Predictive model based on Adaptive Neuro-Fuzzy Inference System for estimation of Cephalexin adsorption on the Octenyl Succinic Anhydride starch

Abstract

The purpose of this study was to investigate the potential of an amphiphilic biopolymer to remove organic contaminants from aqueous solutions. The removal of Cephalexin antibiotic as model compound by adsorption using the Octenyl Succinic Anhydride starch (OSA starch) as amphiphilic polysaccharide was studied. The effects of various parameters such as contact time, pH, temperature and OSA starch dosage were investigated. Different adsorption isotherm models were tested, and it was found that, the best fit with the experimental data is provided by Langmuir and Redlich-Peterson models (R2 = 0.9638 and R2 = 0.9661 respectively). The maximum adsorption capacity was obtained is 0,9236 mg/g in optimal conditions (pH = 6, T = 25 °C and OSA starch initial concentration of 5 g/L). Pseudo-second order model has perfectly represented the kinetic data (R2 = 0.9932). On the other hand, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was optimized for modeling the adsorption process by predicting the OSA starch adsorption capacity as well as the effect of the different parameters. The predicted values are very similar to the experimental results with coefficient of determination (R2) of 0.999 and Root Mean Square Error (RMSE) equal to 3.9 × 10−3. Furthermore, thermodynamic study showed the exothermic nature of Cephalexin adsorption on OSA starch.