Soft sensor of bath temperature in an electric arc furnace based on a data-driven Takagi–Sugeno fuzzy model
ID Blažič, Aljaž (Author), ID Škrjanc, Igor (Author), ID Logar, Vito (Author)

.pdfPDF - Presentation file, Download (1,03 MB)
MD5: BB32CE922E200A5040560C34623D4675
URLURL - Source URL, Visit https://www.sciencedirect.com/science/article/pii/S1568494621008711 This link opens in a new window

Electric arc furnaces (EAFs) are intended for the recycling of steel scrap. One of the more important variables in the recycling process is the tapping temperature of the steel. Due to the nature of the process, continuous measurement of the melt temperature is complicated and requires sophisticated measuring equipment; therefore, for most EAFs, separate temperature samples are taken several times before the melt is tapped, to verify whether the melt temperature is within the prescribed range. The measurements are obtained using disposable probes; when measurement is performed, the furnace must be switched off, leading to increased tap-to-tap time, unnecessary energy losses, and consequently, lower efficiency. The following paper presents a novel approach to EAF bath temperature estimation using a fuzzy model soft sensor obtained using Gustafson–Kessel input data clustering and particle swarm optimization of model parameters. The model uses the first temperature measurement as an initial condition, and measurements of the necessary EAF inputs to estimate continuously the bath temperature throughout the refining stage of the recycling process. The results have shown that the prediction accuracy of the proposed model is very high and that it fulfils the required tolerance band. The model is intended for parallel implementation in the EAF process, with the aim of achieving fewer temperature measurements, shorter tap-to-tap times, and decreased energy losses. Furthermore, if information about bath temperature is accessible in a continuous manner, operators can adjust the control of the EAF to achieve optimal tapping temperature and thus higher EAF efficiency.

Keywords:electric arc furnace, fuzzy modeling, Gustafson–Kessel clustering, particle swarn optimization, temperature estimation
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FE - Faculty of Electrical Engineering
Publication status:Published
Publication version:Version of Record
Number of pages:11 str.
Numbering:Vol. 113, pt. B, art. 107949
PID:20.500.12556/RUL-138854 This link opens in a new window
ISSN on article:1568-4946
DOI:10.1016/j.asoc.2021.107949 This link opens in a new window
COBISS.SI-ID:79852547 This link opens in a new window
Publication date in RUL:23.08.2022
Copy citation
Share:Bookmark and Share

Record is a part of a journal

Title:Applied soft computing
COBISS.SI-ID:16080679 This link opens in a new window


License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

Keywords:elektroobločna peč, mehko modeliranje, rojenje Gustafson-Kessel, optimizacija z roji delcev, ocena temperature


Funder:EC - European Commission
Funding programme:H2020
Project number:869815
Name:Optimization and performance improving in metal industry by digital technologies

Funder:ARRS - Slovenian Research Agency
Project number:P2-0219
Name:Modeliranje, simulacija in vodenje procesov

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections: