HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Schulthess, C.P. Articles by Dey, D.K. Search for Related Content PubMed Articles by Schulthess, C.P. Articles by Dey, D.K. Agricola Articles by Schulthess, C.P. Articles by Dey, D.K.

Estimation of Langmuir Constants using Linear and Nonlinear

Dep. of Plant Science (U-67)

D.K. Dey

Dep. of Statistics (U-120), Univ. of Connecticut, Strorrs, CT 06269

*Corresponding author (cschulth{at}canrl.cag.uconn.edu).

ABSTRACT

A nonlinear least squares (NLLS) regression analysis of the Langmuir equation is described here based on minimizing the sum of the normal distance of the data to the isotherm. This regression can yield different Langmuir constants when compared with linear regression methods and another NLLS method — one based on minimizing the sum of the squares of the vertical distance of the data to the isotherm. The two NLLS regressions can also result in two different conclusions (or suggestions) about the physicochemical characteristics of the adsorption phenomena. There is no fundamental, mathematical requirement that the nonlinear regression be based on a vertical minimum save that it is easier to evaluate. More importantly, the vertical NLLS regression is strongly biased toward fitting the low-concentration data; this is remedied by using the normal NLLS regression. None of the regressions are endorsed per se since they should all agree if the isotherm is Langmuirian. The normal NLLS regression method is not sensitive to the goodness-of-fit criteria and, therefore, is considered to be robust. The criteria for choosing a regression method should consider both its sensitivity to data error plus its sensitivity to non-ideality. A deviation in the data is not necessarily due to random measurement errors only (which are often easy to identify when the data are numerous and duplicated), but may also be due to the presence of a partially non-Langmuir adsorption phenomenon. In the latter case, the Langmuir constants usually remain useful, but they must be used cautiously.

Storrs Agric. Exp. Stn. scientific contribution no. 1525.

Received for publication February 16, 1994.

This article has been cited by other articles:

				C. H. Bolster Revisiting a Statistical Shortcoming when Fitting the Langmuir Model to Sorption Data J. Environ. Qual., August 8, 2008; 37(5): 1986 - 1992. [Abstract] [Full Text] [PDF]

				J. E. Lopez-Periago, M. Arias-Estevez, J. C. Novoa-Munoz, D. Fernandez-Calvino, B. Soto, C. Perez-Novo, and J. Simal-Gandara Copper Retention Kinetics in Acid Soils Soil Sci. Soc. Am. J., January 11, 2008; 72(1): 63 - 72. [Abstract] [Full Text] [PDF]

				C. H. Bolster and G. M. Hornberger On the Use of Linearized Langmuir Equations Soil Sci. Soc. Am. J., September 28, 2007; 71(6): 1796 - 1806. [Abstract] [Full Text] [PDF]

				null Zhao Hongting, H. Zhao, Y. Deng, J. B. Harsh, M. Flury, and J. S. Boyle ALTERATION OF KAOLINITE TO CANCRINITE AND SODALITE BY SIMULATED HANFORD TANK WASTE AND ITS IMPACT ON CESIUM RETENTION Clays and Clay Minerals, February 1, 2004; 52(1): 1 - 13. [Abstract] [Full Text] [PDF]

				H. Wijnja and C.P. Schulthess Interaction of Carbonate and Organic Anions with Sulfate and Selenate Adsorption on an Aluminum Oxide Soil Sci. Soc. Am. J., May 1, 2000; 64(3): 898 - 908. [Abstract] [Full Text]

				A. H. Alsaeedi and A. M. Elprince Critical Phosphorus Levels for Salicornia Growth Agron. J., March 1, 2000; 92(2): 336 - 345. [Abstract] [Full Text]