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َAbdolraouf Samadi-Maybodi

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

Research

Title
Artificial neural network (ANN) technique for modeling the mercury adsorption from aqueous solution using Sargassum Bevanom algae
Type
JournalPaper
Keywords
Mercury; Alga; Adsorption; Thermodynamic; Kinetic; ANN
Year
2016
Journal Desalination and Water Treatment
DOI
Researchers Hossein Esfandian ، Mehdi Parvini ، Behnam Khoshandam ، َAbdolraouf Samadi-Maybodi

Abstract

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.