Predictive Model for Polysulfone Membrane Reinforced with Gum Arabic and Biogenic Zinc Oxide Nanoparticles Using CCD Response Surface Methodology for Membrane Performance Enhancement

Response surface methodology (RSM) was used to find the best ratio in preparing the polysulfone (PSf) ultrafiltration (UF) membranes via phase inversion. The central composite design was used to make predictive regression models and study the effects of different parameters, such as the concentration of PSf polymer, the concentration of gum Arabic (GA), and the content of biosynthesized zinc oxide nanoparticles, on water flux, rejection, and porosity. Twenty tests were done to build a quadratic model, and the membranes were evaluated with scanning electron microscopy (SEM), atomic force microscopy (AFM), and water contact angle measurements. Using a Lab-scale cross-flow filtering device, performance testing was undertaken. The variance analysis showed that all three independent parameters were statistically important, and the model made from them was pretty good. To explain the regression equations, response surfaces, and outlines were plotted. The best experimental conditions were evaluated for water permeation flux, rejection, and porosity. The best parameters led to a maximum permeation flow of 381.5 L m −2 h −1 , a rejection of 54%, and a porosity of 85.1%. With R 2 values of more than 90%, the correlation between the measured and predicted values of pure water flux, rejection, and porosity values of the membranes validates the model’s correctness.