Abstract
Electric arc furnaces (EAFs) contribute to almost one third of the global steel production. Arc furnaces use a large amount of electrical energy to process scrap or reduced iron and are relevant to study because small improvements in their efficiency account for significant energy savings. Optimal controllers need to be designed and proposed to enhance both process performance and energy consumption. Due to the random and chaotic nature of the electric arcs, neural networks and other soft computing techniques have been used for modeling EAFs. This study proposes a methodology for modeling EAFs that considers the time varying arc length as a relevant input parameter to the arc furnace model. Based on actual voltages and current measurements taken from an arc furnace, it was possible to estimate an arc length suitable for modeling the arc furnace using neural networks. The obtained results show that the model reproduces not only the stable arc conditions but also the unstable arc conditions, which are difficult to identify in a real heat process. The presented model can be applied for the development and testing of control systems to improve furnace energy efficiency and productivity.
Highlights
An electric arc furnace (EAF) [1] is an industrial device used for steel production and other important applications (e.g., [2])
The inclusion of the varying arc length by means of a variable voltage gradient makes it possible to capture the dynamic behavior of the system, allowing for a more real and accurate waveform reproduction of the EAF system model
The convergence of the system model obtained was corroborated by carrying out a case study and using measurements gathered from an EAF under operation during the whole melting process
Summary
An electric arc furnace (EAF) [1] is an industrial device used for steel production and other important applications (e.g., [2]). EAFs work in very large electrical loads ranging from several megavolt amperes (MVA) to 250 MVA in modern facilities, which means that small improvements in efficiency represent significant electrical energy savings. This transformer is fed with high voltage, which delivers energy to the electrodes. In order to keep the electric arcs burning, the electrodes’ currents need to be controlled by means of an electrode control system
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