Isotherms for the adsorption of Cu(II) onto lignin: Comparison of linear and non-linear methods

Equilibrium studies were carried out for the adsorption of Cu(II) onto Kraft lignin as an adsorbent. The experimental data were fitted to the Freundlich, Langmuir and Redlich-Peterson isotherms by linear and non-linear method. Comparison of linear and non-linear regression method was given in select...

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Bibliographic Details
Published in:Hemijska industrija Vol. 66; no. 4; pp. 497 - 503
Main Authors: Brdar Mirjana M., Takači Aleksandar A., Šćiban Marina B., Rakić Dušan Z.
Format: Journal Article
Language:English
Published: Association of Chemical Engineers of Serbia 2012
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Summary:Equilibrium studies were carried out for the adsorption of Cu(II) onto Kraft lignin as an adsorbent. The experimental data were fitted to the Freundlich, Langmuir and Redlich-Peterson isotherms by linear and non-linear method. Comparison of linear and non-linear regression method was given in selecting the optimum isotherm for the experimental data. The coefficient of correlation r2 and Chi-square test χ2 was used to select the best linear theoretical isotherm. The best linear model is Redlich-Peterson isotherm model, where r2=0,985 and χ2=0,02. In order to predict the error ERRSQ, HYBRD, MPSD, ARE and EABS were used. Moreover, by minimizing these error functions the optimal values of parameters and also the optimum isotherm was found. The Redlich-Peterson isotherm was found to be the best representative for adsorption of Cu(II) on the adsorbent in the cases when ERRSQ, HYBRD, MPSD functions were used. There coefficients of determination are 0.986, 0.985, 0.984, respectively and Chi-square is 0.02 in all cases. Freundlich isotherms which were obtained by minimization of the ERRSQ, HYBRD, MPSD, ARE and EABS function showed very good agreement with experimental data. In all cases the coefficients of determination are greater than 0.91. Besides, it was observed that non-linear isotherm models were better for representation of equilibrium data than linearized models.
ISSN:0367-598X
DOI:10.2298/HEMIND111114003B