Adopting Sustainable Oil Bio-Based Polymer Membranes as Alternatives for Environmental Remediation.

This study aimed to optimize the removal of Cu(II) ions from an aqueous solution using a oil bio-based membrane blended with 0.50 wt% graphene oxide (JPU/GO 0.50 wt%) using a central composite model (CCD) design using response surface methodology. The input factors were the feed concentration (60-140) ppm, pressure (1.5-2.5) bar, and solution pH value (3-5). An optimum Cu(II) ions removal of 87% was predicted at 116 ppm feed concentration, 1.5 bar pressure, and pH 3.7, while the validated experimental result recorded 80% Cu(II) ions removal, with 95% of prediction intervals. A statistically non-significant term was removed from the analysis by the backward elimination method to improve the model's accuracy. Using the reduction method, the predicted R value was increased from -0.16 (-16%) to 0.88 (88%), suggesting that the reduced model had a good predictive ability. The quadratic regression model was significant (R = 0.98) for the optimization prediction. Therefore, the results from the reduction model implied acceptable membrane performance, offering a better process optimization for Cu(II) ions removal.