Next Article in Journal
Deep Borehole Disposal Safety Case
Next Article in Special Issue
Ancillary Services Provided by Hybrid Residential Renewable Energy Systems through Thermal and Electrochemical Storage Systems
Previous Article in Journal
Future Generation 5G Wireless Networks for Smart Grid: A Comprehensive Review
Previous Article in Special Issue
Analytical and Experimental Investigation of the Solar Chimney System
Open AccessArticle

Comprehensive Electric Arc Furnace Electric Energy Consumption Modeling: A Pilot Study

1
Štore Steel Ltd., Železarska cesta 3, SI-3220 Štore, Slovenia
2
Faculty of Mechanical Engineering, University in Ljubljana, Aškerčeva 6, SI-1000 Ljubljana, Slovenia
3
Institute of Metals and Technology, Lepi pot 11, SI-1000 Ljubljana, Slovenia
*
Author to whom correspondence should be addressed.
Energies 2019, 12(11), 2142; https://doi.org/10.3390/en12112142
Received: 28 February 2019 / Revised: 15 May 2019 / Accepted: 3 June 2019 / Published: 4 June 2019
(This article belongs to the Special Issue Heat and Mass Transfer in Energy Systems)
The electric arc furnace operation at the Štore Steel company, one of the largest flat spring steel producers in Europe, consists of charging, melting, refining the chemical composition, adjusting the temperature, and tapping. Knowledge of the consumed energy within the individual electric arc operation steps is essential. The electric energy consumption during melting and refining was analyzed including the maintenance and technological delays. In modeling the electric energy consumption, 25 parameters were considered during melting (e.g., coke, dolomite, quantity), refining and tapping (e.g., injected oxygen, carbon, and limestone quantity) that were selected from 3248 consecutively produced batches in 2018. Two approaches were employed for the data analysis: linear regression and genetic programming model. The linear regression model was used in the first randomly generated generations of each of the 100 independent developed civilizations. More accurate models were subsequently obtained during the simulated evolution. The average relative deviation of the linear regression and the genetic programming model predictions from the experimental data were 3.60% and 3.31%, respectively. Both models were subsequently validated by using data from 278 batches produced in 2019, where the maintenance and the technological delays were below 20 minutes per batch. It was possible, based on the linear regression and the genetically developed model, to calculate that the average electric energy consumption could be reduced by up to 1.04% and 1.16%, respectively, in the case of maintenance and other technological delays. View Full-Text
Keywords: steelmaking; electric arc furnace; consumption; electric energy; melting; refining; tapping; modeling; linear regression; genetic programming steelmaking; electric arc furnace; consumption; electric energy; melting; refining; tapping; modeling; linear regression; genetic programming
Show Figures

Figure 1

MDPI and ACS Style

Kovačič, M.; Stopar, K.; Vertnik, R.; Šarler, B. Comprehensive Electric Arc Furnace Electric Energy Consumption Modeling: A Pilot Study. Energies 2019, 12, 2142.

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.

Article Access Map by Country/Region

1
Back to TopTop