Comparative optimization of alizarin red S adsorption on hyper-cross-linked resin using response surface methodology and artificial neural network techniques

Abstract

Given the extensive use of synthetic dyes across various industries as well as their association with environmental and health risks, this study focuses on the adsorptive removal of highly toxic dye, alizarin red S (ARS), employing a novel hyper-cross-linked isocyanurate-based resin (ICY-AM). Comparative optimization studies were carried out using response surface methodology (RSM) and artificial neural network (ANN). According to five statistical error standards, the RSM showed a superior performance as compared to ANN. Moreover, RSM exhibited a higher value in correlation coefficient in the parity plot, although the difference was subtle. However, the ANN model was proven to be superior in predicting the experimental outcome, outside of the training range. Furthermore, mechanistic modeling was carried out using kinetic, isothermal, and thermodynamic experiments. These experiments revealed that the ICY-AM possessed a high adsorption capacity towards alizarin red S (315.4 mg g⁻¹). The highest adsorption performance was recorded at pH 7.2 with the adsorbent dosage at 1 g L⁻¹. The ionic form of the dye, dependent upon the solution's pH, was crucial towards the adsorbate–adsorbent interaction during the adsorption process. Moreover, the kinetic and isothermal data suggested that the interaction of the components was favorable, and the adsorption process was not limited to a single component alone. Moreover, ICY-AM also showed an excellent reusability albeit with a slight decline in adsorption efficiency after five cycles of adsorption–desorption.