A neural network model for the numerical prediction of the diameter of electro-spun polyethylene oxide nanofibers

This paper investigates the viability of neural network as a tool for predicting the diameter of fiber formed by an electrospinning process. Published experimental data for polyethylene oxide (PEO) aqueous solution is used to train and test the neural network model. Concentration, conductivity, flow rate, and electric field strength are used as the input variables to the neural network model. Network model selection, training and testing were conducted using the k-fold cross validation technique which is demonstrated to be the most suitable scheme for the size of dataset used in this study. A statistical study was conducted to establish 95% confidence intervals on the bias and on the limits of agreement between the experimental data and the predicted data. The computer simulation results show a very good agreement between the data, demonstrating the viability of neural network as a promising tool for predicting fiber diameter. While the proposed neural network approach is not intended to model the complete complex physics of the electrospinning process, it is demonstrated to provide an accurate nonlinear mapping between the four salient input variables and the diameter of the formed fiber. This study provides some potential insights into exploring neural network model-based feedback control techniques to regulate nanofiber diameter in an electrospinning process.