Introduction

Increasing scrap usage is a strategy to help decarbonise the UK steel industry and maintain its sustainability considering the excessive scrap supply in the UK and significantly reduced CO2 emissions of using recycled scrap in steelmaking. However, there are various challenges associated with this. The first and most well-known challenge is the scrap quality – its measurement and control. The scrap supplied to steelmakers in general is a mixed material without chemistry measurement. The main measures are “trust”, visual inspection, and occasional spot check by hand-held XRF (which requires direct contact between XRF analyser and the sample surface). This limits the accuracy, consistency, and assurance of compatibility with product chemistry and value-in-use (VIU) that the materials (recycled scraps and associated raw materials) can offer.

The research in Task 19 aims to develop the industry highly sought-after, artificial intelligence (AI)-based tool for quantifying the scrap quality, and subsequently improve the scrap value-in-use (VIU) model for optimising its usage in steelmaking. The project will build on existing work in SUSTAIN, as well as the latest developments from the wider community such as Viability, RAP Prosperity Partnership, CircularMetal, DEFRA projects.

Initially, this Task will quantify the scrap quality by applying the computer vision-based system for scrap quality monitoring. This will provide assurance of scrap quality compatible with steel products. Work will then quantify the true value (contribution and penalty) associated with the use of a particular scrap, from metallurgical understanding of the effects of scrap quality (e.g. combined residuals and alloying elements, sterile) on steelmaking process (e.g. productivity, energy/materials efficiency, costs, CO2 emissions), downstream process, and steel properties via theoretical simulation, laboratory experiments and industry data. Finally, the scrap usage in producing high quality steel grades can be improved/optimised.

Outcomes and Impact

• Application of artificial intelligence models (a computer vision-based system) in scrap identification and quality quantification, enabling the accuracy, consistence, and assurance of steel scraps compatible with product chemistry.

• Quantification of the true value associated with the use of any particular scraps considering its effects on steelmaking (e.g. productivity, energy/ materials efficiency, costs), environmental impacts, scrap characteristics and latest understanding on residual elements obtained from SUSTAIN, Prosperity Partnership, Viability and beyond.

• The ultimate impact of this project to the steel industry is to help achieve net zero by increasing scrap utilisation, reduce the costs by maximising the scrap value/optimising scrap usage for producing high quality steels, and reduce environmental impact by reducing CO2 emissions.