The aim of this work was to develop three artificial intelligence-based methods to model the ternary adsorption of heavy metal ions {Pb 2+ , Hg 2+ , Cd 2+ , Cu 2+ , Zn 2+ , Ni 2+ , Cr 4+ } on different adsorbates {activated carbon, chitosan, Danish peat, Heilongjiang peat, carbon sunflower head, and carbon sunflower stem). Results show that support vector regression (SVR) performed slightly better, more accurate, stable, and more rapid than least-square support vector regression (LS-SVR) and artificial neural networks (ANN). The SVR model is highly recommended for estimating the ternary adsorption kinetics of a multicomponent system.

The objective of this study was to model the removal efficiency of ternary adsorption system using feed-forward back propagation artificial neural network (FFBP-ANN). The ANN model was trained with Levenberg-Marquardt back propagation algorithm and the best model was found with the architecture of {9-11-4-3} neurons for the input layer, first and second hidden layers, and the output layer, respectively, based on two metrics, namely, mean squared error (MSE) = (0.2717-0.5445) and determination coefficient (R 2 ) = (0.9997-0.9999). Results confirmed the robustness and the efficiency of the developed ANN model to model the adsorption process.