Bees metaheuristic algorithm with the aid of artificial neural networks for optimization of acid red 27 dye adsorption onto novel polypyrrole/SrFe12O19/graphene oxide nanocomposite

Abstract

In this work, the capability of bees-inspired algorithm with the aid of artificial neural networks (ANN-BA) for optimization of acid red 27 dye removal was studied using polypyrrole/SrFe12O19/graphene oxide (GO) as a novel nanocomposite. The nanocomposite fabricated through in situ polymerization and its characteristics were investigated by means of several instrumental techniques. The prepared polypyrrole/SrFe12O19/GO nanocomposite was employed to adsorb acid red 27 from aqueous solution. Based on the cost function as a nonlinear relation between some factors influencing the adsorption efficiency including adsorbent dosage, initial concentration, pH, shaking rate and contact time with percentage removal which were obtained by a multilayer perceptron artificial neural networks, bees metaheuristic algorithm was utilized to optimize of the batch sorption process. In addition, D-optimal response surface methodology (RSM) was also employed as a comparative study. In comparison with the D-optimal RSM, the ANN-BA model gave higher percentage removal (99%) about 4%. Under optimal conditions obtained by ANN-BA, equilibrium isotherms, kinetic behaviors and thermodynamics of the dye adsorption were thoroughly investigated. The pseudo-second-order model and the Langmuir adsorption model [with maximum capacity (qmax) of 294.11 mg g−1] fitted the experimental results with the determination coefficients (R2) of 0.94 and 0.99, respectively. The thermodynamic parameters have also been evaluated which showed the sorption procedure was endothermic and spontaneous. The findings obtained from the sorbent usage in wastewater treatment and its regeneration investigation revealed that the nanocomposite can be applied as an effective dye sorbent for removing acid red 27 dye in real samples with a reusable property.