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Machine learning-based prediction of a BOS reactor performance from operating parameters
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Rahnama, Alireza, Li, Zushu and Seetharaman, Sridhar (2020) Machine learning-based prediction of a BOS reactor performance from operating parameters. Processes, 8 (3). 371. doi:10.3390/pr8030371 ISSN 2227-9717.
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Official URL: https://doi.org/10.3390/pr8030371
Abstract
A machine learning-based analysis was applied to process data obtained from a Basic Oxygen Steelmaking (BOS) pilot plant. The first purpose was to identify correlations between operating parameters and reactor performance, defined as rate of decarburization (dc/dt). Correlation analysis showed, as expected a strong positive correlation between the rate of decarburization (dc/dt) and total oxygen flow. On the other hand, the decarburization rate exhibited a negative correlation with lance height. Less obviously, the decarburization rate, also showed a positive correlation with temperature of the waste gas and CO2 content in the waste gas. The second purpose was to train the pilot-plant dataset and develop a neural network based regression to predict the decarburization rate. This was used to predict the decarburization rate in a BOS furnace in an actual manufacturing plant based on lance height and total oxygen flow. The performance was satisfactory with a coefficient of determination of 0.98, confirming that the trained model can adequately predict the variation in the decarburization rate (dc/dt) within BOS reactors. View Full-Text
Item Type: | Journal Article | ||||||
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Subjects: | T Technology > TN Mining engineering. Metallurgy | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
Library of Congress Subject Headings (LCSH): | Steel -- Metallurgy -- Oxygen processes, Machine learning, Neural networks (Computer science) | ||||||
Journal or Publication Title: | Processes | ||||||
Publisher: | MDPI | ||||||
ISSN: | 2227-9717 | ||||||
Official Date: | 23 March 2020 | ||||||
Dates: |
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Volume: | 8 | ||||||
Number: | 3 | ||||||
Article Number: | 371 | ||||||
DOI: | 10.3390/pr8030371 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 24 March 2020 | ||||||
Date of first compliant Open Access: | 25 March 2020 | ||||||
RIOXX Funder/Project Grant: |
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