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Ternary Multicomponent Adsorption Modelling Using ANN, LS-SVR, and SVR Approach – Case Study
Copyright policy )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. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna.
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.
Microsoft Academic Graph classification: Ternary operation Materials science Adsorption Thermodynamics
multicomponent adsorption, heavy metals, artificial neural networks, support vector regression, least-square support vector regression, Chemistry, QD1-999, multicomponent adsorption; heavy metals; artificial neural networks; support vector regression; least-square support vector regression, višekomponentna adsorpcija; teški metali; umjetne neuronske mreže; regresija potpornih vektora; regresija potpornih vektora najmanjih kvadrata, General Chemical Engineering, General Chemistry
multicomponent adsorption, heavy metals, artificial neural networks, support vector regression, least-square support vector regression, Chemistry, QD1-999, multicomponent adsorption; heavy metals; artificial neural networks; support vector regression; least-square support vector regression, višekomponentna adsorpcija; teški metali; umjetne neuronske mreže; regresija potpornih vektora; regresija potpornih vektora najmanjih kvadrata, General Chemical Engineering, General Chemistry
Microsoft Academic Graph classification: Ternary operation Materials science Adsorption Thermodynamics
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. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna.
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.