2024 : 7 : 14

َAbdolraouf Samadi-Maybodi

Academic rank: Professor
Education: PhD.
Faculty: Faculty of Chemistry
Phone: 011-35302396


Artificial neural network (ANN) technique for modeling the mercury adsorption from aqueous solution using Sargassum Bevanom algae
Mercury; Alga; Adsorption; Thermodynamic; Kinetic; ANN
Journal Desalination and Water Treatment
Researchers Hossein Esfandian ، Mehdi Parvini ، Behnam Khoshandam ، َAbdolraouf Samadi-Maybodi


In this study, the removal of mercury (Hg(II)) ions from aqueous solutions was carried out using the brown algae Sargassum bevanom (S. bevanom) as a low-cost adsorbent. The sorption of Hg(II) was facilitated through the batch method. The following are the optimum conditions of sorption: a sorbent amount of 0.4 g in 100 mL of Hg(II) solution (50 mg L−1), contact time of 90 min, pH and temperature 7 and 20˚C. In order to study the kinetics of removal process, three equations were employed, namely Morris–Weber, Lagergren, and pseudo-second-order. To estimate sorption capacity, the sorption data were imported in the Langmuir, Freundlich, Dubinin–Radushkevich (D–R) and Temkin models. Also, an evaluation of thermodynamic parameters, namely ΔH, ΔS, and ΔG was done subsequently. These parameters explain that the Hg(II) sorption onto the S. bevanom is feasible, spontaneous, and exothermic under the aforementioned conditions. The data prediction phase related to the Hg(II) sorption onto the S. bevanom was conducted using the artificial neural networks (ANN). A comparison was made between the Hg(II) sorption data through the ANN model. The experimental results suggested that the ANN model has a high potential for predicting the Hg(II) sorption onto S. bevanom.