Chemical Engineering Research & Design, Vol.156, 263-272, 2020
Prediction of the droplet spreading dynamics on a solid substrate at irregular sampling intervals: Nonlinear Auto-Regressive eXogenous Artificial Neural Network approach (NARX-ANN)
This paper introduces a Nonlinear Auto-Regressive eXogenous Artificial Neural Network (NARX-ANN) model for prediction of spreading dynamics. 1220 experimental data of spreading dynamics of different droplets on various substrates have been collected from literature for model development. The model input parameters are Weber number, Ohnesorge number, and the tangent of equilibrium contact angle. An auxiliary input has been also added to take into account the irregular time sampling. D-optimal design of experiments has been utilized to determine the best parameters for the NARX model. It was found that the prediction capability of NARX model was better in comparison with KC and AGM models. The statistical parameters of the best developed NARX model including mean square error (MSE) = 3 257 x 10(-4), average absolute relative deviation (AARD) = 4.240%, and coefficient of determination (R-2) = 0 993 demonstrate the excellent prediction capability of the proposed model. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.