Optimization of Disulphine Blue Dye Adsorption Process on ZnO-Cr Loaded on Activated Carbon Using Response Surface Methodology and Modeling by Means of Artificial Neural Network

In this research, ZnO-Cr nanoparticles loaded on active carbon were used for the removal of disulphine blue dye. The influence of variables such as the amount of adsorbent, initial concentration of dyes, pH and stirring time on removal percentage were optimized and investigated by central composite design. The kinetic models, adsorption isotherms and thermodynamic parameters also reviewed, and the ability to use in the optimal conditions assessment. After analyzing the results and compared the optimal points, for ZnO-Cr nanoparticles loaded on active carbon, removal percentage of disulphine blue 98.70 respectively. Finally, the adsorption process was modeled by the artificial neural network. In this study, time, the amount of absorbent, pH, the concentration of dye as the network inputs and removal percentage of dyes is considered as network targets. In the modeling removal of dye processes by the artificial neural network, neuron 15 as the optimum neuron was chosen in the removal of disulphine blue. The mean square errors in the optimal neuron for the process of adsorption using ZnO-Cr nanoparticles loaded on active carbon 7.17×10-5 respectively obtained that numerical is close to zero, also according to the calculated AAD% values in the model the results show that the artificial neural network model is more in agreement with experimental data is compared to the response surface methodology.