Optimal artificial neural network design for simultaneous modeling of multicomponent adsorption

Abstract

In this work, ultrasound-modified chitin (UM-chitin) was used to adsorb cobalt (Co2+), methylene blue (MB), and nickel (Ni2+) in single, binary and ternary systems, at different temperatures. The single adsorption isotherms showed that the affinity of the UM-chitin towards the metals was higher than that of the dye. Antagonist adsorption was found for the binary adsorption of Co2+ and MB; however, the presence of Ni2+ in the ternary system promoted the adsorption of MB and inhibited the adsorption of Co2+. Therefore, an optimal artificial neural network was developed to predict these synergistic and antagonistic interactions simultaneously. Different ANN configurations were tested using the initial concentration of each adsorbate (0–650 mg L−1) and the solution temperature (298–328 K) as input variables. The optimal ANN structure, which uses 2 hidden layers with 5 and 10 neurons, tangent sigmoid activation function (tansig) at both hidden and output layers, and Bayesian Regulation as its backpropagation algorithm demonstrated suitable ability in simultaneously predict the adsorption capacity for each adsorbate (MSE < 0.0003 and R > 0.9995).