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

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 substrate content (25-43%) and catalyst-solvent properties (14-21%). The error between experimental and ML-predicted total monomer yields ranges from 2% to 2.6%, demonstrating robust performance of the model. Scaling this approach has the potential to process 140 million tons of aspen biomass annually, can reduce CO₂ emissions by 20.6 million tons, and yield $4,729 million in socioeconomic savings. This ML-enhanced strategy offers a scalable and environmentally viable pathway for data-driven lignocellulose valorization, advancing the development of low-carbon, economically competitive biorefineries.