Scalable Lignin Monomer Production Via Machine Learning-Guided Reductive Catalytic Fractionation of Lignocellulose.

ABSTRACT: Efficient valorization of lignocellulosic biomass into high-value lignin monomers is a cornerstone of sustainable biorefineries, yet the complexity of optimizing reductive catalytic fractionation limits industrial scalability. This study presents a machine learning (ML)-driven framework that harnesses 3,451 experimental data points from 54 peer-reviewed studies to model and optimize lignin monomer production. Among four advanced ML models developed, eXtreme Gradient Boosting Regression is found to achieve the highest predictive accuracy (R = 0.80-0.86) with low prediction errors (root mean square error: 3.99-8.31; mean absolute error: 2.85-6.90) for monomer production. Feature importance analysis reveals that operational parameters account for the largest influence (40-57%), followed by su

SUBMITTER: Madadi M 

PROVIDER: S-EPMC12622498 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature