Least-squares regression of adsorption equilibrium data: Comparing the options

Abstract

Experimental and simulated adsorption equilibrium data were analyzed by different methods of least-squares regression. The methods used were linear regression, nonlinear regression, and orthogonal distance regression. The results of the regression analysis of the experimental data showed that the different regression methods produced different estimates of the adsorption isotherm parameters, and consequently, different conclusions about the surface properties of the adsorbent and the mechanism of adsorption. A Langmuir-type simulated data set was calculated and several levels of random error were added to the data set. The results of regression analysis of the simulated data set showed that orthogonal distance regression gives the most accurate and efficient estimates of the isotherm parameters. Nonlinear regression and one form of the linearized Langmuir isotherm also gave accurate estimates, but only at low levels of random error.