Carbon dioxide solubility in polyethylene glycol polymer: an accurate intelligent estimation framework.

ABSTRACT: Polyethylene glycol (PEG), a synthetic polymer made up of repeating ethylene oxide units, is widely recognized for its broad utility and adaptable properties. Precise estimation of CO₂ solubility in PEG plays a vital role in enhancing processes such as supercritical fluid extraction, carbon capture, and polymer modification, where CO₂ serves as a solvent or transport medium. This study focuses on building advanced predictive models using machine-learning approaches, such as random forest (RF), decision tree (DT), adaptive boosting (AdaBoost), k-nearest neighbors (KNN), and ensemble learning (EL) to forecast CO₂ solubility in PEG across a wide range of conditions. The data utilized for model development is sourced from previously published literature, and an

SUBMITTER: Sead FF 

PROVIDER: S-EPMC12015362 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature