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

Projektinformationen

ALCHIMIA

ID Finanzhilfevereinbarung: 101070046

DOI

10.3030/101070046

Projekt abgeschlossen

EK-Unterschriftsdatum 13 Juni 2022

Startdatum 1 September 2022

Enddatum 30 November 2025

Finanziert unter

• Digital, Industry and Space

Gesamtkosten

€ 3 182 500,00

EU-Beitrag

€ 2 576 987,50

Koordiniert durch

ATOS IT SOLUTIONS AND SERVICES IBERIA SL
Spain

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

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.

Veröffentlichungen

Conference proceedings (23)

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

Autoren: Dean Stroud, Rachel Hale; Martin Weinel; Luca Antonazzo; Vinicio Di Iorio
Veröffentlicht in: 2026
Herausgeber: ELGAR - Handbook of Disruptive Digital Technology

Journal of Workplace Learning

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

Autoren: Federico Rossi, Fabio Irado, Monia Niero
Herausgeber: Resources, Conservation, and Recycling ELSEVIER

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

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

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

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

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

Machine Learning models to forecast defects occurrence on foundry products

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

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

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

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

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

Autoren: David Blazquez, Irene Garcia and Barbara Fernandez
Veröffentlicht in: 2026
Herausgeber: Steel Times International

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

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

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

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

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

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

Autoren: Dean Stroud and Martin Weinel
Veröffentlicht in: 2026
Herausgeber: Steel Times International

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

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

Artificial Intelligence and Transformations of Work

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

Intelligenza artificiale applicata alla modellazione del ciclo siderurgico elettrico

Autoren: A. Zaccara, S. Dettori, L. Laid, I. Matino, V. Colla
Herausgeber: 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

Autoren: Federico Rossi, Monia Niero, Marco Frey
Herausgeber: N/A

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

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

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

Autoren: Monia Niero, Federico Rossi, Francesca Albano, Fabio Iraldo, Marco Frey
Herausgeber: N/A

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

Autoren: D. Stroud, R. Hale, M. Weinel
Herausgeber: Journal of Workplace Learning

Unsupervised Anomaly Detection Combining PCA and Neural Gases

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

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

Other (1)

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

Veröffentlicht in: Automaatioväylä, Ausgabe 41(1), 2025, ISSN 0784-6428
Herausgeber: Finnish Society of Automation
DOI: 10.5281/ZENODO.14741719