The Artificial Neural Network (ANN) model was used to predict dye (Reactive Blue 19) removal efficiency from aqueous
solution using modified pomegranate residual based on 124 experimental sets. Three-layer ANN models with different neurons
numbers at a hidden layer were developed. The optimum network yielded a network error of 0.0054 and 0.9606 for mean square
error and coefficient of determination, respectively. Furthermore, sensitivity analysis of the network revealed that the adsorbent
dose and initial dye concentration were the most and least important variables, respectively. The effect of operation parameters
such as initial pH, contact time, adsorbent dose, and initial dye concentration was examined and compared with the ANN
prediction. The optimum conditions were determined to be 11, 15 min, 5 g/L, and 150 mg/L for initial pH, contact time,
adsorbent dose, and initial dye concentration, respectively.