Prediction and optimization of stability parameters for titanium dioxide nanofluid using response surface methodology and artificial neural networks

The effect of various process parameters on the stability of TiO2 nanofluid, which can mostly be defined as zeta potential and particle size, was studied using response surface methodology (RSM) by the design of experiments and was predicted through a trained artificial neural network (ANN). The process parameters studied were weight percentage of surfactant (sodium lauryl sulfate) (0.01-0.2 wt%) and the value of pH (10-12). Central composite design and the RSM were employed to develop a mathematical model as well as to define the optimum condition. A three-layered feed-forward ANN model was designed and used for the prediction of the stability parameters. From the analysis of variance, the significant factors that affected the experimental design responses were also identified. The predicted stability parameters using the RSM and ANNs were compared using figures and tables. It is shown that the trained ANN outperformed the RSM in terms of accuracy and prediction of obtained results.