Article (Périodiques scientifiques)
A DEM modeling of biomass fast pyrolysis in a double auger reactor
QI, Fenglei; Wright, Mark
2020In International Journal of Heat and Mass Transfer, 150
Peer reviewed vérifié par ORBi
 

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Résumé :
[en] Thermochemical conversion of biomass via fast pyrolysis is a proven pathway to product low-carbon crude bio-oils. In this research, an extended discrete element method (DEM) is proposed for simulating biomass fast pyrolysis reacting granular flows in a double auger reactor, in which particle hydrodynamics and interparticle heat transfer processes are involved and coupled with chemical reactions in solid particles. An adaptive time step algorithm is proposed to achieve a stable coupling between the integration of reaction ordinary differential equations and the DEM solver, and the algorithm is proven computationally efficient. A multi-component fast pyrolysis kinetics is adopted and its modeling accuracy is assessed by carrying out simulations of benchmark biomass pyrolysis experiments and comparing the prediction results with experimental data. The predicted product yields of bio-oil, char and non-condensable gas from the simulation of the biomass fast pyrolysis in the auger reactor are in satisfactory agreement with experimental measurements. The decomposition rates of biomass components in the reactor are revealed from the simulation and the pyrolysis number Py is calculated from the decomposition rate of biomass and the heat transfer coefficient. The Py number illustrates that the biomass fast pyrolysis process is limited by the heat transfer process at particle size of 2 mm.
Disciplines :
Energie
Auteur, co-auteur :
QI, Fenglei ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Wright, Mark
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
A DEM modeling of biomass fast pyrolysis in a double auger reactor
Date de publication/diffusion :
avril 2020
Titre du périodique :
International Journal of Heat and Mass Transfer
ISSN :
0017-9310
eISSN :
1879-2189
Maison d'édition :
Elsevier, Royaume-Uni
Volume/Tome :
150
Peer reviewed :
Peer reviewed vérifié par ORBi
Focus Area :
Computational Sciences
Disponible sur ORBilu :
depuis le 23 janvier 2020

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