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

TY - JOUR

T1 - Multiscale CFD Simulation of Biomass Fast Pyrolysis with a Machine Learning Derived Intra-Particle Model and Detailed Pyrolysis Kinetics

AU - Lu, Liqiang

AU - Brennan Pecha, M.

AU - Wiggins, Gavin M.

AU - Xu, Yupeng

AU - Gao, Xi

AU - Hughes, Bryan

AU - Shahnam, Mehrdad

AU - Rogers, William A.

AU - Carpenter, Daniel

AU - Parks, James E.

PY - 2021/11/27

Y1 - 2021/11/27

N2 - 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.

AB - 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.

U2 - 10.1016/j.cej.2021.133853

DO - 10.1016/j.cej.2021.133853

M3 - 文章

SN - 1385-8947

JO - Chemical Engineering Journal

JF - Chemical Engineering Journal

ER -