Cluster characteristics of Geldart Group B particles in a pilot-scale CFB riser. I. Monodisperse systems

Chew, Jia Wei; Hays, Roy; Findlay, John G.; Knowlton, T.M.; Karri, S.B. Reddy; Cocco, Ray; Hrenya, Christine M.

doi:10.1016/j.ces.2011.09.012

Cited by 91 publications

(56 citation statements)

References 35 publications

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“…There is a general lack of reliable modeling and experimental information in the literature on Circulating Fluidized Beds (CFBs) working with large materials. Furthermore, published work on Group B particles does not focus on the impact of particles shape [9,10]. Further studies are required for the reliable scale-up and optimization of the CLC process.…”

Section: Accepted Manuscriptmentioning

confidence: 97%

CFD modeling of riser with Group B particles

et al. 2015

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International audience– A series of tests were conducted in a large cold flow CFB unit (18 m long riser and 30 cm in diameter) with two different Group B powders. The impact of particle nature on the riser hydrodynamics was investigated at similar operating conditions through pressure taps and extraction probes. To study the impact of particle size and sphericity, sand and glass beads were used. Resulting axial pressure profiles are very different in both the acceleration and in the fully developed region. These differences can most probably be explained by the difference in shape between the solids. Higher sphericity seems to generate smaller pressure drop. Trough extraction probe measurements, the core-annulus regime was found on the developed zone of the riser. An assessment of a commercial CFD code to predict riser flow was carried out. For glass beads, simulation results agree reasonably well with experimental pressure profiles but the core annulus structure is underpredicted. However, pressure drop along the riser is strongly underestimated in sand simulations

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Section: Accepted Manuscriptmentioning

confidence: 97%

CFD modeling of riser with Group B particles

et al. 2015

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“…Cocco et al (2010) used a fiber optical probe to provide cluster size distribution in and above fluidized beds. Also Chew et al (2012) investigated the formation, duration and frequency of appearance of particle clusters at different operating conditions along a riser for different materials. Gokaltun et al (2012) used shadow-sizing technique to measure cluster size and velocity distribution in a circulating fluidized bed.…”

Section: Introductionmentioning

confidence: 99%

Development of a gas–solid drag law for clustered particles using particle-resolved direct numerical simulation

Mehrabadi

Murphy

Subramaniam

2016

Chemical Engineering Science

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Particle-resolved direct numerical simulation (PR-DNS) is used to quantify the drag force on clustered particle configurations over the solid phase volume fraction range of 0.1 ≤ φ ≤ 0.35 and the mean slip Reynolds number range of 0.01 ≤ Re m ≤ 50. The particle configurations and flow parameters correspond to gas-solid suspensions of Geldart A particles in which formation of clusters have been reported. In our PR-DNS, we use clustered particle configurations that match cluster statistics observed in experimental studies.To generate the particle configurations, we perform discrete element method (DEM) simulations of homogeneous cooling gas (HCG) systems with cohesive and inelastic particles in the absence interstitial fluid. Clustered particle subensembles are then extracted from HCG simulations to match the statistics of cluster size distributions observed in experiments. These sub-ensembles are used for PR-DNS. It is found that the mean drag on clustered configurations decreases when compared to the drag laws for uniform particle configura- * tions. The maximum drag reduction belongs to the configuration with low solid-phase volume fraction φ = 0.1 in Stokes flow, and is about 35%. The drag reduction reduces with increase in both φ and Re m . A clustering metric is introduced to explain the behavior of the drag reduction with respect to solid-phase volume fraction. Also the behavior of the drag reduction with mean slip Reynolds number is related to the Brinkman screening length. PR-DNS results are then used to propose a clustered drag model for the range of flow parameters considered in this study. This clustered drag model provides a smooth transition between the uniform and clustered states by means of a weighting function with two model parameters.

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“…Gas-solid flow inside the riser of a CFB is inherently dynamic and chaotic, leading to the formation of transient mesoscale flow structures manifesting as particle clusters [1,2], referred to as particle groups with high height-to-width ratios, higher solids contents than the surrounding, and significant existence in the time scale [3]. Particle clusters are mainly distributed near the wall, affecting significantly the flow hydrodynamics and riser performance [1], and if evolving to particle agglomerates, severe fluidisation faults (e.g., hot spots, explosive polymerisation, reactor shutdown) are likely to be caused [4].…”

Section: Introductionmentioning

confidence: 99%

“…times of the standard deviations above the mean voltages) have been applied to solids holdup signals from capacitance and optical fibre probes [9,27,28]. Considering that such thresholds may lead to dynamic information loss, wavelet transform was then used for the threshold determination of optical fibre signals, because of its capability of demarcating different flow scales (microscale, mesoscale, macroscale) [1,2,29]. Although an electrostatic sensor cannot directly provide solids holdup data, it is still possible to identify clusters from the electrostatic signals through an appropriate threshold separation, because of the strong dependence of the electrostatic signals on the solids holdup.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, capacitance and optical fibre probes have been used to sense solids holdup fluctuations in risers, based on which the cluster sizes and distributions [5][6][7][8], cluster counts [7], and cluster appearance probabilities, durations, and frequencies [1,2,[9][10][11] have been characterised. However, these probes are intrusive and hence suffer from a disadvantage of interferences to the flow fields.…”

Section: Introductionmentioning

confidence: 99%

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Non-intrusive characterisation of particle cluster behaviours in a riser through electrostatic and vibration sensing

Sun

Yan

2017

Chemical Engineering Journal

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Particle clusters are important mesoscale flow structures in gas-solid circulating fluidised beds (CFBs). An electrostatic sensing system and two accelerometers are installed on the riser of a CFB test rig to collect signals simultaneously. Cross correlation, Hilbert-Huang transform (HHT), V-statistic analysis, and wavelet transform are applied for signal identification and cluster characterisation near the wall. Solids velocities are obtained through cross correlation. Non-stationary and non-linear characteristics are distinctly exhibited in the Hilbert spectra of the electrostatic and vibration signals, and the cluster dynamic behaviours are represented by the energy distributions of the signal intrinsic mode functions (IMFs). The cycle feature and main cycle frequency of cluster motion are characterised through V-statistic analysis of the vibration signals. Consistent characteristic information about particle clusters is extracted from the electrostatic and vibration signals.Furthermore, a cluster identification criterion for electrostatic signals is proposed, including a fixed and a wavelet dynamic thresholds, based on which the cluster time fraction, average cluster duration time, cluster frequency, and average cluster vertical size are quantified. Especially, the cluster frequency obtained from this criterion agrees well with that from the aforementioned V-statistic analysis. Results from this 3 work provide a new non-intrusive approach to the characterisation of cluster dynamic behaviours and their effects on the flow field.

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Cluster characteristics of Geldart Group B particles in a pilot-scale CFB riser. I. Monodisperse systems

Cited by 91 publications

References 35 publications

CFD modeling of riser with Group B particles

CFD modeling of riser with Group B particles

Development of a gas–solid drag law for clustered particles using particle-resolved direct numerical simulation

Non-intrusive characterisation of particle cluster behaviours in a riser through electrostatic and vibration sensing

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