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

Información del proyecto

ALCHIMIA

Identificador del acuerdo de subvención: 101070046

DOI

10.3030/101070046

Proyecto cerrado

Fecha de la firma de la CE 13 Junio 2022

Fecha de inicio 1 Septiembre 2022

Fecha de finalización 30 Noviembre 2025

Financiado con arreglo a

• Digital, Industry and Space

Coste total

€ 3 182 500,00

Aportación de la UE

€ 2 576 987,50

Coordinado por

ATOS IT SOLUTIONS AND SERVICES IBERIA SL
Spain

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

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.

Publicaciones

Conference proceedings (23)

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

Autores: Dean Stroud, Rachel Hale; Martin Weinel; Luca Antonazzo; Vinicio Di Iorio
Publicado en: 2026
Editor: ELGAR - Handbook of Disruptive Digital Technology

Journal of Workplace Learning

Autores: S. Dettori, A. Zaccara, L. Laid, I. Matino, V. Colla, C. Parea, D. Esteban
Editor: 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

Autores: Federico Rossi, Fabio Irado, Monia Niero
Editor: Resources, Conservation, and Recycling ELSEVIER

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

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

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

Autores: Valentina Colla, Antonella Zaccara,Stefano Dettori, Laura Laid, Ismael Matino, Silvia Cateni, Teresa Annunziata Branca, Lorenzo Vannini
Editor: 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

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

Machine Learning models to forecast defects occurrence on foundry products

Autores: S. Dettori, A. Zaccara, L. Laid, I. Matino, M. Vannucci, V. Colla, G. Bontempi, L. Forlani
Publicado en: IFAC-PapersOnLine, Edición 58, 2024, ISSN 2405-8963
Editor: Elsevier BV
DOI: 10.1016/J.IFACOL.2024.09.300

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

Autores: Rossi F., Niero M., Colla V., Zaccara A., Dettori S., Laid L., Branza T.A., Cateni S., Vannini L., Iraldo F.
Editor: 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

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

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

Autores: David Blazquez, Irene Garcia and Barbara Fernandez
Publicado en: 2026
Editor: Steel Times International

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

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

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

Autores: Valentina Colla, Antonella Zaccara, Stefano Dettori, Laura Laid, Ismael Matino, Silvia Cateni, Teresa Annunziata Branca, Lorenzo Vannini
Editor: 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.

Autores: Hale, R., Stroud, D. and Weinel, M. 
Publicado en: Book of Abstracts 21st STS Conference Graz 2023 Critical Edicións in Science, Technology and Society Studies 8 – 10 May 2023
Editor: N/A

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

Autores: Dean Stroud and Martin Weinel
Publicado en: 2026
Editor: Steel Times International

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

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

Artificial Intelligence and Transformations of Work

Autores: Dr. Dean Stroud, Dr. Martin Weinel, Dr. Rachel Hale, Dr. Vini di Iorio
Publicado en: Speakers’ Booklet International Conference Artificial Intelligence and Transformations of Work November 20-22, 2023 Grenoble, Edición N/A
Editor: N/A

Intelligenza artificiale applicata alla modellazione del ciclo siderurgico elettrico

Autores: A. Zaccara, S. Dettori, L. Laid, I. Matino, V. Colla
Editor: 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

Autores: Federico Rossi, Monia Niero, Marco Frey
Editor: N/A

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

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

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

Autores: Monia Niero, Federico Rossi, Francesca Albano, Fabio Iraldo, Marco Frey
Editor: N/A

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

Autores: D. Stroud, R. Hale, M. Weinel
Editor: Journal of Workplace Learning

Unsupervised Anomaly Detection Combining PCA and Neural Gases

Autores: Marco Vannucci, Valentina Colla, Antonella Zaccara, Stefano Dettori, Laura Laid
Publicado en: Engineering Applications of Neural Networks, ISBN 978-3-031-62494-0
Editor: 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

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

Other (1)

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

Publicado en: Automaatioväylä, Edición 41(1), 2025, ISSN 0784-6428
Editor: Finnish Society of Automation
DOI: 10.5281/ZENODO.14741719