Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures

Thermophysical properties of fluid mixtures are important in many fields of science and engineering. However, experimental data are scarce in this field, so prediction methods are vital. Different types of physical prediction methods are available, ranging from molecular models over equations of state to models of excess properties. These well-established methods are currently being complemented by new methods from the field of machine learning (ML). This review focuses on the rapidly developing interface between these two approaches and gives a structured overview of how physical modeling and ML can be combined to yield hybrid models.We illustrate the different options with examples from recent research and give an outlook on future developments.

Automated summary

Thermophysical properties of fluid mixtures are important in various fields, and prediction methods are essential due to the lack of experimental data. Physical prediction methods, including molecular models, equations of state, and excess property models, are being complemented by new methods from machine learning (ML). This review provides an overview of the interface between these approaches and showcases how physical modeling and ML can be combined to create hybrid models, with examples from recent research and an outlook on future developments.