Department of Chemical Engineering

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Department Process Engineering now has a new name, and will be known from March 2023, as Department of Chemical Engineering.

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Browsing Department of Chemical Engineering by Subject "Acoustical engineering"

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    Acoustic monitoring of DC placma arcs
    (Stellenbosch : Stellenbosch University, 2008-03) Burchell, John James; Eksteen, J. J.; Niesler, T. R.; Aldrich, C.; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.
    ENGLISH ABSTRACT: The arc, generated between the cathode and slag in a dc electric arc furnace (EAF), constitutes the principal source of thermal energy in the furnace. Steady state melting conditions rely on efficient control of the arc's power. This is achieved by keeping the arc's length constant, which is currently not directly measured in the industry, but relies on an external voltage measurement. This voltage measurement is often subject to inaccuracies since it may be influenced by voltage fluctuations that are not necessarily related to the arc itself, such as the variable impedance of the molten bath and the degradation of the graphite electrode. This study investigated whether or not it is possible to develop a sensor for the detection of arc length from the sound that is generated by the arc during operation. Acoustic signals were recorded at different arc lengths using a 60 kW dc electric arc furnace and 600 g of mild steel as melt. Using a filterbank kernel (FB) based Fisher discriminant analysis (KFD) method, nonlinear features were extracted from these signals. The features were then used to train and test a k nearest neighbour (kNN) classifier. Two methods were used to evaluate the performance of the kNN classifier. In the first, both test and train features were extracted from acoustic signals recorded during the same experimental run and used a ten fold bootstrap method for integrity. The second method tested the generalized performance of the classifier. This involved training the kNN classifier with features extracted from the acoustic recordings made during a single or multiple experimental runs and then testing it with features drawn from the remaining experimental runs. The results from this study shows that there exists a relationship between arc length and arc acoustic which can be exploited to develop a sensor for the detection of arc length from arc acoustics in the de EAF. Indications are that the performance of such a sensor would rely strongly on how statistically representative the acoustic data are, used to develop the sensor, to the acoustics generated by industrial dc EAFs during operation.