• Otoole, Michael D.
• Williams, Kane C.
• Peyton, Antony J.

Abstract

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.<br/>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 &gt;80% for most metal groups and waste streams, rising to &gt;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.

Topics

• impedance spectroscopy
• stainless steel
• aluminium
• aluminium alloy
• copper
• random
• machine learning