An open-source, kinetic model for downdraft biomass gasification was developed in Python to provide a transparent and reproducible reactor-scale framework. The approach integrates coupled mass and energy balances with a 26-reaction kinetic set describing drying, multi-stage pyrolysis, oxidation, reduction, and tar cracking in a plug-flow configuration. Validation against laboratory-scale experimental data for Scots pine under air-blown conditions yielded an average mean absolute percentage error (MAPE) of 3.03% and a root mean square error (RMSE) of 0.6693 for major syngas species (CO, CO 2 , H 2 , CH 4 ). Additional validation using Miscanthus briquettes and rubber wood confirmed predictive robustness without parameter re-tuning, maintaining RMSE values below the established acceptance threshold (RMSE <1.60). Parametric analysis was conducted across representative ranges of equivalence ratio (ER), addition of steam(S/B), and pyrolysis temperature. Increasing ER reduced tar from 27.63 to 1.21 g Nm −3 but decreased syngas energy content beyond the optimal point (ER ≈ 0.32). The higher heating value (HHV) ranged from 4.02 to 5.24 MJ Nm −3 , while lower heating value (LHV) reached 4.97 MJ Nm −3 at elevated pyrolysis temperatures. Steam addition increased H 2 up to 12.99 vol% (≈37% increase) with limited tar reduction (<5%). Temperature exerted the strongest influence, promoting near-complete tar conversion (99.7%). Overall, the proposed framework supports parametric, and reproducible model-based design of downdraft gasifiers. • Open-source, fully kinetic Python model for downdraft biomass gasification. • Model predicts syngas composition accurately (MAPE 3.03%, RMSE 0.6693). • Increasing ER (0.25–0.40) raises CO to 22.74 vol% and lowers H 2 and CH 4 . • At 1000 K, syngas LHV reaches 4.97 MJ/Nm 3 ; CH 4 drops to near zero. • Transparent, reproducible, extensible tool for gasifier design optimization.
Chuquín-Vasco et al. (Sat,) studied this question.