Enhanced mixing in double-cone blenders

Brone, Dean; Muzzio, Fernando J.

doi:10.1016/s0032-5910(99)00204-1

Cited by 74 publications

(28 citation statements)

References 14 publications

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“…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%

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

Dubey

Sarkar

Ierapetritou

et al. 2011

Macro Materials & Eng

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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

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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

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“…The mixing process is highly complicated with a number of affecting parameters, such as the particle properties, the structure and performance of the mixer, the mixing process parameters and the particle feeding order [3].…”

Section: Problem Statementmentioning

confidence: 99%

“…However, in these studies the mixing performance of the particles was only assessed qualitatively not quantitatively and the information provided by the experimental study was very limited [3].…”

Section: Problem Statementmentioning

confidence: 99%

Simulation of Components Mixing in Order to Determine Working Bodies Rational Parameters

Stadnyk¹,

Vitenko²,

Dróździel³

et al. 2016

Adv. Sci. Technol. Res. J.

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One of important things in the production of vitally foods is bakery industry. The quality of baked goods can be achieved by improving the quality of dough kneading. In order to achieve this goal a new structure of the working body and methods of mathematical and computer modelling are offered, aimed to determine the working body design factors and visualize the process. Mathematical models of the dependency of dough viscosity from working body structural features and the frequency of its rotation are made. The obtained models are used to determine rational values of controlled parameters. It has been found that for wheat dough mixing the use of equipment with pin working bodies is more preferable. One of them -the main one -is larger in diameter, and two others -supporting ones -are smaller, able to ensure effective implementation of first and second stages of components mixing. The necessity of changing rotation direction of working bodies is substantiated. At the initial stage of mixing it is necessary to ensure larger diameter pin heating and at the stage of plasticization -vice versa -smaller diameter pins heating.

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“…Mc Carthy et al (2000 claimed that there is no accepted set of universal governing equations describing granular flow. There are many studies on the process of mixing particulate materials mainly in laboratory scale, typically for two or three components (Arratia et al, 2006;Basinskas and Sakai, 2016;Brone and Muzzio, 2000;Mahmood et al, 2016;Lemieux et al, 2007, Seville et al, 2005). There are not many publications presenting the results of experimental studies conducted in industrial conditions.…”

Section: Introductionmentioning

confidence: 99%

Analysis of kinetics of multicomponent, heterogeneous granular mixtures - Laminar and turbulent flow approach

Królczyk

2016

Chemical and Process Engineering

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Mixing of granular materials is unquestionably important. Mixing solids is common in industrial applications and frequently represents a critical stage of the processes. The effect of mixing determines the quality of the products. Achieving a gas or liquid mixture ideally homogeneous in terms of composition in the case of dissolving components is not that difficult, while in case of granular materials that usually differ in sizes and densities, obtaining a homogenous mixture is practically impossible. The aim of the paper is to present the kinetics of mixing of a multicomponent, nonhomogeneous granular mixture. For the first time in mixing of granular materials, a reference has been made to the terminology used in kinematics of fluid mixtures to determine the state of the mixture: turbulent or laminar. By means of statistical analysis the mixing process was divided into two stages. The initial phase of the process was called the stage of turbulent changes, due to large differences in the quality of the observed mixtures; the final step of the process was called the stage of laminar, stable changes, where further mixing did not result in a significant improvement in quality. The research was conducted in industrial conditions in a twotonne mixer.

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Enhanced mixing in double-cone blenders

Cited by 74 publications

References 14 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

Simulation of Components Mixing in Order to Determine Working Bodies Rational Parameters

Analysis of kinetics of multicomponent, heterogeneous granular mixtures - Laminar and turbulent flow approach

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