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Machine learning models show promise in replacing traditional equations of state (EoS) for fluids. These AI models can accurately predict thermophysical properties, offering a new approach for engineering design.

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Area of Science:

  • Thermodynamics
  • Chemical Engineering
  • Computational Chemistry

Background:

  • Traditional equations of state (EoS) have limitations in accuracy and applicability due to their fixed mathematical structure and physical models.
  • Developing accurate EoS requires extensive experimental data fitting, which can be time-consuming and resource-intensive.

Purpose of the Study:

  • To explore the potential of machine-learned models as replacements for analytical equations of state.
  • To demonstrate the effectiveness of machine learning in replicating a specific EoS, the Statistical Associating Fluid Theory (SAFT-VR Mie) for pure fluids.
  • To assess the capability of machine learning to correlate and predict thermophysical properties based on molecular descriptors.

Main Methods:

  • Generated a large dataset of pseudodata from the SAFT-VR Mie EoS for training.
  • Employed artificial neural networks (ANNs) and Gaussian process regression (GPR) as machine-learning models.
  • Utilized molecular descriptors as input features for the machine-learning models.
  • Correlated and predicted thermodynamic properties including critical pressure and temperature, vapor pressures, and densities.

Main Results:

  • Machine-learned models successfully replicated the SAFT-VR Mie EoS.
  • ANNs and GPR demonstrated effectiveness in correlating and predicting thermophysical properties.
  • The accuracy of the machine-learned models was validated against the pseudodata generated from the SAFT-VR Mie EoS.

Conclusions:

  • Machine learning offers a promising alternative to traditional analytical equations of state for fluid property prediction.
  • This approach shows potential for accurate correlation and prediction of thermophysical properties using molecular descriptors.
  • The study serves as a proof of concept for integrating machine learning into fluid thermodynamics and chemical engineering practices.