Statistical modeling and optimization of heterogeneous Fenton-like removal of organic pollutant using fibrous catalysts: a full factorial design

Abstract

This work focuses on the optimization of heterogeneous Fenton-like removal of organic pollutant (dye) from water using newly developed fibrous catalysts based on a full factorial experimental design. This study aims to approximate the feasibility of heterogeneous Fenton-like removal process and optionally make predictions from this approximation in a form of statistical modeling. The fibrous catalysts were prepared by dispersing zerovalent iron nanoparticles on polyester fabrics (PET) before and after incorporation of either polyamidoamine (PAMAM, –NH2) dendrimer, 3-(aminopropyl) triethoxysilane (APTES, –Si–NH2) or thioglycerol (SH). The individual effect of two main factors [pH (X1) and concentration of hydrogen peroxide-[H2O2]μl (X2)] and their interactional effects on the removal process was determined at 95% confidence level by an L27 design. The results indicated that increasing the pH over 5 decreases the dye removal efficiency whereas the rise in [H2O2]μl until equilibrium point increases it. The principal effect of the type of catalysts (PET–NH2–Fe, PET–Si–NH2–Fe, and PET–SH–Fe) did not show any statistical significance. The factorial experiments demonstrated the existence of a significant synergistic interaction effect between the pH and [H2O2]μl as expressed by the values of the coefficient of interactions and analysis of variance (ANOVA). Finally, the functionalization of the resultant fibrous catalysts was validated by electrokinetic and X-ray photoelectron spectroscopy analysis. The optimization made from this study are of great importance for rational design and scaling up of fibrous catalyst for green chemistry and environmental applications.