Three-dimensional numerical simulation of the co-combustion of oil shale retorting solid waste with cornstalk particles in a circulating fluidized bed reactor

Liu, Hongpeng; Li, Jiawei; Wang, Qing

doi:10.1016/j.applthermaleng.2017.10.107

Cited by 22 publications

(3 citation statements)

References 34 publications

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“…The fluid-wall heat transfer coefficient ( h fw ) is deemed as a combination of contributions from the lean gas phase heat transfer coefficient ( h l ) and the dense particle phase coefficient ( h d ): where θ p is the particle volume fraction and θ cp is the close pack volume fraction.…”

Section: Numerical Methodologymentioning

confidence: 99%

“…Due to the rapid development of computer technology, numerous mathematical models have been adopted to assist with design, scale-up, and optimization of FBRs/SBRs. − Among them are the computational fluid dynamic (CFD) models, such as the two-fluid model (TFM) , and the computational particle fluid dynamic (CPFD) models that are based on the discrete particle/element method (DPM/DEM), , or multi-phase particle-in-cell (MP-PIC). , As for the former, although CFD has been widely used in many fields, − especially for the dense gas–solid reactor, it has some inevitable limitations: (i) the method has trouble modeling flows with particle type and size distributions, although the particle size distribution significantly influences the performance of gas–solid reactors; (ii) the method cannot account for the characteristics of the realistic particles, such as shear stresses and interparticle cohesive forces for particles when treated as a pseudo fluid . By contrast, the particle-scale model (e.g., CPFD) differentiates itself from the CFD in tracking particles in the Lagrangian coordinate. , CPFD is generically divided into two categories depending on how particle collision is handled.…”

Section: Introductionmentioning

confidence: 99%

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Numerical Investigations of the Oxidative Dehydrogenation of Propane in a Spouted Bed Reactor

Wang

et al. 2020

Energy Fuels

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The oxidative dehydrogenation of propane (ODHP) is still in its infancy stage of development. Compared to great emphases on the catalyst selection and design, the reactor type and operation for the ODHP are paid much less attention. In this work, spouted bed reactors (SBR) without and with an internal draft tube are evaluated for the ODHP by using a 3D comprehensive model based on the multi-phase particle-in-cell (MP-PIC) approach. The model is also used to study on the effects of operating parameters, including the static bed height, catalyst particle size, reaction temperature, and oxygen content at different gas inlets (i.e., bottom and lateral), on the propane conversion, propylene selectivity, and yield. Findings indicate that numerical predictions are in very good agreement with experimental data. The performances of the SBRs without and with a draft tube for the ODHP are different due to the various gas–solid hydrodynamics in the reactors. Higher propane conversion is observed in the SBR without draft tubes, while higher propylene selectivity can be obtained in the SBR with a draft tube. Under the static bed height of 0.30 m, the particle diameter of 1.0–1.2 mm, the reaction temperature of 500 °C, and the ratio of propane to oxygen of 1.0 and oxygen content of 1.0%, the propane conversion and propylene selectivity in the SBR with a draft tube reach 18.01 and 29.78%, respectively. It is believed that the SBR is appropriate for the ODHP and might assist to make the ODHP a feasible alternative route toward propylene production.

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Section: Numerical Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Numerical Investigations of the Oxidative Dehydrogenation of Propane in a Spouted Bed Reactor

Wang

et al. 2020

Energy Fuels

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“…This interaction between solid phase and liquid phase can be seen in other applications, such as nanofluid matter 14 . But the problem of staying with them is that their complex hydrodynamics is not fully understood, leading to severe difficulties in the scale-up of these solid–gas interactions 15 – 17 .…”

Section: Introductionmentioning

confidence: 99%

Simulation of liquid flow with a combination artificial intelligence flow field and Adams–Bashforth method

Babanezhad

Behroyan

Nakhjiri

et al. 2020

Sci Rep

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Direct numerical simulation (DNS) of particle hydrodynamics in the multiphase industrial process enables us to fully learn the process and optimize it on the industrial scale. However, using high-resolution computational calculations for particle movement and the interaction between the solid phase and other phases in fine timestep is limited to excellent computational resources. Solving the Eulerian flow field as a source of solid particle movement can be very time-consuming. However, by the revolution of the fast and accurate learning process, the Eulerian domain can be computed by smart modeling in a very short computational time. In this work, using the machine learning method, the flow field in the square shape cavity is trained, and then the Eulerian framework is replaced with a machine learning method to generate the artificial intelligence (AI) flow field. Then the Lagrangian framework is coupled with this AI flow field, and we simulate particle motion through the fully AI framework. The Adams–Bashforth finite element method is used as a conventional CFD method (Eulerian framework) to simulate the flow field in the cavity. After simulating fluid flow, the ANFIS method is used as an AI model to train the Eulerian data-set and represents AI fluid flow (framework). The Lagrangian framework is coupled with the AI method, and the particle freely migrates through this artificial framework. The results reveal that there is a great agreement between Euler-Lagrangian and AI- Lagrangian in the cavity. We also found that there is an excellent agreement between AI overview with the Adams–Bashforth approach, and the new combination of machine learning and CFD method can accelerate the calculation of the flow field in the square-shaped cavity. AI model can mimic the vortex structure in the cavity, where there is a zero-velocity structure in the center of the domain and maximum velocity near the moving walls.

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Dynamical and thermal property of rising bubbles in the bubbling fluidized biomass gasifier with wide particle size distribution

et al. 2020

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Three-dimensional numerical simulation of the co-combustion of oil shale retorting solid waste with cornstalk particles in a circulating fluidized bed reactor

Cited by 22 publications

References 34 publications

Numerical Investigations of the Oxidative Dehydrogenation of Propane in a Spouted Bed Reactor

Numerical Investigations of the Oxidative Dehydrogenation of Propane in a Spouted Bed Reactor

Simulation of liquid flow with a combination artificial intelligence flow field and Adams–Bashforth method

Dynamical and thermal property of rising bubbles in the bubbling fluidized biomass gasifier with wide particle size distribution

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