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 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.