Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Comparative Analysis of Models used to Predict the Temperature Decreases in the Steel Making Process using Soft Computing Techniques

철강 생산 공정에서 Soft Computing 기술을 이용한 온도하락 예측 모형의 비교 연구

  • Published : 2007.02.01
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Abstract

This paper is to establish an appropriate model for predicting the temperature decreases in the batch transferred from the refining process to the caster in steel-making companies. Mathematical modeling of the temperature decreases between the processes is difficult, since the reaction mechanism by which the temperature changes in a molten steel batch is dynamic, uncertain and complex. Three soft computing techniques are examined using the same data, namely the multiple regression, fuzzy regression, and neural net (NN) models. To compare the accuracy of these three models, a limited number of input variables are selected from those variables significantly affecting the temperature decrease. The results show that the difference in accuracy between the three models is not statistically significant. Nonetheless, the NN model is recommended because of its adaptive ability and robustness. The method presented in this paper allows the temperature decrease to be predicted without requiring any precise metallurgical knowledge.

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References

  1. K. W. Park, 'Determination of important factors for production planning of steel making plants,' Master Thesis, POSTECH, 2000
  2. J. J. Buckley, T. Feuring, 'Linear and non-linear fuzzy regression: Evolutionary algorithm solutions,' Fuzzy Sets and Systems, 112(3), pp. 381-394, 2000 https://doi.org/10.1016/S0165-0114(98)00154-7
  3. T. Feuring, J. J. Buckley, and Y. Hayashi 'Fuzzy regression: an evolutionary algorithm approach,' Proceedings of the 1998 IEEE International Conference on Fuzzy, pp. 704-708, 1998
  4. Li S, D. C. Wunsch, O'Hair E, and M. G. Giesselmann, 'Comparative analysis of regression and artificial neural network models for wind turbine power curve estimation,' J. of Solar Energy Engineering, vol 123, pp 327-332, 2001 https://doi.org/10.1115/1.1413216
  5. Y. O. Chang and B. M. Ayyub, 'Fuzzy regression methods - a comparative assessment,' Fuzzy Sets and Systems, vol. 119, pp. 187-203, 2001 https://doi.org/10.1016/S0165-0114(99)00091-3
  6. F. Karlsruche, 'Piecewise linear networks (PLN) for process control,' Proceedings of the 2001 IEEE Systems, Man, and Cybernetics Conference, 2001
  7. J. Finol, Y. K. Guo, and X. D. Jing, 'A rule based fuzzy model for the prediction of petrophysical rock parameters,' J. of Petroleum Science and Engineering, vol. 29, pp. 97-113, 2001 https://doi.org/10.1016/S0920-4105(00)00096-6
  8. J. Neter, W. Wasserman, and M. H. Kutner, Applied Linear Statistical Models, Richard D. Irwin, Inc, 1974
  9. S. S. Rao, Optimization, Theory and Applications, 2nd ed. John Wiley & Sons, 1984
  10. The MathWorks, Inc, Neural Network Toolbox User's Guide, The MathWorks, Inc., 2000