JACIII Vol.14 No.6 pp. 722-728
doi: 10.20965/jaciii.2010.p0722


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

February 10, 2010
April 10, 2010
September 20, 2010
arc furnace, Adaptive Neuro-Fuzzy Inference System (ANFIS), black-box modelling approach, conventional modelling approach
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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