1 research outputs found
Fitting Error vs Parameter PerformanceHow to Choose Reliable PC-SAFT Pure-Component Parameters by Physics-Informed Machine Learning
State of the art
thermodynamic models, such as the Perturbed-Chain
Statistical Associating Fluid Theory (PC-SAFT), require a thorough
parametrization (three pure-component parameters for nonassociating
molecules) of the molecules considered. In our previous work (J. Habicht,
C. Brandenbusch, G. Sadowski, Fluid Phase Equilibria, 2023, 565, 113657), we introduced
a Machine Learning approach for a predictive parametrization of nonassociating
components. Within this approach, training is performed using a Huber-loss
function, comparing the ML-predicted parameter set with the original
one, e.g., from literature. However, often multiple pure-component
parameter sets exist for one molecule. This fact makes the training
to only one “true” parameter set questionable. Within
this work, we thus performed a detailed analysis on the fact of multiparameter
set existence. We further expanded our ML-approach by developing a
choice of two physics-informed loss functions that allow for the consideration
of multiple “true” parameter sets during training. Results
indicate that reliable pure-component parameters have a certain orientation
when plotted in the three-dimensional parameter space. The results
of this work will lead to a more reliable ML-based parametrization
and ensure the prediction of optimized pure-component parameters for
a given molecule