Comparison between Artificial Neural Network and Response Surface Methodology in the Prediction of the Production Rate of Polyacrylonitrile Electrospun Nanofibers

This paper focused on using response surface methodology (RSM) and artificial neural network (ANN) to analyze production rate of electrospun nanofibers. The three important electrospinning factors were studied including polymer concentration (wt %), applied voltage (kV) and the nozzle-collector distance (cm). The predicted production rates were in agreement with the experimental results in both ANN and RSM techniques. High regression coefficient between the variables and the response (R-2=0.975) indicates excellent evaluation of experimental data by second-order polynomial regression model. The regression coefficient was 0.988, which indicates that the ANN model was shows good fitting with experimental data. The obtained results indicate that the performance of ANN was better than RSM. It was concluded that applied voltage plays an important role (relative importance of 42.8 %) against production rate of electrospun nanofibers. The RSM model predicted the 2802.3 m/min value of the highest production rate at conditions of 15 wt % polymer concentration, 16 kV of the applied voltage, and 15 cm of nozzle-collector distance. The predicted value showed only 4.4 % difference with experimental results in which 2931.0 m/min at the same setting was observed.