Data-driven approach augmented by attention mechanism in critical and boiling thermophysical properties prediction of fluorine/chlorine-based refrigerants

Yichuan He, Yanhui Feng, Lin Qiu and Dawei Tang

Energy, 2024, vol. 306, issue C

Abstract: Refrigerants are ubiquitous in modern society, indispensable for various applications ranging from air conditioners and refrigerators to spacecraft and hospitals. Improving predictive methodologies for designing and implementing novel fluids is therefore essential. This paper presents an integrated prediction model augmented by attention mechanism for the molecular weight, boiling point, critical temperature, and critical pressure of fluorine/chlorine - based refrigerants. The model employs molecular groups as molecular structure descriptors and utilizes a machine learning algorithm with attention mechanisms. The prediction accuracy is good (all R2 > 0.9), far ahead of other methods, such as Lydersen, Joback, Klincewicz-Reid, Constantinou-Gani, and multilayer perceptron methods. Furthermore, to gain insights into the influence of group features on property prediction, an interpretable method known as Shapley Additive Explanation (SHAP) was employed. This method computes the contributions of group features, thus providing a deeper understanding of the relationship between molecular groups and thermophysical properties. The results verified that the proposed models effectively establish this relationship.

Keywords: Thermophysical property; Machine learning; Attention mechanism; Molecular groups (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224022643
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:306:y:2024:i:c:s0360544224022643

DOI: 10.1016/j.energy.2024.132490

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().