Optimization and analysis of solar-driven biomass gasification using a CFD-ANN-GA framework

This study investigates solar-driven biomass gasification using a silicon carbide (SiC) foam packed bed reactor, examining interactions between structural parameters (porosity ϕ_SiC and aperture d_SiC) and reaction conditions (solar radiation intensity Q_G and steam equivalence ratio ER) with respect to effective conversion ratio (ECR) and energy efficiency (EE). A hybrid CFD-ANN-GA approach was employed to reduce computational cost while systematically evaluating and globally optimizing these variables. Results demonstrate that reaction conditions influence ECR and EE more significantly than SiC foam structures, with ϕ_SiC outweighing d_SiC. Furthermore, ER dominates the change rate of ECR due to its dual role as a reactant and a cooling source. Under distinct variable configurations, ECR and EE achieve optimal values of 82.64 % and 91.37 %, respectively. Pareto front analysis further reveals a trade-off between ECR and EE, with their respective optimization ranges of 60–80 % and 80–92 %. Q_G spans a broader range on the Pareto front, providing greater flexibility to achieve near-optimal performance even under suboptimal solar radiation conditions. This study underscores the effectiveness of the CFD-ANN-GA method in reducing computational burden, as well as in analyzing and optimizing complex biomass gasification systems.