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.
Madadi et al. (2025) studied this question.