An Artificial Intelligence Approach to Develop a Time-Series Prediction Model of the Arc Furnace Resistance

Abu Mohammad Osman Haruni; Michael Negnevitsky

doi:10.20965/jaciii.2010.p0722

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JACIII Vol.14 No.6 pp. 722-728

doi: 10.20965/jaciii.2010.p0722

(2010)

Paper:

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An Artificial Intelligence Approach to Develop a Time-Series Prediction Model of the Arc Furnace Resistance

Abu Mohammad Osman Haruni and Michael Negnevitsky

Centre of Renewable Energy and Power Systems (CREPS), University of Tasmania, Tasmania, Australia

Received:

February 10, 2010

Accepted:

April 10, 2010

Published:

September 20, 2010

Keywords:

arc furnace, Adaptive Neuro-Fuzzy Inference System (ANFIS), black-box modelling approach, conventional modelling approach

Abstract

The control scheme of an arc furnace electrode positioning system aims to deliver an optimum stable reaction zone below the electrodes by maintaining a fixpoint resistance. However, because of random movement of melted materials during melting period, the resistance of the arc furnace changes randomly. As a result, the electrodes have to move accordingly to obtain a fix-point resistance. Moreover, it is often found that the arc furnace resistance changes very fast and it is impossible for the electrode to track the random change of resistance. Consequently, the furnace becomes unstable and it is often impossible to achieve required production per unit power. Hence, the control system often relies on prediction tools. However, it is difficult to predict the arc furnace resistance using conventional mathematical models. As a result, in this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to capture the random and time-varying nature of arc furnace resistance. The performance of the proposed model is evaluated by presenting a case study where the outputs of the proposed model are compared with the data recorded from an actual metallurgical plant.

Cite this article as:

A. Haruni and M. Negnevitsky, “An Artificial Intelligence Approach to Develop a Time-Series Prediction Model of the Arc Furnace Resistance,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.6, pp. 722-728, 2010.

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References

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[18] X.-H. Liu, R. Kuai, P. Guan, X.-M. Ye, and Z.-L. Wu, “Fuzzy-PID control for arc furnace electrode regulator system based on Genetic Algorithm,” 2009 Int. Conf. on Machine Learning and Cybernetics, Vol.2, pp. 683-689, July 12-15, 2009.
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[21] J.-S. R. Jang, C.-T. Sun, and E. Mizutani, “Neuro-Fuzzy and Soft Computing,” A Computational Approach to Learning and Machine Intelligent, Prentice Hall, Englewood Cliffs, NJ, 1993.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] ‘world steel organization,’
http://www.worldsteel.org/index.php?action=storypages&id=196

[2] [2] http://en.wikipedia.org/wiki/Electric_arc_furnace

[3] [3] E. A. C. Plata and H. E. Tacca, “Arc furnace modelling in ATP-EMPT,” Proc. of the Int. Conf. on power system transients (IPST’05), Montreal, Canada, June 19-23, 2005.

[4] [4] A. E. Emanuel and J. A. Orr, “An improved method of simulation of the arc voltage-current characteristics,” Proc. 9th Int. Conf. on harmonics and quality of power, Orlarndo, Florida, pp. 148-150, October 1-4, 2000.

[5] [5] R. C. Dugan, “Simulation of arc furnace power system,” IEEE Trans. on industrial application, Vol.IA-16, No.6, pp. 813-818, November 1980.

[6] [6] M. Negnevitsky, “Artificial Intelligence: a guide to intelligent systems,” 2nd Edition, Addison-Wesley, Harlow, England, 2005.

[7] [7] A. R. Sadeghian and J. D. Lavers, “Application of radial basis function networks to model electric arc furnaces,” International Joint Conference on Neural Network (IJCNN ’99), Vol.6, pp. 3996-4001, July 10-16, 1999, Vol.6, Digital Object Identifier 10.1109/IJCNN.1999.830798.

[8] [8] S. Varadan, E. B. Makram, and A. Girgis, “A new time domain voltage source model for an arc furnace using EMPT,” IEEE Trans. on power delivery, Vol.11, No.3, July 1996.

[9] [9] M. A. P. Alonso and M. P. Donsion, “An improved time domain arc furnace model for harmonic analysis,” IEEE Trans. on power delivery, Vol.19, No.1, pp. 367-373, January 2004.

[10] [10] G. C. Montanari, M. Loggini, and A. Cavallini, “Arc furnace model for the study of flicker compensation in electrical networks,” IEEE Trans. on Power Delivery, Vol.9. No.4, pp. 2026-2033, October 1994.

[11] [11] G. C. Montanari, M. Loggini, and A. Cavallini, “Arc furnace model for the study of flicker compensation in electrical networks,” IEEE Trans. on Power Delivery, Vol.9, No.4, pp. 2026-2033, October 1994.

[12] [12] T. Zheng, E. B. Makram, and A. A. Girgis, “Effect of different arc furnace models on voltage distortion,” Proc. on the 8th Int. Conf. on Harmonics And Quality of Power, Vol.2, pp. 1079-1085, 14-16 Oct. 1998.

[13] [13] H. Schau and D. Stade, “Mathematical Modeling of Three-Phase Arc Fumace,” Proc. of IEEE ICHPS VI, Bologna, pp. 422-428, Sep. 21-23, 1994.

[14] [14] E. O’Neill-Carrillo, G. T. Heydt, E. J. Kostelich, S. S. Venkata, and A. Sundaram, “Nonlinear Deterministic Modeling of Highly Varying Loads,” IEEE Trans. on Power Delivery, Vol.14, No.2, pp. 537-542, April 1999.

[15] [15] G. Jang, W. Wang, G. T. Heydt, S. S. Venkata, and B. Lee, “Development of Enhanced Electric Arc Furnace Models for Transient Analysis,” Electric power component and systems, Vol.29, pp. 1061-1074, 2001.

[16] [16] O. Ozgun and A. Abur, “Flicker Study Using a Novel Arc Furnace Model,” IEEE Trans. on Power Delivery, Vol.17, No.4, pp. 1158-1163, October 2002.

[17] [17] G. W. Chang, C.-I. Chen, and Y.-J. Liu, “A Neural-Network-Based Method of Modeling Electric Arc Furnace Load for Power Engineering Study,” IEEE Trans. on Power Systems, Vol.25, No.1, pp. 138-146, February 2010.

[18] [18] X.-H. Liu, R. Kuai, P. Guan, X.-M. Ye, and Z.-L. Wu, “Fuzzy-PID control for arc furnace electrode regulator system based on Genetic Algorithm,” 2009 Int. Conf. on Machine Learning and Cybernetics, Vol.2, pp. 683-689, July 12-15, 2009.

[19] [19] G. Ping, L. Ji-chao, and L. Xiao-he, “Direct adaptive fuzzy sliding mode control of arc furnace electrode regulator system,” Chinese Control and Decision Conf., pp. 2776-2781, June, 17-19 2009.

[20] [20] Z. Hui and X. Wang, “Prediction Model of Arc Furnace Based on Improved BP Neural Network,” Int. Conf. on Environmental Science and information Application Technology, ESIAT 2009, Vol.3, pp. 664-669, July 4-5, 2009.

[21] [21] J.-S. R. Jang, C.-T. Sun, and E. Mizutani, “Neuro-Fuzzy and Soft Computing,” A Computational Approach to Learning and Machine Intelligent, Prentice Hall, Englewood Cliffs, NJ, 1993.