Predictive modelling of hydrogen production from agricultural and forestry residues through a thermo-catalytic reforming process

Hydrogen produced from renewable sources is crucial for decarbonizing hard-to-abate sectors and achieving net-zero targets. This study examines hydrogen production through the novel thermo-catalytic reforming (TCR) process using agricultural and forestry residues. The research aims to develop and optimize regression models that integrate feedstock properties (ash, hydrogen-to-carbon molar ratio, and lignin) and process parameters (reactor and reformer temperatures) to predict yields of hydrogen (H₂), syngas, methane (CH₄) and carbon dioxide (CO₂). Three biomass feedstocks – softwood pellets (SWPs), hardwood pellets (HWPs), and wheat straw pellets (WSPs) – were analyzed at reactor temperatures of 400–550 °C and reformer temperatures of 500–700 °C. Predictive models for H₂ (R² = 0.9642, RMSE = 1.0639) and syngas (R² = 0.9894, RMSE = 0.0140) yields show strong agreement and accuracy between the predicted and experimental values. In contrast, the models for CH₄ and CO₂ yields show higher variability in the predictions. Reformer temperature was the most significant parameter influencing the yields of H₂ and syngas. The optimal H₂ yields predicted for the model were obtained for HWPs at 550/700 °C (26.67 g H₂/kg dry biomass), followed by SWPs at 550/700 °C (24.11 g H₂/kg dry biomass) and WSPs at 550/685.2 °C (18.78 g H₂/kg dry biomass). The volumetric syngas yields were highest for HWPs at 550/700 °C (0.831 Nm³/kg dry biomass), followed by SWPs (0.777 Nm³/kg dry biomass) and WSPs (0.634 Nm³/kg dry biomass). This study demonstrates that regression modelling accurately predicts H₂ and syngas yields, which would help to expand the applicability of TCR technology for large-scale hydrogen production, contributing to the decarbonization of the energy sector.