Acoustic Monitoring of Plasma Arcs in Direct Current Electric Arc Furnaces

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.

New Arc Stability Index for Industrial AC Three-Phase Electric Arc Furnaces Based on Acoustic Signals

“…When the basket was fully fused, and consequently the arcs exhibited more regular behavior, the 1-12 kHz spectral components reduced their levels considerably The behavior of the filtered signals shown in Figure 7b, evidenced the presence of irregularities in the acoustic signal with a presence of higher amplitude peaks for the most unstable conditions (unmelted) that may be associated with short-circuits and restarts of the arcs. In this first stage, the signals showed higher energy in the evaluated frequency range, caused by the erratic movement of the arcs due to the state of the load [15]. The condition of greater stability (molten) presented a filtered signal of less energy and with less dispersion of the peaks associated with the instabilities of the electric arcs.…”

“…Modeling the process from experimental data for arch height classification tasks based on Kernel Fisher discriminant analysis was proposed by Burchell et al [15]. In this case, the authors performed studies in a 60 kW DC EAF and extracted AE signal characteristics under stable conditions, to evaluate 5, 15, and 25 mm arc heights.…”

Analysis of electric arc furnaces efficiency via frequency spectrum-based arc coverage detection

“…Experiments have been done on scale furnaces, as Burchell et al reported. 19 Authors used a 60 kW furnace with arcs of 5, 15 and 25 mm. They recorded using acoustic signals in order to find a possible correlation of the arc length and the noise spectrum generated by the arc.…”