Evaluation of adsorption potential of illite/quartz/kaolinite/montmorillonite for Cr(VI), Ni(II), and Cu(II) ions and modeling of experimental results by artificial neural networks

Abstract

An artificial neural network (ANN) was used to predict the removal efficiency of Cr(VI), Ni(II), and Cu(II) ions on riverbed sand containing illite/quartz/kaolinite/montmorillonite (IQKM) clay minerals. The effect of operational parameters such as initial metal ion concentration (10–100 mg/L), initial pH (2–10), adsorbent dosage (0.025–0.15 g/L), contact time (15–90 min), agitation speed (100–800 rpm), and temperature (303–323 K) is studied to optimize the conditions for greatest removal of metal ions. Employment of equilibrium isotherm models for the description of adsorption capacities for IQKM explored better efficiency of the Langmuir model for the best representation of experimental data with the highest adsorption capacity of 8.802, 7.5125, 6.608 mg/g for Cr(VI), Ni(II), and Cu(II) ions in the solution. The kinetics of the proposed adsorption processes efficiently followed pseudo-second-order and intraparticle diffusion kinetic models. . HIGHLIGHTS IQKM was used as an adsorbent for the removal of Cr(VI), Ni(II), and Cu(II) ions from an aqueous solution.; FT-IR, XRD, SEM EDAX, and BET were performed to confirm the clay minerals.; An artificial neural network (ANN) was used to predict the removal efficiency of Cr(VI), Ni(II), and Cu(II) ions.; The maximum adsorption capacity of IQKM is 8.802, 7.5125, and 6.608 mg/g for Cr(VI), Ni(II), and Cu(II) ions in the solution.;

Keywords

• artificial neural network
• ft-ir
• levenberg–marquardt
• riverbed sand