Imagine trying to sort tiny, mixed-up pieces of industrial ceramics that are all different colors and types. Instead of doing it by hand, this system uses a high-tech 'digital eye' and lasers to identify materials instantly. It then shoots them into the right bins with precision, even for particles smaller than a grain of sand. This turns industrial waste back into valuable raw materials.
The refractory industry relies on energy-intensive primary raw materials that have a massive carbon footprint. Current sorting technology is too imprecise to recover these materials from waste, especially for particles smaller than 1 mm.
An AI-supported multi-sensor sorting system combining laser-induced breakdown spectroscopy and hyperspectral imaging. The project also produced two industrial demonstrators and a techno-economic assessment (TEA) model.
If you are a lining producer dealing with high costs of primary raw materials—this project developed an AI-supported multi-sensor sorting equipment that reduces the need for extractive mining by 1 million tons. This allows you to use secondary raw materials to lower your carbon footprint.
If you are a recycling plant dealing with complex industrial waste that is too difficult to sort manually—this project developed a system combining laser spectroscopy and hyperspectral imaging. It enables the sorting of particles below 1 mm, unlocking previously unexploited raw material sources.
If you are a supplier dealing with the volatility of critical materials like bauxite and graphite—this project developed a recycling process chain that fosters material autarky. It reduces the reliance on primary extraction by recovering these materials from waste.
Based on available project data, it reduces energy consumption by up to 760 GWh and lowers the need for expensive primary raw material extraction by 1 million tons.
Yes, the project includes the construction of two demonstrators specifically designed to ensure straightforward exploitation and implementation in the European industry.
Based on available project data, the consortium is led by RHI Magnesita GmbH with 9 partners; however, specific licensing terms are not detailed in the summary.
The system helps companies meet CO2 reduction targets, with a potential annual reduction of up to 800 kilo tonnes of CO2 in the EU.
The technology integrates a full process chain including optimized pre-processing, AI-supported multi-sensor sorting, and automated ejection.
The consortium is heavily industry-driven with a 56% industry ratio, featuring 5 industrial partners and 4 SMEs. Led by RHI Magnesita GmbH, the group spans 5 countries (AT, DE, IE, NO, UK), combining the commercial weight of large enterprises with the agility of SMEs and the technical depth of 4 research/university entities. This structure suggests a strong focus on commercial viability and direct industrial application rather than pure academic research.
Contact RHI Magnesita GmbH in Austria for licensing and implementation details.
Contact us to connect with the ReSoURCE consortium for pilot integration.
