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ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction
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DOI：10.1016/j.cej.2024.151999.png)
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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.
Keyword:
Deep learning
PC-SAFT
Thermodynamics
Property predictions
期刊
IF:
13.2
论文数:
7.6W
被引数:
48.5W
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Perturbed-chain SAFT: An equation of state based on a perturbation theory for chain molecules扰动链SAFT: 基于链分子扰动理论的状态方程
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