Next Article in Journal
Olive Mill Wastewater: From a Pollutant to Green Fuels, Agricultural Water Source and Bio-Fertilizer—Part 1. The Drying Kinetics
Previous Article in Journal
The Influence of Eroded Blades on Wind Turbine Performance Using Numerical Simulations
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Energies 2017, 10(9), 1424; https://doi.org/10.3390/en10091424

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

1
Deparment of Engineering, University of Quintana Roo, Chetumal 77019, Mexico
2
Research Center in Optics, Aguascalientes 20200, Mexico
3
Department of Electronics, Systems and IT, ITESO, Tlaquepaque 45604, Mexico
*
Author to whom correspondence should be addressed.
Received: 13 June 2017 / Revised: 3 August 2017 / Accepted: 3 August 2017 / Published: 16 September 2017
(This article belongs to the Section Electrical Power and Energy System)
View Full-Text   |   Download PDF [4817 KB, uploaded 16 September 2017]   |  

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. View Full-Text
Keywords: arc length modeling; artificial neural networks (ANN); electric arc furnace; EAF simulation arc length modeling; artificial neural networks (ANN); electric arc furnace; EAF simulation
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Garcia-Segura, R.; Vázquez Castillo, J.; Martell-Chavez, F.; Longoria-Gandara, O.; Ortegón Aguilar, J. Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient. Energies 2017, 10, 1424.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top