Studies on multicomponent solids mixing and mixtures Part III. Mixing indices

Fan, L.T.; Too, J. R.; Rubison, R. M.; Lai, F. S.

doi:10.1016/0032-5910(79)80009-1

Cited by 34 publications

(17 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A number of other mixing indices, summarized in refs. [32][33][34][35] may be expressed in terms of the RSD. [33,34] RSD is relatively easy to obtain experimentally using standard sampling methods, and has been used in regulatory guidance documents for pharmaceutical manufacturing, hence is a popular mixing index.…”

Section: Performance Characterization Of Continuous Blendersmentioning

confidence: 99%

“…[32][33][34][35] may be expressed in terms of the RSD. [33,34] RSD is relatively easy to obtain experimentally using standard sampling methods, and has been used in regulatory guidance documents for pharmaceutical manufacturing, hence is a popular mixing index. The limitations of the RSD (or RSD-related mixing indices) are that it requires a multi-component system or a set of marked tracer particles, is sensitive to the initial configuration of these tracer particles, and more importantly, is dependent on the sample size.…”

Section: Performance Characterization Of Continuous Blendersmentioning

confidence: 99%

“…Following the approach in Sherritt et al, [50] Equation (33) was fitted to the computed RTD from DEM to obtain the fitting parameters u z and D zz . The advantage of this approach is that the axial diffusion coefficient may be computed indirectly using the RTD without using any detailed information regarding particle trajectories.…”

Section: Dem Modeling Approach Ii: Periodic Slice Simulationsmentioning

confidence: 99%

“…In experimental systems, measurement of RTD is fairly straightforward, thus simplifying determination of D zz . Figure 12 plots the RTD obtained from DEM, and the fit of Equation (33) for some representative cases from data set III. The values of the fitted axial diffusion coefficient D zz and the R 2 values are indicated in the respective figures.…”

Section: Dem Modeling Approach Ii: Periodic Slice Simulationsmentioning

confidence: 99%

See 3 more Smart Citations

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

Section: Performance Characterization Of Continuous Blendersmentioning

confidence: 99%

Section: Performance Characterization Of Continuous Blendersmentioning

confidence: 99%

Section: Dem Modeling Approach Ii: Periodic Slice Simulationsmentioning

confidence: 99%

Section: Dem Modeling Approach Ii: Periodic Slice Simulationsmentioning

confidence: 99%

See 2 more Smart Citations

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

show abstract

“…According to Fitzpatrick et al (2004) the effect of moisture content and particle size on the powder flowability may be coupled. Since mixing is a process where two or more powders are dispersed by random movement of particles (Fan and Too, 1979) it is important that the powders being mixed can move easily throughout each other. However, ease of motion may also induce segregation mechanisms due to differences in particle size and powder densities.…”

Section: Introductionmentioning

confidence: 99%

Dry mixing of food powders: Effect of water content and composition on mixture quality of binary mixtures

Shenoy

Xanthakis

Innings

et al. 2015

Journal of Food Engineering

View full text Add to dashboard Cite

Mixing of Solids

Sommer

2000

Ullmann's Encyclopedia of Industrial Chemistry

View full text Add to dashboard Cite

The article contains sections titled: 1. Mixedness 1.1. Theoretical Variance 1.2. Variance of the Precision of Measurement 1.3. Variance of Random Mixing 1.4. Mixedness versus Time 2. Practical Determination of Mixedness 2.1. Sampling 2.2. Empirical Variance 2.2.1. Frequency Distribution and Chi‐Square Distribution 2.2.2. Confidence Intervals 2.2.3. Determination of Mixing Time 3. Designs of Solid ‐ Solid Mixers 3.1. Tumblers 3.2. Screw Mixers 3.3. Paddle Mixers 3.4. Pneumatic Mixers 3.5. Silo Mixers 3.6. Pile Mixing

show abstract

Studies on multicomponent solids mixing and mixtures Part III. Mixing indices

Cited by 34 publications

References 8 publications

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

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

Dry mixing of food powders: Effect of water content and composition on mixture quality of binary mixtures

Mixing of Solids

Contact Info

Product

Resources

About