Kavli Affiliate: Xiang Zhang
| First 5 Authors: Peilin Cao, Ying Geng, Nan Feng, Xiang Zhang, Zhiwen Qi
| Summary:
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.
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