Original scientific paper
https://doi.org/10.15255/KUI.2020.069
Practical Artificial Neural Network Tool for Predicting the Competitive Adsorption of Dyes on Gemini Polymeric Nanoarchitecture
Abdelmadjid El Bey
orcid.org/0000-0002-5525-074X
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Algeria
Maamar Laidi
orcid.org/0000-0002-8977-9895
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Médéa, Algeria
Amina Yettou
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Médéa, Algeria
Salah Hanini
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Médéa, Algeria
Abdellah Ibrir
orcid.org/0000-0003-0332-1398
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Médéa, Algeria
Mohamed Hentabli
orcid.org/0000-0002-6693-0708
; Laboratory Quality Control, Physico-Chemical Department, Antibiotical Saidal of Médéa, Algeria
Hasna Ouldkhaoua
; Laboratory Quality Control, Physico-Chemical Department, Antibiotical Saidal of Médéa, Algeria
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.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Keywords
competitive adsorption; artificial neural networks; modelling; dyes
Hrčak ID:
261416
URI
Publication date:
23.8.2021.
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