Use of an artificial neural network algorithm to predict hydrate dissociation conditions for hydrogen+water and hydrogen+tetra-n-butyl ammonium bromide+water systems
Résumé
In this communication, a feed-forward artificial neural network algorithm is developed to estimate the hydrate dissociation conditions for the hydrogen+water and hydrogen+tetra-n-butyl ammonium bromide+water systems. To develop this algorithm, the experimental data reported in the literature for hydrate dissociation conditions of the latter two systems with different concentrations of tetra-n-butyl ammonium bromide in aqueous phase below its stoichiometric concentration (i.e., ≈0.037 mole fraction or 0.43 mass fraction) have been used. Independent experimental data (not used in training and developing this algorithm) have been employed to examine the reliability of this method. It is shown that the predicted and the experimental data are in acceptable agreement demonstrating the reliability of this algorithm as a predictive tool.