This research studied the modeling of malachite green (MG) adsorption onto novel polyurethane/SrFe12O19/clinoptilolite (PU/SrM/CLP) nanocomposite from aqueous solutions by the application of biogeography-based optimization (BBO) algorithm-assisted multilayer neural networks (MNN-BBO) as a new evolutionary algorithm in environmental science. The PU/SrM/CLP nanocomposite was successfully fabricated and characterized by some spectroscopic analyses. Four variables influencing the removal efficiency were modeled by MNN-BBO and response surface methodology (RSM). The MNN-BBO model gave higher percentage removal (99.6%) about 7.6% compared to the RSM technique. Under optimal conditions obtained by MNN-BBO, the four independent variables including pH, shaking rate, initial concentration, and adsorbent dosage were 6.5, 255 rpm, 50 mg.L-1, and 0.08 g, respectively. Under these conditions, the results were fitted well to the Langmuir isotherm with a monolayer maximum amount of sorbate uptake (qmax) of 68.49 mg.g-1 and the pseudo-first-order kinetic pattern with the rate constant (K1) of 0.01 min-1 with the R2 values of 0.9248 and 0.9980, respectively. The results of thermodynamics demonstrated that the MG uptake was not spontaneous due to the positive value of the adsorption ΔG. In addition, the positive values of ΔS (0.079 kJ/mol K) and ΔH (30.816 kJ/mol) indicated the feasible operation and endothermic approach, respectively. Besides, the wastewater investigations showed that the nanocomposite could be used as a new promising sorbent for efficient removal of MG (R% > 72) and magnetically separable from the real samples.
Keywords: Artificial neural network; Biogeography-based optimization; Clinoptilolite; Malachite green; Polyurethane composite; Strontium hexaferrite.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.