Can I read Nonlinear Regression for Treating Adsorption Isotherm Data to Characterize New Sorbents: Advantages Over Linearization Demonstrated with Simulated Data on EtoBox?

Nonlinear Regression for Treating Adsorption Isotherm Data to Characterize New Sorbents: Advantages Over Linearization Demonstrated with Simulated Data by Renan Vitek; Jorge Cesar Masini is a Economics, Econometrics and Finance article available to read on EtoBox.

What is Nonlinear Regression for Treating Adsorption Isotherm Data to Characterize New Sorbents: Advantages Over Linearization Demonstrated with Simulated Data about?

This paper demonstrates that the determination of new sorbents' adsorption capacity and affinity through data fitting of adsorption isotherms is more accurate by nonlinear regression (NLR) than by the linearized Langmuir equations. Linearization errors and the subjective choice of data points used to apply the linear regression analysis deviate the fitted adsorption parameters (constants and adsorption capacities) from the simulated values. The deviation magnitude increases for heterogeneous sorbents such as environmental particles and molecularly imprinted polymers, which interact through either more than one sorption mechanism or adsorption sites of diverse chemical natures. For instance, Lineweaver-Burk linearization of isotherms simulated considering the presence of two kinds of adsorption sites provide excellent linear regression fittings, but for only one kind of adsorption site. Contrary, Scatchard and Eadie-Hoffsiee's equations indicate the presence of more than one kind of adsorption site, but if the difference between the adsorption constants is not significant, the choice of points used to perform the computation becomes subjective. On the contrary, NLR analysis consider

Who reads Nonlinear Regression for Treating Adsorption Isotherm Data to Characterize New Sorbents: Advantages Over Linearization Demonstrated with Simulated Data?

Author

Renan Vitek; Jorge Cesar Masini

Published

2022

Language

EN

Field

Economics, Econometrics and Finance (Social Sciences)