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We developed graph neural networks (GE-GNNs) to accurately predict mixture properties. This method ensures thermodynamic consistency for activity coefficients, crucial for chemical process modeling.

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Area of Science:

  • Thermodynamics
  • Machine Learning
  • Chemical Engineering

Background:

  • Predicting activity coefficients is essential for chemical process design.
  • Existing models often face limitations in thermodynamic consistency and applicability.
  • Accurate prediction of composition-dependent properties is a key challenge.

Purpose of the Study:

  • To introduce a novel graph neural network (GE-NN) approach for predicting activity coefficients.
  • To ensure thermodynamic consistency in predictions using fundamental thermodynamic principles.
  • To develop a model free from thermodynamic modeling limitations.

Main Methods:

  • Developed excess Gibbs free energy graph neural networks (GE-GNNs).
  • Utilized automatic differentiation for end-to-end learning of activity coefficients.
  • Ensured thermodynamic consistency by predicting molar excess Gibbs free energy.

Main Results:

  • Achieved high accuracy in predicting activity coefficients for binary mixtures.
  • Demonstrated inherent thermodynamic consistency without additional loss terms.
  • The GE-GNN model showed no thermodynamic modeling limitations.

Conclusions:

  • GE-GNNs offer a powerful and thermodynamically consistent method for predicting mixture properties.
  • This approach advances the application of machine learning in chemical thermodynamics.
  • The model provides reliable predictions essential for chemical process optimization.