Competitive adsorption of methylene blue and brilliant green onto graphite oxide nano particle following: Derivative spectrophotometric and principal component-artificial neural network model methods for their simultaneous determination

Abstract In this work, the competitive adsorption of methylene blue (MB) and brilliant green (BG) onto graphite oxide (GO) nanoparticles followed by their accurate and reproducible determination by second order derivative spectrophotometry (SODS) and principal component–artificial neural network model (PCA–ANN) model has been studied. The evaluation of kinetic and isotherm studies was investigated at optimum experimental conditions set as pH = 7.0, 8 mg of GO and 14 min contact time in binary systems. The equilibrium amounts of MB and BG dyes in binary mixture adsorbed onto GO-NP has opposite correlation with their initial concentration. Principal component analysis (PCA) used to minimize the dimensionality of large data sets via reducing the number of spectral data by a three-layered feed-forward artificial neural network (ANN) trained by Levenberg–Marquardt back-propagation algorithm. The ANN model was able to predict the concentrations of both dyes in mixtures with a tangent sigmoid transfer function (tansig) at hidden layer with 20 neurons and a linear transfer function (purelin) at output layer. Several isotherm models were applied to experimental data and the isotherm constants were calculated for BG and MB dyes. Among the applied models, the extended Freundlich isotherm model adequately predicts the multi-component adsorption equilibrium data at moderate ranges of concentration.