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

Informazioni relative al progetto

ALCHIMIA

ID dell’accordo di sovvenzione: 101070046

DOI

10.3030/101070046

Progetto chiuso

Data della firma CE 13 Giugno 2022

Data di avvio 1 Settembre 2022

Data di completamento 30 Novembre 2025

Finanziato da

• Digital, Industry and Space

Costo totale

€ 3 182 500,00

Contributo UE

€ 2 576 987,50

Coordinato da

ATOS IT SOLUTIONS AND SERVICES IBERIA SL
Spain

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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.

Pubblicazioni

Conference proceedings (23)

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

Autori: Dean Stroud, Rachel Hale; Martin Weinel; Luca Antonazzo; Vinicio Di Iorio
Pubblicato in: 2026
Editore: ELGAR - Handbook of Disruptive Digital Technology

Journal of Workplace Learning

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

Autori: Federico Rossi, Fabio Irado, Monia Niero
Editore: Resources, Conservation, and Recycling ELSEVIER

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

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

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

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

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

Machine Learning models to forecast defects occurrence on foundry products

Autori: S. Dettori, A. Zaccara, L. Laid, I. Matino, M. Vannucci, V. Colla, G. Bontempi, L. Forlani
Pubblicato in: IFAC-PapersOnLine, Numero 58, 2024, ISSN 2405-8963
Editore: Elsevier BV
DOI: 10.1016/J.IFACOL.2024.09.300

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

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

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

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

Autori: David Blazquez, Irene Garcia and Barbara Fernandez
Pubblicato in: 2026
Editore: Steel Times International

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

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

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

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

Autori: Hale, R., Stroud, D. and Weinel, M. 
Pubblicato in: Book of Abstracts 21st STS Conference Graz 2023 Critical Numeros in Science, Technology and Society Studies 8 – 10 May 2023
Editore: N/A

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

Autori: Dean Stroud and Martin Weinel
Pubblicato in: 2026
Editore: Steel Times International

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

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

Artificial Intelligence and Transformations of Work

Autori: Dr. Dean Stroud, Dr. Martin Weinel, Dr. Rachel Hale, Dr. Vini di Iorio
Pubblicato in: Speakers’ Booklet International Conference Artificial Intelligence and Transformations of Work November 20-22, 2023 Grenoble, Numero N/A
Editore: N/A

Intelligenza artificiale applicata alla modellazione del ciclo siderurgico elettrico

Autori: A. Zaccara, S. Dettori, L. Laid, I. Matino, V. Colla
Editore: 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

Autori: Federico Rossi, Monia Niero, Marco Frey
Editore: N/A

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

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

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

Autori: Monia Niero, Federico Rossi, Francesca Albano, Fabio Iraldo, Marco Frey
Editore: N/A

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

Autori: D. Stroud, R. Hale, M. Weinel
Editore: Journal of Workplace Learning

Unsupervised Anomaly Detection Combining PCA and Neural Gases

Autori: Marco Vannucci, Valentina Colla, Antonella Zaccara, Stefano Dettori, Laura Laid
Pubblicato in: Engineering Applications of Neural Networks, ISBN 978-3-031-62494-0
Editore: 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

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

Other (1)

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

Pubblicato in: Automaatioväylä, Numero 41(1), 2025, ISSN 0784-6428
Editore: Finnish Society of Automation
DOI: 10.5281/ZENODO.14741719