Large-scale numerical investigation of solids mixing in a V-blender using the discrete element method

Lemieux, M. Joanne; Leonard, G. W.; Doucet, Jocelyn; Leclaire, L.-A.; Viens, F.; Chaouki, Jamal; Bertrand, François

doi:10.1016/j.powtec.2006.12.009

Cited by 120 publications

(53 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, due to computational boundaries, uncertainty in material properties, and limitations in terms of process representation, these approaches are applicable only to simplified cases. For example, mixing simulations using DEM [7,29,21,34] are common, yet the largest number of particles used within these simulations was 250,000 for a period of 120 s [24]. Under these conditions, the CPU time on a Beowulf cluster (performed in parallel using up to 128, 3.6 GHz processors of a 1152 processor) was about 5 to 8 h per second of simulation.…”

Section: Introductionmentioning

confidence: 99%

Using Compartment Modeling to Investigate Mixing Behavior of a Continuous Mixer

2008

View full text Add to dashboard Cite

In this paper, the development of a compartment model to simulate mixing within a continuous blender is reported. The main benefit of the method is that it can generate extensive modeling predictions in very short computational time. The model can also be used to explore the effect of sampling parameters on estimated mixing performance, a topic that has been central to pharmaceutical manufacturing for the past 15 years and that remains a central issue in the PAT initiative. However, this method requires more input than conventional particle dynamics methods. Thus, we investigate the effects of modeling parameters on mixing performance to develop general guidance needed to adapt this modeling framework to any continuous process. An experimental technique based on longitudinal sampling is used to examine the content uniformity of the blend along the continuous mixer. The model compares favorably with continuous mixing experiments, capture the effects of feeding rate variability, active product ingredient concentration, and blender processing angle, while effectively capturing and making explicit the effect of sampling parameters such as number of samples and sample size. The modeling approach provides a convenient tool for process design.

show abstract

Section: Introductionmentioning

confidence: 99%

Using Compartment Modeling to Investigate Mixing Behavior of a Continuous Mixer

2008

View full text Add to dashboard Cite

show abstract

“…Mathematical modeling and numerical simulation techniques have been applied for understanding mixing mechanisms in many types of batch blenders such as the Vblender, [10][11][12] tote-blender, [13,14] double cone blender, [15,16] and simple rotating drum. [17] Several other types of blenders have been studied using computational techniques.…”

mentioning

confidence: 99%

“…[17] Several other types of blenders have been studied using computational techniques. [11,[16][17][18] These methods predominantly use DEMbased approaches to simulate flow and mixing, although other methods such as continuum modeling, [19][20][21] Markov chain modeling, [22][23][24] and compartment modeling [25,26] have also been applied. DEM simulations have been shown to be in good agreement with experimental studies of mixing using techniques such as positron emission particle tracking (PEPT) [27,28] in batch mixers such as the Vblender [29] and simple rotating cylinders.…”

mentioning

confidence: 99%

Computational Approaches for Studying the Granular Dynamics of Continuous Blending Processes, 1 – DEM Based Methods

Dubey

Sarkar

Ierapetritou

et al. 2011

Macro Materials & Eng

View full text Add to dashboard Cite

Computational methods using mechanistic modeling with a specific application in the area of continuous blending are presented. These methods complement experimental designs and aim to reduce the amount of time, effort, and material required to characterize a device or a process. The discrete element method is applied to the specific case of continuous mixing using two approaches. The first approach models an entire blender and studies the impact of changing impeller speed on residence time distribution (RTD), number of blade passes experienced by the powder, and mean centered variance of the particle residence time. The mean centered variance and the number of blade passes exhibit a maximum with increasing impeller speed, indicating optimal mixing behavior at an intermediate speed range. The second DEM approach utilizes a periodic slice of the full blender in order to explore in detail the effect of speed, fill level, and cohesion on mixing performance. The results show that the transverse mixing rates were generally higher than the corresponding axial mixing rates. Transverse diffusion was highest for large impeller speeds at larger fills, and axial diffusion was highest for large impeller speeds at smaller fills. Variations in particle–particle cohesion within the investigated range do not affect diffusion values significantly. The axial diffusion coefficient can be reasonably predicted from the residence time distribution using the 1D advection‐diffusion model. magnified image

show abstract

“…As can be deduced from this definition, this index depends on the number, size and location of the samples, as discussed by Lemieux et al (2008). Employing the RSD in the context of DEM simulations is effortless since the particle positions are available in each time interval and no perturbation occurs inside the granular bed during the sampling procedure .…”

Section: Global Viewpoint: Relative Standard Deviationmentioning

confidence: 99%

“…The first approach is Eulerian and assumes the particles flow as a fluid (Aranson & Tsimring, 1999;Khakhar, McCarthy, & Ottino, 1997;Khakhar, Orpe, & Ottino, 2001) while the second approach is Lagrangian and considers each single particle as a separate entity. In the second approach, the discrete element method (DEM), originally proposed by Cundall and Strack (1979), has been applied to investigate granular mixing and segregation inside tumbling blenders (Lemieux et al, 2007(Lemieux et al, , 2008Rapaport, 2007). The DEM method has been shown to provide new insights into the phenomena occurring in granular systems and afford extensive details about the flow and mixing of granules.…”

Section: Introductionmentioning

confidence: 99%