ORCID

Document Type

Article

Abstract

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.

Publication Date

2026-09-25

Publication Title

Biomass and Bioenergy

Volume

217

Issue

Part D

ISSN

0961-9534

Acceptance Date

2026-09-21

Deposit Date

2026-09-28

Keywords

Fast pyrolysis, Temperature- and moisture-driven uncertainty, analysis, Cold gas efficiency, Stress analysis, Regression-based Monte-Carlo method

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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