An Online Prediction Model for BFG Output in Steel Industry
2012 ◽
Vol 542-543
◽
pp. 507-512
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Keyword(s):
The output prediction of blast furnace gas (BFG), influenced by many complex production factors, is a very important and difficult problem concerning the byproduct gas balance in steel industry. A new online least squares support vector machine (LSSVM) prediction model is proposed in this paper, in which the training data is filtered by an improved empirical mode decomposition threshold filtering (IEMDTF). The model is solved and optimized by an online learning algorithm and an online bayesian parameters optimization, respectively. The experimental results using practical BFG output data from BaoSteel Co. Ltd., China show the proposed model is effective and enable to offer reasonable gas balance scheduling for operators.
2021 ◽
2020 ◽
2012 ◽
Vol 16
(6)
◽
pp. 687-695
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Keyword(s):
2019 ◽
Vol 180
◽
pp. 196-205
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2009 ◽
Vol 15
(2)
◽
pp. 241-271
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2015 ◽
Vol 2015
◽
pp. 1-7
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2011 ◽
Vol 08
(03)
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pp. 579-606
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Keyword(s):
2016 ◽
Vol XLI-B3
◽
pp. 447-452
Keyword(s):