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

Chemical Engineering Journal, vol. 492, pp. 151999

Abstract

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.

Authors 8

1. Kobi Felton Aachen Process Systems Engineering (AVT.SVT)

   University of Cambridge · RWTH Aachen University

   Affiliation as printed

   Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, 52074 Aachen, Germany

2. Lukas Raßpe-Lange Aachen Institute of Technical Thermodynamics

   RWTH Aachen University

   Affiliation as printed

   Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany

3. Jan G. Rittig Aachen Process Systems Engineering (AVT.SVT)

   RWTH Aachen University

   Affiliation as printed

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, 52074 Aachen, Germany

4. Kai O. Leonhard Aachen Institute of Technical Thermodynamics

   RWTH Aachen University

   Affiliation as printed

   Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany

5. Alexander Mitsos Aachen Process Systems Engineering (AVT.SVT)

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Institute for Energy and Climate Research IEK-10: Energy Systems Engineering, Forschungszentrum Jülich GmbH, Jülich 52425, Germany

   JARA-ENERGY, Aachen 52056, Germany

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, 52074 Aachen, Germany

6. Julian Meyer‐Kirschner

   BASF (Germany)

   Affiliation as printed

   BASF SE, 67056 Ludwigshafen am Rhein, Germany

7. Carsten Knösche

   BASF (Germany)

   Affiliation as printed

   BASF SE, 67056 Ludwigshafen am Rhein, Germany

8. Alexei A. Lapkin corresponding

   University of Cambridge

   Affiliation as printed

   Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK

   Innovation Centre in Digital Molecular Technology, Yusuf Hamied Department of Chemistry, University of Cambridge, UK