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َAbdolraouf Samadi-Maybodi

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

Research

Title
Development of a novel method for the removal of diazinon pesticide from aqueous solution and modeling by artificial neural networks (ANN)
Type
JournalPaper
Keywords
Diazinon, Perlite, Sodalite zeolite, Thermodynamic, ANN
Year
2016
Journal JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY
DOI
Researchers Hossein Esfandian ، َAbdolraouf Samadi-Maybodi ، Mehdi Parvini ، Behnam Khoshandam

Abstract

In this study, perlite was used as a low-cost source of Si and Al to synthesize the sodalite zeolite via hydrothermal synthesis method. Cu2O nanoparticles (30–60 nm) were coated on a bed of sodalite zeolite. Toward this aim, a series of batch adsorption experiments was carried out and the sorption of diazinon from aqueous solutions on acid treated zeolite (ATZ) with dilute H2SO4 solutions and modified zeolite by Cu2O nanoparticles (MZ) were also determined. Results showed that Cu2O nanoparticles have a significant effect on the diazinon removal processing from aqueous solution. Maximum adsorption rates were 98.2% (with 0.2, 20, 6 of adsorbent dose, contact time and pH, respectively, for MZ) and 63.4% (with 0.3, 80, 6 of adsorbent dose, contact time and pH, respectively, for ATZ). Three equations, i.e., Morris–Weber, Lagergren (pseudo first order) and pseudo second order have been applied to study the kinetics of removal process. The diazinon sorption process was well described by the pseudo second order (type 2) kinetic model. The Langmuir, Freundlich, Temkin and Dubnin–Randkovich (D–R) models were tested on sorption data to estimate the sorption capacity, intensity and energy. Langmuir (type 1) isotherm provided the best fit to the equilibrium data with maximum adsorption capacity of 61.73 and 15.10 mg/g for MZ and ATZ, respectively. The thermodynamic parameters DH, DS and DG were evaluated. Thermodynamic parameters showed that the sorption of diazinon onto zeolite was feasible, spontaneous and exothermic under studied conditions. Artificial neural network (ANN) model was also applied for modeling of diazinon removal from aqueous solution by ATZ and MZ. There was a good agreement between the experimental and predicted values with seven neurons in hidden layer.