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

Informations projet

ALCHIMIA

N° de convention de subvention: 101070046

DOI

10.3030/101070046

Projet clôturé

Date de signature de la CE 13 Juin 2022

Date de début 1 Septembre 2022

Date de fin 30 Novembre 2025

Financé au titre de

• Digital, Industry and Space

Coût total

€ 3 182 500,00

Contribution de l’UE

€ 2 576 987,50

Coordonné par

ATOS IT SOLUTIONS AND SERVICES IBERIA SL
Spain

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Documents, reports (4)

Project Handbook

T1.1-T1.3. It shall describe all operational aspects of the project, project execution and management in order to provide the consortium partners with background information about specific procedures and norms to be followed during the project lifetime. Quality procedures, risk identification, management and mitigation procedures, as well as guidelines on knowledge management for information sharing, IPR protection and innovation will be here described.

Plan for impact creation, standardisation and exploitation

T6.1-T6.3 Report on the communication, dissemination, and standardisation. This report will include detailed plans for dissemination, communication, standardization and exploitation activities and IPR to be followed by all partners to publicise the ALCHIMA results. This deliverable will also include a preliminary Business Plan for the sustainability of the project

Use-cases preparation

T5.1. Report that describes the readiness of each pilot for the development and deployment activities based on T5.1.

Requirements and human-centric recommendations

T2.1, T2.3 This deliverable will include the stakeholders’ expectations. Also, the findings of the research with survey results and interviews provide insights and foresight recommendations for human-centric technology development and insertion.

Data Management Plan (1)

Data Management Plan

T1.5. It defines the guidelines for data management in order to ensure a high level of data quality and accessibility for final users and stakeholders and to allow the application of data analytics techniques. Note: Financial and progress reporting will be provided at the end of each reporting period through the EC tool.

Other (1)

Federated Learning framework_version 1

T3.1-T3.3 Report and prototype implementation of ALCHIMIA Federated Learning framework, Transfer Learning and domain adaptation techniques.

Publications

Conference proceedings (23)

Artificial Intelligence in Steel Production: Questions of Augmentation, Optimisation and Accountability

Auteurs: Dean Stroud, Rachel Hale; Martin Weinel; Luca Antonazzo; Vinicio Di Iorio
Publié dans: 2026
Éditeur: ELGAR - Handbook of Disruptive Digital Technology

Journal of Workplace Learning

Auteurs: S. Dettori, A. Zaccara, L. Laid, I. Matino, V. Colla, C. Parea, D. Esteban
Éditeur: 18th International Conference on Society & Materials, SAM18, 2024

Unlocking the LCA recycling modelling dilemma in steelmaking industry through the inclusion of temporal, spatial, and steel grades variability

Auteurs: Federico Rossi, Fabio Irado, Monia Niero
Éditeur: Resources, Conservation, and Recycling ELSEVIER

AI-based modelling techniques for input materials optimisation in the EAF route

Auteurs: S. Dettori, A. Zaccara, L. Laid, I. Matino, V. Colla, C. Parea, D. Esteban
Éditeur: SAM Society

Decreasing the environmental impact of the electric steel route through advanced modelling techniques

Auteurs: Valentina Colla, Antonella Zaccara,Stefano Dettori, Laura Laid, Ismael Matino, Silvia Cateni, Teresa Annunziata Branca, Lorenzo Vannini
Éditeur: 10th European Oxygen Steelmaking Conference 10th EOSC – 7th Conference on Clean Technologies in the Steel Industry 7th CTSI EOSC-CTSI 2025

Machine learning-based models applied for improving electric steelworks sustainability

Auteurs: Valentina Colla, Stefano Dettori, Antonella Zaccara, Laura Laid, Ismael Matino, Teresa Annunziata Branca, Silvia Cateni
Éditeur: AISTech-Iron and Steel Technology Conference Proceedings - AISTECH2025

Machine Learning models to forecast defects occurrence on foundry products

Auteurs: S. Dettori, A. Zaccara, L. Laid, I. Matino, M. Vannucci, V. Colla, G. Bontempi, L. Forlani
Publié dans: IFAC-PapersOnLine, Numéro 58, 2024, ISSN 2405-8963
Éditeur: Elsevier BV
DOI: 10.1016/J.IFACOL.2024.09.300

18th International Conference on Society & Materials, SAM18, 2024

Auteurs: Rossi F., Niero M., Colla V., Zaccara A., Dettori S., Laid L., Branza T.A., Cateni S., Vannini L., Iraldo F.
Éditeur: 19th International Conference on Society & Materials, SAM19, 2025

Application of Federated Learning to enhance model-based Decision Support for EAF online Monitoring and Control at multiple plants

Auteurs: B.Kleimt, P. Kannisto, A. Changude, N. Clarevanne, X. Dacquet, N. Garcia, R. Lazcano
Éditeur: European Steel and Application Days (ESTAD) 2025

ALCHIMIA: what it really takes to build industrial AI in European steelmaking

Auteurs: David Blazquez, Irene Garcia and Barbara Fernandez
Publié dans: 2026
Éditeur: Steel Times International

Artificial Intelligence-based decision support system for optimising the ladle furnace process in the ALCHIMIA project

Auteurs: S. Dettori, L. Laid, A. Zaccara, V. Colla, I. Matino, L. Vannini, T.A. Branca, M. Vannucci, A. Siddique, F. Rossi, M. Niero
Éditeur: European Steel and Application Days (ESTAD) 2025

Descreasing the environmental impact of the electric steelmaking route through advanced modelling techniques

Auteurs: Valentina Colla, Antonella Zaccara, Stefano Dettori, Laura Laid, Ismael Matino, Silvia Cateni, Teresa Annunziata Branca, Lorenzo Vannini
Éditeur: Steel Research International (extended version of the one we presented at the CTSI conference in Vienna)

Understanding the digitalization of work in the steel industry using the sociology of work, industrial sociology and STS.

Auteurs: Hale, R., Stroud, D. and Weinel, M. 
Publié dans: Book of Abstracts 21st STS Conference Graz 2023 Critical Numéros in Science, Technology and Society Studies 8 – 10 May 2023
Éditeur: N/A

Embedding Human-Centred AI in Steelmaking: Lessons from the Alchimia Project

Auteurs: Dean Stroud and Martin Weinel
Publié dans: 2026
Éditeur: Steel Times International

19th International Conference on Society & Materials, SAM19, 2025

Auteurs: Rossi F, ALbano F, Frey M, Iraldo F, Niero M
Éditeur: Abstract Book - SETAC Europe 34th Annual Meeting

Artificial Intelligence and Transformations of Work

Auteurs: Dr. Dean Stroud, Dr. Martin Weinel, Dr. Rachel Hale, Dr. Vini di Iorio
Publié dans: Speakers’ Booklet International Conference Artificial Intelligence and Transformations of Work November 20-22, 2023 Grenoble, Numéro N/A
Éditeur: N/A

Intelligenza artificiale applicata alla modellazione del ciclo siderurgico elettrico

Auteurs: A. Zaccara, S. Dettori, L. Laid, I. Matino, V. Colla
Éditeur: La Metallurgia Italiana - International Journal of the Italian Association for Metallurgy

Application of LCA to circular economy strategies in steelmaking industry: state-of-the-art and recommendations

Auteurs: Federico Rossi, Monia Niero, Marco Frey
Éditeur: N/A

Artificial Intelligence for Steelmaking: optimizing processes, augmenting workers, blurring accountability

Auteurs: Dr. Dean Stroud, Dr. Martin Weinel, Dr. Rachel Hale, Dr. Vini di Iorio, Dr. Luca Antonazzo 
Éditeur: N/A

Life Cycle Assessment as a decision support tool for the implementation of circular economy and decarbonization strategies in the steel industry

Auteurs: Monia Niero, Federico Rossi, Francesca Albano, Fabio Iraldo, Marco Frey
Éditeur: N/A

Learning for AI in Industrial Settings: The Importance of Transversal Skills in the Metallurgy Sector

Auteurs: D. Stroud, R. Hale, M. Weinel
Éditeur: Journal of Workplace Learning

Unsupervised Anomaly Detection Combining PCA and Neural Gases

Auteurs: Marco Vannucci, Valentina Colla, Antonella Zaccara, Stefano Dettori, Laura Laid
Publié dans: Engineering Applications of Neural Networks, ISBN 978-3-031-62494-0
Éditeur: Springer Nature
DOI: 10.1007/978-3-031-62495-7_32

Framework with Digital Twins and Federated Learning for Decision Support in Multi-plant Schemes for Electric Steelmaking and Beyond

Auteurs: P. Kannisto
Éditeur: La Metallurgia Italiana - International Journal of the Italian Association for Metallurgy

Other (1)

Optimizing Steelmaking with Models, AI, and Federated and Continual Learning

Publié dans: Automaatioväylä, Numéro 41(1), 2025, ISSN 0784-6428
Éditeur: Finnish Society of Automation
DOI: 10.5281/ZENODO.14741719