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Machine Learning-Based Models for Supporting Optimal Exploitation of Process Off-Gases in Integrated Steelworks

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Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry (ESTEP 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1338))

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Abstract

Within integrated steelworks, several sub-processes produce off-gases, which are suitable for reuse as energy sources for other internal processes as well as for the production of energy. An adequate and optimal distribution of these gases among their users allows valorizing at best their energy content by minimizing the need to both burn them through torches due to storage issues and to acquire natural gas to satisfy the internal energetic demand. To this purpose, the volume and energetic value of produced gases as well as the demands from internal users must be known in advance, in order to implement model-predictive control strategies aimed at satisfying the demands on the short-medium term based on the production scheduling. Such forecasting knowledge also enhances the capability to react to the variability of the process scheduling as well as to other unforeseen events.

The paper depicts an application of Machine Learning-based models to forecast off-gases and further energy carriers productions and demands within integrated steelworks. The forecasting models are integrated into a complex hierarchical control strategy aimed at optimizing the distribution of such gases.

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Acknowledgments

The work described in the paper was developed within the project “Optimization of the management of the process gases network within the integrated steelworks - GASNET” (Contract No. RFSR-CT-2015-00029) and received funding from the Research Fund for Coal and Steel of the European Union, which is gratefully acknowledged. The sole responsibility of the issues treated in the present paper lies with the authors; the Union is not responsible for any use that may be made of the information contained therein.

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Authors and Affiliations

  1. Scuola Superiore Sant’Anna, TeCIP Institute, ICT-COISP Center, Pisa, Italy

    Ismael Matino, Stefano Dettori, Angelo Castellano, Ruben Matino, Claudio Mocci, Marco Vannocci, Alessandro Maddaloni & Valentina Colla

  2. Betriebsforschungsinstitut, Sohnstrasse 65, 40237, Düsseldorf, Germany

    Andreas Wolff

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  1. Ismael Matino
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Correspondence to Ismael Matino .

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Editors and Affiliations

  1. TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy

    Valentina Colla

  2. Danieli Automation, Buttrio, Italy

    Costanzo Pietrosanti

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Matino, I. et al. (2021). Machine Learning-Based Models for Supporting Optimal Exploitation of Process Off-Gases in Integrated Steelworks. In: Colla, V., Pietrosanti, C. (eds) Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry. ESTEP 2020. Advances in Intelligent Systems and Computing, vol 1338. Springer, Cham. https://doi.org/10.1007/978-3-030-69367-1_9

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