Machine learning based analysis of reaction phenomena in catalytic lignin depolymerization

Heterogeneously catalyzed lignin solvolysis opens the possibility of transforming low value biomass into high value, useful aromatic chemicals, however, its reaction behavior is poorly understood due to the many possible interactions between reaction parameters. In this study, a novel predictive model for bio-oil yield, char yield and reaction time is developed using Random Forest (RF) regression method using data available from the literature to study the impact of surface properties of the catalyst and the weight averaged molecular weight of the lignin (M-w) used in the reaction. The models achieved a coefficient of determination (R2) score of 0.9062, 0.9428 and 0.8327, respectively, and feature importance for each case was explained and tied to studies that provide a mechanistic explanation for the performance of the model. Surface properties and lignin Mw showed no importance to the prediction of bio-oil yield and average pore diameter contributed 3% of feature importance to reaction time.

Automated summary

A predictive model was developed using the Random Forest regression method to study the impact of catalyst surface properties and lignin molecular weight on bio-oil yield, char yield, and reaction time in lignin solvolysis. The models showed high coefficients of determination and explained the feature importance for each case, with average pore diameter contributing 3% to reaction time.