Artificial neural network optimization for methyl orange adsorption onto polyaniline nano-adsorbent: Kinetic, isotherm and thermodynamic studies

Abstract

This study aims to synthesize a polyaniline nano-adsorbent and use it to adsorb methyl orange dye from the aqueous solution. The average particle size of nano-adsorbent was about 70nm. The effects of various parameters have been analysed, as are pH, temperature, adsorption time, initial concentration and adsorbent dosage, and they were optimized by an artificial neural network model. The multilayer feed forward neural network with five inputs and one output has been trained with eight neurons in the hidden layer. A comparison of the experimental data with the dye adsorption efficiency predicted by the artificial neural network model showed that this model can estimate the behavior of the adsorption process of methyl orange dye on the polyaniline nano-adsorbent under different conditions. The study yielded the result that dye adsorption capacity of the nano-adsorbent increased from 3.34 to 32.04mg/g and from 3.28 to 30.28mg/g as the dye initial concentration was increased from 10 to 100mg/L, at 65°C and 25°C, respectively. Also, dye adsorption equilibrium was achieved in 60min for methyl orange dye. The adsorption kinetics was studied based on pseudo-first-order, pseudo-second-order, intraparticle diffusion and Elovich models. The results showed that the adsorption data at all levels of initial concentration have the best consistency with the pseudo-second order equation. Furthermore, two-parameter isotherm models (Langmuir, Freundlich and Temkin) and three-parameter isotherm models (Hill, Sips and R-P) were used to ascertain the nature of the adsorption isotherm. Based on this study, the maximum adsorption capacity was estimated to be 75.9mg/g. Thermodynamic studies indicated that methyl orange dye adsorption was endothermic on the polyaniline nano-adsorbent.