Layer-by-layer (LBL) self-assembly technology has become a new research hotspot in the fabrication of nanofiltration membranes in recent years. However, there is a lack of a systematic approach for the assessment of influencing factors during the membrane fabrication process. In this study, the process optimization of LBL deposition was performed by a two-step statistical method. The multiple linear regression was performed on the results of single-factor experiments to determine the major influencing factors on membrane performance, including the concentration of Poly (allylamine hydrochloride) (PAH), glutaraldehyde, and the NaCl concentration in PAH solution. The Box-Behnken response surface method was then used to analyze the interactions between the selected factors, while their correlation with the membrane performance was obtained by polynomial fitting. The R2 value of the regression models (0.97 and 0.94) was in good agreement with the adjusted R2 value (0.93 and 0.86), indicating that the quadratic response models were adequate enough to predict the membrane performance. The optimal process parameters were finally determined through dual-response surface analysis to achieve both high membrane permeability of 14.3 LMH·MPa-1 and MgSO4 rejection rate of 90.22%.
Keywords: layer-by-layer self-assembly; multiple regression analysis; nanofiltration membrane; process optimization; response surface methodology.