Ternary Multicomponent Adsorption Modelling Using ANN, LS-SVR, and SVR Approach – Case Study

• Main Authors: Amina Yettou, Maamar Laidi, Abdelmadjid El Bey, Salah Hanini, Mohamed Hentabli, Omar Khaldi, Mihoub Abderrahim
• Format: Article
• Language: English
• Published: Croatian Society of Chemical Engineers 2021-08-01
• Series: Kemija u Industriji
• Subjects: multicomponent adsorption; heavy metals; artificial neural networks; support vector regression; least-square support vector regression
• Online Access: http://silverstripe.fkit.hr/kui/assets/Uploads/4-509-518-KUI-9-10-2021.pdf
• ISSN: 0022-9830; 1334-9090

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.