Izvorni znanstveni članak
https://doi.org/10.15255/KUI.2020.071
Ternary Multicomponent Adsorption Modelling Using ANN, LS-SVR, and SVR Approach – Case Study
Amina Yettou
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Médéa, Alžir
Maamar Laidi
orcid.org/0000-0002-8977-9895
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Médéa, Alžir
Abdelmadjid El Bey
orcid.org/0000-0002-5525-074X
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Médéa, Alžir
Salah Hanini
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Médéa, Alžir
Mohamed Hentabli
orcid.org/0000-0002-6693-0708
; Laboratory Quality Control, Physico-Chemical Department, Antibiotical Saidal of Médéa, Alžir
Omar Khaldi
; Material and Environment Laboratory (LME), University Yahia Fares of Medea, Médéa, Alžir
Mihoub Abderrahim
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Médéa, Alžir
Sažetak
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.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Ključne riječi
multicomponent adsorption; heavy metals; artificial neural networks; support vector regression; least-square support vector regression
Hrčak ID:
261417
URI
Datum izdavanja:
23.8.2021.
Posjeta: 1.153 *