Experimental adsorption data were analysed by fitting them to nonlinear forms of Langmuir and Freundlich isotherms. Optimization of the parameters was performed by nonlinear least square regression with different forms of error function, namely: vertical, horizontal, orthogonal, normal and squared normal. The results showed, that isotherm parameters may be affected by the selection of error function and that they are more sensitive to its’ form in case of Langmuir equation. We did not find any correlation between a type of the function and performance of the regression – procedure requires optimization for every experimental dataset and every model being fitted.
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