A machine learning-based dual system coupling model of catalytic-gasification: a calcium-based case study with mechanistic interpretability.
TL;DR
This framework provides a tool for catalyst screening and process parameter optimization in biomass clean energy conversion systems and confirms that the model predicts syngas composition with less than five percentage points of absolute deviation.
Abstract
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.