https://doi.org/10.15255/KUI.2020.069
Published: Kem. Ind. 70 (9-10) (2021) 481–488
Paper reference number: KUI-69/2020
Paper type: Original scientific paper
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Practical Artificial Neural Network Tool for Predicting the Competitive Adsorption of Dyes on Gemini Polymeric Nanoarchitecture

A. El Bey, M. Laidi, A. Yettou, S. Hanini, A. Ibrir, M. Hentabli and H. Ouldkhaoua

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.

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Keywords

competitive adsorption, artificial neural networks, modelling, dyes