Grant Description:

These awards were for first-time Innovate UK grant winners and over the next six months will deliver affordable, adoptable and investable innovations in areas such as:

➡️ Semiconductors
➡️ AI
➡️ Quantum
➡️ Engineering Biology

Read full details of the story here about the grant:  https://ow.ly/7aie50P3Lxz

Project Description:

This project will explore the feasibility of the Digital Product Passport (DPP) on the materials stream of steel, in particular railway tracks. It will carry out a pilot study using DPPs for the first time for management of the railway tracks’ lifecycle, from manufacturing to end-of-life . 

Currently up to 87% of all scrap metal is exported for recycling, to areas with much lower labour, health & safety and environmental standards, before being re-imported. Steel is our material stream of choice because the major raw material used, iron ore, is imported, it has a 100% reusable coefficient and recycled steel uses about 60% less energy compared to smelting virgin material. 

Our solution will innovate the data management practices around railway track infrastructure. Currently data (such as material origin and batch IDs) is recorded separately by railway track manufacturers. Similarly, data (such as maintenance and usage) is recorded by the railway tracks management organisations. Using DPP for the first time in the railway industry, we can provide a complete set of data on each and every segment of railway track in one place, eliminating inter- and intra-organisational duplication while bridging the data systems. The aim is to facilitate implementation of AI-based assistive tools for predictive maintenance, and link DPP to leasing, to achieve transparency of ownership aiming to maintain the material in the country. 

The proposed solution has the potential for substantial benefits to the UK, in financial and environmental terms. The major financial gains are that steel is expected to be produced cheaper (as less contaminated feedstock requires less energy) whilst improved predictive maintenance can lead to a 30% reduction in costs. The key environmental benefit stems from circularity improvements, currently at 9%, leading to significant Scope 3 emissions reductions across the entire supply chain. 

Current DPP pilots focus mainly on construction, textiles and chemicals. Using the findings of the current, mainly EU focused, DPP related pilot projects, provides a valuable starting point for us. Our approach has the potential to improve transparency, traceability, and sustainability, in an economically viable way. 

Our DPP design for railway tracks includes the following relevant information:

Maintenance history: Repairs, replacements, and other maintenance activities. 

Composition, ownership and manufacturing process: Quality control measures, inspections, and testing. 

Predictive maintenance: A set of proposed maintenance activities which will change over time using ML models.

End-of-life considerations: Expected lifespan of the tracks and recyclability.