RSM versus ANN for modeling and optimization of magnetic adsorbent based on montmorillonite and CoFe2O4

Abstract

Abstract A highly resourceful, environmentally benign, and recyclable magnetic montmorillonite composite (MMT/CF) was obtained through a simple one-step hydrothermal method and exhibited excellent Pb (II) removal. The as-synthesized adsorbent was then characterized by XRD, SEM–EDX, FTIR, BET, and TGA-DTA. The operating parameters including adsorbent dosage, initial Pb (II) concentration, solution pH, and time were studied. Also, a comparative approach was formed between response surface methodology (RSM) and artificial neural network (ANN) to optimize and model the removal efficiency of Pb (II) by MMT/CF. The results indicated that the ANN model was more precise and quite trusted optimization tool than RSM in consideration of its higher correlation coefficient (R 2 = 0.998) and lower prediction errors (RMSE = 0.851 and ADD = 0.505). Langmuir isotherm provided the best fit to the experimental data, and the maximum adsorption capacity was 101.01 mg/g. Additionally, the kinetic studies showed that the pseudo-second-order model fitted well with the experimental data. The magnetic MMT/CF composite possesses high adsorption capacity and is suitable for reuse. Therefore, this study shows that MMT/CF composite can be a potential adsorbent in Pb (II) uptake from aqueous media.

Keywords

• Montmorillonite/CoFe2O4 composite
• Adsorption
• Response surface methodology
• Artificial neural network