Multiscale CFD Simulation of Biomass Fast Pyrolysis with a Machine Learning Derived Intra-Particle Model and Detailed Pyrolysis Kinetics

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

Research output: Contribution to journalArticlepeer-review

25 Scopus Citations

Abstract

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). 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.

Original languageAmerican English
Article number133853
Number of pages13
JournalChemical Engineering Journal
Volume431
Issue numberPart 1
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2021 Elsevier B.V.

NREL Publication Number

  • NREL/JA-2800-81831

Keywords

  • Biomass pyrolysis
  • Fluidized bed
  • Intra-particle model
  • Machine learning
  • Multiscale CFD
  • Pyrolysis kinetics

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