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Surrogate modelling of VLE: Integrating machine learning with thermodynamic constraints
2020
Chemical Engineering Science: X
An easy-to-implement methodology to develop accurate, fast and thermodynamically consistent surrogate machine learning (ML) models for multicomponent phase equilibria is proposed. The methodology is successfully applied to predict the vapour-liquid equilibrium (VLE) behavior of a mixture containing CO 2 , monoethanolamine (MEA), and water (H 2 O). The accuracy of the surrogate model predictions of VLE for this system is found to be satisfactory as the results provide an average absolute
doi:10.1016/j.cesx.2020.100080
fatcat:7wcvqdax2bcv3d2fvulp5uxdgu