Department of Electrical and Electronic Engineering
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Electrical and Electronic Engineering is an exciting and dynamic field. Electrical engineers are responsible for the generation, transfer and conversion of electrical power, while electronic engineers are concerned with the transfer of information using radio waves, the design of electronic circuits, the design of computer systems and the development of control systems such as aircraft autopilots. These sought-after engineers can look forward to a rewarding and respected career.
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Browsing Department of Electrical and Electronic Engineering by browse.metadata.advisor "Aldrich, C."
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- ItemThe development of a one-dimensional quasi-steady state model for the desulphurisation process at Saldanha Steel(Stellenbosch : University of Stellenbosch, 2003-04) Scheepers, Emile; Eksteen, J. J.; Aldrich, C.; University of Stellenbosch. Faculty of Engineering. Dept. of Process Engineering.ENGLISH ABSTRACT: The pneumatic injection of reagent powder into molten iron has become the preferred way to carry out iron and steel desulphurisation. It is therefore essential to not only understand the thermodynamic implications, but also the kinetic principles that govern the desulphurisation process. Key variables that influence the kinetics of the procedure are the condition and composition of the top slag and the melt as well as the injection conditions. Notable injection parameters include reagent flowrate, injection-lance depth and carrier gas flowrate. Owing to sampling restrictions, the subsequent data from Saldanha Steel®, South Africa does not provide adequate insight into the kinetic behaviour of the desulphurisation process and it was therefore the focus of this research to provide an improved quantitive comprehension of the calcium carbide injection procedure at Saldanha Steel. For this purpose a one-dimensional quasi-steady state model for momentum, heat- and mass transfer in rising gas-liquid-powder plumes has been developed for conditions relevant to the Saldanha Steel refining process. Combined with a model predicting the contribution of the topslag to the process, the overall rate of desulphurisation as a function of time can be determined, thus affording the ability to quantitatively explore and analyse the influence of the afore-mentioned injection parameters, as well as the nature of both the topslag and the melt, on the kinetics of the desulphurisation process. Sensitivity analyses concluded that individual increases in the calcium carbide flowrate, the depth of injection and the amount of carry-over slag will result in a reduction in the injection time, while a decrease in the reagent particle diameter and the initial mass of iron in the ladle will have the same effect. Molten iron temperature losses brought about by prolonged injection needs to be electrically recovered within a steelmaking furnace at a high cost. Owing to the high cost of the desulphurising agent, any reduction in the required injection time, while still maintaining product specifications, will therefore result in diminishing overall production costs. Although all the results contained in this study is of particular interest to the Saldanha Steel scenario, it also provides invaluable information and insights into the important variables and parameters playing a role in injection desulphurisation processes in general, along with the influence that changing conditions can have on the end result of such a procedure.
- ItemEmpirical state space modelling with application in online diagnosis of multivariate non-linear dynamic systems(Stellenbosch : Stellenbosch University, 1999-12) Barnard, Jakobus Petrus; Aldrich, C.; Gerber, M.; University of Stellenbosch. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. .ENGLISH ABSTRACT: System identification has been sufficiently formalized for linear systems, but not for empirical identification of non-linear, multivariate dynamic systems. Therefore this dissertation formalizes and extends non-linear empirical system identification for the broad class of nonlinear multivariate systems that can be parameterized as state space systems. The established, but rather ad hoc methods of time series embedding and nonlinear modeling, using multilayer perceptron network and radial basis function network model structures, are interpreted in context with the established linear system identification framework. First, the methodological framework was formulated for the identification of non-linear state space systems from one-dimensional time series using a surrogate data method. It was clearly demonstrated on an autocatalytic process in a continuously stirred tank reactor, that validation of dynamic models by one-step predictions is insufficient proof of model quality. In addition, the classification of data as either dynamic or random was performed, using the same surrogate data technique. The classification technique proved to be robust in the presence of up to at least 10% measurement and dynamic noise. Next, the formulation of a nearly real-time algorithm for detection and removal of radial outliers in multidimensional data was pursued. A convex hull technique was proposed and demonstrated on random data, as well as real test data recorded from an internal combustion engine. The results showed the convex hull technique to be effective at a computational cost two orders of magnitude lower than the more proficient Rocke and Woodruff technique, used as a benchmark, and incurred low cost (0.9%) in terms of falsely identifying outliers. Following the identification of systems from one-dimensional time series, the methodological framework was expanded to accommodate the identification of nonlinear state space systems from multivariate time series. System parameterization was accomplished by combining individual embeddings of each variable in the multivariate time series, and then separating this combined space into independent components, using independent component analysis. This method of parameterization was successfully applied in the simulation of the abovementioned autocatalytic process. In addition, the parameterization method was implemented in the one-step prediction of atmospheric N02 concentrations, which could become part of an environmental control system for Cape Town. Furthermore, the combination of the embedding strategy and separation by independent component analysis was able to isolate some of the noise components from the embedded data. Finally the foregoing system identification methodology was applied to the online diagnosis of temporal trends in critical system states. The methodology was supplemented by the formulation of a statistical likelihood criterion for simultaneous interpretation of multivariate system states. This technology was successfully applied to the diagnosis of the temporal deterioration of the piston rings in a compression ignition engine under test conditions. The diagnostic results indicated the beginning of significant piston ring wear, which was confirmed by physical inspection of the engine after conclusion of the test. The technology will be further developed and commercialized.