Polymer reaction engineering meets explainable machine learning

doi:10.1016/j.compchemeng.2023.108356

Polymer reaction engineering meets explainable machine learning

2023-09-01

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AI

DOI：10.1016/j.compchemeng.2023.108356

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Abstract

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Due to the complex polymerization technique and statistical composition of the polymer, tailoring its charac-teristics is a challenging task. Modeling of the polymerizations can contribute to deeper insights into the process. This study applies state-of-the-art machine learning (ML) methods for modeling and reverse engineering of polymerization processes. ML methods (random forest, XGBoost and CatBoost) are trained on data sets generated by an in house developed kinetic Monte Carlo simulator. The applied ML models predict monomer concentration, average molar masses and full molar mass distributions with excellent accuracy (R2 > 0.96). Reverse engineering results delivering the polymerization recipe for a targeted molar mass distribution are less accurate, but still only minor deviations from the targeted molar mass distribution are seen. The influences of the input variables in ML models obtained by explainability methods correspond to the expert expectations.

Keywords:

Polymers

Machine learning

Kinetic Monte Carlo simulation

Multi-target-regression

Reverse engineering

explainable AI

Polymer reaction engineering meets explainable machine learning

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