The EURO-TITAN EC funded project under Grant Agreement 101135077 is set to transform titanium production in Europe by tapping into continuous titanium (Ti) resources from metallurgical waste streams generated during alumina and titanium dioxide production. With a goal of producing 54 ktpy of Ti metal, EURO-TITAN aims to end Europe’s reliance on Russian Ti-metal sponge imports—a critical material for the transportation and medical industries.

Building on the success of projects like Scavenger, SCALE, SCALE-UP, and Valore, EURO-TITAN unites an industry-driven consortium to pioneer a game-changing green hydrogen-based direct titanium reduction process. Unlike the conventional Kroll process, which releases 10 tons of CO₂ per ton of titanium, EURO-TITAN’s method will cut emissions by over 90%. This cleaner, more efficient process—demonstrated in an industrial setting at the Bosnian Al-plant—will produce tailor-made titanium products at a 15% lower cost (€6,800 per ton) compared to imports from Russia and China (€8,500 per ton).

But EURO-TITAN’s innovation doesn’t stop there. The project incorporates real-time data monitoring and AI to optimize production, reducing energy and water use on-site by 10% while minimizing downtime. Waste materials will be repurposed into low-carbon construction products, emitting 30% less CO₂ than cement alternatives. Additionally, the project promotes circularity by recycling water and reusing excess heat to benefit local households, ensuring a cleaner, more sustainable future for titanium production in Europe.

SAIS LAB is developing the Data Management Platform which offers a complete, all-in-one solution for managing data—from collection and storage to hypothesis testing and advanced algorithm-driven insights. Using tools like distribution statistics and exploratory data analysis, we uncover trends, patterns, and connections between process parameters, data levels, and environmental factors. To make predictions even more accurate and reliable, the system incorporates anomaly detection methods to address data uncertainty head-on. This ensures deeper insights and a stronger foundation for decision-making.