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Insight into lignocellulosic biomass torrefaction kinetics with case study of pinewood sawdust torrefaction

Yantao Yang, Xia Qu, Guorun Huang, Suxia Ren, Lili Dong, Tanglei Sun, Peng Liu, Yanling Li, Tingzhou Lei and Junmeng Cai

Renewable Energy, 2023, vol. 215, issue C

Abstract: Torrefaction, as a promising pretreatment of lignocellulosic biomass, can upgrade its physicochemical properties and thermochemical conversion performance. The kinetics of lignocellulosic biomass torrefaction is fundamental for elucidating its reaction mechanism and optimizing its process. Although some kinetic models have been studied to describe the kinetic characteristics of lignocellulosic biomass torrefaction, a systematical analysis of the kinetic models is still missing. In this study, the derivation, numerical calculations, parametric studies and characteristics of those kinetic models for lignocellulosic biomass torrefaction were comprehensively studied. The reaction order model, two-step model and distributed activation energy model (DAEM) for lignocellulosic biomass torrefaction were used to analyze the experimental kinetic data of pinewood sawdust torrefaction at three different final torrefaction temperatures of 523, 548 and 573 K. The results show that (i) the reaction order model cannot provide accurate fit to the experimental kinetic data of pinewood sawdust torrefaction; (ii) the two-step model can provide the best fit but it contains too many parameters and involves intermediate reactions making it difficult to apply; (iii) the DAEM can describe the kinetic behaviors of pinewood sawdust torrefaction with adequate accuracy and reflect the reactivity distribution of thermal decomposition reactions involving in pinewood sawdust torrefaction.

Keywords: Lignocellulosic biomass; Torrefaction; Kinetics; Activation energy; Reaction order; Distributed activation energy model (DAEM) (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:215:y:2023:i:c:s0960148123008479

DOI: 10.1016/j.renene.2023.118941

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