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.
This article proposes an arc stability index for Electrical Arc Furnaces (EAF) based on the real-time processing of the line to ground three-phase voltage measurements typically available at the secondary side of the EAF's power transformer. This methodology uses the definition of a virtual neutral for the open delta connection to calculate the neutral to ground voltage waveform and its Root Mean Square value that can be considered in a simple formula as stability indicator of the arc itself and for the power delivery to the arc. This proposed stability indicator may be used for process monitoring and power control.
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