A predictive KH-based model to enhance the performance of industrial electric arc furnaces

Kavousi Fard, Abdollah, Su, Wencong, Tao, Jin, Al-Sumaiti, Ameena Saad, Samet, Haidar and Khosravi, Abbas 2018, A predictive KH-based model to enhance the performance of industrial electric arc furnaces, IEEE transactions on industrial electronics, vol. 66, no. 10, pp. 7976-7985, doi: 10.1109/TIE.2018.2880710.

Attached Files
Name Description MIMEType Size Downloads

Title A predictive KH-based model to enhance the performance of industrial electric arc furnaces
Author(s) Kavousi Fard, Abdollah
Su, Wencong
Tao, Jin
Al-Sumaiti, Ameena Saad
Samet, Haidar
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Journal name IEEE transactions on industrial electronics
Volume number 66
Issue number 10
Start page 7976
End page 7985
Total pages 10
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2018-10
ISSN 0278-0046
Keyword(s) Electric arc furnace (EAF)
Prediction
Reactive power compensation
Static VAr compensator (SVC)
Uncertainty
Language eng
DOI 10.1109/TIE.2018.2880710
Field of Research 08 Information and Computing Sciences
09 Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30120579

Document type: Journal Article
Collection: Institute for Intelligent Systems Research and Innovation (IISRI)
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 50 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Thu, 11 Apr 2019, 14:48:42 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.