Acoustic Monitoring of Plasma Arcs in Direct Current Electric Arc Furnaces |
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Authors: | JJ Burchell C Aldrich JJ Eksteen TR Niesler GT Jemwa |
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Affiliation: | (1) Department of Process Engineering, University of Stellenbosch, Private Bag X1, Matieland, 7602, Stellenbosch, South Africa;(2) Department of Electrical and Electronic Engineering, University of Stellenbosch, Private Bag X1, Matieland, 7602, Stellenbosch, South Africa; |
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Abstract: | In this article, the extraction of features from acoustic signals generated by a 60-kW direct current electric arc furnace
and the use of these features to infer the arc length of the plasma jets in the furnace were considered. A sensor capable
of such measurements would be more robust to the unobservable fluctuations of the arc length and would, in principle, allow
better control of smelting operations. The collected data comprised sets of five separate 10-second recordings of the acoustic
signal, furnace current, and voltage, each at nominal arc lengths of 5, 15, and 25 mm. In the approach, time-frequency features
initially were obtained through filter bank analysis of the signals. Reduction of the dimensionality of these filter bank
features was then performed using a nonlinear subspace method called kernel Fisher discriminant analysis. Finally, kernel
discriminant features were used to infer the arc length via a nearest neighbor classification model that associated three classes of arc lengths (5, 15, and 25 mm) with their corresponding
features. The results of the small number of experiments suggest that a significant statistical relationship exists between
the length of a plasma arc and its acoustic signal despite potentially large variations in arc phenomena inside the furnace. |
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