A computer‐aided methodology for the optimization of electrostatic separation processes in recycling

Matteo Borrotti, Antonio Pievatolo, Ida Critelli, Andrea Degiorgi and Marcello Colledani

Applied Stochastic Models in Business and Industry, 2016, vol. 32, issue 1, 133-148

Abstract: The rapid growth of technological products has led to an increasing volume of waste electrical and electronic equipments (WEEE), which could represent a valuable source of critical raw materials. However, current mechanical separation processes for recycling are typically poorly operated, making it impossible to modify the process parameters as a function of the materials under treatment, thus resulting in untapped separation potentials. Corona electrostatic separation (CES) is one of the most popular processes for separating fine metal and nonmetal particles derived from WEEE. In order to optimize the process operating conditions (i.e., variables) for a given multi‐material mixture under treatment, several technological and economical criteria should be jointly considered. This translates into a complex optimization problem that can be hardly solved by a purely experimental approach. As a result, practitioners tend to assign process parameters by few experiments based on a small material sample and to keep these parameters fixed during the process life‐cycle. The use of computer experiments for parameter optimization is a mostly unexplored area in this field. In this work, a computer‐aided approach is proposed to the problem of optimizing the operational parameters in CES processes. Three metamodels, developed starting from a multi‐body simulation model of the process physics, are presented and compared by means of a numerical and simulation study. Our approach proves to be an effective framework to optimize the CES process performance. Furthermore, by comparing the predicted response surfaces of the metamodels, additional insight into the process behavior over the operating region is obtained. Copyright © 2015 John Wiley & Sons, Ltd.

Date: 2016
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https://doi.org/10.1002/asmb.2128

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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:32:y:2016:i:1:p:133-148

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