Pyrolysis of biomass feedstocks for hydrogen-enriched syngas production: Cold gas efficiency modelling, latin hypercube uncertainty analysis, and parametric stress analysis

Biomass-derived syngas offers a viable pathway for producing renewable, hydrogen-rich fuel gas. However, its conversion performance is influenced by feedstock type, temperature, feed rate, and moisture content. This study develops a Cold Gas Efficiency (CGE) model for four agricultural feedstocks: wheat, barley, oilseed rape, and beans. Temperature-dependent regression correlations are developed for the main syngas components and gas flow rate using experimental data. Results show that CGE is highly sensitive to temperature, with low-temperature stress (0–5th percentile) reducing mean CGE by up to 22%. High-moisture stress (95–100th percentile) produced an even larger decline, lowering mean CGE from ∼44% to ∼35% for wheat and compressing the distribution into a low-performance regime. Regression-based tornado charts indicate that temperature is the strongest linear driver (coefficient = +0.44), while Spearman rank correlations highlight feed rate as the most monotonic negative driver (−0.41). Feedstock probability density functions (PDFs) show that wheat as the most efficient and least variable performer, while Oilseed Rape (OSR) and beans exhibited broader distributions due to higher ash-related inhibition of char reactivity. The thermochemical and statistical results demonstrate that operational uncertainty, such as temperature and moisture, dominates CGE variability. This underscores the need for robust control strategies in small-scale pyrolysis systems.