Computational Fluid Dynamics/Discrete Element Method Investigation on the Biomass Fast Pyrolysis: The Influences of Shrinkage Patterns and Operating Parameters

Hu, Chenshu; Luo, Kun; Wang, Shuai; Sun, Libin; Fan, Jianren

doi:10.1021/acs.iecr.8b05279

Cited by 33 publications

(13 citation statements)

References 56 publications

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“…Computational Fluid Dynamic (CFD) simulation can provide more details on the mixing, heat transfer, and reactions of such complex multiphase flow reactors 7,8 . However, CFD is also challenging due to the need to track large number of sand particles, with a wide size and shape distributions for biomass, and lack of an accurate drag corrections between phases 9 .…”

Section: Introductionmentioning

confidence: 99%

Coarse grained computational fluid dynamic simulation of sands and biomass fluidization with a hybrid drag

Gao

Shahnam

et al. 2019

AIChE Journal

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The bubbling fluidized bed reactor is widely used in fast pyrolysis of biomass. Discrete simulation of this reactor is challenging due to many sand particles and lack of accurate drag corrections accounting for the interaction of two different solid particles with different properties. In this research, the computational cost is reduced by using the Coarse-Grained Computational Fluid Dynamic Discrete Element Method (CFD-DEM), where many sand particles are lumped into a larger numerical parcel. The Syamlal-O'Brien drag model is used for sand, while Ganser correction coupled with Gidaspow model is used for the nonspherical biomass particles. This hybrid approach shows superior behavior over other drag models using pressure drops as a benchmark. The predicted bed height and pressure fluctuating frequencies compare well with experiment. The mixing of biomass is close to perfect if the superficial velocity is larger than four times the minimum fluidization velocity.

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Section: Introductionmentioning

confidence: 99%

Coarse grained computational fluid dynamic simulation of sands and biomass fluidization with a hybrid drag

Gao

Shahnam

et al. 2019

AIChE Journal

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“…With regard to the dense two-phase reaction system, such as the fluidized bed combustion, it involves complex particle–turbulence interaction, particle–particle interaction, heat and mass transfer, and chemical reactions, and the complex two-phase flow characteristics are more dominant . In addition, the dense particle dynamic characteristics also influence the particle devolatilization behavior . The many complicated factors of the dense two-phase reaction system are beyond the scope of the present work, and the optimal devolatilization treatment for the dense two-phase reaction system is not discussed in the present work but will be explored using CFD-DEM in a future study.…”

Section: Introductionmentioning

confidence: 95%

Comparative Study on Different Treatments of Coal Devolatilization for Pulverized Coal Combustion Simulation

et al. 2020

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Currently, volatile matter is generally treated as a postulate substance or a mixture of light gases and tar with given proportion in pulverized coal combustion (PCC) simulation. Whether those treatments can well characterize the PCC or not remains unknown. Here, current different coal devolatilization treatments are numerically evaluated under the configuration of laminar stagnation PCC (Xia, M.; et al. Proc. Combust. Inst. 2017, 36, 2123–2130). The results show that the one-step model fitted from the Chemical Percolation Devolatilization model prediction together with detailed components and mechanism could give the best prediction of the PCC characteristics. Different detailed volatile components give similar predictions on the gas and particle properties with a slight difference in the CO mass fraction due to their different components fractions. The simplified global mechanism cannot reproduce the PCC characteristics such as those of the detailed mechanisms, with earlier particle ignition and much higher gas temperature and mass fractions of production species. Assuming the volatile matter is pure methane is not reasonable with obvious discrepancies on the particle and gas properties compared with those of the optimal treatments even when a detailed mechanism is used. Devolatilization model also plays a significant role with the two-step model giving a lower flame location, lower NO x , and higher polycyclic aromatic hydrocarbons emissions compared with those of the optimal treatment. The generality of the above conclusions will be further verified on more realistic configurations in the future, such as the benchmark coal flames in the Coal and Biomass Conversion (CBC) workshop.

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“…This model is used to simulate fast pyrolysis in a double-screw auger reactor. The results show that the DEM approach is more efficient than CFD in describing the dynamic and thermochemical properties of solid particles through the reactor, while the flow dynamic and heat transfer are numerically solved separately on each single particle. − Nevertheless, the DEM approach is limited to relatively small numbers of solid particles, which makes the model not applicable to the simulation of the pilot or industrial scales. Additionally, the heat transfer takes place only by conduction (particle-to-particle contact or particle-to-surface contact).…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of the Thermo-catalytic Reforming Process: Up-scaling Study

Elmously

Neidel

Apfelbacher

et al. 2021

Ind. Eng. Chem. Res.

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The up-scaling of the pyrolysis technologies is the next step to achieve the industrial scale and to fulfill the energy and petrochemical demand in large-scale units. The overall goal of this study is to up-scale the Thermo-Catalytic Reforming (TCR) technology from the laboratory to the pilot unit. In the previous part of the study, the up-scaling was studied experimentally in regard to the product yields and qualities. Therefore, a computational fluid dynamics (CFD) study is carried out to investigate the effects of up-scaling of the TCR system on the temperature distribution through the intermediate pyrolysis and the catalytic reforming process. A multifluid model and K-ε model are employed to simulate the TCR process for the mixture flow of sewage sludge as a solid phase and pyrolysis vapor as a gas phase. The results reveal a CFD model that can predict the heat distribution and flow velocities through the TCR system, while the deviations between simulation data and experimental work are considered small. The errors in the maximum biomass temperature within the auger reactor are 0.0 and 4.2% for TCR2 and TCR30, and the deviations in the solid residence time are about 0.8 and 0.74 s, respectively. Furthermore, the deviations in the vapor residence time within the post-reformer are 0.35 and 0.81 s for TCR2 and TCR30, respectively. Additionally, the CFD model provides a good platform for further simulation of the chemical reaction kinetics.

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Computational Fluid Dynamics/Discrete Element Method Investigation on the Biomass Fast Pyrolysis: The Influences of Shrinkage Patterns and Operating Parameters

Cited by 33 publications

References 56 publications

Coarse grained computational fluid dynamic simulation of sands and biomass fluidization with a hybrid drag

Coarse grained computational fluid dynamic simulation of sands and biomass fluidization with a hybrid drag

Comparative Study on Different Treatments of Coal Devolatilization for Pulverized Coal Combustion Simulation

Numerical Simulation of the Thermo-catalytic Reforming Process: Up-scaling Study

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