A machine learning-based dual system coupling model of catalytic-gasification: a calcium-based case study with mechanistic interpretability.

Predictive modeling of catalytic biomass gasification suffers from simplified categorical representations of catalysts. This study developed a dual system machine learning framework coupling catalyst properties with gasification outputs. The Bayesian-optimized gradient boosting was utilized to train a catalytic model on 149 experimental datasets, achieving a carbon conversion efficiency prediction accuracy of R2 = 0.937. Principal component analysis was applied to compress seven catalyst descriptors (including specifies surface area and active site size) into a single performance score (PC1), explaining 47.3% of the variance. This score serves as the continuous input for the multi-output gasification model to predict CH4, H2, CO, and CO2 yields (R2 = 0.940, 0.976, 0.916, and 0.920, respectively). Independent validation using pine sawdust and calcium oxide confirmed that the model predicts syngas composition with less than five percentage points of absolute deviation. This framework provides a tool for catalyst screening and process parameter optimization in biomass clean energy conversion systems.