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Conference Paper/Proceeding/Abstract 755 views 155 downloads

Flame intensity analysis for hot molten metal pouring in the steel industry by applying image segmentation

Ivan Popov, Grazia Todeschini

Global Congress on Manufacturing and Management, 20-22 April 2021

Swansea University Author: Grazia Todeschini

Abstract

Pouring large quantities of hot metal (HM) can release substantial amounts of flame. This problem is frequently encountered within the Basis Oxygen Furnace (BOF) steelmaking process where large quantities of HM (frequently exceeding 300 t) are poured into the converter vessels. The HM is contained i...

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Published in: Global Congress on Manufacturing and Management, 20-22 April 2021
Published: 2021
URI: https://cronfa.swan.ac.uk/Record/cronfa55771
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spelling 2022-01-28T17:04:46.1051913 v2 55771 2020-11-27 Flame intensity analysis for hot molten metal pouring in the steel industry by applying image segmentation c4ff9050b31bdec0e560b19bfb3b56d3 Grazia Todeschini Grazia Todeschini true false 2020-11-27 FGSEN Pouring large quantities of hot metal (HM) can release substantial amounts of flame. This problem is frequently encountered within the Basis Oxygen Furnace (BOF) steelmaking process where large quantities of HM (frequently exceeding 300 t) are poured into the converter vessels. The HM is contained in specially designed ladles and poured using overhead girder cranes. Excess release of flame may damage surrounding components such as crane ropes and consequently reduce their lifecycle. Therefore, limiting the release of flame during pouring, allows extending the lifetime of the components located in proximity of the ladle. The scope of this paper is to characterise flame generation during different pouring operations at a BOF steelmaking plant and to relate the amount of flame generated to process factors. Due to the complexity of the process under investigation, this paper does not aim to eliminate flame generation, but rather to identify approaches to its mitigation. The proposed approach utilises a standard CCTV camera to record videos of pours. An image segmentation analysis is then performed, where the flame is separated from the background image using pixel information in the CIE L*a*b* colour space. For each frame, flame intensity is then calculated. This process is partially automated for each video making use of MATLAB. A total of 169 videos are analysed and the pours that cause higher flame intensity are identified. In the last steps of the analysis, the process factors with the most significant impact on the flame release are identified and mitigating solutions are proposed. Conference Paper/Proceeding/Abstract Global Congress on Manufacturing and Management, 20-22 April 2021 20 4 2021 2021-04-20 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2022-01-28T17:04:46.1051913 2020-11-27T08:34:26.6064601 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Ivan Popov 1 Grazia Todeschini 2 55771__18752__c9284f3d34af46e39f615866c2d494d2.pdf Flame intensity analysis for hot molten metal pouring in the steel industry by applying image segmentation.pdf 2020-11-27T08:36:57.2856779 Output 495880 application/pdf Accepted Manuscript true 2021-05-01T00:00:00.0000000 false
title Flame intensity analysis for hot molten metal pouring in the steel industry by applying image segmentation
spellingShingle Flame intensity analysis for hot molten metal pouring in the steel industry by applying image segmentation
Grazia Todeschini
title_short Flame intensity analysis for hot molten metal pouring in the steel industry by applying image segmentation
title_full Flame intensity analysis for hot molten metal pouring in the steel industry by applying image segmentation
title_fullStr Flame intensity analysis for hot molten metal pouring in the steel industry by applying image segmentation
title_full_unstemmed Flame intensity analysis for hot molten metal pouring in the steel industry by applying image segmentation
title_sort Flame intensity analysis for hot molten metal pouring in the steel industry by applying image segmentation
author_id_str_mv c4ff9050b31bdec0e560b19bfb3b56d3
author_id_fullname_str_mv c4ff9050b31bdec0e560b19bfb3b56d3_***_Grazia Todeschini
author Grazia Todeschini
author2 Ivan Popov
Grazia Todeschini
format Conference Paper/Proceeding/Abstract
container_title Global Congress on Manufacturing and Management, 20-22 April 2021
publishDate 2021
institution Swansea University
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description Pouring large quantities of hot metal (HM) can release substantial amounts of flame. This problem is frequently encountered within the Basis Oxygen Furnace (BOF) steelmaking process where large quantities of HM (frequently exceeding 300 t) are poured into the converter vessels. The HM is contained in specially designed ladles and poured using overhead girder cranes. Excess release of flame may damage surrounding components such as crane ropes and consequently reduce their lifecycle. Therefore, limiting the release of flame during pouring, allows extending the lifetime of the components located in proximity of the ladle. The scope of this paper is to characterise flame generation during different pouring operations at a BOF steelmaking plant and to relate the amount of flame generated to process factors. Due to the complexity of the process under investigation, this paper does not aim to eliminate flame generation, but rather to identify approaches to its mitigation. The proposed approach utilises a standard CCTV camera to record videos of pours. An image segmentation analysis is then performed, where the flame is separated from the background image using pixel information in the CIE L*a*b* colour space. For each frame, flame intensity is then calculated. This process is partially automated for each video making use of MATLAB. A total of 169 videos are analysed and the pours that cause higher flame intensity are identified. In the last steps of the analysis, the process factors with the most significant impact on the flame release are identified and mitigating solutions are proposed.
published_date 2021-04-20T04:10:13Z
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score 11.035655

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