Modélisation des Isothermes d’Adsorption Expérimentales du Colorant Jaune Basique 28 (BYE 28) sur Trois Photocatalyseurs (TiO2, ZnO et Fe2O3) par des Modèles à Deux, Trois, Quatre et Cinq Paramètres.

Abstract

This work has for object, to study and modelize the adsorption of yellow basic Bye 28 on three adsorbents zinc oxide (ZnO), iron oxide (Fe2O3) and titanium dioxide (TiO2). The first part is therefore dedicated to a bibliographic study carrying essential data on adsorption, the different sort of adsorbents, a distinction between physisorption and chemisorption, the different parameters influencing the adsorption as well as the different adsorption models applied to the exploitation of the experimental results. The second part is devoted to experimental results that have been adapted by several models of isothermal adsorption described in the literature: 2, 3, 4 and 5 parameters. Each model provides access to important information on the absorption mechanisms; the calculated values RL (between 0 and 1) and (n>1) of Langmuir and Freundlich models allowed us to conclude that the adsorption is favorable.The Temkin equation leads to the determination of the variation of the energy of adsorption. The equation of kiselev makes it possible to determine a constant of complexation between molecules of adsorbate. The interaction energy W between adsorbed molecules can be determined using the Fowler-Guggenheim equation and an association energy with that of Hill and Boer. A comparative study between linear regression (which is frequently used to determine the most suitable isotherm) and nonlinear (which is used to avoid errors affecting R 2 during linearity) for this an SSE error function (the sum of the least carree) has been used for the isothermal optimization pre-release although the coefficient of determination (R2 ) was used for the best linear appropriate. Linear and nonlinear plots of models will result in different parameters estimates which showed that the nonlinear regression is more accurate than the linear regression