10076415Developing computer vision algorithms for ferrous scrap and secondary materials grading using images and videos as input, incorporating them into digital passports for increasing UK usage of domestically generated scrap.ClosedGrant for R&DInnovate UKDuring the six-month project, the Agave Networks team will develop a computer vision algorithm assessing ferrous scrap's physical quality characteristics to incorporate into their existing online platform for the ferrous secondary materials domestic market. They create and use digital passports using real-time images of the materials to enable UK SME to scrap generators and collectors to market their stock directly to foundries without intermediaries. UK steel processors and SME scrap yards use low-cost or existing hardware to gather images of their materials and their system creates individual digital passports to validate the material's location, date, and time of existence, providing UK foundries with a verified procurement tool, eliminating the need for additional intermediaries. Their easy-to-use and efficient solution helps close the recycling loop and trace UK-recovered metal materials from their origin to the furnace. Their proposed innovation is developing a computer vision and machine learning algorithm that extracts characteristics of recovered ferrous metal batches from real-time images including: * % of contaminant materials * % oxidation and degradation * Pieces dimensions * Potential end-of-life sources * Approximate batch volume and weight Currently, ferrous recovered stakeholders currently rely on visual inspection for materials verification for trading purposes, which is prone to results' variability, error, delays and abuse. Other grading technologies like XRF and Eddy current magnetic separators are neither suitable nor efficient and non-cost effective for mixed ferrous materials; hence are little or not utilized across the supply chain. According to DEFRA 2021, the UK steel foundry industry can increase domestic recovered steel usage from 2.6 to 3.5 million tonnes p.a. without additional investment. However, data disparities and mismatches between scrap stakeholders, decreasing UK domestic scrap capacity usage and being exporting more attractive, translated into £225M in losses p.a. in addition to avoidable CO2 scope 2 and 3 emissions. This project focuses on the challenge of Sustainable UK Materials and Manufacturing - By helping the UK steel manufacturing and recycling to adopt more resource-efficient solutions and technologies, including artificial intelligence, computer vision and data communication platforms to: * Create more resilient procurement supply chains for recovered ferrous materials. * Design and develop a more efficient business model for closed-loop metals * Provide traceability of recovered ferrous materials from origin to finished product. * Reduce CO2 equivalent emissions on scopes 2 and 3 for UK steel manufacturers and recyclers. * Increase the international competitiveness of UK-manufactured steel by increasing the recycled content of traceable recovered materials.