Data-driven nonlinear modeling studies on removal of Acid Yellow 59 using Si-doped multi-walled carbon nanotubes

Abstract

Data-driven modeling of removal of color index name of Acid Yellow 59 from aqueous solutions using multi-walled carbon nanotubes by multiple (non)linear regression and artificial neural networks (ANN) models based on leave-one-out cross-validation to predict the adsorbed dye amount per unit mass of adsorbent (mg g−1) and performance evaluation of the proposed multiple (non)linear regression and ANN models is the main novel contributor of the present study. Initial dye concentration, adsorbent concentration, reaction time, and temperature were determined as explanatory variables and input neurons for multiple (non)linear regression and ANN models, respectively. The total number of experiments was determined as 1280 statistically. The results showed that multilayer perception ANN model ( $$R^{2}_{\text{training}}$$ = 0.9997, $$R^{2}_{\text{testing}}$$ = 0.9993, RMSE = 0.7678, MAE of 0.0007) predicted q t better than multiple (non)linear regression model ( $$R^{2}_{\text{adj}}$$ = 0.9645, $$R^{2}_{\text{pred}}$$ = 0.9633, SE = 9.55) and MLR (R 2 = 0.9543, SE = 10.87) models. The results justified the accuracy of ANN in prediction of q t , significantly.