UK startup MatAlytics has secured a £619,000 Frontier AI grant from Innovate UK to accelerate development of CITRUS, its physics-based AI platform for modelling steel reheating processes. The funding follows an earlier £100,000 Innovate UK award and supports the UK's wider AI sovereignty ambitions.
The technology could help manufacturers improve efficiency, reduce energy costs and lower emissions by replacing traditional simulation methods that can take hours or even days to generate results.
CITRUS delivers steel manufacturing insights in seconds
According to MatAlytics, CITRUS uses neural networks trained on finite element (FE) simulation data to predict thermomechanical and microstructural changes in steel during extreme reheating conditions.
By leveraging pre-trained datasets, the platform can generate predictions in seconds rather than relying on lengthy simulations and significant computing resources. The company says this enables faster decision-making and greater process optimisation.
Improving efficiency, quality and sustainability
By predicting how steel will respond during reheating, CITRUS can help manufacturers optimise time and temperature settings, reducing energy consumption and operating costs while improving product quality.
More efficient reheating processes could also lower carbon emissions by reducing furnace operating times, supporting wider industry sustainability goals and the UK's net-zero agenda.
Funding to support commercial growth
MatAlytics said the latest funding will help fast-track development and validation of the CITRUS platform as it moves towards commercial deployment.
While initially focused on steel manufacturing, the company sees potential applications across thermal power generation, aerospace, defence, automotive and the hydrogen economy. The funding will also support recruitment across its technical and commercial teams.
To secure the grant, MatAlytics demonstrated the scalability of its AI technology and its potential contribution to the UK's wider AI innovation strategy.
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