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Data-Driven Dynamic Modeling for Prediction of Molten Iron Silicon Content Using ELM with Self-Feedback
Joint Authors
Zhou, Ping
Yuan, Meng
Wang, Hong
Chai, Tianyou
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-05-27
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Silicon content ([Si] for short) of the molten metal is an important index reflecting the product quality and thermal status of the blast furnace (BF) ironmaking process.
Since the online detection of [Si] is difficult and larger time delay exists in the offline assay procedure, quality modeling is required to achieve online estimation of [Si].
Focusing on this problem, a data-driven dynamic modeling method is proposed using improved extreme learning machine (ELM) with the help of principle component analysis (PCA).
First, data-driven PCA is introduced to pick out the most pivotal variables from multitudinous factors to serve as the secondary variables of modeling.
Second, a novel data-driven ELM modeling technology with good generalization performance and nonlinear mapping capability is presented by applying a self-feedback structure on traditional ELM.
The feedback outputs at previous time together with input variables at different time constitute a dynamic ELM structure which has a storage ability to tackle data in different time and overcomes the limitation of static modeling of traditional ELM.
At last, industrial experiments demonstrate that the proposed method has a better modeling and estimating accuracy as well as a faster learning speed when compared with different modeling methods with different model structures.
American Psychological Association (APA)
Zhou, Ping& Yuan, Meng& Wang, Hong& Chai, Tianyou. 2015. Data-Driven Dynamic Modeling for Prediction of Molten Iron Silicon Content Using ELM with Self-Feedback. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1073528
Modern Language Association (MLA)
Zhou, Ping…[et al.]. Data-Driven Dynamic Modeling for Prediction of Molten Iron Silicon Content Using ELM with Self-Feedback. Mathematical Problems in Engineering No. 2015 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1073528
American Medical Association (AMA)
Zhou, Ping& Yuan, Meng& Wang, Hong& Chai, Tianyou. Data-Driven Dynamic Modeling for Prediction of Molten Iron Silicon Content Using ELM with Self-Feedback. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1073528
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1073528