Soft computing-based models and decolorization of Reactive Yellow 81 using Ulva Prolifera biochar

Abstract

The present research explores the application of optimization tools namely Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the decolorization of Reactive Yellow 81 (RY81) from an aqueous solution. The characterization of the biochar was carried out using FTIR, elemental analysis, proximate analysis, BET analysis and Thermogravimetric analysis. Five independent variables namely solution pH, biochar dose, contact time, initial dye concentration and temperature were analyzed using RSM, ANN and ANFIS models. The maximum removal efficiency of 86.4% was obtained and the statistical error analysis was calculated. The correlation coefficient of 0.9665, 0.9998 and 0.9999 was obtained for RSM, ANN and ANFIS models, respectively. Adsorption Isotherm models and kinetic models were used to understand the adsorption mechanism. Maximum monolayer adsorption of 225 mg g−1 was predicted by Hill isotherm model. A partition coefficient of 4.09 L g−1 was obtained at an initial dye concentration of 250 mg L−1. It was revealed from the thermodynamic studies that reactions are endothermic and spontaneous. Further, to check the potential of the biochar, regeneration cycle was studied. The desorption efficiency of 99.5% was achieved at an S/L ratio of 3, regeneration cycles of 2, and sodium hydroxide was found as the best elutant for the desorption.