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CORDIS - EU research results
CORDIS

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

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Project Handbook (opens in new window)

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 (opens in new window)

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 (opens in new window)

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 (opens in new window)

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 (opens in new window)

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.

Federated Learning framework_version 1 (opens in new window)

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

Publications

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

Author(s): Dean Stroud, Rachel Hale; Martin Weinel; Luca Antonazzo; Vinicio Di Iorio
Published in: 2026
Publisher: ELGAR - Handbook of Disruptive Digital Technology

Journal of Workplace Learning

Author(s): S. Dettori, A. Zaccara, L. Laid, I. Matino, V. Colla, C. Parea, D. Esteban
Publisher: 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

Author(s): Federico Rossi, Fabio Irado, Monia Niero
Publisher: Resources, Conservation, and Recycling ELSEVIER

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

Author(s): S. Dettori, A. Zaccara, L. Laid, I. Matino, V. Colla, C. Parea, D. Esteban
Publisher: SAM Society

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

Author(s): Valentina Colla, Antonella Zaccara,Stefano Dettori, Laura Laid, Ismael Matino, Silvia Cateni, Teresa Annunziata Branca, Lorenzo Vannini
Publisher: 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

Author(s): Valentina Colla, Stefano Dettori, Antonella Zaccara, Laura Laid, Ismael Matino, Teresa Annunziata Branca, Silvia Cateni
Publisher: AISTech-Iron and Steel Technology Conference Proceedings - AISTECH2025

Machine Learning models to forecast defects occurrence on foundry products (opens in new window)

Author(s): S. Dettori, A. Zaccara, L. Laid, I. Matino, M. Vannucci, V. Colla, G. Bontempi, L. Forlani
Published in: IFAC-PapersOnLine, Issue 58, 2024, ISSN 2405-8963
Publisher: Elsevier BV
DOI: 10.1016/J.IFACOL.2024.09.300

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

Author(s): Rossi F., Niero M., Colla V., Zaccara A., Dettori S., Laid L., Branza T.A., Cateni S., Vannini L., Iraldo F.
Publisher: 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

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

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

Author(s): David Blazquez, Irene Garcia and Barbara Fernandez
Published in: 2026
Publisher: Steel Times International

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

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

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

Author(s): Valentina Colla, Antonella Zaccara, Stefano Dettori, Laura Laid, Ismael Matino, Silvia Cateni, Teresa Annunziata Branca, Lorenzo Vannini
Publisher: 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.

Author(s): Hale, R., Stroud, D. and Weinel, M. 
Published in: Book of Abstracts 21st STS Conference Graz 2023 Critical Issues in Science, Technology and Society Studies 8 – 10 May 2023
Publisher: N/A

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

Author(s): Dean Stroud and Martin Weinel
Published in: 2026
Publisher: Steel Times International

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

Author(s): Rossi F, ALbano F, Frey M, Iraldo F, Niero M
Publisher: Abstract Book - SETAC Europe 34th Annual Meeting

Artificial Intelligence and Transformations of Work

Author(s): Dr. Dean Stroud, Dr. Martin Weinel, Dr. Rachel Hale, Dr. Vini di Iorio
Published in: Speakers’ Booklet International Conference Artificial Intelligence and Transformations of Work November 20-22, 2023 Grenoble, Issue N/A
Publisher: N/A

Intelligenza artificiale applicata alla modellazione del ciclo siderurgico elettrico

Author(s): A. Zaccara, S. Dettori, L. Laid, I. Matino, V. Colla
Publisher: 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

Author(s): Federico Rossi, Monia Niero, Marco Frey
Publisher: N/A

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

Author(s): Dr. Dean Stroud, Dr. Martin Weinel, Dr. Rachel Hale, Dr. Vini di Iorio, Dr. Luca Antonazzo 
Publisher: N/A

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

Author(s): Monia Niero, Federico Rossi, Francesca Albano, Fabio Iraldo, Marco Frey
Publisher: N/A

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

Author(s): D. Stroud, R. Hale, M. Weinel
Publisher: Journal of Workplace Learning

Unsupervised Anomaly Detection Combining PCA and Neural Gases (opens in new window)

Author(s): Marco Vannucci, Valentina Colla, Antonella Zaccara, Stefano Dettori, Laura Laid
Published in: Engineering Applications of Neural Networks, ISBN 978-3-031-62494-0
Publisher: 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

Author(s): P. Kannisto
Publisher: La Metallurgia Italiana - International Journal of the Italian Association for Metallurgy

Optimizing Steelmaking with Models, AI, and Federated and Continual Learning (opens in new window)

Published in: Automaatioväylä, Issue 41(1), 2025, ISSN 0784-6428
Publisher: Finnish Society of Automation
DOI: 10.5281/ZENODO.14741719

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