Integrated modelling of dual fluidised-bed biomass steam gasification with hot gas cleaning and upgrading for Fischer–Tropsch liquid fuel synthesis

• An integrated Aspen Plus model was developed for dual fluidised-bed biomass steam gasification. • A hybrid kinetic–equilibrium modelling framework was applied to gasification and syngas upgrading units. • Model predictions showed excellent agreement with experimental data (RMSE < 1.4 for H 2 and CO). • Optimal operating conditions were identified to maximise syngas yield and quality. • Syngas upgrading successfully achieved an H 2 /CO ratio of 2:1 suitable for Fischer–Tropsch synthesis. This study develops a comprehensive Aspen Plus model for a biomass-to-liquid fuel pathway integrating dual fluidised-bed (DFB) steam gasification with high-temperature cleaning and conditioning. The framework combines a rigorous kinetic gasification model with multi-contaminant hot-gas cleaning, including particulate filtration, HCl/H 2 S removal, and catalytic tar reforming, followed by water–gas shift (WGS) and pressure swing adsorption (PSA) to produce clean syngas with an H 2 /CO ratio of 2:1 suitable for Fischer–Tropsch synthesis. Validation against experimental woody biomass gasification data demonstrated excellent predictive accuracy, with root mean square error values below 1.4 for the major syngas components. Sensitivity analysis identified optimal conditions of 800°C and a steam-to-biomass ratio of 0.89, yielding a raw syngas composition on a dry basis of 33.5% H 2 , 38.6% CO, 18.7% CO 2 , and 9.1% CH 4 , with a lower heating value of 14–16 MJ/Nm 3 . The hot-gas cleaning train reduced tar and inorganic impurities (NH 3 , H 2 S, HCl) by more than 99%, meeting FT catalyst thresholds. Thermodynamic analysis confirmed the advantage of hot-gas cleaning, achieving a cold gas efficiency of 74.4%, a carbon conversion efficiency of 92.5%, and an exergy efficiency of 68.2% by preserving sensible heat and minimising irreversibility. This validated, scalable framework supports the design, optimisation, and techno-economic assessment of sustainable biorefineries for synthetic fuel production.