A Dive into the Specific Electric Energy Consumption in Steelworks

  • C. Mocci
  • A. Maddaloni
  • M. Vannucci
  • S. Cateni
  • V. Colla
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)

Abstract

The paper describes an application of optimization techniques for the minimization of the specific electrical energy consumption related to the production of steel for a steelworks situated in Italy. The major electrical consumption derives from two internal plants: the Electric Arc Furnace and the Ladle Furnace. This work addresses the problem of understanding the best settings (based on predefined models) to produce a specific steel, which is mainly characterized by its steelgrade and quality, with the minimum energy consumption.

Keywords

Electrical energy Energy savings Steelworks Electric Arc Furnace Ladle Furnace Mathematical modelling Neural Networks Variable selection 

Notes

Acknowledgments

The work described in the present paper has been developed within the project entitled “Application of a factory wide and product related energy database for energy consumption” (Ref. EnergyDB, Contract No. RFSR-CT-2013-00027) that has 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 Commission is not responsible for any use that may be made of the information contained therein.

References

  1. 1.
  2. 2.
    Birat, J.P., Malfa, E., Colla, V., Thomas, J.S.: Sustainable steel production for the 2030s: the vision of the European steel technology platform’s strategic research agenda (ESTEP’s SRA). In: Technical Proceedings of the 2014 NSTI Nanotechnology Conference and Expo, NSTI-Nanotech 2014, vol. 3, pp. 238–241 (2014)Google Scholar
  3. 3.
    Cateni, S., Colla, V., Vannucci, M.: General purpose input variable extraction: a genetic algorithm based procedure give a gap. In: 9th International Conference on Intelligence Systems design and Applications, ISDA 2009, pp. 1307–1311, November 2009Google Scholar
  4. 4.
    Cateni, S., Colla, V., Vannucci, M.: Variable selection through genetic algorithms for classification purpose. In: IASTED International Conference on Artificial Intelligence and Applications, AIA 2010, pp. 6–11, February 2010Google Scholar
  5. 5.
    Cateni, S., Colla, V., Nastasi, G.: A multivariate fuzzy system applied for outliers detection. J. Intell. Fuzzy Syst. 24(4), 889–903 (2013)MathSciNetGoogle Scholar
  6. 6.
    Cateni, S., Colla, V., Vannucci, M.: A fuzzy logic-based method for outliers detection. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2007, pp. 561–566 (2007)Google Scholar
  7. 7.
    Colla, V., Cirilli, F., Kleimt, B., Unamuno, I., Tosato, S., Baragiola, S., Klung, J.S., Quintero, B.P., Miranda, U.D.: Monitoring the environmental and energy impacts of electric arc furnace steelmaking. Materiaux et Techniques 104(1) (2016)CrossRefGoogle Scholar
  8. 8.
    Colla, V., Matino, I., Cirilli, F., Jochler, G., Kleimt, B., Rosemann, H., Unamuno, I., Tosato, S., Gussago, F., Baragiola, S., Klung, J.S., Quintero, B.P., Alonso, A., Miranda, U.D.: Improving energy and resource efficiency of electric steelmaking through simulation tools and process data analyses. Materiaux et Techniques 104(6–7) (2016)CrossRefGoogle Scholar
  9. 9.
    Fleischer, M., Schwörer, J., Lückhoff, J.: Green technologies for higher energy efficiency in electric steelmaking. In: AISTech - Iron and Steel Technology Conference Proceedings, vol. 1, pp. 753–759 (2013)Google Scholar
  10. 10.
    Fröhling, M., Schwaderer, F., Bartusch, H., Schultmann, F.: Analyzing energy and resource efficiency measures in the steel and zinc industry combining flowsheet simulation with a linear material and energy flow model. Revue de Metallurgie. Cahiers D’Informations Techniques 109(5), 359–367 (2012)CrossRefGoogle Scholar
  11. 11.
    MathWorks Inc.: Matlab 2017a (2017)Google Scholar
  12. 12.
    Matino, I., Alcamisi, E., Colla, V., Baragiola, S., Moni, P.: Process modelling and simulation of electric arc furnace steelmaking to allow prognostic evaluations of process environmental and energy impacts. Materiaux et Techniques 104(1) (2016)CrossRefGoogle Scholar
  13. 13.
    Matino, I., Colla, V., Colucci, V., Lamia, P., Baragiola, S., Cecca, C.D.: Improving sustainability of electric steelworks through process simulations. Chem. Eng. Trans. 52, 763–768 (2016)Google Scholar
  14. 14.
    Milford, R., Pauliuk, S., Allwood, J., Müller, D.: The roles of energy and material efficiency in meeting steel industry CO2 targets. Environ. Sci. Technol. 47(7), 3455–3462 (2013)CrossRefGoogle Scholar
  15. 15.
    Peters, K., Malfa, E., Colla, V., Brimacombe, L.: Resource efficiency in the strategic research agenda of the European steel technology platform. In: 2015 World Congress on Sustainable Technologies, WCST 2015, pp. 34–39 (2016)Google Scholar
  16. 16.
    Rojas-Cardenas, J., Hasanbeigi, A., Sheinbaum-Pardo, C., Price, L.: Energy efficiency in the Mexican iron and steel industry from an international perspective. J. Clean. Prod. 158, 335–348 (2017)CrossRefGoogle Scholar
  17. 17.
    Teng, L., Hackl, H.: Energy efficiency improvement and cost saving opportunities in EAF by arc save. In: Proceedings of 6th International Congress on the Science and Technology of Steelmaking, ICS 2015 (2015)Google Scholar
  18. 18.
    Toulouevski, Y.: Energy efficiency: methods, limitations and challenges. In: Methods for Determination of Energy Efficiency in Electric Arc Furnaces, pp. 153–168. Nova Science Publishers, Inc. (2012)Google Scholar
  19. 19.
    Zuev, M., Babenko, A., Burmasov, S., Zhitlukhin, E., Ushakov, M., Belyov, A., Murzin, A., Stepanov, A., Selivanov, E., Spirin, S.: Set of production and engineering solutions for reducing energy and material consumption of semifinished steel melting in contemporary electric arc furnaces. Metallurgist 58(7–8), 582–587 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • C. Mocci
    • 1
  • A. Maddaloni
    • 1
  • M. Vannucci
    • 1
  • S. Cateni
    • 1
  • V. Colla
    • 1
  1. 1.ICT-COISP Center, TeCIP InstituteScuola Superiore Sant’ AnnaPisaItaly

Personalised recommendations