Adsorption of tetracycline antibiotic onto modified zeolite: Experimental investigation and modeling

Maryam Dolatabadi; Marjan Mehrabpour; Morteza Esfandyari; Saeid Ahmadzadeh

doi:10.1016/j.mex.2020.100885

Adsorption of tetracycline antibiotic onto modified zeolite: Experimental investigation and modeling

MethodsX. 2020 Apr 18:7:100885. doi: 10.1016/j.mex.2020.100885. eCollection 2020.

Authors

Maryam Dolatabadi^{1

2}, Marjan Mehrabpour³, Morteza Esfandyari⁴, Saeid Ahmadzadeh^{5

6}

Affiliations

¹ Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran.
² Environmental Science and Technology Research Center, Department of Environmental Health Engineering, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
³ Health Sciences Research Center, Department of Environmental Health Engineering, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
⁴ Department of Chemical Engineering, Faculty of Engineering, University of Bojnord, Bojnord, Iran.
⁵ Pharmaceutics Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran.
⁶ Pharmaceutical Sciences and Cosmetic Products Research Center, Kerman University of Medical Sciences, Kerman, Iran.

Abstract

Artificial Neural Networks (ANNs) model and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to estimate and predict the removal efficiency of tetracycline (TC) using the adsorption process from aqueous solutions. The obtained results demonstrated that the optimum condition for removal efficiency of TC were 1.5 g L^-1 modified zeolite (MZ), pH of 8.0, initial TC concentration of 10.0 mg L^-1, and reaction time of 60 min. Among the different back-propagation algorithms, the Marquardt-Levenberg learning algorithm was selected for ANN Model. The log sigmoid transfer function (log sig) at the hidden layer with ten neurons in the first layer and a linear transfer function were used for prediction of the removal efficiency. Accordingly, a correlation coefficient, mean square error, and absolute error percentage of 0.9331, 0.0017, and 0.56% were obtained for the total dataset, respectively. The results revealed that the ANN has great performance in predicting the removal efficiency of TC.•ANNs used to estimate and predict tetracycline antibiotic removal using the adsorption process from aqueous solutions.•The model's predictive performance evaluated by MSE, MAPE, and R².

Keywords: Antibiotic; Artificial Neural Network; Tetracycline; Zeolite.