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

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.

应对全球变暖引发的气候变化，亟需采取及时行动以削减温室气体排放，尤以二氧化碳为重点。以生物质为原料的生物燃料是极具前景的化石能源替代方案。将生物质转化为能源的诸多工艺均需在高压高温条件下开展，而此类工艺的设计与尺寸选型需要热物理性质数据，尤其是热导率数据，但这类数据在现有学术文献中往往较为匮乏。本研究提出采用化学信息学方法，探究烃类与含氧化合物的热导率预测模型。研究团队首先搜集整理了实验数据，并经过严格的数据质控流程，最终构建了专用数据库。将支持向量机（Support Vector Machine, SVM）算法应用于该数据库后，得到了预测性能优异的模型。随后，将该支持向量回归（Support Vector Regression, SVR）模型应用于一组未参与模型训练的独立外部化合物样本。结果表明，本研究构建的SVR模型可用于预测现有实验文献尚未覆盖的温度条件或化合物的热导率数值。