Predictive Modeling of Surface Tension in Chemical Compounds: Uncovering Crucial Features with Machine Learning
Paula Cala,
Guilherme Dariani,
Eduardo Veiga
et al.
Abstract:Surface tension (SFT) can shape the behavior of liquids in industrial chemical processes, influencing variables such as flow rate and separation efficiency. This property is commonly measured with experimental approaches such as Du Noüy ring and Wilhelmy plate methods. Here, we present machine learning (ML) methodologies that can predict the SFT of hydrocarbons. A comparative analysis encompassing k-nearest neighbors, random forest, and XGBoost (extreme gradient boosting) methods was done. Results from our stu… Show more
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