Holliday, M. orcid.org/0000-0003-2387-1311, Lai, Y., Hobbs, M. et al. (4 more authors) (2023) Continuous measurement of ferrous sinter size distributions using an optical sensor system. Ironmaking & Steelmaking, 50 (8). pp. 1104-1111. ISSN 0301-9233
Abstract
The size distribution of iron ore sinter is critical to efficient blast furnace operation and is an optimised variable in sinter plants globally. Prompt process control response to discrepancies in sinter size is essential, and the standard sieve measurement test introduces significant delay in data acquisition. We introduce a networked optical sensor system that is shown to accurately measure size distribution within 5 s, collect data continuously at 0.5 Hz, and is well correlated to sieving measurements. This system is deployed at the end of a sinter plant, providing real-time process control data with digital image analysis performed on an integrated microprocessor. The systems performance was assessed with a 12-week validation period, showing excellent correlation with sieve data. Systems such as ours can be widely implemented in sinter plants, and in similar steelmaking applications, due to its cost-effective implementation of continuous data acquisition and the systems versatility to be adapted.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. |
Keywords: | Sinter size; iron ore; process control; optical; blast furnace; machine vision; image processing |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council 2713405 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 May 2023 09:59 |
Last Modified: | 03 Oct 2024 14:23 |
Status: | Published |
Publisher: | Taylor and Francis Group |
Refereed: | Yes |
Identification Number: | 10.1080/03019233.2023.2208979 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:199192 |
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