Artificial neural network – Imperialist competitive algorithm based optimization for removal of sunset yellow using Zn(OH)2 nanoparticles-activated carbon

The effects of variables were modeled using multiple linear regressions (MLR) and artificial neural network (ANN) and the variables were optimized by imperialist competitive algorithm (ICA). Comparison of the results obtained using introduced models indicated the ANN model is better than the MLR model for the prediction of sunset yellow removal using zinc oxide nanoparticles-activated carbon. The coefficient of determination (R2) and mean squared error (MSE) for the optimal ANN model with 9 neurons at hidden layer were obtained to be 0.9782 and 0.0013, respectively. A nano-scale adsorbents namely as Zn(OH)2 was synthesized and subsequently loaded with AC. Then, this new material efficiently applied for sunset yellow (SY) removal, from aqueous solutions in batch process. Firstly the adsorbent were characterized and identified by XRD, FESEM and BET. Unique properties such as high surface area (>1308m2/g) and low pore size (0.999). The factors controlling adsorption process were also calculated and discussed. Equilibrium data fitted well with the Langmuir model at all amount of adsorbent with maximum adsorption capacity of 158.7mgg−1.