Energy-intensive industries, embedded in many strategic value chains, make up more than half of the energy consumption of the European industry and reducing their CO2 intensity is crucial for meeting the objectives of the Paris agreement. Within EIIs, metallurgy poses a major challenge due to the trade-off that must be found between maintaining economic profitability, while progressively implementing the required transformations for a greener production. While digitalisation is generating a data deluge, Artificial Intelligence cannot be fully adopted due to limitations to share data between several factories and the heterogeneity of systems that hinders the replicability of AI. ALCHIMIA aims to build a platform based on Federated Learning and Continual Learning to help big European metallurgy industries unlock the full potential of AI to support the needed transformations to create high-quality, competitive, efficient and green manufacturing processes. The project will address the challenges of the steel sector, creating an innovative system that automates and optimises the production process dynamically with a holistic approach that includes scrap recycling and steelmaking. ALCHIMIA will find an optimal mix to reduce energy consumption, emissions and waste generation of steelmaking while guaranteeing to obtain high-quality products. The replicability and scalability of ALCHIMIA will be enabled through a complementary use case for the manufacturing of automotive parts. The developed system will be used for prognostic optimisation of the mix of input materials charged in the furnaces to obtain a certain product quality that matches the customers' specifications while reducing the environmental impact and the energy consumption. ALCHIMIA will not only seek the optimal mix for the charge of metallurgy furnace, it will also determine the best combination of learning capacities to enable a smooth green transition for all industries thanks to unprecedented collaboration