Interpretable machine learning to model biomass and waste gasification

Machine learning has been regarded as a promising method to better model thermochemical processes such as gasification. However, their black box nature can limit how much one can trust and learn from the developed models. Here seven different machine learning methods have been adopted to model the gasification of biomass and waste across a wide range of operating conditions. Gradient boosting regression has been found to outperform the other model types with a coefficient of determination (R-2) of 0.90 when averaged across ten key gasification outputs. Global and local model interpretability methods have been used to illuminate the developed black box models. The studied models were most strongly influenced by the feedstock's particle size and the type of gasifying agent employed. By combining global and local interpretability methods, the understanding of black box models has been improved. This allows policy makers and investors to make more educated decisions about gasification process design.