A New Approach in Regression Analysis for Modeling Adsorption Isotherms

• Published in: TheScientificWorld Vol. 2014; no. 2014; pp. 1 - 17
• Main Authors: Rajaković, Ljubinka V., Onjia, Antonije E., Rajaković-Ognjanović, Vladana N., Lekić, Branislava M., Marković, Dana D.
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01-01-2014; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Adsorption; Algorithms; Economic models; Equilibrium; Isotherms; Least-Squares Analysis; Models, Theoretical; Monte Carlo method; Monte Carlo simulation; Parameter estimation; Population; Regression Analysis; Research Article; Standard deviation; Studies; Temperature
• ISSN: 2356-6140; 1537-744X; 1537-744X
• DOI: 10.1155/2014/930879

Numerous regression approaches to isotherm parameters estimation appear in the literature. The real insight into the proper modeling pattern can be achieved only by testing methods on a very big number of cases. Experimentally, it cannot be done in a reasonable time, so the Monte Carlo simulation method was applied. The objective of this paper is to introduce and compare numerical approaches that involve different levels of knowledge about the noise structure of the analytical method used for initial and equilibrium concentration determination. Six levels of homoscedastic noise and five types of heteroscedastic noise precision models were considered. Performance of the methods was statistically evaluated based on median percentage error and mean absolute relative error in parameter estimates. The present study showed a clear distinction between two cases. When equilibrium experiments are performed only once, for the homoscedastic case, the winning error function is ordinary least squares, while for the case of heteroscedastic noise the use of orthogonal distance regression or Margart’s percent standard deviation is suggested. It was found that in case when experiments are repeated three times the simple method of weighted least squares performed as well as more complicated orthogonal distance regression method.