Ternary multicomponent adsorption modelling using ANN, LS-SVR, and SVR approach
The aim of this work was to develop three artificial intelligence-based methods to model the ternary adsorption of heavy metal ions {Pb2+, Hg2+, Cd2+, Cu2+, Zn2+, Ni2+, Cr4+} on different adsorbates {activated carbon, chitosan, Danish peat, Heilongjiang peat, carbon sunflower head, and carbon sunflo...
| Permalink: | http://skupnikatalog.nsk.hr/Record/nsk.NSK01001144941/Details |
|---|---|
| Matična publikacija: |
Kemija u industriji (Online) 70 (2021), 9/10 ; str. 509-518 |
| Glavni autori: | Yettou, Amina (Author), Laidi, Maamar, El Bey, Abdelmadjid, Hanini, Salah, Hentabli, Mohamed, Khaldi, Omar, Abderrahim, Mihoub |
| Vrsta građe: | e-članak |
| Jezik: | eng |
| Predmet: | |
| Online pristup: |
https://doi.org/10.15255/KUI.2020.071 Kemija u industriji (Online) Hrčak |
| LEADER | 02951naa a22004454i 4500 | ||
|---|---|---|---|
| 001 | NSK01001144941 | ||
| 003 | HR-ZaNSK | ||
| 005 | 20221219134902.0 | ||
| 006 | m d | ||
| 007 | cr|||||||||||| | ||
| 008 | 220803s2021 ci d |o |0|| ||eng | ||
| 024 | 7 | |2 doi |a 10.15255/KUI.2020.071 | |
| 035 | |a (HR-ZaNSK)001144941 | ||
| 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 54 |2 2011 | |
| 080 | 1 | |a 004 |2 2011 | |
| 100 | 1 | |a Yettou, Amina |4 aut |9 HR-ZaNSK | |
| 245 | 1 | 0 | |a Ternary multicomponent adsorption modelling using ANN, LS-SVR, and SVR approach |h [Elektronička građa] : |b case study / |c Amina Yettou, Maamar Laidi, Abdelmadjid El Bey, Salah Hanini, Mohamed Hentabli, Omar Khaldi, Mihoub Abderrahim. |
| 300 | |b Graf. prikazi. | ||
| 504 | |a Bibliografija: 28 jed. | ||
| 504 | |a Abstract ; Sažetak. | ||
| 520 | |a The aim of this work was to develop three artificial intelligence-based methods to model the ternary adsorption of heavy metal ions {Pb2+, Hg2+, Cd2+, Cu2+, Zn2+, Ni2+, Cr4+} on different adsorbates {activated carbon, chitosan, Danish peat, Heilongjiang peat, carbon sunflower head, and carbon sunflower stem). Results show that support vector regression (SVR) performed slightly better, more accurate, stable, and more rapid than least-square support vector regression (LS-SVR) and artificial neural networks (ANN). The SVR model is highly recommended for estimating the ternary adsorption kinetics of a multicomponent system. | ||
| 520 | |a Cilj ovog rada bio je razviti tri metode temeljene na umjetnoj inteligenciji za modeliranje trostruke adsorpcije iona teških metala {Pb2+, Hg2+, Cd2+, Cu2+, Zn2+, Ni2+, Cr4+} na različitim adsorbatima {aktivni ugljen, kitozan, danski treset, treset Heilongjiang, ugljik glave suncokreta i ugljik stabljike suncokreta). Rezultati pokazuju da se regresija potpornih vektora (SVR) pokazala nešto boljom, preciznijom, stabilnijom i bržom od regresije potpornih vektora najmanjih kvadrata (LS-SVR) i umjetnih neuronskih mreža (ANN). Za procjenu kinetike trostrukog adsorpcijskog sustava višekomponentnog sustava preporučuje se model SVR. | ||
| 653 | 0 | |a Teški metali |a Umjetne neuronske mreže |a Regresija potpornih vektora |a Adsorpcija | |
| 700 | 1 | |a Laidi, Maamar |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a El Bey, Abdelmadjid |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Hanini, Salah |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Hentabli, Mohamed |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Khaldi, Omar |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Abderrahim, Mihoub |4 aut |9 HR-ZaNSK | |
| 773 | 0 | |t Kemija u industriji (Online) |x 1334-9090 |g 70 (2021), 9/10 ; str. 509-518 |w nsk.(HR-ZaNSK)000530475 | |
| 981 | |b Be2021 |b B03/21 | ||
| 998 | |b tino2212 | ||
| 856 | 4 | 0 | |u https://doi.org/10.15255/KUI.2020.071 |
| 856 | 4 | 0 | |u http://silverstripe.fkit.hr/kui/issue-archive/article/809 |y Kemija u industriji (Online) |
| 856 | 4 | 0 | |u https://hrcak.srce.hr/261417 |y Hrčak |
| 856 | 4 | 1 | |y Digitalna.nsk.hr |