Predicting performance-related properties of refrigerant based on tailored small-molecule functional group contribution
This work addresses the need for more precise property prediction in refrigerant design, which is incremental as it adapts existing methods to a specific domain.
The authors tackled the problem of inaccurate property predictions for small refrigerant molecules by tailoring group contribution methods specifically for refrigerants and combining them with machine learning, achieving improved accuracy for five key performance-related properties.
As current group contribution (GC) methods are mostly proposed for a wide size-range of molecules, applying them to property prediction of small refrigerant molecules could lead to unacceptable errors. In this sense, for the design of novel refrigerants and refrigeration systems, tailoring GC-based models specifically fitted to refrigerant molecules is of great interest. In this work, databases of potential refrigerant molecules are first collected, focusing on five key properties related to the operational efficiency of refrigeration systems, namely normal boiling point, critical temperature, critical pressure, enthalpy of vaporization, and acentric factor. Based on tailored small-molecule groups, the GC method is combined with machine learning (ML) to model these performance-related properties. Following the development of GC-ML models, their performance is analyzed to highlight the potential group-to-property contributions. Additionally, the refrigerant property databases are extended internally and externally, based on which examples are presented to highlight the significance of the developed models.