Exploring kinetic mechanisms of biomass pyrolysis using generalized logistic mixture model

• Published in: Energy conversion and management Vol. 258; p. 115522
• Main Authors: Zou, Jianfeng, Hu, Hangli, Xue, Yuan, Li, Chong, Li, Yingkai, Yellezuome, Dominic, He, Fang, Zhang, Xingguang, Maksudur Rahman, Md, Cai, Junmeng
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15-04-2022; Elsevier Science Ltd
• Subjects: Acids; Biofuels; Biomass; Biomass pyrolysis; Cellulose; Complexity; Corn; Cotton; Deconvolution analysis; Distributed activation energy model (DAEM); Hemicellulose; Information processing; Kinetics; Lignin; Lignocellulose; Logistic model; Mixtures; Pyrolysis; Sensitivity analysis; Thermal decomposition; Xylan; Deconvolution analysis; Sensitivity analysis; Distributed activation energy model (DAEM); Kinetics; Logistic model; Biomass pyrolysis
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2022.115522

[Display omitted]
•Generalized logistic model can represent asymmetrical kinetic profiles.•Location parameter in generalized logistic model has the greatest sensitivity.•Generalized logistic mixture model (GLMM) well describes DAEM and parallel processes.•GLMM reflects acid infusion mechanism of corn stover.•GLMM effectively discriminates overlapping processes of cotton stalk pyrolysis.
Pyrolysis is one of promising possible paths for converting waste biomass to heat, bio-fuels, or higher value chemicals. The pyrolysis of biomass involves several overlapping processes due to its complex composition and the complexity of pyrolysis reactions. The characterization and applicability of the generalized logistic mixture model in the kinetic analysis of simulated processes and lignocellulosic biomass pyrolysis processes were investigated. The generalized logistic model showed asymmetrical kinetic profiles, which could fit the simulated DAEM and parallel processes more accurately than the logistic and Weibull models. The pyrolysis kinetic data of xylan, cellulose and lignin could be accurately described by the generalized logistic model with a single component. The pyrolysis kinetics of raw and acid-infused corn stovers was well fitted by the generalized logistic mixture model with three components, and the characterization of individual sub-processes separated from the model could reflect the acid infusion mechanism of corn stover. The generalized logistic mixture model effectively separated the pyrolysis process of raw and acid-washed cotton stalk into three sub-processes, and the kinetic analysis of individual sub-processes using the Friedman isoconversional method showed that those separated sub-processes corresponded to the thermal decomposition of pseudo-components of hemicellulose, cellulose and lignin. Acid washing could reduce the fraction of pseudo-hemicellulose, and increase the pseudo-lignin fraction according to the results from the generalized logistic mixture model. The generalized logistic mixture model enables us to obtain the further kinetic information of the individual processes for lignocellulosic biomass pyrolysis.