Ternary Multicomponent Adsorption Modelling Using ANN, LS-SVR, and SVR Approach – Case Study
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...
Main Authors: | , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Croatian Society of Chemical Engineers
2021-08-01
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Series: | Kemija u Industriji |
Subjects: | |
Online Access: | http://silverstripe.fkit.hr/kui/assets/Uploads/4-509-518-KUI-9-10-2021.pdf |
Summary: | 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. |
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ISSN: | 0022-9830 1334-9090 |