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َAbdolraouf Samadi-Maybodi

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

Research

Title
Removal of sarafloxacin from aqueous solution by a magnetized metal-organic framework; Artificial neural network modeling
Type
JournalPaper
Keywords
Sarafloxacin, Response surface methodology, Artificial neural network, Levenberg-Marquardt
Year
2020
Journal POLYHEDRON
DOI
Researchers َAbdolraouf Samadi-Maybodi ، Seyed Mohsen Nikou

Abstract

Antibiotics are one of the pollutants that their existence in the environment which causes serious problems. The residual of these compounds in the environment has negative influences on human health, and hence, their removal from the environment is a vital task. In this study, the removal of sarafloxacin (SRF) has performed using a magnetized metal-organic framework (Fe3O4/MIL-101(Fe)). Response surface methodology (RSM) was employed to achieve optimized removal efficiency. The optimized removal efficiency of SRF was obtained under the following conditions: the SRF initial concentration 10 ppm, pH value of 7.0, the adsorbent dosage of 20 mg, and the contact time of 40 min. The isotherm models were also investigated. The Langmuir isotherm model (type II) was more consistency in the adsorption process. The artificial neural network (ANN) as a predictive model was applied to predicting the SRF removal efficiency from aqueous solution. The algorithm of Levenberg-Marquardt was used for the training of the ANN. The performance of the model was evaluated based on the coefficient of determination (R2) and mean squared error (MSE). The values of R2 (R2 = 0.9995, 0.9951) and MSE (MSE = 3.98e05, 0.0023) were for training and testing data set, respectively. Results showed a good agreement between experimental and predicted data obtained by the ANN model as a powerful tool for prediction removal percentage of SRF (R2 pred = 0.9861).