Chitosan–Silica Composite Aerogel for the Adsorption of Cupric Ions: Isothermal Remodeling and MOORA-Based Model Selection
Year: 2024
Type: article
Abstract: A remodeling evaluation was conducted on the sorption isotherm data for copper adsorption onto chitosan–silica composite aerogel (Vareda et al., 2024) using nonlinear regression due to the irregular manner in which the regression was done and reported. The remodelling exercise results showed that the top five were Koble-Corrigan, Brouers–Sotolongo, Fritz-Schlunder III, Hill, and Sips models. The Brouers–Sotolongo model was the most precise in estimating the maximum adsorption capacity (qm) because it considers the surface's heterogeneity and offers narrow confidence intervals. On the other hand, the Langmuir model chosen in the original publication had wide confidence intervals, which means that it was less precise. In addition, the study also stresses the need to use various error functions to improve the model selection and increase the validity of the models. In addition, for the first time, to the best of our knowldege, this study reported on the first use of Multiobjective Optimization by Ratio Analysis (MOORA) method for isothermal model selection and ranking in the adsorption field. This approach simplifies the selection process by systematically evaluating and ranking the numerous isotherm and kinetic models that are commonly used in adsorption research. Therefore, this study recommends using advanced and flexible isotherm models such as Brouers-Sotolongo for accurate adsorption modeling but also stresses the need for rigorous statistical validation in adsorption research. The results of this study can be used to enhance the accuracy of adsorption studies in wastewater treatment, environmental remediation, and material science applications.
Author: Mohd Yunus Shukor
Institution: Universiti Putra Malaysia
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4407803228
