No Cover Image

Journal article 79 views 27 downloads

Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II

Matheus Torquato Orcid Logo, German Martinez Ayuso, Ashraf Fahmy Abdo, Johann Sienz Orcid Logo

IEEE Access, Volume: 9, Pages: 149715 - 149731

Swansea University Authors: Matheus Torquato Orcid Logo, German Martinez Ayuso, Ashraf Fahmy Abdo, Johann Sienz Orcid Logo

  • 58644.pdf

    PDF | Version of Record

    This work is licensed under a Creative Commons Attribution 4.0 License

    Download (2.12MB)

Abstract

Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of the EAF-based steel-making process based on a multi-objective optimisation using evolutionary computing and machine learning. Using a large amount of real-w...

Full description

Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa58644
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2021-11-30T12:00:55Z
last_indexed 2021-12-01T04:18:37Z
id cronfa58644
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2021-11-30T12:03:18.1509899</datestamp><bib-version>v2</bib-version><id>58644</id><entry>2021-11-15</entry><title>Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II</title><swanseaauthors><author><sid>7a053c668886b4642286baed36fdba90</sid><ORCID>0000-0001-6356-3538</ORCID><firstname>Matheus</firstname><surname>Torquato</surname><name>Matheus Torquato</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>cb3d95d4daf63de098833a0cc76b68b6</sid><firstname>German</firstname><surname>Martinez Ayuso</surname><name>German Martinez Ayuso</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>b952b837f8a8447055210d209892b427</sid><firstname>Ashraf</firstname><surname>Fahmy Abdo</surname><name>Ashraf Fahmy Abdo</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>17bf1dd287bff2cb01b53d98ceb28a31</sid><ORCID>0000-0003-3136-5718</ORCID><firstname>Johann</firstname><surname>Sienz</surname><name>Johann Sienz</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-11-15</date><deptcode>SCS</deptcode><abstract>Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of the EAF-based steel-making process based on a multi-objective optimisation using evolutionary computing and machine learning. Using a large amount of real-world historical data containing 6423 consecutive EAF heats collected from a melt shop in an established steel plant this work not only creates machine learning models for both EAF and ladle furnaces but also simultaneously minimises the total scrap cost and EAF energy consumption per ton of scrap. In the modelling process, several algorithms are tested, tuned, evaluated and compared before selecting Gradient Boosting as the best option to model the data analysed. A similar approach is followed for the selection of the multi-objective optimisation algorithm. For this task, six techniques are tested and compared based on the hypervolume performance indicator to just then select the Non-dominated Sorting Genetic Algorithm II ( NSGA-II ) as the best option. Given this applied research focus on a real manufacturing process, real-world constraints and variables such as individual scrap price, scrap availability, tap additives and ambient temperature are used in the models developed here. A comparison with an equivalent EAF model from the literature showed a 13% improvement using the mean absolute error in the EAF energy usage prediction as a comparative metric. The multi-objective optimisation resulted in reductions in the energy consumption costs that ranged from 1.87% up to 8.20% among different steel grades and scrap cost reductions ranging from 1.15% up to 5.2%. The machine learning models and the optimiser were ultimately deployed with a graphical user interface allowing the melt-shop staff members to make informed decisions while controlling the EAF operation.</abstract><type>Journal Article</type><journal>IEEE Access</journal><volume>9</volume><journalNumber/><paginationStart>149715</paginationStart><paginationEnd>149731</paginationEnd><publisher>Institute of Electrical and Electronics Engineers (IEEE)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2169-3536</issnElectronic><keywords/><publishedDay>11</publishedDay><publishedMonth>11</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-11-11</publishedDate><doi>10.1109/access.2021.3125519</doi><url/><notes/><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2021-11-30T12:03:18.1509899</lastEdited><Created>2021-11-15T09:53:18.9317017</Created><path><level id="1">College of Engineering</level><level id="2">Engineering</level></path><authors><author><firstname>Matheus</firstname><surname>Torquato</surname><orcid>0000-0001-6356-3538</orcid><order>1</order></author><author><firstname>German</firstname><surname>Martinez Ayuso</surname><order>2</order></author><author><firstname>Ashraf</firstname><surname>Fahmy Abdo</surname><order>3</order></author><author><firstname>Johann</firstname><surname>Sienz</surname><orcid>0000-0003-3136-5718</orcid><order>4</order></author></authors><documents><document><filename>58644__21729__f8204d2bfc484e39b77ec242c38d0774.pdf</filename><originalFilename>58644.pdf</originalFilename><uploaded>2021-11-30T12:01:22.3503885</uploaded><type>Output</type><contentLength>2225313</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>This work is licensed under a Creative Commons Attribution 4.0 License</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2021-11-30T12:03:18.1509899 v2 58644 2021-11-15 Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II 7a053c668886b4642286baed36fdba90 0000-0001-6356-3538 Matheus Torquato Matheus Torquato true false cb3d95d4daf63de098833a0cc76b68b6 German Martinez Ayuso German Martinez Ayuso true false b952b837f8a8447055210d209892b427 Ashraf Fahmy Abdo Ashraf Fahmy Abdo true false 17bf1dd287bff2cb01b53d98ceb28a31 0000-0003-3136-5718 Johann Sienz Johann Sienz true false 2021-11-15 SCS Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of the EAF-based steel-making process based on a multi-objective optimisation using evolutionary computing and machine learning. Using a large amount of real-world historical data containing 6423 consecutive EAF heats collected from a melt shop in an established steel plant this work not only creates machine learning models for both EAF and ladle furnaces but also simultaneously minimises the total scrap cost and EAF energy consumption per ton of scrap. In the modelling process, several algorithms are tested, tuned, evaluated and compared before selecting Gradient Boosting as the best option to model the data analysed. A similar approach is followed for the selection of the multi-objective optimisation algorithm. For this task, six techniques are tested and compared based on the hypervolume performance indicator to just then select the Non-dominated Sorting Genetic Algorithm II ( NSGA-II ) as the best option. Given this applied research focus on a real manufacturing process, real-world constraints and variables such as individual scrap price, scrap availability, tap additives and ambient temperature are used in the models developed here. A comparison with an equivalent EAF model from the literature showed a 13% improvement using the mean absolute error in the EAF energy usage prediction as a comparative metric. The multi-objective optimisation resulted in reductions in the energy consumption costs that ranged from 1.87% up to 8.20% among different steel grades and scrap cost reductions ranging from 1.15% up to 5.2%. The machine learning models and the optimiser were ultimately deployed with a graphical user interface allowing the melt-shop staff members to make informed decisions while controlling the EAF operation. Journal Article IEEE Access 9 149715 149731 Institute of Electrical and Electronics Engineers (IEEE) 2169-3536 11 11 2021 2021-11-11 10.1109/access.2021.3125519 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2021-11-30T12:03:18.1509899 2021-11-15T09:53:18.9317017 College of Engineering Engineering Matheus Torquato 0000-0001-6356-3538 1 German Martinez Ayuso 2 Ashraf Fahmy Abdo 3 Johann Sienz 0000-0003-3136-5718 4 58644__21729__f8204d2bfc484e39b77ec242c38d0774.pdf 58644.pdf 2021-11-30T12:01:22.3503885 Output 2225313 application/pdf Version of Record true This work is licensed under a Creative Commons Attribution 4.0 License true eng https://creativecommons.org/licenses/by/4.0/
title Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II
spellingShingle Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II
Matheus Torquato
German Martinez Ayuso
Ashraf Fahmy Abdo
Johann Sienz
title_short Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II
title_full Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II
title_fullStr Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II
title_full_unstemmed Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II
title_sort Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II
author_id_str_mv 7a053c668886b4642286baed36fdba90
cb3d95d4daf63de098833a0cc76b68b6
b952b837f8a8447055210d209892b427
17bf1dd287bff2cb01b53d98ceb28a31
author_id_fullname_str_mv 7a053c668886b4642286baed36fdba90_***_Matheus Torquato
cb3d95d4daf63de098833a0cc76b68b6_***_German Martinez Ayuso
b952b837f8a8447055210d209892b427_***_Ashraf Fahmy Abdo
17bf1dd287bff2cb01b53d98ceb28a31_***_Johann Sienz
author Matheus Torquato
German Martinez Ayuso
Ashraf Fahmy Abdo
Johann Sienz
author2 Matheus Torquato
German Martinez Ayuso
Ashraf Fahmy Abdo
Johann Sienz
format Journal article
container_title IEEE Access
container_volume 9
container_start_page 149715
publishDate 2021
institution Swansea University
issn 2169-3536
doi_str_mv 10.1109/access.2021.3125519
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str College of Engineering
hierarchytype
hierarchy_top_id collegeofengineering
hierarchy_top_title College of Engineering
hierarchy_parent_id collegeofengineering
hierarchy_parent_title College of Engineering
department_str Engineering{{{_:::_}}}College of Engineering{{{_:::_}}}Engineering
document_store_str 1
active_str 0
description Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of the EAF-based steel-making process based on a multi-objective optimisation using evolutionary computing and machine learning. Using a large amount of real-world historical data containing 6423 consecutive EAF heats collected from a melt shop in an established steel plant this work not only creates machine learning models for both EAF and ladle furnaces but also simultaneously minimises the total scrap cost and EAF energy consumption per ton of scrap. In the modelling process, several algorithms are tested, tuned, evaluated and compared before selecting Gradient Boosting as the best option to model the data analysed. A similar approach is followed for the selection of the multi-objective optimisation algorithm. For this task, six techniques are tested and compared based on the hypervolume performance indicator to just then select the Non-dominated Sorting Genetic Algorithm II ( NSGA-II ) as the best option. Given this applied research focus on a real manufacturing process, real-world constraints and variables such as individual scrap price, scrap availability, tap additives and ambient temperature are used in the models developed here. A comparison with an equivalent EAF model from the literature showed a 13% improvement using the mean absolute error in the EAF energy usage prediction as a comparative metric. The multi-objective optimisation resulted in reductions in the energy consumption costs that ranged from 1.87% up to 8.20% among different steel grades and scrap cost reductions ranging from 1.15% up to 5.2%. The machine learning models and the optimiser were ultimately deployed with a graphical user interface allowing the melt-shop staff members to make informed decisions while controlling the EAF operation.
published_date 2021-11-11T04:15:27Z
_version_ 1737027933693804544
score 10.887713