Effect of Rotating Cylinder on Mixing Performance in a Cylindrical Double-Ribbon Mixer

Harish, Vancha; Cho, Migyung; Shim, Jaesool

doi:10.3390/app9235179

Cited by 7 publications

(4 citation statements)

References 28 publications

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“…A half-filled cylindrical double ribbon mixer was simulated, and the values of SMI were obtained for the following cases: (i) rotating ribbon, (ii) rotating cylinder, (iii) rotating cylinder with a static ribbon, and (iv) rotating cylinder and ribbon. The simulation results found that the SMI values ranged from 0 (segregation condition) to 0.91-0.94 (fully randomly mixed condition) within a time range of 0-60 s [15].…”

Section: Introductionmentioning

confidence: 97%

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Effects of the Geometric Parameters of Mixer on the Mixing Process of Foam Concrete Mixture and Its Energy Efficiency

et al. 2020

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The paper studies the influence of the geometric parameters of the mixer on the mixing process, the construction of the mixing body, its location in the mixer bulk, and the mixer shape and geometry. The technique of calculating the power spent on mixing the foam concrete mixture is described. The effects of the ratio of the mixture height to the mixer diameter, the number and width of reflective partitions, and the shape of the conical part of the mixer on the homogeneity of the foam concrete mixture and the power consumption are considered. The optimal ratios of the foam concrete mixture height to the mixer diameter have been determined. Moreover, the optimal range of the ratios of the partition width to the mixer diameter has been established, in order to obtain a homogeneous foam concrete mixture throughout the volume with lower energy consumption. The optimal values of the angle of the mixer conical part for the preparation of a foam concrete mixture have been determined.

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Section: Introductionmentioning

confidence: 97%

“…The discrete element method (DEM) was used to simulate the mixing of multiple components contained in a typical commercial whey protein mixture [15]. An effective non-sampling mixing index or the subdomain-based mixing index (SMI) was introduced to assess the mixing levels.…”

Section: Introductionmentioning

confidence: 99%

Effects of the Geometric Parameters of Mixer on the Mixing Process of Foam Concrete Mixture and Its Energy Efficiency

et al. 2020

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“…An experimental analysis of the powder behaviour in a Turbula ® mixer has been carried out in [7]. On the other hand, several works [8,9] have analysed the mixing performance of several mixers, as well as the influence of various process parameters on it, by using the DEM simulation [10][11][12]. Therefore, the DEM method was identified as the best option to model the mixing process of the powder detergent and to analyse its mixing performance.…”

Section: Introductionmentioning

confidence: 99%

Mixing Performance Prediction of Detergent Mixing Process Based on the Discrete Element Method and Machine Learning

Cañamero¹,

Doraisingam²,

Álvarez-Leal³

2023

Applied Sciences

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The DIY approach promotes small-scale digital manufacturing for the production of customized, fast moving consumer goods, including powder detergent. In this context, a machine was developed to manufacture a customized detergent according to the needs of the clients indicated on a digital platform connected to the machine. The detergent is produced by a mixing process of the formulation components carried out in a 3D mixer. Analysing the mixing performance of the process is essential to obtain a quality product. In this study, the mixing process of the powder detergent was modelled using the discrete element method. After validating it with experimental test, this model was utilized to study the mixing performance considering the allowable mass fraction range of every formulation component and a mixer speed of 45 rpm, and the dataset generated from this study was employed along with a machine learning algorithm to obtain a model to predict the mixing index. In this sense, twenty-five different combinations of the defined components were simulated and a mixing index of 0.98–0.99 was obtained in a time of 60 s, revealing that all the combinations were completely mixed. In addition, the developed model was validated with results obtained from the DEM model. The model predicts the mixing index in advance and with accuracy.

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“…Among the Lagrangian family, the Discrete Element Method (DEM) consists of predicting the trajectories of each individual particle by modeling the collisions based on interaction laws. This technique has been used by many authors for describing the bed behavior in rotating drums [12][13][14][15][16][17][18][19], like characterizing the angle of repose for different regimes [20] and describing mixing mechanisms [12,13]. DEM provides detailed information at the scale of the particles which can be very valuable for properly describing the granular phase dynamic.…”

Section: Introductionmentioning

confidence: 99%

Discrete and Continuum Approaches for Modeling Solids Motion Inside a Rotating Drum at Different Regimes

et al. 2021

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In this work, the performance of discrete and continuum computational models for addressing granular flow dynamics in a rotating drum at different regimes is studied. The results are compared to the experimental observations obtained by image processing of a high-speed camera on a pilot plant rotating drum. For the discrete modeling, Discrete Elements Method (DEM) through the open-source software LIGGGHTS(R) is used, while for the continuum model, the μ(I)-rheology is implemented in the general structure of a Volume-Of-Fluid (VOF) solver of the OpenFOAM(R) platform. Four test cases consisting of different sets of particles filling and rotational speed are considered and the results are analyzed in terms of solids distribution, the velocity of the particles, and mixing patterns. The solids distribution and velocities for each one of the tests considered are fairly similar between both computational techniques and the experimental observations. In general, DEM results show a higher level of agreement with the experiments, with minor differences that might be irrelevant in some cases (e.g., more splashing of particles for the fastest regimes). Among the drawbacks of the continuum model, it was unable to predict the slumping regime observed experimentally which can be attributed to the lack of a yield criterion and a slower dragging of the granular material when the drum is being accelerated, which can be attributed to the need of adding non-local effects to the rheology. On the other hand, the dynamic of the bed in the rolling and cascading regimes are accurately predicted by the continuum model in less time than DEM, even in a pilot plant scale system. These results suggest that the use of a continuum model with granular fluid rheology is more suited for simulating industrial-scale rotating drums at different regimes than DEM, but only if all the phenomenological features (i.e., yield criteria and non-local effects) are taken into account in the model.

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Effect of Rotating Cylinder on Mixing Performance in a Cylindrical Double-Ribbon Mixer

Cited by 7 publications

References 28 publications

Effects of the Geometric Parameters of Mixer on the Mixing Process of Foam Concrete Mixture and Its Energy Efficiency

Effects of the Geometric Parameters of Mixer on the Mixing Process of Foam Concrete Mixture and Its Energy Efficiency

Mixing Performance Prediction of Detergent Mixing Process Based on the Discrete Element Method and Machine Learning

Discrete and Continuum Approaches for Modeling Solids Motion Inside a Rotating Drum at Different Regimes

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