Genetic algorithm-assisted artificial neural network modelling for remediation and recovery of Pb (II) and Cr(VI) by manganese and cobalt spinel ferrite super nanoadsorbent

Abstract

MnFe2O4 and CoFe2O4 nanoparticles were hydrothermally synthesized to examine their capability in adsorption of Pb (II) and Cr (VI). The adsorbents exhibited a high rate of adsorption, reaching 90% of their adsorption capacity in less than 30 min. Furthermore, the adsorption capability of the Magnetic Nanoparticles (MNPs) was noticeably greater at initial pollutant concentrations smaller than 40 mg/L. Maximum adsorption capacity on MnFe2O4 and CoFe2O4 nanoparticles were 40 and 25.38 mg/g for Cr (VI) and 523.32 and 476.19 mg/g for Pb (II), respectively. A data-driven model of Artificial Neural Network was used for prediction of adsorption capacity at both equilibrium and non-equilibrium condition. The model parameters including the numbers of neuron (n = 7) and data portioning for training (49.5%), validation (40.5%), and testing (10%) were obtained using Genetic Algorithm. The results indicated that the model could predict the data with high accuracy (R2 = 0.998). The input parameters were initial concentration, time, pH, temperature, adsorbent dosage, and other parameters that is dependent to the physico-chemical properties of ions and adsorbents’ surface (ε, α1, α2). The mechanism involved in Cr(VI) and Pb(II) adsorption are electrostatic physisorption and a combination of ion exchange chemisorption and electrostatic physisorption, respectively. Desorption capability and adsorbent reuse capability were also examined.