Machine learning-based models for accessing thermal conductivity of liquids at different temperature conditions

ABSTRACT

Combating global warming-related climate change demands prompt actions to reduce greenhouse gas emissions, particularly carbon dioxide. Biomass-based biofuels represent a promising alternative fossil energy source. To convert biomass into energy, numerous conversion processes are performed at high pressure and temperature conditions, and the design and dimensioning of such processes requires thermophysical property data, particularly thermal conductivity, which are not always available in the literature. In this paper, we proposed the application of Chemoinformatics methodologies to investigate the prediction of thermal conductivity for hydrocarbons and oxygenated compounds. A compilation of experimental data followed by a careful data curation were performed to establish a database. The support vector machine algorithm has been applied to the database leading to models with good predictive abilities. The support vector regression (SVR) model has then been applied to an external set of compounds, i.e. not considered during the training of models. It showed that our SVR model can be used for the prediction of thermal conductivity values for temperatures and/or compounds that are not covered experimentally in the literature.

KEYWORDS:

• QSPR
• thermal conductivity
• temperature
• oxygenated compounds
• hydrocarbons

Acknowledgements

The authors would like to thank Dr Claire Marlière, Lionel Teulé-Gay, and Olivier Nguyen for discussions that preceded this work. Presented at QSAR 2023: 20th International Workshop on (Q)SAR in Environmental and Health Sciences, 5–9 June, 2023, Copenhagen, Denmark.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2023.2244410

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.