The volume of waste from electrical or electronic equipment (WEEE) to be discarded as defective, unused, or obsolete is rising, due to the increasing demand of new technological products. Such new products require a high demand for raw materials that could be, at least partially, obtained from WEEE recycling. In this study, the attention was focused on the implementation of non-ferrous metal concentration strategies (i.e. final product recovery) based on hyperspectral imaging (HSI) to be applied during and/or at end of an industrial recycling process, for process control and/or final concentrate characteristics quality control. HSI is one of the main emerging innovative technologies that can be profitably applied to fulfil an in-depth characterization of WEEE by-products. The main target of this study was to set-up a model, based on a chemometric approach, in order to recognize embedded metals in printed circuit boards (PCBs), with particular reference to the recovery of non-ferrous metals from small and medium appliances WEEE bulk by-products, obtained by an innovative Magnetic Density Separation (MDS) process. The investigated product belongs to an MDS output density fraction ranging between 1300 kg/m3 and 2200 kg/m3, with size < 10 mm. This output is mainly constituted by PCBs, plastic and glass. A small amount of metals still occurs in this output, even if metals should not occur in this density range, at least theoretically. Hyperspectral datasets of the analyzed bulk material, in the SWIR spectral range (1000-2500 nm) were obtained. A Partial Least Square – Discriminant Analysis (PLS-DA) was carried out for predicting the following material classes: black plastic, white plastic, plastic wires, wood, PCBs and glass. The investigated materials were correctly identified, demonstrating that HSI can be a valid solution for quality control and/or sorting of PCBs from WEEE by-product.

Hyperspectral imaging approach for evaluating printed circuit boards separability from bulk waste of electrical and electronic equipment / Bonifazi, Giuseppe; Gasbarrone, Riccardo; Serranti, Silvia. - STAMPA. - (2017). ((Intervento presentato al convegno ECOMONDO 2017 tenutosi a Rimini; Italy [10.13140/rg.2.2.35372.56960].

Hyperspectral imaging approach for evaluating printed circuit boards separability from bulk waste of electrical and electronic equipment

Giuseppe Bonifazi
Primo
;
Riccardo Gasbarrone
Secondo
;
Silvia Serranti
Ultimo
2017

Abstract

The volume of waste from electrical or electronic equipment (WEEE) to be discarded as defective, unused, or obsolete is rising, due to the increasing demand of new technological products. Such new products require a high demand for raw materials that could be, at least partially, obtained from WEEE recycling. In this study, the attention was focused on the implementation of non-ferrous metal concentration strategies (i.e. final product recovery) based on hyperspectral imaging (HSI) to be applied during and/or at end of an industrial recycling process, for process control and/or final concentrate characteristics quality control. HSI is one of the main emerging innovative technologies that can be profitably applied to fulfil an in-depth characterization of WEEE by-products. The main target of this study was to set-up a model, based on a chemometric approach, in order to recognize embedded metals in printed circuit boards (PCBs), with particular reference to the recovery of non-ferrous metals from small and medium appliances WEEE bulk by-products, obtained by an innovative Magnetic Density Separation (MDS) process. The investigated product belongs to an MDS output density fraction ranging between 1300 kg/m3 and 2200 kg/m3, with size < 10 mm. This output is mainly constituted by PCBs, plastic and glass. A small amount of metals still occurs in this output, even if metals should not occur in this density range, at least theoretically. Hyperspectral datasets of the analyzed bulk material, in the SWIR spectral range (1000-2500 nm) were obtained. A Partial Least Square – Discriminant Analysis (PLS-DA) was carried out for predicting the following material classes: black plastic, white plastic, plastic wires, wood, PCBs and glass. The investigated materials were correctly identified, demonstrating that HSI can be a valid solution for quality control and/or sorting of PCBs from WEEE by-product.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1163867
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