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Toba-CPD: An Extended Chemical Percolation Devolatilization Model for Tobacco Pyrolysis

[Image: see text] Tobacco features chemical compositions different from that of raw lignocellulosic biomass. Currently, the performance of network models, like Bio-Chemical Percolation Devolatilization (Bio-CPD), on tobacco pyrolysis is unclear, and only global kinetics have been proposed for tobacc...

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Detalles Bibliográficos
Autores principales: Wei, Hao, Peng, Yuhan, Huang, Hua, Fan, Jianqi, Xing, Jiangkuan, Luo, Kun, Fan, Jianren, Dai, Lu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9583314/
https://www.ncbi.nlm.nih.gov/pubmed/36278092
http://dx.doi.org/10.1021/acsomega.2c05098
Descripción
Sumario:[Image: see text] Tobacco features chemical compositions different from that of raw lignocellulosic biomass. Currently, the performance of network models, like Bio-Chemical Percolation Devolatilization (Bio-CPD), on tobacco pyrolysis is unclear, and only global kinetics have been proposed for tobacco devolatilization, which does not have the versatility for a wide range of heating conditions and tobacco types. To address this issue, the present work first assessed the performance of the Bio-CPD model on tobacco pyrolysis through an a priori study, which showed large deviations. Afterward, an extended Chemical Percolation Devolatilization model for tobacco pyrolysis (Toba-CPD) was developed by modifying the kinetic parameters using a grid-search optimization strategy. The process of grid-search optimization strategy is based on the kinetic parameters of the Bio-CPD model and modified with experimental results of 11 tobacco types under a wide range of heating rates. Finally, the performance of Toba-CPD was measured with experimental results which were not used during parameters optimization. Results demonstrated that the Toba-CPD models could well reproduce the pyrolysis of various tobacco types under a wide range of heating rates (R(2) > 0.957).