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DEM Modelling and Simulation of Banana Screen Classification Efficiency
Chalmers University of Technology.
Chalmers University of Technology.
Chalmers University of Technology.
Chalmers University of Technology.ORCID iD: 0000-0002-3283-067x
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2015 (English)Conference paper, Poster (with or without abstract) (Refereed)
Sustainable development
According to the author(s), the content of this publication falls within the area of sustainable development.
Abstract [en]

Banana screens are popular and frequently used in minerals processing. The screens are characterized by a high separation capacity and low maintenance need. The operation of screening usually takes place after crushing operation. The banana screens have multiple panels with variable slope which enables the feed material to flow rapidly resulting in a high screening rate. The difference between banana screens and other screens is that in banana screens the screen cut size varies with the changing slope of the decks. There are a number of factors affecting the screening operation; the deck panel slope progression, screen deck material, aperture shape, vibrational motion, open area, thickness of deck, feed rate and material properties. The aim of this paper is to simulate the screening performance by using the Discrete Element Method (DEM) and to analyse different motions that affect screening operation efficiency. Three decks with different slopes have been used and two different motions, linear and elliptical, have been evaluated at one feed rate. Figure 1 shows the overview of DEM simulation of screening process. Design of Experiment (DoE) has been used to evaluate the factors that control the value of parameters. The results show that the classification efficiency can be evaluated by conventional comparison between the feed particle size distribution and each of the product streams. The resolution of the model also enables the calculation of a critical efficiency criteria position along the screen deck. This position can be used to define a screening robustness factor. The passage probability and stratification behaviour can also be evaluated.

Place, publisher, year, edition, pages
2015.
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Mechanical Engineering
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URN: urn:nbn:se:hb:diva-14950OAI: oai:DiVA.org:hb-14950DiVA, id: diva2:1237852
Available from: 2018-08-10 Created: 2018-08-10 Last updated: 2022-03-29Bibliographically approved

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Davoodi, AliBengtsson, Magnus

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