Comprehensive experimental investigation on biomass‐glass beads binary fluidization: A data set for CFD model validation

Gao, Xi; Yu, Jia; Li, Cheng; Panday, Rupen; Xu, Yupeng; Li, Tingwen; Ashfaq, Huda; Hughes, Bryan; Rogers, William A.

doi:10.1002/aic.16843

Cited by 34 publications

(29 citation statements)

References 72 publications

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“…In this research, the biomass has a wide size distribution and the Sauter mean diameter is only 130 μm. We believe this uniform bed expansion is caused by the fines in the biomass because this phenomenon was not observed in the previous research with similar sized sand materials but 1.3 mm biomass particles.…”

Section: Resultsmentioning

confidence: 59%

“…The uniform colors of the fluidized bed indicate a good mixing of the biomass–sands mixtures under all the tested gas velocities. Our previous research observed a strong segregation of large 1.3 mm biomass particles with similar 353 microglass beads under a gas velocity around 2 U mf . This non‐segregation characteristic of small biomass–sands mixtures provides more flexible operating options regarding the gas flux.…”

Section: Resultsmentioning

confidence: 86%

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Experimental and numerical investigation of sands and Geldart A biomass co‐fluidization

Gao

et al. 2020

AIChE Journal

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This article investigated the fluidization of sands and small Geldart A biomass mixtures. The mixture fluidized like Geldart A type particles with a uniform bed expansion regime before bubbling. The video recorded color distance between pure sands and sands-biomass mixtures was used to estimate the sands-biomass mixing. The coarse-grained computational fluid dynamics-discrete element method with a hybrid drag model which couples the Syamlal-O'Brien drag and a filtered drag can capture the mixing while the simulation with Gidaspow drag predicted a segregated bed. The simulations were further validated with experimental measured pressure drops. The time averaged pressure drop equals the weight of the bed material, however, its fluctuation is about three times of the bed material fluctuation. K E Y W O R D S biomass, coarse-grained CFD-DEM, drag model, filtered model, fluidized bed

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Section: Resultsmentioning

confidence: 59%

Section: Resultsmentioning

confidence: 86%

Experimental and numerical investigation of sands and Geldart A biomass co‐fluidization

Gao

et al. 2020

AIChE Journal

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“…The particle–particle interaction, like particle–particle conduction, collision, friction, and clustering can significantly affect the pyrolysis reaction 13 . The gas–particle interaction, like drag force, heat, and mass transfer can affect the biomass particles hydrodynamics, residence time, and chemical reactions in the reactor 14‐18 . Also, the reactor geometry and operating conditions, like pressure and heating jacket temperature can affect pyrolyzer performance.…”

Section: Introductionmentioning

confidence: 99%

“…Lu et al 25 proposed off‐line coupling a method of particle resolved simulations and a reactor scale model. In the reactor model, most studies assumed the biomass particles are spherical, while, biomass particles are usually non‐spherical 14 . It is shown that the particle shape has a significant effect on the pressure drop, fluidization hydrodynamics, heat transfer, chemical reactions, and so forth.…”

Section: Introductionmentioning

confidence: 99%

Coupling particle scale model and SuperDEM‐CFD for multiscale simulation of biomass pyrolysis in a packed bed pyrolyzer

Gao

Rogers

2021

AIChE Journal

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An efficient biomass pyrolysis process requires a comprehensive understanding of the chemical and physical phenomena that occur at multi‐length and time scales. In this study, a multiscale computational approach was developed and validated for biomass pyrolysis in a packed‐bed reactor by integrating pyrolysis kinetics, a particle scale model, and Superquadric Discrete Element Method‐Computational Fluid Dynamics (SuperDEM‐CFD) in open‐source code MFiX. A one‐dimensional particle–scale model that discretizes the characteristic length of biomass particle into layers was developed to predict the intraparticle phenomena inside a single particle. The 1D model was validated by comparing it with a single biomass particle pyrolysis experiment. A recently developed SuperDEM‐CFD model was employed to simulate the non‐spherical particle–particle contact and fluid‐particle interaction. The coupled model was applied to simulate the pyrolysis of cubic biomass particles in a packed bed and validated by comparing with experimental data. Simulation with and without particle–scale model was compared, and the effect of the gas–solid heat transfer models was also investigated.

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“…However, the addition of the inert material increases the hydrodynamics complexity due to the differences between the various particle characteristics (e.g., different density, weight, etc.) [37][38][39][40]. Also, the biomass feeding location (bottom, top, side, etc.)…”

Section: Figure 2 Gasification Reaction Diagrammentioning

confidence: 99%

A Study on the Hydrodynamics of a Bench-Scale Top-Fed Bubbling Fluidized Bed Gasifier using Biomass and Coal as Feedstocks

Sivri¹

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The production of synthetic gas (syngas) from renewable or carbon-neutral sources can significantly reduce greenhouse and other emissions associated with conventional fuels. One of the most promising technologies to efficiently convert carbonaceous feedstocks such as biomass, coal, or municipal waste into syngas for transportation, power, heat, electricity generation, and or production of added-value chemicals is the bubbling fluidized-bed gasifier (BFBG). However, the gasification process inside a BFBG is a very complex high-temperature multiphase flow phenomena still not well understood, particularly when binary mixtures are investigated. As a result, despite the numerous correlations in the literature developed to predict the hydrodynamics inside a BFBG, the results are inconsistent, particularly for the minimum fluidization velocity, Umf.As predicting the fluidization hydrodynamics is paramount for optimum gasification, this investigation observed the effect of some of the most important fluidization parameters such as the particle size and shape, fluidizing gas properties, moisture content, bed aspect ratio etc. This study designed and built two separate experimental platforms: a bench-scale BFBG with automated feeding and a cold flow model with the same geometry and dimensions as the BFBG. The experiments used well-characterized (i.e., known size and shape distribution, density, moisture content, initial mixing condition) inert material (sand, glass beads) and feedstock (biomass (sawdust) and coal). The cold flow investigation results showed that the initial mixing conditions for binary mixtures with biomass had a significant effect on the measured Umf. For example, the relative error in predicting Umf using the available correlation in the literature increased for segregated mixtures. Moreover, lower relative errors in Umf suggested that the fluidization quality was better if the mixture was initially well-mixed (premixed). In addition, a larger biomass moisture content decreased Umf of premixed binary mixtures but increased the relative error between the predicted and the experimental Reynolds number, Re. After reaching the minimum fluidization condition, the fluidization behavior and mixing at various flow rates were also recorded with a high-speed camera. The processed images were used to determine the interval for the fluidizing-gas superficial velocity that produced the best mixing for a particular mixture composition and initial conditions. The images showed that while segregated biomass mixtures did not mix if the bed aspect ratio was larger than five, coal mixtures did mix homogeneously along the reactor bed. Finally, experiments performed at temperatures up to 800 C showed a large increase in the bed pressure drop at minimum fluidization velocity with the bed temperature due to the large effect on the fluidizing gas density and viscosity. On the contrary, Umf decreased when the process temperature increased. Finally, preliminary biomass and coal gasification experiments in the BFBG setup produced...

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Comprehensive experimental investigation on biomass‐glass beads binary fluidization: A data set for CFD model validation

Cited by 34 publications

References 72 publications

Experimental and numerical investigation of sands and Geldart A biomass co‐fluidization

Experimental and numerical investigation of sands and Geldart A biomass co‐fluidization

Coupling particle scale model and SuperDEM‐CFD for multiscale simulation of biomass pyrolysis in a packed bed pyrolyzer

A Study on the Hydrodynamics of a Bench-Scale Top-Fed Bubbling Fluidized Bed Gasifier using Biomass and Coal as Feedstocks

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