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Measurement, modelling and optimization of three-phase submerged ARC furnaces (SAF)

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dc.contributor.advisor Wang, Z.
dc.contributor.author Amadi, Amos
dc.date.accessioned 2012-10-09T07:36:10Z
dc.date.available 2012-10-09T07:36:10Z
dc.date.issued 2012-06
dc.identifier.uri http://hdl.handle.net/10500/6588
dc.description.abstract This thesis investigates the modelling and optimization of electro-thermal variable parameters applicable in obtaining an optimal operating point in SAFs. Graphite electrodes that are symmetrically positioned around the furnace are used to convert electrical energy to heat energy via three-phase arcs. The raw materials are fed via conveyor belts from the top of the furnace and are smelted by the arcs produced by the electrodes. The charge constitutes the resistance variable, whilst the heat emitted from the molten charge constitutes the temperature variable. The supply voltage to the furnace constitutes the last variable and it suffers from the network disturbances such as harmonics, dips, surges and others. Although there are many variables that are involved in submerged arc furnace operations, the scope of this thesis is restricted to three electro-thermal variable parameters namely, resistance, voltage and temperature. The measurement of these parameters need to be done accurately and controlled effectively in order to achieve optimum output power during the furnace operation. An amalgamated variable parameter measurement (AVPM) system is proposed for the accurate measurement of these variables by use of mathematically modeled modules. The verification of this proposed measurement system is not considered in this thesis as it is recommended for future study. Modelling is difficult using mathematical functions according to the mechanisms of the actual furnace plant system because of its complexity and many disturbances. The neural networks have been chosen because of its easy to use in modelling nonlinear functions such as the furnace plant. In this thesis, the furnace plant is modeled with the neural networks (NN) algorithm to obtain the SAF NN model. The model is then optimized using the particle swarm optimizer (PSO) algorithm. The formulated PSO based SAF NN model’s results are also validated using the real SAF plant samples. en
dc.format.extent 1 online resource (xv, 96 leaves)
dc.language.iso en en
dc.subject No keywords available en
dc.subject.ddc 621.4021
dc.subject.lcsh Thermodynamics
dc.title Measurement, modelling and optimization of three-phase submerged ARC furnaces (SAF) en
dc.type Dissertation en
dc.description.department Electrical Engineering en
dc.description.degree M. Tech. (Electrical Engineering)


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