Data and decentralized Artificial intelligence for a competitive and green European metallurgy industry

Digital, Industry & SpaceHORIZON-IAID: 101070046
EC Contribution
€25,770
Consortium Size
11 orgs
Start Year
2022
Summary

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

Consortium (11)

Project Results (31)

Source: CORDIS, the EU research results database.

Publications (24)
ALCHIMIA: what it really takes to build industrial AI in European steelmaking
· 2026
David Blazquez, Irene Garcia and Barbara Fernandez
Artificial Intelligence in Steel Production: Questions of Augmentation, Optimisation and Accountability
· 2026
Dean Stroud, Rachel Hale; Martin Weinel; Luca Antonazzo; Vinicio Di Iorio
Embedding Human-Centred AI in Steelmaking: Lessons from the Alchimia Project
· 2026
Dean Stroud and Martin Weinel
Optimizing Steelmaking with Models, AI, and Federated and Continual Learning
Automaatioväylä· 2025DOI
Machine Learning models to forecast defects occurrence on foundry products
IFAC-PapersOnLine· 2024DOI
S. Dettori, A. Zaccara, L. Laid, I. Matino, M. Vannucci, V. Colla, G. Bontempi, L. Forlani
18th International Conference on Society & Materials, SAM18, 2024
Rossi F., Niero M., Colla V., Zaccara A., Dettori S., Laid L., Branza T.A., Cateni S., Vannini L., Iraldo F.
19th International Conference on Society & Materials, SAM19, 2025
Rossi F, ALbano F, Frey M, Iraldo F, Niero M
AI-based modelling techniques for input materials optimisation in the EAF route
S. Dettori, A. Zaccara, L. Laid, I. Matino, V. Colla, C. Parea, D. Esteban
Application of Federated Learning to enhance model-based Decision Support for EAF online Monitoring and Control at multiple plants
B.Kleimt, P. Kannisto, A. Changude, N. Clarevanne, X. Dacquet, N. Garcia, R. Lazcano
Application of LCA to circular economy strategies in steelmaking industry: state-of-the-art and recommendations
Federico Rossi, Monia Niero, Marco Frey
Artificial Intelligence and Transformations of Work
Speakers’ Booklet International Conference Artificial Intelligence and Transformations of Work November 20-22, 2023 Grenoble
Dr. Dean Stroud, Dr. Martin Weinel, Dr. Rachel Hale, Dr. Vini di Iorio
Artificial Intelligence for Steelmaking: optimizing processes, augmenting workers, blurring accountability
Dr. Dean Stroud, Dr. Martin Weinel, Dr. Rachel Hale, Dr. Vini di Iorio, Dr. Luca Antonazzo 
Artificial Intelligence-based decision support system for optimising the ladle furnace process in the ALCHIMIA project
S. Dettori, L. Laid, A. Zaccara, V. Colla, I. Matino, L. Vannini, T.A. Branca, M. Vannucci, A. Siddique, F. Rossi, M. Niero
Decreasing the environmental impact of the electric steel route through advanced modelling techniques
Valentina Colla, Antonella Zaccara,Stefano Dettori, Laura Laid, Ismael Matino, Silvia Cateni, Teresa Annunziata Branca, Lorenzo Vannini
Descreasing the environmental impact of the electric steelmaking route through advanced modelling techniques
Valentina Colla, Antonella Zaccara, Stefano Dettori, Laura Laid, Ismael Matino, Silvia Cateni, Teresa Annunziata Branca, Lorenzo Vannini
Framework with Digital Twins and Federated Learning for Decision Support in Multi-plant Schemes for Electric Steelmaking and Beyond
P. Kannisto
Intelligenza artificiale applicata alla modellazione del ciclo siderurgico elettrico
A. Zaccara, S. Dettori, L. Laid, I. Matino, V. Colla
Journal of Workplace Learning
S. Dettori, A. Zaccara, L. Laid, I. Matino, V. Colla, C. Parea, D. Esteban
Learning for AI in Industrial Settings: The Importance of Transversal Skills in the Metallurgy Sector
D. Stroud, R. Hale, M. Weinel
Life Cycle Assessment as a decision support tool for the implementation of circular economy and decarbonization strategies in the steel industry
Monia Niero, Federico Rossi, Francesca Albano, Fabio Iraldo, Marco Frey
Machine learning-based models applied for improving electric steelworks sustainability
Valentina Colla, Stefano Dettori, Antonella Zaccara, Laura Laid, Ismael Matino, Teresa Annunziata Branca, Silvia Cateni
Understanding the digitalization of work in the steel industry using the sociology of work, industrial sociology and STS.
Book of Abstracts 21st STS Conference Graz 2023 Critical Issues in Science, Technology and Society Studies 8 – 10 May 2023
Hale, R., Stroud, D. and Weinel, M. 
Unlocking the LCA recycling modelling dilemma in steelmaking industry through the inclusion of temporal, spatial, and steel grades variability
Federico Rossi, Fabio Irado, Monia Niero
Unsupervised Anomaly Detection Combining PCA and Neural Gases
Engineering Applications of Neural NetworksDOI
Marco Vannucci, Valentina Colla, Antonella Zaccara, Stefano Dettori, Laura Laid
Deliverables (6)
Other Results (1)
Periodic Reporting for period 1 - ALCHIMIA (Data and decentralized Artificial intelligence for a competitive and green European metallurgy industry)