Adsorption of Triamterene on multi-walled and single-walled carbon nanotubes: Artificial neural network modeling and genetic algorithm optimization

Rapid adsorption of Triamterene using multi-walled carbon nanotube (MWCNT) and single-walled carbon nano tube (SWCNT) was well investigated and elucidated. The impact of influential variables such as temperature, amount of adsorbent, initial drug concentration, contact time was modeled using multiple linear regression (MLR) and artificial neural network (ANN) and the influential variables were optimized using genetic algorithm (GA). The adsorption equilibrium and kinetic data was well fitted and found to be in good agreement with the Langmuir monolayer isotherm model and pseudo second order kinetics mechanism. The maximum adsorption capacity of SWCNT and MWCNTs for the removal of Triamterene was found to be 25.77 mg g(-1) and 33.14 mg g(-1) respectively. The negative value of adsorption enthalpy (OFF) reveals towards the exothermic nature of the adsorption process. Based on the results comparison of the proposed models, results revealed that the applicability of the ANN model is more appropriate in comparison to the MLR model for predicting the adsorption efficiency of the process. The coefficient of determination (R-2) of 0.980 and mean squared error (MSE) of 0.002 for adsorption on SWCNT 0.986 and 5.4e-04 on MWCNT were obtained, respectively using the optimal ANN model. (C) 2016 Elsevier B.V. All rights reserved.