ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction

doi:10.1016/j.cej.2024.151999

ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction

2024-07-01

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Julian Meyer‐Kirschner

DOI：10.1016/j.cej.2024.151999

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Abstract

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The Perturbed Chain Polar Statistical Associating Fluid Theory (PCP-SAFT) equation of state (EoS) is widely used to predict fluid-phase thermodynamics, but parameterization of PCP-SAFT for individual molecules is often challenging. We propose a machine learning framework called ML-SAFT that can turn experimental data in predictive models of PCP-SAFT parameters. We demonstrate methods for automated large scale regression of PCP-SAFT parameters and thus create a large PCP-SAFT parameter dataset in the literature. We then evaluate several machine learning architectures for predicting PCP-SAFT parameters. We find that our best model provides accurate predictions for a wider range of molecules than existing predictive methods with 40 % average absolute deviation (% AAD) in vapor pressure predictions and 8 % AAD in density predictions.

Keywords:

Deep learning

PC-SAFT

Thermodynamics

Property predictions

ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction

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