Particle-Resolved Computational Fluid Dynamics as the Basis for Thermal Process Intensification of Fixed-Bed Reactors on Multiple Scales

Jurtz, Nico; Srivastava, U. C.; Moghaddam, Alireza Attari; Kraume, Matthias

doi:10.3390/en14102913

Cited by 16 publications

(9 citation statements)

References 57 publications

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“…Here, Q is the total heat transfer rate,

is the area available for heat transfer, and

is the logarithmic mean temperature difference, computed as per Equation (3) for a constant wall temperature

with mixed mean fluid temperatures at the inlet

and the outlet

[ 24 ].

…”

Section: Methodsmentioning

confidence: 99%

Influence of Foam Morphology on Flow and Heat Transport in a Random Packed Bed with Metallic Foam Pellets—An Investigation Using CFD

George

Bockelmann

Schmalhorst³

et al. 2022

Materials

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Open-cell metallic foams used as catalyst supports exhibit excellent transport properties. In this work, a unique application of metallic foam, as pelletized catalyst in a packed bed reactor, is examined. By using a wall-segment Computational Fluid Dynamics (CFD) setup, parametric analyses are carried out to investigate the influence of foam morphologies (cell size ϕ=0.45–3 mm and porosity ε=0.55–0.95) and intrinsic conductivity on flow and heat transport characteristics in a slender packed bed (N=D/dp=6.78) made of cylindrical metallic foam pellets. The transport processes have been modeled using an extended version of conventional particle-resolved CFD, i.e., flow and energy in inter-particle spaces are fully resolved, whereas the porous-media model is used for the effective transport processes inside highly-porous foam pellets. Simulation inputs include the processing parameters relevant to Steam Methane Reforming (SMR), analyzed for low (Rep~100) and high (Rep~5000) flow regimes. The effect of foam morphologies on packed beds has shown that the desired requirements contradict each other, i.e., an increase in cell size and porosity favors the reduction in pressure drop, but, it reduces the heat transfer efficiency. A design study is also conducted to find the optimum foam morphology of a cylindrical foam pellet at a higher Rep~5000, which yields ϕ = 0.45, ε = 0.8. Suitable correlations to predict the friction factor and the overall heat transfer coefficient in a foam-packed bed have been presented, which consider the effect of different foam morphologies over a range of particle Reynolds number, 100≤Rep≤5000.

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“…Here, Q is the total heat transfer rate,

is the area available for heat transfer, and

is the logarithmic mean temperature difference, computed as per Equation (3) for a constant wall temperature

with mixed mean fluid temperatures at the inlet

and the outlet

[ 24 ].

…”

Section: Methodsmentioning

confidence: 99%

Influence of Foam Morphology on Flow and Heat Transport in a Random Packed Bed with Metallic Foam Pellets—An Investigation Using CFD

George

Bockelmann

Schmalhorst³

et al. 2022

Materials

View full text Add to dashboard Cite

show abstract

“…Moreover, PRCFD can be used as cost-efficient studies for investigations of novel reactor concepts for process intensification. 95,152,153 On the other hand, rigorous CFD simulations are at the basis of the hierarchical approach. These simulations can be used as well to perform in-silico experiments: diverse parameters and process conditions can be finely adjusted towards the derivation of descriptive, physically driven correlations, describing the transport properties that can be included in conventional reactor models.…”

Section: Industrial Reactor-scale Simulationsmentioning

confidence: 99%

Quo vadis multiscale modeling in reaction engineering? – A perspective

Wehinger

Ambrosetti

Cheula

et al. 2022

Chemical Engineering Research and Design

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“…Existing equations and models quantifying radial temperature distribution and the radial heat transfer problem of reactors are available as frameworks to reconcile with the predicted preparation temperature distribution. [59][60][61] Information about the radial spatial distribution of the biomass particles within the reactor would likely be necessary for this purpose. The resulting radial temperature gradient would correspond to the point at which the axial temperature gradient is greatest.…”

Section: Discussionmentioning

confidence: 99%

Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models

Watts

Potter²,

Mohan³

et al. 2023

Biofuels Bioprod Bioref

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The temperature experienced by reactants during preparation in a reactor is a key component in determining the yield and homogeneity of usable chemical products such as biomass particles. Thermocouples with sensors can be used to monitor spatial temperature gradients within reactors but these sensors are often too expensive and/or invasive. The present work proposes a strategy to identify optimal machine learning models to infer the maximum effective temperature experienced by particles during oxidative biomass torrefaction using key thermochemical combustion parameters. The maximum rate of weight loss, the corresponding temperature, and fixed carbon content on a dry‐ash‐free basis are used as literature‐based predictor variables obtained from thermogravimetric analysis. The evaluation of 24 machine‐learning models using the standard tenfold cross‐validation method suggests that the exponential Gaussian process regression (GPR) model is the most effective, followed by other GPR models. These high‐performing GPR models were also utilized to predict the effective preparation temperature distribution of reactor‐produced biomass particles under eight conditions of varying residence time and air‐to‐biomass ratio. The effective preparation temperature and residence time of individual biomass particles were then encoded into the torrefaction severity factor and used to estimate the energy yield of the reactor output as a novel quality control method.

show abstract

Particle-Resolved Computational Fluid Dynamics as the Basis for Thermal Process Intensification of Fixed-Bed Reactors on Multiple Scales

Cited by 16 publications

References 57 publications

Influence of Foam Morphology on Flow and Heat Transport in a Random Packed Bed with Metallic Foam Pellets—An Investigation Using CFD

Influence of Foam Morphology on Flow and Heat Transport in a Random Packed Bed with Metallic Foam Pellets—An Investigation Using CFD

Quo vadis multiscale modeling in reaction engineering? – A perspective

Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models

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