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◆ Fuel2025-12-03· Pyrolysis

Biomass pyrolysis kinetics considering lignin-hemicellulose interaction

Leon Loni Berkel, Paulo Debiagi, Hendrik Nicolai, Francesca Cerciello, Muhammad Yusuf Suleiman, Osvalda Senneca, Christian Hasse, Tiziano Faravelli

原始摘要(英文原文)· Original abstract
• Predictive biomass pyrolysis modeling with semi-detailed kinetics. • Analysis of component interactions, particularly lignin-carbohydrate complex (LCC). • Shown the importance of LCC during pyrolysis for certain biomass feedstocks. • Novel addition of LCC effect in a practical and predictive pyrolysis model. • Proven improved model performance with quantitative approach. Biomass is a promising near-term clean energy solution, but it poses challenges for predictive simulations due to its complex chemical structure. The chemical interaction among its individual components – cellulose, hemicellulose, and lignin – is a widely debated topic in literature, a major example being the lignin-carbohydrate-complex (LCC). Nevertheless, the effect of interacting components is usually neglected in most modeling approaches. This study is an effort to include the LCC effects in the CRECK-S-B biomass pyrolysis mechanism as a practical modeling approach. Walnut shell TGA measurements serve as a starting point due to the lignin-rich composition of the feedstock, which may lead to more pronounced interaction effects. The linking between hemicellulose and lignin is considered to represent the LCC, and its decomposition is modeled with a second order reaction, without disrupting the previously validated individual component pathways. The impact of the modification is first thoroughly analyzed using walnut shell TGA. Subsequently, various heating rates and different biomass samples are examined to assess the performance changes introduced by the modification. A curve matching approach is employed for a quantitative performance comparison between the proposed model and the base model. The novel approach to include LCC effects in the pyrolysis mechanism highlights a crucial improvement in prediction capabilities. A significant improvement in average model performance is observed, while maintaining accuracy across all individual biomass feedstocks considered.
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