Graphene oxide for the treatment of ranitidine containing solution: Optimum sorption kinetics by linear and non linear methods and simulation using artificial neural network

Abstract

In this present study, Graphene oxide nanoplateletes were synthesized and used as nano-adsorbent for the treatment of ranitidine present in synthetic wastewater. 99% ranitidine was removed using synthesized Graphene oxide. Comparative analysis was performed between linear and non-linear kinetic model for estimating the kinetic parameters. Four linear pseudo-second-order kinetic models were analyzed with non-linear kinetic model. Error analysis between non-linear and linear model were studied for determining the best-fitting model. It was observed that non-linear method proved to be a better alternative than the linear kinetic model for obtaining the important kinetic parameters which can be used further to determine the mechanism of the adsorption study. In addition, the treatment procedure was simulated using artificial neural network analysis to estimate the maximum percentage of ranitidine removal from wastewater in terms of various operational parameters.