Optimization and kinetics study for methylene blue adsorption with GO incorporated waste polystyrene: ANN and BBD studies

This study focused on the development of PS-GO composites synthesized by combining waste polystyrene (PS) and different ratios of graphene oxide (GO) for the effective removal of an organic contaminant, methylene blue (MB). The composites were characterized and analyzed using various techniques to confirm their successful synthesis, structural integrity, and surface properties. The impact of independent parameters including solution pH, various GO ratios in the composite and initial MB concentration were evaluated to find the optimum conditions. In addition, Box-Behnken Design (BBD) and Artificial Neural Network (ANN) models were applied in the design of experiments and in the examination of the adsorption efficiency of MB, as well as to predict and optimize MB removal. The optimum conditions were determined as pH 11, MB concentration 20 mg/L, GO ratio by weight in the composite as 5 %, and the highest MB removal of 99.98 % was achieved after 20 min. This value was predicted with high accuracy by BBD (98.97 %) and ANN (99.92 %). According to kinetic evaluations, MB adsorption exhibited pseudo-second-order behavior. Isotherm studies proved that the MB adsorption onto PS-GO composites followed the Langmuir isotherm model. Furthermore, thermodynamic evaluations indicated that the adsorption process was spontaneous and endothermic, while recyclability tests showed that the PS-GO composite maintained good adsorption performance over multiple adsorption–desorption cycles. The results demonstrated that the PS-GO composite prepared in this study can be proposed as a novel, sustainable, economical and industrially applicable adsorbent material for the treatment of organic pollutants from aquatic environments.