Modeling of simultaneous adsorption of dye and metal ion by sawdust from aqueous solution using of ANN and ANFIS

The current work deals with the investigation of Simultaneous of Basic Red46 (BR46) and Cu (dye and heavy metal) removal efficiency from aqueous solution through the adsorption process using a laboratory scale reactor. In this research, a feed-forward artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) have been utilized to the prediction of adsorption potential of sawdust in simultaneous removal of a cationic dye and heavy metal ion from aqueous solution. Five Operational variables, concluding initial dye, initial Cu (II), pH, contact time, and adsorbent dosage were selected to investigate their effects on the adsorption study. The application of (ANN) and (ANFIS) models for experiments were employed to optimize, create and develop prediction models for dye and Cu (II) adsorption by using sawdust from Melia Azedarach wood. The result reveals that ANN and ANFIS models as a promising predicting technique would be effectively used for simulation of dye and metal ion adsorption. According to this result, in training dataset determination coefficient were obtained 0.99 and 0.98 for dye and a metal ion, respectively. Also, in ANFIS model R2 was calculated 0.99 for both of pollutants.