Optimization studies and artificial neural network modeling for pyrene adsorption onto UiO-66(Zr) and NH2-UiO-66(Zr) metal organic frameworks

Abstract

Optimization studies was conducted for the pyrene (PYR) adsorption onto Zr-based metal organic frameworks (MOFs), UiO-66(Zr) and NH2-UiO-66(Zr) in aqueous medium. Central composite design (CCD) model has shown good fittings of the coefficient of determination (R2) with non-significant lack of fit for both UiO-66(Zr) and NH2-UiO-66(Zr) MOFs. The optimized adsorption efficiency achieved by the UiO-66(Zr) and NH2-UiO-66(Zr) were 99.22 and 95.67% respectively. Artificial neural network (ANN) model was able to predict the experimental findings with high precision at topographic node of 5-4-2 structural layer. The kinetics and isotherms of the process was best described by pseudo-second-order Langmuir models respectively. The process was exothermic and spontaneous with the good reusability of the MOFs.