Adsorptive removal of cobalt(II) from aqueous solutions using multi-walled carbon nanotubes and γ-alumina as novel adsorbents: Modelling and optimization based on response surface methodology and artificial neural network - Dehghani, Mohammad Hadi; Yetilmezsoy, Kaan; Salari, Mehdi; Heidarinejad, Zoha; Yousefi, Mahmood; Sillanpaa, Mika (ELSEVIER, 2020)
The efficiency of new and nano-scale adsorbents including multi-walled carbon nanotubes (MWCNTs) and gamma-alumina in the removal of cobalt(II) from aqueous solutions was experimentally evaluated in a batch-system reactor. To the best of our knowledge, no previous study has specifically attempted to introduce a hybrid strategy based on artificial neural network and genetic algorithm techniques for modelling and optimizing adsorptive removal of cobalt(II) from aqueous solutions via the proposed nanoparticles. The analyses of SEM, TEM, and FTIR were used to characterize both adsorbents. The response surface methodology (RSM) approach suggested a second-order polynomial model with a p-value < 0.0001 and R-2 of 0.9980 for MWCNTs adsorbent and a p-value < 0.0001 and R-2 of 0.9992 for gamma-alumina adsorbent. The artificial neural network (ANN) approach suggested a three-layered feed-forward backpropagation model with R-2 of 0.9794 for MWCNTs adsorbent and R-2 of 0.9823 for gamma-alumina adsorbent. The results linked to optimization by RSM showed that the maximum cobalt(II) removal efficiency of about 90% was achieved in the case of the MWCNTs adsorbent under the conditions of pH = 10, contact time = 38.6 min, MWCNTs dosage = 1.57 mg/L, and initial cobalt(II) concentration = 56.57 mg/L. About 93% of cobalt(II) removal could be obtained in the case of gamma-alumina adsorbent under the conditions of pH = 10, contact time = 35.5 min, gamma-alumina dosage = 1.63 g/L, and initial cobalt(II) concentration = 52.15 mg/L. The optimization values using the genetic algorithm (GA) technique were almost the same as those obtained from the RSM method. The kinetic model of Ho and McKay's pseudo-second order (PSO) and the isotherm model of Dubinin-Radushkevich were found to be the best-fitted to the experimental for both MWCNTs and gamma-alumina. In addition, the maximum monolayer adsorption capacity of MWCNTs and gamma-alumina adsorbents for the adsorption of cobalt(II) was 78.94 mg/g and 75.78 mg/g, respectively. Also, a thermodynamic study exhibited a favorable and spontaneous adsorption process for both materials. The present study clearly concluded that the proposed adsorbents could be effectively used for the removal of cobalt(II) from aqueous solutions at lower adsorbent dose and shorter contact times than various adsorbents reported in literature. (C) 2019 Elsevier B.V. All rights reserved.
Keywords: Adsorption; Cobalt(II); Multi-walled carbon nanotube; gamma-Alumina; Artificial neural network; Genetic algorithm; FACILE HYDROTHERMAL METHOD; WASTE-WATER; METAL-IONS; NANOPARTICLES; KINETICS; EQUILIBRIUM; COMPOSITE; PHOSPHATE; SORPTION; CO(II); Chemistry; Physics