Practical Artificial Neural Network Tool for Predicting the Competitive Adsorption of Dyes on Gemini Polymeric Nanoarchitecture

Abstract

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 (R2) = (0.9997–0.9999). Results confirmed the robustness and the efficiency of the developed ANN model to model the adsorption process.

Keywords

• competitive adsorption
• artificial neural networks
• modelling
• dyes