| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Peyton, Antony J.
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (19/19 displayed)
- 2024Classification of Wrought and Cast Aluminium using Magnetic Induction Spectroscopy and Machine Vision
- 2023Computations and measurements of the magnetic polarizability tensor characterisation of highly conducting and magnetic objectscitations
- 2023A review of the classification of non-ferrous metals using magnetic induction for recyclingcitations
- 2023Scrap metal classification using magnetic induction spectroscopy and machine visioncitations
- 2019Classification of Non-ferrous Scrap Metal using Two Component Magnetic Induction Spectroscopy
- 2019Magnetic characterisation of grain size and precipitate distribution by major and minor BH loop measurementscitations
- 2017Classification of Non-ferrous Metals using Magnetic Induction Spectroscopycitations
- 2017Detection of creep degradation during pressure vessel testing using electromagnetic sensor technologycitations
- 2017Optimized setup and protocol for magnetic domain imaging with in Situ hysteresis measurementcitations
- 2017Electromagnetic tensor spectroscopy for sorting of shredded metallic scrapcitations
- 2017Selective recovery of metallic scraps using electromagnetic tensor spectroscopy
- 2016Defect representation using the electromagnetic tensor formulation for eddy current NDT
- 2015Electromagnetic evaluation of the microstructure of grade 91 tubes/pipescitations
- 2015Rapid Non-Contact Relative Permittivity Measurement of Fruits and Vegetables using Magnetic Induction Spectroscopycitations
- 2014Differential permeability behaviour of P9 and T22 power station Steelscitations
- 2013Magnetic sensing for microstructural assessment of power station steels: Differential permeability and magnetic hysteresiscitations
- 2006Electromagnetic visualisation of steel flow in continuous casting nozzlescitations
- 2006A three-dimensional inverse finite-element method applied to experimental eddy-current imaging datacitations
- 2003Development of a sensor for visualization of steel flow in the continuous casting nozzlecitations
Places of action
| Organizations | Location | People |
|---|
conferencepaper
Selective recovery of metallic scraps using electromagnetic tensor spectroscopy
Abstract
Automobile use continues to show significant increase year on year, with well over 1 billion vehicles now in use globally according to OICA figures. As a result, the recycling of end-of-life vehicles (ELV) has become a major concern, with legislative ELV recycling systems in place in many countries. In the EU for instance, ELV generate approaching 10 million tonnes of waste per year and around 75% of this is currently recycled or recovered, but this percentage falls well short of the 95% target for 2015 set by the ELV European directive. Automobile shredding residue (ASR) includes heavy metals as well as a mass of unclassified fine particles. The non-ferrous metal fraction in ELV scrap contains several metals/alloys; primarily aluminium, copper and brass, whose recovery is important for environmental, economic and resource conservation reasons. The separation of non-ferrous metals from ASR scrap is technically complex and existing technologies suffer from poor cost effectiveness. This paper present a new method for sorting of non-ferritic metallic scrap using electromagnetic tensor spectroscopy. The method combines a vision system, with a novel electromagnetic array to determine the electrical conductivity of each metal piece. The pieces can then be sorted based on conductivity into metal type. Crucial to this process is a fast metal identification algorithm, which allows the line to be operated at conveyor speeds of several m/s and which linearly scales in complexity with conveyor belt width. This study reports that the metal identification algorithms perform adequately when processing machined metal test samples with a wide range of shapes, without the use of any vision information. The challenge is to cope with the diverse range pieces in terms of shape and morphology. In doing so, a A1/4-scale EMTS system has been developed to prove the principle of the technique.