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Evaluate the blend quality of powders using a simplified mixing law

Evaluate the blend quality of powders using a simplified mixing law

Zigan, Stefan, Hettiaratchi, Kaushika and Milledge, John J. ORCID: 0000-0003-0252-6711 (2017) Evaluate the blend quality of powders using a simplified mixing law. In: Reliable Flow of Particulate Solid V (RELPOWFLO V), 13-15 June 2017, Skien, Norway.

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The research presents an easy to follow approach to evaluate blend quality of particle blends which are complex in shape and vary in size. For some materials simulations with discrete modelling approaches are challenging because of size and shape constrains but also of the access to recourses and knowhow of practitioners in the field using blending equipment for evaluating the blend quality of their products. A significant proportion of bulk handling operations happens in agriculture and related industries e.g. producing fertilisers, handling and storing food such as beans, lentils and rice. Different materials such as mung beans, black eye beans, lentils and rice were blended in a variable speed screw blender. The blender could vary the screw speed and then results taken from experiments were analysed to quantify the blend quality. It was found that a screw speed of 120 rpm gave the highest blend “quality” defined as a50 / 50 percent mix for different materials and blending time intervals. It was also found that the sampling position in the variable speed screw blender mattered because of axial and radial segregation occurring in the blender.

Item Type: Conference or Conference Paper (Speech)
Uncontrolled Keywords: Mixing; powders; blend
Subjects: T Technology > TP Chemical technology
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Applied Engineering & Management
Faculty of Engineering & Science > Department of Life & Sports Sciences
Last Modified: 16 May 2018 15:03
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: GREAT c
Selected for GREAT 2019: None

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