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Multiple Neural Network Modeling Method for Carbon and Temperature Estimation in Basic Oxygen Furnace

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

In this paper, a novel multiple Neural Network (NN) models including forecasting model, presetting model, adjusting model and judgment model for Basic Oxygen Furnace (BOF) steelmaking dynamic process is introduced. The control system is composed of the preset model of the dynamic requirement for oxygen blowing and coolant adding, bath [C] and temperature prediction model, and judgment model for blowing-stop. In this method, NN technology is used to construct these models above; Fuzzy Inference (FI) is adopted to derive the control law. The control method of BOF steelmaking process has been successfully applied in some steelmaking plants to improve the bath Hit Ratio (HR) significantly.

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References

  1. Sumi, I., Kawabata, R., Kikuchi, Y., et al.: Technique of Controlling Dust Generation during Oxygen Top Blowing in BOF. Steel Research 74, 14–18 (2003)

    Google Scholar 

  2. Swift, T.: BOF Bath Level Measurement for Lance Height Control at the Sparrows Point Plant. Iron and Steelmaker 29, 37–40 (2002)

    MathSciNet  Google Scholar 

  3. Nirschel, W.F., Stone, R.P., Carr, C.J.: Overview of Steelmaking Process Control Sensors for the BOF. Iron and Steelmaker 28, 61–65 (2001)

    Google Scholar 

  4. Dai, Y.G., Li, W.X., Long, T.C.: Modern BOF Steelmaking. Northeastern University Press, Shenyang (1998) (in Chinese)

    Google Scholar 

  5. Wang, X., Li, S.Y., Wang, Z.J., Tao, J., Liu, J.X.: A Multiple RBF NN Modeling Approach to BOF Endpoint Estimation in Steelmaking Process. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 175–180. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  6. Tao, J., Wang, X., Chai, T.Y., et al.: Intelligent Control Method and Application for BOF Steelmaking Process. In: Proceedings of the IFAC World Congress, pp. 1071–1076 (2002)

    Google Scholar 

  7. Mao, K.Z.: RBF Neural Network Center Selection Based on Fisher Ratio Class Separability Measure. IEEE Transactions on Neural Networks 13, 1211–1217 (2002)

    Article  Google Scholar 

  8. Pedrycz, W.: Conditional Fuzzy Clustering in the Design of Radial Basis Function Neural Networks. IEEE Transactions on Neural Networks 9, 601–612 (1998)

    Article  Google Scholar 

  9. Azeem, M.F., Hanmandlu, M., Ahmad, N.: Generalization of Adaptive Neuro-fuzzy Inference Systems. IEEE Transactions on Neural Networks 11, 1332–1346 (2000)

    Article  Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, X., Wang, ZJ., Tao, J. (2006). Multiple Neural Network Modeling Method for Carbon and Temperature Estimation in Basic Oxygen Furnace. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_127

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  • DOI: https://doi.org/10.1007/11760191_127

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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