Optimization of Pb(II) biosorption by Robinia tree leaves using statistical design of experiments

Abstract

The present study introduces Robinia tree leaves as a novel and efficient biosorbent for removing Pb(II) from aqueous solutions. In order to reduce the large number of experiments and find the highest removal efficiency of Pb(II), a set of full 2 3 factorial design with two blocks were performed in duplicate (16 experiments). In all experiments, the contact time was fixed at 25 min. The main interaction effects of the three factors including sorbent mass, pH and initial concentration of metal-ion were considered. By using Student's t-test and analysis of variances (ANOVA), the main factors, which had the highest effect on the removal process, were identified. Twenty-six experiments were designed according to Doehlert response surface design to obtain a mathematical model describing functional relationship between response and main independent variables. The most suitable regression model, that fitted the experimental data extremely well, was chosen according to the lack-of-fit-test and adjusted R 2 value. Finally, after checking for possible outliers, the optimum conditions for maximum removal of Pb(II) from aqueous solution were obtained. The best conditions were calculated to be as: initial concentration of Pb(II) = 40 mg L −1, pH 4.6 and concentration of sorbet equal to 27.3 g L −1.