Britain’s steel industry is swapping its coal-fired blast furnaces for electric arc furnaces (EAFs), but the scrap metal those furnaces melt carries unwanted impurities that can ruin thin steel sheets for cars and food cans. This partnership between Tata Steel UK, Cambridge, Warwick, and Imperial College London aims to solve that problem by building a computer model that predicts how different scrap compositions and processing steps affect the final steel’s properties. The shift to EAFs will cut UK CO₂ emissions by 1.5% and boost recycling capacity, but only if the resulting steel matches blast-furnace quality. Residual elements like copper and tin accumulate when scrap is recycled repeatedly, making the steel brittle or hard to form into thin panels. The team will use high-throughput alloy prototyping to generate data, then feed it into an AI-driven digital platform that accelerates the design of new steel grades tolerant to those residuals. If successful, the framework will let Tata Steel produce automotive body panels and packaging steel from high-scrap EAF routes without sacrificing performance. That would secure 34,500 direct jobs and £2.4 billion in GDP, plus 43,000 supply-chain roles generating another £3.1 billion, while keeping the UK steel industry competitive in a low-carbon economy.
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The ADAPT-EAF Prosperity Partnership aims to address critical knowledge gaps in electric arc furnace (EAF) steelmaking and design new steels amenable to EAF processing. The UK is undergoing a transition from blast furnace to EAF based steelmaking. This will reduce UK CO2 emissions by 1.5% and increase capacity for steel recycling. However, for this to be achieved, technical challenges must be overcome. Specifically, commercially competitive steels produced through EAF processing requires high scrap contents. This leads to the accumulation of residual elements that can negatively impact properties. In particular, they can severely compromise the processability of products that require thinner cross sections, like those used in the automotive and packaging industries. Understanding the impact of residual elements is therefore imperative for producing economically attractive steel grades. Through collaborative efforts between Tata Steel UK, University of Cambridge, Warwick Manufacturing Group and Imperial College London, our research will develop a comprehensive computational framework for predicting steel properties based on composition and processing variables. The data required for this will be generated through coupled rapid alloy prototyping and testing. Once established, the framework will allow the accelerated design of new EAF steel compositions specifically optimised for automotive and packaging applications. Our research strategy unfolds in four stages. In the first stage, Tata Steel will curate commercial data on scrap quality and conduct a techno-economic analysis to inform alloy design targets. In the second stage, a computational framework will be developed to predict steel properties, with targeted experimental studies filling data gaps. This will initially focus on creating a robust understanding of the interactions between residual elements, microstructure, and processing response. High-throughput alloy prototyping capabilities will generate microstructure and property data, complemented by formability assessments. Stage three enhances the computational framework with processing-microstructure correlations and iteratively refines steel compositions for optimal properties in the target applications. In stage four, alloy assessment transitions to full-scale validation and demonstration, ensuring consistency with small-scale results and customer acceptance. The Partnership's objectives include developing a digital predictive platform using AI and data analytics to predict steel properties, leveraging high-throughput processing capabilities for targeted data generation, and understanding the mechanistic origins of residual element effects on microstructural evolution. Additionally, the project will assess the equivalence or superiority of EAF steel to blast furnace steel, examine tolerance levels for scrap/direct reduced iron balance variations, and explore potential positive effects of residuals on steel properties. Importantly, the project will also focus on developing research staff with metallurgical expertise to support the talent pipeline in the UK steel industry. The outcomes of this research project will underpin the UK steel industry's transition to EAF production, and accelerate the development of environmentally sustainable steel grades with enhanced properties. By bridging critical knowledge gaps and leveraging advanced computational and experimental techniques, the partnership aims to enable Tata Steel to maintain global competitiveness and contribute to the UK's clean growth agenda. These initiatives are critical to secure the future prosperity of UK steelmaking, which directly employs 34,500 individuals and generates £2.4 billion of UK GDP as well as indirectly supporting an additional 43,000 jobs that creates an additional £3.1 billion in economic activity through supply chains.
Bin Xiao (Co-Investigator)Carl Slater (Co-Investigator)Catrin Davies (Co-Investigator)Claire Davis (Co-Investigator)David Collins (Co-Investigator)Didier Farrugia (Co-Investigator)Howard Stone (Principal Investigator)Janka Cafolla (Co-Investigator)Jianguo Lin (Co-Investigator)Jun Jiang (Co-Investigator)Sumitesh Das (Co-Investigator)Yi Gao (Co-Investigator)Zushu Li (Co-Investigator)
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