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

SAR QSAR Environ Res. 2023 Jul-Sep;34(8):605-617. doi: 10.1080/1062936X.2023.2244410. Epub 2023 Aug 29.ABSTRACTCombating 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.PMID:37642367 | DOI:10.1080/1062936X.2023.2244410

Source: SAR and QSAR in Environmental Research - August 29, 2023 Category: Environmental Health Authors: R Moreno Jimenez B Creton S Marre Source Type: research