Characterization of granular mixing in a helical ribbon blade blender

Simons, Tom; Bensmann, S.; Zigan, Stefan; Feise, Hermann J.; Zetzener, Harald; Kwade, Arno

doi:10.1016/j.powtec.2015.11.041

Cited by 25 publications

(10 citation statements)

References 29 publications

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“…These samples are then statistically analyzed to reveal the mixing characteristics of the system [39]. The most common technique utilized for sampling is the use of a thief probe (Figure 7) [39,40]. The main advantage of using such devices is that numerous samples of various sizes can be obtained to represent the whole mixture.…”

Section: Experimental Assessment Of Mixture Qualitymentioning

confidence: 99%

Mixing Assessment of Non-Cohesive Mono-disperse and Bi-disperse Particles in a Paddle Mixer – Experiments and Discrete Element Method (DEM) Application

Yaraghi¹

2021

Preprint

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The objective of this study was to assess the mixing performance of a horizontal paddle blender for mono-disperse and bi-disperse particles. The assessment was performed through the application of the Discrete Element Method (DEM) simulations, experiments, and Analysis of Variance (ANOVA). EDEM 2.7 commercial software was utilized for the mono-disperse simulations while LIGGGHTS(R)-PUBLIC 3.3.1, an open source software, was used for the bi-disperse simulations. DEM models were validated with experimental data. Simulations were performed to explore the effect of impeller rotational speed, vessel fill level, particle number composition, and particle loading arrangement on mixing quality defined by the Relative Standard Deviation (RSD) index. The flow pattern and mixing mechanisms were examined through granular temperature, particle diffusivity, and Peclet number. The impeller rotational speed was the most influential parameter on the mixing performance of mono-disperse particles. The particle number composition was the dominating parameter on the mixing quality of bi-disperse particles

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Section: Experimental Assessment Of Mixture Qualitymentioning

confidence: 99%

Mixing Assessment of Non-Cohesive Mono-disperse and Bi-disperse Particles in a Paddle Mixer – Experiments and Discrete Element Method (DEM) Application

Yaraghi¹

2021

Preprint

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“…DEM was used to study granular mixing with stirring, shearing, and ploughing elements. An example of the application of DEM to the study of mixing in a Henschel mixer is illustrated in Figure b.…”

Section: Mixersmentioning

confidence: 99%

Experimental Methods in Chemical Engineering: Discrete Element Method—DEM

et al. 2019

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The discrete element method (DEM) is a time-driven simulation technique based on a Lagrangian description of particle motion that predicts the flow of granular matter and fine powders in conveying, mixing, drying, and heterogeneous gas-(liquid)-solids reactors. Powders flowing out of bins form bridges, they segregate in suboptimal pharmaceutical v-blenders, and a stream may split into large gulf streams as they enter fluidized bed reactors from standpipes and diplegs. To reduce the uncertainty in scaling up these and other powder process unit operations, researchers apply DEM. It integrates Newton's second law (acceleration equals the sum of the forces) for each particle and models contacts between the particles with springs and dashpots (dampers). It is computationally intensive since it calculates the trajectory of all particles. The availability of open source codes, commercial software, and parallel computer architectures has accelerated its adoption in pharmaceutical, agro-industrial, and mineral processes, and geophysics. The accuracy of DEM models depends on how well researchers calibrate the contact model expressions and their parameters: friction coefficients and the coefficient of restitution. Systems exceeding 1 × 10 8 particles can require weeks of computational time on large computer clusters. Current research targets non-spherical particle interactions and multiphysics problems including heat transfer, mass transfer, and chemical reactions within the particles. The field has grown to 750 indexed articles in WoS in 2017. A bibliographic analysis recognized four research clusters: granular materials, behaviour, particle shape, and deformation; flows, fluidized beds, and computational fluid dynamics; particles, impact, and validation; and granular flow, dynamics, and segregation.

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“…The papers within this special issue cover a significant effort by the authors, in the framework of the EU-FP7 Marie Curie ITN (Initial Training Network) PARDEM (see www.pardem.eu) in deploying various aspects of DEM. A range of industrial application examples are studied (silo flow [18], mixing [19], and segregation [20]); for large, granular particles as well as two-phase systems (fluidized beds [21] or pneumatic conveying [22]); and cohesive powders (cone penetration and unconfined strength testing [23], dosing of cohesive food powder [24]), as well as the more fundamental issues of fine powder testing with different devices and the extremely slow stress-relaxation in such systems [25]. On the macroscopic level several important questions are addressed, such as the effect of the particle size-ratio on the bulk stiffness of granular mixtures [26] as well as the asymmetry of stress tensor arising from a micropolar formulation [27].…”

Section: Overview Of the Special Issuementioning

confidence: 99%