Development of artificial intelligence-based models for prediction of vanadium adsorption onto activated carbon nanocomposites

Abstract

Due to environmental concerns and economic value, the adsorption process is one of the suitable methods to separate vanadium from wastewater. In this study, two artificial intelligence models, including an adaptive neuro fuzzy inference system and group method of data handling, were successfully implemented to model the vanadium adsorption process with activated carbon nanocomposites. In the developed smart models, initial pH, contact time, and iron nanoparticle concentration were selected as input variables, and vanadium adsorption was chosen as the target parameter. In order to accomplish modeling, experimental data were extracted from our previous study and utilized to develop artificial intelligence-based models. For the neuro fuzzy approach, the average absolute relative deviation (AARD) and coefficient of determination (R2) were 2.45 % and 0.9858, respectively. The values of AARD and R2 for the group method of data handling technique were obtained 5.06 % and 0.9567, respectively. These results indicated the high accuracy of both intelligent models in predicting vanadium adsorption efficiency with activated carbon nanoparticles. Moreover, the comparison of the proposed smart approaches with the mechanism-based models, revealed the superiority of the neuro fuzzy model in predicting vanadium adsorption.