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Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management

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  • Matino, Ismael
  • Dettori, Stefano
  • Colla, Valentina
  • Weber, Valentine
  • Salame, Sahar

Abstract

The efficient use of resources is a relevant research topic for integrated steelworks. Process off-gases, such as the ones produced during blast furnace operation, are valid substitutes of natural gas, as they are sources of a considerable amount of energy. Currently they are recovered, for instance, by using in hot blast stoves but sometimes part of such gas is flared due to non-optimal management of such resource. In order to exploit the off-gases produced in an integrated steelworks, the interactions between gas producers and users in the whole gas network need to be considered. The paper describes two models exploited by a Decision Support Tool that is under development within a European project. Such models forecast, respectively, the blast furnace gas amount and its heating power by obtaining an error between 1.6 and 6.9% in a time horizon of 2 h and the blast furnace gas demand by hot blast stoves by giving a prediction error between 5.0 and 12.1% in the same time horizon. The forecasted values of blast furnace gas production and main demand allow a continuous optimal planning of the blast furnace gas usage according to its availability and to the needs in the steelworks, by avoiding losses of a valuable secondary resource and related emissions.

Suggested Citation

  • Matino, Ismael & Dettori, Stefano & Colla, Valentina & Weber, Valentine & Salame, Sahar, 2019. "Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:90
    DOI: 10.1016/j.apenergy.2019.113578
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    References listed on IDEAS

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    Cited by:

    1. Wen, Shizhao & Wang, Hongzeng & Qian, Jinhua & Men, Xuanyu, 2023. "A novel combined model based on echo state network optimized by whale optimization algorithm for blast furnace gas prediction," Energy, Elsevier, vol. 279(C).
    2. Teresa Annunziata Branca & Barbara Fornai & Valentina Colla & Maria Ilaria Pistelli & Eros Luciano Faraci & Filippo Cirilli & Antonius Johannes Schröder, 2022. "Skills Demand in Energy Intensive Industries Targeting Industrial Symbiosis and Energy Efficiency," Sustainability, MDPI, vol. 14(23), pages 1-20, November.
    3. Michael Bampaou & Kyriakos Panopoulos & Panos Seferlis & Spyridon Voutetakis & Ismael Matino & Alice Petrucciani & Antonella Zaccara & Valentina Colla & Stefano Dettori & Teresa Annunziata Branca & Vi, 2021. "Integration of Renewable Hydrogen Production in Steelworks Off-Gases for the Synthesis of Methanol and Methane," Energies, MDPI, vol. 14(10), pages 1-24, May.
    4. Sun, Wenqiang & Wang, Qiang & Zhou, Yue & Wu, Jianzhong, 2020. "Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives," Applied Energy, Elsevier, vol. 268(C).
    5. Michael Bampaou & Kyriakos Panopoulos & Panos Seferlis & Amaia Sasiain & Stephane Haag & Philipp Wolf-Zoellner & Markus Lehner & Leokadia Rog & Przemyslaw Rompalski & Sebastian Kolb & Nina Kieberger &, 2022. "Economic Evaluation of Renewable Hydrogen Integration into Steelworks for the Production of Methanol and Methane," Energies, MDPI, vol. 15(13), pages 1-26, June.
    6. Sun, Wenqiang & Wang, Zihao & Wang, Qiang, 2020. "Hybrid event-, mechanism- and data-driven prediction of blast furnace gas generation," Energy, Elsevier, vol. 199(C).
    7. Teresa Annunziata Branca & Barbara Fornai & Valentina Colla & Maria Ilaria Pistelli & Eros Luciano Faraci & Filippo Cirilli & Antonius Johannes Schröder, 2021. "Industrial Symbiosis and Energy Efficiency in European Process Industries: A Review," Sustainability, MDPI, vol. 13(16), pages 1-37, August.

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