optimization of ultrafiltration membrane fabrication using backpropagation neural network and genetic algorithm

Hybrid models based on backpropagation neural network (BPNN) and genetic algorithm (GA) were constructed to optimize the fabrication of polyetherimide (PEI) ultrafiltration (UF) membrane via dry/wet phase inversion. BPNN was employed to capture the detailed relationships between the preparation conditions and the UF membrane performances, and GA was used to choose the initial connection weights and biases of BPNN to avoid convergence at suboptimal solutions. The excellent agreements between the model predictions and the testing data indicate that the hybrid models have sufficient accuracy. The effects of preparation conditions on membrane performances were predicted by the hybrid models successfully, which indicate that PEI/N,N-dimethylacetamide (DMAc)/1,4-butyrolactone (GBL) is the best membrane casting system investigated in this study. Furthermore, the optimal preparation conditions were forecasted, and membranes with desired performances, for instance, higher pure water flux (PWF) and bovine serum albumin (BSA) rejection ratio (RR) 80-90% were fabricated with the standard deviation between the predicted performances and validation experimental values less than 10%. The hybrid models can contribute to collaborative optimization of multiple parameters and designing the preparation conditions to obtain desired UF membrane performances and avoiding large experimental data scattering in the fabrication of phase inversion membranes.