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Open AccessArticle

A Parametric Model of the LARCODEMS Heavy Media Separator by Means of Multivariate Adaptive Regression Splines

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Department of Exploration and Mining, Universidad de Oviedo, EIMEMO, c/ Independencia 13, 33004 Oviedo, Spain
2
Department of Construction and Manufacturing Engineering, Universidad de Oviedo, Campus de Viesques, 33204 Gijón, Spain
*
Author to whom correspondence should be addressed.
Materials 2017, 10(7), 729; https://doi.org/10.3390/ma10070729
Received: 29 May 2017 / Revised: 28 June 2017 / Accepted: 28 June 2017 / Published: 30 June 2017
Modeling of a cylindrical heavy media separator has been conducted in order to predict its optimum operating parameters. As far as it is known by the authors, this is the first application in the literature. The aim of the present research is to predict the separation efficiency based on the adjustment of the device’s dimensions and media flow rates. A variety of heavy media separators exist that are extensively used to separate particles by density. There is a growing importance in their application in the recycling sector. The cylindrical variety is reported to be the most suited for processing a large range of particle sizes, but optimizing its operating parameters remains to be documented. The multivariate adaptive regression splines methodology has been applied in order to predict the separation efficiencies using, as inputs, the device dimension and media flow rate variables. The results obtained show that it is possible to predict the device separation efficiency according to laboratory experiments performed and, therefore, forecast results obtainable with different operating conditions. View Full-Text
Keywords: heavy media separation; density separations; multivariate adaptive regression splines (MARS); LARCODEMS heavy media separation; density separations; multivariate adaptive regression splines (MARS); LARCODEMS
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Álvarez, M.M.; Sierra, H.M.; Lasheras, F.S.; Juez, F.J.C. A Parametric Model of the LARCODEMS Heavy Media Separator by Means of Multivariate Adaptive Regression Splines. Materials 2017, 10, 729.

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