Methyl orange adsorption onto simple chemical route synthesized crystalline α-Fe2O3nanoparticles: kinetic, equilibrium isotherm, and neural network modeling

Abstract

Nanoparticles of α-Fe2O3 were synthesized by simple chemical precipitation method and characterized by X-ray diffraction study, scanning electron microscopy, and Fourier transform infrared spectroscopy. Feasibility of as-synthesized nanoparticles was investigated for adsorptive removal of methyl orange (MO) dye from aqueous solution. The effects of various experimental parameters such as solution pH, initial MO concentration, contact time, and α-Fe2O3 nanoparticles dose were studied in batch mode. More than 90% removal was reported at pH 2.0 with 30 mg L−1 initial MO concentration treated with 1.00 g L−1 adsorbent dose. Isotherm study reveals that Langmuir isotherm model is the most efficient one in explaining the process and maximum adsorption capacity as much as 28.90 mg g−1 is reported. Kinetic study shows that the adsorption process is best explained by second-order kinetic model confirming the dominancy of chemisorption in the process. Subsequently, the experimental data were modeled by artificial neural network to predict the removal efficiency of MO by α-Fe2O3 nanoparticles following conduction of 95 experimental data points. A three-layer feed-forward back-propagation model with Levenberg–Marquardt algorithm was developed which show that the optimal network topology is 4–10–1. Model predicted data shows very good agreement with experimental data set with mean squared error and coefficient of determination (R2) as 0.00152 and 0.9916, respectively.