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Open AccessArticle

Machine Learning-Based Prediction of a BOS Reactor Performance from Operating Parameters

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AI-GARISMO, 1 Sandover House, 124 Spa Road, London SE16 3FD, UK
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WMG, The University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
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George S. Ansell Department of Metallurgical and Materials Engineering, Colorado School of Mines, Golden, CO 80401, USA
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Author to whom correspondence should be addressed.
Processes 2020, 8(3), 371; https://doi.org/10.3390/pr8030371
Received: 25 November 2019 / Revised: 1 March 2020 / Accepted: 13 March 2020 / Published: 23 March 2020
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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
Keywords: machine learning; artificial intelligence; neural network; BOS reactor; steelmaking machine learning; artificial intelligence; neural network; BOS reactor; steelmaking
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Rahnama, A.; Li, Z.; Sridhar, S. Machine Learning-Based Prediction of a BOS Reactor Performance from Operating Parameters. Processes 2020, 8, 371.

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