Machine learning-guided optimization for ionic liquid-based polyethylene terephthalate waste recycling

Abstract

Ionic liquid (IL)-catalyzed polyethylene terephthalate (PET) glycolysis has emerged as a promising method for recycling valuable monomers for high-quality polymer production. However, traditional approaches rely heavily on trial-and-error and time-consuming experiments to explore the large search space with multiple design factors. Here, we introduce a novel multi-objective optimization framework that integrates a graph neural network with process simulation for simultaneous IL design and reaction optimization toward unified economic and environmental metrics. We identified seven ILs unseen from the literature. Experimental validation demonstrates that approximately 47% of the optimized IL and reaction condition combinations outperform the best-reported literature values. This results in an average cost reduction of 29% and CO2e reduction of 2.6% compared to literature results. This work demonstrates the potential of machine learning to guide reaction optimization toward cost-effective and low-carbon targets for the PET recycling process.