Evaluation of least square support vector machine, generalized regression neural network and response surface methodology in modeling the removal of Levofloxacin and Ciprofloxacin from aqueous solutions using ionic liquid @Graphene oxide@ ionic liquid NC

Abstract

This study focused on the preparation, characterization of a supported dicationic ionic liquid nanocomposite (IL@GO@IL NC) and employing of it in the adsorption of Levofloxacin (LVX) and Ciprofloxacin (CPF) antibiotic. Also, the nonlinear least square support vector machine (N-LSSVM), generalized regression neural network (GRNN), along with response surface methodology used for modeling the removal of both antibiotics (i.e. LVX and CPF) from aqueous solution. In this work, the experiments designed based on critical parameters such as adsorbent dose, antibiotic concentration, sonication time and temperature. The nonlinear adsorption isotherms and kinetics models as well as thermodynamic used for fitting the experimental data. The statistical results demonstrated that the LSSVM and GRNN model efficiently predicted the antibiotics removal percentage with very high accuracy, respectively. Furthermore, for an optimal adsorption of LVX and CPF by IL@GO@IL NC, the adsorbent dose, antibiotic concentration, temperature and sonication time should be set to 0.23 and 0.35 g, 95.20 and 100.00 mg L-1, 2.10 and 4.60 min, and 45.00 ℃, for LVX and CPF, respectively. Under these optimal conditions, the removal percentages and adsorption capacity of LVX and CPF will be 98.20 and 97.50%, as well as 730 and 630 mg g−1, respectively. Besides, the IL@GO@IL NC efficiency under optimal conditions drops up by 15% after seven cycles for both antibiotics. Therefore, the IL@GO@IL NC may be considered as a promising option in removing fluoroquinolones antibiotics from water.

Keywords

• Central composite design
• Generalized regression neural network
• Ionic liquid
• Graphene oxide
• Genetic algorithm