Multiscale CFD simulation of biomass fast pyrolysis with a machine learning derived intra-particle model and detailed pyrolysis kinetics

Lu, Liqiang; Pecha, M. Brennan; Wiggins, Gavin M.; Xu, Yupeng; Gao, Xi; Hughes, Bryan; Shahnam, Mehrdad; Rogers, William A.; Carpenter, Daniel; Parks, James E.

doi:10.1016/j.cej.2021.133853

Title: Multiscale CFD simulation of biomass fast pyrolysis with a machine learning derived intra-particle model and detailed pyrolysis kinetics

Journal Article · Sat Nov 27 00:00:00 EST 2021 · Chemical Engineering Journal

DOI:https://doi.org/10.1016/j.cej.2021.133853· OSTI ID:1839239

Lu, Liqiang ^[1];

^[2]; Wiggins, Gavin M. ^[3]; Xu, Yupeng ^[1]; Gao, Xi ^[4]; Hughes, Bryan ^[1]; Shahnam, Mehrdad ^[5]; Rogers, William A. ^[5]; Carpenter, Daniel ^[2]; Parks, James E. ^[3]

National Energy Technology Lab. (NETL), Morgantown, WV (United States); NETL Support Contractor, Morgantown, WV (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States). Renewable Resources and Enabling Sciences Center
Oak Ridge National Lab. (ORNL), Knoxville, TN (United States)
Guangdong Technion Israel Institute of Technology (China)
National Energy Technology Lab. (NETL), Morgantown, WV (United States)

Coupling particle and reactor scale models is as essential as reactor fluid dynamics and particle motion for accurate Computational Fluid Dynamic (CFD) simulations of biomass fast pyrolysis reactors due to intraparticle heat transfer and chemical reactions controlling conversion time and product distributions. Direct online coupling of a particle model with a reactor model is computationally expensive, while offline coupling is case-dependent. In this research, solutions from a series of particle pyrolysis simulations were regressed with Artificial Neural Network (ANN). Furthermoer, this machine learning-derived model predicted the same temperature and conversion profiles compared with particle resolved simulation while the isothermal approach overpredicted the temperature by 130 K and underpredicted the conversion time by 30 s. The ANN model was then integrated into CFD simulations of fluidized bed biomass fast pyrolysis with varied feedstocks via coupling PyTorch and MFiX. The averaged error of simulation predicted bio-oil yields with four feedstocks is 6.4%. This multi-scale approach provides an efficient tool for the coupled particle and reactor scale simulations of biomass pyrolysis.

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Research Organization:: National Renewable Energy Lab. (NREL), Golden, CO (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Bioenergy Technologies Office

Grant/Contract Number:: AC36-08GO28308; AC05-00OR22725

OSTI ID:: 1839239

Alternate ID(s):: OSTI ID: 1862136

Report Number(s):: NREL/JA-2800-81831; MainId:82604; UUID:f1ea594b-8400-4592-be45-4073aabbfab0; MainAdminID:63573

Journal Information:: Chemical Engineering Journal, Vol. 431; ISSN 1385-8947

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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