In this paper, the Langmuir isotherm, originally derived for the adsorption of asphaltene extracted from shale oil and dissolved in toluene on Kaolin, Smectite, Fluorite and Hematite, was modified to fit the adsorption isotherm. The modified Langmuir isotherm parameters obtained from the four linear equations using the linear method differed. The aim of the proposed modification is based on the fact that direct application of the Langmuir isotherm often leads to poor data fitting. In the present communication, it is shown that the level of data fitting to the Langmuir isotherm can be improved by a simple modification through introducing a concentration dependent factor, X. The present paper discusses four modified Langmuir linearized isotherm models and one non-linear isotherm model: their coefficients are estimated and, for the study of non-linear isotherm model, genetic algorithm is used. A genetic algorithm procedure was utilized to optimize the modified Langmuir constants for a more accurate estimation of the set of model parameters. The obtained results demonstrated that the best fit was obtained using genetic algorithm. Furthermore, it was found out that from the surface minerals mentioned, Hematite mineral follows a multilayer adsorption isotherm.

In this paper, the Langmuir isotherm, originally derived for the adsorption of asphaltene extracted from shale oil and dissolved in toluene on Kaolin, Smectite, Fluorite and Hematite, was modified to fit the adsorption isotherm. The modified Langmuir isotherm parameters obtained from the four linear equations using the linear method differed. The aim of the proposed modification is based on the fact that direct application of the Langmuir isotherm often leads to poor data fitting. In the present communication, it is shown that the level of data fitting to the Langmuir isotherm can be improved by a simple modification through introducing a concentration dependent factor, X. The present paper discusses four modified Langmuir linearized isotherm models and one non-linear isotherm model: their coefficients are estimated and, for the study of non-linear isotherm model, genetic algorithm is used. A genetic algorithm procedure was utilized to optimize the modified Langmuir constants for a more accurate estimation of the set of model parameters. The obtained results demonstrated that the best fit was obtained using genetic algorithm. Furthermore, it was found out that from the surface minerals mentioned, Hematite mineral follows a multilayer adsorption isotherm.