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

Citation

Haruni, AMO and Negnevitsky, M, An Artificial Intelligence Approach to Develop a Time-Series Prediction Model of The Arc Furnace Resistance, Journal of Advanced Computational Intelligence, 14, (6) pp. 722-728. ISSN 1343-0130 (2010) [Refereed Article]

DOI: doi:10.20965/jaciii.2010.p0722

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.

Item Details

Item Type:Refereed Article
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Computer Vision
Objective Division:Information and Communication Services
Objective Group:Communication Networks and Services
Objective Field:Fixed Line Data Networks and Services
UTAS Author:Haruni, AMO (Mr Abu Haruni)
UTAS Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:66905
Year Published:2010
Deposited By:Engineering
Deposited On:2011-02-17
Last Modified:2014-12-23
Downloads:0

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