Practical artificial neural network tool for predicting the competitive adsorption of dyes on gemini polymeric nanoarchitecture
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 {...
| Permalink: | http://skupnikatalog.nsk.hr/Record/nsk.NSK01001144944/Details |
|---|---|
| Matična publikacija: |
Kemija u industriji (Online) 70 (2021), 9/10 ; str. 481-488 |
| Glavni autori: | El Bey, Abdelmadjid (Author), Laidi, Maamar, Yettou, Amina, Hanini, Salah, Ibrir, Abdellah, Hentabli, Mohamed, Ouldkhaoua, Hasna |
| Vrsta građe: | e-članak |
| Jezik: | eng |
| Predmet: | |
| Online pristup: |
https://doi.org/10.15255/KUI.2020.069 Kemija u industriji (Online) Hrčak |
| LEADER | 03024naa a22004454i 4500 | ||
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| 001 | NSK01001144944 | ||
| 003 | HR-ZaNSK | ||
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| 006 | m d | ||
| 007 | cr|||||||||||| | ||
| 008 | 220803s2021 ci d |o |0|| ||eng | ||
| 024 | 7 | |2 doi |a 10.15255/KUI.2020.069 | |
| 035 | |a (HR-ZaNSK)001144944 | ||
| 040 | |a HR-ZaNSK |b hrv |c HR-ZaNSK |e ppiak | ||
| 041 | 0 | |a eng |b hrv | |
| 042 | |a croatica | ||
| 044 | |a ci |c hr | ||
| 080 | 1 | |a 66 |2 2011 | |
| 080 | 1 | |a 004 |2 2011 | |
| 100 | 1 | |a El Bey, Abdelmadjid |4 aut |9 HR-ZaNSK | |
| 245 | 1 | 0 | |a Practical artificial neural network tool for predicting the competitive adsorption of dyes on gemini polymeric nanoarchitecture |h [Elektronička građa] / |c Abdelmadjid El Bey, Maamar Laidi, Amina Yettou, Salah Hanini, Abdellah Ibrir, Mohamed Hentabli, Hasna Ouldkhaoua. |
| 300 | |b Graf. prikazi. | ||
| 504 | |a Bibliografija: 21 jed. | ||
| 504 | |a Abstract ; Sažetak. | ||
| 520 | |a 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. | ||
| 520 | |a Cilj ove studije bio je modelirati učinkovitost uklanjanja ternarnog adsorpcijskog sustava pomoću višeslojne unaprijedne neuronske mreže s povratnim rasprostiranjem pogreške (FFBP-ANN). Model ANN-a učen je algoritmom Levenberg–Marquardt, a najbolji model bio je s arhitekturom {9-11-4-3} neurona za ulazni, prvi i drugi skriveni sloj te izlazni sloj, na temelju dvaju metričkih pokazatelja: srednje kvadratne pogreške (MSE) = (0,2717 – 0,5445) i koeficijenta određivanja (R2) = (0,9997 – 0,9999). Rezultati su potvrdili robusnost i učinkovitost razvijenog ANN modela za modeliranje procesa adsorpcije. | ||
| 653 | 0 | |a Umjetne neuronske mreže |a Bojila |a Uklanjanje boje |a Obezbojavanje |a Adsorpcija |a Polimeri |a Nanostruktura | |
| 700 | 1 | |a Laidi, Maamar |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Yettou, Amina |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Hanini, Salah |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Ibrir, Abdellah |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Hentabli, Mohamed |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Ouldkhaoua, Hasna |4 aut |9 HR-ZaNSK | |
| 773 | 0 | |t Kemija u industriji (Online) |x 1334-9090 |g 70 (2021), 9/10 ; str. 481-488 |w nsk.(HR-ZaNSK)000530475 | |
| 981 | |b Be2021 |b B03/21 | ||
| 998 | |b tino2212 | ||
| 856 | 4 | 0 | |u https://doi.org/10.15255/KUI.2020.069 |
| 856 | 4 | 0 | |u http://silverstripe.fkit.hr/kui/issue-archive/article/807 |y Kemija u industriji (Online) |
| 856 | 4 | 0 | |u https://hrcak.srce.hr/261416 |y Hrčak |
| 856 | 4 | 1 | |y Digitalna.nsk.hr |