Modeling, optimization and efficient use of MMT K10 nanoclay for Pb (II) removal using RSM, ANN and GA

Abstract

Regarding the long-term toxic effects of Pb (II) ions on human health and its bioaccumulation property, taking measures for its reduction in the environment is necessary. The MMT-K10 (montmorillonite-k10) nanoclay was characterized by XRD, XRF, BET, FESEM, and FTIR. The effects of pH, initial concentrations, reaction time, and adsorbent dosage were studied. The experimental design study was carried out with RSM-BBD method. Results prediction and optimization were investigated with RSM and artificial neural network (ANN)-genetic algorithm (GA) respectively. The RSM results showed that the experimental data followed the quadratic model with the highest regression coefficient value (R2 = 0.9903) and insignificant lack of fit (0.2426) showing the validity of the Quadratic model. The optimal adsorption conditions were obtained at pH 5.44, adsorbent = 0.98 g/l, concentration of Pb (II) ions = 25 mg/L, and reaction time = 68 min. Similar optimization results were observed by RSM and artificial neural network-genetic algorithm methods. The experimental data revealed that the process followed the Langmuir isotherm and the maximum adsorption capacity was 40.86 mg/g. Besides, the kinetic data indicated that the results fitted with the pseudo-second-order model. Hence, the MMT-K10 nanoclay can be a suitable adsorbent due to having a natural source, simple and inexpensive preparation, and high adsorption capacity.