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Optimization of Sinter Plant Operating Conditions Using Advanced Multivariate Statistics: Intelligent Data Processing

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Abstract

Blast furnace operators expect to get sinter with homogenous and regular properties (chemical and mechanical), necessary to ensure regular blast furnace operation. Blends for sintering also include several iron by-products and other wastes that are obtained in different processes inside the steelworks. Due to their source, the availability of such materials is not always consistent, but their total production should be consumed in the sintering process, to both save money and recycle wastes. The main scope of this paper is to obtain the least expensive iron ore blend for the sintering process, which will provide suitable chemical and mechanical features for the homogeneous and regular operation of the blast furnace. The systematic use of statistical tools was employed to analyze historical data, including linear and partial correlations applied to the data and fuzzy clustering based on the Sugeno Fuzzy Inference System to establish relationships among the available variables.

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References

  1. P.R. Dawson, Ironmak. Steelmak. 20, 137 (1993).

    Google Scholar 

  2. J. Mitterlehner, G. Loeffler, F. Winter, H. Hofbauer, H. Schmid, E. Zwittag, T.H. Buergler, O. Pammer, and H. Stiasny, ISIJ Int. 44, 11 (2004).

    Article  Google Scholar 

  3. W. Yang, R. Changkook, S. Choi, E. Choi, D. Lee, and W. Huh, ISIJ Int. 44, 492 (2004).

    Article  Google Scholar 

  4. G. Szatvanyi, C. Duchesne, and G. Bartolacci, Ind. Eng. Chem. Res. 45, 4706 (2006).

    Article  Google Scholar 

  5. L. Wang, Adaptive Fuzzy System and Control Design and Stability Analysis (New York: Prentice Hall, 1994), p. 232.

    Google Scholar 

  6. N. Gulley and J.R. Jang, Fuzzy Logic Toolbox User’s Guide (Natick: The Math Works, 2005).

    Google Scholar 

  7. J.A. Freeman and D.M. Skapura, Neuronal Network—Algorithms, Application and Programming Techniques (New York: Addison Wesley Publishing Company, 1991), p. 401.

    MATH  Google Scholar 

  8. R.K. Sarma, A. Gupta, and S. Vadhavkar, Neural Network-Based Data Mining Techniques for Steel Making, ed. H. Bozdogan (Florida: CRC Press LLC, 2004), p. 401.

  9. J. Hinnelä and H. Saxén, ISIJ Int. 41, 142 (2001).

    Article  Google Scholar 

  10. P.J. Laitinen and H. Saxén, Ironmak. Steelmak. 34, 109 (2007).

    Article  Google Scholar 

  11. A. Cores, L.F. Verdeja, S. Ferreira, I. Ruiz-Bustinza, and J. Mochón, Dyna-Colombia 80, 152 (2013).

    Google Scholar 

  12. J. Mochón, A. Cores, I. Ruiz-Bustinza, L.F. Verdeja, J.I. Robla, and F. García-Carcedo, Dyna-Colombia 81, 168 (2014).

    Google Scholar 

  13. A. Cores, L.F. Verdeja, S. Ferreira, I. Ruiz-Bustinza, J. Mochón, J.I. Robla, and C. González-Gasca, Dyna-Colombia 82, 227 (2015).

    Google Scholar 

  14. J. Iglesias, J. Sancho, M. Ordiales, D. Fernández, J. Mochón, I. Ruiz-Bustinza, A. Fuentes, and L.F. Verdeja, Ironmak. Steelmak. 42, 618 (2015).

    Article  Google Scholar 

  15. M.R. Martín, Optimización de las condiciones de operación de una planta de sínter y localización del parque de minerales por medio de estadísticas multivariantes avanzadas y lógica difusa (Oviedo, ES: Universidad de Oviedo, PhD. Thesis, 2015).

  16. R. Fletcher, Practical Methods of Optimisation: Constrained Optimisation, vol. 2 (Chichester: Wiley, 1981), p. 224.

    Google Scholar 

  17. R. Barea, J. Mochón, A. Cores, and R. Martín, ISIJ Int. 46, 687 (2006).

    Article  Google Scholar 

  18. R. Martín, J. Mochón, L.F. Verdeja, R. Barea, P. Rusek, and J. Jiménez, Steel Res. Int. 80, 185 (2009).

    Google Scholar 

  19. J.S.R. Jang, C.T. Sun, and E. Mizutani, IEEE Trans. Autom. Control 42, 1482 (1997).

    Article  Google Scholar 

  20. J.S. Bendat and A.G. Piersol, Random Data. Analysis and Measurements Procedures, (New York: Wiley, 1987), p. 566.

  21. J.M. Mendel, IEEE Proc. 83, 345 (1995).

    Article  Google Scholar 

  22. J.S.R. Jang, C.T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing (New York: Prentice Hall, 1996), p. 614.

    Google Scholar 

  23. A. Priyono, M. Ridwan, A.J. Alias, R. Atiq, O.K. Rahmat, A. Hassan, and A. Mohd, J. Teknol. 43, 143 (2005).

    Google Scholar 

  24. A. Lofti, H.C. Andersen, and A.C. Tsoi, IEEE Trans. Syst. Man Cybern. B 26, 332 (1996).

    Article  Google Scholar 

  25. R.R. Yager and D.P. Filev, J. Intell. Fuzzy Syst. 2, 209 (1994).

    Google Scholar 

  26. M.V. Ramos, E. Kasai, J. Kano, and T. Nakamura, ISIJ Int. 40, 448 (2000).

    Article  Google Scholar 

  27. A.K. Lohani, N.K. Goel, and K.K.S. Bhatia, J. Hydrol. 331, 146 (2006).

    Article  Google Scholar 

  28. L.S. Meyers, G. Gamst, and A.J. Guarino, Applied Multivariate Research. Design and Interpretation, 2nd ed. (California, CA: SAGE Publications, Inc. 2013), pp. 337–338.

  29. J.P. Sancho, L.F. Verdeja, and A. Ballester, Metalurgia Extractiva. Volumen II: Procesos de obtención, (Madrid, ES: Síntesis, 2000), pp. 22–27.

  30. A. Babich, D. Senk, H.W. Gudenau, and TTh Mavrommatis, Ironmaking, (Aachen, DE: Department of Ferreous Metallurgy (Aachen: RWTH Aachen University, 2008), pp. 50–64.

    Google Scholar 

  31. E. Lwamba and A.M. Garbers-Craig, J. S. Afr. I Min. Metall. 108, 293 (2008).

    Google Scholar 

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Acknowledgements

The authors wish to extend their appreciation to the Spanish MICYT (MAT 2001-4435-E) for their financial support. The research was also supported by the Spanish Ministry of Education, Culture and Sports via an FPU (Formación del Profesorado Universitario) grant to Daniel Fernández González (FPU014/02436).

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Authors and Affiliations

  1. Research Group in Siderurgy, Metallurgy and Materials, Department of Materials Science and Metallurgical Engineering, Oviedo School of Mines, University of Oviedo, Independencia Street, 13, 33004, Oviedo, Asturias, Spain

    Daniel Fernández-González & Luis Felipe Verdeja

  2. Research Group in Processes, Materials and Energy in Ecological and Sustainable Metallurgy (PROMESS), Department of Primary Metallurgy and Recycling, National Centre of Metallurgical Researches, National Council of Scientific Researches (CENIM-CSIC), Gregorio del Amo Avenue, 8, 28001, Madrid, Spain

    Ramón Martín-Duarte, Íñigo Ruiz-Bustinza & Javier Mochón

  3. Universidad Europea – Laureate International Universities, Calle Tajo s/n, Villaviciosa de Odón, 28670, Madrid, Spain

    Carmen González-Gasca

Authors
  1. Daniel Fernández-González
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  2. Ramón Martín-Duarte
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  3. Íñigo Ruiz-Bustinza
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  4. Javier Mochón
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  5. Carmen González-Gasca
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  6. Luis Felipe Verdeja
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Correspondence to Daniel Fernández-González.

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Fernández-González, D., Martín-Duarte, R., Ruiz-Bustinza, Í. et al. Optimization of Sinter Plant Operating Conditions Using Advanced Multivariate Statistics: Intelligent Data Processing. JOM 68, 2089–2095 (2016). https://doi.org/10.1007/s11837-016-2002-2

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  • DOI: https://doi.org/10.1007/s11837-016-2002-2

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