An intelligent equation for methane hydrate growth kinetics

We explore the use of deep artificial neural networks (DNNs) to impute data to the experimental data reported by the methane hydrate crystal growth at a bubble surface reported in the literature (Ma, et al., 2002; Sun et al., 2007). We use a genetic programming symbolic regressor to propose a novel empirical rate equation as a function of the temperature and the pressure from the new data set. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.