One’s scrap is another’s treasure

Using metallic scrap as a source when manufacturing steel saves both raw materials and energy which results in a more environmentally friendly manufacturing process.
Scrap is a fantastic resource to use which is why researchers at Luleå University of Technology are trying to make the recycling process even better with the new project OptiScrap.

OptiScrap is aligned to the Green Deal objectives put up by the European Commission to transform the EU into a modern and resource-efficient economy. That is why one of the goals of OptiScrap is to be able to use more recycled materials in the steel manufacturing process, the more scrap used the more the emissions will be reduced. When using recycled materials, one must be aware of the amount of impurities as it is critical to the success. Too many impurities such as copper originating from scrapped cars can lead to, for example, cracks in the steel. The ability to accurately quantify the amount of impurities via new sensors in combination with introduction of efficiently sorting technologies are fundamental for the steel sector to increase its share of recycled materials even more and contribute to a cleaner and greener future for our society.

Designing greener technology

OptiScrap is investigating new sensor concepts able to classify the most common materials typically found in waste streams for the presence of unwanted elements and will reduce the need for manual sorting and pave the way for autonomous and fully robotized processing/upgrading of large material flows. The sensor makes use of LIBS (laser-induced breakdown spectroscopy) technology to scan the material which is already an established method for fragmented scrap on conveyor belts. However, if one wish to scan larger chunks at a scrap yard or at a steel mill a new design of the technology is needed. The researchers at Luleå University of Technology are determined to overcome this hurdle by designing a new sensor which will be able to scan large pieces of metals for impurities at a high speed.

–    In order to achieve the goals of the project we will contribute in designing computer vision methods which will result in faster and more accurate categorization of metal scrap, George Nikolakopoulos, Professor of Robotics and AI explains.

The first part of the project will focus on adapting the sensor and the second part will be to implement the new concept at steel mills to determine the scrap composition before the melting starts. The performance will be evaluated by analysing samples from the melt in the laboratory which is expected to generate one-off a kind knowledge for further developments of machine learning applications in the steel- and recycling industry.

Project coordinator: SWERIM AB
Partners: Luleå University of Technology, AB Sandvik Materials Technology, SSAB EMEA AB, Stena Recycling International AB.
Funded by: Vinnova
Project length: 2022-2025.