Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient
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 c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/10/9/1424 |
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| author | Raul Garcia-Segura Javier Vázquez Castillo Fernando Martell-Chavez Omar Longoria-Gandara Jaime Ortegón Aguilar |
| author_facet | Raul Garcia-Segura Javier Vázquez Castillo Fernando Martell-Chavez Omar Longoria-Gandara Jaime Ortegón Aguilar |
| author_sort | Raul Garcia-Segura |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T02:18:35Z |
| format | Article |
| id | doaj.art-537b26a327724e86888ac1d30dd31b1b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T02:18:35Z |
| publishDate | 2017-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-537b26a327724e86888ac1d30dd31b1b2022-12-22T02:18:06ZengMDPI AGEnergies1996-10732017-09-01109142410.3390/en10091424en10091424Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage GradientRaul Garcia-Segura0Javier Vázquez Castillo1Fernando Martell-Chavez2Omar Longoria-Gandara3Jaime Ortegón Aguilar4Deparment of Engineering, University of Quintana Roo, Chetumal 77019, MexicoDeparment of Engineering, University of Quintana Roo, Chetumal 77019, MexicoResearch Center in Optics, Aguascalientes 20200, MexicoDepartment of Electronics, Systems and IT, ITESO, Tlaquepaque 45604, MexicoDeparment of Engineering, University of Quintana Roo, Chetumal 77019, MexicoElectric 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.https://www.mdpi.com/1996-1073/10/9/1424arc length modelingartificial neural networks (ANN)electric arc furnaceEAF simulation |
| spellingShingle | Raul Garcia-Segura Javier Vázquez Castillo Fernando Martell-Chavez Omar Longoria-Gandara Jaime Ortegón Aguilar Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient Energies arc length modeling artificial neural networks (ANN) electric arc furnace EAF simulation |
| title | Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient |
| title_full | Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient |
| title_fullStr | Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient |
| title_full_unstemmed | Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient |
| title_short | Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient |
| title_sort | electric arc furnace modeling with artificial neural networks and arc length with variable voltage gradient |
| topic | arc length modeling artificial neural networks (ANN) electric arc furnace EAF simulation |
| url | https://www.mdpi.com/1996-1073/10/9/1424 |
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