An intelligent equation for methane hydrate growth kinetics

René Pérez-Moroyoqui, Oscar Ibáñez-Orozco and Suemi Rodríguez-Romo

Mathematics and Computers in Simulation (MATCOM), 2022, vol. 192, issue C, 19-32

Abstract: 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.

Keywords: Methane hydrate; Formation kinetics; Artificial Neural Networks; Genetic Programming (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:192:y:2022:i:c:p:19-32

DOI: 10.1016/j.matcom.2021.08.011

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