Electrodynamic Sorting of Industrial Scrap Metal

Nagel, James R.; Cohrs, Dave; Salgado, J. L.; Rajamani, Raj K.

doi:10.14356/kona.2020015

Cited by 9 publications

(3 citation statements)

References 10 publications

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“…An eddy current separation method that uses an electrical vortex to separate crushed material can also be used. The particles are separated according to their conductivity and shape [57].…”

Section: Mechanical Approachmentioning

confidence: 99%

The End of Life of PV Systems: Is Europe Ready for It?

Bošnjaković,

Galović,

Kuprešak

et al. 2023

Sustainability

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Like other plants, every photovoltaic (PV) power plant will one day reach the end of its service life. Calculations show that 96,000 tons of PV module waste will be generated worldwide by 2030 and 86 million tons by 2050. Such large quantities of waste can endanger the environment and people if they are not disposed of properly. This paper investigated how photovoltaic waste is currently handled, how this problem is legally regulated and to what extent reuse, recycling and disposal are represented. As recycling is the best option in terms of environmental protection and a circular economy, an overview of recycling technologies and recovery rates for the materials contained in the PV system is given. Currently, there are a small number of recycling plants for PV modules in Europe, but none in the Balkan countries. The main reason for this is the small amount of PV waste in these countries, which is far below the profitability threshold for the recycling of 19,000 t/year, and even below the reduced threshold of 9000 t/year. The analysis shows that only seven EU member states will exceed this threshold by 2040, and more than half of the EU member states will not even reach this threshold by 2050. For this reason, PV modules (after dismantling the aluminum frame and cables) are mostly disposed of in landfills in these countries. This is an indication that this problem should be seriously addressed in the EU. In this context, the main obstacles to the reuse and recycling of PV modules are listed, together with guidelines for their removal.

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“…An eddy current separation method that uses an electrical vortex to separate crushed material can also be used. The particles are separated according to their conductivity and shape [57].…”

Section: Mechanical Approachmentioning

confidence: 99%

The End of Life of PV Systems: Is Europe Ready for It?

Bošnjaković,

Galović,

Kuprešak

et al. 2023

Sustainability

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“…For instance, the 'Electrodynamic sorting technology' developed recently at the University of Utah [14], [15], uses a tuneable or variable frequency ECS system to be able to sort different metals and smaller fragment. The authors highlight some success extracting aluminium from Zorba [16], brass, copper and other aluminium alloys [14], although in the latter results were drawn from spherical test samples rather than genuine scrap.…”

Section: Introductionmentioning

confidence: 99%

Scrap Metal Classification Using Magnetic Induction Spectroscopy and Machine Vision

Williams

O’Toole

Peyton

2023

IEEE Trans. Instrum. Meas.

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The need to recover and recycle material towards building a circular economy is increasingly a global imperative. Non-ferrous metals in particular are highly recyclable and can be extracted using processes such as eddy current separation. However, their further separation into recyclable groups based on metal or alloy continues to pose a challenge. Recently, we proposed a new technique to discriminate between non-ferrous metals: Magnetic induction spectroscopy (MIS) measures how a metal fragment scatters an excitation magnetic field over different frequencies. MIS is related to conductivity, which can be used to classify the fragment according to this property.In this paper, we demonstrate for the first time the use of MIS with machine learning to classify non-ferrous scrap metals drawn from commercial waste streams. Two approaches are explored: (1) MIS over a bandwidth from 3 kHz to 90 kHz, and (2) the combination of MIS with physical colour of the metal samples. We show that MIS alone can obtain purity and recovery rates >80% for most metal groups and waste streams, rising to >93% for stainless steel. The exception was the Zorba waste stream where the mix of aluminium alloys within the sample set led to poor conductivity contrasts. The introduction of colour substantially improved results in this case, increasing purity and recovery rates by 20-35 percentage points. Of the machine learning models tested, we found that random forest, extra trees and support vector machine algorithms consistently achieved the highest performance.

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“…Electrodynamic sorting was able to remove aluminium from Zorba, with 97.6% purity and 93% recovery. The system could not eject small pieces of aluminium due to the excitation frequency, which reduced the recovery rate [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Shredded Aluminium Scrap Metal Using Magnetic Induction Spectroscopy

Williams,

Mallaburn,

Gagola

et al. 2023

Sensors

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Recycling aluminium is essential for a circular economy, reducing the energy required and greenhouse gas emissions compared to extraction from virgin ore. A ‘Twitch’ waste stream is a mix of shredded wrought and cast aluminium. Wrought must be separated before recycling to prevent contamination from the impurities present in the cast. In this paper, we demonstrate magnetic induction spectroscopy (MIS) to classify wrought from cast aluminium. MIS measures the scattering of an oscillating magnetic field to characterise a material. The conductivity difference between cast and wrought makes it a promising choice for MIS. We first show how wrought can be classified on a laboratory system with 89.66% recovery and 94.96% purity. We then implement the first industrial MIS material recovery solution for sorting Twitch, combining our sensors with a commercial-scale separator system. The industrial system did not reflect the laboratory results. The analysis found three areas of reduced performance: (1) metal pieces correctly classified by one sensor were misclassified by adjacent sensors that only captured part of the metal; (2) the metal surface facing the sensor can produce different classification results; and (3) the choice of machine learning algorithm is significant with artificial neural networks producing the best results on unseen data.

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Electrodynamic Sorting of Industrial Scrap Metal

Cited by 9 publications

References 10 publications

The End of Life of PV Systems: Is Europe Ready for It?

The End of Life of PV Systems: Is Europe Ready for It?

Scrap Metal Classification Using Magnetic Induction Spectroscopy and Machine Vision

Classification of Shredded Aluminium Scrap Metal Using Magnetic Induction Spectroscopy

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