A novel function segmentation methodology for implementing affordable channel emulators

Trejo-Arellano, J. M.; Castillo, J. Vázquez; Longoria-Gándara, O.; Gutiérrez, Carlos; Carrasco-Alvarez, Roberto; Atoche, A. Castillo

doi:10.1109/lamc.2016.7851264

Cited by 1 publication

(1 citation statement)

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“…The ANN used in this study consists of 181 values distributed over 160 weights, 21 biases, and 2 kinds of transfer functions. The transfer functions can be designed via piecewise polynomial approximation techniques [19,20]. In order to corroborate the precision of the ANN-EAF model, a goodness of fit test was carried out [21].…”

Section: Ann Experimental Resultsmentioning

confidence: 99%

Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient

Garcia-Segura

Castillo

Martell³

et al. 2017

Energies

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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.

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Section: Ann Experimental Resultsmentioning

confidence: 99%