2004
DOI: 10.3139/217.1843
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Index for Simultaneous Dispersive and Distributive Mixing Characterization in Processing Equipment

Abstract: Computer simulation of agglomerate dispersion and sequential distribution of all particles obtained in the system allows us to evaluate the overall mixing efficiency of processing equipment. Evaluation was based on a specific mixing index, calculated using the Shannon entropies for different size fractions. The index can be tailored to give preference to different particle size distributions, thus relating the quality of mixing to specific properties of the final product.

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Cited by 15 publications
(7 citation statements)
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“…Furthermore, the dispersion state was evaluated by using Shannon entropy according to the literature. [ 24,25 ] The Shannon entropy Ψ is defined by Equation (3), Ψgoodbreak=goodbreak−c=1Cpcj=1Mpj/clnpj/clnMgoodbreak=goodbreak−c=1CpcSc where C is the total number of species, the index c indicates each species, M is the total number of subregions (bins), the index j indicates the nominal number of bins, pc is the sum of agglomerate areas in species c divided by the total area of all agglomerates, and pj/c is the probability of finding an agglomerate in bin j conditional on being of species c. In this study, the species were categorized by area in pixel 2 into eight species (10 0 –10 0.5 , 10 0.5 –10 1 , 10 1 –10 1.5 , 10 1.5 –10 2 , 10 2 –10 2.5 , 10 2.5 –10 3 , 10 3 –10 3.5 , 10 3.5 –10 4 pixel 2 ).…”
Section: Methodsmentioning
confidence: 99%
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“…Furthermore, the dispersion state was evaluated by using Shannon entropy according to the literature. [ 24,25 ] The Shannon entropy Ψ is defined by Equation (3), Ψgoodbreak=goodbreak−c=1Cpcj=1Mpj/clnpj/clnMgoodbreak=goodbreak−c=1CpcSc where C is the total number of species, the index c indicates each species, M is the total number of subregions (bins), the index j indicates the nominal number of bins, pc is the sum of agglomerate areas in species c divided by the total area of all agglomerates, and pj/c is the probability of finding an agglomerate in bin j conditional on being of species c. In this study, the species were categorized by area in pixel 2 into eight species (10 0 –10 0.5 , 10 0.5 –10 1 , 10 1 –10 1.5 , 10 1.5 –10 2 , 10 2 –10 2.5 , 10 2.5 –10 3 , 10 3 –10 3.5 , 10 3.5 –10 4 pixel 2 ).…”
Section: Methodsmentioning
confidence: 99%
“…Furthermore, the dispersion state was evaluated by using Shannon entropy according to the literature. [24,25] The Shannon entropy Ψ is defined by Equation (3),…”
Section: Characterization Of Dispersion Statementioning
confidence: 99%
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“…We assume continuous feeding of particles at the extruder entrance and thus steady-state conditions for all the results presented in this work. Details of the numerical simulations and the particle tracking algorithm are given in Reference [5].…”
Section: Entropic Characterization Of Simultaneous Dispersive and Dismentioning
confidence: 99%
“…A few authors have also proposed the use of mixing indices to straightforwardly characterize the extent of distributive or dispersive mixing, as these would facilitate direct comparisons between different operating conditions, screw profiles, and material properties. Average particle/droplet size relative to the initial size, number-average diameter [ 22 , 23 ], relative flow strength [ 24 ], shear stress distribution [ 25 , 26 ], and cumulative area ratio [ 27 , 28 ] have been routinely used as indicators of dispersive mixing, whereas Shannon entropy [ 29 ] was suggested as a measure of distributive mixing. Nevertheless, these indices require either lengthy experimental characterization or computationally demanding calculations.…”
Section: Introductionmentioning
confidence: 99%