A quantitative image analysis method for characterizing mixtures of granular materials

Wightman, Carolyn; Muzzio, Fernando J.; Wilder, Joseph

doi:10.1016/s0032-5910(96)03178-6

Cited by 52 publications

(23 citation statements)

References 26 publications

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“…The experimental techniques used in this study have been described in detail elsewhere (Wightman et al, 1995;1996;Brone et al, 1997); only a brief description is presented here. Custom-made disposable aluminum mixing vessels (American Aluminum Co., Mountainside, NJ) that were 76 mm in diameter, 153 mm long, and had a 90-degree angle between shells were used in all the experiments reported here.…”

Section: Methodsmentioning

confidence: 99%

“…. been described in detail in a previous publication (Wightman et al, 1996). Each slice was scanned using a video camera with the aid of a programmable xy-table (Unidex Aerotech Inc., Pittsburgh.…”

Section: Methodsmentioning

confidence: 99%

“…PA) operated remotely by a computer. Image-processing software handled the video signal, data retrieval, and storage (Wightman et al, 1996). During image acquisition, a slice of the mixture was partitioned into separate fields of view, each 5 mm by 6.7 mm.…”

Section: Methodsmentioning

confidence: 99%

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Quantitative characterization of mixing of dry powders in V‐blenders

1998

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative characterization of mixing of dry powders in V‐blenders

1998

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“…Due to computational limitations, simulations are restricted to a much smaller number of particles than in previously communicated laboratory experiments (Wightman and Muzzio, 1997;Wightman et al, 1996). Hence, to allow closer comparison, additional experiments were carried out using modified conditions.…”

Section: Conditions/parametersmentioning

confidence: 99%

Simulation of flow and mixing of particles in a rotating and rocking cylinder

et al. 1998

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Granular material in a cylindrical vessel undergoing rotational and rocking motions is modeled using a discrete element method. Rotational motion supplemented by rocking is compared to purely rotational motion via linear density profiles, velocity fields, and axial concentration profiles. The rocking motion, which imparts a time-dependent flow perturbation to purely rotational motion, dramatically enhanced mixing in laborat o y studies. Simulation results appear to agree well with experimental results and observations. IntroductionMany technical fields deal with mixing of granular materials, including chemical, agricultural, ceramic, mechanical, civil and soil engineering, the life sciences, physics, and pharmacy. Although researchers in these fields consider a wide range of physical characteristics and flow regimes, they share a common interest: the need to understand and characterize bulkflow powder behavior. Powder processing has been recently characterized by chemical engineers as having a legacy of neglect (Ennis et al., 1994). Awareness of this neglect has motivated a recent resurgence of both experimental investigations (McCarthy et al., 1996;Hill and Kakalios, 1995;Brone et al., 1997) and theoretical approaches (Johnson et al., 1990;Edwards and Mounfield, 1996). It has become fashionable in a sense to study powders, not only because of their complexity but also because there is a growing need in industry to reliably manufacture high-quality products.Although practitioners have attempted to handle powder problems using empirical approaches, more detailed studies of the physics of powders are required. Some researchers have suggested that granular materials might be a different state of matter (Jaeger et al., 1996). Interest in the physics of powders has increased dramatically in the past decade, starting with the analysis of sand piles (Baxter et al., 1989(Baxter et al., , 1993Bak and Chen, 1991), prompted by analogies with self-organized critical systems (Bak et al., 1988). Fundamental questions concerning how and why granular materials behave alternaCorrespondence concerning this article should be addressed to F. J. Muzrio tively like a liquid when flowing down an inclined chute and like a solid when forming a sloping surface remain to be answered (Glanz, 1995). A growing research community is currently investigating microscopic theories (Jaeger et al., 1996;Jaeger and Nagel, 1992;Evesque and Stefani, 1991;Evesque et al., 1992; Mehta, 19941, as well as unit operations typically found in industry, such as blending, sampling, discharge, fluidization, and granulation. The kinetic theory developed for fast-flowing low-density solids has achieved some success in modeling Couette flows and flows down inclined planes, but industrial systems are often more complex. Civil and soil engineers are mainly interested in strain effects (failure) produced by external forces and the effect of time on the development of strain. The chemical engineering community, on the other hand, has focused primarily on mixing and tran...

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“…N. Abatzoglou et al (2010) proposed an experimental method to measure the segregation tendency of flowing binary granular mixtures with NIRS methodology [3][4][5]; David Barling (2015) studied the mixing of a binary pharmaceutical powder using a colored tracer powder (sub-micronised iron oxide tracer) [6]; Carolyn Wightman et al (1996) [7], Alvaro Realpe and Carlos Velázquez (2003) have used Image Analysis Method for characterizing mixtures of granular materials [8]; Ixchel Gijón-Arreortúa and Alberto Tecante (2015) have studied a mixing time of cohesive food powders with a horizontal helical double-ribbon impeller [9]; Luke A. Fullard and al.…”

Section: Introductionmentioning

confidence: 99%

Coupling the image analysis and the artificial neural networks to predict a mixing time of a pharmaceutical powder

Mahdi¹,

Mouhi²,

N³

et al. 2018

J. Fundam and Appl Sci.

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In recent years, different laboratories were interested in predicting the mixing time of a pharmaceutical powder. In fact, a nonhomogeneous mixture may lead to under dose and/or overdose of the active ingredient in the drug product. Our study is aimed toward using a new and revolutionary approach in the field of the processes "The Artificial Neural Networks" (ANN) by using the Neural Networks Toolbox TM derived from Matlab ® software. The validation of the neural network was assumed by studying others mixing p o w d e r s and then w e compared the experimental results to the data obtained by the neural network calculations. Experimental results were obtained from a non-destructive method (Image Analysis) which was used in order to characterize the homogeneity of powder mixture in a V-Blender as well as a Cubic Blender which are most used in the pharmaceutical industry. Keywords

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A quantitative image analysis method for characterizing mixtures of granular materials

Cited by 52 publications

References 26 publications

Quantitative characterization of mixing of dry powders in V‐blenders

Quantitative characterization of mixing of dry powders in V‐blenders

Simulation of flow and mixing of particles in a rotating and rocking cylinder

Coupling the image analysis and the artificial neural networks to predict a mixing time of a pharmaceutical powder

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