Feature Importance Analysis of Solar Gasification of Biomass via Machine Learning Models

Solar gasification is a thermochemical process that relies on concentrated solar radiation to heat steam and biomass to produce syngas. This study uses Machine Learning to model solar gasification using steam as an oxidizer, incorporating both thermodynamic simulations and predictive algorithms, developed using Python (version 3.11.13) scripting, to understand the relationship between the input and output variables. Three models—Artificial Neural Networks, Support Vector Machines, and Random Forests—were trained using datasets including biomass composition, solar irradiance (considering a solar furnace), and steam-to-biomass ratios in a downdraft or fluidized bed gasifier. Among the models, Random Forests provided the highest accuracy (average R2 = 0.942, Mean Absolute Error = 0.086, and Root Mean Square Error = 0.951) and were used for feature importance analysis. Results indicate that radiative heat transfer and steam-to-biomass ratio are the parameters that result in the largest increase in the syngas heating value and decrease in the tar contents. In terms of composition, the hydrogen contents have a direct relationship with the H2 and tar formed, while the carbon content affects the carbon conversion efficiency. This work highlights the of feature importance analysis to improve the design and operation of solar-driven gasification systems.